Search

The Köppen‐Geiger (K‐G) climate classification is the most commonly used climate classification method in the world, and there are many K‐G climate classification studies focusing on Türkiye using different datasets. However, the differences in the datasets used in these studies lead to substantial differences and errors in K‐G climate zone maps. The differences and disagreements in these maps also cause significant discrepancies in climate studies. In this respect, accurate identification of climate classes and types is very important for understanding the distribution of climate types and for many climate‐based studies to achieve accurate results. In this study, the K‐G climate types of Türkiye and the regime characteristics of these climate types were determined using the CHELSA dataset corrected based on the measurements of 337 meteorological stations. According to the results that were obtained, 14 climate types were identified in Türkiye. Since the CHELSA dataset reflected topographic conditions well, many microclimates were identified within broad areas of climate types. The distribution of the microclimate types was compared to the distribution of the vegetation, and the accuracy of the results was evaluated. Apart from microclimates, other prominent features of this study were the co‐occurrence of multiple climate types in a limited area in the Eastern Black Sea Region and the detection of the EF climate type for the first time at the summit of Mount Ararat. Climate types vary according to altitude conditions, and temperature changes due to altitude are an important factor in the formation of climate sub‐types within the same main climate type in Türkiye. [ABSTRACT FROM AUTHOR]

This study analyses the spatio‐temporal variability of heatwave characteristics and their association with large‐scale climate drivers across seven climatic sub‐regions in Vietnam, including the Northwest (R1), Northeast (R2), Red River Delta (R3), North Central (R4), South Central (R5), Central Highlands (R6) and the South (R7). The analysis is based on observed daily maximum temperatures from 102 meteorological stations, spanning the period 1980–2020. The obtained results reveal diverse heatwave patterns across the country. Amongst the seven climatic sub‐regions of Vietnam, the R3 and R4 sub‐regions experienced more frequent heatwaves and a higher number of heatwave days, but shorter durations. In contrast, other sub‐regions had fewer heatwave events and heatwave days but experienced longer‐lasting heatwaves. The intensity of heatwave events varies amongst sub‐regions, with the highest value in the R4 sub‐region, and the lowest in R7. Notably, the R1–R5 sub‐regions are affected by heatwaves over larger areas, compared to others. Additionally, the findings confirm that the lagged influence of El Niño–Southern Oscillation (ENSO) is the primary climatic driver of heatwave characteristics in Vietnam. Generally, heatwaves tend to occur more frequently in the years following El Niño events than after La Niña events. This observation provides opportunities for developing a system of seasonal predictions of heatwaves in Vietnam. The impact of ENSO on the number of heatwave events and heatwave days is evident in five out of seven sub‐regions, with less impact in the R2 and R7 sub‐regions. However, it does not significantly affect heatwave intensity. [ABSTRACT FROM AUTHOR]

Since the land 2 m air temperature from European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5-land) is vulnerable to interference by regional climate characteristics and local topography, how to objectively and accurately evaluate its accuracy and then establish a refined model is the key to improve its multifaceted and deep-level applications. This study proposed a systematic and complete refinement method for ERA5-land air temperature based on the densely distributed weather stations in Shandong Province in eastern China, including the compensation of elevation matching bias (EMB) caused by ASTER GDEM and the overall refinement using the remove-and-restore model (RRM). Taking the hottest July and the coldest December in this area, surface air temperature, atmospheric weighted mean temperature (Tm) and GNSS precipitable water vapor (PWV) were used to verify the feasibility of this method. The results show that: (1) Whether the temperature is high in July or low in December, the accuracy can be improved by about 42.2 % when the refinement method was used. Especially for high altitude areas (the summit of Mount Tai, 1553 m above sea level), the accuracy can be improved by 74.5 %. (2) In contrast to only the temperature lapse rate (TLR) correction, the average root mean square (RMS) can be reduced by 7.4 % and 60.4 % in July and 7.0 % and 59.5 % in December for the met stations less than 150 m in altitude and the peak station of Mount Tai, respectively, when the new method was used. (3) Compared with the Tm and PWV computed by interpolated temperature without any correction, the accuracy of Tm can be improved by 31.2 % and 34.1 % in July and December for weather station in the altitude of 100 ∼ 350 m after the TLR correction, EMB compensation and RRM refinement successively. Meanwhile, the accuracy of PWV can be improved by 47.8 % in July and 42.0 % in December, respectively, and especially for PWV high-value moments. Besides, after correction by RRM, both Tm and PWV in low altitude areas achieve at least 20 % accuracy improvement. In summary, the systematic and complete refinement method proposed in this study are important to significantly improve the ERA5-land 2 m temperature availability and GNSS PWV accuracy, and the compensation of EMB is mainly effective for high altitude areas. [ABSTRACT FROM AUTHOR]

The forests of the Amazon basin are threatened by climate and land use changes. Due to the transition towards a drier climate, moisture-dependent organisms such as canopy epiphytes are particularly affected. Even if the topography in the Amazon lowland is moderate, mesoscale nocturnal katabatic flows result from cold air production related to radiative cooling. From a certain level of mass the cold air starts to flow downslope towards the valley centers leading to temperature inversions. The resulting cooling in the valleys drives localized fog formation in the valleys at night. This correlates with high epiphyte abundance and diversity in the valleys, which is much less pronounced upslope. The underlying temperature dynamics are, however, not sufficiently included in coarse-resolution reanalysis models such as ERA5-Land. Since high resolution climate data are needed e.g. for proper niche modeling of locally distributed species such as canopy epiphytes, downscaling models such as microclima have been developed and include micro- and mesoscale effects. However, it is unclear how well the elevation-related diurnal course of air temperature can be simulated. Here, we test functions for downscaling coarse-resolution temperature data to high spatial resolution data implemented in the R-package microclima for the South American tropical lowland forests. To do so we compared microclima-downscaled ERA5-Land air temperature data with meteorological station data. We found that the microclima functions only properly detect 73 temperature inversions out of 412 nocturnal cold air drainage (CAD) events during the dry season study period and only 18 out of 400 during the wet season with default settings. By modifying default values such as the emissivity threshold and time frames of possible CAD condition detection, we found 345 of 412 CAD events during the dry season and 177 out of 400 during the wet season. Despite problems with the distinction between CAD and non-CAD events the microclima algorithms show difficulties in correctly modeling the diurnal course of the temperature data and the amplitudes of elevational temperature gradients. For future studies focusing on temperature downscaling approaches, the modules implemented in the microclima package have to be adjusted for their usage in tropical lowland forest studies and beyond. [ABSTRACT FROM AUTHOR]

