Misclassification of convective precipitation pixels as stratiform precipitation over the Tibetan Plateau (TP) in summer by the precipitation type classification algorithm (CSF algorithm) of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) has been reported in several studies. To investigate the cause of this misclassification, we validated the CSF algorithm using Doppler radar observations of mesoscale wind divergence. The case studies revealed that the algorithm performs well for typical isolated deep convection, whereas embedded convection within stratiform precipitation was misclassified; the divergence profile clearly indicated convective characteristics, although the CSF algorithm classified it as stratiform. This provides the first evidence of misclassification of precipitation type over the TP in summer based on mesoscale wind divergence. We found that the misclassification is primarily caused by the thresholds of the Peakedness function in the H-method, and that reclassification aimed at enhancing the detection of embedded convection and weak convective precipitation led to a more reasonable convective precipitation fraction.

Abstract Lightning flash rate densities measured by a series of low-Earth-orbiting NASA lightning sensors are used to explore lightning hotspots, how they vary with time of year and time of day, the diurnal cycle, annual cycle, and interannual variability. New gridded datasets are available from the Optical Transient Detector (OTD; April 1995–March 2000), Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS; January 1998–April 2015), and International Space Station (ISS) LIS (March 2017–November 2023). Taking all these lightning measurements together, the highest 0.1° flash rate densities are over Lake Maracaibo in northern Venezuela, as reported previously. Other locations emerge as having the highest flash rate densities when limited to smaller subsets of the data. Maxima are located on either side of the Democratic Republic of Congo according to ISS LIS (maximum in the west) and OTD (maximum in the east). ISS LIS also records higher values over mountain slopes in Colombia than over nearby Lake Maracaibo. Summertime flash rate densities are almost as high over northern Pakistan. At the peak of the diurnal and annual cycles, July afternoons over Cuba have almost as much lightning as late nights in September over Lake Maracaibo. Flash rates peak around 1600–1700 LST and gradually decrease through the evening over most continental regions and peak in the summer with little lightning measured during winter. Time series of flash rates show Africa contributing most to the interannual variability, particularly with the sharp downturn in lightning from mid-2019 through 2022. Significance Statement Lightning data from three NASA missions beginning in 1995 and finishing in 2023 enable a variety of analyses. From the full dataset, lightning maxima are noted in northwestern South America and central Africa. The particular location with the most lightning depends on which period of time is examined and how large an area is considered. Lightning peaks in the late afternoon in summer for most places over land. Summer afternoon flash rate densities over Cuba, Florida, and western Mexico rival the peak activity in South America and Africa, but for shorter periods of time. Many regions, especially Africa, had below-normal flash rates from mid-2019 through 2022. Year-to-year variability in the near-global flash rates is largely driven by variability in Africa.

In this study, Wavelet Long Short-Term Memory (WLSTM) and Wavelet Convolutional Neural Network Long Short-Term Memory (WCNNLSTM) are two Artificial Intelligence (AI) methods that are applied for the subseasonal-to-seasonal (S2S) streamflow forecasting in seven rivers in Colorado. Daily and monthly meteorological variables from 1992 to 2022 were collected from Colorado’s Mesonet (CoAgMet) as input data. Daily and monthly discharge data were acquired from the United States Geological Survey (USGS) as output data. In addition, precipitation data were gathered from the National Aeronautics and Space Administration (NASA) global precipitation network, including the Tropical Rainfall Measuring Mission (TRMM) and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). WCNNLSTM-IMERG had the best accuracy in forecasting streamflow in Colorado due to its finer spatial resolution. The findings of this study will be beneficial for modelers and dataset developers in evaluating the performance of NASA products in practice for S2S streamflow forecasting.

