Using NOAA's Gridpoint Statistical Interpolation (GSI) data assimilation system and NCAR's Advanced Research WRF (Weather Research and Forecasting) (ARW-WRF) regional model, six experiments are designed by (1) a control experiment (CTRL) and five data assimilation (DA) experiments with different data sets, including (2) conventional data only (CON); (3) microwave data (AMSU-A + MHS) only (MW); (4) infrared data (IASI) only (IR); (5) a combination of microwave and infrared data (MWIR); and (6) a combination of conventional, microwave and infrared observation data (ALL). One-month experiments in July 2012 and the impacts of the DA on temperature and moisture forecasts at the surface and four vertical layers over the western United States have been investigated. The four layers include lower troposphere (LT) from 800 to 1000 hPa, middle troposphere (MT) from 400 to 800 hPa, upper troposphere (UT) from 200 to 400 hPa, and lower stratosphere (LS) from 50 to 200 hPa. The results show that the regional GSI–WRF system is underestimating the observed temperature in the LT and overestimating in the UT and LS. The MW DA reduced the forecast bias from the MT to the LS within 30 h forecasts, and the CON DA kept a smaller forecast bias in the LT for 2-day forecasts. The largest root mean square error (RMSE) is observed in the LT and at the surface (SFC). Compared to the CTRL, the MW DA produced the most positive contribution in the UT and LS, and the CON DA mainly improved the temperature forecasts at the SFC. However, the IR DA gave a negative contribution in the LT. Most of the observed humidity in the different vertical layers is overestimated in the humidity forecasts except in the UT. The smallest bias in the humidity forecast occurred at the SFC and in the UT. The DA experiments apparently reduced the bias from the LT to UT, especially for the IR DA experiment, but the RMSEs are not reduced in the humidity forecasts. Compared to the CTRL, the IR DA experiment has a larger RMSE in the moisture forecast, although the smallest bias is found in the LT and MT.

Analyzing and forecasting the Indian Summer Monsoon Rainfall (ISMR) is vital for South Asia’s socio-economic stability. Using 35 climate models from the latest generation of the Coupled Model Intercomparison Project (CMIP6) to simulate and project ISMR, we integrated statistical methods, such as Taylor diagrams, comprehensive rating indicators, and interannual variability scores, to compare performance differences between various models and analyze influencing mechanisms. The results show that the majority of models effectively simulate the climatology of the ISMR. However, they exhibit limitations in accurately capturing its interannual variability. Importantly, we observed no significant correlation between a model’s ability to simulate ISMR’s general climatology and its accuracy in representing annual variability. After a comprehensive assessment, models, like BCC-ESM1, EC-Earth3-Veg, GFDL-CM4, INM-CM5-0, and SAM0-UNICON were identified as part of the prime model mean ensemble (pMME), demonstrating superior performance in spatiotemporal simulations. The pMME can accurately simulate the sea surface temperature changes in the North Indian Ocean and the atmospheric circulation characteristics of South Asia. This accuracy is pivotal for CMIP6’s prime models to precisely simulate ISMR climatic variations. CMIP6 projections suggest that, by the end of the 21st century, ISMR will increase under low, medium, and high emission scenarios, with a significant rise in rainfall under the high emission scenario, especially in the western and northern parts of India. Among the pMME, the projected increase in rainfall across India is more moderate, with an estimated increase of 30%. The findings of this study suggest that selecting the best models for regional climate downscaling research will project regional climate changes more accurately. This provides valuable recommendations for model improvements in the Indian region.

Trend uncertainty in the ozone valley over the Tibetan Plateau (OVTP) and the South Asian high (SAH) during 1979–2009 in ERA-Interim (interim reanalysis data from the ECMWF), JRA-55 (55-yr reanalysis data from the Japan Meteorological Agency), and NCEP-CFSR (Climate Forecast System Reanalysis) datasets was evaluated. The results showed that the NCEP-CFSR OVTP became strong in the summers of 1979–2009, whereas it became weak according to ERA-Interim and JRA-55. Satellite data merged with TOMS (Total Ozone Mapping Spectrometer) and OMI (Ozone Monitoring Instrument) agreed with the OVTP trend of NCEP-CFSR. The OVTP strengthening in NCEP-CFSR may have been caused by SAH intensification, a rising tropopause, and increasing ozone over non-TP (non-Tibetan Plateau) areas (27°–37°N, < 75°E and > 105°E). Analogously, the OVTP weakening in ERA-Interim and JRA-55 may have been affected by weakening SAH, descending tropopause, and decreasing non-TP ozone.

The characteristics of tropical cyclones (TCs) and their response to climate change is an issue of broad concern. Based on the Power Dissipation Index (PDI) proposed by Emanuel in 2005, the destructiveness of TCs in the typhoon season (July–October) during the period 1979–2016 over the western North Pacific is investigated. Results show that a regime shift in the destructive potential of TCs took place around 1998. The destructive potential of TCs has a considerable increasing trend from 1998 to 2016 (the P2 period), mainly contributed by the average intensity of TCs (51.20% of PDI change). We find that the PDI of TCs is mainly regulated by the El Niño/Southern Oscillation cycle in whole study period, whereas the Pacific Decadal Oscillation pattern shows significant enhancement in P2, which acts as a more important constraint on the typhoon season PDI over the western North Pacific.

With increasing energy shortages and global warming, clean and renewable energy sources, such as wind and wave energy, have gained widespread attention. In this study, the third-generation wave model WAVEWATCH-III (WW3) is used to simulate wave height in the North Indian Ocean (NIO), from 2008 to 2017, using the wind data from the European Centre for Medium-Range Weather Forecasts Renalysis datasets. The simulated results show good correlation with data obtained from altimetry. Analysis of wind and wave energy resources in the NIO is carried out considering energy density, the exploitable energy, the energy density stability, and monthly and seasonal variability indices. The results show that most areas of the NIO have abundant wind energy and at the Somali Waters are rich in wave energy resources, with wind energy densities above 200 W/m² and wave energy densities above 15 KW/m. The most energy-rich areas are the Somali Waters, the Arabian Sea, and the southern part of the NIO (wind energy density 350–650 W/m², wave energy density 9–24 KW/m), followed by the Laccadive sea (wind energy density 150–350 W/m², wave energy density 6–9 KW/m), while the central part of the NIO is relatively poor (wind energy density less than 150 W/m², wave energy density below 6 KW/m).