ABSTRACT We use a multivariate analysis to study droughts in Finland using the Joint Deficit Index (JDI). Subsequently, the joint probability of occurrence of drought characteristics was analysed using Vine copulas. For this purpose, we used monthly precipitation from 22 meteorological stations across Finland in the period 1980–2021. The JDI time series showed that Finland had a normal wetness condition most of the time during the studied period. Trend analysis of the JDI time series using a modified Mann–Kendall test showed that there was no significant trend in the values of this index during the studied period. The drought characteristics, including severity, duration and inter‐arrival time (IAT), were extracted from the JDI time series for each station. The trend analysis of drought characteristics showed that only the Tohmajärvi Kemie station in eastern Finland had a significant negative trend in drought duration and severity. Furthermore, of the 22 stations studied, only two stations showed a significant increasing trend in the duration and severity of drought at the 10% level. The drought characteristics at the remaining stations showed no significant trend at the 10% level of significance. For stations with non‐stationary drought characteristics, generalised additive models for location, scale, and shape (GAMLSS) were used for frequency analysis. The correlation between the three characteristics of severity, duration and IAT was investigated using Kendall's Tau statistic. The results showed a high correlation between the two variables duration and severity and a moderate and acceptable correlation between drought severity and IAT as well as the pair of duration and IAT. In the following, copula functions were used to construct a trivariate distribution of the drought characteristics. Among the copulas tested, the R‐vine copula and its independent mode have the best fit for the variables under study and provide a suitable tree sequence. Finally, using the aforementioned copulas and their conditional density, the frequency analysis of the three drought variables was performed. The results of this study were presented in the form of four‐dimensional graphs to estimate the joint probability of occurrence of drought characteristics based on the JDI. These graphs are presented according to the precipitation conditions of each station, and by having a drought characteristic, other characteristics can be estimated with different probabilities. The proposed method is very efficient in analysing the joint frequency of drought characteristics due to the consideration of the effective parameters and the use of conditional density. [ABSTRACT FROM AUTHOR]

ABSTRACT The Cerrado biome, renowned for its biodiversity and threatened by rapid land transformation, encompasses a vast savanna ecosystem in Brazil. The region is characterised by a seasonal climate, influenced by a myriad of meteorological systems creating diverse and non‐homogeneous rainfall regimes across the region. To account for this heterogeneity, we propose a novel classification of the Cerrado using rainfall data to delineate three distinct climatic regions: Eastern, Southern and Central‐West Cerrado. The Eastern region exhibited the driest and most seasonal climate, marked by high vapour pressure deficit (VPD) and predominantly open‐canopy vegetation. Conversely, the Southern region, characterised by lower seasonality, boasts a higher proportion of forest cover and lower mean VPD. The Central‐West region, encompassing diverse landscapes, featured areas with higher precipitation levels, particularly along the Amazônia border. Furthermore, we conducted trend analyses on observed station data and used CMIP6 models to evaluate future scenarios under differing emissions trajectories. While observed trends in mean rainfall were marginal, VPD demonstrated a notable upward trend of approximately 1% annually throughout the biome. Climate models indicated a substantial drying close to the Amazônia border, and wetter conditions in the southeast. All Cerrado regions are anticipated to experience amplified seasonality and VPD, with VPD projected to surge by approximately 30% (60%) under low (high) emissions scenarios by the end of the century. Notably, the transition from the dry to wet season was the most affected. Our study provides critical insights on how the climatic heterogeneity of the Cerrado shapes vegetation structure distribution and how future changes will exacerbate water stress throughout the biome. These findings underscore the importance of understanding climate variability for effective conservation and management strategies in the region. [ABSTRACT FROM AUTHOR]

Environmental conditions highly affect decomposition rates and therefore a forensic practitioner should consider context‐specific information when estimating the post mortem interval (PMI). Traditional methods of collecting environmental data, however, are time‐consuming and often impractical for large‐scale studies or routine forensic investigations. This study developed an automated computer method by employing the technology of geographic information systems (GIS) and Python programming language to provide contextual information for bodies found outdoors in Greece. The generated coding script underwent testing on 95 bodies in various stages of decomposition, which were examined between the years 1999 and 2022 at the National and Kapodistrian University of Athens and the Forensic Medical Service of Thessaloniki. Using ArcGIS Pro software and publicly available online data, a multilayer map was developed. Individual layers included high‐resolution aerial images and data on the European Nature Information System ecosystem type, the Köppen–Geiger climatic type, the population density, the elevation, and the slope. Additionally, 99 national weather stations and their corresponding meteorological data were integrated. By leveraging the geographical coordinates of the recovery site of each case and information about the decedent's disappearance and recovery dates, this script automatically generates details from each of the above layers. Additionally, it calculates the accumulated degree days (ADD) and accumulated humidity days (AHD) values by extracting data from the nearest weather station. The GIS‐based approach enables rapid, objective, and reproducible taphonomic profile construction, which can greatly improve the reliability of PMI estimations. By utilizing this method, forensic practitioners can accurately evaluate environmental effects on decomposition, thus standardizing taphonomic profiling globally. [ABSTRACT FROM AUTHOR]

Studies on rainfall variability to Sea Surface Temperature (SST) effects in arid and semi-arid parts of northern Ethiopia have been limited. This paper investigates the SST drivers of rainfall variability in contrasting areas of northern Ethiopia during the main rainy season "Kiremt" on monthly and dekadal (Ten-days) time scales. We analyzed daily rainfall records (1988–2017) from 41 meteorological stations across four homogeneous rainfall zones in northern Ethiopia's highlands and lowlands. Correlation analysis and multiple linear regression models were employed to examine the relationships between global SST and Kiremt rainfall, with the aim of predicting monthly and dekadal rainfall. Results reveal distinct rainfall distributions: the lowland receives bi-modal pattern with rainfall amount < 80 mm during the main rain season, while the highlands exhibit uni-modal pattern with rainfall amount of 350 mm. The variability in Monthly and dekadal rainfall in the lowlands and highlands is mainly linked to SST in the equatorial Pacific Ocean. Changes in SST in the Atlantic and Indian Oceans also significantly impact monthly and dekadal rainfall variability, regardless of their strength and lag-time effect. Warming of the western equatorial Pacific and Indian Ocean, and tropical northeast Atlantic Ocean enhances the rainfall over the highlands and lowlands while the eastern and central equatorial Pacific causes the declining of the rainfall. The prediction models for monthly and dekadal rainfall archived a moderate to good performance, with R2 ranging from 28%–61%. Efficient methods for predicting Kiremt rainfall are profoundly important to support systematic and informed decision-making in agriculture and water resource sectors. [ABSTRACT FROM AUTHOR]

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables. [ABSTRACT FROM AUTHOR]

The Observatorio del Roque de los Muchachos will host the northern site of the Cherenkov Telescope Array Observatory (CTAO), in an area about 200 m below the mountain rim, where the optical telescopes are located. The site currently hosts the MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) telescopes, which have gathered a unique series of 20 yr of weather data. We use advanced profile-likelihood methods to determine seasonal cycles, the occurrence of weather extremes, weather downtime, and long-term trends correctly taking into account data gaps. The fractality of the weather data is investigated by means of multifractal detrended fluctuation analysis. The data are published according to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. We find that the behaviour of wind and relative humidity show significant differences compared to the mountain rim. We observe an increase in temperature of |$0.55\pm 0.07\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$| |$^{\circ }$| C decade−1, the diurnal temperature range of |$0.13\pm 0.04\mathrm{(stat.)}\pm 0.02\mathrm{(syst.)}$| |$^{\circ }$| C decade−1 (accompanied by an increase of seasonal oscillation amplitude of |$\Delta C_m=0.29\pm 0.10\mathrm{(stat.)}\pm 0.04\mathrm{(syst.)}$| |$^{\circ }$| C decade−1), and relative humidity of |$4.0\pm 0.4\mathrm{(stat.)}\pm 1.1\mathrm{(syst.)}$|  per cent decade−1, and a decrease in trade wind speeds of |$0.85\pm 0.12\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$|  (km h−1) decade−1. The occurrence of extreme weather, such as tropical storms and long rains, remains constant over time. We find a significant correlation of temperature with the North Atlantic Oscillation Index and multifractal behaviour of the data. The site shows a weather-related downtime of 18.5 per cent–20.5 per cent, depending on the wind gust limits employed. No hints are found of a degradation of weather downtime under the assumption of a linear evolution of environmental parameters over time. [ABSTRACT FROM AUTHOR]

