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This study evaluates two reanalysis precipitation products (CRA40 and ERA5) over the Ganjiang River Basin with precipitation data from 37 ground rainfall gauges and surface-observed stream flow data from January 1998 to December 2008. Direct comparison with rain gauge observations shows that both CRA40 and ERA5 can capture the spatial and temporal characteristics of precipitation at the basin scale of the Ganjiang River and reflect most of the precipitation events, but there are pronounced differences in the quality of precipitation between them. ERA5 performs better on the daily scale, capturing precipitation changes more accurately over short periods of time, while CRA40 performs better on the monthly scale, providing more stable and long-term precipitation trends. The results of stream flow simulations using two reanalysis precipitation products driving the VIC hydrological model show that (1) CRA40 outperforms ERA5 with a better Nash–Sutcliffe Efficiency (NSE, 0.65 and 0.6) and higher CC (0.96 and 0.91) in daily and monthly scale stream flow simulations, and ERA5 has a good CC (0.86 and 0.93, respectively), but its NSE is poor (0.29 and 0.30, respectively); (2) both CRA40 and ERA5 generally overestimate basin stream flows, especially during the flood season (April–September), with ERA5's overestimation being more pronounced. This study is expected to provide a basis for the selection of reliable reanalysis products for Ganjiang River Basin precipitation and hydrological simulation. [ABSTRACT FROM AUTHOR]

A method is proposed and demonstrated for extracting the spatial and temporal characteristics of individual rainfall events using hourly rainfall measurements at an array of rain gauges. The method is based on identifying the probabilities of overlapping rainfall events at the rain gauge locations. The method is demonstrated in a 47,000 km2 area covered by 150 rain gauges with 17 years of unbroken hourly data. The results show that the length scales of individual events in the wet season are in the range of 1.2–18.3 km depending on the event magnitude, and dry season events with corresponding magnitudes have length scales in the range of 1.5–35.9 km. The timescales of wet and dry season events generally increase with event magnitude in both seasons. The shapes and locations of cluster areas that tend to have overlapping rainfall events are substantially different between seasons, being smaller and more dispersed in the wet season and larger and lesser in number in the dry season. [ABSTRACT FROM AUTHOR]

Multiple record-breaking rainfall events were observed along the Western Ghats (WG) during the recent monsoon seasons (2019–2021). Rainfall amounts of up to > 200 mm/day (Extreme rainfall, ER) were recorded especially over the Mumbai region (19.07 N, 72.8 E) causing flooding, landslides, damage to infrastructure and loss of life. Thus, to enhance the resilience of this region by providing early warning for flooding, the National Center for Medium-Range Weather Forecasting Unified model's regional forecasting system (NCUM-reg) provides rainfall forecasts up to 3 days (72-h), which are utilized in the integrated flood warning system hydrological model. This study focuses on evaluating the performance of NCUM-reg forecasts during ER events. For this purpose, we have systematically performed verification of regional model operational forecasts using the suite of observations (rain gauge, satellite) and newly generated NCMRWF's regional reanalysis, Indian Monsoon Data Assimilation and Analysis (IMDAA). Key findings indicate that NCUM-reg model with explicit convection is performing well in representing the synoptic and dynamic features of the ER events similar to those observed. Quantitative assessment of the forecasts shows the strength of in-situ observations. In addition, the results summarize the importance of continuous and quality-controlled observations and stress the need for collective efforts of observations and new verification metrics (like process-oriented diagnostics) to enhance our understanding and as well as the model's ability in forecasting such events. [ABSTRACT FROM AUTHOR]

This paper describes the first assessment of ISRO's indigenously developed S-band Polarimetric Doppler Weather Radar (DWR) at Sriharikota High Altitude Range (SHAR), Sriharikota (13.66°N, 80.23°E). The assessment is done during the passage of a tropical cyclone 'Phethai' and four different cases of Mesoscale convective systems. A comparison of reflectivity measured from SHAR-DWR with India Meteorological Department (IMD) DWR located at Chennai (13.07°N, 80.28°E) and Ku-band space-borne radar of the Global Precipitation Mission (GPM) is done using the common volume matching method. A good comparison is observed between SHAR-DWR with IMD-DWR and GPM-Ku radar in both space and height. The vertical structure of the cyclone is provided using Contour Frequency by Altitude Diagrams (CFAD) which show the vertical extent up to an altitude of 16 km with a reflectivity between 25 and 30 dBZ. Maximum occurrence of 30 dBZ occurs below 6 km. Bright band signatures show ~ 25 dBZ between 4 and 5 km depicting the stratiform precipitation of the cyclone. Further rain rate is estimated using both reflectivity and the polarimetric product which compared well with rain estimated from IMD-DWR and rainfall observed from rain gauges. Rain rate is observed from 5 to 35 mm hr−1. Hydrometer classification during the passage of tropical cyclone 'Phethai' shows the presence of rain below 4 km and graupels, ice crystals, and large droplets above. [ABSTRACT FROM AUTHOR]

Climate change is a major issue in wastewater management at local and regional levels, as it affects the frequency of flooding and therefore the need to update infrastructure and design regulations. To this end, rainfall data are the main input to hydraulic models used for the design of drainage systems and, in advanced contexts, for their real-time monitoring. Field observations are of great interest and water authorities are increasing the number of existing rain gauges, but at present they are scarce and require maintenance, so their number needs to be considered with their O&M costs. Remote sensors, including both the existing satellite rain products (SRPs) and radar imagery (RI), can complete the spatial distribution of rainfall and optimise the cost of observations. While most SRPs are based on re-analysis and have a lag in availability, RI can be obtained in near real time and is becoming increasingly popular in weather forecasting applications. Unfortunately, actual rainfall forecasts from RI observations are not accurate enough for real-time monitoring of drainage systems. In this paper, the Colour Pattern Regression (CPR) algorithm is used to recalibrate the 6 h rainfall values from RI provided by the Agencia Estatal de Meteorología (AEMET) with the observed rain gauge data, using as a case study the metropolitan area of Palma (Spain). [ABSTRACT FROM AUTHOR]

Using the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections. Plain Language Summary: We ran high‐resolution model simulations at a 3‐km grid spacing with ACM2 and MYNN planetary boundary layer schemes for a 6‐year period, including a 1‐year spin‐up, to investigate precipitation and storm patterns in the Peruvian central Andes region. Other data sets including a 4‐km simulation produced by National Center for Atmospheric Research, three gridded precipitation products, and rain gauge data in Peru and Brazil were collected for comparison and evaluation. We found that all km‐scale simulations capture overall patterns of precipitation and storms at both seasonal and sub‐daily time scales, although some discrepancies exist in precipitation intensity and storm details. Compared to the gridded precipitation products and gauge data, the 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin. Low‐level wind and terrain‐induced uplift are the key drivers for precipitation and storm genesis along the Andes' eastern slopes, while factors associated with vertical structures of temperature and humidity control the precipitation and storm activity in the western Amazon Basin and mountain regions. The study suggests model improvements and better model configurations for future regional climate projections. Key Points: Characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian Central Andes are investigated based on convection‐permitting simulationsWRF3km_MYNN outperforms in simulating mountain precipitation; both it and WRF4km_SAAG show superior performance in western AmazonDynamic factors dominate precipitation and MCSs on the Andean east slope, while thermodynamic factors are dominant in western Amazon Basin [ABSTRACT FROM AUTHOR]

