Jan 2, 2014 - Forward Model Algorithm to test the MCA under simulated wind fields and vessel ... that NWP model skill in the coastal MD region can be highly sensitive to the input data set and ...... ACO--8--88500--01, Final Report, 2010.
Third Quarterly Report to the Maryland Energy Administration January 2014 through March 2014 Contract Title- “Data fusion, analysis and meteorological interpretation for the 2013 geophysical/metocean survey, LiDAR error/sensitivity analysis, and wind climatology for the Maryland Wind Energy Area”
Building a Science-Based Foundation for Assessing the Offshore Wind Resource of Maryland University of Maryland, Baltimore County
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Executive Summary Wind resource evaluation can benefit from new insights into the fundamental physical mechanisms responsible for variability across the rotor span, the boundary layer, and the entire region, including the Maryland Wind Energy Area (WEA). This report describes how data from a variety of in-situ and remote sensing instruments including Lidar are being used to characterize processes in the atmosphere and ocean and improve their representation in simulation models. This report also documents the development of a new Lidar Motion Compensation Algorithm (MCA) for vessels underway. Floating Lidar technology has the potential to replace met towers in many cases, once accepted by the industry. However, current systems are integrated into floating buoys and incorporate proprietary MCAs, which are all designed for motion of an anchored buoy. By developing and validating an algorithm that can be applied to a Lidar unit on a moving vessel, the UMBC MCA is opening the range of deployment possibilities to an entirely new set of platforms. Many types of vessels that could serve as Lidar platforms traverse in or near the MD WEA on a daily basis. During WEA survey and construction operations, vessels could be continuously collecting wind profile data for use in configuring and nudging forecast models, and fine-tuning production estimates. The cost of lidar units will come down significantly as technology improves and economies of scale are realized. Eventually, commercial vessels may be equipped with wind Lidar profilers and the data used in forecasting models, just as is often done with marine weather radar data from ships. The preliminary validation indicates that the UMBC MCA has nearly identical error and uncertainty characteristics to the internal, proprietary Leosphere MCA used by the WindCube. The next stage of the analysis is to use the Forward Model Algorithm to test the MCA under simulated wind fields and vessel speeds and motions. This is currently underway and an update will be provided in the next quarterly report. In the absence of strong synoptic forcing, smaller scale regional mechanisms in the Mid-Atlantic are revealed. These have the potential to generate locally strong winds that may coincide with high grid loads [1,2 ]. Satellite sea surface temperature data often shows cool water upwelling along the coast, creating a low-level local baroclinic zone and changing the low level thermal stability. There has been recent research on the impact of low level thermal stability on winds in the rotor layer [3,4]. The overall impact of such events on the wind resource offshore depends on how often they occur, which remains to be determined. Their predictability depends on whether the larger synoptic scale situation leads to conditions that make such small scale events more likely, and if any synoptic scale precursor conditions can be identified. A detailed investigation of low level jet formation and its precursors is ongoing. In addition, the occurrence of negative shear conditions across the rotor layer may be more frequent than expected, as shown by the preliminary analysis of data from NASA-DAWN1 Lidar, and the NOAA marine met stations designated CHLV2 and NDBC 44010. This has significant implications for rotor design, production estimates, and power forecasting. Further investigation of this phenomenon is ongoing. 1
http://engineering.larc.nasa.gov/common/docs/DAWN-AIR_Overview.pdf
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Numerical Weather Prediction (NWP) models are used to help understand the broader spatial and temporal context of wind resource measurements, and have been used for offshore wind resource assessment. They are especially important in areas where there is a lack of field data, since they can shed light on the driving mechanisms, wind uncertainty, intermittency, or recurring patterns by filling in the gaps in time and space between observational data sets. Third quarter research has demonstrated that NWP model skill in the coastal MD region can be highly sensitive to the input data set and configuration parameters. At this stage of the Weather Research and Forecast (WRF) model sensitivity analysis, CFSRRv2 appears to be the best re-analysis input data set for multi-year climatology, with full nudging of the WRF outer domain. ERA Interim showed the lowest skill of the three re-analysis data sets tested (versus the Horn Point Profiler). This is a significant observation since ERA Interim is used for the NREL WindToolkit (Draxl et al 2013). This is, however, not surprising since it reflects the lack of tall coastal and offshore met towers available for model validation. For shorter case studies where operational model data sets may be used as input, the operational model RAP, using the F02 forecast hr. generally exhibits the highest skill, likely due to its rapid update cycle (1 hr) and high resolution compared to re-analysis data sets. Overall, the sensitivity analysis showed that the use of grid nudging improved the resolution of localscale high wind features compared to “free runs”, which are not updated regularly with input data sets. Free Run climatologies that do not use nudging to steer the model back to reality (the input data set) can drift far from the actual state of the atmosphere. The analysis also showed that use of the RAP 2-hour forecast (F02) data led to better results in general. This illustrates the complexity in evaluating model configurations and the importance of understanding sources of error and uncertainty. Because almost all available re-analysis data sets are provided in standard pressure levels, the lowest wind speed (above the 10m level) is often above 200m height. This introduces significant uncertainty by making large extrapolations based on standard shear profiles necessary. These profiles are based on mixed layer similiarity theory [5] or characterized by power laws [6] for convenience. If offshore wind profiles are frequently different than what is shown by current models and assumed by profile parameterizations, then average annual hub height wind speeds in the WEA would be significantly different than currently estimated. How often such behavior occurs is the subject of ongoing study. This uncertainty is being mitigated by using input data sets with higher vertical resolution and by configuring WRF with more layers in the rotor plane. UMBC is also identifying and archiving additional data sets with better rotor layer resolution for further analysis. The case studies and examples provided in this report represent only a small portion of the available data. They were selected because they represent conditions and phenomena that are not well understood and that can significantly affect power production. Additional case studies, model configuration and testing, and more in-depth statistical analyses of the data are under way, and will be presented in subsequent reports and publications. By identifying and understanding the sources of error and uncertainty and how they affect different models of the MD WEA and surrounding regions, model skill has been significantly improved compared to the setups and configurations used in current climatological data sets. This will ultimately result in more accurate climatologies, more accurate dayahead forecasts, and more efficient wind farm and rotor designs. iii
Contents 1
2
3
Introduction ..................................................................................................................... 1 1.1
Goal of the Project........................................................................................................................ 1
1.2
Scope and Purpose of this Report ................................................................................................ 1
1.3
Sources of Uncertainty Targeted .................................................................................................. 2
1.4
Mechanisms .................................................................................................................................. 2
1.5
Measurements............................................................................................................................... 4
1.6
Modeling ...................................................................................................................................... 5
Deliverable 1- Lidar Motion Compensation and Forward Model Algorithms ................ 5 2.1
Generalized Model for MCA – Wide Application ....................................................................... 7
2.2
Spatial and Temporal Sampling Limitations ............................................................................... 8
2.3
MCA – Preliminary Validation Study.......................................................................................... 8
2.4
MCA Requirements...................................................................................................................... 9
2.5
Conversion of Line of Sight Data to 3D Components in the Ship’s Frame ............................... 10
2.6
Azimuthal Correction for Vessel Heading ................................................................................. 12
2.7
Case Study - Deriving Wind Components from LOS Wind Measurements.............................. 12
2.8
Correction for Vessel Motion..................................................................................................... 14
2.9
Case Study – MCA Validation ................................................................................................... 16
Deliverable 2 – WRF Modeling Configuration/Validation ........................................... 18 3.1
WRF Modeling Background ...................................................................................................... 18
3.1.1
Data Fusion - Measure, Model, Adjust, Repeat .................................................................. 18
3.1.2
Operational Models ............................................................................................................. 19
3.1.3
Research Models ................................................................................................................. 19
3.1.4
ReAnalysis Data Sets .......................................................................................................... 20
3.1.5
Archived Operational Data Sets......................................................................................... 20
3.1.6
Validation Data Sets ........................................................................................................... 20
3.2
Second Quarter Modeling Research – Summary/Recap ............................................................ 21
3.3
Third Quarter Modeling Research.............................................................................................. 21
3.4
Model Uncertainties ................................................................................................................... 22
3.5
WRF Options and Model Permutations ..................................................................................... 23 iv
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5
3.6
Target Weather Regimes – NAM Analysis................................................................................ 23
3.7
Pressure Level Interpolation....................................................................................................... 27
3.8
WRF Vertical Resolution ........................................................................................................... 28
3.9
WRF Grid Resolution................................................................................................................. 29
3.10
Refresh Time and Forecast Hour ............................................................................................ 29
3.11
Outer Domain Data Set (Initial, Boundary Conditions, FDDA Updates) ............................. 30
Climatology-related Research Completed in the Third Quarter .................................... 36 4.1
Laying the Groundwork for WEA Climatology ........................................................................... 39
4.2
Eastern Regional Wind Data Set ................................................................................................ 39
4.3
CHLV2 (43m) vs ERWR (80m) .................................................................................................... 40
4.4
Negative Shear Observations ..................................................................................................... 45
4.5
Inter-Turbine Scales of Variabililty ............................................................................................. 47
Summary Discussion and Implications for Reducing Uncertainty ............................... 49 5.1
Wind Lidar from a Moving Vessel ............................................................................................ 49
5.2
Summertime Local Forcing Mechanisms .................................................................................. 50
5.3
Model Setup, Configuration, and Uncertainty ........................................................................... 50
5.3.1
Input Data Set Sensitivity – Re-analysis ............................................................................. 50
5.3.2
Input Data Set Sensitivity - Operational ............................................................................. 51
5.3.3
Configuration Sensitivity – Vertical Resolution ................................................................. 51
5.4
Conclusion.................................................................................................................................. 51
5.5
Benefits Matrix ........................................................................................................................... 52
6
Work Status and Schedule ............................................................................................. 53
7
References ..................................................................................................................... 54
8
Appendix 1 – Derivation of Equations for Lidar MCA................................................. 56 8.1
Review of Vector Motion Equations (previously submitted) .................................................... 56
8.2 Line of Sight to Wind to Earth Frame Components - Conversion Equations for Leosphere v2 Offshore Lidar. ...................................................................................................................................... 56 8.3 9
Matrix Transforms for Derivation of Eq. 11 (rotational motions) ............................................. 57
Appendix 2 – Variable Definitions and Core Pseudo-Code for Lidar MCA ................ 58
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1 Introduction This introduction provides background information on the project purpose, methodology, and findings of previous project reports. This background information is provided at the beginning of every quarterly report for convenience.
1.1 Goal of the Project The goal of this project is to reduce uncertainty of the wind resource in the MD Wind Energy Area (WEA) by incorporating a variety of data sources with meteorological models (data fusion) to characterize the processes that drive variability of offshore wind, on scales from minutes to decades. The project will develop measurement and modeling strategies for wind regimes that create the most uncertainty in resource estimates, and will improve the diagnosis of model errors. This knowledge will be used to improve model predictive and hindcasting skill. In short, the primary project objective is to develop and execute an integrated measurement and modeling approach that identifies and reduces wind resource uncertainties that arise primarily from misrepresentation of local forcing mechanisms. This, in turn, will reduce the delivered cost of wind energy by directly or indirectly enabling the following improvements across the industry :
improved siting of offshore met towers and other instruments for lower cost, lower uncertainty resource assessment improved siting, design, construction and O&M of turbines. higher confidence in estimates of Annual Energy Production (AEP) more accurate short term (1hr to 48 hrs) wind, wave, and power forecasting for PJM bids and marine operations more reliable long term climatology for planning purposes improved wake modeling and wake loss estimates a better understanding of variability during summer, when market pricing is sensitive to timing
1.2 Scope and Purpose of this Report This document summarizes UMBC efforts under the UMBC/MEA Scope of Work -Data fusion, analysis and meteorological interpretation for the 2013 Geophysical/metocean Survey, LiDAR error/sensitivity analysis, and wind Climatology for the Maryland Wind Energy Area during the third quarter of the contract. It is identified as a deliverable in the contract and outlines third quarter activities on MEA funded tasks.
