Radar-Based Quantitative Precipitation Estimation for ... - AMS Journals

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JOURNAL OF HYDROMETEOROLOGY

VOLUME 13

Radar-Based Quantitative Precipitation Estimation for the Cool Season in Complex Terrain: Case Studies from the NOAA Hydrometeorology Testbed JIAN ZHANG NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

YOUCUN QI Nanjing University of Information Science and Technology, Nanjing, China, and Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, and NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

DAVID KINGSMILL Cooperative Institute for Research in Environmental Science, University of Colorado, and NOAA/OAR/Earth System Research Laboratory, Boulder, Colorado

KENNETH HOWARD NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma (Manuscript received 22 November 2011, in final form 7 May 2012) ABSTRACT This study explores error sources of the National Weather Service operational radar-based quantitative precipitation estimation (QPE) during the cool season over the complex terrain of the western United States. A new, operationally geared radar QPE was developed and tested using data from the National Oceanic and Atmospheric Administration Hydrometeorology Testbed executed during the 2005/06 cool season in Northern California. The new radar QPE scheme includes multiple steps for removing nonprecipitation echoes, constructing a seamless hybrid scan reflectivity field, applying vertical profile of reflectivity (VPR) corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Specific issues in radar rainfall accumulations were addressed, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. The new radar QPE was tested in a 6-month period of the 2005/06 cool season and showed significant improvements over the current operational radar QPE (43% reduction in bias and 30% reduction in root-mean-square error) when compared with gauges. In addition, the new technique minimizes various radar artifacts and produces a spatially continuous rainfall product. Such continuity is important for accurate hydrological model predictions. The new technique is computationally efficient and can be easily transitioned into operations. One of the largest remaining challenges is obtaining accurate radar QPE over the windward slopes of significant mountain ranges, where low-level orographic enhancement of precipitation is not resolved by the operational radars leading to underestimation. Additional high-resolution and near-surface radar observations are necessary for more accurate radar QPE over these regions.

1. Introduction The topography of the western United States poses a significant challenge in creating physically realistic and spatially accurate estimates of precipitation using remote

Corresponding author address: Jian Zhang, 120 David L. Boren Blvd., Norman, OK 73072. E-mail: [email protected] DOI: 10.1175/JHM-D-11-0145.1 Ó 2012 American Meteorological Society

sensing techniques. Simply stated, complex terrain serves as a hostile observing environment for radars of any size and wavelength. This is further compounded by the presence of complex microphysical processes during the cool season as a result of air–sea interactions within coastal regions as well as the orographic modulation and enhancement within the interior regions of the west. The placement and density of operational Weather Surveillance Radar-1988 Doppler (WSR-88D) radars in the

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west cannot meet the needs for watershed and water resource management due to the complex terrain of the region (Westrick et al. 1999; Brown et al. 2002; Maddox et al. 2002). Accordingly, radars are not used extensively and in some locales of the region they are not used at all for the monitoring of precipitation rate and type in watersheds and along associated rivers and streams. However, there is a basic necessity to monitor and predict water inputs into the western hydrological cycle given that freshwater is an increasingly expensive resource, while the effective management and prediction of flooding as well as drought has a direct economic impact on nearly all aspects of society. A rain gauge network does not have sufficient coverage and resolution to meet all needs, especially for flash floods and mudslides that have small time and spatial scales. On the other hand, the WSR-88D radar network provides highresolution observations on a scale of 1–10 km and every 5 min. This study explores the error sources of radarbased quantitative precipitation estimation (QPE) over the complex terrain of the U.S. west coast, with a focus on the cool season. Gourley et al. (2009) performed a detailed study over a small area (the American River basin) of this complex terrain by comparing QPEs from one WSR-88D and two research radars. That study was focused on understanding radar QPE errors related to calibration correction, Z–R relationship, and vertical profile of reflectivity (VPR) for individual radars. The current work investigates similar radar QPE approaches but over a larger area of Northern California with a focus on the network of WSR-88D radars. Further, the current work addresses additional issues such as the radar data quality control, multiradar mosaic, and spatial continuities in rainfall products. The objective of this study is to understand deficiencies in the current operational WSR-88D radar QPE and to identify areas that can be improved. The current study is focused on a radar QPE that is tested in a real-time operational data environment and can be easily transitioned into operations. Many previous studies have discussed radar-based QPE errors (e.g., Wilson and Brandes 1979; Zawadzki 1984; Austin 1987; Joss and Waldvogel 1990; Andrieu et al. 1997; Creutin et al. 1997). In general, the error sources include 1) errors in measuring radar reflectivity (e.g., the calibration bias), 2) contamination from nonprecipitation echoes (e.g., ground clutter due to anomalous propagations), 3) uncertainty in radar reflectivity (Z) and rain-rate (R) relationships, and 4) variability in the VPR. Errors due to source 1 are difficult to quantify in an automated way in real time, and many studies of radar

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FIG. 1. Illustration of the WSR-88D radar sampling issues for stratiform precipitation in complex terrain. The curves above the A, B, and C labels represent the precipitation VPR. A radar is located at the origin, and the bold black lines represent axes of various radar tilts in VCP 21. The numbers denote the corresponding elevation angles. The squared brackets represent the 3-dB beam widths of the first tilt at point A and the second tilt at points B and C.

calibration error involve special observations (e.g., precipitation profiling radars and disdrometers; Williams et al. 2005). The calibration error can be minimized through a close monitoring and vigorous hardware maintenance of the radar network, and it is not a focus of the current study. Radar QPE errors due to source 2 are usually transient and relatively minor assuming that effective reflectivity data quality control (QC) is applied to remove nonprecipitation echoes. An accurate Z–R relationship (error source 3) requires precise information about the raindrop size distribution within each radar sample volume. However, observations of raindrop size distributions in different seasons and geographical regions are limited. The VPR-related uncertainties (error source 4) in radar QPEs depend on vertical gradients of reflectivity and heights of the lowest and unblocked radar beams. If the beams are near the ground, or if the vertical variation of reflectivity within and below the lowest beams is zero, then the radar QPE error associated with the VPR would be negligible. Cool season precipitation over complex terrain exacerbates the aforementioned radar QPE errors associated with nonprecipitation echoes, Z–R relations, and nonuniform VPRs. Further, beam blockage in complex terrain often results in spatially discontinuous radar rainfall maps. Figure 1 illustrates the VPR issues for cool season radar QPEs over complex terrain. This schema assumes a horizontally uniform area of stratiform precipitation that produces the same amount of rainfall everywhere at the surface. Assuming a radar located at the origin and scanning with nine elevation angles (Fig. 1), radar data from the first tilt is used to