The "train effect" is defined as a cloud system in which several convective cells pass over the same place in a short time. Trains produce a large amount of rainfall, frequently leading to flash floods, reported mainly over North America during spring and summer. Thirty train events were identified using radar images calibrated by rain gauges for four winters, all associated with Cyprus lows (CLs). The dynamic factors responsible for their formation in Israel were examined, utilizing the ECMWF Integrated Forecast System with 0.1° resolution. Seventeen out of the 30 events share common features. Each one was found within the cold sector in the southern periphery of a CL at its occluded stage and located in the left flank of a maximum wind belt, where cyclonic shear vorticity exists. The trains cross the Israeli coast near 32.2° N, with a mean length of 35 km; last 2–3 h; and yield a total of 30–50 mm of rainfall. The maximum wind belts to the right of the trains were found to delineate the limit of the precipitative region of the CLs. Unlike classical trains, activated by thermal or frontal forcing, the eastern Mediterranean trains that develop in a cold air mass can be referred to as "cold trains" rather than the classical "warm trains". [ABSTRACT FROM AUTHOR]

The spatiotemporal and cloud features of the extreme rainfall under the warm and cold upper-level anomalies over the central Yungui Plateau (YGP) were investigated using the hourly rain gauge records, ERA5 reanalysis data, TRMM, and Fengyun satellite data, aiming to refine the understanding of different types of extreme rainfall. Extreme rainfall under an upper-level negative temperature anomaly (cold events) presents stronger convective cloud features when compared with the positive temperature anomaly (warm events). The maximum rainfall intensity and duration in cold events is much larger than that of warm events, while the brightness temperature of the cloud top is lower, and the ratio of convective rainfall is higher. In cold events, the middle-to-upper troposphere is dominated by a cold anomaly, and an unstable configuration with upper (lower) cold (warm) anomalies is observed around the central YGP. Although the upper-level temperature anomaly is positive, the anomalous divergence and convergence of southerly and northerly winds, as well as the strong moisture center and upward motions, are also found over the central YGP in warm events. The stronger atmospheric instability and higher convective energy under the upper-level cold anomalous circulation are closely associated with the rainfall features over the central YGP. The results indicate that the upper tropospheric temperature has significant influences on extreme rainfall, and thus more attention should be paid to the upper tropospheric temperature in future analyses. [ABSTRACT FROM AUTHOR]

A common and largely unresolved problem of national-scale landslide early warning systems is their independent evaluation. In a recent paper, Mondini et al. (Nat Commun 14:2466, 2023) proposed a deep-learning system for short-term forecasting of rain-induced shallow landslides in Italy. Here, we independently evaluate the performance of this national-scale system by demonstrating its application between 1 January and 31 May 2021. For the purpose, we use hourly rainfall measurements from the same rain gauge network and different and independent information on the timing and location of 163 rain-induced landslides obtained from the FraneItalia catalogue that occurred in Italy in a period non considered in the construction of the system (https://zenodo.org/records/7923683). Independent demonstration confirmed the good predictive performance of the forecasting system and revealed no geographical or temporal bias in the forecasts. The analysis also showed that the system was more effective at predicting multiple landslides in the same general area than single landslides. This was a good result as multiple landslides are inherently more dangerous than single failures. Analysis of the few misclassified landslides showed that approximately one-third of the landslides were rockfalls, and for approximately another third there was uncertainty about when or where the landslides occurred. We conclude that, despite the inevitable misclassifications inherent in any probabilistically based national-scale landslide forecasting system, the deep-learning system analysed is well suited for short-term operational forecasting of rain-induced shallow landslides in Italy. [ABSTRACT FROM AUTHOR]

In this study, we explored the efficacy of random forest algorithms in downscaling CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) precipitation data to predict Argane stand traits. Nonparametric regression integrated original CHIRPS data with environmental variables, demonstrating enhanced accuracy aligned with ground rain gauge observations after residual correction. Furthermore, we explored the performance of range machine learning algorithms, encompassing XGBoost, GBDT, RF, DT, SVR, LR and ANN, in predicting the Leaf Area Index (LAI) and crop yield of Argane trees using condition index-based drought indices such as PCI, VCI, TCI and ETCI derived from multi-sensor satellites. The results demonstrated the superiority of XGBoost in estimating these parameters, with drought indices used as input. XGBoost-based crop yield achieved a higher R2 value of 0.94 and a lower RMSE of 6.25 kg/ha. Similarly, the XGBoost-based LAI model showed the highest level of accuracy, with an R2 of 0.62 and an RMSE of 0.67. The XGBoost model demonstrated superior performance in predicting the crop yield and LAI estimation of Argania sinosa, followed by GBDT, RF and ANN. Additionally, the study employed the Combined Drought Index (CDI) to monitor agricultural and meteorological drought over two decades, by combining four key parameters, PCI, VCI, TCI and ETCI, validating its accuracy through comparison with other drought indices. CDI exhibited positive correlations with VHI, SPI and crop yield, with a particularly strong and statistically significant correlation observed with VHI (r = 0.83). Therefore, CDI was recommended as an effective method and index for assessing and monitoring drought across Argane forest stands area. The findings demonstrated the potential of advanced machine learning models for improving precipitation data resolution and enhancing agricultural drought monitoring, contributing to better land and hydrological management. [ABSTRACT FROM AUTHOR]

An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain's topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes. [ABSTRACT FROM AUTHOR]

Precipitation is momentous role in the hydrological cycle, crop irrigation, drinking, and ecosystem maintenance. Excessive and inadequate precipitation leads to flooding, landslides, loss of human lives and properties, and drought. Therefore, precipitation prediction is necessary for planning and protecting water resources and handling natural disasters like floods and drought. This paper proposed a cloud segmentation and precipitation prediction using Machine Learning (ML) algorithms. ML cloud segmentation model is implemented to segment the INSAT-3D Thermal Infra-Red (TIR) image into No-cloud, Low-Level (L-L), Mid-Level (M-L), and High-Level (H-L) clouds and determine the percentage of different clouds over the study area. Fuzzy C-means(FCM) clustering, Gaussian Naïve Bayes (GNB), K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are the ML algorithms considered for cloud segmentation. The precipitation intensity was predicted on the weather dataset using ML regression models: KNN, DT, and RF. The estimated cloud percentage is validated with cloud cover attributes of the weather dataset. Qualitative and quantitative results of cloud segmentation show that XGBoost, LightGBM, and CatBoost are the best performing models. Similarly, quantitative results of precipitation prediction show that RF and KNN are the best and least performing ML regression models. Also, the performance of the ML regression model is improved by including dimensionality reduction techniques such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and Independent Component Analysis (ICA). [ABSTRACT FROM AUTHOR]

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures. [ABSTRACT FROM AUTHOR]

Numerous hydrological applications, such as soil erosion estimation, water resource management, and rain driven damage assessment, demand accurate and reliable rainfall erosivity data. However, the scarcity of gauge rainfall records and the inherent uncertainty in satellite and reanalysis-based rainfall datasets limit rainfall erosivity assessment globally. Here, we present a new global rainfall erosivity dataset (0.1° × 0.1° spatial resolution) integrating satellite (CMORPH and IMERG) and reanalysis (ERA5-Land) derived rainfall erosivity estimates with gauge rainfall erosivity observations collected from approximately 6,200 locations across the globe. We used a machine learning-based Gaussian Process Regression (GPR) model to assimilate multi-source rainfall erosivity estimates alongside geoclimatic covariates to prepare a unified high-resolution mean annual rainfall erosivity product. It has been shown that the proposed rainfall erosivity product performs well during cross-validation with gauge records and inter-comparison with the existing global rainfall erosivity datasets. Furthermore, this dataset offers a new global rainfall erosivity perspective, addressing the limitations of existing datasets and facilitating large-scale hydrological modelling and soil erosion assessments. [ABSTRACT FROM AUTHOR]