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The “First Quarterly and Post Survey Report for the MEA, July 15- September 15, 2013” described the offshore deployment of personnel and instrumentation to gather metocean data in the WEA during the geophysical survey from June 19 – August 31 2013. Details of the offshore survey data collection effort can be found in that document, but a brief summary is included in this report for convenience. The “Second Quarterly Report to the Maryland Energy Administration - September 15, 2013 – December 15, 2013”, described how data from a variety of in-situ and remote sensing instruments on satellite, buoy, ship, and land-based platforms are being used to characterize processes in the atmosphere and ocean and improve their representation in simulation models. It also described the methodology of the “data fusion” approach and how it will reduce uncertainty to benefit Maryland’s offshore wind industry. It also presented several case studies of wind and forcing mechanisms during the summer survey of 2013, and discussed apparent error and biases in some of the operational weather forecasting models. This report updates progress on several tasks relating to Lidar data processing and analysis, model configuration and optimization, and atmospheric process investigations. The case studies and examples provided in this report represent only a small portion of the available data. They were selected because they illuminate conditions and phenomena that are not well understood and that can significantly affect power production. Additional case studies, model configuration and testing, and more in-depth statistical analyses of the data are under way, and will be presented in subsequent reports and publications.
1.3 Sources of Uncertainty Targeted Uncertainty is due to a variety of factors, from instrumental precision to poor representation of atmospheric dynamical processes in models, and it spans a range of temporal and spatial scales. Uncertainties and errors arise from low signal-to-noise ratios in measurements, biased or sparse data sampling, simplified statistical models, and an inadequate understanding of local land and sea surface interaction with the atmospheric boundary layer. UMBC research is intended to reduce uncertainty in each of these areas.
1.4 Mechanisms The origin of much of the uncertainty in the low level winds in the Maryland Wind Energy Area (WEA) is due to the complex physiography of the state and the surrounding region, including the elevated terrain to the west, the Chesapeake and Delaware Bays, the flat coastal plain of the Eastern Shore, and the bathymetry of the Mid-Atlantic Bight (MAB), with a relatively shallow continental shelf (Figure 1). The coastal region is influenced by the low level jet (LLJ; red arrow) and downslope wind (DW; yellow arrow) regimes, which may impact the MD WEA. Land-sea thermal and roughness contrasts lead to local circulations that can extend into the WEA, and cool water upwelling along the coast increases the thermal stability and can also lead to shallow baroclinic zones and small scale convergence offshore. In addition, it is not known how far off the Maryland coast internal boundary layers created from land-sea 2
roughness or thermal contrasts extend, and whether coastal measurements are correlated with hub height winds offshore. Other large-scale climatological forcing mechanisms in the Mid-Atlantic are related to the position of the upper level subtropical jet, the location of the Bermuda high, and the Gulf Stream. During the summertime, when there are fewer large (synoptic scale) weather systems moving through, low level winds in the study area are more heavily influenced by weaker localized influences like the mountains and the cycles of cooling and warming of the air by the sun, the ground, and the ocean. During these calm summer conditions, which produce higher temperatures and can coincide with peak electricity demand, the air over the mountains cools rapidly at night, but over the ocean, the air does not cool as rapidly because the ocean retains heat much longer in the top layer of water. Day/night changes in air temperature also depend on cloud cover and soil moisture. Coastal upwelling can also lower the SST along the coast. Lower SST cools the air and increases thermal stability, which in turn can alter the winds by affecting wind profiles and vertical mixing. The interaction of these effects is not well understood, but can have impacts on offshore winds at hub height that are not well represented or captured in weather models. The first component of our project is therefore identifying and characterizing these processes that drive the variability of Mid-Atlantic offshore wind. Reducing uncertainty in the wind resource may also require reducing uncertainty in other key meteorological or oceanographic variables such as land and sea surface temperature, soil moisture, or wave height.
Figure 1 The State of Maryland, USA and the locations of wind profilers (*) buoys(o), National Weather Service stations (x) and other stations. The Maryland Wind Energy Area is outlined in red. . LLNR168 (44009) Air temp height: 4 m asl; anemometer height 5m.
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1.5 Measurements Measurements used in this research come from field campaigns and from many other surface-based and satellite platforms, indicated schematically in Figure 2. A geophysical and met-ocean measurement campaign was conducted during the summer 2013. The data collected are being used for detailed case studies of forcing mechanisms during the warm season, when synoptic forcing is weak and local forcing (due to complex terrain and sea surface temperature) becomes more important. The summer 2013 MD WEA data collection campaign included a a suite of geophysical survey gear, and also a suite of instrumentation for measuring metocean variables such as sea surface temperature (SST), winds with a Leosphere WindCubeTM v2 Offshore, thermal profiling with a microwave radiometer (Radiometrics MP3000A), 10m winds (NRG class 1A cup anemometer and wind vane) and other aerosol, temperature and water vapor sensors. Wind measurements were obtained over the entire area for a near-continuous period of about 6 weeks. The vessel was almost constantly underway, so almost all the data were acquired from a platform that was both translating (heave, surge, sway, and vessel path) and rotating (pitch, roll and yaw). New uncertainties and non-linearities arise when using lidar on a floating platform because the platform/sea surface motion is coupled to the windspeed and direction that is being measured. Existing commercial motion compensation algorithms (MCAs) built into the instrument software are intended for buoys, not moving vessels. To account for this, a new MCA was developed and is being applied directly to the line-of-sight data prior to any vector wind calculations. This MCA is designed to remove motion from Lidar data collected from a moving vessel.
Figure 2-Sensors and Platforms. A- differential GPS (DFPS), 5 cm resolution; B-INU system for orientation (roll, pitch, yaw(heading); C-aircraft downlooking wind lidar; D-shipboard temp and radiometer-thermal profiler; E-balloon radiosondes; F- satellite wind speed; G – Onboard anemometry; H – ocean temperature profiles; I, J, - bottom /sub-bottom sonar 4
1.6 Modeling Numerical Weather Prediction (NWP) models are used to help understand the broader spatial and temporal context of wind resource measurements, and have been used for offshore wind resource assessment [7]. They are especially important in areas where there is a lack of field data, since they can shed light on the driving mechanism, wind uncertainty, intermittency, or recurring patterns. RAP [8] output is used as a 1st-order investigation of meteorological conditions during case studies of interest. However, operational models such as RAP lack the spatial resolution required to resolve small scale phenomena driven by local forcings, such as topography, fine-scale differences in land use, surface temperature gradients, or low-level vertical structure in the atmosphere. Therefore, the Weather Research and Forecasting (WRF) model [9] is used in this research to downscale to finer spatiotemporal resolutions (~ 1km), using initial and boundary conditions that are derived from operational models. These simulations are invaluable for gathering improved wind resource statistics and understanding the underlying meteorology and wind uncertainties. A primary objective of this work is to investigate the origins of small-scale warm season variability that is not resolved, or is mischaracterized in statistical models or numerical weather prediction (NWP) models.
2 Deliverable 1- Lidar Motion Compensation and Forward Model Algorithms Deliverable No. 1 per the contract is a series of reports on the wind LiDAR motion compensation algorithm and forward model algorithm, including numerical simulations of wind fields with varying spatial and temporal variability. The first and second quarterly reports contain details of motion geometries and vector calculations, and Appendix 2, in this report, further develops the mathematical equations and outlines the code used in the Motion Compensation Algorithm (MCA). By simulating Lidar sampling of artificial (numerically simulated) wind fields with spatial and temporal variability, MCAs can be validated against the un-compensated reference data set. This validation methodology is called a Forward Model Algorithm (FMA). The FMA numerically simulates a wind lidar time series as measured from a moving offshore platform. The characteristics of the wind field, the platform motion and the lidar sensor and scan mode are all used to generate a “synthetic” lidar line-ofsight (LOS) measurement of a simulated wind field from a simulated moving platform. The data are then “motion compensated” using the MCA and compared to the reference “true” wind field to quantify differences. The sensitivities and uncertainties of the instrument and the MCA can then be fully characterized under a range of different conditions by varying the lidar parameters, the wind field parameters, and the characteristics of the platform motion in different ways. There are three main inputs to the FMA, described below and outlined in a schematic. These were provided in previous reports, but are summarized here for convenience. 1) The lidar model is the part of the FMA that defines various attributes of the instrument such as the number of beams, the minimum and maximum range, vertical resolution, scan mode and scan rate. A module can be added to the lidar model that prescribes the aerosol spatial distribution and simulates the 5
signal-to-noise ratio (SNR), or a distribution of SNR can be prescribed. The backscatter signal depends on the amount of aerosol and on features of the atmospheric state such as boundary layer height. Aerosol concentration can, in turn, depend on the wind direction, since winds from land carry a heavier aerosol load in the MAB, on average. 2) The wind model prescribes the 3D wind field to be sampled by the simulated lidar. This is the "truth", or reference data set by which to judge the motion-compensated winds that are reconstructed from the simulated measurement. This is done in order to quantify uncertainties inherent in the sensor characteristics and in the motion-compensation algorithm. The wind field is simulated in various ways that are designed to exercise the algorithm, progressing from simple parameterizations given by logarithmic profiles, to Large Eddy Simulations (LES) that account for atmospheric variables affecting wind. The parameterized wind models can include spatial and temporal scales of variability. Wind fields from high resolution mesoscale models can also be used for testing the MCA. 3) The floating platform model incorporates features of the platform that determine its motion in response to a prescribed wave field, such as mass and center of buoyancy. All six degrees of motion of the platform (roll, pitch, yaw, heave, surge, sway) can be prescribed based on realistic timeseries of wave heights and wave periods, or actual INS (inertial navigation system) motion data from a real floating platform can also be used when available. The platform model includes an option for translational motion in order to interpret measurements from ships under way, as well as stationary buoys. Inputs include ship speed and heading. Figure 3 illustrates this process schematically.
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Figure 3- Lidar simulation and forward model algorithm (reproduced from Q1 Appendix for convenience)
2.1 Generalized Model for MCA – Wide Application The aerosol-reflected return laser signal is Doppler shifted proportional to the component of the wind that is along the laser axis (co-axial). This is the fundamental physics used in all wind Lidar remote sensing systems. In order to measure all 3 vector components of the wind, at least 3 LOS scans are required at different angles and this typically takes several seconds. For example, the Windcube V2 used in this project uses a four direction - N,S,E,W scan pattern that generates 4 time series, 7
temporally staggered by 1-2 seconds. Other models use three beams fired simultaneously, once at each height, or a conical scanning pattern over one or two seconds per height. Some systems used for “lookahead” lidar use only two beams to reconstruct only the horizontal wind speed in front of a turbine. To account for these variations, the lidar simulation model and the motion compensation algorithm includes multiple modules/options for different scan sequences and geometries. This ensures the widest possible applicability.
2.2 Spatial and Temporal Sampling Limitations Regardless of the sampling strategy, all profiling wind Lidars share a common trait due to the laser source location - the greater the target scan range, the greater the distance between sample volumes. To calculate wind speed components from multiple LOS probes, there is an inherent assumption of wind speed uniformity between sampled volumes at a given height, roughly speaking. The true accuracy of the wind measurement therefore depends on the time and distance between separate LOS measurements, relative to the spatial and temporal variability of the wind, which is not known. The samples are typically separated by no more than a few seconds and a few tens of meters, so the Nyquist limit would indicate that wind features on scales below about ten seconds and/or 100 meters may not be resolved or even detected. The errors are compounded on a moving platform and under conditions of strong turbulence. Although the effect may not be significant for energy production estimates, it is important to understand the uncertainties and how they are affected by the vessel motion and the MCA processing. This is important for understanding uncertainties in the measurement of turbulence from a floating platform. This research is critically important in order for floating lidar measurements to achieve industry acceptance as an offshore wind resource measurement tool. There is another distinction which affects the data processing in the MCA; in the case of Continuous Wave (CW) Lidar, which uses an optical focus to set sensing range, the sampled volume length also increases with height, since it is a function of the focal length of the receiver lens. A separate module/option will be included for CW Lidar to account for this.