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FIG. 2. A map of the current study domain (the red rectangle). The ‘‘1’’ symbols indicate the WSR-88D radar locations, ‘‘r’’ the S-PROF site, and ‘‘d’’ the disdrometers’ sites. The white lines separate the domain into three regions (from west to east: coastal, Central Valley, and the Sierra Mountains). The black dashed lines indicate transition zones between the adjacent Z–R regions. The four longitudes defining the transitions zones are la(1) 5 121.958W, la(2) 5 120.758W, lb(1) 5 122.358W, and lb(2) 5 121.98W.

estimate rainfall over point A. At points B and C, radar QPEs are derived from the second tilt because the first tilt was severely blocked in the ;(80–100)-km range. The radar rainfall estimate at point A would be accurate if an accurate Z–R relationship is employed. However, the radar QPEs at point B would be overestimated because the data was obtained within the brightband (BB). At point C, the radar QPE would be underestimated because of 1) decreasing reflectivity with increasing altitude in the ice region and 2) the radar beam partially overshooting cloud top. This study addresses radar QPE error sources 2, 3, and 4 and the blockage discontinuities while focusing on cool season precipitation occurring over the complex terrain of Northern California (Fig. 2) by utilizing the National Oceanic and Atmospheric Administration’s (NOAA) Hydrometeorology Testbed (HMT; http://hmt.noaa.gov). The HMT is a program intended to accelerate the infusion of new technologies, models, and scientific results from the research community into daily forecasting operations of the National Weather Service (NWS) and its River Forecast Centers (RFCs). The project focuses on the development and use of hydrometeorological instrumentation and models to aid forecasters, hydrologists,

and water resource managers in their decision-making process. The first demonstration (HMT-West) began in 2003 in the coastal mountains of Northern California, focusing on the Russian River basin north of San Francisco. HMT-West expanded in 2005 to also cover the Sierra Nevada, focusing on the American River basin above Sacramento. This study focuses on data collected in association with HMT-West during the 2005/06 cool season (HMT-West 2006). Several S-band precipitation profiling radars (S-PROF; White et al. 2000) were deployed to obtain fine-resolution vertical precipitation structure. Various procedures were developed, refined, and tested for the radar QPE on two specific intensive operating periods (IOPs) during HMT-West 2006. The two events covered 1200 UTC 30 December 2005 to 1200 UTC 1 January 2006 (IOP4) and 1200 UTC 13 January to 1200 UTC 15 January 2006 (IOP7). Through extensive experimentation using data from the two IOPs, a new radar QPE scheme was developed. The scheme was further tested and refined using 6 months of observations from 1 November 2005 to 30 April 2006. The aforementioned radar QPE error sources are analyzed and documented herein. The new radar QPE scheme developed from the current study is described

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in section 2, and the performance of various scheme components is discussed in section 3. Section 4 provides a summary and conclusions.

2. Methodology The objective of this study was to develop a robust and efficient radar QPE technique that can be easily transferred into real-time operations for the U.S. west coast. The newly developed radar QPE scheme consists of five components: 1) an automated reflectivity QC to minimize ground clutter contaminations, 2) an apparent VPR (‘‘AVPR’’) correction to reduce QPE errors due to the radar beam sampling in the brightband and in ice regions, 3) a seamless hybrid scan reflectivity (HSR) approach to mitigate blockage discontinuities in precipitation accumulation fields, 4) a distance-weightedmean mosaicking scheme to smoothly integrate HSR fields from multiple radars onto a regional QPE grid, and 5) a spatially varying Z–R relationship to convert the mosaicked HSR into precipitation rates.

a. Radar reflectivity QC This module contains three steps. The first step removes radar data bins that have power blockages greater than 50%, or the bottom of the bins is within 50 m of the ground. The 50-m criterion was used to minimize potential clutter echoes caused by trees and other objects above the ground (O’Bannon 1997; Langston and Zhang 2004). This terrain-based QC removed the majority of ground clutter in radar reflectivity observations from the HMT-West 2006 datasets. However, ground clutter can result from super refractive conditions or ‘‘anomalous propagations’’ (AP; Doviak and Zrnic 1993; Delrieu et al. 1995; Berenguer et al. 2006), which cannot be removed by the terrain-based QC. The second step is a neural-network-based QC (QCNN; Lakshmanan et al. 2007) that identifies nonprecipitation echoes that are not removed in the base-level WSR-88D reflectivity data or in the terrain-based QC. These echoes include AP-induced ground clutter; biological returns from birds, bats, and insects; electronic interferences; and chaff. The identification of AP was based on features in the reflectivity, Doppler radial velocity, and spectrum width fields that are associated with ground clutter. These features include zero velocity, narrow spectrum width, noisy texture, and lack of vertical continuity (Kessinger et al. 2003; Zhang et al. 2004; Lakshmanan et al. 2007). The QCNN was successful in removing most AP echoes, especially those confined to the lowest tilt and not embedded in precipitation. Biological returns were not a significant problem for the current study since the analysis was for the winter

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season. However, chaff echoes were found in about 5 days of the HMT-West 2006 datasets. While relatively uncommon, chaff echoes are difficult to remove because of the fact that they are transient and they exist in all tilts. The third step in QC removes reflectivity bins that are associated with static targets, such as wind farms or radar bins severely blocked by ground objects that are not accounted for in the digital terrain data (e.g., large trees, manmade towers, and buildings). Reflectivity data in these premarked areas are automatically removed.