We performed a plateau‐linear reaction norm model (RNM) analysis of number born alive (NBA) in purebred Landrace pigs, where breeding value changes according to maximum temperature at mating day, using public meteorological observation data in Japan. We analysed 52,668 NBA records obtained from 10,320 Landrace sows. Pedigree data contained 99,201 animals. Off‐farm daily temperature data at the nearest weather station from each of the farms were downloaded from the Japan Meteorological Agency website. A plateau‐linear RNM analysis based on daily maximum temperature on mating day (threshold temperature of 16.6°C) was performed. The percentage of the records with daily maximum temperatures at mating days of ≤16.6, ≥25.0, ≥30.0 and ≥35.0°C were 34.3%, 33.6%, 14.0% and 0.8%, respectively. The value of Akaike's information criterion for the plateau‐linear RNM was lower than that for a simple repeatability model (RM). With the plateau‐linear RNM, estimated value of heritability ranged from 0.14 to 0.15, while that from the RM analysis was 0.15. Additive genetic correlation between intercept and slope terms was estimated to be −0.52 from the plateau‐linear RNM analysis. Estimated additive genetic correlations were >0.9 between NBA at different temperatures ranging from 16.6 to 37.6°C. For the 10,320 sows, average values of prediction reliability of the intercept and slope terms for breeding values in the plateau‐linear RNM were 0.47 and 0.16, respectively. Increasing weight for slope term in linear selection index could bring positive genetic gain in the slope part, but prediction accuracy would decrease. Our results imply that genetically improving heat tolerance in sows reared in Japan focusing on NBA using RNM is possible, while RNM is more complex to implement and interpret. Therefore, further study should be encouraged to make genetic improvement for heat tolerance in sows more efficient. [ABSTRACT FROM AUTHOR]

A better understanding of rainfall variability and trends is vital for agricultural production systems which are largely dependent on climate. This study aims to analyze and to quantify the significance of change on annual, seasonal and daily rainfall in North and Central Benin, and to infer future challenges for crop production. Daily rainfall data for the 1970–2016 period measured at three weather stations (Savè, Malanville and Tanguiéta) were obtained from the Benin National Weather Agency. Descriptive statistics, standardized anomaly of rainfall (SAR) and rainfall intensity were used to analyze rainfall variability. For rainfall trends analysis, we tested for auto-correlation and used the Mann–Kendall and modified Mann–Kendall tests for non-auto-correlated and auto-correlated data, respectively. Trend magnitude was estimated using Sen's slope. Globally, a moderate-to-high seasonal rainfall and low variability of yearly rainfall were observed. The SAR indicated more than 50% of the years in the studies period experienced dry years. Between 1970 and 2016, a significant 20% increase was observed in the yearly rainfall in Tanguiéta, whereas no significant trends were observed in Malanville (10% increase) and Savè (0.6% decrease). The general rainfall increase observed during the post-monsoon season (October–November) in the three weather stations potentially increases flood frequencies during the harvest period of some crops, which can reduce crop yields. Adaptation strategies are needed which can mitigate the effects of climate change on agriculture. These findings are essential to the climate risk management in agriculture and to target appropriate adaptive measures for resilience building in the sector. [ABSTRACT FROM AUTHOR]

ABSTRACT In the North China Plain (NCP), the assessment of water surplus or deficit (WSD), which is calculated as precipitation minus reference evapotranspiration (ET0), holds significant implications for water resource management and agricultural irrigation decision‐making, given the region's long‐standing severe shortage of water resources. However, the magnitude, trend and climatic drivers of WSD remain poorly understood in the NCP. This study analysed the spatial and temporal characteristics of WSD, and quantified the contribution of climatic factors to WSD based on the sensitivity and contribution rate analysis methods with climatic data from 75 meteorological stations. The result showed that: (1) Annual WSD decreased mainly in northeastern NCP and increased significantly in southern NCP during 1961–2022. Annual WSD increased slightly from 1961 to 2022 at a rate of 1.63 mm a−2 mainly due to the more significant decrease (−1.88 mm a−2) in ET0 compared to precipitation (−0.25 mm a−2). (2) In terms of the sensitivity of WSD to climatic factors, relative humidity had the highest sensitivity, followed by net radiation, wind speed, precipitation and average air temperature. (3) Significant declines of wind speed were the most dominant factor affecting WSD variation in most part of NCP during most of a year, and net radiation of four stations in the western high‐elevation regions played the most important role. This study enhances comprehension of the impact of climate change on WSD in the NCP and provides a reference for improving management of agricultural water resources under NCP's evolving climatic conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT This work studies the simultaneous and sequential occurrence of hot and dry months in the summer season in Argentina, north of 40°S, based on three different databases: meteorological stations, a gridded observational dataset and a reanalysis product. The influence of the El Niño‐Southern Oscillation (ENSO) over the occurrence of these compound events is specially analysed using a logistic regression model. Monthly maximum temperature and precipitation data are used for the period 1979–2022 in four sub‐regions of Argentina: Northwestern Argentina (NOA), Northeastern Argentina (NEA), Cuyo (central‐western Argentina) and the Pampas (central‐eastern Argentina). Simultaneous hot and dry months and hot months preceded by dry months are the most frequent compound events. The highest frequencies are found in the centre part of the study region and NEA for simultaneous compound events, and in NOA and the Pampas region for sequential ones. In general terms, all datasets show a good representation of the spatio‐temporal variability of hot and dry months. The insights of the influence of ENSO on compound events revealed that La Niña enhances the occurrences of hot and dry months throughout the study region, with the exception of NOA, where El Niño conditions promote the occurrence of these events. Based on logistic regression models, we successfully quantify the relationship between ENSO and hot and dry months and demonstrate that ENSO plays a significant role as a driver of compound hot and dry events in the central region, Cuyo, NEA and a portion of the Pampas. This research contributes to the understanding of compound events in Argentina and how they are influenced by major drivers of climate variability providing useful information for the development of a predictive system for such events. [ABSTRACT FROM AUTHOR]

The article examines the risk, attributable fraction, and attributable number of cause-specific heat-related emergency hospital admissions in Switzerland from 1998 to 2019. It includes figures showing lag-specific risk ratios, exposure-response functions by age and gender, and sensitivity analyses for different temperature variables. Additionally, a table lists weather stations used in the study by canton, detailing median, minimum, and maximum daily maximum temperatures. [Extracted from the article]