2.3 MCA – Preliminary Validation Study The Motion Compensation Algorithm has been derived and compiled, and preliminary validation has been completed using Lidar data from the WEA survey of 2013. The Leosphere WindCube provided motion corrected profile data at about 6 second intervals using Leosphere’s proprietary algorithm, which was designed for an anchored buoy platform. The WindCube provides horizontal wind speed components, u and v, in the earth frame, using an internal compass to correct for vessel heading. For a valid comparison, the raw LOS data from the WindCube was first converted to u and v components in the vessel frame, then corrected for vessel heading to match the earth frame. The manufacturer claims that the Leosphere MCA accounts for all six degrees of motion, but does not correct for a vessel underway (near constant linear translation), since it was designed for buoy deployment. The Leophere MCA output data was compared with the output of the UMBC MCA applied to the same LOS raw data, with excellent results. The methodology and results are discussed below. 8
2.4 MCA Requirements A Lidar compensation algorithm (MCA) processes input time series data from a Doppler LIDAR, a GPS, and an Inertial Navigation Unit (INU). It also accounts for the LIDARs position relative to the origin of the ship’s coordinate system. A state of the art GPS/INU data management system (Octopus F180R) sharing a common data CPU was used during the WEA field campaign. The GPS was positioned near the top of the pilot house, as shown in Figure 4, and provided highly accurate three dimensional velocity measurements derived from changes in geographical position. The INU was positioned on the starboard rail and angular speed and position were calculated by integrating measured accelerations.
Figure 4 - GPS and INU position on the MV Scarlett Isabelle during WEA campaign The position of the LIDAR within the ship’s coordinate system was measured by a handheld laser meter. ⃑ × 𝑟, of the LIDAR motion relative to the ship’s motions (at the INU) was then The cross product , 𝛺 calculated. Figure 5, below, is a schematic of the vectors that define the dynamic relative positions of the sampled volume, the GPS and Lidar units, and the two frames of reference. These are all defined and used to generate the motion compensation algorithm. Vectors in the ship frame of reference are denoted by lower case symbols, while vectors in the earth frame are denoted in Upper Case symbols. ⃑⃑⃑ 𝑙 , ⃑⃑⃑ For example,𝑅 𝑅𝑙idarand 𝑅⃑ shipdesignate the positions and motions of the wind, the Lidar unit, and the ship center in the fixed earth frame, while lower case 𝑟 is used to describe vectors in the ship frame of reference for the LOS wind component and the positions of the GPS and Lidar units. The positional vectors are required for compensating rotational motions. Appendix 1, attached, contains the derivation of the vector motion equations, included for convenience.
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Figure 5 - Position vectors in two frames of reference-
2.5 Conversion of Line of Sight Data to 3D Components in the Ship’s Frame The fundamental wind measurement provided by a Doppler LIDAR is the line of sight (LOS) wind, 𝑣𝐿𝑂𝑆 , which is defined as the wind component along the axis of the beam. 𝑣𝐿𝑂𝑆 = (𝑢 cos 𝛷 + 𝑣 sin 𝛷) sin 𝛩 + 𝑤 cos 𝛩
(1)
Given the zenith angle, Θ, and the azimuth angle, Φ, for at least three different probes at different angles, the three wind vector components can be resolved. The Windcube uses four LOS probes designated North, East, South, and West (NESW), with a cone angle of 52 deg (26 degrees from zenith). The expressions for LOS velocity are shown in terms of a cartesian coordinate system in the ships frame of reference (2). Figure 6 shows the geometry behind the calculations for translating LOS speed (along the laser axis) into reference frame components (u,v, w),. Equation (3) solves for the wind speed components in terms of LOS data and azimuth angle, and Equation (4) gives horizontal wind speed.
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Figure 6 - Profile view looking east for two beams (N and S) of Leosphere’s pulsed LIDAR system
𝑣𝐿𝑂𝑆,𝑁 = 𝑢 sin 𝛩 + 𝑤 cos 𝛩 𝑣𝐿𝑂𝑆,𝐸 = 𝑣 sin 𝛩 + 𝑤 cos 𝛩 𝑣𝐿𝑂𝑆,𝑆 = −𝑢 sin 𝛩 + 𝑤 cos 𝛩 {𝑣𝐿𝑂𝑆,𝑊 = −𝑣 sin 𝛩 + 𝑤 cos 𝛩 𝑢= 𝑣=
𝑤= {
(2)
𝑣𝐿𝑂𝑆,𝑁 −𝑣𝐿𝑂𝑆,𝑆 2 sin 𝛩 𝑣𝐿𝑂𝑆,𝐸 −𝑣𝐿𝑂𝑆,𝑊
(3)
2 sin 𝛩 𝑣𝐿𝑂𝑆,𝑁 +𝑣𝐿𝑂𝑆,𝐸 +𝑣𝐿𝑂𝑆,𝑆 +𝑣𝐿𝑂𝑆,𝑊 4 cos 𝛩
𝑣ℎ = √𝑢2 + 𝑣 2
(4)
The system of equations given by (3) is specific to the laser geometry and sampling strategy of the WindCube v2 LIDAR used during the WEA mission. The equations will be different for other lidar models and configurations, and these will be included in subsequent reports. The final MCA will include several different user-selectable modules, based on the top three or four wind Lidar technologies and a subset of different configurations. These equations hold only under the assumption that conditions are relatively homogeneous within the scanned area and over the scan time. This condition is usually satisfied since wind Lidar probes are only a few tens of meters and/or only a few seconds apart. Variability at these scales tends to average out quickly, so this assumption is generally safe unless the objective is to study the details of the wind impinging on the rotor, small scale turbulence, or extreme ramping of wind speed. 11
2.6 Azimuthal Correction for Vessel Heading For normal onshore deployment, the Lidar unit is oriented so the North laser beam matches true north, but at sea, a correction must be applied for vessel heading, as shown in Figure 7. Horizontal wind speed, given in Equation (4), was used with X, the heading-corrected wind direction (5) to obtain U and V, heading-corrected horizontal wind speed components. U and V may then be expressed as in (6), and LOS wind velocity can then be expressed as in (7), in a frame of reference aligned with true North. 𝛸 =𝜒+𝜓 𝑈 = 𝑣ℎ cos 𝛸 { 𝑉 = 𝑣ℎ sin 𝛸 𝑉𝐿𝑂𝑆 = (𝑈 cos 𝛷 + 𝑉 sin 𝛷) sin 𝛩 + 𝑤 cos 𝛩
(5) (6) (7)
Figure 7- Azimuthal Correction for Vessel Heading - Schematic
2.7 Case Study - Deriving Wind Components from LOS Wind Measurements Although the Leosphere MCA used in the WindCube is proprietary, the ouput may be used as a first order validation of the UMBC method/algorithm. The u and v (horizontal) wind components were derived with the set of equations above, and compared to the data from the Lidar’s internal MCA, using a case study during a low-level jet (LLJ) on the 19th of July. Figure 8 shows that the UMBC algorithm produces nearly identical component wind speeds to the Leosphere WindCube. The two components were compared separately to ensure there was no error or bias in wind direction. The large rapid changes in wind speed are due to the vessel making a 180 degree turn at the end of a transit line. Vessel motion and rotational correction is addressed in the following section. Figure 9 shows scatter plots and 12
a linear regression generated using the same data. The slopes of the linear regression are near unity. No offset was observed.
Figure 8 - u and v output by the WindCube algorithm (blue) overplotted with u and v derived by Equation 3 (red). Data has been corrected to align u with true north, but NOT motion corrected.
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Figure 9 - Scatter plot of UMBC derived wind vector components vs. WindCube internal algorithm wind vector components for July 19 Case Study. Data has been corrected to align u with true north, but NOT motion corrected.
2.8 Correction for Vessel Motion Vessel motion affects the Doppler shift of the LOS laser returns. Therefore a solution that removes all six degrees of motion (translational and rotational) from LOS wind must be applied. This requires a priori knowledge of the orientation, position and velocity of the Lidar unit at any given time. Figure 10, below, shows the basic vector mechanics of rotation; in this case vessel roll, given by the greek letter phi (φ). A detailed treatment of the vector and velocity calculations, and a summary of the corecode of the MatLab algorithm is presented in Appendices 2 and 3 of this report.
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Figure 10 - Roll Angle and Roll PositionVector of WindCube
The vector equations presented in (8), (9) and (10) use the GPS/INU system data and the LIDAR position on the vessel (described earlier) to place LOS wind measurements in a fixed Earth coordinate ⃑ 𝐿𝑂𝑆 , contains all three components of the wind is system (thus compensating for platform motion). 𝑉 ⃑ 𝑟𝑜𝑡 . The second term on the right vectorially added with translational motion, 𝑆, and rotational motion, 𝑉 hand side (RHS) of equation (8) is the superposition of translational motions in three dimensions. These motions are defined as boat speed and surge, B, sway, D, and heave, H. The most dominant contributor to motion for equation (9) is boat speed, which is a relatively steady translational motion as opposed to the other motions which are quasi-periodic, since they are wave induced. The last term on the RHS of ⃑ × 𝑟 term takes (8) defines the rotation about all three coordinate axes for a ship borne LIDAR. The 𝛺 angular speed, measured by the INU, and LIDAR position to quantify tangential velocity. The farther ⃑ × 𝑟 term becomes. The INU was away the LIDAR system is from the ship’s origin, the larger the 𝛺 mounted on the rail where higher accelerations due to angular motion increase the amplitude of the ⃑ × 𝑟 that takes the combined angles of roll, 𝜙, signal. 𝑄 𝑇 (equation 11) is a factor multiplied into 𝛺 pitch, 𝛳, and yaw, 𝜓 of the ship’s coordinate system and places LIDAR measurements into an Earth based coordinate system. For MCA, each LOS position (NESW) is taken into account separately when 15
carrying out motion compensation. The formula for 𝑄 𝑇 is given below, and the derivation can be found in Appendix 1, “Derivations of Motion Compensation Equations”, at the end of this document.
⃑ 𝐿𝑂𝑆,𝑐𝑜𝑟𝑟 = 𝑉 ⃑ 𝐿𝑂𝑆 + 𝑆 + 𝑉 ⃑ 𝑟𝑜𝑡 𝑉 ̂ + 𝐷𝑠̂ + 𝐻𝑧̂ 𝑆 = 𝐵ℎ ⃑ × 𝑟) ⃑ 𝑟𝑜𝑡 = 𝑄 𝑇 (𝛺 𝑉 cos 𝛳 cos 𝜓 𝑄 = ( cos 𝛳 sin 𝜓 − sin 𝛳 𝑇
(8) (9) (10)
− cos 𝜙 sin 𝜓 + sin 𝛳 cos 𝜓 sin 𝜙 cos 𝜙 cos 𝜓 + sin 𝛳 sin 𝜙 sin 𝜓 cos 𝛳 sin 𝜙
sin 𝜙 sin 𝜓 + cos 𝜙 sin 𝛳 cos 𝜓 − cos 𝜓 sin 𝜙 + cos 𝜙 sin 𝛳 sin 𝜓) cos 𝛳 cos 𝜙
(11)
2.9 Case Study – MCA Validation Figure 11 shows results of applying the UMBC MCA, using data from July 19th, with corrected LOS wind data plotted over uncorrected LOS wind data at hub height. The beam setting chosen for this case is for the zero degree “North” position (oriented towards the bow of the vessel). The large jumps in the raw data (blue points) are caused by the vessel making a 180 degree turn at the end of a transit line, which changes the “apparent” (from the vessel’s frame of reference) wind. The corrected LOS wind (red points) removes this effect along with the effects of vessel motion. Data were filtered to remove : \ data points with unacceptably low SNR (based on manufacturers recommendations), data taken during vessel turn-arounds outside the WEA time periods when not all of the three critical instruments (Lidar, GPS, INU) were producing valid data This was done for the entire LIDAR dataset collected during the WEA mission, and profiles were generated at approximately 6 second intervals.
