IPWG training course:

Global Satellite Mapping of Precipitation (GSMaP) project

Kazumasa Aonashi (MRI/JMA) aonashi@mri-jma.go.jp

• Artists like Andoh

Hirosige appreciated rain. • But…

Motivation:

Motivation: Needs for Precip. Remote-sensing

• Global precipitation monitoring is very important for disaster prevention, water resource management, …..

• Direct precipitation observation data are sparse, especially over oceans.

Outline of Lecture

• Overview of GSMaP • MWR algorithms • Microwave-IR merged algorithms • Validation • Next version of GSMaP • Summary

Inhomo-geneity of the data quality

Overview of GSMaP

Algorithms Products

GCOM-W AMSR2

DMSP SSM/I, SSMIS

Global Rainfall Map + Gauge-calibrated

Rainfall Map (0.1 degree grid, Hourly）

Rainfall Data from each Microwave Radiometer

Merged Microwave Rainfall Data

NOAA/MetOp AMSU

Overview of GSMaP Algorithm

（Okamoto et al. 2005, Kubota et al, 2007, Aonashi et al. 2009, Ushio et al. 2009, Shige et al. 2009, Kachi et al. 2011）

IR Imagers

Microwave-IR Merged Algorithm (CMV, K/F)

GSMaP Microwave Radiometer Retrieval Algorithm

Good: high-frequent (wide swath, multi-satellites) Bad: cannot measure vertical structure (need info. from radar)

Microwave Imagers & Sounders

GPM-Core GMI

TRMM PR

Precipitation Radars

GPM-Core DPR

Data Base

Geostationary Satellites

http://sharaku.eorc.jaxa.jp/GSMaP/

Nov. 12, 2015 MRI JICA lecture

Correlation of satellite-estimated and Radar-estimated instantaneous rain rates vs normalized RMS differences

(Ebert & Manton, 1998)

Observed TBs

Precip.

Ts, Temp Precip Profiles DSD Mixed phase inhomogeneity

Look-up Table

Forward calculation Retrieval Calculation

Basic Idea of the MWR Algorithm

RTM Screening Inhomogeneity estimation Scattering part Emission part

Find the optimal precipitation that gives RTM-calculated TBs fitting best with the observed TBs: PCT37, PCT85 (land) TB10v,TB19v, TB37v, PCT37, PCT85 (sea)

GANAL

Statistical Precip-related Variable Models (PR)

Nov. 12, 2015 MRI JICA lecture

Physical Basis of Microwave Precip. Retrival

• Over Land: Scattering by frozen

particles (Higher Freq.) • Over Ocean: Scattering (Higher Freq.) + Emission from Rain (Lower Freq.)

MWR retrievals over ocean are more accurate than those over land and coast.

GCOM-W AMSR2

DMSP SSM/I, SSMIS

Global Rainfall Map + Gauge-calibrated

Rainfall Map (0.1 degree grid, Hourly）

Rainfall Data from each Microwave Radiometer

Merged Microwave Rainfall Data

NOAA/MetOp AMSU

Overview of GSMaP Algorithm

（Okamoto et al. 2005, Kubota et al, 2007, Aonashi et al. 2009, Ushio et al. 2009, Shige et al. 2009, Kachi et al. 2011）

IR Imagers

Microwave-IR Merged Algorithm (CMV, K/F)

GSMaP Microwave Radiometer Retrieval Algorithm

Good: high-frequent (wide swath, multi-satellites) Bad: cannot measure vertical structure (need info. from radar)

Microwave Imagers & Sounders

GPM-Core GMI

TRMM PR

Precipitation Radars

GPM-Core DPR

Data Base

Geostationary Satellites

http://sharaku.eorc.jaxa.jp/GSMaP/

Present IR

GEO IR data

Cloud motion vectors

Past GSMaP data

MWR data observed during present 1 hour

Meridional

Zonal

Present MWR data

GSMaP interpolated by the motion vectors

Kalman filter

1-hour-before IR

1-hour-before GSMaP

Present GSMaP

(MWR overpasses)

(Outsides MWR overpasses)

Flowchart of MWR-IR merged algorithm (GSMaP_MVK algorithm)

Ushio et al. (2009)

MWR-IR merged algorithm To fill the temporal gaps, we developed morphing techniques using Geo IR data. But, the precipitation retrievals loose their quality by +- 90 minutes from the MWI observation time.

Note: High-resolution conical-scanning MWI (TMI) gives retrievals that have the highest correlation with precipitation observation.

GPM-GSMaP Product list

Product name Variables Horizontal resolution

Temporal resolution

Latency Correction

L3 GSMaP Hourly

Hourly Precip Rate (GSMaP_MVK)

0.1×0.1 deg.lat/lon

1 hour 3 days None

Gauge-adjusted Hourly Precip Rate (GSMaP_Gauge)

adjusted by daily rain gauges (NOAA CPC Gauge-Based Analysis, Chen et al. 2008)

Product name Variables Horizontal resolution

Temporal resolution

Latency Correction

L3R GSMaP Hourly

Hourly Precip Rate (GSMaP_NRT)

0.1×0.1 deg.lat/lon

1 hour 4 hours None

Gauge-adjusted Hourly Precip Rate (GSMaP_Gauge_NRT)

Correction by empirical coefficients

Standard product (Latency: 3 days)

Near-real-time product (Latency: 4 hours)

GPM-GSMaP data during Mar. 2000-Feb. 2014 period was reprocessed as reanalysis version (GSMaP_RNL), and was open to the public on Apr. 2016.

