Application of Real Time Dynamic Sensor Web Measurement Techniques to Maximize Aura/TES Useful Science Return Stephen Talabac, Mark Schoeberl, Christopher Lynnes, Patrick Coronado, and Robert Lutz NASA Goddard Space Flight Center Greenbelt, Maryland 20771
[email protected] Abstract— The Sensor Web characterizes a future observing system concept that has the potential to significantly increase useful science data return. It accomplishes this by intelligently and dynamically reconfiguring the measurement and information processing states of its constituent sensor, computing, and storage nodes. To explore the potential benefits of Sensor Web observing strategies for the Earth science community, the Goddard Space Flight Center is developing software to demonstrate the value of real-time, collaborative, “intelligent data collection” between two formation flying spacecraft that comprise NASA’s EOS “ATrain” constellation: Aqua and Aura. After its planned Summer 2004 launch, Aura, with its Troposphere Emission Spectrometer (TES), is to be inserted into the constellation’s orbital plane and “follow” 15 minutes behind Aqua with its Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. MODIS is a nadir pointing multispectral imager, it is always “ON”, and its 36 bands can be used to detect clouds. TES is a high-resolution infrared-imaging Fourier transform spectrometer designed primarily to study ozone in the lower atmosphere. Providing spectral coverage in the mid- to thermal IR bands, it is undesirable for TES to make measurements within cloud obscured fields of view. Of particular significance, TES is a pointable instrument: it can be commanded to image any target within 45° of the local vertical. In our paper we describe a high performance prototype software system to acquire and process direct readout MODIS data to a Level 2 cloud mask product in real time. Using a predefined list of desired targets, in conjunction with the real time MODIS cloud mask information, the software will generate simulated commands and “point” TES to only those targets that are cloud free to demonstrate the value of intelligent data collection measurement techniques. To further refine where within the cloud free regions TES should point, we are also assessing the potential use of atmospheric chemistry models whose outputs may provide an additional set of criteria for high science value target selection. Similar “event-driven” and “model-driven” adaptive observation strategies and dynamic measurement techniques may be applied to future Sensor Web Earth- and space-science missions. Keywords; Sensor Webs, dynamic measurment techniques, adaptive observing strategies, Aqua/MODIS, Aura/TES, formation flying
I.
SENSOR WEBS BACKGROUND
To articulate its 2020-era Earth science goals, NASA’s Earth Science Vision Initiative [1] identified six targeted research applications. The Sensor Web was identified as one of four key candidate technology themes that should be pursued as part of a long term investment strategy to ensure that these six targeted applications could be achieved. We have defined a Sensor Web as a distributed, organized system of “nodes”, interconnected by a communications fabric, that behave as a coherent instrument. It consists of sensor, computing, and storage nodes. Multiple, heterogeneous, remote sensing and in situ sensor nodes may be located in space, deployed within the atmosphere (e.g., radiosondes, UAV’s), and on or below the Earth’s surface (e.g., buoys, autonomous marine craft). Computing nodes, such as data assimilation and weather forecast models [2], [3] and storage nodes, such as intelligent archives [4] complement the sensor nodes. Information from any of these nodes can be used to initiate changes to sensor spatial, spectral, and/or temporal measurement modes. Similarly, a computing node may be reconfigured to change the model’s initial conditions, grid scale, or other information processing properties. This exchange of sensor measurement data (raw or processed) and predictive forecast model information, causes the Sensor Web to adapt and react by initiating new node measurement and information processing states. The potential benefits of this new closed-loop approach include: maximizing the return of only the most useful scientific measurement data; minimizing system response time when monitoring rapidly evolving or transient phenomena; reducing numerical forecast model error growth by performing targeted observations of model-sensitive regions; and improving the system’s ability to identify key precursor signatures of environmental phenomena. II.
