Forecasting rainfall has always been a challenging task due to its unpredictable nature. Nowcasting, or short-term forecasting, provides an effective solution by continuously updating with near-real-time input data to address the variability in rainfall systems. In this paper, we developed the Hydro-Estimator (HE) nowcast model using INSAT-3DR-derived HE precipitation intensity to provide precipitation nowcasts with a lead time of up to three hours. The model was developed within the PySTEPS framework, where motion field vectors from precipitation fields are generated using optical flow algorithms and then advected with an extrapolation method. We conducted a sensitivity study of the model using three different optical flow algorithms: Lucas-Kanade, Proesman, and DART. The analysis revealed that while Proesman achieved the highest skill scores, its processing limitations led us to select the Lucas-Kanade algorithm for further analysis. We validated the model by computing various skill scores, including a Fractional Skill Score (FSS) of 0.9, a Heidke Skill Score (HSS) of 0.58, a Gilbert Skill Score (GSS) of 0.47, and a Mean Absolute Error (MAE) of 0.6 mm/hr for precipitation forecasts over 3 months: April, May, and June 2023. We also analyzed diurnal variation trends in model accuracy, showing improved performance during the initial stages of the forecast. The paper presents case studies of heavy precipitation events in Northeast India on June 17, 2022, and Kerala on May 1, 2022, demonstrating the model’s capability to accurately predict high-intensity rainfall. The model achieved errors of less than 10

Frequent dust storms during the pre-monsoon season in northwest India significantly impact weather, air quality, and health, necessitating accurate predictions. This study demonstrates the first-time assimilation of aerosol optical depth (AOD) data from INSAT-3D into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), alongside previously assimilated AOD data from MODIS AQUA. Focusing on an extreme dust storm over the Indo-Gangetic Plain (IGP) from June 12-17, 2018, identical experiments were conducted with and without AOD assimilation. The Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) method was utilized for data assimilation, with simulations performed for 144 h, adjusting for uncertainties in the model's background error covariance using different inflation factors. The model's performance was validated using the ECMWF Atmospheric Composition Reanalysis 4 (EAC4) product. The assimilated AOD analysis significantly improved bias, root mean square difference (RMSD), and correlation from [0.47, 0.71, 0.40] to [0.07, 0.38, 0.80]. The 24 h AOD forecast improved by approximately 56% and 30% with INSAT-3D and MODIS AOD assimilation, respectively. AOD assimilation directly impacted cloud properties and radiative forcing, enhancing the 24 h forecast of net shortwave downward flux (SWD) by approximately 51 W m-2 and 24 W m-2 for INSAT-3D and MODIS, respectively. These changes also affected vertical levels up to 5 km, modifying the rainwater mixing ratio (QRAIN) and surface rainfall forecasts. This study shows that integrating INSAT-3D AOD data provides substantial improvements in aerosol forecasts, crucial for making reliable and accurate forecasts of severe dust storms and air pollution episodes over the Indian region.

The Global Climate Observing System has listed precipitation, whether liquid or solid, as the most important essential climate variable directly affecting humans. To study the effect of climate on precipitation, its 3-dimensional distribution needs to be examined. While the horizontal distribution of precipitation is available with reasonable accuracies from satellite-borne passive instruments, the most accurate vertical (and surface) distribution of precipitation is provided by precipitation radars. The first precipitation radar, operating at a single frequency, Ku-band, was placed onboard the Tropical Rainfall Measuring Mission (TRMM) satellite in 1997. This was followed by a more advanced dual-frequency precipitation radar on board the Global Precipitation Measurement (GPM) Core Satellite in 2014. Over the last 27 years, the continuity of precipitation radar measurements has led to numerous discoveries in the science of clouds and precipitation. In this review article, precipitation radars such as those flew onboard TRMM, GPM-Core, and CloudSat are discussed, including their measurement methods, strengths, shortcomings, and major discoveries and applications. Additionally, this article explores the potential for rain measurement from wind scatterometers, which were not originally designed for precipitation measurement. However, they can be used to fill the sampling gaps in the global precipitation measurements.

