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Keywords = rain-on-snow events

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54 pages, 18933 KB  
Article
LUME 2D: A Linear Upslope Model for Orographic and Convective Rainfall Simulation
by Andrea Abbate and Francesco Apadula
Meteorology 2025, 4(4), 28; https://doi.org/10.3390/meteorology4040028 - 3 Oct 2025
Abstract
Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and [...] Read more.
Rainfalls are the result of complex cloud microphysical processes. Trying to estimate their intensity and duration is a key task necessary for assessing precipitation magnitude. Across mountains, extreme rainfalls may cause several side effects on the ground, triggering severe geo-hydrological issues (floods and landslides) which impact people, human activities, buildings, and infrastructure. Therefore, having a tool able to reconstruct rainfall processes easily and understandably is advisable for non-expert stakeholders and researchers who deal with rainfall management. In this work, an evolution of the LUME (Linear Upslope Model Experiment), designed to simplify the study of the rainfall process, is presented. The main novelties of the new version, called LUME 2D, regard (1) the 2D domain extension, (2) the inclusion of warm-rain and cold-rain bulk-microphysical schemes (with snow and hail categories), and (3) the simulation of convective precipitations. The model was completely rewritten using Python (version 3.11) and was tested on a heavy rainfall event that occurred in Piedmont in April 2025. Using a 2D spatial and temporal interpolation of the radiosonde data, the model was able to reconstruct a realistic rainfall field of the event, reproducing rather accurately the rainfall intensity pattern. Applying the cold microphysics schemes, the snow and hail amounts were evaluated, while the rainfall intensity amplification due to the moist convection activation was detected within the results. The LUME 2D model has revealed itself to be an easy tool for carrying out further studies on intense rainfall events, improving understanding and highlighting their peculiarity in a straightforward way suitable for non-expert users. Full article
(This article belongs to the Special Issue Early Career Scientists' (ECS) Contributions to Meteorology (2025))
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18 pages, 13697 KB  
Article
A New Anticyclone Identification Method Based on Mask R-CNN Model and Its Application
by Yang Kong, Hao Wu, Ping Xia and Yumin Zhang
Atmosphere 2025, 16(10), 1140; https://doi.org/10.3390/atmos16101140 - 28 Sep 2025
Abstract
In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method [...] Read more.
In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method using the Mask region-based convolutional neural network (Mask R-CNN) model to detect synoptic-scale anticyclones by capturing their two-dimensional structural features and investigating their relationship with snow-ice disasters in Northeast China. It is found that compared with traditional objective identification methods, the new method better captures the overall structural characteristics of anticyclones, significantly improving the description of large-scale, strong anticyclones. Specifically, it incorporates 7.3% of small-scale anticyclones into larger-scale systems. Anticyclones are closely correlated with local cooling and cold air mass changes over Northeast China, with 60% of anticyclones accompanying regional cold air mass accumulation and temperature drops. Two case studies of the rare rain-snow and cold wave events revealed that these events were preceded by the generation and eastward expansion of an upstream anticyclone identified by the new method. This demonstrates that the proposed method can effectively track anticyclones and the evolution of cold high-pressure systems, providing insights into extreme cold events. Full article
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17 pages, 8464 KB  
Article
Spatiotemporal Variations in Observed Rain-on-Snow Events and Their Intensities in China from 1978 to 2020
by Zhiwei Yang, Rensheng Chen, Xiongshi Wang, Zhangwen Liu, Xiangqian Li and Guohua Liu
Water 2025, 17(14), 2114; https://doi.org/10.3390/w17142114 - 16 Jul 2025
Viewed by 392
Abstract
The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and [...] Read more.
