Abstract
In this study, a comprehensive assessment has been provided for the performance of three newly-developed high resolution reanalysis (IMDAA, NGFS, ERA5) datasets w.r.t. IMD rainfall observation over six homogeneous monsoon regions of India during 1999–2018. It is necessary to understand how these reanalyses rainfall datasets are performing spatio-temporally, specifically during monsoon in order to apply them in further research and developmental purposes. Focus has been given to assess their relative performances during monsoonal rainfall extremes against IMD observations. Results reveal that all these three reanalysis datasets captured the spatial patterns of observed rainfall climatology reasonably well in all the seasons, except for hilly regions where there is over-estimation against the observed patterns. Moreover, wet biases over the entire foothills of Himalayas extending up to north-east are more dominant during monsoon for IMDAA reanalysis, whereas bias is mixed type and region-specific in all seasons for NGFS and ERA5 reanalysis. Both IMDAA and NGFS reanalysis captured the probability distribution of observed daily rainfall intensity diligently especially in the range of extremes while ERA5 reanalysis underestimated the same. However, spatial distribution for rainfall extremes (based on intensity and 95th percentile) reveals that all the reanalyses have failed to capture patterns in the hilly topography as well as over peninsular Indian region. In comparison to IMDAA and ERA5 reanalysis, NGFS captured reasonably well for heavy and very heavy rainfall over Western Ghats. ERA5 reanalysis always underestimated all the extreme rainfall categories in comparison to IMDAA and NGFS reanalysis which is also confirmed from probability distribution curve. Moreover, bi-decadal (pre- and post-2008) spatial trend distribution for 95th percentile rainfall extremes indicate a reversal in trend (wetter to drier and vice versa) over India. Both IMDAA and NGFS have captured the moist and desiccated trend region-specifically along with ERA5 reanalysis against observational trends. Thus, the result provides useful insights for the climate modeling to establish appropriate standards for performing climate model evaluation over Indian region.
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