b. Apparent VPR correction This module was developed to address the radar QPE issues associated with radar sampling deficiencies and nonuniform VPRs. As discussed in the introduction, even if the precipitation is horizontally uniform and a perfect Z–R relationship is available, the radar QPE (computed from the lowest and unblocked reflectivity data) will still contain significant errors. These range (height)-dependent errors are very profound for cool season stratiform precipitation (e.g., Koistinen 1991; Andrieu and Creutin 1995; Seo et al. 2000; Bellon et al. 2005; Zhang and Qi 2010) and for areas with significant radar blockages (e.g., Kitchen et al. 1994; Germann and Joss 2002). The majority of precipitation in Northern California falls in the winter and is of a stratiform nature, thus meeting all of the above criteria. Therefore, a VPR correction is necessary to obtain an accurate radar QPE for this region. The methodology used in the current study is an extension of the technique developed by Zhang and Qi (2010, hereafter ZQ10). Below is a review of steps in the ZQ10 methodology and a description of the additional steps developed in the current study.

1) COMPUTE AVPRS The AVPRs were calculated by deriving a 3608 azimuthal mean of reflectivities from predefined precipitation areas, with one AVPR computed for each tilt. The predefined precipitation areas in each tilt are delineated according to the following criteria: d

d d

d

Vertically integrated liquid (VIL; Greene and Clark 1972) , 6.5 kg m22. Blockage , 50%. For BB areas (i.e., when height is within the apparent BB layer, where the apparent BB layer was determined using a procedure developed in ZQ10), reflectivity . 15 dBZ and composite reflectivity (i.e., the maximum reflectivity in the vertical column) . 30 dBZ. For the ice region (i.e., when height is above the apparent BB top), reflectivity . 0 dBZ. These criteria are chosen to include as much reflectivity data as

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FIG. 3. The conceptual VPR model used in the current study. The blue dotted line represents the mean observed VPR, and the red line is the idealized VPR. The horizontal blue lines are the brightband top and bottom. The solid green and dashed black lines are brightband peak and the 08C height, respectively.

FIG. 4. VPR correction process: the black line with the arrows shows how the adjustment is made to an observed reflectivity (dBZobs) at a height of hobs. The dBZcorr represents the VPRcorrected reflectivity.

possible in the ice region, so that the VPR structure in the ice region can be accurately represented.

d

2) VPR PARAMETERIZATION

d

The AVPR for each single tilt is parameterized according to a linear model (Fig. 3), which is described by apparent BB top, peak, and bottom heights and piecewise linear slopes (a, b, and g). Since different reflectivity criteria are used for the BB area and for the ice region, the mean AVPRs are not always continuous between the BB top and the ice region (Fig. 3). However, this has no effect on the VPR correction because the linear model is always continuous (red line in Fig. 3), and only the slopes will determine the amount of corrections made to reflectivities.

3) VPR CORRECTION Reflectivity observations are adjusted based on the parameterized VPR to obtain the corresponding values for rainfall estimation at the ground (Fig. 4). Note that calibration errors would not impact the VPR correction calculation given that the correction was based on the ratio (or difference on the dBZ scale) between reflectivities at the hybrid scan beam height (hobs; Fig. 4) and the reference level, which is usually the ground (ZQ10). The correction is only applied to reflectivities that meet the following criteria:

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d d

height of the reflectivity bin is above the apparent BB bottom, VIL , 6.5 kg m22, reflectivity . 20 dBZ in the BB region, and reflectivity . 5 dBZ in the ice region.

The correction uses one parameterized VPR for each tilt, assuming that the VPR is representative of the vertical precipitation structure over the entire radar domain. This assumption holds relatively well when the vertical gradient of precipitation is much greater than the horizontal variation. In the complex terrain, uneven topography may modulate the BB layer resulting in different BB heights across the radar domain. BB peak heights and intensities will be smeared in apparent tilt VPRs after the spatial averaging, and the AVPR correction may not effectively reduce QPE overestimations in BB areas. In other words, the current VPR correction scheme is not suitable for precipitation events where the horizontal variation of precipitation is comparable to or greater than the vertical variation.

c. Deriving hybrid scan reflectivity Radar QPEs are commonly calculated from the lowest radar bins that are not significantly blocked. Those bins constitute a 2D polar grid called ‘‘hybrid scan’’

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(O’Bannon 1997; Fulton et al. 1998). The HSR in the operational WSR-88D Precipitation Processing System (PPS; Fulton et al. 1998) was derived from the lowest radar bins that have less than 50% blockage and the (3 dB) bottom of the bins clearing the ground by at least 150 m. The reflectivities in partially blocked bins are adjusted to compensate for the amount of power blockages. However, the compensation is not sufficient when the radar wave propagates under superrefractive conditions. This often results in discontinuities in radar rainfall accumulations over a period of time (e.g., several hours). Some examples of such discontinuities will be shown in the next section. To mitigate the discontinuities introduced by blockages, a new ‘‘seamless’’ HSR (sHSR) is developed in the current study. The sHSR is the same as the HSR for all range–azimuth bins with less than 10% blockage. For a bin with partial blockages of 10%–50%, the sHSR is a weighted mean of reflectivities from the PPS-type hybrid scan and from the next tilt above [Eq. (1)]. The weight (w) is a function of the blockage (wblk) and height (whgt) of the radar bin [Eqs. (2a)–(2c)]:

å

ZsHSR 5

k5hst,hst11

wk Zk

å

k5hst,hst11

å

hsHSR 5

k5hst,hst11

å

wk

,

(1a)

,

(1b)

wk hk

k5hst,hst11

wk

w 5 wblk 3 whgt , 8 1 d , 10% > < wblk 5 1 2 d/0:5 10% # d # 50%, > : 0 d . 50%

(2a)

and

(2b)

8 > h $ h0C : exp 2 H

(2c)

