By Sagar Ratna Bajracharya
([email protected]) Wahid Palash
Mandira Shrestha International Centre for Integrated Mountain Development (ICIMOD)
Kathmandu, Nepal
Evaluation of satellite-based rainfall products over the
Brahmaputra basin
7 th IPWG, Tsukuba, Japan, 17-21 November 2014
vStudy Area vData use, Observed data, Data preparation (RFE2.0-Modified) vMethodology vResults vApplication vConclusion
Outline
573,000 sq km, diverse environment as the cold dry plateau of Tibet, mean elevation 3944 m, Physical features, monsoon 80%, Large north-south precipitation gradient
Product Name
CMORPH (NOAA CPC Morphing
Technique) Source:
ftp://ftp.cpc.ncep.noaa.gov/fews/C
MORPH/
GSMaP (Global Satellite Mapping for
Precipitation) Source:
http://sharaku.eorc.jaxa.jp/GSMaP_crest/html/about_data.ht
ml
CPC-RFE2.0 (NOAA Climate Prediction
Centre Rainfall Estimates Version
2.0) Source:
ftp://ftp.cpc.ncep.noaa.gov/fews/S.Asia/d
ata/
RFE2.0-Modified(The
merged version of CPC-RFE2.0
products and local ground observed data improved at
ICIMOD )
TRMM 3B42- V6 ( Tropical Rainfall
Measuring Mission) Source:
http://gcmd.nasa.gov/records/GCMD_GES_DISC_TRMM_3B42_
daily_V6.html
Spatial resolution
0.1 deg. 0.1 deg. 0.1 deg. 0.1 deg. 0.25 deg.
Temporal resolution
30 min 1 hour 24 hours 24 hours 30 min
Domain 60N – 60S (global)
60N – 60S (Global) 70–110E and 5–35N (Regional)
70–110E and 5–35N (Regional)
50N–50S (global)
Product source
IR, SSM/I, AMSU-B, AMSR-E,
TMI (no rain gauge)
MWR-GEO IR combined algorithm with NOAA AMSU-B
products (no rain gauge)
GPI cloud-top IR, SSM/I, AMSU-A, GTS
(rain gauge)
GPI cloud-top IR, SSM/I, AMSU-A,
GTS Merged local
country wise rainfall data (rain gauge)
IR, TMI, SSM/I, AMSR-E, AMSU-B,
MHS, Monthly 1degree rain gauge
grid data (rain gauge)
Agency Climate Prediction Centre
(CPC), NOAA
Earth Observation Research Centre
(EORC), Japan Aerospace
Exploration Agency (JAXA)
Climate Prediction Centre (CPC), NOAA in association with USAID/ FEWS-NET
Climate Prediction Centre (CPC), NOAA and
improved at ICIMOD
National Aeronautics and
Space Administration (NASA)
References
Joyce et al. (2004)
Ushio et al.(2009) Xie and Arkin.(1996)
Xie and Arkin.(1996)
Huffman et al.(1995,1997)
ftp://ftp.cpc.ncep.noaa.gov/fews/CMORPH/ftp://ftp.cpc.ncep.noaa.gov/fews/CMORPH/ftp://ftp.cpc.ncep.noaa.gov/fews/CMORPH/http://sharaku.eorc.jaxa.jp/GSMaP_crest/html/about_data.htmlhttp://sharaku.eorc.jaxa.jp/GSMaP_crest/html/about_data.htmlhttp://sharaku.eorc.jaxa.jp/GSMaP_crest/html/about_data.htmlhttp://sharaku.eorc.jaxa.jp/GSMaP_crest/html/about_data.htmlftp://ftp.cpc.ncep.noaa.gov/fews/S.Asia/data/ftp://ftp.cpc.ncep.noaa.gov/fews/S.Asia/data/ftp://ftp.cpc.ncep.noaa.gov/fews/S.Asia/data/
According to the criteria of minimum density of precipitation given by WMO to ensure a consistent distribution of stations in each country. For blending, detailed information about each local gauge station (name, latitude, longitude, elevation, and others) was added to a master NOAA GTS rain gauge file to calculate the gauge-to-gauge distances and update the number of new local stations in the master file.
