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Kazumasa Aonashi aonashi@mri-jma.go.jp Meteorological Research Institute

Explore enhancements to the existing GSMaP MWI algorithm, resolving problems in over-land retrieval for improved accuracy in precipitation estimation, statistical corrections, and promising results for specific cases. Presenting: The new methodology, findings, and future prospects.

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Kazumasa Aonashi aonashi@mri-jma.go.jp Meteorological Research Institute

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  1. 5th IPWG Workshop New GSMaP over-land MWI algorithm Kazumasa Aonashi aonashi@mri-jma.go.jp Meteorological Research Institute Japan Meteorological Agency 5th IPWG Workshop

  2. Outline • Introduction • GSMaP MWI Algorithm • Problems in over-land retrieval • New over-land MWI algorithm • Basic idea • Estimation of Index of Dtop(r8537) and SRR(sigma85) • Statistical correction using r8537 and sigma85 • Results • Over-land retrieval for 2004 • Pakistan Flood case (July, 2010) • Summary & Future directions 5th IPWG Workshop

  3. GSMaP Project Aonashi (MRI) Kubota (JAXA) Shige (U. Kyoto) TRMM TMI Aqua AMSR-E DMSP SSM/I NOAA AMSU MWR precipretrievalalgotrithm L2 Product from each sensor Geo. Satellite Mix Infrared radiomter Cloud motion vector Near Real Time System At JAXA/EORC Kachi & Kubota (JAXA) L3 Composite product 0.1degree 1 hour. 6 hour 1 day 1 month Ushio (U. Osaka) Composite algorithm of IR and MWR 0.1degree・30min 2010/10/11 5th IPWG Workshop

  4. Basic Idea of the Retrieval Algorithm PCT37, PCT85over land Observed TBs Retrieval Calculation Forward calculation Statistical Precip-related Variable Models RTM Screening Inhomogeneity estimation Scattering part Emission part Look-up Table Precip Profiles DSD Mixed phase inhomogeneity Precip. Find the optimal precipitation that gives RTM-calculated TBs fitting best with the observed TBs: 2010/10/11 5th IPWG Workshop

  5. Statistical precip-related var. modelsfrom TRMM observation Precip type classification Data base 10 types (land 6, sea 4) are classified from TRMM PR data (2.5 deg, 3 monthly) Precip Profile (land)0: thunderstorm, 1: shower, 2: shallow, 3: frontal rain, 4: organized rain 5: highland (sea) 6: shallow 7:frontal rain, 8:transit, 9:organized rain Precip profile data base Height from 1 deg level [km] Example： TRMM PR averaged preciptation profiles for each type, surface precip, conv/stra 1℃ level 2010/10/11 5th IPWG Workshop Rainfall rate [mm/h]

  6. GPROF,GSMaP vs PR for July ‘98 GPROF GSMaP (a) (b) Over Land GPROF retrieval (mm/hr) GSMaP retrieval (mm/hr) PR precip rate (mm hr-1) PR precip rate (mm hr-1) (c ) (d) Over Ocean GPROF retrieval (mm/hr) GSMaP retrieval (mm/hr) PR precip rate (mm hr-1) PR precip rate (mm hr-1) 5th IPWG Workshop

  7. Rainsurf vs. Rainspc (Land ‘98) in terms of Dtop (toplev-ZFLH) and SRR(stratiform rain ratio) Dtop > 4km Dtop <2km SRR > 0.7 SRR < 0.3

  8. New over-land algorithm Estimation of Index of Dtop and SRR Statistical correction using these indexes 2010/10/11 5th IPWG Workshop

  9. Index of Dtop, R8537 • Index of Dtop: the ratio of TB85 depressions to TB 37 depressions (R8537). • R8537 in terms of ratio of precipitation retrieved from TB85 to TB37 using the conventional GSMaP algorithm. Retrain.v4.10.20080417 match-up data(Land ‘98)

  10. Ratio of TMI scattering signals to PR in terms of precip top level (July, 1998) (Rain85/rainsurf) (Rain37/rainsurf) PR top level –FLH (m) PR top level –FLH (m) TB85 depr becomes larger than TB37 depr. for higher Dtop. 5th IPWG Workshop

  11. Forward calculation with different Dtoplev TB85 depression became larger than the TB37 depression for precipitation with higher top levels.