In the central Arctic, high-quality water vapour observations are sparse due to the low density of meteorological stations and uncertainties in satellite remote sensing. Different reanalyses also disagree on the amount of water vapour in the central Arctic. The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition provides comprehensive observations that are suitable for evaluating satellite products and reanalyses. Radiosonde observations provide high-quality water vapour estimates with a high vertical but a low temporal resolution. Observations from the microwave radiometers (MWRs) on board the research vessel Polarstern complement these observations through high temporal resolution. In this study, we demonstrate the high accuracy of the combination of the two MWRs HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds – Passive). For this purpose, we developed new retrievals of integrated water vapour (IWV) and profiles of specific humidity and temperature using a neural network approach, including observations from both HATPRO and MiRAC-P to utilize their different water vapour sensitivity. The retrievals were trained with the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis version 5 (ERA5) and synthetic MWR observations simulated with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA). We applied the retrievals to synthetic and real observations and evaluated them with ERA5 and radiosondes launched during MOSAiC, respectively. To assess the benefit of the combination of HATPRO and MiRAC-P compared to single MWR retrievals, we compared the errors with respect to MOSAiC radiosondes and computed the vertical information content of the specific humidity profiles. The root mean squared error (RMSE) of IWV was reduced by up to 15 %. Specific humidity biases and RMSE were reduced by up to 75 % and 50 %, respectively. The vertical information content of specific humidity could be increased from 1.7 to 2.4 degrees of freedom. We also computed relative humidity from the retrieved temperature and specific humidity profiles and found that RMSE was reduced from 45 % to 15 %. Finally, we show a case study demonstrating the enhanced humidity profiling capabilities compared to the standard HATPRO-based retrievals. The vertical resolution of the retrieved specific humidity profiles is still low compared to radiosondes, but the case study revealed the potential to resolve major humidity inversions. To what degree the MWR combination detects humidity inversions, also compared to satellites and reanalyses, will be part of future work. [ABSTRACT FROM AUTHOR]

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM2.5 and PM10 fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM2.5 and a poor linear correlation for PM10. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the characteristics of tropical cyclones (TCs) over the Bay of Bengal (BoB) that affect snowfall on the Tibetan Plateau (TP) and spatiotemporal distribution of snowfall related to BoB TCs are statistically analysed by using multi‐sources data from 1981 to 2020, with partitioning TC‐influenced snowfall by tracking cloud clusters. The results show that 141 TCs formed during the 40‐year period of 1981–2020, of which about 35% (50 TCs) impacted snowfall at 83% of meteorological stations on the TP during their northward or westward movement, and the average distance between the TC centre and the snowfall stations is 1277 km. The proportion of snowfall‐related TC frequency shows a significantly decreasing trend with a predominant cycle of 10a. The TC‐influenced snowfall frequency (SF), precipitation amount (PA) on a snowfall day and snow depth (SD) during 1981–2020 all show a non‐significant weak decreasing trend, while TC‐influenced snowfall is significantly increased in the eastern and southern edges of Xizang, western Sichuan and the southern margin of Qinghai. PA and SD in December account for more than 75% and 55% of the monthly total, respectively. The spatial pattern of PA could be objectively categorised into west‐type (24%) and southeast‐type (76%). The moisture transported by the BoB TC and a southerly jet stream formed between the trough and the western Pacific subtropical high (WPSH), the convergence of cold air and warm–moist airstream over the TP and the change in position of the south Asian high in the upper troposphere are significant factors causing the different spatial distribution. The results can provide reference for TC‐related snowfall, SD prediction and disaster assessment on the TP. [ABSTRACT FROM AUTHOR]

ABSTRACT Increasing temperatures cause the weather to become more severe. Studying how heatwaves (HWs) impact individual heat exposure and susceptibility is vital for building climate change mitigation and adaptation measures. So, this study explores the impact of HWs on individual heat exposure and susceptibility. The assessment was based on the highly complex topographic region contribution to HWs in the Republic of Armenia (RA) using data from 16 meteorological stations for the extended warm season (May–September). We developed a regional HW catchment program to estimate the HW indices' responses to climatic change and to present them in terms of topography changes. The Mann–Kendall (MK) test was used to determine the statistical significance of the trends for 10 distinct HW indicators using linear and exponential trends along with graphical interpretations. This study reveals a large‐scale, significant increasing trend in annual maximum temperatures (Tmax) and observed HWs over the period 1955–2019. The rising temperature is accompanied by an increase in HW indices, particularly at low altitudes up to 1250 m above sea level (ASL), where the main population centres and national crop production are concentrated. According to our classification, the above‐mentioned areas have faced extreme and severe increases in HW intensity, covering more than 60% of the country's territory. Moreover, not only the intensity and frequency have been identified but the HW period extension as well. This extreme increase has been found in the low‐lying highly populated and intensively cultivated areas, such as in the Ararat valley. These results have implications for future climate assessments, adaptation strategies, agriculture and public health in Armenia. The development of targeted mitigation measures and adaptation strategies informed by these findings is essential for addressing the escalating challenges posed by HWs in the region. [ABSTRACT FROM AUTHOR]

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations. [ABSTRACT FROM AUTHOR]

This study provides a climatological analysis of annual and long-term variability of dry days and sequences of dry days in the Polish Carpathians between 1986 and 2020. The input data for the research was compiled from the daily precipitation totals from 17 meteorological stations and the T. Niedźwiedź catalogue of circulation types over southern Poland. The frequency of sequences of dry days of different duration has been presented. The incidence of long-term periods has been related to the atmospheric circulation. The number of dry days and the averages of both the number and of the duration of sequences of dry days were analysed for the study period. This yielded an average annual number of dry days ranging from 142 to 195 (39–53% of days in a year), depending on the altitude above sea level and longitude. A general pattern emerged in which the number of dry days increased from west to east. At most stations, there were no statistically significant trends with respect to the dry day characteristics analysed. This is an obvious result of high year-to-year variability, which means that only large changes can achieve statistical significance. Dry days have been evidenced to relate to circulation types. The vast majority of dry days are related to anticyclonic systems, particularly the anticyclonic wedge (Ka) and western anticyclonic (Wa) situations. On the other hand, no clear-cut relationship was established between an increase in air temperature and the occurrence of dry days. [ABSTRACT FROM AUTHOR]

The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns. [ABSTRACT FROM AUTHOR]

Wind speed is one of the main control factors of wind erosion and dust emissions, which are major problems in arid and semiarid regions of the world. Accurately simulating highly precise hourly wind speeds is critical and cost-efficient for land management decisions with the goal of mitigating wind erosion and land degradation. The Wind Erosion Prediction System (WEPS) is a process-based, daily time-step model that simulates changes in the soil–vegetation–atmosphere. However, to date, relatively few studies have been conducted to test the ability of the WEPS in simulating hourly wind speeds. In this study, the performance of the WEPS model was tested in the Inland Pacific Northwest (iPNW), where wind erosion is a serious problem. Hourly wind speeds were observed and simulated by the WEPS at 13 meteorological stations from 2009 to 2018 using the WEPS hourly wind speed probability histogram. Owing to increasing wind shear, the model is not as precise in reproducing high wind speeds. The WEPS inadequately simulated the hourly wind speeds at six of the 13 stations, with a low index of agreement (d < 0.5). The complex regional topography may be one of the reasons for this lack of agreement, because the WEPS's performance of interpolation relies on spatial distances and surface complexity. Therefore, we validated the model using another wind-speed database to eliminate the impact of spatial interpolation. The performance of the WEPS was improved after removing the impact of spatial interpolation, producing d values > 0.5 at nine of the 13 stations. Our results suggest that the WEPS can accurately simulate hourly wind speeds and assess wind erosion in the absence of interpolation, whereas the model may be uncertain when invoking spatial interpolation. Some evidence also suggests that the model may have a tendency to underestimate observed hourly wind speeds. Pragmatically, this suggests that model users should consider the possibility that WEPS may underestimate wind erosion risk in the iPNW and plan implementation of conservation practices accordingly. [ABSTRACT FROM AUTHOR]