Abstract The radar “first echo” appears in a fresh growing cloud when hydrometeors grow to large enough sizes to be detected by radar. Using 26‐year observations by the Ku band radars onboard the Global Precipitation Mission and the Tropical Rainfall Measurement Mission satellites, isolated pixels with detectable radar echoes at altitude and without precipitation at the surface are identified as candidate “first echoes.” In general, the appearance of the “first echoes” show three altitude‐related modes in the tropics. The shallow mode at 1.5–2 km is mainly from the growth of warm rain from rapid coalescence. The mid mode between 0°C and −7°C is found mainly over land and coastal regions, that can be explained by the enhanced radar reflectivity by ice particles through heterogenous ice nucleation at relative warmer temperatures, followed by active riming. The deep mode between −12°C and −25°C is frequently found over both land and ocean. Around 12% of isolated echoes, mainly over desert regions, could be the remnant of dissipating precipitation, like virga. Cloud base height and total column water vapor derived from ERA5 reanalysis are found positively related to the “first echo” height. The mid mode first echoes are more likely found in a moist (ice‐supersaturated) environment, a favorable condition for crystal growth following heterogeneous ice nucleation. While the shallow mode over ocean tends to have more sea salt aerosols, large smoke and sulfate aerosol concentrations are often associated with the mid mode, and large dust aerosol concentrations are often involved with the deep mode.

The study focuses on the Oued Ouahrane Ras in the Cheliff Basin, located in north-central Algeria. Accurate prediction of daily runoff is essential for effective water resource management, flood control, and agricultural planning. This study evaluates the performance of three advanced deep learning models—(a) Recursive Feature Elimination with Gated Recurrent Unit–Bidirectional Long Short-Term Memory (RFE-GRU-BiLSTM), (b) RFE with Gated Recurrent Unit–Convolutional Neural Network (RFE-GRU-CNN), and (c) RFE with Convolutional Neural Network–GRU–BiLSTM (RFE-CNN-GRU-BiLSTM)—for forecasting daily runoff in the study region. These models incorporate Recursive Feature Elimination (RFE) for dimensionality reduction and SHapley Additive exPlanations (SHAP) for post hoc feature importance analysis. Two distinct datasets were employed for model training and evaluation: satellite-based precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and ground-based in-situ hydrological observations, covering the period from 1998 to 2012. The results indicate that the RFE-GRU-CNN model achieved the best performance on the TRMM dataset, yielding a minimum RMSE of 3.61 in model M8. Conversely, the RFE-CNN-GRU-BiLSTM model produced superior outcomes for the in-situ dataset, with an RMSE of 3.815. SHAP analysis identified the lagged discharge input (Q t − 2 ) as a key predictor across models, highlighting the catchment’s short-term memory, reflecting rapid runoff persistence controlled by active storage components. Although the implementation of RFE increased the average training time by approximately 43.37 s, the additional computational cost remained within acceptable limits. For example, training durations ranged from 1106.54 to 2425.52 s for the TRMM dataset and from 1224.71 to 1955.10 s for the in-situ dataset. These findings underscore the effectiveness of hybrid deep learning architectures in daily runoff prediction and emphasize the critical roles of both feature selection and dataset type in optimizing model performance. • Hybrid deep learning enhanced runoff prediction using RFE and SHAP insights. • RFE-GRU-CNN achieved RMSE = 3.61 and NSE = 0.893 on TRMM rainfall data. • RFE-CNN-GRU-BiLSTM attained RMSE = 3.79 and R = 0.94 with in-situ data. • SHAP identified lagged discharge (Qt−2) as the dominant runoff predictor. • RFE improved model accuracy with minimal increase in training time.

Climatic variation and land-atmosphere interaction affect the natural resources, particularly in the arid/ semi-arid regions. The present research work has been carried out in the Sabarmati basin to quantify Land surface temperature (LST), factors and their impact on hydrometeorology and biophysical parameters. LST and indices were estimated using Landsat 5, 7 and 8 images on a decadal basis using thermal and spectral bands. The monthly TRMM (Tropical Rainfall Measuring Mission) data was used to analyse the rainfall pattern in the study area. Different indices such as normalised difference vegetation index (NDVI), normalised difference moisture index (NDMI), and normalised difference water index (MNDWI) were calculated. Results suggests that from 1990 to 2020, the maximum LST increased by 4.36°C and the minimum LST increased by 5.90°C. In 2020, high LST zones (areas above the mean LST) increased by 8085.51 sq. km compared to 1990. The study finds a negative correlation coefficient of LST with Vegetation Index (NDVI) (r = −0.41), MNDWI (r = −0.54), and Moisture Index (NDMI) (r = −0.69), whereas a moderate positive correlation exists with elevation (r = 0.33). The surface water body area increased significantly in 2020 compared to 1990 due to the formation of new reservoirs and water channels, as well as increased rainfall during the 2019 monsoon season in the river basin. This study provides valuable insights into climate change impacts, aiding urban planning. It also emphasizes the importance of preserving green spaces and water bodies and expanding vegetation in barren lands to combat LST intensification. • Tempo-spatial variation of Land surface temperature (LST), and its impact on hydro-meteorological variables and Vegetative indices for Sabarmati Basin were computed. • Various satellite data like Landsat, ASTER DEM and TRMM were used in this study. • In the last 30 years (1990–2020), the maximum and minimum LST of the different regions increased by 4.36 ℃ and 5.9 ℃, respectively in the basin. • A negative correlation coefficient of LST with vegetative indices and a moderate positive correlation with elevation were observed. • The study emphasizes the need to preserve water bodies and expand green spaces in barren lands to combat LST intensification and sustainable development, especially in semi-arid regions.