The variables in Eqs. (1a)–(2c) are defined below: ZsHSR: seamless hybrid scan reflectivity (in mm6 m23), hsHSR: height associated with ZsHSR, Zk: VPR-corrected reflectivity (in mm6 m23) on the kth tilt, hst: hybrid scan tilt number, d: the amount of one-way power blockage (dimensionless; d 5 0.5 for 50% blockage), h: beam center height,

h0C: freezing-level height, and H: a height scale factor (default 51 km).

d. Mosaic of hybrid scan reflectivity The ZsHSR fields from individual radars are mosaicked to produce a regional seamless HSR field. The mosaic scheme and associated weighting functions are defined below:

ZmsHSR 5

i å wLi wHi ZsHSR i

å wLi wHi

,

(3)

and

(4)

i

2 d wL 5 exp 2 2 , L h2 wH 5 exp 2 2 . H

(5)

Here ZmsHSR represents the mosaicked seamless HSR, and i is the radar index. The variable d represents the distance between a given analysis point and the radar, and L and H are adaptable parameters with default values of 100 and 3 km, respectively. The default values are determined from the winter 2005/06 dataset, and these values are designed to be easily changeable in a real-time operational environment. It is clear that at any given grid point, reflectivities from lower altitudes and from a closer radar will get higher weights.

e. Spatially varying Z–R relationships Radar reflectivities are converted into precipitation rates through a power law empirical relationship commonly referred as the ‘‘Z–R’’ relationship (e.g., Marshall et al. 1955; Austin 1987; Rosenfeld et al. 1993), and different Z–R relationships are needed for different drop size distributions (DSDs) (e.g., Wilson and Brandes 1979; Zawadzki 1984). Several Joss–Waldvogel disdrometers (Joss and Waldvogel 1969) have been deployed in association with HMT-West to measure DSDs at different mountain sites, and various Z–R relationships have been derived from these observations. In the current study, three different Z–R relationships were applied in three distinctively different regions (Fig. 2): coastal (region 1), Central Valley (region 2), and Sierra (region 3). The Z–R relationships are Coastal (Martner et al. 2008): Z 5 44R1:91 , (6) Central Valley (Marshall et al. 1955): Z 5 200R1:6 , and

(7) Sierra (Matrosov et al. 2007): Z 5 101R1:76 .

(8)

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FIG. 5. The 24-h rainfall accumulations during IOP4 (a) without reflectivity QC, (b) after the automated QC, and (c) after the additional manual QC. The red circles indicate areas where raw reflectivities were contaminated by ground clutter and the purple circles indicate areas of wind farms. The rainfall accumulation was valid from 1200 UTC 31 Dec 2005 to 1200 UTC 1 Jan 2006. Note that a VPR correction was also applied in (c) but not in (a) and (b); thus field is smoother in (c) than in (a) and (b) However, the VPR correction did not have an impact on the wind farm clutter.

The coastal Z–R was derived from a disdrometer deployed at Cazadero (CZD; Fig. 2) in the coast range during the 2003/04 cool season (Martner et al. 2008), and the Sierra Z–R was derived from a disdrometer deployed at the Colfax water center (CFC; Fig. 2) on the lower slope of the Sierra Nevada during the 2005/06 cool season (Matrosov et al. 2007). For the Central Valley, the Marshall–Palmer Z–R relationship was used for its general applicability to stratiform precipitation (e.g., in the NWS operational WSR-88D QPE). To assure the spatial continuity of the radar QPE, an overlapping transition zone was designed between adjacent Z–R regions. At any given point in the transition zone, the precipitation rate was a weighted mean of two precipitation rates calculated using the two adjacent Z–R relationships. The weight is a linear function of the longitudinal distance from the bounds of the transition zone. Precipitation DSDs can also vary in time, and the application of temporally varying Z–R relationships requires automated delineation of different DSD regions every time step of the QPE. Zhang et al. (2011) developed an automated precipitation classification using radar and atmospheric environmental data, which segregates precipitation into stratiform rain, convective rain, tropical rain, hail, and snow. The current scheme will be combined with the Zhang et al. (2011) scheme and will be tested in the real-time national mosaic and multisensor QPE (NMQ; Zhang et al. 2011) system.

3. Case study results Two IOPs during HMT-West 2006 were studied for the development of an optimal radar QPE technique for

the current study domain. The two events covered 1200 UTC 30 December 2005 to 1200 UTC 1 January 2006 (IOP4) and 1200 UTC 13 January to 1200 UTC 15 January 2006 (IOP7). The technique was then applied to the whole winter 2005/06 season and further refinements were made. This section presents various experiments performed during the radar QPE development, and describes the impact of different approaches. Hourly rainfall observations from the Hydrometeorological Automated Data System (HADS; www.weather. gov/oh/hads/) gauges and the special precipitation-gauge network deployed during the HMT-West 2006 field experiment (http://hmt.noaa.gov) were used for the evaluation of various QPE approaches. Manual quality control was performed to remove unreliable gauge data. The quality control procedures included checking spatial and temporal consistency between neighboring gauge observations. The consistency between time series of hourly gauge data and collocated radar reflectivities and rainfall data were also examined. Gauges with significant inconsistencies with its neighbors and with radar observations were removed from the validation.

a. Impact of reflectivity QC The reflectivity QC was important in order to remove any contamination from nonprecipitation echoes. Figure 5 shows a comparison of the 24-h rainfall accumulations during IOP4 with and without the automated reflectivity QC. The red circles indicate ground clutter contamination that was successfully removed by the QC. Some clutter (in pink dashed circles; Fig. 5b) remained because of a wind farm that generated strong echoes in more than one tilts. This clutter was then removed (Fig. 5c)

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FIG. 6. The 1-h rainfall accumulations (a) using the PPS hybrid scan and a single Z–R (Z 5 300R1.4) and (b) using the seamless hybrid scan and the adaptive Z–R relation. The red circles indicate blockage areas on the old hybrid scan. The rainfall accumulation was valid from 0700 to 0800 UTC on 31 Dec 2005 during IOP4. (c),(d) The corresponding scatterplots of the QPEs in (a) and (b) vs gauge observations, respectively.