You can access the products via: http://sharaku.eorc.jaxa.jp/GSMaP/index_e.htm

GSMaP real-time version (GSMaP_NOW) • To reduce latency from 4-hr to “quasi-realtime”

– Using data that is available within 0.5-hour (GMI, AMSR2 direct receiving data, AMSU direct receiving data and Himawari-IR) to produce GSMaP at 0.5-hr before (observation).

– Applying 0.5-hour forward extrapolation (future direction) by cloud motion vector to produce GSMaP at current hour (just now).

• Since Nov. 2015, web site and data (GEO-Himawari region) are open to the public from http://sharaku.eorc.jaxa.jp/GSMaP_NOW/

“GSMaP_NOW” product

Dr. Yamamoto Orographic rainfall correction method

Prof. Shige Orographic rainfall correction method

Dr. Aonashi Current reader, Imager algorithm

Ms. Kachi NRT system

Prof. Takahashi Precipitation physical modeling

Dr. Akimoto Surface emissivity

Dr.Arai NRT system

Drs.Hamada and Yokoyama Precipitation type classification

Dr. Mega MWR-IR merged algorithm, Gauge-adjustment method

Prof. Takayabu Precipitation type classification

Prof.Hirose Precipitation type classification

Group Photo 2016/6/3

Ms. Yamaji DSD DB

Prof. Seto Rain/No-rain classification method

Prof. Higuchi IR algorithm

Prof. Ushio MWR-IR merged algorithm, Gauge-adjustment method

Kubota(me) Imager/sounder algorithm

MWR algorithm

Basic Idea Precipitation cloud models Orographic rain algorithm

• Precipitation structures (Precipitation Profile, Melting layer, DSD) • Atmospheric variables (temperatures,…)

Look-up Table

Retrieval Algorithm

The relationship between rain rate and brightness temperature is tabulated by assumption of precipitation physical model and calculation of the radiative transfer model (RTM).

Precipitation physical model

(Aonashi and Liu 2000, Kubota et al. 2007, Aonashi et al .2009)

RTM calculation

Basic Idea of GSMaP_MWR algorithm

Observed Brightness

Temp.

Rainfall rate

Microwave Radiometers

TRMM/PR (GPM/DPR) database in the algorithm

LUT for convective rain

Hydrometeor profile for

Stratiform rain

Hydrometeor profile for

Convective rain

Convetive/stratiform ratio (Currently, statistical weights are adopted.)

・No brightband model ・Convective DSD model

・Brightband model ・Stratiform DSD model

LUT for stratiform rain

LUT for retrievals • Each frequency • 5.0 × 5.0 deg. lat-lon boxes • 6-hourly

Effects of vertical profiles

Effects of conv/strat ratio

Effects of drop size distribution (DSD)

Look-up table (LUT)

Rain rate(R)

Brig

htne

ss T

emp(

Tb)

• Rain/no-rain classification method • Correction of LUT using estimated

inhomogeneity

Rain estimates by the retrievals

Nov. 23 2012 IHP training course@Nagoya Univ.

Precip type classification

Precip Profile

Data base

Precip profile data base

Precipitation Profile Model

Rainfall rate [mm/h]

Hei

ght

from

1 d

eg

level

[k

m]

1℃ level Example：

TRMM PR averaged preciptation profiles

for each type, surface precip, conv/stra

(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

10 types (land 6, sea 4) are classified from TRMM PR data (2.5 deg, 3 monthly)

Microwave-IR merged algorithms

Basic idea Use of rain guages

Present IR

GEO IR data

Cloud motion vectors

Past GSMaP data

MWR data observed during present 1 hour

Meridional

Zonal

Present MWR data

GSMaP interpolated by the motion vectors

Kalman filter

1-hour-before IR

1-hour-before GSMaP

Present GSMaP

(MWR overpasses)

(Outsides MWR overpasses)

Flowchart of MWR-IR merged algorithm (GSMaP_MVK algorithm)

Ushio et al. (2009)

+ + +

= = =

t t+1 t+2

Validation of the current products

Global validation using TRMM

Regional Validation using ground-based observation

Monthly Sensor map (Jul. 2013)

AMSR2 TMI

SSMIS F16

SSMIS F18

SSMIS F17

MHS Metop-A

MHS NOAA19

Zonal mean analysis (Jul. 2013)

Monthly merged map (Jan. 2013)

MVK_FW

MVK_BW

MVK(CMB)

GSMaP_ Gauge

MWR

PR

CPC Gauge

Zonal mean analysis (Jan. 2013)

GSMaP_MVK (V6) Radar-AMeDAS (5 Jul. 2013)

Comparisons in daily averaged rainfall estimates around Japan with 0.25 x 0.25 deg. resolution with reference to the gauge-calibrated

ground radar dataset (JMA Radar-AMeDAS precipitation analysis).