The ability of numerical weather prediction models to accurately predict extreme weather events, such as thunderstorms marked by heavy rainfall and lightning activities, has consistently been of great importance for human life. The objective of this study is to assess the long-term reliability of the European Centre for Medium-Range Weather Forecasts Reanalysis version 5 (ERA5) rainfall in comparison to the Indian gauge-adjusted Global Satellite Mapping of Precipitation (GSMaP_ISRO) rainfall at the time of lightning flashes measured by the Lightning Imaging Sensor (LIS) onboard the Tropical Rainfall Measuring Mission satellite over the Indian region during the years 2001-2014. This analysis will provide valuable insights into the intricate relationship between lightning flashes and precipitation under various terrain conditions (low, mid, or high), across oceanic regions (Bay of Bengal, Arabian Sea), and during different monsoon phases (normal, active, or deficit). According to a prolonged examination of LIS data, April-June accounts for similar to 50% of the total flashes, with the largest number of flashes occurring over the Himalayan and the northeastern part of India. According to hourly GSMaP_ISRO rainfall, the most substantial lightning-associated rainfall happens an hour prior to lightning flash (T - 1) and within three hours after (T + 3), indicating a robust correlation between heavy rainfall and lightning activity during this time frame. The rainfall in the ERA5 reanalysis misses the intensity as well as duration of the peak rainfall at the time of lightning flashes. Furthermore, the ERA5 reanalysis rainfall depicts under (over)-estimation of rainfall in plain (orographic) regions. The underestimation of ERA5 rainfall is very pronounced over the Indian Ocean and Bay of Bengal regions, mainly between flash time (T) to two hours after the flash time (T + 2). The results indicate that there is a requirement for additional enhancements in the ERA5 reanalysis rainfall for lightning occurrences.

Hydro-Estimator (H-E) method-based rainfall products from INSAT-3DR are operationally available at the web portal of the India Meteorological Department (IMD). These high spatial (4 km) and temporal (30 min) resolution products not only play a significant role in monitoring monsoonal rain over India and the surrounding ocean but also show importance in assessing several meteorological events such as tropical cyclones, floods, cloud bursts and thunderstorms. Thus, a comparison of these rain products with in situ observations and other satellite data is important to evaluate their performance. This study details about the validation procedures and discusses the performance of H-E technique-based rain products from INSAT-3DR over India and surrounding oceans during the Indian summer monsoon (June–September) 2020. The performance of products is evaluated using two sets of data (1) in situ network of IMD rain gauges over India and (2) Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) products. Results show that H-E well captures the spatial distribution of rain as depicted by rain gauges and IMERG. H-E shows a probability of detection of > 95

During September 2021, a tropical cyclone (TC) named Gulab formed in Bay of Bengal (BoB) region of North Indian Ocean (NIO) and by nature, it was quite an unusual one as it crossed the Indian subcontinent and re-emerged as TC Shaheen in Arabian Sea (AS) which made landfall at the coast of Oman. These two cyclones were unique from two aspects: (i) formed in active southwest monsoon period in the month of September, which is a very rare event in NIO and (ii) formation of TCs in BoB and its re-emergence in AS, in the form of a cyclonic storm after crossing the Indian continent is uncommon. Along with these two, another exception was landfall at Oman coast, which is again very rare. The different large scale atmospheric and oceanic parameters, during development of TC Gulab in BoB and its re-emergence as TC Shaheen in AS are analysed using the reanalysis data and satellite derived products. The results suggest that vertical wind shear (VWS) during genesis of TC Gulab was unusually low and favourable for cyclonic storm development in BoB. The middle level relative humidity over central India was also high (positive anomaly), which supported remnants of TC Gulab to survive as a low-pressure weather system in land region. Later, it evolves into as TC Shaheen in AS, and due to favourable Sea Surface Temperature and Oceanic Heat Content it further intensifies to a very severe category cyclonic storm.

A Very Severe Cyclonic Storm ‘Yaas’ developed over the Bay of Bengal (BoB) on 23 May 2021 and crossed over the Odisha coast on 26 May with maximum sustained wind speed of 75 kts. Herein, a pathway has been developed and exemplified for ‘Yaas’ through three-stage cyclone-induced hazard tracking. Days before the cyclone formation, cyclone genesis potential parameter, sea surface temperature (SST) (> 30 °C) and tropical cyclone heat potential (anomaly of 40–80 kJ/cm 2 ) indicated a strong possibility of cyclogenesis in the BoB. A Lagrangian advection model used for its track prediction with 24-h lead-time provided an accuracy of ~ 19 km and ~ 6 h in its landfall location and time. Further, intensity prediction was done using numerical weather prediction model. Geostationary satellites, INSAT-3D/3DR, were used to visualize cyclone structure. Passing of cyclone had its reverbarations in oceans, which are observed in SST drop of ~ 3 °C, salinity and density increase by ~ 1 psu and ~ 2 kg/m 3 , respectively. During the period, 23–26 May 2021, the Ekman suction velocity and chlorophyll concentration were found significantly high at ~ 5 m/day and > 0.5 mg/m 3 , respectively. Forecast of storm surge was found to be between 3.5 and 4 m at coastal locations. Significant wave height was found to be 5.5–9.2 m. The coastal inundation forecast for 24 May 2021 provided its quantitative maximum inland extent. Finally, loss of the crop, fishery and forest areas by strong winds and inundation/ingress of saline water associated with storm surge were examined using SAR and optical data.

