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Hydrometeorology

Hydrometeorology. Dr. Andrew S. Jones* Prof. Pierre Julien Assoc. Prof. Jorge Ramirez. Hydrometeorology. Hydrometeorology. Spatial Data Filters. Satellite. Objective: Measure and Predict the Hydrologic Environment. 1DVAR Water Vapor Profiling. Data Assimilation and Modeling

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Hydrometeorology

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  1. Hydrometeorology Dr. Andrew S. Jones* Prof. Pierre Julien Assoc. Prof. Jorge Ramirez

  2. Hydrometeorology

  3. Hydrometeorology Spatial Data Filters Satellite • Objective:Measure and Predict the Hydrologic Environment 1DVAR Water Vapor Profiling DataAssimilationandModeling Physically links the system together in space and time Physical Hydrological Models Energy and Mass Flux Channel Flow / Erosion / Deposition Emissivity Soil Moisture

  4. Hydrometeorology • Dr. Andrew S. Jones, Mr. Phil Stephens1 • Satellite Soil Moisture data assimilation, Soil Moisture retrieval, advanced spatial filter analysis (~5 to 50 km) • Trafficability, Land surface-weather feedbacks • Prof. Pierre Julian, Dr. Rosalia Rojas 2 • Erosion Simulations: CASC2D-SED (30 to 300 m) • Channel flow, sediment transport, chemical dispersion • Assoc. Prof. Jorge Ramirez, Dr. David Raff 2 • Hillslope Hydrology Model: HYDROR (> 1 cm) • Water/erosion process studies, interactive infiltration 1 PhD Student at Cambridge 2 Recent PhD Graduates

  5. HydrometeorologyCG/AR Theme Interconnections NDDA 4DVAR Soil Moisture HYDROR Clouds Hydromet Future: CDFS II Spatial Filters / Backus-Gilbert CASC2D-SED 1DVAR WV Emissivities TechTransition DPEAS Battlespace

  6. Hydrometeorology Objectives: Develop “deep” soil moisture remote sensing capabilities (therefore must include spatial and temporal soil moisture data assimilation capabilities) Create spatial data filter for improved data assimilation / data fusion Cross-sensor technology transitionData Processing and Error Analysis System (DPEAS) work

  7. Hydrometeorology Principal Results: The Microwave Land Surface Model (MWLSM) Observational Operator has been created and has passed preliminary tests. Publication of work in progress… Current 4DDA work involves case study preparation efforts (physical insertion of IR and MW satellite data sets into the data assimilation system) More details during the NDDA brief…

  8. Hydrometeorology More Principal Results: • The 1D Discrete Backus-Gilbert (DBG) spatial filter has been published (IEEE TGRS), and full technical report delivered to DoD contacts • Extension of DBG to 2D and addition of noise minimization capabilities in the presence of RFI is underway… • The Data Processing and Error Analysis System (DPEAS) has been published (JTECH) Took NOAA opportunity to run DPEAS in an operational test environment at NOAA/NESDIS/OSDPD through related NOAA / High Performance Computing and Communications (HPCC) work

  9. Cross-sensor Technology TransitionConvergence of Research and Operations

  10. Hydrometeorology DoD Contacts: • Pat Phoebus, many other NRL technical contacts,NRL, Monterey:(primarily data assimilation and satellite meteorology groups)formal collaboration proposals submitted(visit to NRL, July 2002) • Chuck Downer, ACE, ERDC, Vicksburg: (visit to CSU, Aug. 2002) • Gary McWilliams, ARL, Adelphi:NPOESS-related work / discussions

  11. Hydrometeorology Future Plans and Deliverables: “Deep” soil moisture experiments using 4D data assimilation of infrared/microwave satellite data MWLSM observational operator publication Complete 2D Discrete Backus-Gilbertspatial filter implementation and testing, extend to new MW sensors as they become available Incorporation of 1DVAR water vapor method into DPEAS for global land emissivity studies (collaborations with Joint Center for Satellite Data Assimilation (JCSDA) participants) Use AMSU to prepare for SSMIS data

  12. Backup Slides Hydrometeorology

  13. HydrometeorologyPublications (2002) Stephens, P. J., and A. S. Jones, 2002: A computationally efficient discrete Backus-Gilbert footprint-matching algorithm. IEEE Trans. Geosci. Remote Sens.,40, 1865-1878. Jones, A. S., and T. H. Vonder Haar, 2002: A dynamic parallel data-computing environment for cross-sensor satellite data merger and scientific analysis. J. Atmos. and Oceanic Technol.,19, 1307-1317. Jones, A. S., 2002: Variational data assimilation of soil moisture using a satellite observational operator and its adjoint. 4th USWRP Symposium, April 22-24,Boulder, CO. (talk). Jones, A. S., and T. H. Vonder Haar, 2002: A cross-sensor Data Processing and Error Analysis System (DPEAS). CSU Atmospheric Science 40th Anniversary Symposium, July 9 2002, Fort Collins, CO. (poster). Jones, A. S., S. Q. Kidder, K. E. Eis, and T. H. Vonder Haar, 2002: The use of an HDF-EOS-based parallel data-computing environment for cross-sensor satellite data merger and technology transition. Preprints, 18th International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology (Joint with the 6th Symposium on Integrated Observing Systems), January 13-17, Orlando, FL, Amer. Meteor. Soc. Jones, A. S., P. J. Stephens, and T. H. Vonder Haar: 2003: An improved Backus-Gilbert spatial filter for satellite data processing. Preprints, 12th Conference on Satellite Meteorology and Oceanography, February 10-13, Long Beach, CA, Amer. Meteor. Soc.