PROJECT SUMARY
A. Spacecraft and Instrument Characteristics Five spacecraft will comprise the EOS afternoon, or “ATrain”, constellation. Aqua, launched on May 4, 2002, leads this formation flying spacecraft train. Following Aqua, in the
same orbital plane, will be Calipso, Cloudsat, Parasol, and Aura respectively. All spacecraft will be in a sun synchronous, 98.2 degree inclination, 705 Km altitude orbit. Aqua's 1:30PM ascending node will precede Aura’s planned 1:38PM ascending node. Orbital characteristics are such that Aqua will lead Aura’s position in orbit by approximately 15 minutes. Six instruments comprise Aqua’s payload. They collect information about Earth’s water cycle and terrestrial and marine ecosystem dynamics such as atmospheric water vapor, clouds, precipitation, soil moisture, snow and ice, aerosols, vegetation, and phytoplankton. Aqua’s MODIS instrument operates at a 100% duty cycle. It has 36 bands with a spectral range of 620nm - 2,125nm for bands 1-19, and 3.66µm 14.386µm for bands 20-36. Having a fixed nadir view, the MODIS detector focal plane array sweeps out a 10Km (along track) by 2,330 Km (cross track) swath of coverage: +55 degrees from its orbital view. The spatial resolution at nadir varies for each band: 250m for bands 1 and 2, 500m for bands 3-7, and 1000m for bands 8-36. In our experiment we will use MODIS data to detect clouds. Aura’s payload consists of four instruments which will be used to study Earth’s atmospheric chemistry and dynamics. The mission science will focus on how and why concentrations and distributions of atmospheric ozone are changing. One of its instruments, TES, is an imaging infrared Fourier-transform spectrometer [5]. Its spectral coverage is 3.2µm to 15.4µm with a spectral resolution of 0.025cm-1. Using a pointable mirror, TES is able to make measurements anywhere within a 45 degree conical FOV. It can also point to the Earth’s limb. When pointing at nadir, each of its 16 detectors provides a spatial resolution of 5.3Km x 0.53Km. Its 1x16 detector array yields a 5.3Km x 8.5 Km FOV. At 45 degrees off nadir, the spatial resolution for each detector and the detector array is reduced to 7.9Km x 0.8Km and 7.9 Km x 12.7Km respectively. Limb measurements provide a 23.1Km x 37Km FOV and a vertical resolution of 2.3Km. TES must stare at each target for 4 seconds before imaging the next one. It can be operated in three modes: downward looking transects, step-and-stare, and limb pointing. In transect mode, TES is commanded to point forward +45 degrees along track. The mirror progressively steps backward along track to make a series of contiguous FOV measurements until the instrument makes its last measurement at -45 degrees aft. In this transect mode TES provides approximately 885Km of contiguous coverage along track. In step-and-stare mode the TES mirror is commanded to step to point to (potentially) non-contiguous targets of interest anywhere within its 45 degree conical FOV. After staring at its target for 4 seconds approximately 4.5 seconds is allocated for the mirror to step (i.e., point) to its next target. We have selected the TES step-and-stare mode of operation to explore adaptive observing strategies and the potential benefits of intelligent data collection techniques. B. TES Targeting and Science Drivers Ozone is harmful pollutant and it is a principal component of smog in large metropolitan areas. Since TES will be used to monitor Tropospheric ozone and other atmospheric pollutants we identified two types of targets for our experiments: fixed targets and targets of opportunity. Our fixed targets are
represented by a list of cities where air quality monitoring is an important element of daily activities. Targets of opportunity are represented by transient phenomena such as erupting volcanoes or wild fires. Targeting volcanoes is important since volcanic gases may serve as precursors to eruptions. Similarly, targeting wildfires can be used to measure ammonia present in biomass burning plumes. C. System Design Considerations Several functional and performance factors were considered during the system design phase including: the availability of real time Aqua/MODIS data; understanding operational Aqua/MODIS data processing systems and their performance characteristics; target selection criteria and instrument viewing geometries; cloud types; and TES commanding. We briefly describe below how these considerations impacted our system design decisions. Aqua/MODIS data is available at Goddard via two alternative paths. The first alternative we considered is to receive this data stream from the White Sands Complex (WSC). The EOS Data and Operations System (EDOS) at Goddard performs level zero processing (LZP) on the received MODIS packets. This alternative would provide us with world wide coverage and therefore a rich target set. However, the standard, research-quality data processing stream for MODIS operates on 2-hour segments of raw data, in concert with 24hour-long ephemeris files. Together with latencies due to spacecraft downlink contacts and various other data systems, the research-quality data is typically available from Aqua some 8 to 36 hours after collection. A second alternative was to use Goddard’s MODIS Direct Readout station. Aqua/MODIS direct readout data are received within seconds of data collection, though the attitude and ephemeris that accompany them are less accurate. Two disadvantages to this approach are that a mid-latitude ground receiving site (i.e., GSFC in Greenbelt, MD) would be constrained to receiving only a limited number of Aqua passes per day and MODIS coverage would be limited to roughly the East coast of the US. This would restrict the number and types of potential targets. The