Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

RPC Review (7/10/07)

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Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

Mississippi State University GeoResources Institute


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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)


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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.


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Identified Decision Support Needs

• Routine analysis land surface state (soil moisture, evaporation, land surface temperature) over the continental involves:

watersoilssunweatherclimatevegetationterrain

watersoilssunweatherclimatevegetationterrain

observe, model, assimilateobserve, model, assimilate

Observations

Analysis / Modeling

Information


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GPM Evaluations: Purpose and Activities


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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.


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Iterative Experimental Design


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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.


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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


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Example Precipitation Products

Name Source PlatformGEOS NASA / GSFC / DAO Model Based

GDAS NOAA / NCEP / EMC Model Based

EDAS NOAA / NCEP / EMC Model Based

RUC NOAA / FSL Model Based

NRL IR Naval Research Laboratory IR

NRL MW Naval Research Laboratory SSM/I / TRMM / AMSU-B

HUFFMAN NASA / GSFC / MAP IR / SSM/I / TRMM

PERSIANN University of Arizona IR / SSM/I / TRMM

NEXRAD NOAA / NCEP Gauge, Ground Based Radar

HIGGINS NOAA / CPC Gauge

GTS NOAA / NCEP Gauge

CMAP NOAA / CPC Gauge, IR, SSM/I, TRMM

CMORPH NOAA/CPC IR, Mircowave


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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.


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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


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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?


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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?


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Intelligent Feature Optimization

Feature Optimization

Precipitation Data

Model Output

LIS

EliminateRedunda

ntFeatures

Precipitation Data Sets

D1

D2

Dn

F1

F2

Fn

G1

G2

Gn

FV

MergedPrecipitation Data Sets

FeatureExtraction

FeatureReduction

EliminateRedundantFeatures


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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

L e v el 0 L e v el 1 L e v el 2L e v el 0 L e v el 1 L e v el 2


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Space-Time Downscaling-Disaggregation


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Downscaled PrecipitationProduct Compared with Radar Observation

Downscaled Product

GCM equivalent Product

Radar Observed

3 km

10 min

3 km

10 min

48 km

180 min


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Expected Results[Example only]

Errors in LSM var due to precip heterogeneity

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

1 2 4 8 16 32 64 128 256

Precip Resolution [KM]

RM

SE

/ M

ea

n /

3-h

r [%

]

Lwnet(W/m2)

Qle(W/m2)

Qh(W/m2)

Qg(W/m2)

Evap(kg/m2s)

AvgSurfT(K)

SoilMoist(kg/m2)


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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.


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Schedule

Task ID Task Mar – Aug 2007 Sep – Feb 2007 Mar – Aug 2008

1.1 Develop and implement data merging technique

1.2 Test merged data in LIS

1.3 Regional validation of optimized forcings

2.1 Develop downscaling methodology

3.1 LIS control simulations at core sites

3.2 LIS simulations with different precipitation forcings

3.3 LIS validation against in-situ data

4.1 Document and report results


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Contact Information

Valentine Anantharaj<[email protected]>

Tel: (662)325-5135

mailto:[email protected]


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Our Sponsor: NASA Applied Sciences …

• NASA's vision is "to improve life here" and our mission is "to understand and protect our home planet".


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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.


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Precipitation Data Model

r = rT+b+n

r: observed data

rT: true data

b: bias (constant systematic error)

n: random error

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Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

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