Cumulative Precipitation Research Articles

Spatial-temporal constraints for surface soil moisture mapping using Sentinel-1 and Sentinel-2 data over agricultural regions

Surface soil moisture (SSM) information could have important applications in agricultural and regional water management. Remote sensing, particularly synthetic aperture radar (SAR), is an important technology for the estimation of spatial–temporal SSM over larger areas. Using Sentinel-1 and Sentinel-2 data, this research developed a general spatial–temporal constrained machine-learning-based method for surface soil moisture mapping over agricultural regions. Central to this method is the construction of spatial and temporal constraints and their implementation in machine-learning models. We first defined the spatial and temporal constraints for SSM estimation by investigating the spatial division of cultivated crop types and the temporal division of cumulative precipitation. Second, under the presumption that the SSM and associated variables are smoothly changing, we extracted the temporal difference variables from the multi-temporal remote sensing data. Finally, we incorporated two constraints as categorical features and temporal differences into a CatBoost-based model to improve surface soil moisture mapping. We verified the proposed model in a Spain study area with multi-temporal remote sensing observations. The experimental results after incorporating the spatial–temporal constraints demonstrate the efficacy of the proposed model for mapping surface soil moisture over agricultural regions, with significantly improved R2 = 0.7328, RMSE = 0.0451 vol, and MAE = 0.0351 vol This study also concluded that using multiple polarization in the machine-learning-based method could reliably and accurately estimate surface soil moisture.

Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

Performance of the Thies Clima 3D Stereo Disdrometer: Evaluation during Rain and Snow Events.

Imaging disdrometers are widely used in field campaigns to provide information on the shape of hydrometeors, together with the diameter and the fall velocity, which can be used to derive information on the shape-size relations of hydrometeors. However, due to their higher price compared to laser disdrometers, their use is limited to scientific research purposes. The 3D stereo (3DS) is a commercial imaging disdrometer recently made available by Thies Clima and on which there are currently no scientific studies in the literature. The most innovative feature of the 3DS is its ability in capturing images of the particles passing through the measurement volume, crucial to provide an accurate classification of hydrometeors based on information about their shape, especially in the case of solid precipitation. In this paper. the performance of the new device is analyzed by comparing 3DS with the Laser Precipitation Monitor (LPM) from the same manufacturer, which is a known laser disdrometer used in many research works. The data used in this paper were obtained from measurements of the two instruments carried out at the Casale Calore site in L'Aquila during the CORE-LAQ (Combined Observations of Radar Experiments in L'Aquila) campaign. The objective of the comparison analysis is to analyze the differences between the two disdrometers in terms of hydrometeor classification, number and falling speed of particles, precipitation intensity, and total cumulative precipitation on an event basis. As regards the classification of precipitation, the two instruments are in excellent agreement in identifying rain and snow; greater differences are observed in the case of particles in mixed phase (rain and snow) or frozen phase (hail). Due to the different measurement area of the two disdrometers, the 3DS generally detects more particles than the LPM. The performance differences also depend on the size of the hydrometeors and are more significant in the case of small particles, i.e., D < 1 mm. In the case of rain events, the two instruments are in agreement with respect to the terminal velocity in still air predicted by the Gunn and Kinzer model for drops with a diameter of less than 3 mm, while, for larger particles, terminal velocity is underestimated by both the disdrometers. The agreement between the two instruments in terms of total cumulative precipitation per event is very good. Regarding the 3DS ability to capture images of hydrometeors, the raw data provide, each minute, from one to four images of single particles and information on their size and type. Their number and coarse resolution make them suitable to support only qualitative analysis of the shape of precipitating particles.