Satellite-based Nowcasting methods are of utmost importance, especially in the Indian monsoonal region, which experiencing hetero-geneity in rainfall structures. In this context, the INSAT-3D/3DRsatellite-based model for Nowcasting of Extreme orographic Rain occurrences (NETRA) for the Western Himalayan region was developed, and was later implemented for the entire Indian landmass, provides near real-time alerts for heavy rainfall events through the web portal https://www.mosdac.gov.in with an update frequency of half an hour. The societal application of this model is tightly linked with its validation over different seasons and years. In the present paper, we have validated two years of rainfall alarm provided by the model with the help of Quantitative Precipitation Estimate (QPE) using the INSAT-3D/3DR Hydro-Estimator (H-E) product. Regarding the frequency of rainfall occurrences, INSAT-3D/3DR satellite rainfall products excelled in capturing the rainfall pattern both spatially and temporally. While the QPE correlation is 0.1, for heavy rainfall events, H-E demonstrates better skill and correlation (r > 0.7) in detecting heavy rainfall with an accuracy of 20 mm and good pattern matching with actualrainfall. Itis observed that for the months of May to October, the probability of detection is quite high (-95%) with a low false alarm rate. In case of extreme events also, the algorithm performs quite well as is shown by extreme dependency indices like the (Extreme Dependency Score)EDS, (Extreme Dependency Index) EDI, and (Symmetric Extreme Dependency Index) SEDI. For the Indian Monsoonal region, which experiences a significant loss of life and property due to heavy rainfall events, this satellite-based nowcasting alert system may have a substantial societal impact.& COPY; 2023 COSPAR. Published by Elsevier B.V. All rights reserved.

Importance of continuous monitoring of rainfall from Indian geostationary satellites INSAT-3D/3DR over India and surrounding oceans is not only necessary to monitor the performance of the monsoon, but is extremely useful in disaster mitigation support system as well. It has been observed that rain products from INSAT-3D/3DR often show overestimation in certain areas with unrealistic instances of intense rain, which need a closer examination. With this objective, the present study is carried out, which suggests that a non-linear calibration equation for thermal infrared brightness temperatures (Tb) of INSAT-3D/3DR is more appropriate than routinely applied linear equation for rainfall estimation. The study elaborates a new calibration correction method and addresses the requirement of such calibration. To evaluate the performance of proposed method, Hydro-Estimator (HE) operational rainfall products from INSAT-3D/3DR are re-estimated from the newly calibrated Tbs during Indian summer monsoon (June-September), 2020. The re-estimated HE rain not only substantially reduces the instances of overestimation, but also show remarkable improvement in capturing rainfall distribution over India and surrounding ocean. Improvement in RMSE from 8.56 mm/hr to 3.23 mm/hr, when compared with the concurrent IMERG (Integrated Multi-satellitE Retrieval for Global Precipitation Mission (GPM)) rain, shows reliability of the new method. Furthermore, the re-estimated HE rain compares better with in-situ rain gauges observations than the operational HE product with RMSE (bias) reduced from 85.43 mm/day (30.23 mm/day) to 40.02 mm/day (17.07 mm/day), and correlation improved from 0.28 to 0.32. The significance of the new calibration method is also recognized in the rain monitoring over complex hilly terrain and coastal regions of India.

Hydro-Estimator method-based rain retrieval technique, successfully providing rain estimates from INSAT-3D/3DR, is modified to provide rain from Sea Surface Temperature Monitor-1 (SSTM-1) payload aboard the upcoming EOS-6 mission of Indian Space Research Organisation. Pre-launch study utilises moderate resolution imaging spectroradiometer instrument aboard Terra and Aqua satellites as a proxy to SSTM-1. Performance of estimations is evaluated with rainfall products from Global Precipitation Measurement (GPM) mission. The results indicate that SSTM-1 can capture not only the global rainfall distribution but also has the potential to resolve rainfall structure of tropical cyclones. Quantitative comparison of the measured rainfall with IMERG (Integrated Multi-satellitE Retrieval for GPM) rain shows a root mean square error of 3.02 mm hr−1, bias of 1.06 mm hr−1 and correlation of 0.48. Present algorithm provides a probability of detection of >65

