A combined microwave and infrared radiometer approach for a high resolution global precipitation map

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A combined microwave and infrared radiometer
approach for a high resolution global
precipitation map in the GSMaP Japan

• Tomoo Ushio, K. Okamoto, K. Aonashi, T. Inoue, T.
  Kubota, H. Hashizume, T. Simomura, T. Iguchi, N.
  Takahashi, R. Oki, M. Kachi

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Outline

• Background
• On the GSMaP project
• Microwave radiometer based precipitation map
• Needs for the Infrared data (IR)
• Methodology
• Cloud motion and Kalman filter approach from the
  Geo-IR data
• Results
• Demonstration of our product
• Initial evaluation of our product
• PEHRPP activities in Japan
• Summary and future directions

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Goals of the project

• Production of high precision and high resolution
  global precipitation map by using satelliteborne
  microwave radiometer data
• -e.g. Spatial resolution 0.1?? 0.1?, Temporal
  resolution 1 day
• -Microwave radiometers (TMI, SSM/I 3,
  AMSR-E, AMSR)
• -Precipitation radar, IR data
• Development of reliable microwave radiometer
  algorithm
• -Based on the common physical precipitation
  model which precipitation radar also uses. Even
  in version 6 TRMM algorithms, about 10-15
  discrepancy can be seen in monthly average
  rainfall rates retrieved by TMI and PR.
• Establishment of precipitation map production
  technique by using multi-satellite data for the
  coming GPM era

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Flow of the GSMaP Project
Ground Radar Obs. G.
Ground Obs.
Microwave Radiometer
Algorithm G.
Verify
Look-upTable
Routine Obs.Campaign Obs.Data base
Obs. Data
Precip. Physical Model
Precip. Retrieval
Algorithm
ParameterSensitivity Exp.
Match-upData Anal.
Precip. Physical Model G.
Global Precip. Map
TRMM/PR
Precip. MapProducts
Meteor. Satellites
Obs.Data
High TemporalResolution Map
Global Precip. Map G.
RadarAlgorithm
Precip. MapData base
Obs. Data
Interpolation Algo.
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How do we get a global precipitation map?

• The accurate estimation of surface rainfall on a
  global scale with high resolution has been one of
  the major goals in global water cycle and its
  related area.
• Ground based approach
• Fairly good estimation
• Generally suffer from spatial coverage problems.
• Satellite based approach
• Fairly good coverage and reasonably good
  estimation
• There is not a single space-born sensor to detect
  surface rainfall in near real time on a global
  basis.
• We need to combine the data from multiple
  satellites.

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Approach of the GSMaP project

• We use the Aonashi Algorithm to retrieve rainfall
  rate.
• The sensors for the analysis are TMI, AMSR-E,
  AMSR, SSMI (F13, 14, 15).

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Monthly precipitation accumulation from DMSP/SSMI
(F13, 14, 15) for Sep. 2003
F13 F15 F14
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6 hourly MWR combined map
TMI
AMSR AMSR-E
Combined 6 hourly
SSM/I (F13, F14, F15)
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How can we get a global precipitation map with
temporal resolution of 3 hours or less?

• Infrared radiometers (IR)
• can provide information on cloud top layers (not
  precipitation)
• Can ensure a global coverage with high temporal
  resolution (gt 30 min) due to the geo-synchronous
  orbit (GEO)
• Microwave radiometers (MW)
• Can detect cloud structure and precipitation with
  high spatial resolution
• The major draw back is temporal sampling due low
  earth orbit satellite (LEO)
• The LEO-MW and GEO-IR radiometry are quite
  complementary for monitoring the highly variable
  parameters like precipitation.

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How do we combine the MWR and IR data?

• Combination of the moving vector and Kalman
  filtering method
• The moving vector method was introduced by Joyce
  et al. 2004.
• Joyce R., J. Janowiak, P. Arkin, and P. Xie,
  CMORPH A method that produces global
  precipitation estimates from passive microwave
  and ifrared data at high spatial and temporal
  resolution, J. Hydrometeorology, 487-503, 2004
• Advantage
• MWR based approach (not Tb but cloud motion)
• Fast processing time
• Disadvantage
• Not include the developing and decaying process
  of precipitation
• Kalman filter approach
• Refine precipitation rate on Kalman gain after
  propagating the rain pixel
• The Kalman gain is determined from the database
  on the relationship between the IR Tb and surface
  rain rate.

New!!
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Algorithm flow

Infrared (IR) Data
11.4 µm Geo IR Present
1 hr Moving Vector
11.4 µm Geo IR 1 hour before
Microwave Radiometer (MWR) Data
Predicted GSMaP
1 hr MWR Present
Kalman Filter
GSMaP Data
GSMaP Present
GSMaP 1 hour before
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State and observation equation used in Kalman
filter
Rain rate at time k Infrared Tb
Rain rate at time k1 System
noise Observation noise
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Kalman Filter
Predicted rain rate
Refinement
IR Tb
Obs. Noise
GSMaP
Prediction
System Noise
Predicted rain rate
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Correlation between radar and the GSMaP product
as a function of the past microwave satellite
overpass
With Kalman filter
Without Kalman filter Moving vector only
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Effect of Kalman Filter(Aug. 2000)-TRMM/TMI only-
GSMaP VS Radar rain gauge network in Japan
Correlation
Time(hr)
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On the PEHRPP web in Japan

• We started to make the evaluation web site using
  the radar-rain gauge network data around Japan in
  2005.
• The IDL codes to make the web are all from Dr.
  Beth Ebert.

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A comparison of the GSMaP with CMORPH from the
PEHRPP web in Japan
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PEHRPP web site in Japan

• http//www.radar.aero.osakafu-u.ac.jp/gsmap/IPWG/
  dailyval.html
• Or you can access this site by clicking the
  address on the DVD.

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Summary

• Initial results of the global precipitation map
  from the MWR and from IR and MWR combined
  algorithm were introduced and demonstrated.
• The details of the GSMaP project are in the DVD I
  brought.

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Acknowledgements

• Thanks to Dr. Bob Adler and Kris Kummerow, we
  could kick off this project.
• Thanks to Dr. Beth Ebert and Dr. Phil Arkin, we
  could make the web site.

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• Thank you!!
• ??!!
• Danke!!
• Merci!!
• ?????!!

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Global Satellite Mapping of Precipitation
projectOrganization of Research Team in FY 2005
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What, When, Where, and How do we analyze for?

• Purpose To map the global precipitation map
  with 0.1 degree/1 hour resolution
• What IR 1hour global IR data from Goddard/DAAC
• MWR TMI, AMSR-E, AMSR, and
  SSM/I3
• When July 2005
• Where 60 degree in latitude around globe
• How By interpolating precipitation between
  MWR overpasses using the cloud motion and
  Kalman filtering inferred from 1 hour IR
  images.