Seasonal prediction of end-of-dry-season watershed behavior in a highly interconnected alluvial watershed in northern California

Abstract. In undammed watersheds in Mediterranean climates, the timing and abruptness of the transition from the dry season to the wet season have major implications for aquatic ecosystems. Of particular concern in many coastal areas is whether this transition can provide sufficient flows at the right time to allow passage for spawning anadromous fish, which is determined by dry season baseflow rates and the timing of the onset of the rainy season. In (semi-) ephemeral watershed systems, these functional flows also dictate the timing of full reconnection of the stream system. In this study, we propose methods to predict, approximately 5 months in advance, two key hydrologic metrics in the undammed rural Scott River watershed in northern California. The two metrics are intended to characterize (1) the severity of a dry year and (2) the relative timing of the transition from the dry to the wet season. The ability to predict these metrics in advance could support seasonal adaptive management. The first metric is the minimum 30 d dry season baseflow volume, Vmin, which occurs at the end of the dry season (September–October) in this Mediterranean climate. The second metric is the cumulative precipitation, starting 1 September, necessary to bring the watershed to a “full” or “spilling” condition (i.e., initiate the onset of wet season storm- or baseflows) after the end of the dry season, referred to here as Pspill. As potential predictors of these two metrics, we assess maximum snowpack, cumulative precipitation, the timing of the snowpack and precipitation, spring groundwater levels, spring river flows, reference evapotranspiration, and a subset of these metrics from the previous water year. Though many of these predictors are correlated with the two metrics of interest, we find that the best prediction for both metrics is a linear combination of the maximum snowpack water content and total October–April precipitation. These two linear models could reproduce historical values of Vmin and Pspill with an average model error (RMSE) of 1.4 Mm3 per 30 d (19.4 cfs) and 25.4 mm (1 in.), corresponding to 49 % and 37 % of mean observed values, respectively. Although these predictive indices could be used by governance entities to support local water management, careful consideration of baseline conditions used as a basis for prediction is necessary.

Analysis of Factors Driving Subtropical Forest Phenology Differentiation, Considering Temperature and Precipitation Time-Lag Effects: A Case Study of Fujian Province

Subtropical forest phenology differentiation is affected by temperature, precipitation, and topography. Understanding the primary contributing elements and their interactions with forest phenology can help people better comprehend the subtropical forest growth process and its response to climate. Meanwhile, the temporal and spatial variations of phenological rhythms are important indicators of climatic impacts on forests. Therefore, this study aimed to analyze both a total area and different forest growth environments within the whole (i.e., coastal site areas (II, IV) and inland site areas (I, III)) as to spatiotemporal patterns associated with subtropical forests in Fujian Province, which is located at the boundary between the middle and south subtropical zones. Considering the asymmetric effects of climate and forest growth, this study chose pre-seasonal and cumulative temperature and precipitation factors and utilized the GeoDetector model to analyze the dominant drivers and interactions within phenology differentiation in Fujian Province. The results show the following: (1) All of the phenological parameters were advanced or shortened over the 19-year observation period; those of shrubland and deciduous broadleaf forests fluctuated greatly, and their stability was poor. (2) The phenological parameters were more distinct at the borders of the site areas. Additionally, the dates associated with the end of the growth season (EOS) and the date-position of peak value (POP) in coastal areas (i.e., II and IV) were later than those in inland areas (i.e., I and III). Among the parameters, the length of the growth season (LOS) was most sensitive to altitude. (3) Precipitation was the main driving factor affecting the spatial heterogeneity of the start of the growth season (SOS) and the EOS. The relatively strong effects of preseason and current-month temperatures on the SOS may be influenced by the temperature threshold required to break bud dormancy, and the relationship between the SOS and temperature was related to the lag time and the length of accumulation. The EOS was susceptible to the hydrothermal conditions of the preseason accumulation, and the variation trend was negatively correlated with temperature and precipitation. Spatial attribution was used to analyze the attribution of phenology differentiation from the perspectives of different regions, thus revealing the relationships between forest phenology and meteorological time-lag effects, the result which can contribute to targeted guidance and support for scientific forest management.