Due to their severity and lack of predictability, understanding and forecasting extreme precipitation events (EPEs) is critical for disaster risk reduction. The present work documents the large‐scale environment of tropical EPEs based on a 42‐year data set combining dense rain‐gauge networks that cover several tropical small islands and coastal regions. Approximately 10%–30% of EPEs are associated with a tropical storm or cyclone (TC), except for Reunion, for which its high topography makes it reach 55%. TCs multiply the EPE probability by a factor of 4–15, especially during TCs of category 1 or higher. A composite analysis demonstrates that the remaining large part of EPEs occurs within large‐scale and strong moist, convective, and cyclonic wind anomalies resulting from the superimposition of intraseasonal, seasonal‐to‐annual, and interannual timescales. These intense anomalies come essentially from intraseasonal variability, and lower frequencies improve the effect of intraseasonal events in creating a favorable environment for EPEs. Plain Language Summary: Floods and landslides, mainly caused by extreme precipitation events (EPEs), are severe disasters that affect populations and economies. However, their prediction, especially in tropical areas, is challenging. Here, we study the main atmospheric configurations leading to EPEs using rain‐gauge data densely sampling several tropical small islands and coastal regions, covering the period 1979–2021. In areas prone to tropical storms and cyclones (TCs), these systems account for 10%–30% of EPEs, except for Reunion, whose high topography increases it up to 55%. TC‐related EPEs are also more intense than non‐TC‐related EPEs. TCs multiply the EPE probability of occurrence by 4–15 times, depending on the region and the TC category. Non‐TC‐related EPEs are shown to occur within specific large‐scale weather patterns in which moisture is highly increased, and convective storms are more active. These patterns involve phenomena occurring at intraseasonal (2–90‐day periods), seasonal‐to‐annual (91–365‐day periods), and interannual (periods above one year) timescales. The intraseasonal variability helps the most to develop this environment favorable to the occurrence of EPEs. Slower signals build a background on which intraseasonal variability can have more effects. The real‐time monitoring of these different features should improve EPE forecasting capabilities and deliver more early warnings. Key Points: Over most of the studied tropical small islands, only 10%–30% of extreme precipitation events occur near a tropical storm or cycloneTropical storms and cyclones favor heavier rainfall and raise the probability of occurrence of extreme precipitation by a factor of 4–15The other largest part of events occur in a large‐scale, intense, moist, and convective anomaly driven mainly by the intraseasonal timescale [ABSTRACT FROM AUTHOR]

The uncertainty of hydrological forecasts is strongly related to the uncertainty of the rainfall field due to the nonlinear relationship between the spatio-temporal pattern of rainfall and runoff. Rain gauges are typically considered to provide reference data to rebuild precipitation fields. However, due to the density and the distribution variability of the rain gauge network, the rebuilding of the precipitation field can be affected by severe errors which compromise the hydrological simulation output. On the other hand, retrievals obtained from remote sensing observations provide spatially resolved precipitation fields, improving their representativeness. In this regard, the comparison between simulated and observed river flow discharge is crucial for assessing the effectiveness of merged precipitation data in enhancing the model's performance and its ability to realistically simulate hydrological processes. This paper aims to investigate the hydrological impact of using the merged rainfall fields from the Italian rain gauge network and the NASA Global Precipitation Measurement (GPM) IMERG precipitation product. One aspect is to highlight the benefits of applying the cellular automata algorithm to pre-process input data in order to merge them and reconstruct an improved version of the precipitation field. The cellular automata approach is evaluated in the Tanaro River basin, one of the tributaries of the Po River in Italy. As this site is characterized by the coexistence of a variety of natural morphologies, from mountain to alluvial environments, as well as the presence of significant civil and industrial settlements, it makes it a suitable case study to apply the proposed approach. The latter has been applied over three different flood events that occurred from November to December 2014. The results confirm that the use of merged gauge–satellite data using the cellular automata algorithm improves the performance of the hydrological simulation, as also confirmed by the statistical analysis performed for 17 selected quality scores. [ABSTRACT FROM AUTHOR]

The diurnal cycle is an important mode of climatic variability associated with different aspects of micro, meso and large scale meteorological phenomena. Thus, we performed a study of the space-time variability of the diurnal cycle of precipitation with hourly sampling and covering all regions of Brazil. The dataset was collected during the period of 13-year, from 1st January 2008 to 31th December 2020. We used data from 411 rain gauges installed in automatic weather stations. To evaluate regional aspects, we conducted a cluster analysis with different configurations (4, 5 and 6 groups). We identified a considerable heterogeneity in the hour of maximum precipitation in Brazil and three main types of diurnal cycle were observed: (i) maximum precipitation at mid- to late afternoon associated with strong local convection activity; (ii) diurnal cycle with intense precipitation during nighttime at the Amazon basin, the coast of Northeast Brazil and the Southern region; (iii) semidiurnal cycles with low precipitation rate at the Northeast Brazil. [ABSTRACT FROM AUTHOR]

Precipitation plays a vital role in various fields, including hydroclimatic modeling, climate change studies, agricultural optimization, and water resources management. Precipitation data can be obtained through observational measurements using the rain gauge approach or as Gridded precipitation products (GPP) derived from satellites or atmospheric models. GPPs provide optimized global estimates of climate data without spatial or temporal gaps, making them a valuable solution for areas with sparse or nonexistent rain gauges. However, it is essential to assess their reliability and limitations across different time scales and regions before usage. This study aims to evaluate the accuracy of two specific GPP datasets, ERA5 and MERRA-2, in comparison with two observational datasets, focusing on the Tocantins-Araguaia watershed and Pará river estuary in Brazil. The results show that both GPPs, ERA5 and MERRA-2, captured the overall precipitation regime for the analyzed period. However, discrepancies emerged, particularly at the daily and annual scales, with better agreement observed at monthly and climatology scales when compared to observational datasets. ERA5 demonstrated a higher number of acceptable stations compared to MERRA-2. Although both reanalysis products showed good agreement in climatological analysis, a more detailed evaluation revealed shortcomings in simulating precipitation during the dry season. While GPPs offer consistent time series with higher temporal and spatial resolutions, the observational precipitation data is deemed the most suitable input for hydrological-hydrodynamic modeling in the Tocantins-Araguaia watershed. Its widespread coverage, numerous rain gauges, and accurate representation of reality make it an ideal choice for hydrological modeling in the region. [ABSTRACT FROM AUTHOR]

