Assessment of the Indian Meteorological Ensemble Dataset (IMED) Performance in Identifying Cloudburst Events over the Northwest Himalayas (NWH)

Cloudbursts over the NWH have become more common in recent years. The uncertainty caused by the sparse density of the station network over NWH encouraged us to employ the developed dataset, Indian Meteorological Ensemble Dataset (IMED), which explicitly accounts for topographical complexity and uncertainties in precipitation estimations. In the NWH, where monitoring stations are sparse, and cloudbursts are hard to discern, this study examines IMED's efficiency in identifying cloudburst events. We aim to use the mean, 70th percentile, 80th percentile, and 99th percentile values from 30 ensembles of IMED data every day with a resolution of 0.25 degrees.  We evaluated 18 events in the NWH between 2014 and 2016, which were documented in different paper publications. Furthermore, we compare the cloudburst identification ability of the CHIRPS dataset to that of the IMED datasets. A pixel-wise analysis shows that IMED performs better than the CHIRPS dataset in this event detection. With the mean value of IMED, it can capture five events, whereas four events are captured by the CHIRPS dataset. With the 70 percentile, 80 percentile, and 99th percentile, IMED can capture more events. This study concludes that IMED performs better than CHIRPS in identifying cloud burst events over the NWH region.