GSMaP

• 2021年の東京付近での積算降水量, 雨量計補正してないmonthlyPrecipRateの積算は3000 mm/yrとかになるけど, 雨量計補正したmonthlyPrecipRateGCの積算は2000 mm/yrとかになるようです。

• https://www.eorc.jaxa.jp/GPM/doc/product/format/ja/07.GPM_GSMaP_Product_Format_V4_J.pdf
• https://www.eorc.jaxa.jp/TRMM/documents/PR_algorithm_product_information/doc_pr_v8/GPM_data_util_handbook_V6_20181004_J.pdf
• https://www.eorc.jaxa.jp/GPM/doc/product_info/release_note_gsmapv05-v8_ja.pdf

• https://www.eorc.jaxa.jp/GPM/doc/product/format/ja/07.GPM_GSMaP_Product_Format_V4_J.pdf P23-
• monthlyPrecipRateは各ピクセルにおける月平均の時間降雨量を表す。負の値は観測データの欠損または、マイクロ波のアルゴリズムで降雨量が算出されなかったことを意味する。unit: mm/hr, NoData: -9999.9
• monthlyPrecipRateGCは雨量計によって補正された、ピクセルの月平均の時間降雨量を表す。unit: mm/hr, NoData: -9999.9

• example: GPMMRG_MAP_2101_M_L3S_MCM_04G.h5
  • 2101 means 2021-01 (January 2021).
  • You can get it from G-Portal direct ftp server:

$ sftp -oPort=2051 username@ftp.gportal.jaxa.jp
sftp> get /standard/GSMaP/3.GSMAP.M/04G/2021/GPMMRG_MAP_2101_M_L3S_MCM_04G.h5

• • You can read and display it with QGIS:

$ qgis GPMMRG_MAP_2101_M_L3S_MCM_04G.h5

• accumulated precipitation 積算雨量

gdal_calc.py -A GPMMRG_MAP_2112_M_L3S_MCM_04G_PRC.tif -B GPMMRG_MAP_2201_M_L3S_MCM_04G_PRC.tif -C GPMMRG_MAP_2202_M_L3S_MCM_04G_PRC.tif --calc="A*24*31+B*24*31+C*24*28" --outfile=Dec_Feb.gif

• Example: Global hourly precipitation during 2022/11/03 01:00UTC - 02:00UTC
  • Filename: GPMMRG_MAP_2211030100_H_L3S_MCH_04H.h5
  • Download: https://gportal.jaxa.jp/download/standard/GSMaP/3.GSMAP.H/04H/2022/11/03/GPMMRG_MAP_2211030100_H_L3S_MCH_04H.h5
  • How to check meta data: $ h5dump -p GPMMRG_MAP_2012_M_L3S_MCM_04G.h5 | less

• example: gsmap_gnrt6.202203.0.25d.monthly.spi02.dat.gz
  • You can get it from GSMaP ftp server (not G-Portal):

(You need to get ID and password first!)
$ sftp *******@hokusai.eorc.jaxa.jp
sftp> get climate/gnrt6/SPI/????/*gz ./

/usr/bin/gdal_calc.py --calc="GPMMRG_MAP_2101_M_L3S_MCM_04G_PRC.tif + GPMMRG_MAP_2102_M_L3S_MCM_04G_PRC.tif" --outfile=t.gif

gdal_edit.py -a_srs "EPSG:4326" -tr 0.1 0.1 -a_ullr 2021_PrecipRate.tif
gdal_edit.py -a_srs "EPSG:4326" -a_ullr -180 -90 180 90 test.tif

gdal_translate "HDF5:\"GPMMRG_MAP_2108_M_L3S_MCM_04G.h5\"://Grid/monthlyPrecipRate" 08.tif