The widely distributed sediments following an earthquake presents a continuous threat to local residential areas and infrastructure. These materials become more easily mobilized due to reduced rainfall thresholds. Before establishing an effective management plan for debris flow hazards, it is crucial to determine the potential reach of these sediments. In this study, a deep learning-based method—Dual Attention Network (DAN)—was developed to predict the runout distance of potential debris flows after the 2022 Luding Earthquake, taking into account the topography and precipitation conditions. Given that the availability of reliable precipitation data remains a challenge, attributable to the scarcity of rain gauge stations and the relatively coarse resolution of satellite-based observations, our approach involved three key steps. First, we employed the DAN model to refine the Global Precipitation Measurement (GPM) data, enhancing its spatial and temporal resolution. This refinement was achieved by leveraging the correlation between precipitation and regional environment factors (REVs) at a seasonal scale. Second, the downscaled GPM underwent calibration using observations from rain gauge stations. Third, mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were employed to evaluate the performance of both the downscaling and calibration processes. Then the calibrated precipitation, catchment area, channel length, average channel gradient, and sediment volume were selected to develop a prediction model based on debris flows following the Wenchuan Earthquake. This model was applied to estimate the runout distance of potential debris flows after the Luding Earthquake. The results show that: (1) The calibrated GPM achieves an average MAE of 1.56 mm, surpassing the MAEs of original GPM (4.25 mm) and downscaled GPM (3.83 mm); (2) The developed prediction model reduces the prediction error by 40 m in comparison to an empirical equation; (3) The potential runout distance of debris flows after the Luding Earthquake reaches 0.77 km when intraday rainfall is 100 mm, while the minimum distance value is only 0.06 km. Overall, the developed model offers a scientific support for decision makers in taking reasonable measurements for loss reduction caused by post-seismic debris flows. [ABSTRACT FROM AUTHOR]

In Indonesia, the monitoring of rainfall requires an estimation system with a high resolution and wide spatial coverage because of the complexities of the rainfall patterns. This study built a rainfall estimation model for Indonesia through the integration of data from various instruments, namely, rain gauges, weather radars, and weather satellites. An ensemble learning technique, specifically, extreme gradient boosting (XGBoost), was applied to overcome the sparse data due to the limited number of rain gauge points, limited weather radar coverage, and imbalanced rain data. The model includes bias correction of the satellite data to increase the estimation accuracy. In addition, the data from several weather radars installed in Indonesia were also combined. This research handled rainfall estimates in various rain patterns in Indonesia, such as seasonal, equatorial, and local patterns, with a high temporal resolution, close to real time. The validation was carried out at six points, namely, Bandar Lampung, Banjarmasin, Pontianak, Deli Serdang, Gorontalo, and Biak. The research results show good estimation accuracy, with respective values of 0.89, 0.91, 0.89, 0.9, 0.92, and 0.9, and root mean square error (RMSE) values of 2.75 mm/h, 2.57 mm/h, 3.08 mm/h, 2.64 mm/h, 1.85 mm/h, and 2.48 mm/h. Our research highlights the potential of this model to accurately capture diverse rainfall patterns in Indonesia at high spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Accurate estimations of extreme precipitation return levels are critical for many hydrological applications. Extreme precipitation is highly variable in both space and time; therefore, to better understand and manage the related risks, knowledge of their probability at different spatial–temporal scales is crucial. We employ a novel non-asymptotic framework to estimate extreme return levels (up to 100 years) at multiple spatial–temporal scales from weather radar precipitation estimates. The approach reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case). We focus on the eastern Mediterranean, an area of high interest due to its sharp climatic gradient, containing Mediterranean, semi-arid, and arid areas across a few tens of kilometres, and its susceptibility to flash flood. At-site intensity–duration–area–frequency relations are derived from radar precipitation data at various scales (10 min–24 h, 0.25–500 km 2) across the study area, using ellipses of varying axes and orientations to account for the spatial component of storms. We evaluate our analysis using daily rain gauge data over areas for which sufficiently dense gauge networks are available. We show that extreme return levels derived from radar precipitation data for 24 h and 100 km 2 are generally comparable to those derived from averaging daily rain gauge data over a similar areal scale. We then analyse differences in multi-scale extreme precipitation over coastal, mountainous, and desert regions. Our study reveals that the power-law scaling relationship between precipitation and duration (simple scaling) weakens for increasing area sizes. This finding has implications for temporal downscaling. Additionally, precipitation intensity varies significantly for different area sizes at short durations but becomes more similar at long durations, suggesting that, in the region, areal reduction factors may not be necessary for computing return levels over long durations. Furthermore, the reverse orographic effect, which causes decreased precipitation for hourly and sub-hourly durations, diminishes for larger areas. Finally, we discuss the effects of orography and coastline proximity on extreme precipitation intensity over different spatial–temporal scales. [ABSTRACT FROM AUTHOR]

Precipitation is a fundamental component of the Earth's hydrological cycle. Therefore, monitoring precipitation is paramount, as accurate information is needed to prevent natural hydrological disasters, such as floods and droughts. However, measuring precipitation using rain gauges is complicated due to their sparse spatial distribution. Satellite precipitation products (SPPs) are an alternative source of rainfall data. This study aimed to evaluate the performance of PERSIANN-CCS and PDIR-Now SPPs over the Tulijá River Basin (Chiapas, Mexico) using scatter plots, categorical statistics, descriptive statistics, and decomposing total bias. Additionally, bias correction was performed using the quantile mapping (QM) method. QM is a technique used to improve the fit of SPPs with respect to rainfall observations through a transfer function, aiming to reduce systematic errors in SPPs. The results indicate that the PDIR-Now product tends to overestimate rainfall to a large extent, thus showing better performance in detecting rain events. Meanwhile, PERSIANN-CCS underestimates precipitation to a lesser extent. The findings of this study demonstrate that correcting the bias of SPPs improves estimations of rainfall records, thereby reducing the percentage bias and root mean square error. [ABSTRACT FROM AUTHOR]

The present research endeavors to examine the effectiveness of four gridded precipitation datasets, namely Integrated Multi-satellite Retrievals for GPM (IMERG), Tropical Precipitation Measuring Mission (TRMM), Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), with the observed rainfall data of eight rain gauge stations of India Meteorological Department (IMD) from 2001 to 2019 in Kosi River basin, India. Various statistical metrics, contingency tests, trend analysis, and rainfall anomaly index were utilized at daily, monthly, seasonal, and annual time scales. The categorical metrics namely probability of detection (POD) and false alarm ratio (FAR) indicate that MERRA-2 and IMERG datasets have the highest level of concurrence with the observed daily data. Statistical analysis of gridded datasets with observed dataset of IMD showed that the performance of the IMERG dataset is better than MERRA-2, PERSIANN, and TRMM datasets with "very good" coefficient of determination (R2) and Nash–Sutcliffe Efficiency values for monthly data. Trend analysis of gridded seasonal data of IMERG showed similar trends of observed seasonal data whereas other dataset differs. IMERG also performed well in identifying wet and dry years based on annual data. Discrepancies of the satellite sensor in capturing the precipitation have also been discussed. Thus, the IMERG dataset can be effectively used for hydro-meteorological and climatological investigations in cases of lack of observed datasets. [ABSTRACT FROM AUTHOR]

Dengue fever is a source of substantial health burden in Vietnam. Given the well‐established influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a world‐leading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (May–October) ensemble precipitation and 2‐m temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001–2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce district‐level epidemiological forecasts for urban areas where dengue is most prevalent. [ABSTRACT FROM AUTHOR]