A case study on the impacts of future climate change on soybean yield and countermeasures in Fujin city of Heilongjiang province, China

Global climate change poses a great impact on crop growth, development and yield. Soybean production in Northeast China, which is one of the traditional dominant soybean production areas in China, is of great significance for developing the domestic soybean industry and reducing dependence on imported soybeans. Therefore, it is crucial to evaluate the impacts of future climate change on soybean yield in Northeast China, and to propose reasonable adaptation measures. In this study, we took Fujin city of Heilongjiang province in Northeast China as an example, and used the CROPGRO-soybean model in DSSAT (Decision Support System for Agrotechnology Transfer) to simulate the impacts of future climate change on soybean yield in the four periods of the 2020s (2021-2030), 2030s (2031-2040), 2040s (2041-2050) and 2050s (2051-2060) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), and further determine the best agronomic management practices. The results showed that the calibrated and validated model is suitable for simulating soybean in the study area. By analyzing the meteorological data under future climate scenarios RCP4.5 and RCP8.5 from the PRECIS regional climate model, we found that the average temperature, cumulative precipitation and cumulative solar radiation would mostly increase during the growing season in Fujin city of Heilongjiang province. Combined with the model simulation results, it is shown that under the effect of CO2 fertilization, future climate change will have a positive impact on soybean yield. Compared to the baseline (1986-2005), the soybean yield would increase by 0.6% (7.4%), 3.3% (5.1%), 6.0% (16.8%) and 12.3% (20.6%) in the 2020s, 2030s, 2040s and 2050s under RCP4.5 (RCP8.5).Moreover, the optimal sowing dates and the optimal supplemental irrigation amount under RCP4.5 (RCP8.5) are May 10 (May 5) and 50 mm (40mm), respectively. Under future climate conditions, the agronomic management practices, such as advancing the sowing date and supplementary irrigation in the key stage of soybean growth would increase soybean yield and make soybean growth more adaptable to future climate change.

Simulating the effects of optimizing sowing date and variety shift on maize production at finer scale in northeast China under future climate.

Global warming and the rising occurrences of climate extremes have become formidable challenges for maize production in northeast China. The optimization of sowing date and variety choice stand out as two economic approaches for maize to enhance its resilience to climate change. Nevertheless, assessment of the potential of optimizing sowing date and variety shift on maize yield at finer scale remains underexamined. This study investigated the implications of optimizing sowing date and implementing variety shift on maize yield from a regional perspective. Compared to the reference period (1986-2005), climate change would decrease by 11.5-34.6% (the range describes the differences among climate scenarios and agro-ecological regions) maize yield in the 2050s (2040-2059) if no adaption measure were to be implemented. The combined adaption (optimizing sowing date and variety shift) can improve maize yield by 38.8 ± 11.3%, 42.7 ± 9.7% and 33.9 ± 7.6% under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, respectively. The current sowing window typically falls within the projected optimal sowing window, defined as the period capable of achieving 90% of the maximum yield within the potential sowing window under future climate conditions. Consequently, the potential of the effect of optimizing sowing window on maize yield is limited. In contrast, variety shift results in higher yield improvement, as temperature rise creates favorable conditions for transplanting varieties with an extended growth period, particularly in high latitudes and mountainous regions. Under future climate, cumulative precipitation and compound drought and hot days during maize growing seasons are two key factors influencing maize production. The optimization of sowing date and variety choice can improve maize yield in northeast China. In addition, maize production should consider varieties with longer growth period and drought and heat tolerance to adapt to climate change. © 2023 Society of Chemical Industry.

Role of Madden–Julian Oscillation in predicting the 2020 East Asian summer precipitation in subseasonal-to-seasonal models

The 2020 summer monsoon season in East Asia was unusually long and intense, and the Madden–Julian Oscillation (MJO) has been proposed as an underlying reason. This study analyzes the role of the MJO in the 2020 East Asian precipitation forecasts of the subseasonal-to-seasonal (S2S) model. The S2S models underestimated the cumulative precipitation over East Asia, and the models with good forecast performance yielded a distinct precipitation band over East Asia and a western pacific subtropical high (WPSH) during the analysis period. East Asian precipitation forecast performance was more closely related to the location of the center than the strength of the WPSH, with precipitation increasing with a decrease in the latitude at the center. MJO Phases 1–3 activation intensified the WPSH and shifted the center of WPSH to lower latitudes. Our results confirm that the strong East Asian precipitation in summer 2020 was partly due to changes in the characteristics of the MJO and indicate the importance of accurately estimating the MJO-WPSH coupling for reliable East Asian precipitation forecasts.

