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The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products.

This study introduces methodologies for constructing joint probability distribution functions utilizing the Copula function and neural networks, and evaluates their efficacy in marine and civil engineering projects. Through an analytical comparison of both models using a numerical example, it is revealed that the neural network model exhibits superior adaptability to large sample sizes. This adaptability is attributed to the neural network's ability to learn complex relationships within the data, which is especially beneficial when dealing with large datasets. The neural network model also demonstrates higher accuracy in constructing joint probability distribution functions compared to the Copula function model. In marine and civil engineering, the adaptability and accuracy of neural networks are of paramount importance due to the variable and complex nature of weather patterns. A practical engineering application is presented, wherein a joint probabilistic distribution neural network model of wind velocity and rain intensity is established for the Lanzhou–Xinjiang high-speed railroad in China. This model illustrates the promising application of neural networks in engineering projects where weather factors play a critical role. Subsequent to the construction of the joint probability distribution functions, a feature importance analysis is incorporated to quantify the contribution of different weather parameters such as wind velocity and rain intensity to the joint distribution function. This analysis provides an objective assessment of the relative importance of various weather factors and offers data-driven insights that are essential for engineering applications where weather conditions are a significant consideration. The study concludes by highlighting the potential benefits of neural network models in marine and civil engineering, suggesting areas for future exploration.

Integrated Multi-satellite Retrievals for GPM (IMERG) data have been widely used to analyze extreme precipitation, but the data have never been validated for the Indonesian Maritime Continent (IMC). This study evaluated the capability of IMERG Early (E), Late (L), and Final (F) data to observe extreme rain in the IMC using the rain gauge data within five years (2016–2020). The capability of IMERG in the observation of the extreme rain index was evaluated using Kling–Gupta efficiency (KGE) matrices. The IMERG well captured climatologic characteristics of the index of annual total precipitation (PRCPTOT), number of wet days (R85p), number of very wet days (R95p), number of rainy days (R1mm), number of heavy rain days (R10mm), number of very heavy rain days (R20mm), consecutive dry days (CDD), and max 5-day precipitation (RX5day), indicated by KGE value >0.4. Moderate performance (KGE = 0–0.4) was shown in the index of the amount of very extremely wet days (R99p), the number of extremely heavy precipitation days (R50mm), max 1-day precipitation (RX1day), and Simple Daily Intensity Index (SDII). Furthermore, low performance of IMERG (KGE < 0) was observed in the consecutive wet days (CWDs) index. Of the 13 extreme rain indices evaluated, IMERG underestimated and overestimated precipitation of nine and four indexes, respectively. IMERG tends to overestimate precipitation of indexes related to low rainfall intensity (e.g., R1mm). The highest overestimation was observed in the CWD index, related to the overestimation of light rainfall and the high false alarm ratio (FAR) from the daily data. For all indices of extreme rain, IMERG showed good capability to observe extreme rain variability in the IMC. Overall, IMERG-L showed a better capability than IMERG-E and -F but with an insignificant difference. Thus, the data of IMERG-E and IMERG-L, with a more rapid latency than IMERG-F, have great potential to be used for extreme rain observation and flood modeling in the IMC.

This study provides an updated analysis of the evolution of seasonal rainfall intensity in the Amazon basin, considering the 1981–2017 period and based on HOP (interpolated HYBAM observed precipitation) and CHIRPS (The Climate Hazards Group Infrared Precipitation with Stations) rainfall data sets. Dry and wet day frequencies as well as extreme percentiles are used in this analysis, producing the same results. Dry-day frequency (DDF) significantly increases in the Southern Amazon (p < 0.01), particularly during September–November (SON) in the Bolivian Amazon, central Peruvian Amazon and far southern Brazilian Amazon. Consistently, total rainfall in the southern Amazon during SON also shows a significant diminution (p < 0.05), estimated at 18%. The increase in SON DDF in the southern Amazon is related to a warming of the northern tropical Atlantic Ocean and a weakening of water vapour flux from the tropical Atlantic Ocean. The increase in DDF in the southern Amazon is related to enhanced wind subsidence (ascendance) over the 10°S–20°S (5°S–5°N) region and to a deficit (excess) of specific humidity at 1000–300 hPa south of 10°S (north of the 5°S), which suggest a reduction of deep convection over southern Amazonia. Subsidence over the southern Amazon shows a significant trend (p < 0.01), which can explain the significant increase in DDF. Wet-day frequency (WDF) significantly increases in the northern Amazon, particularly during the March–May (MAM) period (p < 0.01), producing an estimated rainfall increase during MAM of 17% (p < 0.01) between 1981 and 2017. Significant changes in both WDF and rainfall in northern Amazon have been detected in 1998 (p < 0.01). After 1998, the increase in MAM WDF and rainfall is explained by enhanced moisture flux from the tropical North Atlantic Ocean and an increase in deep convection over the northern and northwestern Amazon. These evolutions in DDF and WDF and in the tropical atmosphere occur simultaneously with an increase in sea surface temperature in the northern Atlantic Ocean, particularly after the mid-1990s. These results provide new insight into rainfall variability and climatic features related to increasing dry season length in southern Amazonia. Severe recent droughts may be associated with the increase in DDF in the South. In addition, the increase in MAM rainfall intensity in northern Amazon after 1998 may be associated with several historical floods that occurred after this date.