Abstract. Understanding the characteristics of the rain cell, the most basic unit in the natural precipitation system, is helpful in improving the cognition of the precipitation system. In this study, based on the merged precipitation profile data, reflectance and infrared data, and microwave brightness temperature data observed by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR), visible and infrared scanner (VIRS) and TRMM microwave imager (TMI), rain cells were identified in the PR swath. For the identified valid rain cells, two fitting methods (the minimum bounding rectangle (MBR) and the best fit ellipse (BFE)) were applied to fit the external frame. Then, the geometric and physical parameters of rain cells were also calculated. By analyzing the geometric parameters (length, width, height, and so on) and physical parameters (rain rate, visible reflectance and thermal infrared brightness temperature from cloud top, and microwave brightness temperature from cloud column) of two rain cells (weak rain cell and strong rain cell), the results indicate that the strong rain cell is filled with deep convective precipitation and has low thermal infrared brightness temperature at the cloud top, while the weak rain cell is mainly characterized by stratiform precipitation with low rain rate. Compared to the BFE method, the area of the external frame calculated by the MBR method is generally larger. The filling ratio of the BFE method is slightly higher than that of the MBR method. In general, the results indicate that the rain cell definition parameters using the two fitting methods are reasonable and intuitive. The data used in this paper are freely available at https://doi.org/10.5281/zenodo.15387988 (Wu and Fu, 2025).

Abstract New gridded lightning climatology datasets are compiled and released from a series of low Earth orbit NASA lightning sensors: the Optical Transient Detector (OTD), Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS), and International Space Station (ISS) LIS. OTD collected data during April 1995 – March 2000; TRMM LIS during December 1997 – April 2015; ISS LIS during March 2017 – November 2023. From these, gridded lightning datasets depict the near-global distribution of annual mean flash rate on a 0.1° grid. Hourly mean and monthly mean averages allow examination of diurnal and annual cycles, although some smoothing of the released data is warranted. This paper describes the released datasets, thoroughly examines data quality and sources of bias, and makes recommendations for potential users. Examples from these datasets are shown, with well-known lightning hotspots in central Africa, northwestern South America, and along the base of the Himalayas. Comparison of how often thunderstorms are observed to those storms’ conditional mean flash rates shows some striking differences, with frequent storms but relatively low per-storm flash rates over the Maritime Continent. Conversely, storms are less frequent but produce higher flash rates over central North America, subtropical South America, Pakistan, and the coasts of northern Australia.

Abstract. This study provides a comprehensive evaluation of eight high-spatial-resolution gridded precipitation products in semi-arid regions of Tamil Nadu, India, focusing specifically on Coimbatore, Madurai, Tiruchirappalli, and Tuticorin, where both irrigated and rainfed agriculture is prevalent. The study regions lack sufficiently long-term and spatially representative observed precipitation data, which are essential for agro-hydrological studies and better understanding and managing the nexus between food production and water and soil management. Hence, the present study evaluates the accuracy of five remote-sensing-based precipitation products, namely the Tropical Rainfall Measuring Mission (TRMM), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN CDR), the CPC MORPHing technique (CMORPH), the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), and three reanalysis-based precipitation products, namely the National Centers for Environmental Prediction Reanalysis 2 (NCEP2), the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 Land (ERA5-Land), and the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), against the station data. Linearly interpolated precipitation products were statistically evaluated at two spatial (grid and district-wise) and three temporal (daily, monthly, and yearly) resolutions for the period 2003–2014. Based on overall statistical metrics, ERA5-Land was the best-performing precipitation product in Coimbatore, Madurai, and Tiruchirappalli, with MSWEP following closely behind. In Tuticorin, however, MSWEP outperformed the others. On the other hand, MERRA2 and NCEP2 performed the worst in all the study regions, as indicated by their higher root mean square error (RMSE) and lower correlation values. Except in Coimbatore, most of the precipitation products underestimated the monthly monsoon precipitation, which highlights the need for a better algorithm for capturing convective precipitation events. Moreover, the percent mean absolute error (%MAE) was higher in non-monsoon months, indicating that product-based agro-hydrological modelling, like irrigation scheduling for water-scarce periods, may be less reliable. The ability of the precipitation products to capture extreme-precipitation intensity differed from the overall statistical metrics, where MSWEP performed the best in Coimbatore and Madurai, PERSIANN CDR in Tiruchirappalli, and ERA5-Land in Tuticorin. This study offers crucial guidance for managing water resources in agricultural areas, especially in regions with scarce precipitation data, by helping to select suitable precipitation products and bias correction methods for agro-hydrological research.