Study on spatiotemporal dynamic characteristics of precipitation and causes of waterlogging based on a data-driven framework

The discernible alterations in regional precipitation patterns, influenced by the intersecting factors of urbanization and climate change, exert a substantial impact on urban flood disasters. Based on multi-source precipitation data, a data-driven model fusion framework was constructed to analyze the spatial and temporal dynamic distribution characteristics of precipitation in Beijing. Wavelet analysis method was used to reveal the periodic variation characteristics and multi-scale effects of precipitation, and the machine learning method was used to characterize the spatiotemporal dynamic change pattern of precipitation. Finally, geographical detector was used to explore the causes of waterlogging in Beijing. The research outcomes reveal a disparate distribution of precipitation across the year, with 78 % of the total precipitation occurring during the flood season. The principal periodic cycles observed in annual cumulative precipitation (ACP) were identified at 21, 13, and 9-year intervals. Spatially, while a decreasing trend in precipitation was observed in most areas of Beijing, 63.4 % of the region exhibited an escalating concentration trend, thereby heightening the risk of urban waterlogging. Machine learning model clustering elucidated three predominant spatial dynamic distribution patterns of precipitation in Beijing. The utilization of web crawler technology to acquire water accumulation data addressed challenges in obtaining urban waterlogging data, and validation through Landsat8 images enhanced data reliability and authenticity. Factor detection shows that road network density, topography, and precipitation were the main factors affecting urban waterlogging. These findings hold significant implications for informing flood control strategies and emergency management protocols in urban areas across China.

Hysteresis analysis reveals how phytoplankton assemblage shifts with the nutrient dynamics during and between precipitation patterns

The escalation of global eutrophication has significantly increased due to the impact of climate change, particularly the increased frequency of extreme rainfall events. Predicting and managing eutrophication requires understanding the consequences of precipitation events on algal dynamics. Here, we assessed the influence of precipitation events throughout the year on nutrient and phytoplankton dynamics in a drinking water reservoir from January 2020 to January 2022. Four distinct precipitation patterns, namely early spring flood rain (THX), Plum rain (MY), Typhoon rain (TF), and Dry season (DS), were identified based on rainfall intensity, duration time, and cumulative rainfall. The study findings indicate that rainfall is the primary driver of algal dynamics by altering nutrient levels and TN:TP ratios during wet seasons, while water temperature becomes more critical during the Dry season. Combining precipitation characteristics with the lag periods between algal proliferation and rainfall occurrence is essential for accurately assessing the impact of rainfall on algal blooms. The highest algae proliferation occurred approximately 20 and 30 days after the peak rainfall during the MY and DS periods, respectively. This was influenced by the intensity and cumulative precipitation. The reservoir exhibited two distinct TN/TP ratio stages, with average values of 52 and 19, respectively. These stages were determined by various forms of nitrogen and phosphorus in rainfall-driven inflows and were associated with shifts from Bacillariophyta-dominated to Cyanophyta-dominated blooms during the MY and DS seasons. Our findings underscore the interconnected effects of nutrients, temperature, and hydrological conditions driven by diverse rainfall patterns in shaping algal dynamics. This study provides valuable insights into forecasting algal bloom risks in the context of climate change and developing sustainable strategies for lake or reservoir restoration.

Management practices benefit endangered Poweshiek skipperling (Oarisma poweshiek) in Manitoba tall grass prairie

Poweshiek skipperling ( Oarisma poweshiek Parker, 1870) populations have declined precipitously in the past few decades, and their global range is now restricted to two isolated regions, one of which is the managed Manitoba tall grass prairie in Canada. In this paper, we used a decade of survey data from 2010 to 2019 to understand how habitat features, management practices, and extreme weather impact Poweshiek skipperling abundance in Manitoba. The strongest predictor of abundance was the density of black-eyed Susans ( Rudbeckia hirta L.), a primary nectar plant for adults. Poweshiek skipperling abundance also had a negative relationship with both the number of years since a burn occurred and the number of years since grazing occurred. Cumulative precipitation during their active period (May–June) had a negative relationship with skipperling abundance, whereas warm early springs and cool temperatures during the active period had positive relationships. These results suggest that management actions that maintain tall grass prairie habitat in an early successional stage (burning and grazing) and maintain important nectar sources benefit this population. In contrast, extreme weather events had varying effects on Poweshiek skipperling abundance. Results from this study inform ongoing management practices in the Manitoba tall grass prairie to support this endangered population.