In this study, analysis of the long term climatology, variability and trends in the daily rainfall events of ≥5 mm [or daily rainfall (DR) events] during the southwest monsoon season (June–September) over four regions of India; south central India (SCI), north central India (NCI), northeast India (NEI) and west coast (WC) have been presented. For this purpose, a new high spatial resolution (0.25° × 0.25°) daily gridded rainfall data set covering 110 years (1901–2010) over the Indian main land has been used. The association of monsoon low pressure systems (LPSs) with the DR events of various intensities has also been examined. Major portion of the rainfall over these regions during the season was received in the form of medium rainfall (≥5–100 mm) or moderate rainfall (MR) events. The mean seasonal cycle of the daily frequency of heavy rainfall (HR) (≥100–150 mm) or HR events and very heavy rainfall (VHR) (≥150 mm) or VHR events over each of the four regions showed peak at different parts of the season. The peak in the mean daily HR and VHR events occurred during middle of July to middle of August over SCI, during late part of June to early part of July over NCI, during middle of June to early July over NEI, and during late June to middle July over WC. Significant long term trends in the frequency and intensity of the DR events were observed in all the four geographical regions. Whereas the intensity of the DR events over all the four regions showed significant positive trends during the second half and the total period, the signs and magnitude of the long term trends in the frequency of the various categories of DR events during the total period and its two halves differed from the region to the region. The trend analysis revealed increased disaster potential for instant flooding over SCI and NCI during the recent years due to significant increasing trends in the frequency (areal coverage) and intensity of the HR and VHR events during the recent half of the data period. However, there is increased disaster potential over NEI and WC due to the increasing trends in the intensity of the rainfall events. There is strong association between the LPS days and the DR events in both the spatial and temporal scales. In all the four regions, the contributions to the total MR events by the LPS days were nearly equal. On the other hand, there was relatively large regional difference in the number of combined HR and VHR events associated with LPS days particularly that associated with monsoon depression (LPS stronger than monsoon depression) days. The possible reasons for the same have also been discussed. The increasing trend in the monsoon low (low pressure) days post 1970s is the primary reason for the observed significant increasing trends in the HR and VHR events over SCI and NCI and decreasing trend in HR events over NEI during the recent half (1956–2010). This is in spite of the decreasing trend in the MD days.

Recent studies have examined the relationship between the intensity of extreme rainfall and temperature. Two main reasons justify this interest. First, the moisture-holding capacity of the atmosphere is governed by the Clausius–Clapeyron (CC) equation. Second, the temperature dependence of extreme-intensity rainfalls should follow a similar relationship assuming relative humidity remains constant and extreme rainfalls are driven by the actual water content of the atmosphere. The relationship between extreme rainfall intensity and air temperature (P extr–Tₐ) was assessed by analyzing maximum daily rainfall intensities for durations ranging from 5 min to 12 h for more than 100 meteorological stations across Canada. Different factors that could influence this relationship have been analyzed. It appears that the duration and the climatic region have a strong influence on this relationship. For short durations, theP extr–Tₐ relationship is close to the CC scaling for coastal regions while a super-CC scaling followed by an upper limit is observed for inland regions. As the duration increases, the slope of the relationshipP extr–Tₐ decreases for all regions. The shape of theP extr–Tₐ curve is not sensitive to the percentile or season. Complementary analyses have been carried out to understand the departures from the expected Clausius–Clapeyron scaling. The relationship between dewpoint temperature and extreme rainfall intensity shows that the relative humidity is a limiting factor for inland regions, but not for coastal regions. Using hourly rainfall series, an event-based analysis is proposed in order to understand other deviations (super-CC, sub-CC, and monotonic decrease). The analyses suggest that the observed scaling is primarily due to the rainfall event dynamic.

Global warming has a profound impact on the vulnerable environment of West Africa; hence, robust climate projection, especially of rainfall extremes, is quite important. Based on two representative concentration pathway (RCP) scenarios, projected changes in extreme summer rainfall events over West Africa were investigated using data from the Coordinated Regional Climate Downscaling Experiment models. Eight (8) extreme rainfall indices (CDD, CWD, r10mm, r20mm, PRCPTOT, R95pTOT, rx5day, and sdii) defined by the Expert Team on Climate Change Detection and Indices were used in the study. The performance of the regional climate model (RCM) simulations was validated by comparing with GPCP and TRMM observation data sets. Results show that the RCMs reasonably reproduced the observed pattern of extreme rainfall over the region and further added significant value to the driven GCMs over some grids. Compared to the baseline period 1976–2005, future changes (2070–2099) in summer rainfall extremes under the RCP4.5 and RCP8.5 scenarios show statistically significant decreasing total rainfall (PRCPTOT), while consecutive dry days and extreme rainfall events (R95pTOT) are projected to increase significantly. There are obvious indications that simple rainfall intensity (sdii) will increase in the future. This does not amount to an increase in total rainfall but suggests a likelihood of greater intensity of rainfall events. Overall, our results project that West Africa may suffer more natural disasters such as droughts and floods in the future.

