Validation of satellite rainfall estimation in the summer monsoon dominated area of the Hindu Kush Himalayan Region - PowerPoint PPT Presentation

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Validation of satellite rainfall estimation in the summer monsoon dominated area of the Hindu Kush Himalayan Region PowerPoint Presentation
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Validation of satellite rainfall estimation in the summer monsoon dominated area of the Hindu Kush Himalayan Region
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Validation of satellite rainfall estimation in the summer monsoon dominated area of the Hindu Kush Himalayan Region

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  1. Validation of satellite rainfall estimation in the summer monsoon dominated areaof the Hindu Kush Himalayan Region Sagar Ratna Bajracharya, Mandira Shrestha and Pradeep Mool sagbajracharya@icimod.org Integrated Water and Hazard Management International Centre for Integrated Mountain Development (ICIMOD) www.icimod.org 4th Workshop of the International Precipitation Working Group 13-17 October, 2008, Beijing, CHINA

  2. Outline • General description and climatic condition of HKH region • What is NOAA CPC-RFE 2.0 • Methodology and Analysis • Results • Recommendations and Road Ahead

  3. 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

  4. 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…

  5. Track of Monsoon Depression

  6. Active Monsoon Trough L, 990 mbar

  7. Kitini Khola 1993 flood

  8. Orography and Rain Shadow Orographic lift occurs when an air mass is forced a low elevation to higher elevation as it moves over rising terrain. As the air mass gains altitude it expands and cools adiabatically. This cooler air cannot hold the moisture as well as warm air and this effectively raises the relative humidity to 100%, creating clouds and frequently precipitation.

  9. Note: -ve value indicates Ocean Precipitation • Southern 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 Source: World Water and Climate Atlas, IWMI

  10. Seasonal Variation of Precipitation Monsoon Pre Monsoon Winter Post Monsoon

  11. 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

  12. Final Product Source: Tim Love

  13. NOAA CPC RFE Domain

  14. GTS Inputs 15 15 Source: Tim Love

  15. Methodology for validation Data Preparation Daily independent rain gauge data in word file convert into appropriate format provided by individual country from 2002-2004 • Estimated Data • NOAA CPC_RFE Product • Whole HKH Daily product (24 hours) • 0.1 degree spatial resolution • In Lambert Azimuthal Area Data Quality Control Data Conversion - RFE2 Data downloaded by NOAA ftp server - Observed rain gauge data in GIS format Comparison or Overlay Change the projection parameter of GIS dataset Interpolation a) Kriging - 0.1˚ spatial resolution for individual country -0.25 to 2.5˚for regional level Validation a) Visual analysis b) 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 Working Area - ICIMOD Whole HKH (Regional) - Partner institutes their individual country Considered Scales Individual 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

  16. Comparison

  17. Validation of RFE • 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

  18. Results • 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

  19. Next Steps in SRE application • 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. 

  20. Thank You!