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Figure 11 - Comparison of uncorrected LOS wind (blue) and corrected LOS wind (red) at hub height during July 19th (UTC). Figure 12 shows a curtain plot profile of wind speeds generated using 12 hours of Lidar profile data from the same period of analysis as Figure 4. A Low Level Jet is apparently sustained for about 6 hrs, after which it drops off quickly. This feature was also observed in output of the Weather Forecasting Research (WRF) model runs and Horn Point RADAR profiler, providing additional validation of the UMBC MCA. Before final processing and analysis of all the Lidar profile data from the WEA survey, further calibration and sensitivity testing of the MCA and data processing methodologies will be required. The next step is to test the MCA using the Forward Model Algorithm with different simulated wind fields. This is currently underway and results are expected for the next quarterly report.
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Figure 12 - Curtain plot of corrected wind speed derived from (3) and (4) during July 19th (UTC). Warm colors indicate higher wind speed
3 Deliverable 2 – WRF Modeling Configuration/Validation Deliverable No. 2 is a series of reports on comparisons of high resolution WRF simulations with data from field campaign and other measurements. The following section summarizes the first installment and provides the second installment of Deliverable 2. For convenience, the first few subsections provide some background and summarize previous efforts. The remainder of Section 3 includes additional case studies and analysis, and recommendations for optimal selection and configuration of NWP models for the WEA.
3.1 WRF Modeling Background 3.1.1 Data Fusion - Measure, Model, Adjust, Repeat An understanding of the mechanisms responsible for the variability in the wind resource allows a more productive diagnosis of model skill. It also points the way toward any modifications in model physics (such modifications are beyond the scope of this contract) . This leads to the development of more accurate models both for resource assessment, climatology, and forecasting. This “data fusion” is an iterative process that incorporates new observational data sets or new understandings of the forcing mechanisms into each model improvement. Understanding the origins of variability also leads to better measurement strategies. 18
3.1.2 Operational Models Operational models are models that are used continuously for live forecasting based on current observations. Current observations from ground based sensors, balloon sondes, satellite data, and other sources are assimilated into operational models on a cycle of one or more hours. RAP and NAMM are two operational models that cover the study area and are used in the UMBC analysis, as outlined below. Rapid Refresh (RAP) The Rapid Refresh2 (RAP) model is an operational NWP with relatively high spatial resolution of 13 km. RAP provides an 18 hr forecast that is continually corrected through an hourly data assimilation cycle that mitigates forecast drift. Data assimilation incorporates observation data into the model by minimizing both observational and model errors into an optimal analysis state, from which subsequent forecasts are made. RAP output is used as a 1st-order investigation of meteorological conditions during case studies of interest. However, operational models lack the spatial resolution required to resolve small scale phenomena driven by local forcings, such as topography, fine-scale differences in land use, surface temperature gradients, or low-level vertical structure in the atmosphere. North American Mesoscale (NAM) The North American Mesoscale3 (NAM) is also used for the outer domain in some simulations, since it provides an 84-hour forecast(F84) and a resolution of 12 km. NAM is re-initialized every 6 hours, and is vertically gridded on standard pressure levels. NAM is useful because it has a higher resolution than reanalysis data sets and the relatively long forecast period makes it easier to identify weather regimes that create model instability and increase forecast drift.
3.1.3 Research Models Research models are used for case studies of specific times and regions, and can be user-configured. The most common experimental NWP model in the U.S. is the Weather Research and Forecasting (WRF) model, which is open source. Proprietary models such as MASS, from AWS Truepower, may also be used if desired. Therefore, the Weather Research and Forecasting (WRF) model4 is used in this research to simulate finer spatio-temporal variability (~ 1km), using initial and boundary conditions that are derived from operational models, such as RAP. These simulations are invaluable for gathering improved wind resource statistics and understanding the underlying meteorology and wind uncertainties. 2
http://www.ncdc.noaa.gov/data-access/model-data/model-datasets/rapid-refresh-rap
3
http://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-mesoscale-forecast-system-nam
4
http://www2.mmm.ucar.edu/wrf/users/pub-doc.html
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3.1.4 ReAnalysis Data Sets Re-analysis data sets are a time series of states of the atmosphere, defined on a fixed grid, for a defined historic period of multiple years. They are generated using historic observational records that are input to a NWP model that does not change with time. Operational models are periodically improved and updated, so any modeling based on their compiled, long term output is subject to a shifting baseline, which creates great uncertainties for long term climatology. Re-analysis data sets avoid this problem by re-running the simulation over the designated time span while assimilating the historic observational data sets using an unchanging model, for consistency. Three re-analysis data sets are used in this study/report; NARR5, ERA6, and CFSR7. These are discussed further in section. 3.11. Other data sets are currently under evaluation for possible use in additional case studies 3.1.5 Archived Operational Data Sets For case studies or shorter periods than climatologies, operational model data sets can also be used for initial and boundary conditions, but they must first be downloaded and stored. One of the innovative strategies used by UMBC is to collect and preserve the “online rolling archives” from operational models. These represent the output of the most up-to-date operational models, but they are not normally used in research studies because these datasets are so large that they are only posted in “rolling archives” anywhere between two and 30 days after. The number of files and storage required makes them difficult to manage. For example, one year (2013) of NAM output takes about 1.8 TerraBytes of storage. However, for periods of analysis less than a few years, it is feasible to collect and analyze a selection of these datasets for case studies. UMBC has developed protocols and set up storage facilities for automatically archiving these rolling data sets for use in wind characterization and model testing and validation. The archived data presents a unique opportunity to characterize forecast errors that will eventually be important for wind forecasts. This process began in 2013, and will continue as long as funding is available. 3.1.6 Validation Data Sets Modeling results must be compared with actual observations in order to establish a "truth" metric that can be used for model validation. Modeling sensitivity tests must then be conducted under a range of conditions to better understand the dependence of skill on the prevailing meteorology, and the relative contributions of all the sources of uncertainty discussed above. Strategies may then be developed for reducing these uncertainties. This is especially important when few coincident observation pairs are available. It is also necessary to understand the local small scale forcings that can disrupt the spatial 5
http://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr
6
http://rda.ucar.edu/datasets/ds627.0/
7
http://www.ncdc.noaa.gov/data-access/model-data/model-datasets/climate-forecast-system-reanalysis-and-reforecast-cfsrr
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correlations between coastal and offshore low level winds in order to interpret differences between models and different types of measurements. While onshore measurements are abundant, there are typically fewer observations offshore. There are, however, two data sets available that were used for the purpose of model validation at rotor heights; one onshore, and one offshore.
WEA Lidar profiles - Wind lidar measurements were taken with a Leosphere v2 lidar during the 6-week MEA-sponsored summer 2013 offshore measurement campaign. The UMBC motion compensation algorithm was used to remove the effects of ship rotation and translation.
Horn Point –The wind profiling systems at Horn Point (HPLMD) Maryland is a 915 MHz radar wind profiler (RWP) and Radio Acoustic Sounding System (RASS). Horn Point is the closest profiler installation to the MD WEA, and is about 110 km west of the WEA. The systems have been operational since 2012, and provide wind speed and direction measurements up to 4 km above ground level. The first bin heights are 152-m , with a vertical resolution of approximately 60-m. Virtual temperature measurements are also provide by the RASS system. The temporal resolution is every 6 minutes with 30 minute smoothing. The continuous wind and thermal profiling, while not over the WEA, are nevertheless extremely useful for diagnosing model skill.
3.2 Second Quarter Modeling Research – Summary/Recap In the second quarter, field observations were compared to model simulations and re-analysis data sets using case studies during weather regimes that are dominated by local forcing. Preliminary analysis of the case studies, previously presented in the second Quarterly Report, shows that these phenomena can have a significant affect on wind regimes across the rotor span. The comparisons of observational data and model simulations revealed significant weaknesses in the models, including an inability to resolve small scale features and large errors in the evolution and locations of low level jets. Additional evidence suggests that sea surface temperatures are also playing a significant role in atmospheric thermal stability during the summer months, and this is not accurately simulated in the models. The previous analysis also showed that the widely used RAP analysis tends to underestimate the strength of Low Level Jets (LLJ) along the coast. Equally as important, model skill was shown to be dependent on the outer domain selected and the model configuration, including spatial resolution, re-intialization cycle, and forecast hour.
3.3 Third Quarter Modeling Research In the third quarter, efforts were focused on selecting the best modeling tools, strategies, and configurations to optimize the WRF model configuration and skill based on the findings of previous research. Another primary task was identifying weather regimes for case studies in order to assess model skill where it is lacking (during chaotic weather forecast regimes, as opposed to deterministic).
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Any mesoscale model-based wind characterization or climatology has to start with the “big picture” the input data set from which (e.g.,) WRF derives its initial and boundary conditions for the simulation period. WRF can also periodically “nudge” any or all of its domains toward the input data source (interpolated to each 3D gridpoint) at regular time intervals. Reanalysis data sets use observation data assimilation methods so that it does not stray too far from the true state of the atmosphere. When running WRF, inner domain nests interact with their coarser parent domain through a 2-way feedback process. Therefore, when grid nudging is only applied to the outer domain, it will influence the inner nests not to drift too far from "truth" during a simulation, but still allow the fine-scale nests to resolve their own hi-resolution meteorological features. This type of nudging is called Four Dimensional Data Assimilation (FDDA) since it occurs periodically, over time. The grid nudging process gently pushes WRF's outer domain toward the input data source states through successive corrections at each model time step, which ramp up (down) in strength on either side of the nominal analysis nudging time. Abrupt forcing of the model to any particular state would create unrealistic artifacts such as ”shockwaves” or convective instabilities. Simply stated, “grid nudging” attenuates the correction to limit rates of change in certain atmospheric parameters in order to avoid these artifacts.
3.4 Model Uncertainties These processes introduce uncertainties into model simulations. First,the initial three-dimensional state of the atmosphere can never be fully known. Thus, flawed input from the outer domain input source often leads to a flawed forecast. Selection of the input data set (e.g., a re-analysis data set) is therefore a critical step in developing a modeling strategy. Second, there are computational limits to modeled grid resolution, especially for a larger, regional domain. For example, currently available climatological datasets such as North American Regional Reanalysis (NARR) are gridded on standard barometric pressure levels on the vertical axis and a 32 km horizontal grid spacing. Winds forced by synoptic features can have significant local variability below this mesoscale. This introduces representative uncertainty since the model grid cell is assuming uniformity over a region with surface characteristics that are not uniform, especially near the coast. Sparse sampling in space and time would not be a problem if the wind statistics were homogeneous and stationary. However, it becomes a significant problem if key wind regimes are not resolved in the input data set, if the grid sampling is biased, or if quasi-stationary patterns of variability linked to the local features are not known and therefore not sampled. Third, parameterizations are necessary in models to approximate unresolved sub grid-scale processes in a statistical way that depends on the resolved variability. There is no single parameterization scheme that performs equally well under all meteorological conditions because certain processes tend to dominate during specific weather regimes, and it becomes impossible to fundamentally include all possibilities. Therefore parameterizations can lead to biases in model output that are linked to the atmospheric state. An understanding of the scales of variabilty in wind features versus the grid resolution is therefore extremely important. It is also critical to identify target weather regimes that produce poor model skill so the model may be evaluated and improved. 22
3.5 WRF Options and Model Permutations Once the input data set (or model) is selected, there are numerous options for running the WRF model. Below is a sample list of questions that can be tested by selecting different options related to grid nudging alone;
Do pressure level or native model level input data yield better results? What are the ranges of vertical and horizontal resolution that can produce good results? oes increased grid nudging frequency (1, 3, or 6-hr) improve skill? Does using 2-hour forecast (F02) input data yield better results than zero-hour analysis (F00) data (as observed in RAP simulations)? Do runs using full vertical profile grid nudging, non-PBL grid nudging, or free runs (no grid nudging) yield better results? Should all atmospheric variables be nudged? What are the benefits of utilizing surface nudging + grid nudging (interior)?