Accurate and timely surface precipitation measurements are crucial for water resources management, agriculture, weather prediction, climate research, as well as ground validation of satellite-based precipitation estimates. However, the majority of the land surface of the earth lacks such data, and in many parts of the world the density of surface precipitation gauging networks is even rapidly declining. This development can potentially be counteracted by using received signal level data from the enormous number of microwave links used worldwide in commercial cellular communication networks. Along such links, radio signals propagate from a transmitting antenna at one base station to a receiving antenna at another base station. Rain-induced attenuation and, subsequently, path-averaged rainfall intensity can be retrieved from the signal’s attenuation between transmitter and receiver. Here, we show how one such a network can be used to retrieve the space–time dynamics of rainfall for an entire country (The Netherlands, ∼35,500 km ²), based on an unprecedented number of links (∼2,400) and a rainfall retrieval algorithm that can be applied in real time. This demonstrates the potential of such networks for real-time rainfall monitoring, in particular in those parts of the world where networks of dedicated ground-based rainfall sensors are often virtually absent.

This study examines trends and change points in agroclimatic variables at 56 meteorological stations’ locations and region levels in Rwanda’s Eastern Province from 1981 to 2021. We used the Mann–Kendall and Regional Kendall tests, along with Sen’s Slope and Sequential Mann–Kendall Rank Statistic tests, to analyse six key agricultural indicators: seasonal rainfall totals, number of rainy days, rainfall intensity (light, moderate, heavy), onset and cessation dates, and season duration. In the March to May (MAM) season, 39 out of 56 stations recorded a decreasing rainfall trend, with significant trends observed at eight stations in the south. Conversely, 17 stations showed increasing trends, with only one in the north being significant. Regionally, the trend was a non-significant decrease. In the September to December (SOND) season, 31 stations (one significant) experienced decreasing rainfall trends. Among the 25 stations showing increasing trends, only one was significant. The regional trend indicates a non-significant increase. Onset days showed a decreasing trend at 41 stations (12 significant) in both MAM and SOND and a significant regional trend in SOND. Season duration increased at 43 stations in MAM (five significant) and 48 stations in SOND (six significant), with the regional trend being significant only in SOND. Heavy rainy days indicate a significantly regional decreasing trend in MAM. The change point of most stations with decreasing and increasing trends occurred between 2000–2020 and 1980–2000, respectively. These fluctuations have affected agricultural practices and led to crop failures, emphasizing the region’s need for better climate information services and adaptation strategies.

Precipitation is one of the integral components of the global hydrological cycle. Accurate estimation of precipitation is vital for numerous applications ranging from hydrology to climatology. Following the launch of the Global Precipitation Measurement (GPM) Core Observatory, the Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product was released. The IMERG provides global precipitation estimates at finer spatiotemporal resolution (e.g., 0.1°/half-hourly) and has shown to be better than other contemporary multi-satellite precipitation products over most parts of the globe. In this study, near-real-time and research products of IMERG have been extensively evaluated against a daily rain-gauge-based precipitation dataset over India for the southwest monsoon period. In addition, the current version 6 of the IMERG research product or Final Run (IMERG-F V6) has been compared with its predecessor, version 5, and error characteristics of IMERG-F V6 for pre-GPM and GPM periods have been assessed. The spatial distributions of different error metrics over the country show that both near-real-time IMERG products (e.g., Early and Late Runs) have similar error characteristics in precipitation estimation. However, near-real-time products have larger errors than IMERG-F V6, as expected. Bias in all-India daily mean rainfall in the near-real-time IMERG products is about 3–4 times larger than research product. Both V5 and V6 IMERG-F estimates show similar error characteristics in daily precipitation estimation over the country. Similarly, both near-real-time and research products show similar characteristics in the detection of rainy days. However, IMERG-F V6 exhibits better performance in precipitation estimation and detection of rainy days during the GPM period (2014–2017) than the pre-GPM period (2010–2013). The contribution of different rainfall intensity intervals to total monsoon rainfall is captured well by the IMERG estimates. Furthermore, results reveal that IMERG estimates under-detect and overestimate light rainfall intensity of 2.5–7.5 mm day−1, which needs to be improved in the next release. The results of this study would be beneficial for end-users to integrate this multi-satellite product in any specific application.