Spatial extrapolation of statistically downscaled weather data over the Northwest Himalayas at major glacier sites. (March 2022) - Singhal, Akshay; Cheriyamparambil, Athul; Jha, Sanjeev K.
Abstract: Generating fine-resolution weather data over the Himalayan region is difficult due to complicated topography and harsh weather. In this study, we set up a Multiple-Point Statistics (MPS) based statistical model to spatially extrapolate fine-resolution rainfall and temperature data in five spatial domains and at ten-point locations of glaciers over the northwest Himalayas. The training domain consists of thirteen years (2001–2013) of daily rainfall and temperature data at 30 and 10-km spatial resolution which generates multiple realizations of fine-resolution information for the year 2014 in the larger domain. Small range of RMSE (2.46–6.68 mm) and MAE (1.12–4.15 mm) for rainfall suggests that MPS performs well in spatial domains. Furthermore, at the glacier sites, we observe that the training data of smaller training domains are insufficient while data of larger domains perform well. Overall, MPS shows encouraging results in extrapolating fine-resolution weather information over the complex region of northwest Himalayas. Highlights: The MPS technique can spatially extrapolate weather data over the Himalayas. Training data of smaller domains are used to simulate data for larger domains. Large-sized training domains simulate well in comparison to small-sized domains. The extrapolated data is also estimated for selected point locations of glaciers. MPS shows good accuracy in extrapolating data both in domains and at glacier sites.