Search Results (40)

By integrating EarthCARE W-band doppler cloud radar observations with GPM Ku/Ka-band dual-frequency precipitation radar data, this study constructs a novel global “pseudo tripe-frequency” radar coincidence dataset comprising 2886 coincidence events (about one-third of the events detected precipitation), aiming to systematically investigating band-dependent responses to cloud and precipitation structure. Results demonstrate that the W-band is highly sensitive to high-altitude cloud particles and snowfall (reflectivity < 0 dBZ), yet it experiences substantial signal attenuation under heavy precipitation conditions, and with low-altitude reflectivity reductions exceeding 50 dBZ, its probability density distribution is more widespread, with low-altitude peaks increasing first, and then decreasing as precipitation increases. In contrast, the Ku and Ka-band radars maintain relatively stable detection capabilities, with attenuation differences generally within 15 dBZ, but its probability density distribution exhibits multiple peaks. As the precipitation rate increases, the peak value of the dual-frequency ratio (Ka/W) gradually rises from approximately 10 dBZ to 20 dBZ, and can even reach up to 60 dBZ under heavy rainfall conditions. Several cases analyses reveal clear contrasts: In stratiform precipitation regions, W-band radar reflectivity is higher above the melting layer than below, whereas the opposite pattern is observed in the Ku and Ka bands. Doppler velocities exceeding 5 m s⁻¹ and precipitation rates surpassing 30 mm h⁻¹ exhibit strong positive correlations in convection-dominated regimes. Furthermore, the dataset confirms the impact of ice–water cloud phase interactions and terrain-induced precipitation variability, underscoring the complementary strengths of multi-frequency radar observations for capturing diverse precipitation processes. Full article

Accelerating climate change poses significant risks to water security and ecological stability in arid regions due to the increasing frequency and intensity of extreme precipitation events. As a climate-sensitive area, the inland river basin (IRB) of Northwest China—a critical water source for local ecosystems and socioeconomic activities—remains insufficiently studied in terms of future extreme precipitation dynamics. This study evaluated the spatiotemporal evolution of extreme precipitation in the IRB under a new low radiative forcing scenario (SSP1-1.9) by employing four global climate models (GCMs: GFDL-ESM4, MRI-ESM2, MIROC6, and IPSL-CM6A-LR). Eight core extreme precipitation indices were analyzed to quantify changes during the near future (NF: 2021–2050) and far future (FF: 2071–2100) periods. Our research demonstrated that all four models were capable of capturing seasonal patterns and exhibited inherent uncertainty. The annual total precipitation (PRCPTOT) in mountainous regions showed minimal variation, while desert areas were projected to experience a 2-6-fold increase in precipitation in the NF and FF. The Precipitation Intensity Index (SDII) weakened by approximately −10% in mountainous areas but strengthened by around +10% in desert regions. Most mountainous areas showed an increase in the maximum consecutive dry days (CDD), whereas desert regions exhibited extended maximum consecutive wet days (CWD). Moderate rainfall (P1025) variations primarily ranged between −5% and +20%, with greater fluctuations in desert areas. Heavy rainfall (PG25) fluctuated between −40% and +40%, reflecting stark contrasts in extreme precipitation between arid basins and mountainous zones. The maximum 1-day precipitation (Rx1day) and maximum 5-day precipitation (Rx5day) both showed significant increases, which indicated heightened risks from extreme rainfall events in the future. Moreover, the IRB region experienced increased total precipitation, enhanced rainfall intensity, more frequent alternations between drought and precipitation, more frequent moderate-to-heavy rainfall days, and higher daily precipitation extremes in both the NF and FF periods. These findings provide critical data for regional development planning and emergency response strategy formulation. Full article

Rainfall variability in the sensitive Kalahari semi-desert in Southern Africa, a region of strong climatic gradients, has not been much studied and is poorly understood. Here, anomalies in rainfall totals and moderate and heavy rain day frequencies are examined for both the summer half of the year and three bi-monthly seasons using CHIRPS rainfall data and ERA5 reanalysis. Peak rainfall occurs in January–February, with anomalously wet summers marked by a significant increase in the number of rainy days rather than rainfall intensity. Wet summers are linked to La Niña events, cyclonic anomalies over Angola, and a weakened Botswana High, which enhances low-level moisture transport and convergence over the region as well as mid-level uplift. Roughly the reverse patterns are found during anomalously dry summers. On sub-seasonal scales, ENSO and the Botswana High (the Southern Annular Mode) are negatively (positively) significantly correlated with early summer rainfall, while in mid-summer, and for the entire November–April season, only ENSO and the Botswana High are correlated with rainfall amounts. In the late summer, weak negative correlations remain with the Botswana High, but they do not achieve 95% significance. Full article