Validation using JMA Radar-AMeDAS analyssis

(White: missing values)

The evaluation system at the Kyoto Univ. was imported to the EORC.

• List of Statistics – Correlation coefficient

– Root Mean Square Error (RMSE)

– Probability of Detection(POD) • The hit rate, an index of rain detection

ability.

• POD=a/(a+c)

– False Alarm Ratio(FAR) • An index of false rainfall.

• FAR=b/(a+b)

– Equitable threat score, frequency bias (not shown here)

radar observing

rain

radar observing no-

rain satellite estimate

giving rain a b

satellite estimate giving no-rain

c d

2 × 2 contingency table

Statistics were computed for daily estimates. 15-day-running mean was adopted.

Method : Statistics

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Corr

ela

tion

Date

MVK_Correlation

Gauge_Correlation

MFW_Correlation

NRT_Correlation

MVK_15日移動平均

Gauge_15日移動平均

MFW_15日移動平均

NRT_15日移動平均

Results: Correlation coefficients

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RMSE

(mm

/hou

r)

Date

MVK_RMSE

Gauge_RMSE

MFW_RMSE

NRT_RMSE

MVK_15日移動平均

Gauge_15日移動平均

MFW_15日移動平均

NRT_15日移動平均

Results: RMSE

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PO

D

Date

MVK_POD

Gauge_POD

MFW_POD

NRT_POD

MVK_15日移動平均

Gauge_15日移動平均

MFW_15日移動平均

NRT_15日移動平均

Results:Probability of Detection(POD)

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FAR

Date

MVK_FAR

Gauge_FAR

MFW_FAR

NRT_FAR

MVK_15日移動平均

Gauge_15日移動平均

MFW_15日移動平均

NRT_15日移動平均

Results: False Alarm Ratio(FAR)

Next version

GSMaP V04 [Algorithm version V7] is scheduled to be released

in Dec. 2016.

Utilization of NOAA/NESDIS Snow/Ice Cover Maps

• False rainfall signals were sometimes found over surface snow or sea ice. – related to difficulty to distinguish falling snow and surface snow in MW

frequencies such as 89GHz. • MW 165 and 183GHz channels are preferable for falling snow measurement,

but several microwave imagers are lack of them.

• Kubota is now trying to use NOAA/NESDIS Snow/Ice Cover Maps (autosnow) in order to mitigate of the misidentification.

Example of autosnow data (white: snow, sea ice: yellow)

Snowfall estimation with Prof. Liu’s method (Kubota)

GSMaP

GSMaP +

Liu

grey: missing values

• In the current GSMaP, no snowfall estimates (missing values) Kubota is now trying to integrate the method by Prof. Liu (Florida State University) into the GSMaP algorithm.

• Snow-Rain Separation method (Sims and Liu 2015)

• Snowfall estimation (Liu and Seo 2013)

Development of classification in precipitation types using GPM/KuPR

• Takayabu, Hamada, Yamaji, and Kubota are now developing precipitation type/profile/DSD database using GPM/DPR data for the GSMaP_MWR algorithm

OLD NEW Precip type for Jan

Land

Sea

Statistical error analyses of the forward-calculated MWI TBs

• The forward calculation using the conventional first guess of CLWC overestimated TBs with very weak precipitation.

GMI TB37v for Pr (0-0.2 mm/h) vs. PWC over Sea (prtype=8) TBo: Blue, TBc (0.5 kg/m2) : Green, TBc (CLWC=0) : brown

• The forward calculation from the PR surface precipitation tended to underestimate subtropical TBs with large PWC..

PR Rainsurf vs. TMI (TB19v-TBc0mm) for SST (290-295 K) over Sea (prtype=11) TBo: Red, TBc (CLWC=0.5 kg.m2):Green

Modification of the first guess of physical variables

• We set the CLWC first guess as a function of PWC and SST.

• This improved the precipitation retrieval by enlarging weak precipitation areas and reducing positive biases for subtropical precipitation.

Monthly mean Precip. (mm/dy) for Jul. 13 (up) TRMM PR 2A25 (mid) TMI conventional (dwn) TMI improved

NRT basis rain-gauge (empirical) adjustment

• Ushio and Mega will substantially improve NRT basis rain gauge (empirical) adjustment method in the next version.

Flow of GSMaP Gauge NRT

30 days

c, σv, (σw, μw)

GSMaP Gauge NRT

1 2 3 ..………….. 29 30 31 32 33 day

GSMaP NRTGSMaP MVKGSMaP Gauge

GSMaP Gauge NRT model

24 hour

By Dr. Mega and Prof. Ushio (Osaka Univ.)

Summary

• Overview of GSMaP • MWR algorithms • Microwave-IR merged algorithms • Validation • Next version of GSMaP

Thank you for your attention!