The study of the micro-physical characteristics of spray droplets provides crucial insights into the impact of sprinkler water on soil erosion, leaf impact, and the microclimate of agricultural environments. However, the variability in measurement outcomes across different technologies due to their distinct measurement principles is a significant challenge. This research aims to evaluate and compare the droplet diameter, velocity, application rate, and kinetic energy rate using four distinct measurement technologies: the paper stain method (PS), flour pellet method (FP), laser precipitation monitor (LPM), and two-dimensional video disdrometer (2DVD), alongside a rain gauge (RG) in the context of sprinkler irrigation conditions. The results reveal that: (1) The FP method struggled to capture small droplets under the same spray conditions, while the PS method recorded a maximum droplet diameter exceeding 7 mm. The LPM registered the highest droplet count per unit area and time, notably capturing a significant number of small droplets (< 1 mm). Conversely, the 2DVD provided a more uniform distribution of droplet sizes, with the LPM's mean equivalent droplet diameter (dv) being 0.86 times that of the 2DVD. (2) Although droplet diameters and velocities measured by the 2DVD, LPM, FP, and PS, decreased with increased working pressure, these technologies concurred when assessing low-velocity and small-diameter droplet. (3) The 2DVD's larger sampling area compared to other methods enables the acquisition of more representative droplet characteristics, including their irregular shapes, suggesting its utility in measuring the micro-physical properties of sprayed droplets. (4) Based on the kinetic energy rate measured by the 2DVD under identical conditions, kinetic energy rate calibration factors of 0.88, 1.15, and 1.10 are suggested for the LPM, FP, and PS, respectively. This study provides essential data for calibrating and applying various droplet measurement technologies. [ABSTRACT FROM AUTHOR]

Climate change in the Mediterranean region is manifesting itself as an increase in average air temperature and a change in the rainfall regime: the value of cumulative annual rainfall generally appears to be constant, but the intensity of annual rainfall maxima, between 1 and 24 h , is increasing, especially in the period between late summer and early autumn. The associated ground effects in urban areas consist of flash floods and pluvial floods, often in very small areas, depending on the physical-geographical layout of the region. In the context of global warming, it is therefore important to have an adequate monitoring network for rain events that are highly concentrated in space and time. This research analyses the meteo-hydrological features of the 27 and 28 August 2023 event that occurred in the city of Genoa, Italy, just 4 d after the record maximum air temperature was recorded: between 19:00 and 02:00 UTC almost 400 mm of rainfall was recorded in the eastern sector of the historic centre of Genoa, with significant ground effects such as flooding episodes and the overflowing of pressurised culverts. Rainfall observations and estimates were made using both official or "authoritative" networks (rain gauges and meteorological radar) and rain gauge networks inspired by citizen science principles. The combined analysis of observations from authoritative and citizen science networks reveals, for the event analysed, a spatial variability of the precipitation field at an hourly and a sub-hourly timescale that cannot be captured by the current spatial density of the authoritative measurement stations (which have one of the highest densities in Italy). Monthly total rainfall and short-duration annual maximum time series recorded by the authoritative rain gauge network of the Genoa area are then analysed. The results show significant variation even at distances of less than 2 km in the average rainfall depth accumulated over sub-hourly duration. Extreme weather monitoring activity is confirmed as one of the most important aspects in terms of flood prevention and protection in urban areas. The integration between authoritative and citizen science networks can prove to be a valid contribution to the monitoring of extreme events. [ABSTRACT FROM AUTHOR]

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. [ABSTRACT FROM AUTHOR]

This study focuses on the Subash Reservoir area in the Aksu Prefecture, Xinjiang, China, employing various data sources, including merged precipitation, rainfall station, digital elevation model, and land-use data. Using the FloodArea model, a refined simulation of typical torrential rain-induced flood processes in the Subash Reservoir is conducted. By analyzing the inundation depth and runoff volume, and the simulation results of two precipitation data sources are compared, and a precipitation- inundation depth relationship model is established, determining critical rainfall thresholds corresponding to the sediment-flushing gate bottom elevation, flood limit water level, spillway gate weir top elevation, and dam crest elevation. The results reveal that the FloodArea model driven by multi-source precipitation fusion data has higher accuracy and closer approximation to the actual values for the inundation depth and calculated runoff volume in the Subash Reservoir than the model driven by rain gauge precipitation data. The error simulation rates for inundation depth of the two kind of data are 8.59% and 18.67%, respectively. The cumulative 4-hour precipitation thresholds for reaching inundation levels corresponding to the sediment-flushing gate bottom elevation, flood limit water level, spillway gate weir top elevation, and dam crest elevation of the Subash Reservoir are 7.1 mm, 20.1 mm, 32.9 mm, and 44.7 mm, respectively. The results provide a theoretical reference for torrential rain-induced early flood warning in the Subash Reservoir region to enhance the capability of early flood warning for such scenarios. [ABSTRACT FROM AUTHOR]

Areal rainfall is routinely estimated based on the observed rainfall data using distributed point rainfall gauges. However, the data collected are sparse and cannot represent the continuous rainfall distribution (or field) over a large watershed due to the limitations of weather station networks. Recent improvements in remote-sensing-based rainfall estimation facilitate more accurate and effective hydrological modeling with a continuous spatial representation of rainfall over a watershed of interest. In this study, we conducted a systematic stepwise comparison of the areal rainfalls estimated by a synoptic weather station and radar station networks throughout South Korea. The bias in the areal rainfalls computed by the automated synoptic observing system and automatic weather system networks was analyzed on an hourly basis for the year 2021. The results showed that the bias increased significantly for hydrological analysis; more importantly, the identified bias exhibited a magnitude comparable to that of the low flow. This discrepancy could potentially mislead the overall rainfall-runoff modeling process. Moreover, the areal rainfall estimated by the radar-based approach significantly differed from that estimated by the existing Thiessen Weighting approach by 4%–100%, indicating that areal rainfalls from a limited number of weather stations are problematic for hydrologic studies. Our case study demonstrated that the gauging station density must be within 10 km2 on average for accurate areal rainfall estimation. This study recommends the use of radar rainfall networks to reduce uncertainties in the measurement and prediction of areal rainfalls with a limited number of ground weather station networks. [ABSTRACT FROM AUTHOR]

High-quality satellite precipitation products (SPPs) are needed for hydrological simulations and water resource management, especially in remote regions where rain gauges are scarce, therefore the comprehensive evaluation of SPPs is critical. In this study, an improved rank score (RS) method was used to comprehensively and quantitatively evaluate the performance of 14 SPPs at the site and basin scales on the Chinese mainland based on gauge-observed precipitation data from 2000 to 2018 in terms of continuous statistics, detection of precipitation events, and extreme metrics. The results demonstrate the following: (1) The performance of the SPPs varied significantly when evaluated using different metrics and at spatial and temporal scales. However, the Climate Prediction Center morphing technique gauge blended product (CMORPH-BLD), Multi-Source Weighted-Ensemble Precipitation (MSWEP), and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG)-final run generally showed the best performance. (2) MSWEP performed best in the Yellow River basin (YeRB), the Tibetan Plateau's endorheic basins (TPEBs), and the Northwest endorheic basins (NWEBs), whereas CMORPH-BLD showed the best performance in the other basins of the comprehensive quantitative assessment. (3) The spatial and temporal performances of SPPs varied considerably, and the performance of SPPs was generally better in the summer (rainy season) and in the southeastern coastal basins than in the winter (dry season) and in the northwestern basins. Overall, this study provides a reference for selecting appropriate SPPs for precipitation analysis over mainland China. [ABSTRACT FROM AUTHOR]

