1. RPC Review (7/10/07) 1 Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications Mississippi State University GeoResources Institute

2. RPC Review (7/10/07) 2 GPM Optimization Team & Collaborators MSU Team –Valentine Anantharaj –Lori Bruce –Jenny Du –Yangrong Ling –QiQi Lu –Georgy Mostovoy –Louis Wasson –Nicholas Younan –Graduate students External Collaborators –Paul Houser (GMU CREW) –Joe Turk (Naval Research Laboratories,Monterey, CA) Partner Agencies –Garry Schaeffer (USDA NRCS) –Steve Hunter (United States Bureau of Reclamation)

3. RPC Review (7/10/07) 3 Team Activity MSU GRI: Precipitation merging & optimization, modeling, project management, and RPC Integration. NRL: GPM data, precipitation sensitivity analysis. GMU CREW: Ensemble Kalman Filter based optimal merging, downscaling, and science expertise.

4. RPC Review (7/10/07) 4 Identified Decision Support Needs Routine analysis land surface state (soil moisture, evaporation, land surface temperature) over the continental involves: water soils sun weather climate vegetation terrain water soils sun weather climate vegetation terrain observe, model, assimilate Observations Analysis / Modeling Information

5. RPC Review (7/10/07) 5 GPM Evaluations: Purpose and Activities

6. RPC Review (7/10/07) 6 Purpose of RPC Experiment Optimize the GPM precipitation estimates for decision support in water resources management and other cross-cutting applications. –Characterize and optimize GPM precipitation data by blending and merging with other precipitation measurements and estimates using a Four-Dimensional Objective Analysis (4D-OA) scheme and other intelligent methods.

7. RPC Review (7/10/07) 7 Iterative Experimental Design

8. RPC Review (7/10/07) 8 Experimental Objectives of GPM Optimization Develop dynamic 4D-OA techniques (EnKF) and intelligent methods (ANN, Bayesian merging) to optimally merge various precipitation estimates. Evaluate and implement spatial downscaling and temporal disaggregation techniques to derive precipitation forcings for land surface modeling. Evaluate the optimized and downscaled products by running land surface model experiments at 1 -10 km resolutions in selected domains. Characterize uncertainties in merged products and in LSM simulations.

9. RPC Review (7/10/07) 9 Tasks to Achieve Objectives Precipitation Merging  ANN Method  Feature Optimization Technique  EnKF Objective Ananlysis  Bayesian Merging Precipitation Downscaling  Stochastical-Physical Hybrid Method Hydrological Modeling  Merged forcings  Downscaled forcings Analyze results and publish

10. RPC Review (7/10/07) 10 Precipitation Datasets

11. RPC Review (7/10/07) 11 Sources of Rainfall Data In-situ gage observations RADAR measurements Estimates from satellite data Numerical Weather Prediction Models (NWP)

12. RPC Review (7/10/07) 12 Example Precipitation Products NameSourcePlatform GEOSNASA / GSFC / DAOModel Based GDASNOAA / NCEP / EMCModel Based EDASNOAA / NCEP / EMCModel Based RUCNOAA / FSLModel Based NRL IRNaval Research LaboratoryIR NRL MWNaval Research LaboratorySSM/I / TRMM / AMSU-B HUFFMANNASA / GSFC / MAPIR / SSM/I / TRMM PERSIANNUniversity of ArizonaIR / SSM/I / TRMM NEXRADNOAA / NCEPGauge, Ground Based Radar HIGGINSNOAA / CPCGauge GTSNOAA / NCEPGauge CMAPNOAA / CPCGauge, IR, SSM/I, TRMM CMORPHNOAA/CPCIR, Mircowave

13. RPC Review (7/10/07) 13 Example Precipitation Products NameDomainSpace/Time Resolution GEOS90 N – 90 S, 180 W – 180 E 1.25x1.00  3 hourly GDAS90 N – 90 S, 180 W – 180 E ~ 0.70  6 hourly EDASNorth America40 km3 hourly RUCNorth America40 km3 hourly NRL IR60 N – 60 S, 180 W – 180 E 0.25x0.25  6 hourly NRL MW60 N – 60 S, 180 W – 180 E 0.25x0.25  6 hourly HUFFMAN50 N – 50 S, 180 W – 180 E 0.25x0.25  3 hourly PERSIANN50 N – 50 S, 180 W – 180 E 0.25x0.25  Hourly NEXRADContinental US4 kmHourly HIGGINSContinental US 0.25x0.25  Daily GTS90 N – 90 S, 180 W – 180 EStation DataDaily CMAP90 N – 90 S, 180 W – 180 E 2.50x2.50  Pentad CMORHP60 N – 60 S, 180 W – 180 E 0.073x0.073  0.5 hourly

