Impact of Kalpana-1 derived land surface albedo on short-range weather forecasting over the Indian subcontinent

Abstract

The skill of weather forecasts at high spatial resolution depends on accurate representation of land surface states at appropriate spatial and temporal scales that modulate flux partitioning in the numerical weather prediction models. In this study, the Weather Research and Forecasting (WRF) model is customized to assess the impact of land surface albedo (LSA) derived from Kalpana-1 Very High Resolution Radiometer (K1VHRR) in comparison to default monthly climatological albedo from the United States Geological Survey (USGS). A two-stage upscaling of ground-measured albedo from Agro-Met Stations is performed to derive K1VHRR LSA. This combines multispectral reflectance at intermediate scales from the Advanced Wide Field Sensor on board Resourcesat-2 at Low Earth Orbiting platform and the planetary (Earth-atmosphere system) albedo from Kalpana-1 visible band at Geostationary Earth Orbiting platform. Two separate experiments, with real-time K1VHRR LSA and USGS climatological LSA (CNT), are performed to evaluate the impact of real-time K1VHRR LSA on daily WRF model forecasts during July 2009. Additional experiments are performed to assess the impact of real-time and climatological K1VHRR albedo against USGS climatological albedo based experiment. Results show that real-time K1VHRR albedo improves the surface temperature, specific humidity, and wind speed forecasts as compared to CNT experiments. The impact of climatological and real-time K1VHRR LSA is small compared to the advantage of using K1VHRR over USGS. Moreover, real-time K1VHRR albedo has additional benefits to improve the representation of seasonal variability. Results show that the real-time K1VHRR LSA has slight positive impact on rainfall forecast.