The final precipitation estimates retain the station's rain gauge value, while as distance from a station increases, the estimates rely more heavily on satellite derived precipitation. This indicates that the spatial pattern of the CPC-RFE2.0 does not change, only the magnitude. If short-term convective rainfall events which were not well observed by the CPC-RFE2.0 were measured at a particular station, this would be included in the RFE2.0 Modified.
RFE2.0-Modified from 2006-05-12 CPC-RFE2.0 from 2006-05-12
The merging algorithm of CPC-RFE2.0 defines the analysis of daily precipitation in two steps. First, to reduce the random error inherent in the individual data sources, the three kinds of satellite data (GPI cloud-top IR, SSM/I, AMSU) are combined linearly through the Maximum Likelihood Estimation Method, in which the weighting coefficients are inversely proportional to the individual error variance. This provides the shape of precipitation. Since the shape of precipitation contains bias passed through from original inputs, a second step is introduced to remove the bias by blending the shape of precipitation with the gauge data using the method of Reynolds (1988) . In this blending process, the gauge data are used to define the magnitude of the precipitation field
To better understand the impact of precipitation inputs on hydrological applications, the accuracy of satellite precipitation should be assessed against the reference data considering basin average precipitation.
Location of Brahmaputra river Basin
Grid-to-Grid Analysis - Sample no. = 4602 (daily data at 4602 grids)- Gives daily performance over whole basin (i.e. basin-wide daily performance)- Averaging daily performance over month and season to calculate month and season-wise daily performance
Catchment-to-Catchment Analysis- Sample no. = 217 (sub-basin or catchment average daily rainfall of 217 catchments)- Gives daily performance over whole basin (i.e. basin-wide daily performance)- Averaging daily performance over month and season to calculate month and season-wise daily performance
Rainfall station
Catchment boundary
Raster rainfall grid
Legends
Mei et al [2014] stated that catchment average rainfall approach allows a more direct inference on the hydrological impact of the satellite rainfall estimation error and similarly, size of catchments also influence the satellite rainfall errors. Hydrological model relies on catchment average rainfall data, comparison of catchment average rainfall thus gives an idea of how useful the selected satellite rainfall products are in a hydrological modelling study..
The monsoon season was the primary focus in this study as more than 80% of annual rainfall falls during this period, and it is the most important season for flood prediction and warning and also from the agriculture point of view
0
3
6
9
12
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MAE (mm/day)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
5
10
15
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
RMSE (mm/day)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.2
0.4
0.6
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Correlation coeffecient (r)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
20
40
60
80
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mbias
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
Monthly average of daily error statistics of different satellite products ( G-G analysis).
0
0.2
0.4
0.6
0.8
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
FAR
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.2
0.4
0.6
0.8
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
POD
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
Month average of daily error categorical statistics of satellite rainfall for 2004 to 2006 ( G-G analysis)
The categorical statistics are very much important if SRE products will be used in modelling of floods because of precipitation detection. Both POD (hits) and FAR (misses) help to understand the hydrological consequences of the sources of errors in SRE products ( Y.Derin and K.Yilmaz, 2014)
0
50
100
150
200
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MAE (mm/month)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
100
200
300
400
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
RMSE (mm/month)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.2
0.4
0.6
0.8
1
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Correlation coeffecient (r)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
5
10
15
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mbias
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
Monthly rainfall error statistics of different satellite products for 2004 to 2006 (G-G analysis)
0
100
200
300
400
500
600
Pre-monsoon Monsoon Post-monsoon Winter
MAE (mm/season)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.2
0.4
0.6
0.8
1
Pre-monsoon Monsoon Post-monsoon Winter
Correlation coeffecient (r)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.5
1
1.5
2
2.5
Pre-monsoon Monsoon Post-monsoon Winter
Mbias
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
200
400
600
800
Pre-monsoon Monsoon Post-monsoon Winter
RMSE (mm/season)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
Seasonal error statistics of different satellite products for 2004 to 2006 ( G-G analysis)
0
3
6
9
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
MAE (mm/day)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
5
10
15
20
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
RMSE (mm/day)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
0.2
0.4
0.6
0.8
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Correlation coeffecient (r)
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
0
10
20
30
40
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Mbias
CPC-RFE2.0 RFE2.0-ModifiedCMORPH GSMaPTRMM 3B42
Monthly average of daily error statistics of different satellite products for 2004 to 2006 (C-C analysis)
0
5
10
15
20
1-Jan-04 30-Apr-04 28-Aug-04 26-Dec-04 25-Apr-05 23-Aug-05 21-Dec-05 20-Apr-06 18-Aug-06 16-Dec-06
Rain
fall
(mm
/dek
ade)
Time
Whole Brahmaputra basin-wide dekadal average rainfall
Observed RF CPC-RFE2.0 RFE2.0-Modified CMOPRH GSMaP TRMM 3B42
Time series of basin average dekadal rainfall for different satellite rainfall products from 2004 to 2006
To demonstrate the utility in flood forecasting, because depending on upstream basin size, flood routing lag time may vary from daily to
dekadal or so
Application
.