Crowdsourced Meteorological Data to Supplement Limited Official Sources: A Survey and Case Study of Precipitation Monitoring in Guangzhou, China

Abstract Crowdsourced meteorological data may provide a useful supplement to operational observations. However, the willingness of various parties to share their data remains unclear. Here, a survey on data applications was carried out to investigate the willingness to participate in crowdsourcing observations. Of the 21 responses, 71% expressed difficulty in meeting the requirement of data services using only their own observations and revealed that they would be willing to exchange data with other parties under some framework; moreover, 90% expressed a willingness to participate in crowdsourcing observations. The findings suggest that in a way the social foundation of crowdsourcing has been established in China. Additionally, a case study on precipitation monitoring was performed in Guangzhou, the capital city of Guangdong Province, South China. Three sources of hourly measurements were combined after data quality control and calibration and interpolated over Guangzhou (gridded precipitation was based on combined data, and it is referred to as the COM grid). Subsequently, the COM grid was compared with the grid data based only on observations from the China Meteorological Administration using three indices, namely, cumulative precipitation, precipitation intensity, and heavy rain hours. The results indicate that requirement for more observations could benefit from crowdsourced data, especially on uneven terrain and in regions covered by sparse surface stations.

АКТИВИЗАЦИЯ ОПОЛЗНЕЙ ПРИ ОСВОЕНИИ ГОРНЫХ ТЕРРИТОРИЙ

The development of mountainous areas for recreation increases the level of risk of activation of dangerous exogenous processes, primarily landslides and mudflows. Water accumulation in the ground in the regions prone to landslides and mudflows is one of the important factors determining the intensity of manifestation of these processes and significantly influencing their characteristics. The paper considers the case of landslide activation at the Gornyi Vozduh ski resort in December 2021. A predicive model is presented for rainfall-induced landslides on saturated soil slopes, whose parameters were adjusted to the conditions of southern Sakhalin. Data on landslides and mudflows within a 30 km radius from the Yuzhno-Sakhalinsk meteorological station were collected to calibrate the model. The predictive model uses data on daily precipitation, pre-event precipitation, and cumulative precipitation. All these data are publicly available allowing model calibration for the conditions of any nearest meteorological station. The daily precipitation threshold for likely landslide and mudflow initiation in the territory under consideration was determined to be 51.4 mm.

Spatiotemporal distribution and influencing factors of impervious surface evaporation in the Baiyangdian catchment from 1980 to 2020

AbstractEvaporation from impervious surfaces plays a vital role in the catchment water cycle. Exploring the spatiotemporal variation patterns and influencing mechanisms of impervious surface evaporation at the catchment scale can improve the understanding and evaluation of the evaporation process. This study downloaded 0.5 m resolution images of the Baiyangdian catchment (BYD) from Google Earth and used deep learning to identify impervious surfaces. This was used to revise impervious surfaces of the China land cover dataset in 1985 and 1990–2020. Potential evaporation (PET) from three types of impervious surfaces (roofs, ground affected or not affected by the building height) was calculated by modifying the parameters of the Penman–Monteith equation, and daily precipitation and water‐storage capacity of impervious surfaces were taken into account to estimate impervious surface evaporation (E). The results showed that E values of the three types of impervious surfaces were between 72.1 and 178.2 mm/year and all exhibited the spatial distribution of high in the northwest and low in the southeast of the BYD in 1980–2020. Compared with that in 1980, in 2020, the cumulative evaporation (EAP) increased by 134.4%. The ratio of EAP to cumulative precipitation ranged from 3.0% to 6.9%, increasing significantly in a fluctuating manner. The increments in precipitation days and impervious surface area played a major role in the increase of EAP, and the decrease in precipitation was the fundamental reason for the increase in the proportion of impervious surface evaporation and water resource pressure in the BYD. Excluding the continuous evaporation from the remaining water can likely lead to underestimating the impervious surface evaporation. This study provides an efficient and reasonable novel approach for calculating impervious surface evaporation in long series and large‐scale catchments.