The spatiotemporal changes and driving mechanisms of rain-on-snow (ROS) events and their intensities are crucial for responding to disasters triggered by such events. However, there is currently a lack of detailed assessment of the seasonal variations and driving mechanisms of ROS events and their intensities in China. Therefore, this study utilized daily meteorological data and daily snow depth data from 513 stations in China during 1978–2020 to investigate spatiotemporal variations of ROS events and their intensities. Also, based on the detrend and partial correlation analysis model, the driving factors of ROS events and their intensity were explored. The results showed that ROS events primarily occurred in northern Xinjiang, the Qinghai–Tibet Plateau, Northeast China, and central and eastern China. ROS events frequently occurred in the middle and lower Yangtze River Plain in winter but were easily overlooked. The number and intensity of ROS events increased significantly (p < 0.05) in the Changbai Mountains in spring and the Altay Mountains and the southeast part of the Qinghai–Tibet Plateau in winter, leading to heightened ROS flood risks. However, the number and intensity of ROS events decreased significantly (p < 0.05) in the middle and lower Yangtze River Plain in winter. The driving mechanisms of the changes for ROS events and their intensities were different. Changes in the number of ROS events and their intensities in snow-rich regions were driven by rainfall days and quantity of rainfall, respectively. In regions with more rainfall, these changes were driven by snow cover days and snow water equivalent, respectively. Air temperature had no direct impact on ROS events and their intensities. These findings provide reliable evidence for responding to disasters and changes triggered by ROS events. Full article
(This article belongs to the Section Hydrology)
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16 pages, 2149 KB  
Article
ZR Relationships for Different Precipitation Types and Events from Parsivel Disdrometer Data in Warsaw, Poland
by Mariusz Paweł Barszcz and Ewa Kaznowska
Remote Sens. 2025, 17(13), 2271; https://doi.org/10.3390/rs17132271 - 2 Jul 2025
Viewed by 357
Abstract
In this study, the relationship between radar reflectivity and rain rate (Z–R) was investigated. The analysis was conducted using data collected by the OTT Parsivel1 disdrometer during the periods 2012–2014 and 2019–2025 in Warsaw, Poland. As a first step, the [...] Read more.
In this study, the relationship between radar reflectivity and rain rate (Z–R) was investigated. The analysis was conducted using data collected by the OTT Parsivel1 disdrometer during the periods 2012–2014 and 2019–2025 in Warsaw, Poland. As a first step, the parameters a and b of the power-law Z–R relationship were estimated separately for three precipitation types: rain, sleet (rain with snow), and snow. Subsequently, observational data from all 12 months of the annual cycle were used to derive Z–R relationships for 118 individual precipitation events. To date, only a few studies of this kind have been conducted in Poland. In the analysis limited to rain events, the estimated parameters (a = 265, b = 1.48) showed relatively minor deviations from the classical Z–R function for convective rainfall, Z = 300R1.4. However, the parameter a deviated more noticeably from the Z = 200R1.6 relationship proposed by Marshall and Palmer, which is commonly used to convert radar reflectivity into rainfall estimates, including in the Polish POLRAD radar system. The dataset used in this study included rainfall events of varying types, both stratiform and convective, which contributed to the averaging of Z–R parameters. The values for the parameter a in the Z–R relationship estimated for the other two categories of precipitation types, sleet and snow, were significantly higher than those determined for rain events alone. The a values calculated for individual events demonstrated considerable variability, ranging from 80 to 751, while the b values presented a more predictable range, from 1.10 to 1.77. The highest parameter a values were observed during the summer months: June, July, and August. The variability in the Z–R relationship for individual events assessed in this study indicates the need for further research under diverse meteorological conditions, particularly for stratiform and convective precipitation. Full article
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28 pages, 8561 KB  
Article
Ice Ice Maybe: Stream Hydrology and Hydraulic Processes During a Mild Winter in a Semi-Alluvial Channel
by Christopher Giovino, Jaclyn M. H. Cockburn and Paul V. Villard
Water 2025, 17(13), 1878; https://doi.org/10.3390/w17131878 - 24 Jun 2025
Viewed by 924
Abstract
Warm conditions during typically cold winters impact runoff and resulting hydraulic processes in channels where ice-cover would typically dominate. This field study on a short, low-slope reach in Southern Ontario, Canada, examined hydrologic and hydraulic processes with a focus on winter runoff events [...] Read more.
Warm conditions during typically cold winters impact runoff and resulting hydraulic processes in channels where ice-cover would typically dominate. This field study on a short, low-slope reach in Southern Ontario, Canada, examined hydrologic and hydraulic processes with a focus on winter runoff events and subsequent bed shear stress variability. Through winter 2024, six cross-sections over a ~100 m reach were monitored near-weekly to measure hydraulic geometry and velocity profiles. These data characterized channel processes and estimated bed shear stress with law of the wall. In this channel, velocity increased more rapidly than width or depth with rising discharge and influenced bed shear stress distribution. Bed shear stress magnitudes were highest (means ranged ~2–6 N/m2) and most variable over gravel beds compared to the exposed bedrock (means ranged ~0.05–2 N/m2). Through a rain-on-snow (ROS) event in late January, bed shear stress estimates decreased dramatically over the rougher gravel bed, despite minimal changes in water depth and velocity. Pebble counts before, during, and after the event, showed that the proportion of finer-sized particles (i.e., <5 cm) increased while median grain size did not vary. These observations align with findings from both flume and field studies and suggest that milder winters reduce gravel-bed roughness through finer-sized sediment deposition, altering sediment transport dynamics and affecting gravel habitat suitability. Additionally, limited ice-cover leads to lower bed shear stresses and thus finer-sized materials are deposited, further impacting gravel habitat suitability. Results highlight the importance of winter hydrologic variability in shaping channel processes and inform potential stream responses under future climate scenarios. Full article
(This article belongs to the Section Hydrology)
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18 pages, 9721 KB  
Article
A Multi-Year Investigation of Thunderstorm Activity at Istanbul International Airport Using Atmospheric Stability Indices
by Oğuzhan Kolay, Bahtiyar Efe, Emrah Tuncay Özdemir and Zafer Aslan
Atmosphere 2025, 16(4), 470; https://doi.org/10.3390/atmos16040470 - 17 Apr 2025
Viewed by 1482
Abstract
Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of [...] Read more.