common practice of processing a direct broadcast swath as one unit would also mean that the processing would not begin until 8-12 minutes after collection. Furthermore, the processing algorithms themselves can introduce additional latencies. For example, the MODIS science algorithm for computing cloud mask and related parameters takes about 1600 seconds to process 300 seconds of data (250-MHz SGI Origin 2000). The calibrated radiance and geolocation algorithms add lesser, but significant, times to the latency. We felt that the ability to collect direct readout MODIS data outweighed these disadvantages since it would provide us with the opportunity to demonstrate real time targeting decision making. We have therefore selected Goddard’s direct readout station to serve as the source of Aqua/MODIS data for our Sensor Web research experiment. Goddard’s research-quality data processing systems and their aggregate system throughput characteristics reflect a serial sequential approach to ingest and to perform LZP, Level 1 (L1) processing, and Level 2 (L2) cloud mask processing. That is, LZP must complete before L1 processing begins, which in turn
must be completed before applying the L2 cloud mask algorithm. We are therefore taking a threefold approach to solve the latency problem: a) Inexpensive, powerful Linux-based hardware. Linuxbased CISC hardware in the range of ~3 GHz is now widely available and inexpensive. While faster clock speeds do not always translate to commensurately faster processing, the algorithms in use for MODIS processing are computeintensive and scale well with increasing clock speed. b) Pass granularization. A key to our approach is to break up each Aqua/MODIS pass into small 30 second granules. This has two important benefits. First, it allows us to perform pipelined processing of the MODIS scans earlier (i.e., we estimate about 32-35 seconds after the data are collected). In addition, it provides a simple path to parallelize the problem (without reprogramming the rather complex algorithms), processing more than one granule at a time on multiple processors. c) Bayesian classification of clouds. The most compute intensive algorithm in the pipeline is the research-quality cloud mask algorithm. Our approach is to replace this algorithm with a faster, simpler algorithm based on machine learning, in this case Bayesian classification. The Bayesian classifier is trained using the output products from the research-quality algorithm, yielding what amounts to a highspeed approximator of the latter. While there are some differences (particularly at night and in polar regions), the algorithms perform similarly in temperate latitudes. We are compiling a list of metropolitan area targets (represented as latitude, longitude pairs) for each desired TES step-and-stare measurement. In lieu of volcanoes, we have selected wildfires to serve as targets of opportunity. A database of large fires tracked by the National Interagency Fire Center (NIFC) will be electronically retrieved as part of our daily prepass setup activities. We have also identified candidate internet sites where air quality alerts are made available from predictive models. We will use these regions to demonstrate model-driven TES targeting. To compute target viewing geometries, swath coverage, target contact times, and perform target scheduling we have selected a commercially available product, Satellite Tool Kit (STK). We are also considering the use of STK/Scheduler to schedule target sequencing based upon in-view times and other criteria. We will use the STK/Connect application programming interface to exchange target identification and scheduling information with our custom application software. We considered the impact of cloud types relative to TES targeting selection criteria. Although our Bayesian cloud mask provides cloud identification information, we decided for simplicity to categorize our targets simply as either cloudy or cloud free. As of this writing Aura is not expected to be launched until July 2004. As with any new spacecraft it will undergo on-orbit checkout before commencing routine on-orbit operations. We will neither generate nor uplink commands to Aura/TES during these critical mission phases. Instead, TES pointing will be
simulated using STK’s Visualization Option (STK/VO) which we will use to demonstrate, in real time, how the Aqua/MODIS cloud mask information will be used to have TES point to and image only cloud free targets. As the MODIS data is received and processed to L1 and L2 cloud mask products using our pipelined processing solution, it will also be transformed to an equirectangular cylindrical mapped grid. The remapped MODIS imagery will be provided to STK/VO which will drape it over a 3D visualization of the globe. STK/VO will also be used to provide us with an on-orbit visualization of Aqua/MODIS and Aura/TES and a real time animation of TES pointing to only the cloud free targets. D. Hardware and Software Architecture In selecting a computer architecture we evaluated the hardware configurations and operating system environments of the computer platforms that currently perform Aqua/MODIS LZP, L1, and L2 cloud mask processing. Our objective was to select an architecture that would ensure our performance requirements would be met while minimizing potential changes to existing software. We first examined GSFC’s MODIS Direct Readout system: the Real Time Satellite Telemetry Processing System (RT-STPS). It is implemented using a PC class computer configured with a single 1.8GHz Intel Pentium 4 processor, 1GB RAM, and 120GB disk storage. The RT-STPS Aqua/MODIS ingest and LZP processing software executes under a Linux operating system. It also requires a PCIbus interface