# PrecipRate
# convert HDF5 to GeoTiff
yy=21
for mm in 01 02 03 04 05 06 07 08 09 10 11 12; do
 gdal_translate "HDF5:\"GPMMRG_MAP_${yy}${mm}_M_L3S_MCM_04G.h5\"://Grid/monthlyPrecipRate" ${mm}.tif
done
gdal_merge.py -separate -o 20${yy}_monthlyPrecipRate.tiff [0-1][0-9].tif
# total
yy=21
gdal_calc.py -A 01.tif -B 02.tif -C 03.tif -D 04.tif -E 05.tif -F 06.tif -G 07.tif -H 08.tif -I 09.tif -J 10.tif -K 11.tif -L 12.tif --calc="(A*31+B*28+C*31+D*30+E*31+F*30+G*31+H*31+I*30+J*31+K*30+L*31)*24" --outfile=20${yy}_PrecipRate.tif --overwrite
gdal_edit.py -a_srs "EPSG:4326" -a_ullr -90 -180 90 180 20${yy}_PrecipRate.tif
rm ??.tif
 
# PrecipRateGC
# convert HDF5 to GeoTiff
yy=21
for mm in 01 02 03 04 05 06 07 08 09 10 11 12; do
gdal_translate "HDF5:\"GPMMRG_MAP_${yy}${mm}_M_L3S_MCM_04G.h5\"://Grid/monthlyPrecipRateGC" ${mm}.tif
done
gdal_merge.py -separate -o 20${yy}_monthlyPrecipRateGC.tiff [0-1][0-9].tif
# total
yy=21
gdal_calc.py -A 01.tif -B 02.tif -C 03.tif -D 04.tif -E 05.tif -F 06.tif -G 07.tif -H 08.tif -I 09.tif -J 10.tif -K 11.tif -L 12.tif --calc="(A*31+B*28+C*31+D*30+E*31+F*30+G*31+H*31+I*30+J*31+K*30+L*31)*24" --outfile=20${yy}_PrecipRateGC.tif --overwrite
gdal_edit.py -a_srs "EPSG:4326" -a_ullr -90 -180 90 180 20${yy}_PrecipRateGC.tif
rm ??.tif

gdal_translate 'HDF5:"GPMMRG_MAP_2101_M_L3S_MCM_04G.h5"://Grid/Latitude' lat.tif
gdal_translate 'HDF5:"GPMMRG_MAP_2101_M_L3S_MCM_04G.h5"://Grid/Longitude' lon.tif
gdal_calc.py -A lat.tif --calc="A*1.0" --outfile=lat1.tif --overwrite
gdal_calc.py -A lon.tif --calc="A*1.0" --outfile=lon1.tif --overwrite
gdal_edit.py -a_srs "EPSG:4326" -a_ullr -90 -180 90 180 lat1.tif
gdal_edit.py -a_srs "EPSG:4326" -a_ullr -90 -180 90 180 lon1.tif

G01=GPMMRG_MAP_2201_M_L3S_MCM_04G_PRC.tif
G02=GPMMRG_MAP_2202_M_L3S_MCM_04G_PRC.tif
G03=GPMMRG_MAP_2103_M_L3S_MCM_04G_PRC.tif
G04=GPMMRG_MAP_2104_M_L3S_MCM_04G_PRC.tif
G05=GPMMRG_MAP_2105_M_L3S_MCM_04G_PRC.tif
G06=GPMMRG_MAP_2106_M_L3S_MCM_04G_PRC.tif
G07=GPMMRG_MAP_2107_M_L3S_MCM_04G_PRC.tif
G08=GPMMRG_MAP_2108_M_L3S_MCM_04G_PRC.tif
G09=GPMMRG_MAP_2109_M_L3S_MCM_04G_PRC.tif
G10=GPMMRG_MAP_2110_M_L3S_MCM_04G_PRC.tif
G11=GPMMRG_MAP_2111_M_L3S_MCM_04G_PRC.tif
G12=GPMMRG_MAP_2112_M_L3S_MCM_04G_PRC.tif

gdal_calc.py -A $G01 -B $G02 -C $G03 -D $G04 -E $G05 -F $G06 -G $G07 -H $G08 -I $G09 -J $G10 -K $G11 -L $G12 --calc="(A*31+B*28+C*31+D*30+E*31+F*30+G*31+H*31+I*30+J*31+K*30+L*31)*24" --outfile=prcp_all_year.tif