Drought is a recurring natural hazard impacting agriculture, water resources, and various socioeconomic sectors. This study evaluated the performances of multiple meteorological and hydrological drought index estimation methods in the Tekeze River basin, northwestern Ethiopia. Monthly rainfall and temperature data from 15 meteorological stations with varying record lengths (28–59 years) and streamflow data from nine functional stations (1991–2018) were used to compute hydrometeorological drought indices. Standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), reconnaissance drought index (RDI), rainfall anomaly index (RAI), decile index (DI), and streamflow drought index (SDI) were calculated using DrinC software, R programming, and empirical formulas at 1, 3, 6, and 12‐month timescales. Results from the analysis revealed a basin‐wide prevalence of mild drought, with SPI indicating severity and negative values over longer timescales. The RDI analyses showed lower drought severity but also decreased its frequency over longer timescales. The DI calculation using basin station averages indicated 48.4%, 10.7%, and 8.0% incidence rates for severe, moderate, and mild droughts, respectively. RAI had generally lower positive and negative values compared to SPI. SPEI‐1 analysis classified almost all stations as experiencing mild drought. Conversely, SDI6 analysis at a 6‐month timescale revealed diverse impacts, with the Tekeze River basin experiencing sustained severe drought (SDI6 < −2.0) for three consecutive years (2004–2006). Spearman's rank correlation coefficients of the indices were positive, with RAI showing particularly strong and statistically significant positive correlations with other indices. This study provides valuable insights for policymakers, researchers, and water resource managers in the Tekeze River basin, aiding in the development of effective drought mitigation and preparedness strategies. [ABSTRACT FROM AUTHOR]

We study the air temperature with 10 years of hourly measured in 12 automatic weather stations, located in the State of Bahia (Brazil), considering mainly its auto‐ and cross‐correlation. We found that, in general, there is a positive linear trend, indicating warming in this variable for all stations. Also, we observe that the probability distribution function clearly exhibits a bimodal distribution, meaning that there are two main seasonal cycles (the largest relating to winter, the other to summer). In order to have a more robust statistical analysis, detrended fluctuation analysis (DFA) method was applied in these time‐series to analyzing data where traditional methods might struggle due to nonstationary. In this case, DFA was useful in identifying long‐range autocorrelations in these time‐series (with persistent and antipersistent behavior) with specific seasonal time‐scales (1 day and 1 year). However, if we quantify the cross‐correlations between the air temperature in Salvador and the other stations, by ρDCCA, we found that detrended cross‐correlation analysis cross‐correlation coefficient is always positive with two peaks, the first is located at n = 24 (1 day) and the second at n = 8766 (1 year). In this sense, ρDCCA coefficient provides insights how air temperature in Salvador is correlated with air temperature in other weather stations over different time‐scales, helping to understand their long‐term interactions. These results, for example, allow us to identify temporal scales that may be useful to better understand trends in the air temperature and that can help us further answer global warming questions. [ABSTRACT FROM AUTHOR]

The Sun is the main source of energy on planet Earth, and its activity is known to vary according to several cycles. Solar activity studied through sunspot variations led for the first time to the discovery of a widely accepted 11-year cycle that can be used to measure such variations, which feature pre-defined cycles over previous years, thus resulting in possible projections future ones. In this article, climate data were collected from meteorological stations dating back up to 59 years for each Brazilian state, totaling 27 units, with the aim of determining solar irradiation in recent years and, possibly, inferring the influence of solar cycles. As solar irradiation data only began to be collected in the 2000s in some locations in the country, solar insolation data were converted into solar irradiation using the Ångström–Prèscott formula. To have greater reliability in the prediction results, this formula was adjusted to the specific characteristics of each location using coefficients for all Brazilian states, as such information is not yet available in the literature. With these data, a predominant cycle was observed for each Brazilian state in relation to solar irradiation, lasting approximately 70 years, with durations whose solar maximum will occur in 2025. These results show that this is a very favorable situation for a growing scenario of implementation of new technologies aimed at photovoltaic generation of electrical energy. [ABSTRACT FROM AUTHOR]

This study presents a novel reanalysis dataset of dead fuel moisture content (DFMC) across California from 2000 to 2020 at a 2 km resolution. Utilizing a data assimilation system that integrates a simplified time-lag fuel moisture model with 10-h fuel moisture observations from remote automated weather stations (RAWS) allowed predictions of 10-h fuel moisture content by our method with a mean absolute error of 0.03 g/g compared to the widely used Nelson model, with a mean absolute error prediction of 0.05 g/g. For context, the values of DFMC in California are commonly between 0.05 g/g and 0.30 g/g. The presented product provides gridded hourly moisture estimates for 1-h, 10-h, 100-h, and 1000-h fuels, essential for analyzing historical fire activity and understanding climatological trends. The methodology presented here demonstrates significant advancements in the accuracy and robustness of fuel moisture estimates, which are critical for fire forecasting and management. [ABSTRACT FROM AUTHOR]

The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET0). However, key variables such as shortwave radiation (Rs) and net longwave radiation (Rln) are often unavailable at most weather stations. While previous studies have focused on calibrating Rs, the influence of large Rln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating Rs and Rln to accurately calculate ET0. The findings reveal that the average daily Rln was 8.172 MJ m−2 d−1 at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET0 estimates due to compensating errors: underestimated Rln offsetting underestimated net shortwave radiation (Rsn). Merely calibrating Rs does not improve ET0 accuracy but may exacerbate errors. The Liu-S was the empirical model for Rs estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for Rln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET0 estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET0 estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET0 in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau. [ABSTRACT FROM AUTHOR]

ERA5-Land is a valuable reanalysis data resource that provides near-real-time, high-resolution, multivariable data for various applications. Using daily precipitation data from 301 meteorological stations in the Yellow River Basin from 2001 to 2013 as benchmark data, this study aims to evaluate ERA5-Land's capability of monitoring extreme precipitation. The evaluation study is conducted from three perspectives: precipitation amount, extreme precipitation indices, and characteristics of extreme precipitation events. The results show that ERA5-Land can effectively capture the spatial distribution patterns and temporal trends in precipitation and extreme precipitation; however, it also exhibits significant overestimation and underestimation errors. ERA5-Land significantly overestimates total precipitation and indices for heavy precipitation and extreme precipitation (R95pTOT and R99pTOT), with errors reaching up to 89%, but underestimates the Simple Daily Intensity Index (SDII). ERA5-Land tends to overestimate the duration of extreme precipitation events but slightly underestimates the total and average precipitation of these events. These findings provide a scientific reference for optimizing the ERA5-Land algorithm and for users in selecting data. [ABSTRACT FROM AUTHOR]

This study assessed the past changes in extreme precipitation and temperature events across the Himalayan Mountains of Pakistan. This cold and mountainous environmental region has witnessed a significant increase in climate-related disasters over the past few decades. Spatiotemporal changes in extreme temperature and precipitation events were analyzed using 24 indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this study, in situ data of 16 national meteorological stations were obtained from the Pakistan Meteorological Department (PMD) for the past three decades (1991–2020). The significance of the trends was assessed using the modified Mann–Kendall (MMK) test, and the Theil–Sen (TS) slope estimator was used to estimate the slope of the trend. The results showed that there has been a consistent decline in the total precipitation amount across the Himalayan Mountains of Pakistan. The trend exhibited a decrease in the annual average precipitation at a rate of −6.56 mm/year. Simultaneously, there was an increasing trend in the annual average minimum and maximum temperatures at rates of 0.02 °C/year and 0.07 °C/year, respectively. The frequencies of consecutive wet days (CWDs) and maximum 5-day precipitation (RX5day) have decreased significantly, with decreasing rates of −0.40 days/year and −1.18 mm/year, respectively. The amount of precipitation during very wet days (R95p) and extremely wet days was decreased by −19.20 and −13.60 mm/decade, respectively. The warm spell duration (WSDI) and the frequency of warm days (TX90p) across the Himalayan Range both increased by 1.5 and 1.4 days/decade. The number of cold days (TX10p) and cold nights (TN10p) decreased by 2.9 and 3.4 days/decade. The average temperature of the hottest nights (TXn) and the diurnal temperature range (DTR) were increased by 0.10 and 0.30 °C/decade. The results indicated an increasing tendency of dry and warm weather in the Himalayan region of Pakistan, which could have adverse consequences for water resources, agriculture, and disaster management in the country. Therefore, it is essential to prioritize the implementation of localized adaptation techniques in order to enhance sustainable climate resilience and effectively address the emerging climate challenges faced by these mountainous regions. [ABSTRACT FROM AUTHOR]

