India has experienced significant Land-Use and Land-Cover Change (LULCC) over the past few decades. In this context, careful observation and mapping of LULCC using satellite data of high to medium spatial resolution is crucial for understanding the long-term usage patterns of natural resources and facilitating sustainable management to plan, monitor and evaluate development. The present study utilizes the satellite images to generate national level LULC maps at decadal intervals for 1985, 1995 and 2005 using onscreen visual interpretation techniques with minimum mapping unit of 2.5 hectares. These maps follow the classification scheme of the International Geosphere Biosphere Programme (IGBP) to ensure compatibility with other global/regional LULC datasets for comparison and integration. Our LULC maps with more than 90% overall accuracy highlight the changes prominent at regional level, i.e., loss of forest cover in central and northeast India, increase of cropland area in Western India, growth of peri-urban area, and relative increase in plantations. We also found spatial correlation between the cropping area and precipitation, which in turn confirms the monsoon dependent agriculture system in the country. On comparison with the existing global LULC products (GlobCover and MODIS), it can be concluded that our dataset has captured the maximum cumulative patch diversity frequency indicating the detailed representation that can be attributed to the on-screen visual interpretation technique. Comparisons with global LULC products (GlobCover and MODIS) show that our dataset captures maximum landscape diversity, which is partly attributable to the on-screen visual interpretation techniques. We advocate the utility of this database for national and regional studies on land dynamics and climate change research. The database would be updated to 2015 as a continuing effort of this study.

Accurate nowcasting of short‐lived extreme weather events is essential for saving millions of lives and property. Traditional methods of nowcasting are majorly focused on extrapolation of precipitation derived from radar reflectivity data, which often fail to capture the initiation and decay of weather systems. Earlier studies have shown the ability of high‐resolution Numerical Weather Prediction models to better capture the structure and lifecycle of storms compared to data‐driven methods. However, the initial value problem of NWP makes it more challenging to be implemented for nowcasting applications. To handle such uncertainty from initial conditions, we have designed an NWP nowcasting system based on variational approach using Weather Research and Forecast (WRF) model. Since one of the major challenges of the variational methods in the nowcasting system is the choice of control variables used for generating background error statistics. Thus we have investigated the impact of control variable options on improving the skill of variational based NWP nowcasting system. The proposed nowcasting system was tested for a heavy rainfall event occurred over the Chennai city, India on 1^st December 2015, by assimilating Doppler Weather Radar (DWR) data using different control variable options in WRF‐ 3DVAR and 4DVAR based nowcasting system. Results show that control variables (CV) choices have a positive impact on 4DVAR analysis, particularly on radial velocity. Our results also indicate that assimilation of DWR data with zonal and meridional momentum CV in a 4DVAR system shows more than 30% improvement in precipitation forecast skill compared to the 3DVAR system.

Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES.

The South-West Indian Ocean (SWIO) region is prone to heavy rainfall, particularly during the passage of tropical cyclones. Given the topography of SWIO islands, orography plays an important role in the spatial distribution of precipitation. The ESPOIRS project was designed to investigate such dynamics in Reunion Island, Seychelles, and Madagascar using a mobile X-band radar. Reunion Island served as a testbed to evaluate X-band radar retrieved QPE using specific comparisons between several radar approaches and available rain gauges. This is the first study to use an X-band dual-polarization radar in the SWIO region. Our research focuses on the intense tropical cyclone event Batsirai in Reunion Island and shows the effectiveness of dual-polarization radar when compared to single-polarization radar in mitigating reflectivity attenuation. Both the Hitschfeld and Bordan and the philinear algorithms were employed and evaluated for this purpose. As study encountered challenges related to noisy and low-resolution differential phase (ϕ_dp) data, the pre-processing steps used to extract reliable ϕ_dp data from the observed measurements are detailed. Furthermore, two precipitation estimators were tested, R(Z) and R(kdp). Results show that R(Z) is sensitive to the attenuation correction method. Additionally, using the extracted ϕ_dp, empirical model for R(kdp) for Reunion Island is proposed and discussed. This model provided better results compared to the R(Z) estimates for heavy precipitation, which can be explained by the fact that kdp is directly linked to precipitation concentration and does not require attenuation correction. The findings highlight that the accuracy of the radar QPE is strongly influenced by local topography, which in turn governs local rainfall patterns, while the accuracy of QPE also depends on the type of precipitation.

In recent years, the application of compact and cost-effective deployable X-band polarimetric radars has gained in popularity, particularly in regions with complex terrain. The deployable radars generally use a radome constructed by joining multiple panels using metallic threads to facilitate easy transportation. As a part of the ESPOIRS project, Laboratoire de l'Atmosphère et des Cyclones has acquired an X-band meteorological radar with a four-panel radome configuration. In this study, we investigated the effect of the radome on the measured polarimetric variables, particularly differential reflectivity and differential phase. Our observations reveal that the metallic threads connecting the radome panels introduce power loss at vertical polarization, leading to a positive bias in the differential reflectivity values. To address the spatial variability bias observed in differential reflectivity and differential phase, we have developed a novel algorithm based on the discrete Fourier transform. The algorithm's performance was tested during an intense heavy-rainfall event caused by Cyclone Batsirai on Réunion Island. The comparative and joint histogram analysis demonstrates the algorithm's effectiveness in correcting the spatial bias in the polarimetric variables.