Thunderstorms are weather phenomena that comprise thunder and lightning. They typically result in heavy precipitation, including rain, snow, and hail. Thunderstorms have adverse effects on flight at both the ground and the upper levels of the troposphere. The characteristics of the thunderstorm of Istanbul International Airport (International Civil Aviation Organization (ICAO) code: LTFM) have been investigated because it is currently one of the busiest airports in Europe and the seventh-busiest airport in the world. Geopotential height (m), temperature (°C), dewpoint temperature (°C), relative humidity (%), mixing ratio (g kg−1), wind direction (°), and wind speed (knots) data for the ground level and upper levels of the İstanbul radiosonde station were obtained from the Turkish State Meteorological Service (TSMS) for 29 October 2018 and 1 January 2023. Surface data were regularly collected by the automatic weather stations near the runway and the upper-level data were collected by the radiosonde system located in the Kartal district of İstanbul. Thunderstorm statistics, stability indices, and meteorological variables at the upper levels were evaluated for this period. Thunderstorms were observed to be more frequent during the summer, with a total of 51 events. June had the highest number of thunderstorm events with a total of 32. This averages eight events per year. A total of 72.22% occurred during trough and cold front transitions. The K index and total totals index represented the thunderstorm events better than other stability indices. In total, 75% of the thunderstorm days were represented by these two stability indices. The results are similar to the covering of this area: the convective available potential energy (CAPE) values which are commonly used for atmospheric instability are low during thunderstorm events, and the K and total totals indices are better represented for thunderstorm events. This study investigates thunderstorm events at the LTFM, providing critical insights into aviation safety and operational efficiency. The research aims to improve flight planning, reduce weather-related disruptions, and increase safety and also serves as a reference for airports with similar climatic conditions. Full article
(This article belongs to the Special Issue Weather and Climate Extremes: Past, Current and Future)
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27 pages, 13326 KB  
Article
Observations of the Microphysics and Type of Wintertime Mixed-Phase Precipitation, and Instrument Comparisons at Sorel, Quebec, Canada
by Faisal S. Boudala, Mathieu Lachapelle, George A. Isaac, Jason A. Milbrandt, Daniel Michelson, Robert Reed and Stephen Holden
Remote Sens. 2025, 17(6), 945; https://doi.org/10.3390/rs17060945 - 7 Mar 2025
Viewed by 894
Abstract
Winter mixed-phase precipitation (P) impacts transportation, electric power grids, and homes. Forecasting winter precipitation such as freezing precipitation (ZP), freezing rain (ZR), freezing drizzle (ZL), ice pellets (IPs), and the snow (S) and rain (R) boundary remains challenging due to the complex cloud [...] Read more.