Hydrological analyses carried out based on precipitation in the Brazilian Legal Amazon (BLA) are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, data series with short periods and many gaps, especially at the daily scale, are a limitation in this region. In order to improve precipitation analysis, a non-parametric stochastic model based on Artificial Neural Networks (ANNs) was used to estimate daily precipitation in the BLA. For this purpose, 22 rain gauge stations were adopted and organized, taking into account the complete series and the seasonal periods (rainy and dry).The results obtained showed a good performance of the model, with ranges of MSE (0.0022–0.2665), MAPE (0.0083–1.5343) and RMSE (0.0017–0.0214), which characterize an acceptable estimate for the estimation daily precipitation, especially in those with a wetter climate and more frequent precipitation during the year, as is the case in those located in the Amazon Biome. However, in regions that suffer from droughts, such as the Amazon-Cerrado ecotone areas, the results were less satisfactory due to the greater recurrence of zeros in the historical series. The seasonal division into dry and rainy periods did not provide better estimates for the model, except for some rain gauge stations located at latitudes close to the equator. However, this study could support future research on the estimation of daily precipitation in the region. [ABSTRACT FROM AUTHOR]

The actual application of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) is restricted by the bias revealed by ground data. This study established seven regression models (RMs) to generate the adjusted IMERG estimates. Relatively stable parameters of the regression can be gained during the calibration. The calibration was performed by climatological adjustment, building a relationship between the 18-year data series of the original IMERG estimates and simultaneous daily data derived from 687 rain gauges in mainland China. A one-time modeling scheme was designed using all daily precipitation data as a calibration dataset. A two-time modeling scheme was established by dividing the calibration period into cold and warm seasons. Then, the relative bias (RB) and root-mean-square error (RMSE) were the evaluation indicators inspected during the validation. Three rounds of modeling were designed to provide corrections when near-real-time and post-real-time IMERG products produce time series. The main conclusions are as follows: (1) The two-time modeling scheme had a higher proportion than the one-time modeling scheme in having the lowest RMSE and absolute RB values. (2) Compared with the original IMERG estimates, the model-generated estimates in rounds 1 and 2 reduced the magnitude of the RB and RMSE at around 75% and 85% of gauges, respectively. (3) Polynomial RMs were the best-performing models in rounds 2 and 3. (4) The gauges where the RM failed to reduce the magnitude of the RB were mainly found in humid, plain, and low-latitude areas of mainland China. [ABSTRACT FROM AUTHOR]

Many efforts have been made by the scientific community to produce gridded datasets with high spatial resolution because they are essential for climate change assessment, impact studies, decision‐making, etc. This study fits into this context and describes the methods used to prepare a 5‐km gridded product of precipitation and minimum and maximum temperatures by merging observed data from meteorological stations, from 1981 to 2016, of Bangladesh, Nepal, and Pakistan with ERA5 reanalysis. The step‐by‐step methods for station data quality control and the development of the 5‐km gridded data are presented. Additionally, we use the 5‐km dataset to show the main climate features of the three countries, which facilitate comparison with other data sources in the literature. [ABSTRACT FROM AUTHOR]

The relationship between intensity-duration-frequency of rainfall is a significant tool for estimating flood discharge. According to the sparsely available rain gauge stations and the development of technology, it is possible to use satellite rainfall data with different temporal and spatial resolutions. PERSIANN rainfall data with a time resolution of 1 and 6 hours were used in this research. Also, the spatial resolution of these data is 0.04 x 0.04 degrees. Rainfall data from synoptic stations around the Kan basin were also used. Three common continuous probability distributions of Gamble, Pearson type 3, and Log Pearson type 3 with return periods of 2, 5, 10, 25, 50, and 100 years were investigated to calculate and check the IDF curve. In general, the precipitation intensity obtained from Gumble's method was more than Pearson Type 3's method. Log Pearson type 3 distribution did not provide acceptable results in this research. The two interpolation methods of inverse distance weighting and empirical Bayesian kriging were used to generalize the frequency intensity curves to the entire Kan basin. The results showed little difference between these two methods, except for Pearson type 3 probability distribution. [ABSTRACT FROM AUTHOR]

Heavy rainfall events prediction at the local scale imposes a big challenge for meteorological agencies over the complex terrain areas in India such as Assam, Uttarakhand, and Himachal Pradesh and causes flash floods with severe consequences throughout the area causing a huge socio-economical loss over these regions. Assam is currently experiencing severe flooding in June 2023. Due to the limits of deterministic numerical weather models in accurately forecasting these events, this work investigates the incorporation of deep learning (DL) models, particularly spatial attention-based U-Net, using simulated daily collected rainfall outputs from various parametrization schemes. This is a pioneering effort to improve district-scale rainfall using the spatio-attention U-Net DL method, particularly over the orographically complex region such as Assam. The proposed model outperformed individual and ensemble Weather Research and Forecasting (WRF) model outputs over four days in June 2022, demonstrating greater abilities to forecast rainfall at the district scale with a mean absolute error of less than 10 mm. Additionally, the proposed model considerably outperformed WRF models by 51.3% in categorical rainfall prediction, achieving a high prediction accuracy of 91.9%. Furthermore, the proposed model has demonstrated improved spatial variation as compared to the WRF model by correctly predicting severe rainfall occurrences at the district scale, including Barpeta, Kamrup, Kokrajhar, and Nalbari. The WRF projections regularly underestimated rainfall intensity (< 100 mm), whereas the DL model's estimates matched actual rainfall readings from the India Meteorological Department (> 150 mm). On the quantitative estimation of rainfall thresholds using different skill scores, Equitable threat score values are more than 0.5 for all categories for the proposed model. In a nutshell, the findings of the study have direct implications for improving early warning systems and associated follow-up action in terms of developing efficient strategies toward better preparedness, mitigation, and adaptation measures over complex hilly regions to reduce loss of lives and properties. [ABSTRACT FROM AUTHOR]

Earth – satellite microwave links such as TV-SAT can help for rainfall monitoring and could be a complement or an alternative to ground-based weather radars, rain gauges or satellites dedicated to Earth observation. Rain induced attenuation which is harmful for telecommunication is exploited here as an opportunistic way to estimate rain rate along the path link. This technique makes it possible to obtain rain measurements at a fine temporal resolution (a few tens of seconds) and with a spatial resolution of few kilometers, which is a good compromise for human activities such as civil security (watershed monitoring, flash flood), agriculture or transport. Among the advantages of this technique, one can note the low cost of the hardware used (which can be commercial) as well as that of its maintenance on site. However, measured attenuation does not directly provide rain intensity and some parameters have to be estimated. Among these, it is necessary to take into account the contribution of the natural radiation of the atmosphere. In this paper, we detail a theoretical framework allowing to estimate rainfall from the measurements of a low-cost sensor operating simultaneously over two parts of the Ku-frenquency band. Then this framework is assessed in a densely instrumented area in south of France, where it is shown that very good results are obtained when compared to rain gauges measurements, both in terms of overall rain accumulation and in terms of rainfall rate distribution. Then we apply this dual channel method in Ivory Coast, in the metropolitan area of Abidjan, where such an approach is very promising. It is shown that this technique when compared to rain gauge measurements give results far better than a single-channel naive approach neglecting natural radiation of atmosphere, but that there are still significant errors in the rain assessment, leading to a persistent underestimation of rain accumulation. Finally we discuss various effects that could lead to this remaining underestimation, opening the door to further studies. [ABSTRACT FROM AUTHOR]