This study aims to analyze how the climatic conditions in the city of João Pessoa, Brazil, influence sustainable tourism, with a specific focus on Climate–Tourism/Transfer–Information–Scheme (CTIS), Physiologically Equivalent Temperature (PET), and rainfall patterns. It also compares these aspects with those of Salvador and Rio de Janeiro to identify climatic patterns, local challenges, and adaptive strategies relevant to the growing tourism context, based on hourly and monthly climate data from 2014 to 2024. The results show that João Pessoa presents a more stable thermal regime with fewer extreme heat events, yet consistently higher daytime PET values, especially between 9:00 and 15:00, throughout the year. The city also experiences a greater frequency of moderate-to-heavy rainfall during its defined wet season (April to July), often influenced by low-predictability atmospheric systems such as Easterly Wave Disturbances (EWDs). CTIS results confirm high climatic suitability for tourism and recreation during the dry season but reduced suitability during the rainy season. These findings suggest that integrating climate adaptation strategies into tourism planning, such as diversifying attractions beyond sun-and-beach tourism and improving real-time climate communication, may help reduce the impact of seasonal variability on visitor experience. Full article

Catastrophic precipitation is an inherent feature of temperate climates. Its occurrence is a manifestation of climate change, but also of the variability of atmospheric circulation. Mountainous areas may be particularly vulnerable as they receive more precipitation and are also areas where relief plays an important role in modifying the distribution of precipitation. One such area is the Polish Western Carpathian Mountains, especially the area around the city of Bielsko-Biała, located at their foot and directly exposed to rain-bearing winds. In 2024, two episodes of unusually heavy precipitation in quick succession occurred in this area, resulting in severe damage to infrastructure. This painful experience inspired a study focusing on the frequency of such catastrophic precipitation events and their synoptic circumstances spanning the period from the mid-20th century to the present day. Daily precipitation totals covering the study period of 74 years were used to identify a category of catastrophic precipitation (here set at above 100 mm). The six events identified to match the criteria appeared from May to September, always accompanied by cyclonic circulation types with advection from the northern sector and with a cyclonic trough situation over southern Poland. The study showed that the leading role in their formation was played by deep convection, especially a Genoa low moving along the Vb Van Bebber track. The damage and destruction suffered as a result were a consequence of the cumulative impact of high-intensity rainfall, itself caused by a combination of specific synoptic thermodynamic and orographic conditions. Full article

Heavy rainfall significantly impacts agriculture by damaging crops and causing substantial economic losses. The Paravanar River Basin, a coastal river basin in India, experiences heavy rainfall during the monsoon season. This study analyzed both ground-level rainfall measurements and farmers’ experiences to understand the effects of heavy rainfall on agriculture. Rainfall data from nine rain gauge locations were analyzed across three cropping seasons: Kharif 1 (June to August), Kharif 2 (September to November), and Rabi (December to May). To determine the frequency of heavy rainfall events, a detailed analysis was conducted based on the standards set by the India Meteorological Department (IMD). Villages near stations showing increasing rainfall trends and a higher frequency of heavy rainfall events were classified as vulnerable. The primary crops cultivated in these vulnerable areas were identified through a questionnaire survey with local farmers. A detailed analysis of these crops was conducted to determine the cropping season most affected by heavy rainfall events. The impacts of heavy rainfall on the primary crops were assessed using the Delphi technique, a score-based crop risk assessment method. These impacts were categorized into eight distinct types. Among them, yield reduction, waterlogging, crop damage, soil erosion, and crop failure emerged as the most significant challenges in the study area. Additional impacts included nutrient loss, disrupted microbial activity, and disease outbreaks. Based on this evaluation, risks were classified into five categories: low risk, moderate risk, high risk, very high risk, and extreme risk. This categorization offers a framework for understanding potential consequences and making informed decisions. To address these challenges, the study recommended mitigation measures such as crop management, soil management, and drainage management. Farmers were also encouraged to conduct a cause-and-effect analysis. This bottom-up approach raised awareness among farmers and provided practical solutions to reduce crop losses and mitigate the effects of heavy rainfall. Full article