Blue-green roofs as nature-based solutions for urban areas: hydrological performance and climatic index analyses.

The water-regulating capacity of nature-based solutions (NBSs) plays a crucial role in providing a full range of ecosystem services and enhancing the resilience of urban systems. This work focuses on the hydrological performance of a particular NBS, the so-called blue-green roof (BGR). The BGR is designed to collect infiltrated rainfall in a water storage layer beneath the soil to support vegetation maintenance, enhance evapotranspiration and cooling, and minimize runoff and drainage system load. The study aims to evaluate the hydrological performance of the BGR at global and event scale and, for the first time, to model climatic factors (easy to measure using common sensors) that affect its stormwater retention capacity. The data collected over 2years and 2months at a 5-min resolution from a pilot study in Central Italy were analysed. Additionally, a new climatic index called AWWP-x (Antecedent Wet Weather Period index) was introduced and calculated. Results show that the BGR has an overall relevant retention rate (67.1%), although the value depends on the rainfall of the observed period. Approximately 50% of the rainfall events did not produce any runoff, and during the dry season, all events were totally absorbed by the BGR. Four climatic variables were identified as significant factors for predicting BGR retention performance (R2 = 0.50). The results suggest that AWWP-130 (number of days to reach 130mm cumulative precipitation) could be a possible proxy for the BGR stormwater retention rate. In general, this study demonstrates the potential for evaluating, planning, and designing NBSs by considering the annual and interannual climatic variability of the investigated specific location.

Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease discovered in China in 2009. The purpose of this study was to describe the spatiotemporal distribution of SFTS and to identify its environmental influencing factors and potential high-risk areas in Shandong Province, China.MethodsData on the SFTS incidence from 2010 to 2021 were collected. Spatiotemporal scan statistics were used to identify the time and area of SFTS clustering. The maximum entropy (MaxEnt) model was used to analyse environmental influences and predict high-risk areas.ResultsFrom 2010 to 2021, a total of 5705 cases of SFTS were reported in Shandong. The number of SFTS cases increased yearly, with a peak incidence from April to October each year. Spatiotemporal scan statistics showed the existence of one most likely cluster and two secondary likely clusters in Shandong. The most likely cluster was in the eastern region, from May to October 2021. The first secondary cluster was in the central region, from May to October 2021. The second secondary cluster was in the southeastern region, from May to September 2020. The MaxEnt model showed that the mean annual wind speed, NDVI, cattle density and annual cumulative precipitation were the key factors influencing the occurrence of SFTS. The predicted risk map showed that the area of high prevalence was 28,120 km2, accounting for 18.05% of the total area of the province.ConclusionsThe spatiotemporal distribution of SFTS was heterogeneous and influenced by multidimensional environmental factors. This should be considered as a basis for delineating SFTS risk areas and developing SFTS prevention and control measures.

Changes in Temperature and Precipitation Trends in Selected Polish Cities Based on the Results of Regional EURO-CORDEX Climate Models in the 2030–2050 Horizon