Other WRF options include;
(12) different Boundary Layer Parameterizations (11) different Cumulus/Cloud Parameterizations
In addition, certain PBL schemes are more effective under specific conditions related to stability, convection initiation, and turbulent kinetic energy. The selection of cloud parameterizations can also be optimized based on shallow vs. deep convection, neighborhood subsidence, or other parameters. The result is that there are hundreds, if not thousands of permutations of model selection and configuration. A comprehensive analysis of each is beyond the scope of this study, so the focus of preliminary sensitivity testing is to determine which configuration options or parameters can have a significant effect on model skill, and which do not. In order to evaluate model skill, the weather regimes that manifest poor model skill must first be identified and targeted.
3.6 Target Weather Regimes – NAM Analysis As one of the first offshore wind energy projects in the Mid-Atlantic, resource assessment challenges derive from unique regional processes that are not yet fully understood and features such as the Appalachian mountains, the complex coastal morphology (bays), strong thermal ocean stratification, and coastal upwelling. UMBC research indicates that these processes can act collectively and lead to complex local wind regimes that are not captured in long-term NOAA/NASA coarse-grained climate reanalysis data sets. One of the main purposes of UMBC research is to develop a modeling strategy and configuration that is able to reproduce these locally forced winds. During certain synoptic scale weather patterns, atmospheric motion can be described as “deterministic” because the forcings are 23
large scale and relatively predictable, and models exhibit greater skill because forecasts tend to converge with time. During other periods, when atmospheric flow is unstable or when local forcings tend to be the primary driving mechanisms (such as strong surface temperature gradients) these phenomena are smaller scale and less predictable., and can sometimes be described as “chaotic” regimes. They are chaotic because numerical weather prediction models show low skill and forecasts tend to be divergent among themselves or from observations. The WRF model performance is not expected to be sensitive to different configurations during deterministic regimes, so sensitivity tests are conducted during chaotic weather regimes which are more problematic. To identify chaotic weather regimes, a unique approach was developed whereby we analyzed forecast data over the entire year of 2013, part of which overlapped with wind lidar measurements at different times and locations. NAM provides 84-hour forecasts on a 12 km grid. NAM output was downloaded, archived, and subsetted over the study area for all of 2013, which comprised over 70,000 files and 1.8 TB of data. Plots were generated for wind speed and direction at 10-200m (every 10m), 200-600m (every 100m), 750m, 1km, 1.5km, and 2km. Other plots were generated for temperature, humidity, wind speed shear, and wind direction shear. A statistical summary will be provided in the final report, but samples are provided here for illustration and discussion. Figure 13, below, shows the results at 10 m ASL in July. From the top down, the plots show forecasts for surface pressure, wind speed, and wind direction over the MD WEA. The forecast hours start with red dots for hour-0 (the present analysis state, or most near-term forecast, and proceed up the color spectrum to purple at the 72-hour forecast (the most long-term three-day model projection). As would be expected, the periods of deterministic weather regimes show very little spread between forecast hours, as in Jul 5-8. Periods with large forecast spread can indicate chaotic weather regimes, as Jul 2025 illustrates divergent forecasts for both wind speed and direction. Figure 14 shows the wind speed error for each forecast time compared to a) the most near-term forecast (middle panel), and b) the wind speed error compared to observations from NDBC buoy 44009 (bottom panel). During the chaotic regimes, the 24 hr forecast often deviates by 3-4 m/s from the near-term forecast, while the error relative to the buoy often exceeds 4 m/s. This illustrates the inherent model error present in the 12-km operational models. High resolution WRF simulations could mitigate some of these errors, but only if grid nudging is utilized to reduce model drift, otherwise WRF will also drift in time similar to the NAM 3-day forecast.
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25Jul 8 - 26 2013 Figure 13 - NAM Results by Forecast hour,
Figure 14 - NAM Error by Forecast26Hour
3.7 Pressure Level Interpolation There is another source of uncertainty and error inherent in most operational and reanalysis data sets (excluding RAP). Modern NWP models typically calculate atmospheric motions on a native terrainfollowing vertical coordinate (often referred to as “eta” or “sigma” levels). This coordinate system resolves flow over complex mountainous terrain better by eliminating discontinuous stepped-level artifacts near the surface that would be inherent in using either a pressure or height vertical coordinate system. However, model output is always post-processed to standard pressure levels when datasets are posted online for the public. This is partly because, in the past, upper-air forecasts were primarily used by pilots with altimeter instruments, and therefore upper air charts were plotted on pressure levels. This tradition continues even though most modern NWP models have moved away from calculations on pressure levels, partly because eta/sigma coordinates are less intuitive for plotting, and because file sizes are typically smaller using standard pressure levels than they would be if output was on the model's native levels. The main problem with using pressure level output for sites near sea level is that the lowest level is 1000 hPa, which corresponds to a (variable) height range of 0-300 meters AGL, and the second level at 975 hPa which has a height range of 200-500m in the WEA. However, unlike eta/sigma coordinates, the height of a pressure level surface depends on the mean temperature of the column of air beneath it and depends on the weather regime. This is very problematic for calculating turbine-height wind statistics when using these types of datasets because vertical interpolation (or extrapolation) is needed to calculate values at consistent heights spanning the rotor, essentially filling-in the data voids and possible introducing major errors into statistical calculations. This is also problematic from the standpoint of initializing WRF with these input data sources. WRF must also interpolate/extrapolate these pressure level values to its native eta coordinates. It is important to note that these issues are almost inevitable when downscaling from a coarse resolution climate dataset to a higher resolution grid, and the uncertainties are not always taken into account by standard methodologies used within the wind resource community. Figure 15 shows a plot of NAM forecast pressure levels and their heights for Jan 2014. There are several weather regimes where the first pressure level is above 200m, and thus above rotor height. This illustrates the importance of identifying conditions and weather regimes that cause large-domain models to miss turbine-heightwind features. For the above reasons UMBC is making every effort to download and archive any datasets that output data on the model's native levels. These datasets are hard to locate and are typically not posted online for multiple months or years, unlike pressure level datasets.
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Figure 15 - Heights of NAM Pressure Levels, Jan 2 - Jan 31 2014
3.8 WRF Vertical Resolution Vertical resolution is another parameter that can affect model skill and uncertainty. Figure 16 shows vertical levels for the NREL Wind ToolKIT (Draxl et al, 2013) and those selected for the UMBC WRF model configuration. Gray shading roughly indicates the wind-turbine height span, although newer models can exceed 200m to blade tip height. As observed in the below figure, UMBC WRF runs double the number of vertical levels compared to other wind datasets. Higher vertical resolution allows wind shear profiles , turbulent processes, and low level features to be better resolved.
Figure 16 - Vertical Profile REsolution, WRF vs. Wind Toolkit 28
3.9 WRF Grid Resolution The UMBC-WRF setup runs with three telescoping (nested) domains in order to increase spatial resolution from the outer domain to coastal areas of interest, including the MD WEA. Figure 17 shows the boundaries of each domain and the size of the grid cells within each. Topographic features are rendered at the true model resolution. The final domain produces gridded data at a resolution of 1.3 km across Delmarva and the WEA. The outer domain resolution was chosen to approximately match input data sources from NAM and RAP. The innermost nest is able to resolve kilometer-scale features. Domains/nests are generally placed with large buffers on the sides of prevailing winds (in this case the west) to allow time for fine-scale model features to fully develop before they advect to specific locations of interest within the domain. It is desirable to avoid, as much as possible, placing domain boundaries over major orographic features as they can introduce boundary artifacts as the atmosphere flows between domains. Therefore, the inner nest was placed to avoid the Appalachian Mountains but still include the major urban centers and the Beltsville wind profiler.
Figure 17 - Three WRF Domains Used and Corresponding Grid Cell Sizes
3.10 Refresh Time and Forecast Hour 29
Figure 18 shows NAM plots of the 80m wind speed and direction over the MD WEA on Jul 20-25, selected to represent a chaotic regime. The spectral color key indicates forecast hour, starting with red at zero, identical to previous plots. When the chaotic regime begins around July 22, the forecast divergence is extreme, often exceeding 4 m/s for the day-ahead forecast (vertical distance from red to lt. green) and 8 m/s for the 3 day forecast (vertical distance from red to blue/purple). This is about two to three times the difference observed during deterministic regimes, as on July 20. This illustrates the impact of forecast hour on model skill, as the initial conditions (boundary and internal) become more distant in time, or “stale”. The effect is more severe during chaotic regimes, giving model results that have little connection to reality beyond forecast hour 12. To improve WRF model skill, it is important to understand and wisely choose the input data source used for the simulation, whether that is a reanalysis data set or archived operational model output. To address this issue, the UMBC modeling strategy uses grid -nudging to continually limit forecast drift by nudging at regular intervals.
Figure 18 - Example of Forecast Drift, Jul23
3.11 Outer Domain Data Set (Initial, Boundary Conditions, FDDA Updates) Counting different grid sizes and physics packages, there are at least 17 different model data sets available that cover the MD WEA, so a comprehensive evaluation of each is beyond the scope of currently funded research. Of these 17, five were selected for the initial analysis based on their spatial resolution, temporal resolution, period of coverage, vertical levels, and frequency of use in wind climatology studies. Future research will narrow down the remaining data sets to select the most promising for additional case studies in subsequent phases of sensitivity testing. Table 1 shows the five data sets (two operational – NAM and RAP - and three reanalysis) that were used as input to WRF simulations in the first phase of sensitivity testing. The table includes the horizontal grid resolution, whether the models are based on pressure levels or user-selected heights, what forecast 30
hour was used, and how often the model was updated (nudged) with observational data. In every case, WRF was configured to match the update cycle of the input data source. Table 1 – Input Datasets and Key Parameters Type
Model/Dataset
Operational
NAM-218 RAP-130 NARR CFSRRv2 ERA- Intm
Re-analysis
Grid Size 12km 13km 32km ~55km* ~77km*
press levels? y no, model y y y
F-Hour 00 02** 00 00 00
U-Cycle 6 1 3 6 6
(*) - translated from degrees lat/long (**) - Previous UMBC analysis compared the RAP data to Lidar profiles and other reference data sets in or near the WEA, (see Second Quarterly Report) and it was found that RAP forecast hour 2 (F02) provides the highest forecasting skill, so this was the data set selected from RAP output.