using predefined lookup tables. There are hundreds of such wind farms that cause contaminations in WSR-88D reflectivity observations across the United States, and the number is still growing. The NWS Radar Operations Center is developing geographic reference tables for all the wind farms that can be used to remove such contaminations in radar observations. There remain some challenges for the reflectivity QC in Northern California—namely, AP and chaff echoes. As discussed in section 2a, these echoes are transient and not confined to the lowest tilt. It is difficult to distinguish between strong AP echoes and storm cells at far ranges unless there are clear features identifiable in the velocity (zero for AP, nonzero for storms) and spectrum width (very narrow for AP) fields. Identifying AP beyond 230 km is practically impossible since radial velocity and

spectrum width data are not available, and the vertical resolution of radar data is very poor. Further, chaff echoes move through space, making them difficult to be identified even with velocity data. Polarimetric radar capabilities will hopefully improve the QC performance for such nonprecipitation echoes (e.g., Ryzhkov et al. 2005; Park et al. 2009).

b. Impact of the seamless hybrid scan and Z–R relationships The impact of the new seamless hybrid scan approach is shown in Fig. 6. Hourly rainfall accumulations from the PPS hybrid scan showed discontinuities due to very severe beam blockages to the west of KDAX (area 1 in Fig. 6a) and to the southeast of KBHX (area 2 in Fig. 6a). The seamless HSR largely mitigated the discontinuities

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in area 1, but the artifacts to the southeast of KBHX remained. KBHX was running the volume coverage pattern (VCP) 21 during the event, and the hybrid scan consisted of the first (0.58) and second (1.458) tilts in area 2. The radar is located at an altitude of 760 m above mean sea level (MSL), and the top of the 1.458 tilt in area 2 was about 7–8.5 km MSL. Examination of volume scan reflectivity data indicated that the cloud top was about 7 km MSL at this time. Therefore the 1.458 tilt was partially overshooting the precipitating cloud and the next tilt above was completely overshooting. The inclusion of the upper tilt in the seamless HSR did not provide better information than the lower and blocked tilt. This example shows that the seamless HSR is only beneficial when the hybrid scan tilt is severely blocked (e.g., .30%–40%) and the upper tilt is not overshooting. Had KBHX been operated at VCP 12, the seamless HSR would have been using a lower tilt (0.98) that would provide better observations than the 1.458 tilt and result in fewer discontinuities. It was apparent that the single Z–R (Z 5 300R1.4) and the traditional hybrid scan resulted in significant underestimation (;50%; Fig. 6c). Using adaptive Z–R relations along with the seamless hybrid scan, the radar QPE–gauge bias ratio was improved from 0.51 to 0.76 (a 25% reduction in bias), the correlation increased from 0.39 to 0.44, and RMSE reduced from 5.06 to 4.05 mm (Figs. 6c and 6d). The impact of adaptive Z–R relations was apparent on the east side of the Sierra Nevada where KRGX had no blockages in the lowest tilt and the seamless HSR was not used. This event (IOP4) contained a significant period (2100 UTC 30 December to 1100 UTC 31 December 2005) of stratiform rain associated with both a brightband and low-level precipitation growth due to orographic enhancement. Figure 7 shows hourly mean VPRs observed by Alta, California (ATA), S-PROF during this time period, where reflectivities increased with decreasing height below the brightband bottom, especially for the early part (2200 UTC on 30 December to 0600 UTC on 31 December 2005). White et al. (2003) classified this type of structure as ‘‘hybrid’’ rain. If low-level precipitation growth was not evident in the presence of a brightband, White et al. (2003) classified the structure as ‘‘brightband’’ rain. The Sierra Z–R relation was derived from disdrometer data collected over the entire HMT-West 2006 season, during which both brightband and hybrid rain events occurred. Since brightband rain has a lower rain rate than the hybrid rain for the same reflectivity (White et al. 2003), the Sierra Z–R relation might have partially contributed to the remaining underestimation for this specific event. Another contributing factor was the nonuniform VPR to be discussed next.

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FIG. 7. Hourly mean VPRs observed by the S-PROF located at ATA between 2100 UTC 30 Dec and 1100 UTC 31 Dec 2005. Times in the legend indicate the end of the hour where the mean VPRs were calculated.

c. Impact of the VPR correction Figure 8 shows an example HSR field during IOP7 before and after the VPR correction. The VPR correction reduced reflectivities in the brightband area (the red line) and increased those in the ice region (the yellow line). The radar-based 1-h rainfall accumulations had significant overestimations when compared to rain gauges (Fig. 8c) because of the brightband. After the VPR correction there was less bias (1.01 verses 1.74) and rootmean-square error (1.6 versus 3.21 mm). The correlation coefficient between the radar QPE and gauge observations was also improved from 0.54 to 0.59. Figure 9 shows an example of VPR correction results during IOP4. In this case, the VPR correction reduced the overestimation in the brightband area to the west of KDAX (area ‘‘I’’) and alleviated the underestimation to the southeast of KDAX (area ‘‘III’’). These corrections yielded a slightly improved correlation coefficient (0.46 versus 0.44) between the radar QPE and the gauge observations. However, the underestimation in area II was worsened by the VPR correction. The gross underestimation resulted in a worse bias ratio (0.64 versus 0.76) and RMSE (4.31 versus 4.05 mm) scores for the radar QPE with the VPR correction than those without (Figs. 9e and 9f). The detrimental effect of the VPR correction in area II appears to result from a combination of orographic precipitation enhancement in the mid- to upper slopes of the Sierra and insufficient radar sampling. In area III, the first tilt from KDAX was used for the QPE, while in area II, the second and third tilts were used because of the severe blockages. Figure 10 shows a 5-min-averaged VPR (black line) observed by

