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
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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:
Observations
water
soils
sun
weather
climate
vegetation
terrain
Information
Analysis / Modeling
observe, model, assimilate
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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
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Precipitation Downscaling
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•
Stochastical-Physical Hybrid Method
Hydrological Modeling
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•
ANN Method
Feature Optimization Technique
EnKF Objective Ananlysis
Bayesian Merging
Merged forcings
Downscaled forcings
Analyze results and publish
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Example Precipitation Products
Name
Source
Platform
GEOS
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
Precipitation
Data
Feature
Optimization
D1
Precipitation
Data Sets
D2
Feature
Extraction
Dn
LIS
Model
Output
F1
G1
F2
G
2
Feature
Reduction
Fn
Eliminate
Redundant
Features
Merged
Precipitation
Data Sets
Gn
Eliminate
Redunda
nt
Features
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FV
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
Space-Time Downscaling-Disaggregation
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Downscaled Precipitation
Product Compared with
Radar Observation
GCM equivalent Product
48 km
180 min
Radar Observed
Downscaled Product
3 km
3 km
10 min
10 min
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Expected Results
[Example only]
RMSE / Mean / 3-hr [%]
Errors in LSM var due to precip heterogeneity
35.0%
Lwnet(W/m2)
30.0%
Qle(W/m2)
25.0%
Qh(W/m2)
Qg(W/m2)
20.0%
Evap(kg/m2s)
15.0%
AvgSurfT(K)
SoilMoist(kg/m2)
10.0%
5.0%
0.0%
1
2
4
8
16
32
Precip Resolution [KM]
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64
128
256
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
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
Mar – Aug 2007
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Sep – Feb 2007
Mar – Aug 2008
Contact Information
Valentine Anantharaj
<val@gri.msstate.edu>
Tel: (662)325-5135
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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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