This study presents the potential impacts of climate change in 49 Polish cities by analyzing seven climate indicators. The analysis was carried out for the following three horizons: the current climate conditions (an average period spanning 2006 to 2015), near-future conditions (an average period spanning 2026 to 2035), and future conditions (an average period spanning 2046 to 2055). Climate indices were computed with bias-corrected EURO-CORDEX model ensembles from two Representative Concentration Pathway scenarios, RCP4.5 and RCP8.5. The systematic error was reduced using the quantile mapping method with a non-parametric approach of robust empirical quantiles (RQUANT). Data were used as references in the period of current climate conditions, and those required for bias correction consisted of historical ground-based observations provided by the Institute of Meteorology and Water Management. The analysis encompassed various key climate indices, including the annual average temperature, the count of hot days, cold days, and frost days, the cumulative annual precipitation, the frequency of days with precipitation, and instances of extreme precipitation (defined as the days with precipitation exceeding 10 mm/day). These findings reveal a noteworthy rise in the average annual temperature of approximately 1 °C and an uptick in the number of hot days by 3.7 days. Conversely, a decline in the number of cold days by approximately 19 days and frost days by 8 days was observed. Additionally, there was an augmentation in the annual precipitation sum, reaching up to 80 mm in RCP 8.5, accompanied by an increase in the number of days with precipitation (up by 3.3 days) and days with extreme precipitation (up by 2 days).

Aerosol impacts on summer precipitation forecast over the North China Plain by using Thompson aerosol-aware scheme in WRF: Statistical analysis and significant threat score improvements in polluted condition during June to August 2018

The North China Plain (NCP) is one of the most polluted areas in China, and the impacts of such high-levels of aerosols on clouds and precipitation remain an interesting issue with great uncertainties. From the perspective of regional numerical weather prediction, how to consider the role of aerosols is still a controversial issue considering the balance between the complexities/uncertainties of the model (aerosol-radiation-cloud interactions) and computational costs. To evaluate the reliability of the Thompson aerosol-aware schemes and promote operational applications in the Weather Research and Forecasting model (WRF), summer precipitation forecast with three experiments that considered no aerosol-radiation-cloud interactions (Thomp), aerosol-cloud interactions only (ThompAI) and full aerosol-radiation-cloud interactions (ThompAD) from June to August 2018 were carried out. Detailed statistics of the 3-h and 24-h cumulative precipitation skills were calculated for two groups, which included the entire summer season and selected polluted-precipitation events (26 days) only, were analyzed. The results showed that the overall Threat Scores (TS) for the 26 polluted-precipitation events were lower than those of the other group (the whole summer season)for either Thomp, ThompAI or ThompAD, which revealed that the forecasting abilities for polluted-precipitation cases in the model were obviously lower than those for the nonpolluted precipitation cases, indicating deficiencies in parameterizing the special conditions on polluted days. Nevertheless, for both groups, after using the aerosol-aware Thompson schemes (either ThompAI or ThompAD), the TS scores for most magnitudes (0.1, 1, 5, 10, and 25 mm) were significantly increased compared to the original Thomp experiment, with the exception being for precipitation amounts >50 mm. Meanwhile the BIAS of each magnitude also increased for both ThompAI and ThompAD due to the spatial increase in areas with precipitation compared to the original Thomp experiment. The TS improvements were more significant in the ThompAD experiment than in the ThompAI experiment. Compared with the original Thomp experiment, the ThompAD improvement ratios for the 24-h cumulative precipitation TSs were 3.01%, 4.91%, 2.05%, 13.58%, 7.80% for 0.1 mm, 1 mm, 5 mm, 10 mm, 25 mm precipitation magnitudes respectively; and the improvements were significantly higher when the statistics window was changed from 24-h to 3-h, with 3-h cumulative precipitation TS improvements reaching 10.23%, 5.09%, 11.50%, 17.4%, 14.30%, respectively, indicating the impacts that considering aerosol-radiation-cloud interactions in changing the precipitation patterns (fall zone and amounts) in the model were more effective for 3-h windows compared to 24-h windows. Case studies of selected light-rain, medium-scale rain and heavy rain events further revealed the positive impacts of the Thompson aerosol-aware schemes, and the precipitated areas were much closer to the observations. The reason why ThompAD did not show obvious advantages for large-scale precipitation (50 mm magnitude) may be due to the complex relationship involving aerosol-cold cloud interactions in the model and the randomness caused by inadequate an individual number of cases. The statistics for positive precipitation forecast skill improvements for the NCP region provide confidence in the application of the Thompson aerosol-aware scheme in the WRF model in the future.

Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)

ABSTRACT Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.