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955–1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95

Tropical cyclone Ockhi (2017) had a very unusual track with unprecedented rapid intensification (RI) and dynamical evolution. During its early phases, a C-band polarimetric Doppler Weather Radar (DWR), installed in Thiruvananthapuram, Kerala, continuously monitored it. The present study focuses on the observations and analysis of the extremely tall precipitation features overshooting Troposphere, called hot towers, prior to and during the RI stages of Ockhi. The maximum height of such features within the inner core exceeded 20 km, with the maximum observed areal coverage over 800 km2. The peak convective burst (CB) activity was seen 9–12 h prior to the onset of RI of Ockhi. The differences among the Maximum Sustained Wind speed (MSW) and Mean Sea Level Pressure (MSLP) obtained from various best track data sets were in the range 5–25 knots and 1–28 hPa, respectively, with the maximum difference seen during the CB phase. Ockhi also exhibited differential reflectivity enhancements, often collocated with the hot towers. Further, we investigated upper ocean Tropical Cyclone Heat Potential (TCHP), derived from the Global Temperature and Salinity Profile Programme (GTSPP) profiles. During the peak CB activity, the TCHP was around 121 kJ cm−2, almost 50 kJ cm−2 higher than the climatological mean of that area. Significant drop in TCHP was observed in the next 24 h concurrent with the RI. The radar-based observations of the hot towers in conjunction with the TCHP and other relevant ocean parameters could prove to be valuable indicators in predicting the RI phase of the storms and gaining a better insight for estimating the damage potential of the cyclones.

Hydro-Estimator (H-E) is an operational algorithm developed by National Oceanic and Atmospheric Administration to provide high-resolution (pixel-scale) rainfall measurements from geostationary satellites. The success of H-E offers a pathway to implement this retrieval technique to estimate rain from Indian geostationary satellite observations (Kalpana-1, INSAT-3D/3DR). H-E shows great potential in estimating rain over most parts of India and surrounding oceans but displays weakness over the hilly terrains. To overcome this weakness, the reason behind the discrepancies in the original version of H-E are explored and modifications are carried out accordingly. Suggested modifications for H-E mainly focus on to addressing dry bias in Numerical Weather Prediction model fields and corrections for orographic and warm rain. Rain products from the modified version of H-E (referred to as HEM) are made operational from Kalpana-1 and INSAT-3D/3DR satellite observations. The present paper describes the need for a modified version of H-E to estimate rain over the Indian subcontinent. In addition, the paper elaborates on the modification procedures and describes the importance of their implementation for rain monitoring particularly over the hilly terrain of India. Performance of HEM rain products with in situ surface observations and other satellite products supports the efficacy of applied modifications.

The percentage occurrence of titled deep convective clouds (DCCs) is rarely examined but it is crucial to overcome the challenges of precise measurement of rainfall intensity and location during dreadful weather events. Present study is carried out with this objective and analysed 2451 globally distributed DCCs identified by Ku-band (13.6 GHz) radar reflectivity profiles of Dual-frequency Precipitation Radar aboard Global Precipitation Measurement mission under varying conditions of environmental vertical wind shear (EVWS) during 2018. Globally (tropics) 60.68% (55.29%) of DCCs that produced intense rain (>50 mm/hr) are strengthened under strong EVWS and thus may have possibility of vertically tilted structure. Substantial 60.49% of DCCs that developed under strong EVWS grow deeper (up to 12 km) in atmosphere. Statistics on the global distribution of DCCs developed in strong EVWS with different categories of rainfall and cloud height would be an important source of information for precise assessment of rainfall distribution, especially during extreme weather. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.

Tropical cyclones (TCs) are primarily characterised by strong winds and torrential rain. Often, strong environmental vertical wind shear (EVWS) displaces the location of TC’s eye at the cloud level from that near the surface and modulates its rain structure significantly. Thus, knowledge of EVWS and its role in displacing TC’s eye at vertical level is important to resolve rain structure of TC. Present study examines the role of EVWS in displacing TC’s eye location and associated rainfall distribution by analysing 27 TCs over global ocean basins in the Northern Hemisphere from October 2016 to December 2019. Study utilizes Indian Space Research Organisation’s Scatsat-1 Ku-band (13.53 GHz) scatterometer derived global ocean surface vector winds, National Oceanic and Atmospheric Administration provided global merged Infra-Red imagery product, U and V components of winds from Global Forecast System, and rainfall from Integrated Multi-satellitE Retrieval for Global Precipitation Measurement mission. High intensified TCs that developed in strong EVWS (> 10 ms−1) show eye dislocation of > 30 km. However, this locational displacement is negligible for severe TCs strengthen in weak EVWS (≤ 10 ms−1). Rain structure of TC is controlled by the direction of shifted eye, particularly when a severe TC exhibits significant shift of > 30 km. On the other hand, rainfall distribution is nearly uniform around the TC’s eye that has a marginal dislocation of < 15 km. Findings of this study are not only important to understand rain structure of TC but also beneficial for saving coastal lives and properties that are often impacted by torrential rain from TC.

