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Near-surface (2m) air temperature estimates from Indian Monsoon Data Assimilation and Analysis (IMDAA) regional reanalysis are verified against observations (Global Historical Climatology Network daily data (GHCNd)) and compared with the similar estimates from ERA5 at 14 selected stations over India. These selected stations represent different climatic locations like northwest, north-central, west coast, interior peninsula, and east coast. The summer maximum temperature, winter minimum temperature, and annual daily mean temperature from IMDAA are analysed for 19 yrs in the 21st century, 2000–2018. A warm (cold) bias is seen in the maximum (minimum) temperatures estimated from IMDAA, whereas the biases are opposite in the similar estimates from ERA5 over the subtropical region, north of 20°N. Over the coastal regions, both west and east, the biases in the reanalyses against the observation are high in both maximum and minimum temperature estimates. ERA5 performs better in the subtropical zone (north India), while IMDAA performs better in the tropical region (south India), particularly over the orographic regions, in terms of mean bias and root mean square error (RMSE). The relatively better performance of the high-resolution regional IMDDA within peninsular south India may be the result of better resolving the orographic and coastal areas, together with the assimilation of more local observations over this region. Though the biases and RMSE show better performance of IMDAA over the tropics, ERA5 estimates are highly correlated with the observation over all the selected locations.

Operational forecasting of tropical cyclone (TC) track and intensity in the India Meteorological Department (IMD) relies more and more on the numerical weather prediction (NWP) model guidance from national and international agencies particularly, on the medium range (24–120 h). Any improvement in TC forecasts by the NWP models enhances the operational forecaster's confidence and capability. The real-time information from the National Centre for Medium Range Weather Forecasting (NCMRWF) global NWP model (NCUM-G) is routinely used by operational forecasters at IMD as model guidance. The present study documents the improved skill of NCUM-G in forecasting the North Indian Ocean (NIO) TCs during 2015–2019, based on a collection of 1810 forecasts involving 22 TC cases. The study highlights three significant changes in the modelling system during the recent five years, namely (i) increased grid resolution from 17 to 12 km, (ii) use of hybrid 4D-Var data assimilation (DA), and (iii) increased volume of assimilated data. The study results indicate a consistent improvement in the NCUM-G model forecasts during the pre-monsoon (April–May, AM) and post-monsoon (October–December, OND) TC seasons. In addition to a 44% reduction in the initial position error, the study also reports a statistically significant decrease in the direct position error (DPE) and error in the intensity forecast, resulting in a forecast gain of 24 hrs. Comparing NWP models with IMDs official track error shows that NCUM-G and ECMWF model forecasts feature lower DPE than IMD in 2019, particularly at higher (96, 108, and 120 h) lead times.