Operational – NAM and RAP Some results from one of the NAM simulations are shown in Figure 19. The top panel shows a 2 km vertical scale curtain plot of wind speed over time at Horn Point, MD, and the two plots below show WRF model output using NAM 218 data as input. The middle plot shows the WRF "free run" simulation results without nudging, and the bottom panel shows simulation results using 6-hourly grid nudging of WRF's outer domain toward NAM's 3D analysis states (interpolated to WRF's grid points) throughout the 7.5 day simulation period (in other words, nudged toward 30 different NAM analysis states during the course of the WRF simulation). Results are somewhat mixed, but overall WRF with grid nudging does a better job reproducing the wind features. The HP profiler (top panel) shows a low level jet on Jul 20 and Jul 21, and a weaker event on Jul 23. Yellow circles show events of interest observed by the profiler and red circles indicate which strategy showed the highest skill. During the strong event on Jul 20, the addition of nudging reduced the model skill, as evidenced by the weaker and dislocated jet in the bottom panel. However, nudging improved the model skill in capturing the event of Jul 21. Events on Jul 23 and 24 were generally captured better with nudging than without. Overall, nudging improved model skill. Without nudging, the feature on 7/23 would have been completely missed, changing the weekly statistics significantly. Figure 20 shows curtain plots of the same HP data compared to WRF output using RAP F02 as input. Nudging with RAP 2-hour forecast files led to higher LLJ core wind speeds that are closer to profiler values. The high frequency of nudging (1-hour) introduced a little more noise than NAM, but overall, represented wind features quite well. The main difference between NAM and RAP simulations were the LLJ core wind speed strength, and some erroneous wind artifacts introduced on 7/22 – however, these artifacts were also present in the “No FDDA” run (unlike the NAM runs- see Figure 19) which means
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that these were not introduced by nudging but rather carried over from the model’s initial condition field at the simulation start. RAP simulates the 7/19 & 7/25 LLJ feature shapes better than NAM. The comparison between WRF runs using NAM and RAP operational input data produced mixed results; some features were resolved better than others, or some were artificially generated, depending on nudging. Overall, the sensitivity analysis showed that the use of grid nudging improved the resolution of high wind features compared to free runs. “Free Run” climatologies that do not use nudging to steer the model closer to reality can drift far from the actual state of the atmosphere. This illustrates the complexity in assessing model configuration and the importance of understanding sources of uncertainty. Further research is under way to improve nudging strategies and model skill.
Re-analysis - ERA-Interim, CFSRRv2, and NARR Variability and uncertainty in the offshore wind resource has a variety of origins including measurement errors or temporally and spatially sparse or biased sampling [10, 11]. Errors are also introduced from coarse model grids that do not resolve small scale variability due to local forcing. This is the case for the currently available climatological datasets such as North American Regional Reanalysis (NARR), gridded on standard pressure levels and with a 32 km grid spacing. Winds forced by larger scale mesoscale or synoptic features can have strong local finescale variability that is not captured correctly in these larger grids. Figure 21 shows the same case study period using the three re-analysis data sets as WRF input, all with nudging. The top panel shows the same curtain plot as the previous figure, from Horn Point, July 18-26, and the three plots below it show results from using three different re-analysis data sets as input; ERAInterim, CFSRRv2, and NARR. As the notations indicate, ERA-Interim tended to oversmooth features, severely underestimating wind speeds at hub height and above, while CFSRRv2 captured the strength, structure and timing of the low level jet better than the others. The sensitivity tests showed that CFSRR is the best of the tested reanalysis models, with NARR coming in a close second. The case studies also showed that ERA data does not resolve local wind features well, likely due to its 77 km grid spacing. This has significant implications since the recently released NREL Wind Toolkit relies on ERA for input data, and is re-initialized on a 30 day cycle (Draxl et al 2013).
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Figure 19 - Curtain Plots - HP Profiler vs. NAM w/ and w/out FDDA 33
Figure 20 - Curtain Plots, HP vs. RAP F02, with and w/out nudging, PBL nudging 34
Figure 21 - Curtain Plots, HD Profiler vs. Reanalysis: ERA, CFSRR, NARR 35
4 Climatology-related Research Completed in the Third Quarter One of the components of laying the groundwork for a climatology is to begin with an examination of data from the most up-to-date operational models. Although this limits the analysis to one or two years it can be instructive for identifying seasonal trends and variability. Figure 22 shows wind roses at 100m over the MD WEA using data from NAM, and split up by season. With the caveat that this is only one year, there is a distinctly seasonal modality to wind direction, with summertime 2013 winds coming almost exclusively from the SSW sector, and winter winds coming predominantly from the NW sector. Future climatology research will include multiple years of data and evaluation of other models, with greater grid resolution and optimized model configurations. These preliminary investigations are, however, useful for guiding selection of the models and development of an eventual strategy for assessing wake effects and power production for various grid layouts in the Mid-Atlantic where the wind direction is multi-modal, unlike, for example, the North Sea.
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Figure 22 - Seasonal wind roses for MD WEA 2013, from NAM 218, 100 m level Figure 23 shows the wind speed distributions for Mar-May 2013 (top panel) and for Jun-Aug 2013 (bottom panel) at 10m. 50m, and 80m. Weibull curves were fitted to the data and the Weibull parameters are given in the figures, along with the mode. Although this only represents one year of data, it generally confirms the basic premise that wind speeds increase with height and also indicates that springtime winds are stronger than summertime. It is also apparent that a single season of data does not conform well to the commonly used Weibull fit.
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Figure 23 - NAM 218, WS Dist for Spring and Summer, 3 heights at MD WEA 38
4.1 Laying the Groundwork for WEA Climatology There are various methodologies for developing a climatology at a wind project site, but all of them require the use of regional mesoscale models. High resolution models used for downscaling all require low resolution climate reanalysis fields to prescribe the meteorological fields as initial conditions and on the boundary of the high resolution grid. If there are errors or biases in these boundary and initial conditions, they will be inherited to some extent in the high resolution domain. Reconstructing a climatology is computationally expensive, so it is important to first evaluate the quality of the reanalysis data. It is also important to examine other available multi-year high resolution mesoscale model simulations and to diagnose possible errors by comparison to observations. This analysis therefore begins with an evaluation of some key existing data sets, including the input data sets that were used for existing climatologies of the MD WEA.
4.2 Eastern Regional Wind Data Set The Eastern Regional Wind Resource (ERWR) Dataset was created by AWS Truewind for NREL to provide wind resource data based on mesoscale modeling for the Eastern Wind Integration and Transmission Study (EWITS) to evaluate the impact of wind power on the regional grid (Brower 2010). From the report: "NREL required a set of data that would capture in a realistic fashion both the temporal and spatial variability of the wind resource and associated wind power generation of onshore and offshore projects totaling 300 GW. These data were to be based on high--resolution simulations of the historical climate performed by a mesoscale numerical weather prediction (NWP) model covering 2004 to 2006. " The simulations used the Mesoscale Atmospheric Simulation System (MASS), a proprietary model developed by AWS Truewind partner MESO, Inc., and were compared to simulations run using the WRF. model, which is an open-source, community weather model. Both were run at 2 km resolution. The MASS (v. 6.8) runs were initialized with the NCEP/NCAR Global Reanalysis (NNGR) dataset (2.5 deg resolution) or by the North American Regional Reanalysis (NARR) dataset (32 km resolution). Comparisons were made to simulations with the WRF model using both the NNGR and NARR datasets as boundary conditions. All model results were compared to proprietary tower data at 10 locations, however only one offshore tower was used, and only for a single year. The location of the offshore tower was proprietary and not disclosed. The configurations with the best performance were the MASS/NNGR and the WRF/NARR and these were comparable. The MASS/NARR and WRF/NNGR simulations showed significant differences compared to observations. Some of the differences were attributed to insufficient vertical resolution of the MASS model. The MASS model made a number of adjustments and normalizations to the ERWR dataset to correct for biases and other discrepancies by comparing to the available (mostly land-based) tower observational data. 39
4.3 CHLV2 (43m) vs ERWR (80m) It is not clear whether the ERWR results were compared to the wind data set from the NDBC station CHLV2 (Chesapeake Light Tower). One possible reason it may not have been used is that the 43m ASL data was considered to be too far below their 80 and 100m levels. This data may be useful as a model diagnostic, however, as demonstrated in the series of plots shown in Figure 24 of the hourly mean wind speed for each month, for each of the years 2004-2006. The main features of these plots are: 1) The 80m windspeed is higher than the 43m wind during the day. At night, they are often comparable, or the 43m wind is stronger, throughout the year. 2) The diurnal cycle at 43m is quite different from the diurnal cycle at 80m. Peak 80m winds during the warm season (April-September) occur from 3-7pm, and are therefore load-coincident. The 43m wind, on the other hand has a mid-day minimum and the wind max often appears later in the day and is not as strong. If the 80m model data is accurate, the differences between the 43m and 80m data suggest a high degree of variability in the offshore wind profile across the rotor and therefore significant uncertainty in power generation, at least at the CHLV2 site. If simple statistical assumptions such as wind speed profiles based on power laws were valid, then the two curves should track one another closely since the 80m wind is just a scaling of the 43m wind. It is also possible that the model offshore data has some problems that may not have been apparent due to the limited amount of offshore data used for validation. In either case, these results point to significant uncertainty in the wind profile near hub height.
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41
42
43
Figure 24 - Comparison of the 80m ERWR and 43m Chlv2 hourly mean windspeed for each month and for each year 2004-2006. Figure 25 is a detailed comparison of the 43m and 80m timeseries; these examples were chosen at random, since the differences are representative of other time periods. There is good agreement in the variability on time scales of a few days. This indicates that the model represents the large scale synoptic variability in a realistic way, but this is not a high standard since all models should be able to perform at this level. The models show much lower skill capturing the hourly changes throughout the diurnal cycle, especially in July (bottom panel), when small scale land-sea thermal contrasts and other local forcings dominate synoptic forcing of low level winds.
Figure 25 - Timeseries comparisons of 43m (CHLV2) and 80m (EWITS/ERWR) winds for two arbitrarily selected 2-week time periods, Jan 1-15, 2004 (top) and Jul 1-15, 2005 (bottom).
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A first order comparison of the windspeed pdfs based on the hourly data for the 3 years combined is shown in Figure 26. The pdfs are very similar, and mask the large differences seen in the previous two figures. This illustrates that while the overall distribution of the wind speeds is nearly the same, temporal variation can be significant on hourly scales. This can have a significant effect on the projected market value of the energy and on any production estimates based on the EWITS data set.
Figure 26 - Comparison of 43m (chlv2) and 80m (ERWR model) windspeed pdfs for all hourly data for years 2004 and 2006. (2005 was excluded due to missing chlv2 data). Left: warm season (MJJASO);Right: cool season (NDJFMA).
4.4 Negative Shear Observations For a brief time, NDBC 44010 was located about 2 km from CHLV2 for 1985 and again during 1994-5, and winds were measured at 3m ASL. Figure 27 is the pdf of the difference in the wind at the two levels, i.e. windspeed at 43m minus the windspeed at 3m. Note that the summer months were not adequately sampled. This plot shows that surface winds were greater than the winds at 43 m more than 1/3 of the time. This occurred during the cool season also, so it cannot be explained by a sea-breeze effect. This violates the traditional assumption that windspeed increases with height. The cause is not yet clear, but it may be related to SST or wind variability related to the location of CHLV2 at the mouth of the Chesapeake Bay, or possible the impact of the Gulf Stream moving closer to shore in the region. This is another example of the impact of local forcings and the uncertainty that can arise by assuming that the statistics of offshore wind are homogeneous in the MidAtlantic. It illustrates why the unique characteristics of the MidAtlantic must be taken into account in a wind resource assessment. Given the limited amount of offshore observations of the wind profile in the Mid-Atlantic, it is tempting to use the CHLV2 data to generalize about wind speeds and profiles in the region. However, it is highly unlikely that this site is representative of locations over 200 km to the north (MD WEA) due to the localized forcings and features discussed above.
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Figure 27 - Pdf of the difference in windspeed at CHLV2 (43m) vs 44010 (3m) during the time when data from both locations was available. Red line indicates zero shear (difference).