Measurements of downburst outflows using standard meteorological instruments (e.g., anemometers) are rare due to their transient and localized nature. However, video recordings of such events are becoming more frequent. This short communication (Technical Note) study presents a new approach to estimating the kinematics of a downburst event using video footage recordings of the event. The main geometric dimensions of the event, such as downdraft diameter, cloud base height, outflow depth, and the radius of the outflow at a given moment in time, are estimated by sizing them against reference structures of known dimensions that are present in the video footage. From this analysis, and knowing the frame rate of the video recording, one can estimate the characteristic velocities in the downburst event, such as the mean downdraft velocity and the mean velocity of the radial outflow propagation. The proposed method is tested on an August 2015 downburst event that hit Tucson, Arizona, United States. The diameter of the downburst outflow increased with the time from approximately 1.10 km to 3.35 km. This range of values indicates that the event was a microburst. The mean descending velocity of downburst downdraft was 8.9 m s−1 and the horizontal velocity of outflow propagation was 17.7 m s−1. The latter velocity is similar to the measured wind gust at the nearby weather station and Doppler radar. The outflow depth is estimated at 160 m, and the cloud base height was approximately 1.24 km. Estimating the kinematics of downbursts using video footage, while subject to certain limitations, does yield a useful estimation of the main downburst kinematics that contribute to a better quantification of these localized windstorms. [ABSTRACT FROM AUTHOR]

Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region. [ABSTRACT FROM AUTHOR]

Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions. [ABSTRACT FROM AUTHOR]

A drought is a complex event characterized by low rainfall and has negative implications for agricultural and hydrological systems, as well as for community life. A common meteorological drought index used for drought monitoring and water resource management is the Standardized Precipitation Evapotranspiration Index (SPEI). Using SPEI can assist in predicting drought onset and estimating drought severity. The objective of this research is to assess the accuracy of machine learning models in estimating the SPEI-1 (one-month) index in semi-arid climates. To achieve this goal, the data will be analyzed using remote sensing parameters, a worldwide database, and meteorological station information. SPEI-1 was predicted in Tabriz, Iran, between 1990 and 2022 using multilayer perceptron (MLP) and random forest (RF) techniques combined with genetic algorithm (GA) methods. The parameters used are average air temperature, average relative humidity, monthly precipitation, wind speed, sunny hours, as well as the one-month standard precipitation index (SPI-1) (from ground data), daily precipitation products from satellites named PERSIANN (PRC-PR) (from remote sensing), and SPEIbase data (from global databases). The results suggest that the use of satellite remote sensing characteristics and global databases has significantly enhanced the precision and efficiency of prediction models. Based on the GA-RF model with an R2 of 0.992 and an RMSE of 0.124, it exhibits the best performance among all models in Scenario 1. By combining remote sensing parameters, this study presents an innovative approach to predicting the SPEI index and demonstrates their capabilities in drought management and mitigation. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive drought analysis using climate data obtained from 219 homogeneously distributed meteorological stations in Türkiye between 1991 and 2022. In this context, Standard Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI) drought indices were used and comparative analysis was made. Türkiye. The study demonstrates that below-normal precipitation over extended periods and increasing temperatures have contributed to the increased frequency of meteorological drought events. Türkiye's topographic conditions, particularly its location in the Mediterranean basin, significantly influence drought occurrences. It is noted that over the past 20 years, Türkiye has been trending towards drier conditions, with rising temperatures reinforcing this trend. The study observes that the moderate drought class range is the most frequently recurring in the SPI, SPEI, and RDI methods utilized. Regarding atmospheric conditions affecting the climate in Türkiye, it is observed that increased drought severity stands out prominently in years when the North Atlantic Oscillation is positive. During these years, increased drought severity is evident in the SPI, SPEI, and RDI indices, particularly in winter and autumn, while a wide area experiences drought effects in the summer months. Long-term analyses emphasize that drought periods occur less frequently but have more prolonged impacts, attributed to variations in precipitation patterns from year to year and the influence of rising temperatures due to global climate change. The potential future increase in drought in the Mediterranean basin due to global climate change and Türkiye's vulnerability to this situation could have adverse effects on water resources, food security, energy sources, and ecosystems. [ABSTRACT FROM AUTHOR]

The current research on urban heat island (UHI) effect mostly focuses on the analysis of land use type changes and the surface UHI intensity. Few studies on the urban canopy heat island effect at a block scale of local climate zone (LCZ), although the canopy heat island effect is a key factor affecting human thermal comfort. Therefore, this study will combine the LCZ classification system and the urban weather generator (UWG) model to simulate and quantitatively analyse the urban canopy heat island effect in Beijing at the block scale. First, based on Sentinel-2 Multispectral remote sensing images, the residual neural network (ResNet) method was used to obtain the LCZ of Beijing, and the results of LCZs was validated based on the google earth engine (GEE). Then, according to the classification results of local climate zones, the input parameters of the UWG and their corresponding value ranges are calculated. Finally, the UWG model is used to simulate the canopy temperature in different local climate zones, and the urban canopy temperature is validated using the meteorological station dataset. We quantitatively analyse the temperature differences between different types of LCZs. The results shows that the canopy heat island effect in Beijing gradually weakened outward from the city centre. This is mainly due to the relatively dense distribution of compact local climate zones in the centre of Beijing, while the surrounding areas of Beijing have lower building density and better natural coverage. The canopy urban heat island intensity of built-up LCZs is significantly stronger than that of natural-covered LCZs. The heat island intensity of the compact LCZ is higher than that of the open LCZ with the same building height. However, for LCZs with comparable compactness, the heat island intensity of high-level LCZs is higher than that of low-level LCZs. [ABSTRACT FROM AUTHOR]