Product before and after blending the local rain gauge
Figure : RFE on 09/07/2003 for all Nepal
Figure : Modified RFE on 09/07/2003 for all
Nepal
0
1000
2000
3000
4000
5000
6000
Dis
char
ge (m
3/se
c)
Discharge from Interpolated gaugeObserved DischargeDischarge from RFEDischarge from modified RFE
0
30
60
90
120
150
Rai
nfal
l (m
m)
Average basin observed rainfallAverage basin Modified RFEAverage basin RFE
Description Predicted
peak
discharge
(m3/sec)
NSEC Coefficient
of
determinatio
n
Flow
ratio
Observed peak
discharge
(m3/sec)
Modified RFE 5,178 0.91 0.92 1.1
5,380 RFE 3,702 0.45 0.49 0.69
Interpolated observed
rain gauge (Shrestha et
al. 2008)
5,210 0.91 0.9 1.1
vThe evolution of regional and global SRE products with high temporal and spatial resolution has opened up new opportunities for hydrological applications in data sparse regions.
vIn sum-up, there was general agreement in the overall pattern of rainfall distribution between observed and satellite estimated data over the Brahmaputra river basin, with SRE following the same trend of high and low rainfall intensity as the observed-interpolated rainfall. However, the amount of rainfall was generally underestimated. RFE2.0-Modified showed the good correspondence with observed rainfall followed by TRMM 3B42, CMORPH, CPC-RFE2.0, and GSMaP. Overall, in the rugged topography of the Brahmaputra river basin, SRE products which incorporated gauge data performed better than the products that only used remotely sensed data. The effect of additional local gauges on the quality of the products was clear. It also revealed that evaluation of SRE products at monthly and seasonal temporal resolution provided better results which could be considered as useful for overall water resource assessment of the basin.
Summary
vBajracharya, S.R, M.S. Shrestha, and A.B. Shrestha, (2014) Assessment of high-resolution satellite rainfall estimation products in a streamflow model for flood prediction in the Bagmati basin, Nepal. J. Flood Risk Management. DOI: 10.1111/jfr3.12133
vBajracharya S.R., Palash W., Shrestha M., Khadgi V., Duo C., Das P. & Dorji. Systematic evaluation of satellite-based rainfall products over the Brahmaputra basin for hydrological application. Advances in Meteorology 2014
Thank you
7 th IPWG, Tsukuba, Japan, 17-21 November 2014
Evaluation of satellite-based rainfall products over the Brahmaputra basin ��スライド番号 2スライド番号 3スライド番号 4スライド番号 5スライド番号 6スライド番号 7スライド番号 8スライド番号 9スライド番号 10スライド番号 11スライド番号 12スライド番号 13スライド番号 14スライド番号 15スライド番号 16スライド番号 17スライド番号 18スライド番号 19スライド番号 20Applicationスライド番号 22Product before and after blending the local rain gaugeスライド番号 24スライド番号 25スライド番号 26スライド番号 27スライド番号 28スライド番号 29スライド番号 30
