Rainfall is probably the most important parameter that is predicted by numerical weather prediction models, though the skill of rainfall prediction is the poorest compared to other parameters, e.g., temperature and humidity. In this study, the impact of rainfall assimilation on mesoscale model forecasts is evaluated during Indian summer monsoon 2011. The Weather Research and Forecasting (WRF) model and its four‐dimensional variational data assimilation system are used to assimilate the Tropical Rainfall Measuring Mission 3B42 and Japan Aerospace Exploration Agency Global Satellite Mapping of Precipitation retrieved rainfall. A total of five experiments are performed daily with and without assimilation of rainfall data during the entire month of July 2011. Separate assimilation experiments are performed to assess the sensitivity of WRF model forecast with strict and less strict quality control. Assimilation of rainfall improves the forecast of temperature, specific humidity, and wind speed. Domain average improvement parameter of rainfall forecast is also improved over the Indian landmass when compared with NOAA Climate Prediction Center Morphing technique and Indian Meteorological Department gridded rainfall.

This paper describes, for the first time, the impact of Oceansat-2 scatterometer (OSCAT) surface winds in the Weather Research and Forecasting (WRF) 3-D variational (3-D-Var) assimilation system. Before using OSCAT winds into WRF assimilation system, we compared OSCAT surface wind retrievals against National Centers for Environmental Prediction analyzed winds, Advanced Scatterometer retrievals, and buoy-measured winds. After the initial assessment of the quality of the OSCAT winds, the control (CNT) (without OSCAT) as well as experimental (which assimilated OSCAT surface winds) runs were made for 48 h starting daily at 0600 Universal Time Coordinated (UTC) during July 2010. The assimilation experiments demonstrated positive impact of OSCAT winds on both the analysis state as well as subsequent short-term forecasts of surface winds. Compared to CNT run, the assimilation of the OSCAT winds improved the surface wind analysis as large as 25%, when compared with Advanced Microwave Scanning Radiometer (AMSR-E) measured winds. The assimilation of OSCAT winds also showed small, but positive, impact on the forecast (particularly later hours of forecast) of midtropospheric moisture, temperature, and upper tropospheric winds. Compared to the CNT run, the assimilation of OSCAT winds improved precipitation forecast for moderate to heavy rainfall thresholds when validated against Tropical Rainfall Measuring Mission precipitation.

Clouds play a tangible role in the Earth's atmosphere and in particular, the cloud base height (CBH), which is linked to cloud type, is one of the most important characteristics to describe the influence of clouds on the environment. In the present study, CBH observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03° N, 72.54° E), India. A detailed comparison has been performed with the use of ground-based CBH measurements from Ceilometer CL31 and CBH retrieved from MODIS (Moderate Resolution Imaging Spectroradiometer) onboard Aqua and Terra satellite. CBH retrieved from MODIS is ∼ 1.955 and ∼ 1.093 km on 25 July 2014 and 1 January 2015 respectively, which matches well with ceilometer-measured CBH ( ∼ 1.92 and ∼ 1.097 km). Some interesting features of cloud dynamics viz. strong downdraft and updraft have been observed over Ahmedabad which revealed different cloud characteristics during monsoon and post-monsoon periods. CBH shows seasonal variation during the Indian summer monsoon and post-monsoon period. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and the MODIS satellite provides accurate retrieval of high-level clouds over this region. The CBH algorithm used for the MODIS satellite is also able to capture the low-level clouds.

This study investigates the impact of temperature and moisture profiles from Atmospheric Infrared Sounder (AIRS) on the prediction of the Indian summer monsoon, using the variational data assimilation system annexed to the Weather Research and Forecasting model. In this study, three numerical experiments are carried out. The first is the control and includes no assimilation; in the second, named Conv, assimilation of conventional Global Telecommunication System data is performed. The third one, named ConvAIRS, is identical to the Conv except that it also includes assimilation of AIRS profiles. The initial fields of tropospheric temperature and water vapor mixing ratio showed significant improvement over the model domain. Assimilation of AIRS profiles has significant impact on predicting the seasonal mean monsoon characteristics such as tropospheric temperature, low‐level moisture distribution, easterly wind shear, and precipitation. The vertical structure of the root‐mean‐square error is substantially affected by the assimilation of AIRS profiles, with smaller errors in temperature, humidity, and wind magnitude. The consequent improved representation of moisture convergence in the boundary layer (deep convection as well) causes an increase in precipitation forecast skill. The fact that the monsoonal circulation is better captured, thanks to an improved representation of thermal gradients, which in turn leads to more realistic moisture transport, is particularly noteworthy. Several previous data impact studies with AIRS and other sensors have focused on the short or medium range of the forecast. The demonstrated improvement in all the predicted fields associated with the Indian summer monsoon, consequent to the month long assimilation of AIRS profiles, is an innovative finding with large implications to the operational seasonal forecasting capabilities over the Indian subcontinent.