14. RPC Review (7/10/07) 14 Artificial Neural Networks Approach for Merging Multiple Precipitation Products Jenny Q. Du and Nicholas Younan GeoResources Institute Department of Electrical and Computer Engineering Mississippi State University

15. RPC Review (7/10/07) 15 Research Objectives –To combine precipitation-related information from satellite estimates, model predictions, and rain gauge measurements in order to capitalize on the advantages of each product. –To study the impact and sensitivity on land surface states of the final precipitation estimates.

16. RPC Review (7/10/07) 16 ANN Data Merging Neural Network –Multilayer Back Propagation Neural Network (BPNN) –Training: Inputs: satellite estimates, model predictions, a bias term Output: gauge measurements –The weights in the BPNN are used to adjust the errors. –Nonlinear regression

17. RPC Review (7/10/07) 17 ANN Data Merging (Cont’d) Investigations to be conducted –Is it reasonable to use gauge measurements as the desired outputs for the neural network training? –When gauge measurements are unavailable, can the interpolated gauge measurements be used as the desired outputs? –If gauge measurements are considered to be noisy, how to modify the neural network training algorithm to accommodate the inaccuracy? –Is there any other choice for the desired outputs?

18. RPC Review (7/10/07) 18 ANN Data Merging (Cont’d) Investigations to be conducted (… continued) –What is the spatial scale for a specific neural network to remain effective (i.e., spatial generalization property)? –What is the temporal scale for a specific neural network to remain effective (i.e., temporal generalization property)? –In addition to the current existing unsupervised neural network-based data merging approach, can a new unsupervised neural network be developed for precipitation data merging?

19. RPC Review (7/10/07) 19 Intelligent Feature Optimization Feature Optimization Precipitation Data Model Output LIS Eliminate Redunda nt Features Precipitation Data Sets D1 D2 Dn F1 F2 Fn G1 G2G2 Gn FV Merged Precipitation Data Sets Feature Extraction Feature Reduction Eliminate Redundant Features

20. RPC Review (7/10/07) 20 Combined Physical and Statistical Approach for Downscaling Precipitation Products Paul Houser Center for Research on Environment and Water George Mason University

21. RPC Review (7/10/07) 21 A Hybrid Approach for Downscaling and Disaggregation of Precipitation Statistical Downscaling Physical Downscaling Hybrid Approach –Stochastic downscaling in space –Physical process based disaggregation in time

22. RPC Review (7/10/07) 22 Space-Time Downscaling-Disaggregation

23. RPC Review (7/10/07) 23 Downscaled Precipitation Product Compared with Radar Observation Downscaled Product GCM equivalent Product Radar Observed 3 km 10 min 3 km 10 min 48 km 180 min

24. RPC Review (7/10/07) 24 Expected Results [Example only]

25. RPC Review (7/10/07) 25 Issues / Risks There may not be a physical basis for the performance of the techniques; i.e. the performance (good or poor) may not be explained by relating to a set of physical processes.

26. RPC Review (7/10/07) 26 Schedule Task IDTaskMar – Aug 2007Sep – Feb 2007Mar – Aug 2008 1.1Develop and implement data merging technique 1.2Test merged data in LIS 1.3Regional validation of optimized forcings 2.1Develop downscaling methodology 3.1LIS control simulations at core sites 3.2LIS simulations with different precipitation forcings 3.3LIS validation against in-situ data 4.1Document and report results

27. RPC Review (7/10/07) 27 Contact Information Valentine Anantharaj Tel: (662)325-5135val@gri.msstate.edu

28. RPC Review (7/10/07) 28 Our Sponsor: NASA Applied Sciences … NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet".

29. RPC Review (7/10/07) 29 Our Sponsor: NASA Applied Sciences … NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet". Applications extend the NASA vision and mission by enabling and facilitating the assimilation of Earth observations and prediction outputs into decision support tools. The purpose is to enhance the performance of the decision support resources to serve society through Earth exploration from space.

30. RPC Review (7/10/07) 30 Precipitation Data Model r = r T +b+n r: observed data r T : true data b: bias (constant systematic error) n: random error