Evidence for a decrease of windspeed with height was also found in the MD WEA during the summer 2013 campaign. This can be seen in the left panel of Figure 28, which depicts wind speed and direction profiles taken with the DAWN Lidar during the June 24 NASA overfly. This does not appear to be a sea breeze circulation, since the right panel shows that winds were from the SSW at this time. In addition, this also shows a clockwise veer in wind direction of about 10o across the rotor layer. The frequency of occurrence of decreasing winds with height (negative shear) during the summer 2013, and the conditions under which they occur, are currently under investigation.
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Figure 28 - Wind speed and direction vertical profiles taken with the DAWN airborne Lidar on June 24, 2013. Left panel: wind speed. Right panel: wind direction. 110 profiles were measured, but for clarity, only every 5th profile is shown.
4.5 Inter-Turbine Scales of Variabililty Uncertainties in AEP also arise from unknown smaller scale spatiotemporal variability across the wind farm and on inter-turbine scales, and how the variability for a “wake-free” base state compares to perturbations induced by wakes. Very little is known about this without a high resolution 3D grid of actual observations. The closest approximation to a WEA-wide 3D observational data set was obtained from the NASA/Langley DAWN lidar scans in June 2013. The observations were separated by distances on the order of turbine spacing. Figure 29 shows horizontal variability at hub height that was sampled as the aircraft criss-crossed over the WEA from top to bottom, then again from bottom to top. Wind speeds at turbine height varied from about 6 to 15 m/s and wind directions varied over a 40 deg range at turbine height over the 70 minute timespan of the survey. Assumptions of constant wind speed and direction across a wind farm in the absence of wake effects would clearly not apply to this situation.
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Figure 29 - Km scale variabiilty of wind speed and direction across the MD WEA, DAWN Lidar data, June 24 2013 A high degree of variability was also observed by the wind lidar during the summer 2013 campaign. Figure 30 shows an example of two profiles separated in time by 5 minutes and in space by 1 km, which is approximately the interturbine spacing (10D). The plot shows differences in turbine-height wind speeds that are on the order of 6 m/s in the absence of wake effects. A more detailed investigation of wind speed and direction variability on inter-turbine scales is underway.
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Approx. rotor span
Figure 30 - Comparison of two wind speed profiles separated in time by 5 minutes and in space by 5 km. Data taken with the Leosphere V2 wind lidar. Arrow shows approximate rotor span of 6 MW turbine.
5 Summary Discussion and Implications for Reducing Uncertainty 5.1 Wind Lidar from a Moving Vessel Emerging wind Lidar technologies will be needed to sample at blade tip height, which can be over 200 m - far beyond the practical limits of offshore met towers. Floating Lidar technology has the potential to replace met towers in many cases, once proven and accepted by the industry. However, current systems are integrated into floating buoys and incorporate proprietary MCAs, which are all designed for motion of an anchored buoy. By developing and validating an algorithm that can be applied to a Lidar unit on a moving vessel, the UMBC MCA is opening the range of deployment possibilities to an entirely new set of platforms. Many types of vessels that could serve as Lidar platforms traverse in or near the MD WEA on a daily basis. During WEA survey and construction operations, vessels could be continuously collecting wind profile data for use in configuring and nudging forecast models, and fine-tuning production estimates. The cost of lidar units will come down significantly as technology improves and economies of scale are realized. Eventually, every commercial vessel may be equipped with a wind 49
Lidar profiler, and the data used in forecasting models, just as is often done with weather radar. For example, recent research has shown how a strategic synthesis of data from offshore wind lidars and satellite microwave wind retrievals can significantly reduce uncertainty in resource mapping [12]. Project Norsewind8, a consortium of govt., industry, and academia, used a similar methodology to create high resolution, low error wind atlases of the North and Baltic Seas. The preliminary validation to date indicates that the UMBC MCA has nearly identical error and uncertainty characteristics to the internal Leosphere MCA. The next stage of the analysis is to use the Forward Model Algorithm to test the MCA under simulated wind fields and platform motions. This is currently underway and results are expected in the next quarterly report.
5.2 Summertime Local Forcing Mechanisms In the absence of strong synoptic forcing, smaller scale regional mechanisms in the Mid-Atlantic are revealed. These have the potential to generate locally strong winds that may coincide with high grid loads [1.2]. Satellite SST data often shows cool water upwelling along the coast, creating a low-level local baroclinic zone and changing the low level thermal stability. There has been recent research on the impact of low level thermal stability on winds in the rotor layer [3,4]. The overall impact of such events on the wind resource offshore depends on how often they occur, which remains to be determined. Their predictability depends on whether the larger synoptic scale situation leads to conditions that make such small scale events more likely, and if any synoptic scale precursor conditions can be identified. A detailed investigation of low level jet formation and its precursors is ongoing. In addition, the occurrence of negative shear conditions across the rotor layer may be more frequent than expected, as shown by the preliminary analysis of data from DAWN Lidar, CHLV2, and NDBC 44010. This has significant implications for rotor design, production estimates, and power forecasting. Further investigation of this phenomenon is ongoing.
5.3 Model Setup, Configuration, and Uncertainty A primary focus of Q3 modeling research was to test WRF sensitivity to different configurations including input data sets, grid nudging strategies, and horizontal and vertical resolutions. 5.3.1 Input Data Set Sensitivity – Re-analysis Third quarter research has demonstrated that model skill in the coastal MD region can be highly sensitive to the setup and configuration parameters, and the sensitivity can depend on the weather regime. Weather regimes that caused the poorest model skill were targeted first since they represent the “low hanging fruit”, where model instability is magnified. By identifying and selecting the input data sets that result in the highest skill during these target weather regimes, WRF model skill has been 8
http://www.norsewind.eu/
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significantly improved. At this stage of the WRF sensitivity analysis, CFSRRv2 appears to be the best input data set for multi-year climatology, with full nudging of the WRF outer domain. ERA Interim showed the lowest skill of the three re-analysis data sets tested (versus the Horn Point Profiler). This is a significant observation since ERA Interim is used for the NREL WindToolkit (Draxl et al 2013). This is, however, not surprising since it reflects the lack of tall coastal and offshore met towers available for model validation. For shorter case studies, the operational model RAP, using the F 02 forecast hr. generally exhibits the highest skill as an input data set, likely due to its rapid update cycle (1 hr). 5.3.2 Input Data Set Sensitivity - Operational The comparison between WRF runs using NAM and RAP operational input data produced mixed results; some features were resolved better than others, or some were artificially generated, depending on nudging. Overall, the sensitivity analysis showed that the use of grid nudging improved the resolution of high wind features compared to free runs. It also showed that use of the RAP F02 data led to better results in general. “Free Run” climatologies that do not use nudging to steer the model back to reality can drift far from the actual state of the atmosphere. This illustrates the complexity in evaluating model configurations and the importance of understanding sources of uncertainty. 5.3.3 Configuration Sensitivity – Vertical Resolution Because almost all available re-analysis data sets are provided in standard pressure levels, the lowest wind speed provided is often above 200m height. This introduces significant uncertainty by making large extrapolations based on standard shear profiles necessary. These profiles are based on mixed layer similiarity theory [5] or characterized by power laws [6] for convenience. These may be reasonable approximations in the lowest part of the boundary layer or under conditions of neutral stability where a statistical description is robust. However, previous case studies showed dramatic departures from these simple laws. If offshore wind profiles are frequently different than what is shown by current models and profile parameterizations, then average annual hub height wind speeds in the WEA would be significantly different than currently estimated. How often such behavior occurs is the subject of ongoing study. This uncertainty is being mitigated by using input data sets with higher vertical resolution, on “native” scales, (such as RAP) and by configuring WRF with more layers in the rotor plane. UMBC is also identifying and archiving additional data sets on native levels for further analysis.
5.4 Conclusion By identifying and understanding the sources of error and uncertainty and how they affect different models of the MD WEA and surrounding regions, model skill has been significantly improved compared to the setups and configurations used in current climatological data sets. This will ultimately result in more accurate climatologies, more accurate day-ahead forecasts, and more efficient wind farm and rotor designs. 51
5.5 Benefits Matrix The table below provides a matrix of UMBC research activities, including those under a linked but separate MEA contract (PI- Delgado) and how the benefits will apply to different areas/stages of development. Forecasting skill is important for reducing the costs and risks of marine operations and also for increasing revenue. Day-Ahead forecasting skill helps reduce non-performance deviation penalties on the PJM day-ahead market9, increasing market revenue and reducing ratepayer impacts.
9
Penalties for non-performance are levied when hourly average production deviates from previously bid amounts, whether it is too high or too low
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6 Work Status and Schedule The 20 steps in Table 1 below outline major tasks and milestones of the project and their current status. Some tasks can be performed concurrently and some must be performed sequentially. Task/Milestone 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Campaign Planning Instrument Acquisition Redundant disk array, high speed server setup Data QA/QC Public archive /website /portal for data / reports Measurement campaign offshore Measurement campaign onshore Develop underway profiling lidar MCA Validate underway profiling lidar MCA Gather other data sets / archives Prelim. Model comparisons – WRF Studies PI Delgado - Offshore Shear Analysis PI Delgado – Scanning Lidar Analysis ID case studies and co located data sets Statistical Studies and Correlations Evaluate and reconfigure models Model skill testing Model sensitivity testing Sample-based climatology / wind statistics Publish and Present
Started
Finished
x x
July 2013 July 2013 Oct 2013
Nov 2013 Jan 2013 July 2013 July 2013 Oct 2013 Feb 2013 Oct 2013 Oct 2013 Oct 2013 Oct 2013 Oct 2013 Mar 2013 Jan 2013 May 2013 May 2013 Proj. YR2 Nov 2013
Status Notes
ongoing by June Aug 2013 ongoing Feb 2013 May 2013 ongoing May 2013 ongoing ongoing ongoing ongoing ongoing ongoing ongoing ongoing
Acknowledgments Sponsorship of this work from the Maryland Energy Administration is gratefully acknowledged. The authors also wish to thank Frank Monaldo of JHU-APL, Bruce Bailey of AWS Truepower and Bill Boicourt of UMCES for their gratis feedback and advice.