Using monthly data from 19 meteorological stations in Guangxi Zhuang Autonomous region from 1961 to 2020, this study calculated the standardized precipitation evapotranspiration index (SPEI) based on the Penman-Monteith evapotranspiration model (SPEI_PM). The temporal and spatial variation of meteorological drought were analyzed via linear tendency estimation, and Mann-Kendall trend test. The correlation between El Niño-Southern Oscillation (ENSO) and meteorological drought in Guangxi was also explored by cross-correlation function, cross wavelet transform and wavelet coherence. The values for summer, autumn, and annual SPEI_PM from 1961 to 2020 exhibited a significant linear trend of increasing wetness over time. Annual and seasonal droughts throughout the region were predominately mild and moderate, with less frequent severe and extreme droughts. Spring droughts were concentrated in the central part of the region, summer droughts in the southwest, autumn droughts in the southeast and northeast, and winter droughts spread throughout the region. Annual droughts were concentrated in the northeast. The rate of drought occurrence as measured by the stations in Guangxi exhibited a distinctly chronological character. Large-scale droughts (especially severe and extreme droughts) across the entire region occurred most often in the 1960s, 1970s, and 2000s. The impact of ENSO on the meteorological drought in Guangxi was characterized by spatial heterogeneity. Correlation between the ocean Niño index (ONI) and meteorological drought was highest in the northeast and southeast, as well as the northwest. There was no significant correlation between drought and ONI in coastal areas and most southwestern parts of the region. As a typical example from meteorological stations in northeastern Guangxi, SPEI-3 and ONI at the Guilin Meteorological station showed interannual oscillation cycles of 16-48 months in the period 1962-1976, and 12-64 months in the period 1990-2019. These results can serve as a useful reference for future drought forecasting and assessment in Guangxi. [ABSTRACT FROM AUTHOR]

Freezing damage in spring is one of the serious agricultural meteorological disasters for winter wheat production in the Huang-Huai-Hai plain. In order to effectively identify freezing damages of winter wheat in spring, this paper used a deep learning long short-term memory model LSTM to identify the occurrences of freezing damages of winter wheat in spring in Henan province, which was an important winter wheat planting area in China. The spatiotemporal distributions and decadal changes of freezing damages of winter wheat in spring based on the multi-source meteorological, crop, and disaster data from 99 meteorological stations from 1981 to 2020 were explored. The results indicated that: (1) the optimized LSTM model effectively identified freezing damages of winter wheat in spring. The daily minimum temperature simulated by LSTM model from 2017 to 2020 showed an average absolute percentage error (MAPE) of 8.73% and a goodness of fit (R2 ) with 0.90 compared to the actual minimum temperature. Based on the disaster data and freezing damage index for winter wheat in spring, the actual disaster situations from 2017 to 2020 were found to be consistent with the simulated results by the optimized LSTM model. (2) From 1981 to 2020, the harm of mild freezing damage to winter wheat in spring in Henan province had gradually weakened, and the high frequency area of mild freezing damage moved from the Northeast to the East. The overall distribution of mild freezing damage of winter wheat in spring in Henan province between 1981 and 2020 was higher in the northeastern Henan, and lower in the southwestern and southeastern Henan, with an average frequency of 0-1.75 times·10y-1 during the past 40 years. The frequency of mild freezing damage of winter wheat in spring decreased per decade, gradually decreasing from 0.843 times per decade to 0.157 times per decade. The high frequency area of mild freezing damage of winter wheat in spring moved from the Northeast to the East. (3) From 1981 to 2020, the frequency of severe freezing damage of winter wheat in spring in Henan province showed a trend of first increasing and then decreasing. The high frequency area of severe freezing damage of winter wheat in spring moved from the Northeast to the East. The overall distribution of severe freezing damage during the 40a period was higher in the East than in the West, and higher in the North than in the South. The average frequency of severe freezing damage in the 40a period was 0-2.75times·10y-1 . The frequency of severe freezing damage of winter wheat in spring increased from 0.508 times per decade to 0.857 times per decade and then decreased to 0.289 times per decade. The high frequency area of severe freezing damage moved from the Northeast to the East. The overall trend of severe freezing damage of winter wheat in spring in Henan Province was decreasing, but the frequency of extreme weather events under climate warming was increasing. Strengthening the researches on the impacts of major agricultural meteorological disasters on agricultural production in various regions in China under climate change was still one of the future research focuses. Various accuracy evaluation indicators of the deep learning LSTM model for identifying freezing damage of winter wheat in spring proposed in this study were improved, and the simulation results of freezing damage by LSTM model were basically consistent with the actual occurrences. Our results can provide ideas and methods for large-scale freezing damage identification of winter wheat under global climate change, and also have certain reference values for other agricultural meteorological disaster identification. [ABSTRACT FROM AUTHOR]

The hourly average temperature and average relative humidity data of 260 meteorological stations in Chongqing from February to June in 2019-2023 were used to simulate the geographic distribution of spring potato late blight infection risk using the CARAH late blight model. The accuracy of the simulation was tested by using the late blight infection data of 26 monitoring stations in Wuxi county, Chongqing in 2022. Based on the geographic distribution of spring potato late blight infection risk simulated, the maximum entropy model was constructed using the climate grid data of the monthly average temperature, maximum temperature, minimum temperature, water vapor pressure and precipitation from February to June in 1970-2000 to analyze the climate impact factors of spring potato late blight in Chongqing, and to evaluate the climate risk of spring potato late blight, providing a reference for the prediction and scientific prevention of the disease. The results showed that simulations of late blight infection based on hourly weather data had high accuracy, with a false positive rate of 12.5%, false negative rate of 18.5% and TS score of 0.73. The mean area under curve (AUC) of the receiver operating characteristic (ROC) was above 0.9, indicating higher accuracy of the simulation results. Precipitation was the dominant climate factor affecting the risk distribution of late blight of spring potato in Chongqing, while relative humidity and temperature were important climate factors. Climate variables at the seedling stage and bud flowering stage had a great impact on the distribution of late blight risk. The low risk area of late blight of each maturity(early/late) and susceptibility (resistant/susceptible) combination of spring potato was less than or close to 10000km², with an average area proportion of 10.2%. The medium risk area and high risk area were both more than 30000km², with an average area proportion of 43.7% and 46.1%, respectively. The climate risk of spring potato late blight showed a spatial distribution characteristic of "high in the middle and low in the periphery" in Chongqing. The high risk area was concentrated in the parallel valley area in eastern Sichuan, the medium risk area was mainly distributed in Daba mountain area in northeast Chongqing, Wuling mountain area in southeast Chongqing, and the hilly area in central Sichuan in west Chongqing, and the low risk area was scattered in the fringe of Chongqing. Spring potato production in Chongqing area faces a high climate risk of late blight, with significant spatial differentiation characteristics. It should be addressed through reasonable production layout and improved cultivation techniques. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

The Normalized Differential Spectral Attenuation (NDSA) technique was proposed years ago as an active method for measuring integrated water vapor (IWV) along a Ku/K-band radio link immersed (totally or partially) in the troposphere. The approach is of the active kind, as it relies on the transmission of a couple of sinusoidal signals, whose power is measured at the receiver, thus providing the differential attenuation measurements from which IWV estimates can be in turn derived. In 2018, a prototype instrument providing such differential attenuation measurements was completed and set up for a first measurement campaign aimed at demonstrating the NDSA method. By the end of June 2022, the instrument was profoundly modified and upgraded so that a second measurement campaign could be carried out from 1 August to 30 November 2022. The transmitter was placed on the top of Monte Gomito (44.1277°lat, 10.6434°lon, 1892 m a.s.l.) and the receiver on the roof of the Department of Information Engineering of the University of Florence (43.7985°lat, 11.2528°lon, 50 m a.s.l.). The resulting radio link length was 61.15 km. Four ground weather stations of the regional weather service were selected among those available. In this paper, we describe the upgraded instrument and present the outcomes of the new measurement campaign, whose purpose was mainly to compare the IWV estimates provided by the instrument with the ground sensor measurements of air temperature, air humidity, barometric pressure, and rainfall. In particular, we show that the temporal trends of the two IWV estimates are qualitatively consistent, and that the instrument is able to provide IWV estimates also in the presence of fog and rainfall. Conversely, a quantitative evaluation through comparison with IWV data from point weather station measurements appears challenging due to the significant spatial variability in temperature and relative humidity, even between couples of stations that are quite close to each other. [ABSTRACT FROM AUTHOR]