Rainfall variability and its underlying physical mechanisms are crucial for improving the predictive accuracy of July rainfall patterns in the Sichuan-Shaanxi (SS) region of Southwestern China. This study utilized observational 24 h accumulated rainfall data from China in conjunction with reanalysis products sourced from the European Centre for Medium-Range Weather Forecasts (ECMWF). The purpose of this study was to elucidate the relationship between daily variations in the daily Asian-Pacific Oscillation (APO), atmospheric circulation, and daily rainfall patterns in the SS region, and to evaluate the impact of atmospheric circulation anomalies on these relationships. The results reveal a discernible intensification in the sea–land thermal contrast associated with atmospheric circulation anomalies transitioning from the daily extremely low APO (ELA) to the extremely high APO (EHA) days. These conditions lead to an increased presence of water vapor and widespread anomalies in rainfall that exceed normal levels in the SS region. Concurrently, the increase in stations experiencing extreme rainfall events (EREs) accounts for 21.3% of the overall increase in stations experiencing rainfall. The increase in rainfall amount contributed by EREs (RA-EREs) accounts for 73.5% of the overall increase in the total rainfall amount (TRA) across the SS region. Specifically, heavy rainfall (HR) and downpour rainfall (DR) during EREs accounted for 65.7% (HR) and 95.3% (DR) of the overall increase in the TRA, respectively. Relative to the ELA days, there was a substantial 122.6% increase in the occurrence frequency of EREs and a 23.3% increase in their intensity. The study suggests that the daily APO index emerges as a better indicator of July rainfall events in the SS region, with EREs significantly contributing to the overall increase in rainfall in this region. These findings indicate the importance of improving predictive capabilities for daily variability in the APO index and their correlation with rainfall events in the SS region. The results may inform the development of effective adaptation and mitigation strategies to manage the potential impacts of EREs on agriculture, water resources, sustainable development, and infrastructure in the region. Full article

Heavy rainfall under the conditions of the changing climate has recently garnered considerable attention. The statistics on heavy daily rainfall offer vital information for assessing present and future extreme events and for clarifying the impacts of global climate variability and change, working to form a favorable background. By analyzing a set of large-ensemble simulations using a global atmospheric model, this study demonstrated that two different physical processes in global climate variability control the interannual fluctuations in the 99th- and 90th-percentile values of summertime daily rainfall (i.e., the potential amounts) on Kyushu Island in western Japan. The 90th-percentile values were closely related to large-scale horizontal moisture transport anomalies due to changes in the subtropical high in the northwestern Pacific, which was usually accompanied by basin-scale warming in the Indian Ocean subsequent to the wintertime El Niño events. The contributions of the sea surface temperatures over the northern Indian Ocean and the eastern tropical Pacific Ocean showed low-frequency modulations, mainly due to the influences of the global warming tendency and the interdecadal variability in the climate system, respectively. In contrast, tropical cyclone activity played a major role in changing the 99th-percentile value. The potentials of both the tropical cyclone intensity and the existence density fluctuated, largely owing to the summertime sea surface temperature over the tropical Pacific, which can be modulated by the El Niño diversity on interdecadal timescales. Full article

This study analyzes the spatial distribution and trends in five extreme daily rainfall indices in the Uruguay River Basin (URB) from 1993 to 2022 using the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset. The main findings reveal a predominantly positive trend in heavy precipitation (R95p) and extreme precipitation (R99p) events over the mid URB, while a negative trend is observed in the upper and low URB. Significant trends in the frequency of heavy and extreme rainfall were observed during autumn (MAM), with positive trends across most of the mid and upper URB and negative trends in the low URB. In the upper URB, negative trends in the frequency of extremes were also found during spring (SON) and summer (DJF). Overall, there was a reduction in the number of consecutive wet days (CWD), particularly significant in the upper URB and the northern half of the mid URB. Additionally, the upper URB experienced an overall increase in the duration of consecutive dry days (CDD). Full article

The Yangtze River Delta (YRD) is an important economic region in China. Heavy winter rainfall may pose serious threats to city operations. To ensure the safe operation of the city, meteorological departments need to provide forecast results for the Spring Festival travel rush weather service. Therefore, the extended-range forecast of winter rainfall is of considerable importance. To solve this problem, based on the analysis of low-frequency rainfall and the intra-seasonal oscillation of atmospheric circulation, an extended-range forecast model for winter rainfall is developed using spatiotemporal projection methods and is applied to a case study from 2020. The results show that: (1) The precipitation in the YRD during the winter has a significant intra-seasonal oscillation (ISO) with a periodicity of 10–30 d. (2) The atmospheric circulations associated with winter rainfall in the YRD have a significant characteristic of low-frequency oscillation. From a 30-day to a 0-day lead, large modifications appear in the low-frequency atmospheric circulations at low, mid, and high latitudes. At low latitudes, strong wet convective activity characterized by a negative OLR combined with a positive RH700 correlation coefficient moves northwestward and covers the entire YRD. Meanwhile, the Western Pacific subtropical high (WPSH) characterized by a positive Z500 anomaly enhances and lifts northward. At mid and high latitudes, the signal of negatively correlated Z500 northwest of Lake Balkhash propagates southeastward, indicating the cold is air moving southward. Multiple circulation factors combine together and lead to the precipitation process in the YRD. (3) Taking the intra-seasonal dynamical evolution process of the atmospheric circulation as the prediction factor, the spatiotemporal method is used to build the model for winter mean extended-range precipitation anomaly tendency in the YRD. The hindcast for the recent 10 years shows that the ensemble model has a higher skill that can reach up to 20 days. In particular, the skill of the eastern part of the YRD can reach 25 days. (4) The rainfall in the 2019/2020 winter has a significant ISO. The ensemble model could forecast the most extreme precipitation for 20 days ahead. Full article

