Optimization of GPM Precipitation Estimates for
Land Data Assimilation Applications
Mississippi State University
Geosystems Research Institute
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)
NASA RPC Review (4/14/08)
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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: Bayesian merging, downscaling,
and science expertise.
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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 intelligent methods.
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Iterative Experimental Design
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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.
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Tasks to Achieve Objectives
•
Precipitation Merging



•
Precipitation Downscaling

•
Nelder-Mead Method
Hydrological Modeling


•
Stochastical-Physical Hybrid Method
Precipitation Optimization

•
ANN Method
Feature Optimization Technique
Bayesian Merging
Merged forcings
Downscaled forcings
Analyze results, evaluate against applications metrics and publish
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Precipitation Datasets
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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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Wavelet-based Data Fusion
• 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
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Intelligent Feature Optimization
Precipitation
Data
Feature
Optimization
D1
Precipitation
Data Sets
D2
Feature
Extraction
Dn
LIS
Model
Output
F1
G1
F2
G2
Feature
Reduction
Fn
Eliminate
Redundant
Features
FV
Merged
Precipitation
Data Sets
Gn
Eliminate
Redundan
t
Features
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Machine Learning Method – Vector Space Transform & ANN
•
•
•
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
Preliminary Results
• 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.
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
Level 0
Level 1
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Level 2
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Optimization / Evaluation of GPM Precipitation Estimation
RPC Downscaling Experiment
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.
NASA RPC Review (4/14/08)
Level 0
Level 1
Level 220
Downscaling Approach
Stochastic Model
Disaggregate
Precipitation from
scale A to scale B
Scale
A
t+
Physical Model
t
Scale
B
Translate
Precipitation from t
to t+
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.
Downscaling Model
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.
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Space-Time Downscaling-Disaggregation
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GCM equivalent Product
Downscaled Precipitation
Product Compared with
Radar Observation
48 km
180 min
Radar Observed
Downscaled Product
3 km
3 km
10 min
10 min
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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)
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Contact Information
Valentine Anantharaj
<vga1@msstate.edu>
Tel: (662)325-5135
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