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 Mool Integrated Water and Hazard Management International Centre for Integrated Mountain Development (ICIMOD) 4 th Workshop of the International Precipitation Working Group October, 2008, Beijing, CHINA

International Centre for Integrated Mountain Development 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…

International Centre for Integrated Mountain Development Track of Monsoon Depression

International Centre for Integrated Mountain Development L, 990 mbar Active Monsoon Trough

International Centre for Integrated Mountain Development Kitini Khola 1993 flood

International Centre for Integrated Mountain Development 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.

International Centre for Integrated Mountain Development 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 Note: -ve value indicates Ocean Source: World Water and Climate Atlas, IWMI

International Centre for Integrated Mountain Development Seasonal Variation of Precipitation Pre Monsoon Monsoon Post Monsoon Winter

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 Development Final Product Source: Tim Love

International Centre for Integrated Mountain Development NOAA CPC RFE Domain

International Centre for Integrated Mountain Development 15 Source: Tim Love GTS Inputs

International Centre for Integrated Mountain Development Data Preparation Daily independent rain gauge data in word file convert into appropriate format provided by individual country from 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 Interpolation a) Kriging - 0.1˚ spatial resolution for individual country to 2.5˚for regional level Working Area - ICIMOD Whole HKH (Regional) - Partner institutes their individual country Considered Scales Individual country to 0.25˚ spatial resolution - 24 hours, 10 and 30 days temporal ICIMOD 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 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 Methodology for validation

International Centre for Integrated Mountain Development

Comparison

International Centre for Integrated Mountain Development 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

International Centre for Integrated Mountain Development

Sumarized statistical summary of regional validation Continuous verification statistics Categorical verification statistics DaysPixel No Bias (mm) CorrRMSE (mm) % error PODFARSkill (Max) (Min) (Max) (Min) (Max) (Min)

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

International Centre for Integrated Mountain Development