Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications

Mississippi State University Geosystems Research Institute

NASA RPC Review (4/14/08) 2

GPM Optimization Team & Collaborators• MSU Team

– Robert Moorhead– Valentine Anantharaj– Nicholas Younan– Georgy Mostovoy– Graduate students (Anish Turlapaty and Majid Mahroogy)

• External Collaborators– Paul Houser (GMU & CREW)– Joe Turk (Naval Research Laboratories and JPL)

• Partner Agencies– Garry Schaeffer (USDA NRCS)– Steve Hunter (United States Bureau of Reclamation)


Team Activity

• MSU GRI: Precipitation merging & optimization, modeling, project management, and RPC Integration.

• NRL: GPM data, precipitation sensitivity analysis.

• GMU CREW: Bayesian merging, downscaling, and science expertise.


GPM Evaluations: Purpose and Activities


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 intelligent methods.


Iterative Experimental Design


Experimental Objectives of GPM Optimization

• Develop 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.

• Characterize uncertainties in merged products and in LSM simulations.


Tasks to Achieve Objectives• Precipitation Merging

ANN Method Feature Optimization Technique Bayesian Merging

• Precipitation Downscaling Stochastical-Physical Hybrid Method

• Precipitation Optimization Nelder-Mead Method

• Hydrological Modeling Merged forcings Downscaled forcings

• Analyze results, evaluate against applications metrics and publish


Precipitation Datasets


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.


• Input Data:– Precipitation observations from different satellites

sources (products)• Objective: – To develop a fused data set which is better than

individual data sets• Fusion Tool:– Redundant Wavelet Transform, Selection Rules

Wavelet-based Data Fusion


Intelligent Feature Optimization

Feature Optimization

Precipitation Data

Model Output

LIS

EliminateRedundant

Features

Precipitation Data Sets

D1

D2

Dn

F1

F2

Fn

G1

G2

Gn

FV

MergedPrecipitation Data Sets

FeatureExtraction

FeatureReduction

EliminateRedundantFeatures

• Goal: Based on the definition of data fusion, the objective is to develop a fused product that is better than the best individual data set at any time or location

• Method: Vector Space Transformation (for data transformation) Artificial Neural Networks (for Classification)

• The training data is the set of selected feature vectors from the transformed data space

Machine Learning Method – Vector Space Transform & ANN

• Heidke Skill Score (HSS) is the performance metric

• For a given grid cell, if the merged time series beats the input time series in terms of HSS, it is a success

• The maps show the skill score distribution of the merged data and SCAMPR data for summer 2007, when compared with reference data

• The merged data has a success rate of 89% in summer 07, 76% in fall 07, 56 % in winter 07/08 and 74% in spring 08.

Preliminary Results


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

NASA RPC Review (4/14/08) 20L 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

Datasets for GPM validation and evaluation experiments come from multiple sources and of different resolutions, which is difficult to use in optimization experiment because of scale mismatch. Rescaling the datasets into desired resolution is a challenging task, as it requires sophisticated methodology to properly fill up subgrid scale information into the downscaled product that are not available in coarse resolution datasets.

The optimization of GPM precipitation for decision support analysis would benefit from the downscaling experiment as it would preserve high resoulution information, which would be vital in the land surface modeling or in hydrological analysis tools

A hybrid downscaling approach, that combines both stochastic and physical processes, is utilized in the downscaling scheme.

A coarse resolution grid cell is divided into cascade of subgrids assigning a generator for each of them that multiplies with parent generator. The process of multiplicative random cascade generator is stochastic process that execute spatial downscaling yielding subgrid scale precipitation intensity.

Optimization / Evaluation of GPM Precipitation Estimation RPC Downscaling Experiment


Stochastic Model

Physical Model

Translate Precipitation from t to t+

Disaggregate Precipitation from scale A to scale B

tt+Scale

AScale

B

Downscaling Model

The physical process involves the precipitation cluster advection technique, which is executed simultaneously with the stochastic process to assist temporal downscaling and to compensate potentially introduced arbitrary stochastic gains. The combined stochastic and physical (thus a hybrid) approach performs space-time downscaling.

Preliminary Results:

4 km resolution StageIV precipitation data was upscaled to 32 km. The 32 km resolution test data was downscaled to 4 km and compared, See figure the white cells are downscaled precip and the colored cells are the original StageIV precip.

The downscaling model is also applied to CMORPH data to obtain 1-km precip test data. The test data are being used in merging and optimiation investigations.

Downscaling Approach


Space-Time Downscaling-Disaggregation


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


Optimization Approach:Using NLDAS forcing data as control run, synthetic soil moisture fields are estimated. Using these soil moisture data as truth, 5 precipitation products (RUC, TRMM, NRL, PERSIANN, NEXRAD) were optimally merged using the described methodology (on the right). Minimized soil moisture errors were compared with the errors from each precipitation product individually. Results showed that optimally merged precipitation product with minimized soil moisture errors also minimized the errors in other fields like evapotranspiration, temperature and run-off.

Currently, the methodology is being applied over a time-window rather than single time-step. The precipitation weights will be constant through this window where optimization goal is to minimize the flux errors over a window rather than single time step.

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Summary of Progress

• Completed Tasks– ANN & VST Intelligent Method for merging (manuscript submitted to

Patten Recognition Letters)– Nelder-Mead Optimization Method (manuscript in draft)

• Final Steps (in progress)– Hybrid downscaling and Bayesian merging (in evaluation)– LSM simulations using LIS (models already configured)– Final evaluation (document and publish)

NASA RPC Review (3/2/09)


Contact Information

Valentine Anantharaj<[email protected]>

Tel: (662)325-5135

mailto:[email protected]

Documents

Optimization of GPM Precipitation Estimates for Land Data Assimilation Applications