After multiple simultaneous landslides caused by heavy rainfall, expanding landslides continue to occur for a certain duration. Evaluation of the influencing period of sediment yield due to expanding landslides is vital for comprehensive sediment management of the basin. In this study, we investigated a region with a low frequency of heavy rainfall that has not received its due level of attention until now. Consequently, the transition of expanding landslides depends on the transition of the number of remaining landslides, based on the difference in the frequency of heavy rainfall. Furthermore, the transition of expanding landslides depends on the maximum daily rainfall after the landslides. These findings indicate that “the number of remaining landslides” and “maximum daily rainfall after a landslide” are related factors that determine the period during which expanding landslides frequently occur. An estimation formula based on elapsed time was developed to calculate the number of remaining landslides. An empirical formula for the number of expanding landslides was obtained by multiplying the function of the daily maximum rainfall after the landslide by the estimation formula for the number of remaining landslides. The developed empirical formula can be used effectively for evaluation during periods when rainfall-induced landslides are subject to subsequent expansion. Full article

The High Resolution Model Intercomparison Project (HighResMIP) experiment within the Coupled Model Intercomparison Project Phase 6 (CMIP6) has enabled the evaluation of the performance of climate models over complex terrain for the first time. The study aims to evaluate summer (June to August) precipitation characteristics over the Tibetan Plateau (TP). Precipitation derived from HighResMIP models and ERA5 are compared against the China Merged Precipitation Analysis (CMPA). The nineteen models that participated in HighResMIP are classified into three categories based on their horizontal resolution: high resolution (HR), middle resolution (MR), and low resolution (LR). The multimodel ensemble means (MMEs) of the three categories of models are evaluated. The spatial distribution and elevation dependency of the hourly precipitation characteristics, which include the diurnal peak hour, diurnal variation amplitude, and frequency–intensity structure, are our main focus. The MME-HR and ERA5 both show comparable ability in simulating precipitation in the TP. The MME-HR has a smaller deviation in the precipitation amount and diurnal variation at various altitudes. The ERA5 can better simulate the elevation dependence of the frequency–intensity structure, but its elevation dependence of diurnal variation shows a trend opposite to the observations. Although the MME-HR produces the best simulation results among the three MMEs, the simulation effects of HighResMIP’s precipitation in the TP do not necessarily improve with increasing the horizontal resolution from LR to MR. The finer model resolution has a small impact on the simulation effect of precipitation intensity, but the coarser model resolution will limit the generation of heavy precipitation. These findings give intensive measures for evaluating precipitation in complex terrain and can help us in comprehending rainfall biases in global climate model simulation. Full article