The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local–global–temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT–LGTA–LSTM. The proposed model was compared to five comparative models (LGTA–LSTM, GAT–GTA–LSTM, GTA–LSTM, GAT–GA–LSTM, GA–LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT–LGTA–LSTM model demonstrated the best forecasting performance among the comparative models. The Nash–Sutcliffe Efficiency (NSE) of GAT–LGTA–LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA–LSTM benchmark model, the GAT–LGTA–LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling–Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff. [ABSTRACT FROM AUTHOR]

Introduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring. [ABSTRACT FROM AUTHOR]

Eight spatial interpolation models were used to map the spatial distribution of precipitation in the southwestern sector of the Iberian Peninsula (22330 km2) over 40 years (1980/1981–2019/2020). Rainfall data from 103 meteorological stations were used to generate the interpolation models, namely inverse distance weight (IDW) with 6, 12 and 24 points, regression spline (RS), thin spline (TS), universal kriging with spherical and Gaussian variogram (UK_Sphe and UK_gauss, respectively) and multilinear regression (MR), based on physiographic and geographic variables. Furthermore, 32 rainfall stations were used to assess the performance of the previous methods through 7 statistical metrics Ordinary Least Squares (OLS), Root Mean Square Error (RMSE), Normalized root mean square error (NRMSE), Coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Mean Absolute Error (Bias and MAE). Based on these metrics, UK_gauss and IDW_6 provided the best adjustments, whereas MR presented the highest errors. All methods were suitable to predict the spatial distribution of rainfall, but adjustments are conditioned by the features of the study area, gauge density and gauge spatial distribution. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the applicability of remote sensing precipitation products in southwest China, to reveal the spatial and temporal characteristics of precipitation in Southwest China, and to provide scientific references for the assessment of droughts and floods in southwest China and the utilization of water resources. [Methods] Firstly, combined with precipitation data from 100 ground- based meteorological stations in the study region, the applicability of TRMM and GPM precipitation products were evaluated on both the multi-year average and inter-annual scales, as well as the meteorological station scale. Subsequently, based on the preferred remote sensing precipitation product, the coefficient of variation method, Theil-Sen median trend analysis coupled with Mann-Kendall significance test and Hurst exponent were adopted to analyze spatiotemporal changes of precipitation and assess their sustainability. Moreover, our effort predicted the precipitation trends in the future. [Results (1) Both TRMM and GPM products demonstrated good consistency and applicability in southwest China, albeit with some overestimation. However, compared with TRMM, GPM displayed a higher spatial resolution and better data quality, which was overall closer to the measured data. (2) Precipitation in southwest China had experienced low and moderate fluctuations over the past 19 years, with 87.54% of the region. The trend for precipitation changes in this region had mostly been increasing (83.71%), mainly weak to moderate sustainability (50.38% weak. 24.30% moderate). The prediction for future precipitation trend as a whole suggested a general increase in precipitation, with 62.24% of the region showing increasing trends, mainly in the central part of the study region, and 13.23% showing trends, mainly in the central Yunnan and southwestern Sichuan regions. This effort provided valuable insights for decision-making related to agricultural production and water resources management in southwest China. [Conclusion GPM is more suitable than TRMM to characterize the precipitation characteristics in southwest China and can be effectively used to carry out spatiotemporal dynamic analysis of precipitation. [ABSTRACT FROM AUTHOR]

In arid and semi-arid regions like Iran, sustainable urban and agricultural development is intimately intertwined with the severity, frequency, and duration of meteorological droughts. Prolonged meteorological droughts can trigger hydrological and socio-economic droughts, posing significant challenges to the region's sustainability. Therefore, the primary objective of this study is to perform a comprehensive Regional Frequency Analysis (RFA) for extreme meteorological drought events across Iran. To this end, we meticulously calculated the severity, duration, and magnitude of the Standardized Precipitation Index (SPI) over the 3-, 6-, 9-, and 12-month accumulation periods. The data was derived from monthly precipitation records collected at 106 meteorological stations during the period spanning 1993–2016. Subsequently, we employed a rigorous approach, utilizing Ward's clustering analysis and the test for heterogeneity using L-moment, to effectively classify and categorize the extreme drought characteristics prevalent across the country. The analysis revealed that the generalized Pareto and Pearson type 3 models are the most suitable distribution functions for RFA across almost all the examined clusters. Additionally, the spatiotemporal analysis of the SPI series at various time scales indicated that the extreme events' characteristics, such as severity, duration, and magnitude, remain surprisingly unaffected by the local climate conditions. The results also showed that even in the typically humid regions located in the north, west, and northwest of Iran, areas that receive considerable annual precipitation, severe droughts can still occur. These droughts primarily stem from temporal events rather than being solely dependent on the local climate conditions. Therefore, the study concludes that the drought events can largely be attributed to mismanagement and inadequate regional water allocation. Moreover, these situations might have been further exacerbated by the occurrence of extreme climatic conditions. [ABSTRACT FROM AUTHOR]

Copyright of Afyon Kocatepe University Journal of Science & Engineering / Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi is the property of Afyon Kocatepe University, Faculty of Science & Literature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration. Design/methodology/approach: Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated. Findings: This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed. Practical implications: Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation. Originality/value: Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions. [ABSTRACT FROM AUTHOR]

Flood early warning requires rainfall data with a high temporal and spatial resolution for flood risk analysis to simulate flood dynamics in all small and large basins. However, such high-quality data are still very scarce in many developing countries. In this research, in order to identify the best and most up-to-date rainfall estimation tools for early flood forecasting in arid and semi-arid regions, the northeastern region of Iran with 17 meteorological stations and four rainfall events was investigated. The rainfall products of satellites (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, Global Satellite Mapping of Precipitation, Climate Hazards Group InfraRed Precipitation with Station, European Reanalysis (ERA5), Global Precipitation Measurement) along with the most widely used microphysical schemes of Weather Research and Forecasting (WRF) model (Purdue-Lin (Lin), WRF Single-Moment class 3, 6, and WRF Double-Moment class 6. were used for rainfall modeling. The efficiency of each of these models to forecasting the amount of rainfall was verified by four methods: Threat Scores (TS), False Alarm Ratio, Hit Rate (H), and False Alarm (F). Analysis of research findings showed that the WRF meteorological model has better accuracy in rainfall modeling for the next 24 h. In this model, Lin's microphysical scheme has the highest accuracy, and its threat score (TS) quantity is up to 98% efficient in some stations. The best accuracy of satellite products for estimating the amount of rainfall is up to 50%. This accuracy value is related to the satellite product (ERA5). In this method, an 18 km distance from the ground station is the best distance for setting up the space station, which is used for input to hydrological/hydraulic models. Based on the results of this research, by using the connection of the WRF model with hydrology/hydraulic models, it is possible to predict and simulate rainfall-runoff up to 72 h before its occurrence. Also, by using these space stations, the amount of rainfall is estimated for the entire area of the basin and an early flood warning is issued. [ABSTRACT FROM AUTHOR]

Purpose: In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers' adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies. Design/methodology/approach: This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation. Findings: (1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers' decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas. Originality/value: By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers' adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies. [ABSTRACT FROM AUTHOR]