Machine learning‐based approaches demonstrate a significant potential in radar quantitative precipitation estimation (QPE) applications. In contrast to conventional methods that depend on local raindrop size distributions, deep learning (DL) can establish an effective mapping from three‐dimensional radar observations to ground rain rates. However, the lack of transparency in DL models poses challenges toward understanding the underlying physical mechanisms that drive their outcomes. This study aims to develop a DL‐based QPE system and provide a physical explanation of radar precipitation estimation process. This research is designed by employing a deep neural network consisting of two modules. The first module is a quantitative precipitation estimation network that has the capability to learn precipitation patterns and spatial distribution from multidimensional polarimetric radar observations. The second module introduces a quantitative precipitation estimation shapley additive explanations method to quantify the influence of each radar observable on the model estimate across various precipitation intensities. Plain Language Summary: Ground radars can provide continuous spatial observations over large areas with high spatiotemporal resolutions, so they form the infrastructure for precipitation monitoring and observation in many countries. Recently, deep learning (DL) techniques have shown great potential for use in polarimetric radar‐based precipitation estimates. Nevertheless, the black‐box and turn‐key characteristics of DL models make it difficult for researchers to understand the model decision‐making process and cast doubt on the reliability of the model results. This study introduces a physically explainable polarization radar‐based quantitative precipitation estimation (QPE) system built on DL technology that can explain the causes of the precipitation estimates provided by deep learning models under different rainfall amounts. An experiment indicates that our model achieves better estimates than the conventional methods. Furthermore, the explainability methodology allows for visualization of the microphysical precipitation information. Being the initial attempt to apply explainability learning in the QPE domain, the explainability results may offer valuable guidance for rainfall estimation. Key Points: A polarimetric radar‐based rainfall estimation system is developed using deep neural networksThe deep learning‐based rainfall estimates generally outperform products derived from traditional parametric relationsThe proposed deep learning interpretation method can provide physical and statistical explanations of the model decision‐making process [ABSTRACT FROM AUTHOR]

Anomalous tropical longwave cloud‐radiative heating of the atmosphere is generated when convective precipitation occurs, which plays an important role in the dynamics of tropical disturbances. Defining the observed cloud‐radiative feedback as the reduction of top‐of‐atmosphere longwave radiative cooling per unit precipitation, the feedback magnitudes are sensitive to the observed precipitation data set used when comparing two versions of Global Precipitation Climatology Project, version 1.3 (GPCPv1.3) and the newer version 3.2 (GPCPv3.2). GPCPv3.2 contains larger magnitudes and variance of daily precipitation, which yields a weaker cloud‐radiative feedback in tropical disturbances at all frequencies and zonal wavenumbers. Weaker cloud‐radiative feedbacks occur in GPCPv3.2 at shorter zonal lengths on intraseasonal timescales, which implies a preferential growth at planetary scales for the Madden‐Julian oscillation. Phase relationships between precipitation, radiative heating, and other thermodynamic variables in eastward‐propagating gravity waves also change with the updated GPCPv3.2. Plain Language Summary: High‐altitude, widespread anvil clouds are generated when heavy convective precipitation occurs in the tropics. These clouds are not only a passive product produced by convection, but they also can subsequently enhance convection by trapping upward infrared radiative flux emitted by the Earth, effectively heating the atmosphere. This additional radiative heating effect can induce upward motion in the tropics, supporting the convective systems by transporting more humid air from below. However, the strength of this cloud‐radiative feedback is hard to estimate because global, continuous observations of surface precipitation are difficult to derive. In this study, the strength of the radiative feedback is calculated using the same product of observed radiative heating against two different observational precipitation products. A newer improved precipitation product yields much weaker radiative feedback strengths for all types of tropical weather systems. In addition, cloud‐radiative heating is found to substantially lag behind precipitation in certain fast, eastward‐propagating tropical rainfall systems in the newer precipitation product, unlike the older one. Why such a lag exists is unclear. The discrepancy of the estimation of cloud‐radiative feedback strengths and properties in the older versus the newer precipitation products indicates that our understanding of mechanisms supporting tropical disturbances is still incomplete. Key Points: The updated Global Precipitation Climatology Project (GPCP) precipitation product has more frequent high rain rates, yielding a weaker longwave radiative feedbackThe updated radiative feedback supports less moistening of the Madden‐Julian oscillation, but imposes stronger planetary scale selectionThe phase relationship between precipitation and thermodynamic fields in eastward‐propagating tropical waves are sensitive to GPCP versions [ABSTRACT FROM AUTHOR]

This study evaluated the accuracy of two new generation satellite rainfall estimates (SREs): Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and Integrated Multi-satellite Retrieval for GPM (IMERG) over Bolivia's complex terrain. These SREs were compared against rainfall data from rain gauge measurements on a point-to-pixel basis for the period 2002–2020. The evaluation was performed across three regions with distinct topographical settings: Altiplano (Highland), Valles (Midland), and Llanos (Lowland). IMERG exhibited better accuracy in rainfall detection than CHIRPS, with the highest rainfall detection skills observed in the Highland region. However, IMERG's higher rainfall detection skill was countered by its higher false alarm ratio. CHIRPS provided a more accurate estimation of rainfall amounts across the three regions, exhibiting low random errors and relative biases below 10%. IMERG tended to overestimate rainfall amounts, with marked overestimation by up to 75% in the Highland region. Bias decomposition revealed that IMERG's high false rainfall bias contributed to its marked overestimation of rainfall. We showcase the utility of long-term CHIRPS data to investigate spatio-temporal rainfall patterns and meteorological drought occurrence in Bolivia. The findings of this study offer valuable insights for choosing appropriate SREs for informed decision-making, particularly in regions of complex topography lacking reliable gauge data. [ABSTRACT FROM AUTHOR]

Evaluating drought parameters at the basin level is one of the fundamental processes for planning sustainable crop production. This study aimed to evaluate both short-term and long-term meteorological drought parameters within the Vaippar Basin, located in southern India, by employing the standardized precipitation index (SPI). Gridded rainfall values developed from 13 rain gauge stations were employed to calculate the SPI values. Drought parameters, encompassing occurrence, intensity, duration, frequency, and trends, were assessed for both short-term and long-term droughts. The study findings indicated that the occurrence of short-term drought was 51.7%, while that of long-term drought was 49.82%. Notably, the basin experienced extreme short-term droughts in 1980, 1998 and 2016 and long-term droughts in 1981, 2013, and 2017. Utilizing an innovative trend identification method for SPI values, a significant monotonic upwards trend was identified in October and December for short-term drought and in December for long-term drought. This study defined the minimum threshold rainfall, which represents the critical amount required to prevent short-term drought (set at 390 mm) and long-term drought (set at 635 mm). The drought severity recurrence curves developed in this study indicate that when the SPI values fall below − 1.0, short-term drought affects 25% of the basin area, while long-term drought impacts 50% of the basin area at a 20-year recurrence interval. Additionally, the drought hazard index (DHI), which combines drought intensity and severity, demonstrated higher values in the northwestern regions for short-term drought and in the southern areas for long-term drought. The study's findings, highlighting areas of drought vulnerability, severity, and recurrence patterns in the basin, direct the attention for timely intervention when drought initiates. [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