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R2 value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions.

This study provides a comprehensive comparison between Global Precipitation Measurement (GPM) Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) measurements through analysis of collocated precipitation at the 19 GHz footprint scale for pixels during hemispheric summer seasons (JJA for Northern Hemisphere and DJF for Southern Hemisphere). Precipitation pixels exceeding 0.2 mm/h are categorized into convective, stratiform, and mixed types based on DPR classifications. While showing generally good agreement in spatial patterns, the GMI and DPR exhibit systematic differences in precipitation intensity measurements. The GMI underestimates convective precipitation intensity by 13.8% but overestimates stratiform precipitation by 12.1% compared to DPR. Mixed precipitation shows the highest occurrence frequency (47.6%) with notable differences between instruments. While measurement differences for convective precipitation have significantly improved from previous Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and Precipitation Radar (PR) estimates (62% to 13.8%), the overall difference has increased (from 2.6% to 12.6%), primarily due to non-convective precipitation. Latitudinal analysis reveals distinct precipitation regimes: tropical regions (below ~30°) produce intense convective precipitation that contributes about 40% of total precipitation despite lower frequency, while mid-latitudes (beyond 30°) shift toward stratiform-dominated regimes where stratiform precipitation accounts for 60–90% of the total. Additionally, geographical variation in GMI-DPR differences shows a see-saw pattern across latitude bands, with opposite signs between tropical and mid-latitude regions for convective and stratiform precipitation types. A fundamental transition in precipitation characteristics occurs between 30° and 40°, reflecting changes in precipitation mechanisms across Earth’s climate zones. Analysis shows that tropical precipitation systems generate approximately three times more precipitation per unit area than mid-latitude regions.

Assessing landslide susceptibility is essential for reducing disaster risks and facilitating informed decisionmaking.However, securing geospatial data poses challenges due to potential data tampering and unauthorized access.This study presents a pioneering blockchain-based Multi-Criteria Decision-Making (MCDM) framework that combines the Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to enable automated, secure, and real-time geospatial data analysis.Unlike traditional methods that depend on centralized storage and manual calculations, this innovative approach employs blockchain smart contracts for immutable weight storage, automated AHP-TOPSIS computations, and transparent validation of geospatial data.These self-executing contracts guarantee unbiased decision-making without human oversight, thereby boosting trust and reliability.Landslide susceptibility data were derived from multiple sources: Shuttle Radar Topography Mission (SRTM) for elevation data, Digital Elevation Model (DEM) for terrain morphology, Tropical Rainfall Measuring Mission (TRMM) for rainfall intensity, and Sentinel-2 imagery for land cover classification.These datasets enable a comprehensive evaluation of topographic, climatic, and environmental factors influencing landslides.The experimental results demonstrate an F1-score of 90.7% and an accuracy of 92.5%, outperforming conventional methods in both precision and efficiency.This blockchain-integrated Multi-Criteria Decision Making (MCDM) framework presents an innovative approach to decision-making in disaster mitigation, facilitating real-time validation, securing geospatial data, and minimizing computational inefficiencies.