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FIG. 8. Hybrid scan reflectivity fields (a) before and (b) after the VPR correction, and scatterplots of 1-h radar QPEs vs gauge observations (c) before and (d) after the VPR correction. The hybrid scan reflectivity fields were from IOP7 and valid at 1300 UTC on 14 Jan 2006, and the 1-h accumulations were for 1200–1300 UTC on 14 Jan 2006. The red solid line indicates the brightband area and the yellow dashed line indicates where the radar beam was sampling in the ice region.

the ATA S-PROF valid between 0725 and 0730 UTC on 31 December 2005 during IOP4. This S-PROF VPR, if sampled by the KDAX radar at 0.98 tilt, would produce a tilt AVPR equivalent to the red line as shown in Fig. 10. Based on this equivalent S-PROF VPR, the VPR correction to the KDAX 0.98 tilt reflectivity to the ground level (1.29 km MSL) at ATA would be ;0.6 dBZ (DZt). However, the tilt AVPR (green triangles in Fig. 10) derived from the KDAX volume scan data showed significantly different structure than the S-PROF observations, especially below 3.5 km MSL. Based on the KDAX-observed tilt AVPR, a correction of 23.6 dBZ was applied to the KDAX 0.98 tilt reflectivity at ATA (DZo), which caused the significant underestimation. The difference between the S-PROF- (red) and KDAX-observed (green) tilt AVPRs in Fig. 10 indicated

that large variations of VPR existed across the KDAX domain. The ATA S-PROF profile is an observation that represents the vertical reflectivity structure above that local point. The KDAX VPR, on the other hand, is a spatially averaged vertical reflectivity structure over the entire radar domain. The different spatial scales result in different BB peak heights in the two profiles. The orographic enhancement observed by the ATA S-PROF was not captured by the KDAX-observed tilt AVPR because the lower part of the AVPR can only come from areas close to KDAX. The area closest to KDAX is the Central Valley region (Fig. 1), where orographic precipitation processes may not be prevalent. At ATA, the lowest unblocked KDAX tilt was at 0.98. The beam is centered at ;2.4 km (MSL) with a width of ;1.8 km (Fig. 10), which could not resolve the structure

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FIG. 9. (a),(b) Hourly radar rainfalls, (c),(d) radar/gauge bias ratios, and (e),(f) radar/gauge scatterplots and histograms of bias. Here, (a),(c), (e) correspond to the radar QPE without the VPR correction and (b),(d),(f) with the correction. The circle size in (c),(d) represents the gauge rainfall amount and the circle color represents the radar/gauge bias (orange , 1, white ’ 1, blue . 1). The data are valid at 0800 UTC 31 Dec 2005 in IOP4. The VPR correction reduced radar QPE in areas ‘‘I’’ and ‘‘II’’ and increased it in area III.

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the fundamental limitation of the WSR-88D VPR correction in the high slopes of Sierra Mountains, which is due to a lack of high-resolution observations near the ground. To overcome this limitation and to obtain accurate radar QPEs in this region, operational precipitation profiler radars or gap-filler radars are necessary.

d. Spatial continuity issues

FIG. 10. The ATA S-PROF observed VPR (black), the simulated KDAX 0.98 tilt VPR (red) from the S-PROF VPR, and the KDAX 0.98 tilt VPR derived from KDAX volume scan observations (green) valid at 0730 UTC on 31 Dec 2005. The vertical purple lines represent the KDAX 0.98, 1.38, and 1.88 tilt beam heights and widths at ATA site. The gray dashed line represents the 08C height derived from a nearby sounding. The DZt and DZo represent VPR corrections to the KDAX 0.98 reflectivity at ATA when using the S-PROF-observed VPR and the KDAX-derived VPR, respectively.

of the brightband and the orographic precipitation growth below. If the radar was running in VCP 21, the results could be even worse because the lowest unblocked tilt would have been 1.458. This example shows

In addition to underestimations of orographic precipitation, spatial continuity is a big challenge for radar QPEs in complex terrain. Even though the seamless hybrid scan mitigates some azimuthal discontinuities caused by blockage (Fig. 6), the artifacts remain in areas where blockage effects and overshooting issues coexist (see discussions in section 3b). Figure 11 shows beam blockage for the 0.58 and 0.98 tilts and the PPS hybrid scan maps associated with the KBHX and KDAX radars under VCP 12. There is severe blockage to the west and northwest of KDAX as well as to the southeast of KBHX, and the amount of blockage varies considerably in the azimuthal direction (e.g., Figs. 11a,b,e,f). As a result, the hybrid scan reflectivity jumps from 0.58 in one azimuth to 0.98 (or 1.458 for VCP 21) in the next, causing azimuthal discontinuities in the storm total QPEs (white circle in Fig. 12a). To further reduce this discontinuity, the hybrid scans for KDAX and KBHX were manually modified (Figs. 11d and h) to minimize changes of tilts in

FIG. 11. KBHX beam blockages at (a) 0.58 and (b) 0.98 tilts and the KBHX PPS hybrid scan tilt map for (c) VCP 12 and (d) the manually modified hybrid scan tilt map. The ‘‘PPS hybrid scan’’ referred here is the same as the hybrid scan in the WSR-88D PPS except that a 50-m ground clearance rule is used instead of 150 m. (e)–(h) As in (a)–(d), but for KDAX fields.

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FIG. 12. VPR-corrected 48-h radar rainfall accumulations for IOP4 ending at 1200 UTC on 1 Jan 2006 calculated from four experiments: (a) using the seamless HSR based on the PPS hybrid scans (Figs. 11c and g) and mosaicking weighting function factors of L 5 50 km and H 5 2 km; the white circle indicates the area with blockage artifacts. (b) Using the seamless HSR based on the manually modified hybrid scan (Figs. 11d and h) and mosaicking weighting function factors of L 5 50 km and H 5 2 km; the white circle indicate the discontinuity midway between KDAX and KBHX radars. (c) As in (b) except that L 5 100 km and H 5 3km; (d) as in (c) except that a 20-point smoothing is applied across the tilt boundaries in the manually modified hybrid scan.