  14. HydrometeorologyPublications (2002) (cont.) Jones, A. S., T. Vukicevic, and T. H. Vonder Haar, 2003: Variational data assimilation of soil moisture using 6 and 10 GHz passive microwave data. Preprints, 7th Symposium on Integrated Observing Systems, February 10-13, Long Beach, CA, Amer. Meteor. Soc. McKague, D. S., J. M. Forsythe, A. S. Jones, S. Q. Kidder, T. H. Vonder Haar, 2003: A passive microwave optimal-estimation algorithm for near real-time atmospheric profiling, Preprints, 12th Conference on Satellite Meteorology and Oceanography, February 10-13, Long Beach, CA, Amer. Meteor. Soc. P. J. Stephens, and A. S. Jones, 2002: Derivation and analysis of a computationally efficient discrete Backus-Gilbert footprint-matching algorithm. CIRA Tech. Report, Colorado State University, Fort Collins, CO, 59 pp. [ISSN: 0737-5352-55]. Vukicevic, T., D. Zupanski, M. Zupanski, T. Greenwald, A. Jones, T. Vonder Haar, K. Eis, 2002: Data assimilation research in CIRA: Regional Atmospheric Modeling and Data Assimilation System (RAMDAS). CSU Atmospheric Science 40th Anniversary Symposium, July 9, Fort Collins, CO. (poster). Vukicevic, T., M. Zupanski, D. Zupanski, T. Greenwald, A. Jones, T. Vonder Haar, D. Ojima, and R. Pielke, 2002: An overview of a mesoscale 4DVAR data assimilation research model: RAMDAS. Preprints, The Symposium on Observations, Data Assimilation, and Probabilistic Prediction, January 13-17, Orlando, FL, Amer. Meteor. Soc. (poster P3.1). Zupanski, M., D. Zupanski, T. Vukicevic, T. Greenwald, A. Jones, K. Eis, T. Vonder Haar, 2002: Estimation theory and data assimilation: a challenge of mesoscale data assimilation. CSU Atmospheric Science 40th Anniversary Symposium, July 9, Fort Collins, CO. (poster).

  15. Backup Slides Discrete Backus-GilbertSpatial Filter Results

  16. Adaptive Spatial Filter Work for SatelliteData Assimilation Use and RFI Avoidance A discrete Backus-Gilbert algorithm for dynamic and more efficient sensor footprint matching (Stephens and Jones, 2002) Discretization creates a new diagonal matrix form of the BG method with the Stogryn minimization constraints Up to 250% more efficient Similar RMS errors (~± 5 %) (compare to the ref. line) This allows for faster calc.of BG coefficients where noise is dynamic (e.g., in RFI contaminated environs)

  17. Data Fusion Aspects • Spectral/spatial/temporal/radiometric influences of instrument characteristicsvs.physical phenomenology • Q: How to extract known instrument-dependent effects (resolution behaviors, sampling issues, RFI problems?) AND objectively quantify this for an operational data assimilation system running in near real-time?

  18. Data Fusion aspects of 3D and 4DVAR Why use a Backus-Gilbert spatial filter? • Cross-sensor observations have different spatial resolutions and noise • Quantitatively trade spatial resolution for signal/noise strength • Accurately manage data information while going from multi-source observational spaces to the model grid space • Focus on important spatial areas in high-gradient environments (e.g., littoral zones, rivers, mountainous terrain)

  19. Backus-Gilbert method: Pros and Cons • Advantages of Backus-Gilbert methods • Mathematically-consistent error propagation behaviors • Antenna gain patterns are explicitly used • Analysis function is highly tunable • Heritage within the remote sensing community • Disadvantagesof Backus-Gilbert methods that are solved by the Discrete BG method • Computationally expensive and rigid(not dynamic) • Difficult to use in RFI-contaminated environments

  20. Discrete Backus-Gilbert Method Performance RMS error(K) CPU time(s) > 2X faster # Discrete Intervals

  21. T x T x est est 250 320 300 249 280 248 260 240 247 220 246 200 x x - 15 - 10 - 5 5 10 15 - 15 - 10 - 5 5 10 15 T x T x est est 320 320 300 300 280 280 260 260 240 240 220 220 200 200 x x - 15 - 10 - 5 5 10 15 - 15 - 10 - 5 5 10 15 T x T x est est 320 320 300 300 280 280 260 260 240 240 220 220 200 200 x x - 15 - 10 - 5 5 10 15 - 15 - 10 - 5 5 10 15 An example of how the DBG Singular Value Decomposition (SVD) approximation works on a coastline cross-section 1% 10% Only ~15%of the termsare needed 3% 15% 5% 100%

  22. DBG Optimization Performance Approximately 2X faster due to SVD optimizations,> 4X faster overall when compared to prior methods SVD Optimization Performance Increases

  23. Improved computational method to integrate data with models • The new method is available and is 4X faster than existing Backus-Gilbert methods • Computationally flexible and efficient • Handles the spatial analysis and data uncertainties • Suitable for multi-source data • Conducive to additional optimizations • With additional work, the performance of the method could be dynamically adjustablee.g., requests like “I’d like 0.2 K precision in my temperature data for my NWP data assimilation grid” are now possible.

  24. Future DBG Work Near-term • Construct an AMSR/Windsat-specific DBG spatial filter • Use within the 4DVAR DA system for deep soil moisture retrievals using 6 and 10 GHz MW data Long-term • Use the BG-derived spatial error characteristics within the 4DVAR DA system • Use within RFI-contaminated environments • Use as a quantitative data compression method using spatial adaptive grid techniques and specified S/N

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