High-resolution, satellite-retrieved precipitation products are useful input data for hydrological predictions and water resources management, especially in developing countries where the availability of ground-based rainfall measurements with high spatial coverage is very limited. This research explores the temporal variability of rainfall, crucial for understanding hydrology and water resource management, particularly in vulnerable regions like Bihar, India. satellite-based precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM) was evaluated using observed rainfall at meteorological station using key statistical parameters and also carried long-term trend analysis during 2000–2023 by applying Mann–Kendall Test and estimating Standardized Anomaly Index (SAI). The results reveal that, Rainfall was underestimated by satellite product and Before bias correction, TRMM data exhibited significant discrepancies in rainfall estimates, with varying biases and mean errors across grid points. After bias correction, the agreement between TRMM and observed rainfall significantly improved, with Pearson correlation coefficients stabilizing between 0.8 and 1.0, bias reduced to − 0.1 to 0.2, and mean errors minimized to − 0.1 to 0.1. Additionally, root mean square error (RMSE) improved, and R2 values, indicating enhanced reliability of the corrected data. The analysis of annual rainfall and the Standardized Anomaly Index (SAI) indicates significant variability without a clear trend. This variability exposes the region to extreme weather, with flooding in wet years like 2007, 2008, 2011 and 2021 and droughts in dry years such as 2005 and 2019. Thus, there is an urgent need for adaptive water management and agricultural strategies.

The study aims to assess future streamflow forecasts in the Godavari basin of India under climate change scenarios. The primary objective of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) was to evaluate future streamflow forecasts across different catchments in the Godavari basin, India, with an emphasis on understanding the impacts of climate change. This study employed both conceptual and machine learning models to assess how changing precipitation patterns and temperature variations influence streamflow dynamics. Seven satellite precipitation products CMORPH, Princeton Global Forcing (PGF), Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC), Infrared Precipitation with Stations (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN-CDR) were evaluated in a gridded precipitation evaluation over the Godavari River basin. Results of Multi-Source Weighted-Ensemble Precipitation (MSWEP) had a Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2), and root mean square error (RMSE) of 0.806, 0.831, and 56.734 mm/mon, whereas the Tropical Rainfall Measuring Mission had 0.768, 0.846, and 57.413 mm, respectively. MSWEP had the highest accuracy, the lowest false alarm ratio, and the highest Peirce’s skill score (0.844, 0.571, and 0.462). Correlation and pairwise correlation attribution approaches were used to assess the input parameters, which included a two-day lag of streamflow, maximum and minimum temperatures, and several precipitation datasets (IMD, EC-Earth3, EC-Earth3-Veg, MIROC6, MRI-ESM2-0, and GFDL-ESM4). CMIP6 datasets that had been adjusted for bias were used in the modeling process. R, NSE, RMSE, and R2 assessed the model’s effectiveness. RF and M5P performed well when using CMIP6 datasets as input. RF demonstrated adequate performance in testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6) and extremely good performance in training (0.75 < NSE < 1 and 0.7 < R < 1). Likewise, M5P demonstrated good performance in both training and testing (0.4 < NSE < 0.50 and 0.5 < R2 < 0.6). While RF was the best performer for both datasets, Indian Meteorological Department outperformed all CMIP6 datasets in streamflow modeling. Using the Indian Meteorological Department gridded precipitation, RF’s NSE, R, R2, and RMSE values during training were 0.95, 0.979, 0.937, and 30.805 m3/s. The test results were 0.681, 0.91, 0.828, and 41.237 m3/s. Additionally, the Multi-Layer Perceptron (MLP) model demonstrated consistent performance across both the training and assessment phases, reinforcing the reliability of machine learning approaches in climate-informed hydrological forecasting. This study underscores the significance of incorporating climate change projections into hydrological modeling to enhance water resource management and adaptation strategies in the Godavari basin and similar regions facing climate-induced hydrological shifts.

Accurate rainfall measurement is vital when investigating spatio-temporal precipitation variability, especially in arid lands. However, there are regions worldwide where only a few ground-based observations are made. This research evaluated the applicability of six satellite precipitation products (SPPs) in detecting rainfall variability in North Darfur State, Sudan. The SPPs were Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). The SPPs were assessed at daily, monthly, and annual timescales for 2000–2019. Four categorical indices, i.e., the probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., the Pearson correlation coefficient (r), the root mean square error (RMSE), the per cent bias (Pbias), and the Nash–Sutcliffe model efficiency coefficient (NSE) were used to evaluate the accuracy of the SPPs. Results of the statistical analysis showed that (1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; (2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the other SPPs. Therefore, the two could reasonably estimate rainfall amounts in North Darfur State.