  12. Index of Dtop, R8537 Index of Dtop: the ratio of TB85 depressions to TB 37 depressions (R8537). R8537 in terms of ratio of precipitation retrieved from TB85 to TB37 using the conventional GSMaP algorithm. Retrain.v4.10.20080417 match-up data(Land ‘98)

  13. Index of SRR, sigma85 • First guess of Sigma85 from Rain85 within the TB10v FOVs. • Adjustment using the statistical relationship between Sigma85 and PR (Kubota et al. 2009). Retrain.v4.10.20080417 match-up data(Land ‘98)

  14. Statistical correction using r8537 and sigma85 R8537 and Sigma85

  15. Rainsurfvs.rain37 Land ’98 Class by (r8537, sigma85) R8537<1 Sigma85>1 R8537>1 Sigma85>1 R8537<1 Sigma85<1 R8537>1 Sigma85<1

  16. Rainsurfvs.rain85Land ’98 class by (r8537, sigma85) R8537 1-2 Sigma85>1 R8537>2 Sigma85>1 R8537<1 Sigma85>1 R8537 1-2 Sigma85<1 R8537>2 Sigma85<1 R8537<1 Sigma85<1

  17. Rainsurf vs. rain37 Land ’98 (ktype !=6)class (r8537,sigma85): Seasonal change Winter Spring Summer Autumn

  18. Rainsurf vs. rain37 Land ’98 class (r8537,sigma85): Precip type change ZKTYPE=5 ZKTYPE=6 TRMM data sets for 1998 are classified by (R8537,sigma85) for each precip type.

  19. Statistical correction using (R8537,sigma85) • TRMM data sets for 1998 are classified by (R8537,sigma85) for each precip type. • Linear fitting coefficients between rain37, rain85 and PR surface precipitation rates. • Weighted sum of corrected rain37 and rain85.

  20. Results Over-land retrieval for 2004Pakistan Flood case (July, 2010)

  21. Correction of over-land retrievals using (R8537, sigma85) Rainspc vs.rainsurf for Land Jan. ‘04 Before Correction After Correction (20090310) (20101001)

  22. Rainsurf vs. Rainspc (Land Jan. ‘04)Class by Dtop and SRR R8537<1 Sigma85>1 R8537>1 Sigma85>1 R8537<1 Sigma85<1 R8537>1 Sigma85<1

  23. Rainsurf vs. Rainspc (Land Jan. ‘04)Class by Dtop and SRR R8537<1 Sigma85>1 R8537>1 Sigma85>1 R8537<1 Sigma85<1 R8537>1 Sigma85<1

  24. Pakistan Flood case (July 21, 2010)

  25. Future Directions Improvement of the forward calculation Apri-ori information for precip-related variables

  26. Improvement of the forward calculation: Non-spherical particle effects Introducing the effect into RTM: 1) Realistic non-spherical model 2) FDTD calculation 3) Parameterization for fast RTM 5th IPWG Workshop

  27. Realistic non-spherical frozen particle model (Ishimoto, 2008) 1.8 1.9 2.0 2.1 2.2 2.3 2.4 Fractal dimension Ｄｆ Monte Carlo calculation of particles for given fractal dimension 5th IPWG Workshop

  28. Keeping the single scattering properties Parameterization introduced to the Fast RTM code 1) Approximation of Roeff in terms of Dmax 2) Softness parameter in terms of df, f, and the frequency Non- Sphere SP=(D-D0)/(Dmax-D0) Sphere D Dmax D0: diameter of solid sphere 5th IPWG Workshop

  29. Orographic Rainfall (Shige & Taniguchi, 2010) Warm rain with less ice precipitation Freezing Level A large amount of condensates Orographic Low-level orographic lifting Ice Water Maritime Air Mountain Ocean

  30. Classification of Orographic/No-Orographic Rain (Shige & Taniguchi, 2010) Topographically induced upward motion Low-level moisture convergence Slope from GTOP Winds and Water Vapor from GANAL Winds from GANAL

  31. Summary • New over-land algorithm: • Estimation of Index of Dtop and SRR • Statistical correction of LUTs • Results • Future directions 2010/10/11 5th IPWG Workshop

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