New GSMaP over-land MWI algorithm 5th IPWG Workshop New GSMaP over-land MWI algorithm Kazumasa Aonashi aonashi@mri-jma.go.jp Meteorological Research Institute Japan Meteorological Agency My name is Kazumasa Aonashi, MRI/JMA. 2010/10/11 5th IPWG Workshop

Outline Introduction New over-land MWI algorithm Results 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 The outline of my today’s talk is as follows: 2010/10/11 5th IPWG Workshop

MWR precip retrieval algotrithm GSMaP Project Aonashi (MRI) Kubota (JAXA) Shige (U. Kyoto) TRMM TMI Aqua AMSR-E DMSP SSM/I NOAA AMSU MWR precip retrieval algotrithm L2 Product from each sensor Geo. Satellite Mix Infrared radiomter Cloud motion vector As Aonashi-san pointed out, microwave radiometer is an excellent tool for the accurate rainfall estimation. Up untill now, we have four sensors. The TRMm/tmi, aqua/amser-e, adeos/amsr, and dmsp/ssmi .by combininb these sensosrs, we have the composite product. As is showed earlier, currently we have several satellites aboard microwave radiometere available now 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 4

Basic Idea of the Retrieval Algorithm PCT37, PCT85　over 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 The basic idea of this algorithm is to find the optimal precipitation that gives RTM-calculated TBs fitting best with the observed TBs: PCT37, PCT85 over land; TB10v,TB19v, TB37v, PCT37, PCT85 over sea. This algorithm consists of two parts: Forward calculation part that computes LUTs between precip and TBs by incorporating the precip cloud models into RTM; Retrieval part that derives precip from the observed TBs using the LUTs. Precip. Find the optimal precipitation that gives RTM-calculated TBs fitting best with the observed TBs: 2010/10/11 5th IPWG Workshop

Statistical precip-related var. models from 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 As for the precipitation profile, we used statistical profile data produced from TRMM PR 2A25 datasets. Height from 1 deg level [km] Example： TRMM PR averaged preciptation profiles for each type, surface precip, conv/stra 1℃ level 2010/10/11 Rainfall rate [mm/h] 5th IPWG Workshop 9

GPROF,GSMaP vs PR for July ‘98 (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) 2010/10/11 5th IPWG Workshop

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

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

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)

Ratio of TMI scattering signals to PR in terms of precip top level (July, 1998) (Rain37/rainsurf) (Rain85/rainsurf) In addition, the over-land algorithm tends to overestimate precip for disturbances with high top levels. As these scatter diagrams show, ratio of rain85 to surface precip has significant sensitivity to PR top levels, compared to rain37 to precip ratio. This frequency dependency suggests problems in our PSD model. PR top level –FLH (m) PR top level –FLH (m) TB85 depr becomes larger than TB37 depr. for higher Dtop. 2010/10/11 5th IPWG Workshop

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

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)

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)

Statistical correction using r8537 and sigma85

Rainsurf vs. rain37 Land ’98 Class by (r8537, sigma85)

Rainsurf vs. rain85 Land ’98 class by (r8537, sigma85)

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

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.

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.

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

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

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

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

Pakistan Flood case (July 21, 2010)

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

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 2010/10/11 5th IPWG Workshop

Realistic non-spherical frozen particle model (Ishimoto, 2008) Fractal dimension Ｄｆ 1.8 1.9 2.0 2.1 2.2 2.3 2.4 Monte Carlo calculation of particles for given fractal dimension 2010/10/11 5th IPWG Workshop

D0: diameter of solid sphere 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) Keeping the single scattering properties Sphere We are also planning to introduce scattering properties of non-spherical frozen particles. Though some studies used DDA for this purpose, we think this method is too expensive. Hence we are trying to use SP proposed by Liu (2004) that seeks spherical particles with the same single scattering properties of the particles. D Dmax D0: diameter of solid sphere 2010/10/11 5th IPWG Workshop 45 45

Orographic Rainfall (Shige & Taniguchi, 2010) Warm rain with less ice precipitation Freezing Level Orographic A large amount of condensates Low-level orographic lifting Ice Water Maritime Air Mountain Ocean 47

Topographically induced upward motion 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

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