Abstract. Calibration techniques refine numerical model outputs for climate research, often preferred for their simplicity and suitability in many climate impact applications. Atmospheric pattern classifications for conditioned transfer function calibration, common in climate studies, are seldom explored for satellite product calibration, where significant biases may occur compared to in situ meteorological observations. This study proposes a new adaptive calibration approach, applied to the Tropical Rainfall Measuring Mission (TRMM) precipitation product across multiple stations in the South Pacific. The methodology involves the daily classification of the target series into five distinct weather types (WTs) capturing the diverse spatio-temporal precipitation patterns in the region. Various quantile mapping (QM) techniques, including empirical quantile mapping (eQM), parametric quantile mapping (pQM), and generalized Pareto distribution quantile mapping (gpQM), as well as an ordinary scaling, are applied to each WT. We perform a comprehensive validation by evaluating 10 specific precipitation-related indices that hold significance in impact studies, which are then combined into a single ranking framework (RF) score, which offers a comprehensive evaluation of the performance of each calibration method for every weather type. These indices are assigned user-defined weights, allowing for a customized assessment of their relative importance to the overall RF score. Thus, the adaptive approach selects the best performing method for each WT based on the RF score, yielding an optimally calibrated series. Our findings indicate that the adaptive calibration methodology surpasses standard and weather-type-conditioned methods based on a single technique, yielding more accurate calibrated series in terms of mean and extreme precipitation indices consistently across locations. Moreover, this methodology provides the flexibility to customize the calibration process based on user preferences, thereby allowing for specific indices, such as extreme rainfall indicators, to be assigned higher weights. This ability enables the calibration to effectively address the influence of intense rainfall events on the overall distribution. Furthermore, the proposed adaptive method is highly versatile and can be applied to different scenarios, datasets, and regions, provided that a prior weather typing exists to capture the pertinent processes related to regional precipitation patterns. Open-source code and illustrative examples are freely accessible to facilitate the application of the method.

Extremely severe cyclonic storms over the North Indian Ocean increased by approximately 10% during the past 30 years. The climatological characteristics of tropical cyclones for 38 years were assessed over the Bay of Bengal (BoB). A total of 24 ESCSs formed over the BoB, having their genesis in the southeast BoB, and the intensity and duration of these storms have increased in recent times. The Advanced Research version of the Weather Research and Forecasting (ARW) model is utilized to simulate the five extremely severe cyclonic storms (ESCSs) over the BoB during the past two decades using the Indian Monsoon Data Assimilation and Analysis (IMDAA) data. The initial and lateral boundary conditions are derived from the IMDAA datasets with a horizontal resolution of 0.12° × 0.12°. Five ESCSs from the past two decades were considered: Sidr 2007, Phailin 2013, Hudhud 2014, Fani 2019, and Amphan 2020. The model was integrated up to 96 h using double-nested domains of 12 km and 4 km. Model performance was evaluated using the 4 km results, compared with the available observational datasets, including the best-fit data from the India Meteorological Department (IMD), the Tropical Rainfall Measuring Mission (TRMM) satellite, and the Doppler Weather Radar (DWR). The results indicated that IMDAA provided accurate forecasts for Fani, Hudhud, and Phailin regarding the track, intensity, and mean sea level pressure, aligning well with the IMD observational datasets. Statistical evaluation was performed to estimate the model skills using Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), the Probability of Detection (POD), the Brier Score, and the Critical Successive Index (CSI). The calculated mean absolute maximum sustained wind speed errors ranged from 8.4 m/s to 10.6 m/s from day 1 to day 4, while mean track errors ranged from 100 km to 496 km for a day. The results highlighted the prediction of rainfall, maximum reflectivity, and the associated structure of the storms. The predicted 24 h accumulated rainfall is well captured by the model with a high POD (96% for the range of 35.6–64.4 mm/day) and a good correlation (65–97%) for the majority of storms. Similarly, the Brier Score showed a value of 0.01, indicating the high performance of the model forecast for maximum surface winds. The Critical Successive Index was 0.6, indicating the moderate model performance in the prediction of tracks. It is evident from the statistical analysis that the performance of the model is good in forecasting storm structure, intensity and rainfall. However, the IMDAA data have certain limitations in predicting the tracks due to inadequate representation of the large-scale circulations, necessitating improvement.

