International Centre for Integrated Mountain Development
Validation of satellite rainfall estimation in the summer monsoon dominated area
of the Hindu Kush Himalayan Region
Sagar Ratna Bajracharya, Mandira Shrestha and Pradeep [email protected]
Integrated Water and Hazard ManagementInternational Centre for Integrated Mountain Development (ICIMOD)
www.icimod.org
4th Workshop of theInternational Precipitation Working Group
13-17 October, 2008, Beijing, CHINA
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Outline General description and climatic condition of HKH region
What is NOAA CPC-RFE 2.0 Methodology and Analysis
Results
Recommendations and Road Ahead
International Centre for Integrated Mountain Development
The Himalayan Region Extends over 3500 km from Afghanistan,
Pakistan, India, China, Nepal, Bhutan to Bangladesh and Myanmar
Geologically youngest mountain range in the world, giving rise to the high degree of slope instability and landslide hazards
High mountains, Plane and Tibetan Plateau
Variable background – snow cover etc
High spatial variations with widely varying physical and climatic conditions
International Centre for Integrated Mountain Development
The Hindu Kush-Himalayan Context
Meteorologically diverse
•Orography and continental influences•Convective precipitation, Cloud burst, Monsoon Influence•Seasonal variations – Extremely cold vs hot and humid temperatures•Variety and variability of climate due to complex topography•Plenty of intense rain intensities…
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Orography and Rain Shadow Orographic lift occurs when an air mass is forceda low elevation to higher elevation as it moves over rising terrain. As the air mass gains altitude it expands and cools adiabatically. This cooler aircannot hold the moisture as well as warm air and this effectively raises the relative humidity to 100%,creating clouds and frequently precipitation.
International Centre for Integrated Mountain Development
PrecipitationSouthern part of the Himalayas receive higher rainfall whereas northern receive less rainfall
Higher in the east and gradually decreases towards west
More than 80% rainfall during monsoon (June-September)
High seasonal and spatial variation
Note: -ve value indicates Ocean
Source: World Water and Climate Atlas, IWMI
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Ocean0 - 1010 - 5050 - 100100 - 250250 - 500> 500
Pre Monsoon (Mar-May)
Ocean0 - 1010 - 5050 - 100100 - 250250 - 500> 500
Post Monsoon (Oct-Nov)
Ocean0 - 1010 - 5050 - 100100 - 250250 - 500> 500
Post Monsoon (Oct-Nov)
Winter (Oct-Nov)
Ocean0 - 1010 - 5050 - 100100 - 250250 - 500> 500
Winter (Oct-Nov)
Ocean0 - 1010 - 5050 - 100100 - 250250 - 500> 500
Seasonal Variation of PrecipitationPre Monsoon Monsoon
Post Monsoon Winter
Monsoon (Jun-Sep)
> 25002000 - 25001500 - 20001000 - 1500500 - 1000100 - 5000 - 100Ocean
International Centre for Integrated Mountain Development
NOAA CPC RFE2.0 Initial version became operational in May 2001
Originally run over the African continent then expanded to southern Asia and western Asia / eastern Europe
Product is a combination of surface and satellite precipitation information
Spatial resolution: 0.1 degree
Temporal resolution: daily
Availability: 5°-35°N; 70°-110°E
International Centre for Integrated Mountain DevelopmentData Preparation
Daily independent rain gauge data in word file convert into appropriate format provided
by individual country from 2002-2004
Data Quality Control
Data Conversion- RFE2 Data downloaded by NOAA ftp server
- Observed rain gauge data in GIS format
Change the projection parameter of GIS dataset
Interpolationa) Kriging
- 0.1˚ spatial resolution for individual country
-0.25 to 2.5˚for regional level
Working Area- ICIMOD Whole HKH (Regional)
- Partner institutes their individual country
Considered ScalesIndividual country
- 0.1 to 0.25˚ spatial resolution- 24 hours, 10 and 30 days temporal
ICIMOD-0.25 to 2.5˚spatial resolution
- 24 hours, 10 and 30 days temporal
Estimated Data-NOAA CPC_RFE Product-Whole HKH Daily product (24 hours)-0.1 degree spatial resolution-In Lambert Azimuthal Area
Comparison or Overlay
Validationa) Visual analysisb) Descriptive statistics - through contingency tables - POD, FAR (e.g. with zero and 1mm/day rain/no rain threshold) c) Statistical analysis-Bias-RMSE-linear correlation coefficient-Skill score index-% error-etc
Methodology for validation
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Visual Analysis
Scatter plot of Observed V Estimated rainfall
Descriptive statistics
Contingency tables use of POD and FAR
Statistical analysis
Bias, RMSE, Correlation, Skill, % error etc
Validation of RFE
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Sumarized statistical summary of regional validation
Continuous verification statistics
Categorical verification statistics
Days Pixel No
Bias(mm)
Corr RMSE(mm)
% error
POD FAR Skill
2002203(Max) 9466 -5 0.72 23.42 -24.27 0.85 0.8 0.02
2002243(Min) 9763 -0.74 0.1 5.62 -28.46 0.38 0.49 0.3
2003190(Max) 9763 -7 0.54 22.2 -40.65 0.85 0.01 0.98
2003153(Min) 9763 -0.62 0.01 5.96 -31.2 0.48 0.42 0.41
2004190(Max) 9763 -9 0.55 26.36 -35.47 0.88 0 0.5
2004162(Min) 9763 -0.14 0.34 5.44 -5.5 0.5 0.3 0.3
International Centre for Integrated Mountain Development
The CPC-RFE technique overestimates rainfall particularly over a region where there is persistence of cirrus cloud, snow and ice.
Underestimates rainfall in a region where there is orographic precipitation and precipitation by warm cloud.
Rainfall occurrence is underestimated by about half and more than half in monsoon during heavy rainfall and overestimates in pre monsoon
Limitation of SRE is that it cannot produce more than certain amount of rainfall in 24 hours
Results
International Centre for Integrated Mountain Development
How lag time of the data can be reduced? Improving Orographic effects in rainfall estimation . RFE- the shape of precipitation is given by the combination of
satellite estimates, magnitude is inferred from GTS station data, need the maximum availability of the rain gauge stations
Including radar data for validation where available in HKH and Incorporate more gauge data for validation
Validation considering different rainfall regimes. Validation considering temporal variable like decadal, monthly,
yearly, rainy season etc using different spatial resolution (0.25˚, 0.5˚, 1˚, etc)
Improve Satellite estimates over the ice and snow cover estimates over the Himalayas
Application of improved RFE in flood early warning and flood monitoring activities in flood season.
Next Steps in SRE application