the azimuthal direction. This change results in an improved azimuthal continuity of the radar QPE fields (Fig. 12b versus Fig. 12a). Blockage associated with the KBBX radar (not shown) is much worse than from KDAX and KBHX. KBBX is blocked across a majority of the radar’s azimuth sector between 08 and 1508 up to 1.58–28 elevation angles. Because of the severe blockage, higher-tilt data ($2.48) from KBBX has to be used for precipitation estimation over the northern Sierra Nevada Mountains. These higher tilts were often sampling the ice region of precipitating clouds or partially overshooting cloud tops,

which resulted in severe underestimation of accumulated precipitation. Experiments revealed that KDAX and KRGX radar data in this region were less blocked than the data from KBBX. As a result, inclusion of KBBX data led to significant degradation of precipitation estimates in the northern Sierra Nevada region owing to the assumption that a closer radar provides better QPEs than does a radar at longer range. Therefore KBBX data was excluded from the current study. The impact of KBBX will be further studied in the future work and a special weighting function may be developed for integrating this radar into the regional QPE mosaic.

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FIG. 13. The VPR-corrected seamless HSR fields from (a) KBHX at 0640 UTC on 31 Dec 2005 and from (b) KDAX at 0637 UTC on 31 Dec 2005. (c) The mosaicked seamless HSR field at 0640 UTC 31 Dec 2005. The white dashed lines indicate the midway between KDAX and KBHX radars, and the yellow line shows a tilt boundary on the KBHX hybrid scan (re: Fig. 11d) because this volume scan was in VCP 21 and the HSR change was significant (a jump from 0.58 to 1.458).

Another spatial discontinuity was found between the KDAX and KBHX radars (inside the white circle in Fig. 12b) when the mosaicking weighting function factors were set to L 5 50 km and H 5 2 km. A closer look at the weighting function revealed that the mosaic weights changed dramatically (on the order of 10 per 10 km) across the midpoint between the two radars. Meanwhile, the VPR-corrected HSR fields from KDAX and KBHX were largely different owing to the fact that the KBHX beam was partially overshooting the cloud top. The steep mosaic weighting function caused a dominant contribution from KDAX on one side of the midpoint and from KBHX on the other side, resulting in a discontinuity at the midway between the two radars (Fig. 13). After a relatively flat weighting function (L 5 100 km and H 5 3 km) was used, the discontinuity between radars was significantly diminished (Fig. 12c).

Figure 14 shows a 24-h radar QPE ending at 0000 UTC 29 November 2005. During this event, KDAX was running VCP 21 instead of VCP 12 as in IOP4. The large difference between the heights of the first (0.58) and second (1.458) tilts caused major discontinuities in the 24-h rainfall accumulation at the tilt boundary (Fig. 14a). Such discontinuities are not physically realistic, which can cause problems in hydrologic model simulations and predictions if the radar QPE is used as a model input. To alleviate the discontinuity, a 20-point azimuthal smoothing is applied across all the tilt boundaries in the hybrid scan (Fig. 14b).

e. Comparison of the HMT radar QPE with the Stage II radar-only QPE The VPR-corrected radar QPEs are compared with the Stage II radar-only QPE product (Lin and Mitchell 2005) for the two IOPs. The Stage II radar-only QPE is

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FIG. 14. The 24-h radar QPE derived from seamless HSR with VPR correction without the 20-point azimuthal smoothing cross (a) the hybrid scan tilt boundaries and (b) with the smoothing.

a mosaic of the operational WSR-88D radar-only QPEs (i.e., the PPS; Fulton et al. 1998) from individual radars. Figures 15 and 16 show the event total (48 h) rainfall maps, the radar–gauge 48-h rainfall bias ratio distributions, and the radar – gauge 48-h rainfall scatterplots for IOP4 and IOP7, respectively. The operational QPE had ;80% underestimation for both IOPs. The underestimation is clearly shown in the bias ratio histograms

(Figs. 15c and 16c). The VPR correction reduced the underestimation and moved the bias ratio mode in the histograms toward unity for both events (Figs. 15f and 16f). The RMSE errors were reduced from 136 to 91 mm for IOP4 (Figs. 15c and 16f) and from 30 to 18 mm for IOP7 (Figs. 16c and 16f). However, because of the radar sampling deficiency in this complex terrain, underestimation still exists after the VPR correction

FIG. 15. IOP4 48-hr radar QPEs ending at 1200 UTC 1 Jan 2006 from (a) the operational WSR-88D radar QPE and (b) the current study. The corresponding radar/gauge bias ratio charts are shown in (b) and (e); and the radar/gauge scatterplots are shown in (c) and (f), respectively.

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FIG. 16. Same as in Fig. 15 except for IOP7 ending at 1200 UTC 15 Jan 2006.

(;46% for IOP4 and ;37% for IOP7). For IOP4, heavy rainfall over the high terrain was dominated by the orographic enhancement process, which was not captured well by the WSR-88Ds. The VPR correction performed well in the central valley and surrounding lower terrains but not as well in the higher terrain of the Sierra Nevada (Fig. 15e versus Fig. 15b). Further, the winter Sierra Z–R relationship (Z 5 101R1.76) may not accurately represent the hybrid rain process during this event (see discussions in section 3b). The spatially variant performance of the VPR correction resulted in a larger radar–gauge 48-h rainfall scatter (Fig. 15f) than without the VPR correction. Meanwhile, the bias error in Stage II had similar spatial variances (Fig. 15b) and a mean field bias would not be able to correct the error. Future work using profiler data and/or spaceborne radar VPRs is necessary to improve the VPR correction for areas like the upper slopes of the Sierra Nevada where the orographic enhancement of precipitation is not captured by the WSR-88Ds. For IOP7, the freezing level (1.2–1.5 km MSL) was much lower than that in IOP4 (2.7–2.9 km MSL), and the event consisted mostly of brightband rain with almost no hybrid rain (i.e., limited amount of low-level precipitation growth due to orographic enhancement). The VPR correction was relatively effective in that the radar QPE underestimation was much less than in IOP4. The VPR correction produced a 48-h radar QPE that correlated much better with gauge observations (0.73;

