Relating forecast and satellite precipitation to generate future skillful ensemble forecasts over the northwest Himalayas at major avalanche and glacier sites

Abstract

Availability of skillful precipitation forecast is crucial for regions such as the Himalayas which directly influence millions of people in the Indian subcontinent. The deterministic raw forecasts obtained from the numerical weather prediction (NWP) models are often biased. In this work, we present a novel approach of considering the spatial mismatch between the precipitation forecast and observation data in developing a statistical model. To deal with the mismatch in the spatial scale, a famous alternative approach could be ‘spatial downscaling and bias correction (SDBC)’. We attempt to blend the traditional approach of SDBC with a multiple-point geostatistics (MPS) approach to generate better-skilled forecasts. We consider the slope, aspect, and other topographical variables, along with the NWP model output, and observation data in a multivariate setup of the MPS (with SDBC) model to generate skillful forecast ensembles. We evaluate the daily generated 25 ensemble forecast members at avalanche and glacier sites. The generated multiple forecast members allow us to assess the uncertainty in the precipitation forecast. The outcome of the study would be helpful to water resource managers, disaster relief authorities, avalanche forecast agencies, and climate change experts for the sustainable development of the Himalayas.