board, and software drivers, to interface it to the direct readout system’s bit synchronizer. Since the RT-STPS computer is used to routinely collect MODIS data we could not use or modify that system for our experiment. We therefore developed a new, modified version of the RT-STPS computer hardware and software. RF splitters provide a parallel telemetry signal path from the MODIS direct readout antenna and frontend electronics to the new system. To reduce implementation risk, accommodate the additional L1 and L2 processing workload, and to maximize hardware and software commonality, we selected a loosely coupled, distributed computer architecture using two identically configured dualprocessor Intel-based computers that are interconnected via a Gigabit Ethernet. Each of these two computers is configured with dual Intel 3.06GHz Xenon processors, 1MB cache, 4GB SDRAM, 40GB system disk, and four 250GB disks to facilitate MODIS data capture, storage, and processing. To minimize operating system porting issues, we selected the Red Hat Linux operating system as it was compatible with the existing LZP, L1, and L2 MODIS processing software. The first computer serves primarily as a modified, pipelined version of the RTSTPS: it will ingest and the CCSDS formatted Aqua/MODIS packets from the 15Mbps data stream. The second computer is primarily responsible for receiving ingested 30-second MODIS granules via the Gigabit Ethernet interface and perform pipelined LZP, L1, and L2 processing also in 30-second granule increments. In particular, a portion of the RT-STPS software on the first computer will stream data, using a socket connection, to a counterpart software component that resides on one of the two processors in the second computer. The RTSTPS software component on the second computer will breakup the streamed MODIS data into granules which will be stored on the second computer as 30-second-long files. These
files will be accessed by new pipelined applications software to produce L1 and L2 files. To maximize system throughput we will also combine the L1, L2, and remapping processes into a single tightly coupled application that executes on the second computer. E. Atmospheric Chemistry Models A stated goal of TES is to contribute to the study of the distribution of Tropospheric ozone as modified by natural and anthropogenic sources of its precursors and the consequent changes in the oxidizing power of the troposphere. Ozone is the source of photochemical smog, a significant greenhouse gas in the upper troposphere and a known phytotoxicant, causing damage to trees and plants. The global distribution of Tropospheric ozone is largely unknown and the processes of ozone formation and destruction are complex. Atmospheric chemistry models may be used to study and forecast photochemical air quality, which pertains to air quality with respect to ambient ozone concentrations. As part of our research we examined many types of atmospheric chemistry models (e.g. box models, Eulerian models, 1 and 2 dimensional, global) and their model components (i.e. treatment of large scale transport, representation of chemical processes). This part of our research was performed so that we could identify a candidate model that might be used to forecast potential targets of opportunity for TES (e.g., regions of high ozone content). To complement our experimental intelligent data collection demonstration, we will initially focus on the potential applicability and use of the Variable grid Urban Airshed Model (UAM-V) system. It is a three-dimensional photochemical grid model that calculates concentrations of pollutants by simulating the physical and chemical processes in the atmosphere.1 It is designed to calculate concentrations of both inert and chemically reactive pollutants by simulating the physical and chemical process in the atmosphere that affect pollutant concentrations. When used to calculate ozone concentrations it simulates the factors that affect photochemical air quality:
UAM-V allows variable vertical layer numbers and spacing, specification of three-dimensional meteorological variables (derived from the Regional Atmospheric Model System) and explicit treatment of subgrid-scale photochemical plumes. Two-way grid nesting allows multiple urban areas to be simulated within a larger region. The typical time step is 10 to 30 minutes for coarse (10 to 40 km) grids and a few minutes for fine (1 to 2 km) grids. The model is usually applied to 48 to 72 hour periods for urban applications. The model has been utilized to facilitate the identification of urban-scale air quality management actions over diverse areas such as: Los Angeles, California; the eastern United States (as part of the Ozone Transport Assessment Group); and Atlanta, Georgia. It is envisioned that this model would be run for a preselected target (e.g., Atlanta) several hours before a TES pass. We would expect that maximum science return could be achieved by pointing TES to regions that are forecast to have the highest ozone levels and that are cloud-free. F. Experimental Results At the time of this paper we are completing the software implementation, integration, and test phases for our demonstration system. We expect to complete these activities in August and we will compile and present our findings at the IGARSS conference. ACKNOWLEDGMENT The authors would like to acknowledge the Goddard Technology Management Office which provided funding support for this Sensor Web research project. We would also like to express our appreciation to the Goddard Space Flight Center, Science Data Systems Branch for providing the capital equipment that we required to perform our research. REFERENCES
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