An assessment of the performance of CYGNSS operational wind products is done against a multitude of reference datasets which include in-situ wind speed from Indian National Centre for Ocean Information Services (INCOIS) network of buoys (hereafter, INCOIS-buoys) in the Indian Ocean, SCATSAT-1 operational wind products and cyclone best track data from India Meteorological Department (IMD). The validation study is done for CYGNSS data version 2.1 for the period 17th March 2017 till 31st December 2017. The performance of the operational wind products is examined through a two-tier approach-first one covering low-to-moderate wind regimes (less than or equal to 15 m/s) where in-situ data from INCOIS-buoys and rain-free SCATSAT-1 products are used. While the former gives an idea of CYGNSS winds in the Indian Ocean basin, the latter can be treated as global reference data set. In the second approach, high wind (greater than15 m/s) performance of CYGNSS winds are evaluated on a case study basis during some of the tropical cyclones (TC) of 2017. The first approach gives good accuracy of minimum variance estimator (MVE) wind speed products in terms of standard deviations of 1.85 m/s (INCOIS-buoys) and 2.07 m/s (SCATSAT-1). MVE is the best product amongst all the five different operational wind products viz. MVE, FDS-NBRCS, FDS-LES, YSLF-NBRCS, YSLF-LES of CYGNSS. Time series analyses of high wind performance during the TC-Mora and Ockhi show better sensitivity of young sea wind products especially YSLF-NBRCS compared to YSLF-LES. However, the fully developed wind product, FDS-NBRCS shows equally good sensitivity of wind speed. (C) 2022 COSPAR. Published by Elsevier B.V. All rights reserved.

Abstract This study aims to create a 21‐year, high spatiotemporal resolution Global Satellite Mapping of Precipitation (GSMaP) rainfall product adjusted by rain gauge measurements over the Indian mainland and highlighted the importance of the Indian Meteorological Department (IMD) daily gridded rainfall to generate gauge adjusted GSMaP rainfall products over Indian landmass. The targeted resolutions of the GSMaP are hourly and 0.1° × 0.1°. The National Oceanic and Atmospheric Administration Climate Prediction Center daily gauge analysis (0.5° × 0.5°) and IMD daily gridded rainfall (0.25° × 0.25°) were utilized to generate long‐term rainfall products, GSMaP_CPC and GSMaP_IMD rainfall, respectively. After preliminary verification of the GSMaP_CPC and GSMaP_IMD rainfalls with IMD gauges, these rainfall products are evaluated for the Indian Summer Monsoon periods of 2000–2020 with comparisons of other gauge adjusted rainfall products such as the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement final‐run. The results suggest GSMaP_IMD has a smaller root‐mean‐square difference (RMSD) and higher correlation than GSMaP_CPC, evaluated against independent rainfall products. In the 3‐hour mean analysis with spaceborne precipitation radar data, it is found that the value of RMSD decreases in GSMaP_IMD with respect to GSMaP_CPC throughout the day. The statistics against the hourly dense gauge network suggests that the GSMaP_IMD is more effective in capturing large spatiotemporal rainfall variation. Thus, validation results with the independent sources suggest that GSMaP_IMD rainfall generally improved over GSMaP_CPC rainfall. These improvements are significant in orographic regions with high rainfall amounts, mainly the western Ghats and northeastern parts of India.

Recently, calibration methods of the ground-based weather radars with reference to spaceborne precipitation radars, such as the Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR), have attracted lot of attention, because of their cost effectiveness and near real-time inputs. This paper describes evaluations for radar reflectivity factors (Z-factors) of S-band and C-band ground-based radars over the Indian region with reference to the GPM/DPR observations. By statistical analyses, using a matching program between the ground-based radar and the GPM/DPR, the S-band ground-based radar measurements at Cherrapunji, Meghalaya, India tended to be significantly underestimated. In this case, a mean bias in the Z-factors was 13.203dBZ was computed. On the other hand, the C-band ground-based radar at Shar, Sriharikota, India was well-corresponded with the GPM/DPR and the mean bias tended to be small (0.688 dBZ).