As wildfire increases in the western United States, so do postfire debris-flow hazards. The U.S. Geological Survey (USGS) has developed two separate models to estimate (1) rainfall intensity thresholds for postfire debris-flow initiation and (2) debris-flow volumes. However, the information necessary to test the accuracy of these models is seldom available. Here, we studied how well these models performed over a 2-year period in the 2020 Grizzly Creek Fire burn perimeter in Glenwood Canyon, Colorado, USA, through the development of a debris-flow response inventory. The study area had the advantage of a network of 11 rain gauges for rainfall intensity measurements and repeat lidar data for volume estimates. Our observations showed that 89 % of observed debris flows in the first year postfire were triggered by rainfall rates higher than the fire-wide rainfall threshold produced by the current USGS operational model (M1). No debris flows were observed in the second year postfire, despite eight rainstorms with intensities higher than the modeled rainfall threshold. We found that the operational model for debris-flow initiation rainfall thresholds works well in this region during the first year but may be too conservative in year 2 due to vegetation recovery and sediment depletion. However, rainfall thresholds in the second year can be improved by using updated remote sensing imagery to recalculate the debris-flow initiation likelihood with the M1 model. The current volume model overestimates for this region by a median value of 4.4 times. However, the offset between the model estimates and observations is approximately linear, and the volumes from the Grizzly Creek debris flows had a similar magnitude to historic postfire debris flows in the region. Consequently, the current volume model could be adjusted with a regional correction factor. [ABSTRACT FROM AUTHOR]

Rainfall time series with high temporal resolution play a crucial role in various hydrological fields, such as urban hydrology, flood risk management and soil erosion. Understanding the future changes in rainfall extreme values is essential for these applications. Since climate models typically offer daily resolution only, statistical downscaling in time seems a relevant and computationally effective solution. The micro-canonical cascade model conserves the daily rainfall amounts exactly, and having all model parameters expressed as physical interpretable probabilities avoids assumptions about future rainfall changes. Taking into account that short-duration rainfall extreme values are linked with high temperatures, the micro-canonical cascade model is further developed in this study. As the introduction of the temperature dependency increases the number of cascade model parameters, several modifications for parameter reduction are tested for 45 locations across Germany. To ensure spatial coherence with the climate model data, a composite product of radar and rain gauges with the same resolution was used for the estimation of the cascade model parameters. For the climate change analysis the core ensemble of the German Weather Service, which comprises six combinations of global and regional climate models, is applied for both RCP4.5 and RCP8.5 scenarios. For parameter reduction two approaches were analysed: (i) the reduction via position-dependent probabilities and (ii) parameter reduction via scale independency. A combination of both approaches led to a reduction in the number of model parameters (48 parameters instead of 144 in the reference model) with only a minor effect on the disaggregation results. The introduction of the temperature dependency improves the disaggregation results, particularly regarding rainfall extreme values and is therefore important to consider for future studies. For the disaggregated rainfall time series of climate scenarios, an intensification of the rainfall extreme values is observed. Analyses of rainfall extreme values for different return periods for a rainfall duration of 5 min and 1 h indicate an increase of 5 %–10 % in the near-term future (2021–2050) and 15 %–25 % in the long-term future (2071–2100) compared to the control period (1971–2000). [ABSTRACT FROM AUTHOR]

Comprehensive evaluations of global precipitation datasets are imperative for gaining insights into their performance and potential applications. However, the existing evaluations of global precipitation datasets are often constrained by limitations regarding the datasets, specific regions, and hydrological models used for hydrologic predictions. The accuracy and hydrological utility of eight precipitation datasets (including two gauged-based, five reanalysis and one merged precipitation datasets) were evaluated on a daily timescale from 1982 to 2015 in this study by using 2404 rain gauges, 2508 catchments, and four lumped hydrological models under varying climatic conditions worldwide. Specifically, the characteristics of different datasets were first analyzed. The accuracy of precipitation datasets at the site and regional scale was then evaluated with daily observations from 2404 gauges and two high-resolution gridded gauge-interpolated regional datasets. The effectiveness of precipitation datasets in runoff simulation was then assessed by using 2058 catchments around the world in combination with four conceptual hydrological models. The results show that: (1) all precipitation datasets demonstrate proficiency in capturing the interannual variability of the annual mean precipitation, but with magnitudes deviating by up to 200 mm/year among the datasets; (2) the precipitation datasets directly incorporating daily gauge observations outperform the uncorrected precipitation datasets. The Climate Precipitation Center dataset (CPC), Global Precipitation Climatology Center dataset (GPCC) and multi-source weighted-ensemble precipitation V2 (MSWEP V2) can be considered the best option for most climate regions regarding the accuracy of precipitation datasets; (3) the performance of hydrological models driven by different datasets is climate dependent and is notably worse in arid regions (with median Kling–Gupta efficiency (KGE) ranging from 0.39 to 0.65) than in other regions. The MSWEP V2 posted a stable performance with the highest KGE and Nash–Sutcliffe Efficiency (NSE) values in most climate regions using various hydrological models. [ABSTRACT FROM AUTHOR]

Decomposition is a key determinant of forest functioning, controlling nutrient and carbon cycling. Although litter-mixing effects on decomposition (that is, using mixtures of litter of different species) have been studied extensively, less is known about the indirect effects of modified microenvironments via overstory tree species mixing. To investigate the effects of tree species diversity on decomposition, we installed 384 standardized litterbags, filled with leaf litter of four broadleaved tree species with contrasting litter quality, in a large, 10-year-old tree diversity experiment. To quantify microenvironments, we used microclimate sensors, below-canopy rain gauges and measured soil characteristics. We then analysed indirect tree species diversity effects, that is, tree species richness effects on mass loss rates via tree species-induced alterations in the microclimate, throughfall and soil characteristics. We found that understory microenvironmental conditions indeed affect mass loss rates, with the main drivers differing among incubation stages. Predominantly soil phosphorus, but also vapour pressure deficit and throughfall amounts, was negatively associated with mass loss rates across litter types during the first 2 months of the decomposition process. After 6 months of the decomposition, soil moisture was found to be the key determinant positively affecting mass loss rates. In sum, our research contributes to a better understanding of the determinants of decomposition and shows an important pathway in which tree species diversity affects decomposition, via modified microenvironmental conditions acting via the soil, microclimate and throughfall. [ABSTRACT FROM AUTHOR]

To enhance the capabilities of the Subseasonal to Seasonal (S2S) database for forecasting Iran's southwest precipitation from 1 to 4 weeks ahead, we compared observed precipitation and atmospheric variables with the corresponding hindcasts generated by the KMA, UKMO, ECWMF, and Meteo-France (MF) research centers. This analysis involved several deterministic and probabilistic metrics. Our reference datasets included daily precipitation data from 176 rain gauge stations and the NOAA-based atmospheric circulations data for Dec-April 1995–2014. Most hindcasts underestimated wet events in southern and eastern districts but overestimated them in the western and northern regions. Additionally, all hindcasts overforecasted the frequency of wet events across all lead times. The correlation scores were highest in the first week and declined as lead times increased. The ECMWF had the best correlation in all regions, showing superior deterministic and probabilistic forecast skills in western districts. The UKMO hindcasts, whose accuracy has the highest dependancy on precipitation amount, effectively captured signals of the El-Niño Southern Oscillation (ENSO) and Madden Julian Oscillation (MJO) over the study area and the Middle East. They accurately forecasted the 850 hPa moisture transport and 500 hPa vertical velocity features in these regions, particularly during the rainy phases of MJO. These findings offer valuable insights to enhance the accuracy of operational S2S precipitation forecasts for planning and decision-making in Iran and the Middle East. [ABSTRACT FROM AUTHOR]