This paper utilizes high-resolution ERA5 hourly data from 1980 to 2020 and long-term normalized difference vegetation index (NDVI) time series obtained from remote sensing and applies trend analysis, correlation analysis, lag analysis, and other methods to study the spatiotemporal characteristics of extreme rainfall at daily and hourly scales in the Huang-Huai-Hai Plain. The paper explores the NDVI’s variability and its relationship with extreme hourly precipitation and analyzes the main factors affecting it. The study made the following observations: (1) The extreme daily precipitation in the Huang-Huai-Hai Plain shows a decreasing trend, with a 13.6 mm/yr reduction rate. In contrast, the proportion of extreme rainfall to total precipitation generally exceeds 20%, and the intensity of extreme rain has gradually increased. The spatial distribution pattern of extreme rainfall follows the distribution pattern of China’s rain belts, with the terrain being an important influencing factor. The high-incidence areas for extreme rainfall are the Huaihe River region and the Shandong Peninsula. (2) The observed significant increase in hourly extreme precipitation events in the Shandong and Henan provinces of the Huang-Huai-Hai Plain has led to an increased risk of flooding, while the corresponding events in the northwest region of the Plain have exhibited a gradual weakening trend over time. (3) The extreme hourly precipitation in the Huang-Huai-Hai plain shows a frequent and scattered pattern, with decreasing intensity over time. Extreme precipitation mainly occurs in the first half of the night, especially between 19:00 and 21:00, with extreme hourly rainfall intensity fluctuating between 0.2 and 0.25 and the proportion of rainfall to total precipitation reaching as high as 10%. The spatial distribution of extreme hourly rainstorms during the peak period (19:00–21:00) exhibits a high rainfall volume, intensity, and frequency pattern in the eastern region, while the western part exhibits low rainfall volume, intensity, and frequency. (4) The incidence of extremely heavy rainfall in an hour has exhibited a more significant increase compared to extreme daily events in the Huang-Huai-Hai Plain, primarily in the form of backward-type precipitation. Hourly extreme precipitation events in the Huang-Huai-Hai Plain are affected by terrain and land use/cover change (LUCC), with the micro-topography of hilly areas leading to a concentrated distribution of precipitation and LUCC suppressing extreme precipitation events in arid climates. (5) At the ten-day scale, the spatial distribution of the NDVI shows a gradually increasing trend from northwest to southeast, with the highest NDVI value reaching up to 0.6 in the southern part of the study area. For extreme hourly precipitation, there is no significant change observed at the multi-year ten-day scale; while the NDVI in the northern and central parts of the Huang-Huai-Hai Plain shows a significant decreasing trend, in contrast, it presents a significant increasing trend in the southern region. (6) Finally, the correlation between NDVI at the ten-day scale and extreme hourly precipitation exhibits a decreasing pattern from north to south, with a correlation coefficient decreasing from 0.48 to 0.08. The lagged correlation analysis of extreme hourly rainfall and NDVI for one, two, and three ten-day periods shows that the lagged effect of extreme hourly precipitation on NDVI is negligible. Analyzing the correlation between extreme hourly rainfall and NDVI for different months, the impact of extreme hourly precipitation on NDVI is predominantly negative, except for June, which shows a positive correlation (0.35), passing the significance test. This study offers a scientific foundation for enhancing disaster warning accuracy and timeliness and strengthening the research on disaster reduction techniques. Full article

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency. Full article

Climate change has increased the frequency and intensity of weather events with heavy precipitation, making communities worldwide more vulnerable to flash flooding. As a result, accurate fore- and nowcasting of impending excessive rainfall is crucial for warning and mitigating these hydro-meteorological hazards. The measurement of integrated water vapour along slant paths is made possible by ground-based global positioning system (GPS) receiver networks, delivering three-dimensional (3D) water vapour distributions at low cost and in real-time. As a result, these data are an invaluable supplementary source of knowledge for monitoring storm events and determining their paths. However, it is generally known that multipath effects at GPS stations have an influence on incoming signals, particularly at low elevations. Although estimates of zenith total delay and horizontal linear gradients make up the majority of the GPS products for meteorology to date, these products are not sufficient for understanding the full 3D distribution of water vapour above a station. Direct utilization of slant delays can address this lack of azimuthal information, although, at low elevations it is more prone to multipath (MP) errors. This study uses the convective storm event that happened on 27 July 2017 over Bulgaria, Greece, and Turkey, which caused flash floods and severe damage, to examine the effects of multipath-corrected slant wet delay (SWD) estimations on monitoring severe weather events. First, we reconstructed the one-way SWD by adding GPS post-fit phase residuals, describing the anisotropic component of the SWD. Because MP errors in the GPS phase observables can considerably impact SWD from individual satellites, we used an averaging technique to build station-specific MP correction maps by stacking the post-fit phase residuals acquired from a precise point positioning (PPP) processing strategy. The stacking was created by spatially organizing the residuals into congruent cells with an optimal resolution in terms of the elevation and azimuth at the local horizon.This enables approximately equal numbers of post-fit residuals to be distributed across each congruent cell. Finally, using these MP correction maps, the one-way SWD was improved for use in the weather event analysis. We found that the anisotropic component of the one-way SWD accounts for up to $20\%$ of the overall SWD estimates. For a station that is strongly influenced by site-specific multipath error, the anisotropic component of SWD can reach up to 4.3 mm in equivalent precipitable water vapour. The result also showed that the spatio-temporal changes in the SWD as measured by GPS closely reflected the moisture field estimated from a numerical weather prediction model (ERA5 reanalysis) associated with this weather event. Full article