Tamil Nadu and Kerala achieve the coastal flood disaster that leads to the maximum social and economic loss for India. The coastal flood prone methods used Sentinel 1A, Tropical Rainfall Measuring Mission (TRMM), and Gravity Recovery and Climate Experiment with Terrestrial Water Storage Anomaly (GRACE‐TWSA) product in the Tamil Nadu and Ernakulam flood events. These data estimate the change detection of pre‐flood and post‐flood events, flood inundation mapping, and Reager’s Flood Potential Index (RFPI). Sentinel 1A was overlaid for four consecutive days to determine the portion of flood inundation mapping. Using GRACE‐TWSA and TRMM data, the Flood Potential Index (FPI) used the RFPI method at a monthly scale. Then, RFPI, GRACE‐TWSA, and TRMM data were compared for four cities of the flooded year in Chennai, Tamil Nadu, and Ernakulam, Kerala. Finally, flood inundation determined the 90‐sq·km of the 2015 Chennai flood and 25‐sq·km of the 2018 Ernakulam flood with the limited remote sensing data. Maximum RFPI between the postflood and preflood period (12 months) achieves the maximum, approximately 80% from the monsoon rainfall with approximately 20% of the total water storage to attain 100% at RFPI at both flooded years. The RFPI results monitor the prone locations from these extreme past floods on the south coast of India. The research insight shares the surplus water resources by the downpour from northeast monsoon in Tamil Nadu and southwest monsoon in Kerala. Downpour naturally disputes the water resources to sustain the socioeconomic reforms between interstates. The study also supports the policy of sustainable development goals in 11, 13, 14, and 15 in the future.

Abstract Soil Erosion is a serious environmental concern, which severity can assess using the Revised Universal Soil Loss Equation (RUSLE) alongside remote sensing and geographic information system (RSGIS) techniques. This study investigates the spatiotemporal changes in soil erosion potential between 2007 and 2017, due to the impact of rainfall patterns and land use/land cover (LULC) changes in the Irga River catchment, which lies in the Chhota Nagpur Plateau and is susceptible to severe soil erosion due to anthropogenic pressures. The study incorporates 20 years of average annual rainfall data (1998-2017) from the Tropical Rainfall Measuring Mission (TRMM) to calculate the Rainfall-Runoff Erosivity factor (R) and utilises FAO soil data for soil erodibility factor (K) determination. Digital elevation models (DEM) are utilized to derive the Topographic factor (LS), and LULC maps for both years are used to create Crop Management (C) and Soil Conservation Practice (P) factor maps. Soil erosion estimates for 2017 averaged 0.2814 t/ha/year, with rates ranging from 0 to 36.1185 t/ha/year, while for 2007, the average was 0.3057 t/ha/year, with rates from 0 to 44.2149 t/ha/year. The study reveals that the catchment experiences very mild and slight erosion, with most wasteland in the northwest and north. Over a decade (2007-2017), the average erosion rate decreased by 8.6%, highlighting the impact of LULC changes and rainfall patterns on soil erosion.

ABSTRACT Remote sensing contributes valuable information to streamflows. Usually, streamflow is directly measured through ground-based hydrological monitoring stations. However, in many countries like Ethiopia, ground-based hydrological monitoring networks are either sparse or nonexistent. The lack of reliable in situ observational data severely limits our ability to manage water resources in a well-informed way in these regions. In such cases, satellite remote sensing is an alternative means of acquiring such information. In this study, the application of remotely sensed rainfall data in streamflow modeling for the Gilgel Ghibe catchment in Ethiopia is reported. Ten years (2001–2010) of satellite-based-precipitation-products (SBPPs) from Tropical Rainfall Measuring Mission (TRMM) and WaterBase, were combined with a PyTOPKAPI model to generate daily streamflows. We compared the results with that of the observed streamflows at the Gilgel Ghibe Nr, Assendabo gauging station using four statistical tools (bias, R2, NS, and RMSE). The result indicates that the bias-adjusted SBPPs agree well with gauged-rainfall. Without bias-adjustment, the SBPPs tend to overestimate the simulated streamflows. We further conclude that the general streamflow patterns were well captured at daily time scale from SBPPs after bias-adjustment. However, the simulated streamflow using gauged-rainfall is superior to those obtained from SBPPs including bias-adjusted ones.