Fig. 16f) than without the VPR correction (0.47; Fig. 16c). Further studies using multiple years and multiple sites of S-PROF data from the HMT-West experiments will be conducted to refine the VPR correction scheme so that orographic precipitation processes are better represented. More disdrometer data should be collected and analyzed to improve our understanding of spatial Z–R relationship variations in complex terrain. A comparison of the new radar QPE technique against the Stage II radar-only QPE was also conducted for the entire HMT-West 2006 experimental period (1 November 2005 to 30 April 2006). Three statistical scores were calculated for both radar QPEs with precipitation-gauge observations from the Hydrometeorological Automated Data System (HADS; www. nws.noaa.gov/oh/hads/WhatIsHADS.html) and from the HMT-West 2006 field experiment used as reference. The scores included the bias ratio, correlation coefficient, and RMSEs between the domain-averaged 24-h radar QPE and the domain-averaged 24-h rainfall observed by gauges. The scores for all event days with a domain average daily rainfall greater than 5 mm are shown in Fig. 17. The new technique improved the bias ratio significantly for all the events, with a seasonal average of 0.59 compared to 0.16 from the operational radar QPE. The seasonal mean RMSE was reduced by 26% from 22.30 to 16.44 mm. However, there was little to no improvement in the correlation coefficient (0.53 versus 0.51). These results are consistent with the findings from

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FIG. 17. (upper left) Time series of domain average 24-hr gauge observed rainfall, (upper right) radar/gauge 24-hr rainfall bias ratio, (lower left) correlation coefficient, and (lower right) root-mean-square error for all events with the average gauge rainfall greater than 5 mm. The notation of ‘‘Stage-2’’ represents the operational WSR-88D QPE product, and ‘‘new’’ represents the radar QPE developed in the current study. The dashed and solid horizontal lines and the associated numbers represent the mean scores for Stage-2 and for the new radar QPE, respectively.

the two IOPs and reflect the challenges that the radar QPE faces in the cool season over complex terrain.

4. Summary and conclusions A new radar QPE VPR correction scheme was developed for cool season precipitation in the complex terrain of Northern California. The new scheme included multiple steps for identifying and removing nonprecipitation echoes, constructing the hybrid scan reflectivity, applying VPR corrections to the reflectivity, and converting the reflectivity into precipitation rates using adaptive Z–R relationships. Additional procedures were developed to address specific issues in radar rainfall accumulations, which include wind farm contaminations, blockage artifacts, and discontinuities due to radar overshooting. This new radarbased QPE was designed to run in an operational data

environment and is computationally efficient. The processing time combing all algorithms was about 10 s for each 5-min volume scan based on a heavy rain event. Therefore it can be easily transitioned into operations. The new scheme was tested on all precipitation events during the NOAA HMT-West 2006 experiment. The new hybrid scan approach combining unblocked higher tilts with partially blocked lower tilts was found to mitigate some azimuthal discontinuities in rainfall accumulations. The VPR correction was effective in reducing radar QPE overestimation in the brightband area. It can also reduce the radar QPE underestimation in the ice region, but the improvement was limited because of 1) less accurate VPR representations at the higher altitudes than within the brightband and 2) radar beam overshooting cloud tops, leaving insufficient information from which the VPR correction could start.

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The new radar QPE scheme showed significant improvements to the Stage II radar-only QPE in terms of mean field bias and root-mean-square error when compared with gauges. Thus the new QPE scheme has a potential of improving the operational WSR-88D radar QPEs. The new radar QPE will also provide enhancements to the real-time national mosaic and multisensor QPE products along the U.S. west coast. The current NMQ radar-only QPE performs similar to the Stage II along the U.S. west coast even though additional QC steps were applied to remove nonprecipitation echoes and a VPR correction for brightband was implemented in the former. Some of the key issues for the mountainous west coast remain to be addressed in the NMQ, which include VPR correction for radar beams sampling in the ice region, spatially varying Z–R relationships in the coastal and complex terrain, and discontinuities in rainfall fields due to severe blockages. Various procedures developed during the current study could enhance the NMQ QPE products. Several challenges remain for WSR-88D radar QPEs, one of which is the underestimation for orographically enhanced precipitation processes. The existing WSR88D radars cannot adequately resolve the orographic processes below the freezing level over the mountainous areas. Additional high-resolution and near-surface level radar observations are necessary for an accurate radar QPE over these regions. Further, the spatial and temporal variations of Z–R relationships in this complex terrain deserve more study. Future work will focus on analyzing the S-PROF data collected from multiple sites in the HMT-West domain over many field seasons. These S-PROF data contain high vertical-resolution precipitation structure and can provide useful information for improving the current WSR-88D VPR correction scheme, especially for orographic precipitation. Gap-filler radars deployed during the HMT-West project made additional observations over the Sierra Mountains, and precipitation estimations will be derived from these data. The gap-filling radar QPE will be merged with the WSR-88D radar QPE, and the impact of gap-filler radars will be assessed. In addition, mountain precipitation climatologies and atmospheric environmental data contain useful information about the precipitation physics. Integration of these physically based background fields with the radar QPE may provide more accurate spatial distributions of precipitation in complex terrain than radar-only QPEs. Further, high-resolution VPRs from spaceborne radars (e. g., those from the current Topical Rainfall Measuring Mission and the future Global Precipitation Measurement satellites) will be explored for their potential to correct ground radar QPEs. Only by assimilating information

from multiple sensors can the large spatial and temporal variations of precipitation in complex terrain be resolved in a manner that facilitates the generation of accurate high-resolution QPE. Acknowledgments. The authors are very thankful to Carrie Langston and Katherine Willingham for processing some of the data used in the current study. Dr. Brian Kaney developed the web-based display tool, with which most of the figures in this paper were created. We are also grateful to the anonymous reviewers for their comments that helped improve the quality of this manuscript. Major funding for this research was provided under NOAA’s Hydrometeorology Testbed (HMT) program and partial funding was provided by NOAA/ Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreement NA17RJ1227, U.S. Department of Commerce.

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