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7 References 1. Colle BA, Novak DR. The New York Bight jet: climatology and dynamical evolution. Monthly Weather Review 2010; 138; 2385–2404. 2. Dvorak, MJ, Corcoran BA, Ten Hoeve JE, McIntyre NG and Jacobson MZ. US East Coast offshore wind energy resources and their relationship to peak-time electricity demand. Wind Energy 2013; 16; 977–997. 3. Lange B, Larsen S, Højstrup J, Barthelmie R. The Influence of Thermal Effects on the Wind Speed Profile of the Coastal Marine Boundary Layer. Boundary-Layer Meteorology, September 2004, Volume 112, Issue 3, pp 587-617. 4. Wharton S, Lundquist JK. Atmospheric stability affects wind turbine power collection. Environmental Research Letters, 2012,Volume 7 Number 1. 5. Obukhov AM. Turbulence in an atmosphere with a non-uniform temperature. Boundary Layer Meteorology 2 (1971 English Translation). 7–29. 6. Burton T, Jenkins N, Sharpe D, Bossanyi E. Wind Energy Handbook: Edition 2. John Wiley & Sons, 2011; 219. 7. Bailey B and Freedman J. A Regional Assessment of the US Offshore Wind Energy Resource Through the Use of Mesoscale Modeling. 2008, Marine Technology Soc. Journal, 42, 8-18. 8. RAP information available at (http://rapidrefresh.noaa.gov/) and at http://www.ncdc.noaa.gov/dataaccess/model-data/model-datasets/rapid-refresh-rap 9. Skamarock WC et al. A Description of the Advanced Research WRF Version 2. NCAR TECHNICAL NOTE 468, June 2005. 10. Brower MC. Wind Resource Assessment: A Practical Guide to Developing a Wind Project. John Wiley & Sons, 2012. New Jersey, 280 pp. 11. Lackner M, Rogers AL, Manwell JF. Uncertainty Analysis in MCP-Based Wind Resource Assessment and Energy Production Estimation. Journal of Solar Energy Engineering 130.3 (2008). 12. Williams B. New Applications of Remote Sensing Technology for Offshore Wind Power. Graduate Thesis. University of Delaware, May 2013. available at (http://www.ceoe.udel.edu/windpower/resources/Williams-Final-Thesis-14-May.pdf) 54
Rabenhorst SD. Field Observations and Model Simulations of Low-Level Flows Over The Mid-Atlantic During August 1-5, 2006, Atmospheric and Oceanic Science, 2012, University of Maryland, College Park, 217 pp. Brower 2010 -Truewind final report, M. Brower, Development of Eastern Regional Wind Resource and Wind Plant Output Datasets Subcontract No. ACO--8--88500--01, Final Report, 2010 Draxl, C., Hodge, B-M., Orwig, K., Jones, W., Searight, K., Getman, D. Harrold, S. McCaa, J. Cline, J. and Clark, C., Advancements in Wind Integration Study Data Modeling: The Wind Integration National Dataset (WIND) Toolkit Preprint C, Donference Paper, 12th International Workshop on the Large Scale Integration of Wind Power into Power Systems London, England, October 2013
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8 Appendix 1 – Derivation of Equations for Lidar MCA 8.1 Review of Vector Motion Equations (previously submitted) Please refer to Appendix 1 of the First Quarterly Report, incorporated herein by reference.
8.2 Line of Sight to Wind to Earth Frame Components - Conversion Equations for Leosphere v2 Offshore Lidar. 𝛷 = 0° 𝑣𝐿𝑂𝑆,𝑁 = (𝑢 cos 0 + 𝑣 sin 0) sin 𝛩 + 𝑤 cos 𝛩 = 𝑢 sin 𝛩 + 𝑤 cos 𝛩 𝛷 = 90° 𝑣𝐿𝑂𝑆,𝐸 = (𝑢 cos 90 + 𝑣 sin 90) sin 𝛩 + 𝑤 cos 𝛩
= 𝑣 sin 𝛩 + 𝑤 cos 𝛩
𝛷 = 180° 𝑣𝐿𝑂𝑆,𝑆 = (𝑢 cos 180 + 𝑣 sin 180) sin 𝛩 + 𝑤 cos 𝛩 = −𝑢 sin 𝛩 + 𝑤 cos 𝛩 𝛷 = 270° 𝑣𝐿𝑂𝑆,𝐸 = (𝑢 cos 270 + 𝑣 sin 270) sin 𝛩 + 𝑤 cos 𝛩 = −𝑣 sin 𝛩 + 𝑤 cos 𝛩
Subtract complementary LOS wind positions (i.e. North from South and East from West for u and v) 𝑣𝐿𝑂𝑆,𝑁 − 𝑣𝐿𝑂𝑆,𝑆 = 𝑢 sin 𝛩 + 𝑤 cos 𝛩 − (−𝑢 sin 𝛩 + 𝑤 cos 𝛩) = 2 𝑢 sin 𝛩 = 𝑢=
𝑣𝐿𝑂𝑆,𝑁 −𝑣𝐿𝑂𝑆,𝑆 2 sin 𝛩
𝑣𝐿𝑂𝑆,𝐸 − 𝑣𝐿𝑂𝑆,𝑊 = 𝑣 sin 𝛩 + 𝑤 cos 𝛩 − (−𝑣 sin 𝛩 + 𝑤 cos 𝛩) = 2 𝑣 sin 𝛩 = 56
𝑣=
𝑣𝐿𝑂𝑆,𝐸 −𝑣𝐿𝑂𝑆,𝑊 2 sin 𝛩
Add all LOS wind positions (i.e. North, East, South, and West) 𝑣𝐿𝑂𝑆,𝑁 + 𝑣𝐿𝑂𝑆,𝐸 + 𝑣𝐿𝑂𝑆,𝑆 + 𝑣𝐿𝑂𝑆,𝑊 = 𝑢 sin 𝛩 + 𝑤 cos 𝛩 + 𝑣 sin 𝛩 + 𝑤 cos 𝛩 + −𝑢 sin 𝛩 + 𝑤 cos 𝛩 + − 𝑣 sin 𝛩 + 𝑤 cos 𝛩 = 4 𝑤 cos 𝛩 = 𝑤=
𝑣𝐿𝑂𝑆,𝑁 +𝑣𝐿𝑂𝑆,𝐸 +𝑣𝐿𝑂𝑆,𝑆 +𝑣𝐿𝑂𝑆,𝑊 4 cos 𝛩
8.3 Matrix Transforms for Derivation of Eq. 11 (rotational motions)
Figure A-1: Diagram of individual axis rotations. . Three individual rotational matrices defined by roll, pitch, and yaw must be multiplied to form the complete set of rotations as experienced by a ship.
𝑀𝑟𝑜𝑙𝑙
1 = (0 0
𝑀𝑝𝑖𝑡𝑐ℎ
0 cos 𝜙 − sin 𝜙
cos 𝛳 =( 0 sin 𝛳
0 sin 𝜙 ) cos 𝜙
(A.1)
0 − sin 𝛳 1 0 ) 0 cos 𝛳
(A.2)
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cos 𝜓 𝑀𝑦𝑎𝑤 = (− sin 𝜓 0
sin 𝜓 cos 𝜓 0
0 0) 1
(A.3)
𝑄 = 𝑀𝑟𝑜𝑙𝑙 𝑀𝑝𝑖𝑡𝑐ℎ 𝑀𝑦𝑎𝑤 = cos 𝛳 cos 𝜓 (− cos 𝜙 sin 𝜓 + sin 𝛳 cos 𝜓 sin 𝜙 sin 𝜙 sin 𝜓 + cos 𝜙 sin 𝛳 cos 𝜓
cos 𝛳 sin 𝜓 cos 𝜙 cos 𝜓 + sin 𝛳 sin 𝜙 sin 𝜓 − cos 𝜓 sin 𝜙 + cos 𝜙 sin 𝛳 sin 𝜓
− sin 𝛳 cos 𝛳 sin 𝜙 ) cos 𝛳 cos 𝜙
(A.4)
The transpose of A.4, defined by 𝑄 𝑇 , converts all orientations from the ship coordinate system to the Earth coordinate system. 𝑄𝑇 = cos 𝛳 cos 𝜓 𝑡𝑟𝑎𝑛𝑠𝑝𝑜𝑠𝑒 (− cos 𝜙 sin 𝜓 + sin 𝛳 cos 𝜓 sin 𝜙 sin 𝜙 sin 𝜓 + cos 𝜙 sin 𝛳 cos 𝜓 cos 𝛳 cos 𝜓 = ( cos 𝛳 sin 𝜓 − sin 𝛳
cos 𝛳 sin 𝜓 cos 𝜙 cos 𝜓 + sin 𝛳 sin 𝜙 sin 𝜓 − cos 𝜓 sin 𝜙 + cos 𝜙 sin 𝛳 sin 𝜓
− cos 𝜙 sin 𝜓 + sin 𝛳 cos 𝜓 sin 𝜙 cos 𝜙 cos 𝜓 + sin 𝛳 sin 𝜙 sin 𝜓 cos 𝛳 sin 𝜙
− sin 𝛳 cos 𝛳 sin 𝜙 ) cos 𝛳 cos 𝜙
sin 𝜙 sin 𝜓 + cos 𝜙 sin 𝛳 cos 𝜓 − cos 𝜓 sin 𝜙 + cos 𝜙 sin 𝛳 sin 𝜓) cos 𝛳 cos 𝜙
9 Appendix 2 – Variable Definitions and Core Pseudo-Code for Lidar MCA The source code for the motion compensation algorithm for ship-based lidar for a single line of sight probe is written in Matlab. Multiple scan directions are required to get the 3 components of the wind velocity, and each must be motion-corrected separately because the scan directions are not instantaneous. The positions and output locations are in meters, expressed in the flat Earth-fixed frame, (XX=east, YY=north, ZZ=zenith) with whatever origin has been used to define the input data. The earth frame is called the local-vertical-local-horizontal (LVLH) frame. This coordinate frame is used instead of the earth spherical coordinate system to simplify the vector geometry of the problem. The ship body frame is (X=starboard, Y=forward, Z=upward normal to deck). 58
Variable definitions: Input variable structure, dat(:,17): 1 t (sec, measurement time) 2:4 pos (m, GPS pos in LVLH)…( Rgps ) r 5:7 vel (m/s, GPS vel in LVLH)…( dRgps / dt ) 8:10 w (deg/s, rotation rate about ship X,Y,Z axes)… ( ) 11:14 quaternion (transformation from LVLH-to-ship frame)* 15 laser_index (index of beam for multi-beam system) 16 distance_index (index of distance along beam where measurement is made, i.e. range gate)…(ri) 17 raw_speed (m/s, raw wind component measurement)…(vr) (wind speed away from the lidar is positive) *The quaternion is an algebraic representation of rotational transformation matrices that is input from the ship or lidar Inertial Navigation Unit (INU) if available, or computed from the orientational INU data. Output motion compensated data and other variables: mc_dat(:,13) Corrected wind data 1 t (sec, measurement time) 2:4 vsrc (m/s, vel of laser source in LVLH)…( Vlidar ) 5:7 loc (m, location where wind is measured in LVLH)…( R ) 8:10 dir (unit vector direction of laser beam in LVLH)…( kˆ ) 11 laser_index (index of beam for multi-beam lidar) 12 distance_index (index of distance along beam where measurement is made 13 speed (m/s, compensated wind component observation)…( Vlos ) (wind speed away from the lidar is positive) Pseudo-code: 1) Initialize sensor, ship, and geo parameters needed for motion compensation from a namelist file and raw data input file is selected. 2) Read input sensor data file containing ship position, velocity, rotation rate, attitude (orientation angles), and doppler lidar sensor measurements 3) Unpack input data structure tobs = dat(1,:); pos = dat(2:4,:); vel = dat(5:7,:); 59
w = dat(8:10,:)*dtr; q = dat(11:14,:); iind = dat(15,:); jind = dat(16,:); smeas = dat(17,:); npt = length(tobs); 4) Define local variables: AT = (9,npt) array of ship-to-LVLH transforms (transposed attitude matrices) at = (3,3) ship-to-LVLH transform for n-th wind measurement N = (3x3) laser frame-to-ship frame alignment transformation r d = (3x1) GPS-to-laser source vector in ship frame (m) ( rlidar rgps ) c = (3x1) direction of laser beam in ship frame u = (3x1) direction of laser beam in LVLH frame ( kˆ ) b = (1x1) distance (m) along the beam where measurement is made (ri) w = (3xnpt) rotation rate (rad/s) of ship relative to LVLH, expressed in ship frame ( ) 5) Combine to get LVLH values for: r = location of wind measurement = GPS_position + AT*(d + c*b) v = velocity of laser source = GPS_velocity + AT*cross(w,d) 6) Conjugate quarternions and convert quaternions to direction-cosine rotation matrices Input: array of quarternions ( q(4xn)) that is supplied by the INU Output: A(3x3n), an array of direction-cosine rotation matrices used to transform between the earth and ship frames. 7) Compute motion-corrected data, i.e. velocity of laser source, the wind vector and location of measurement in the earth frame (LVLH). 8) The algorithm is repeated for each of the 4 orientations of the laser beam, and the components are combined to give the 3D wind vector.
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Relationships between the various data sources in the motion compensation algorithm
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