Winter mixed-phase precipitation (P) impacts transportation, electric power grids, and homes. Forecasting winter precipitation such as freezing precipitation (ZP), freezing rain (ZR), freezing drizzle (ZL), ice pellets (IPs), and the snow (S) and rain (R) boundary remains challenging due to the complex cloud microphysical and dynamical processes involved, which are difficult to predict with the current numerical weather prediction (NWP) models. Understanding these processes based on observations is crucial for improving NWP models. To aid this effort, Environment and Climate Change Canada deployed specialized instruments such as the Vaisala FD71P and OTT PARSIVEL disdrometers, which measure P type (PT), particle size distributions, and fall velocity (V). The liquid water content (LWC) and mean mass-weighted diameter (Dm) were derived based on the PARSIVEL data during ZP events. Additionally, a Micro Rain Radar (MRR) and an OTT Pluvio2 P gauge were used as part of the Winter Precipitation Type Research Multi-Scale Experiment (WINTRE-MIX) field campaign at Sorel, Quebec. The dataset included manual measurements of the snow water equivalent (SWE), PT, and radiosonde profiles. The analysis revealed that the FD71P and PARSIVEL instruments generally agreed in detecting P and snow events. However, FD71P tended to overestimate ZR and underestimate IPs, while PARSIVEL showed superior detection of R, ZR, and S. Conversely, the FD71P performed better in identifying ZL. These discrepancies may stem from uncertainties in the velocity–diameter (V-D) relationship used to diagnose ZR and IPs. Observations from the MRR, radiosondes, and surface data linked ZR and IP events to melting layers (MLs). IP events were associated with colder surface temperatures (Ts) compared to ZP events. Most ZR and ZL occurrences were characterized by light P with low LWC and specific intensity and Dm thresholds. Additionally, snow events were more common at warmer T compared to liquid P under low surface relative humidity conditions. The Pluvio2 gauge significantly underestimated snowfall compared to the optical probes and manual measurements. However, snowfall estimates derived from PARSIVEL data, adjusted for snow density to account for riming effects, closely matched measurements from the FD71P and manual observations. Full article
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17 pages, 3782 KB  
Article
Identification Method of Highway Accident Prone Sections Under Adverse Meteorological Conditions Based on Meteorological Responsiveness
by Yanyang Gao, Chi Zhang, Maojie Ye and Bo Wang
Appl. Sci. 2025, 15(2), 521; https://doi.org/10.3390/app15020521 - 8 Jan 2025
Viewed by 913
Abstract
To mitigate the prevalence of highway accidents in Southwest China during adverse weather conditions, this study introduces a novel method for identifying accident-prone sections in complex meteorological circumstances. The technique, anchored in data mining’s support index, pioneers the concept of meteorological responsiveness, which [...] Read more.
To mitigate the prevalence of highway accidents in Southwest China during adverse weather conditions, this study introduces a novel method for identifying accident-prone sections in complex meteorological circumstances. The technique, anchored in data mining’s support index, pioneers the concept of meteorological responsiveness, which includes the elucidation of its mechanisms and the development of computational methodologies. Historical meteorological data and accident records from mountainous highways were meticulously analyzed to quantify the spectrum of adverse weather impacts on driving risks. By integrating road geometry, weather data, and accident site information, meteorological events were identified, categorized, and assigned a meteorological responsiveness score. Outlier sections were processed for preliminary screening, enabling the identification of high-risk segments. The Meteorological Response Ratio Index was instrumental in highlighting and quantifying the influence of adverse weather on traffic safety, facilitating the prioritization of critical sections. The case study of the SC2 highway in Southwest China validated the method’s feasibility, successfully pinpointing eight high-risk sections significantly affected by adverse weather, which constituted approximately 19.05% of the total highway length. Detailed analysis of these sections, especially those impacted by rain, fog, and snow, revealed specific zones prone to accidents. The meteorological responsiveness method’s efficacy was further substantiated by correlating accident mechanisms under adverse weather with the road geometry of key sections. This approach stands to significantly enhance the safety management of operational highways. Full article
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14 pages, 6673 KB  
Article
Impact of Cyclonic Storm “Sitrang” over the Bay of Bengal on Heavy Rain and Snow in Eastern Tibet
by Xiaotao Zhao, Lunzhu Danzeng, Qu Chi, Xulin Ma, Yuting Tan, Luozhu Duodian and Ranzhen Danzeng
Atmosphere 2025, 16(1), 30; https://doi.org/10.3390/atmos16010030 - 29 Dec 2024
Viewed by 1251
Abstract
Rainstorms and blizzards are common extreme weather events occurring in the eastern Tibet region. Their complex dynamic and thermodynamic mechanisms present challenges for regional meteorological research and forecasting. Based on station observation data and ERA5 atmospheric reanalysis datasets, a diagnostic analysis of the [...] Read more.
Rainstorms and blizzards are common extreme weather events occurring in the eastern Tibet region. Their complex dynamic and thermodynamic mechanisms present challenges for regional meteorological research and forecasting. Based on station observation data and ERA5 atmospheric reanalysis datasets, a diagnostic analysis of the heavy rain and snow event in eastern Tibet from 24 to 27 October 2022 was conducted. The results indicate that (1) the influence of the cloud systems surrounding the Bay of Bengal storm “Sitrang” was a significant factor contributing to the occurrence of this heavy rain and snow weather. (2) Sustained stability of the southern branch trough and the western Pacific subtropical high favored the establishment and maintenance of the mid-level jet stream ahead of the storm. Storm “Sitrang” transported warm and moist air to eastern Tibet through the southwest mid-level jet stream, providing favorable moisture, dynamic, and thermal conditions for the heavy rain and snow. (3) Most importantly, symmetrical instability generated by the inclined motion of the storm’s warm and moist air emerged as the decisive mechanism driving the occurrence and development of the heavy rain and snow. Full article
(This article belongs to the Section Meteorology)
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32 pages, 13260 KB  
Article
Flood Susceptibility Mapping in Punjab, Pakistan: A Hybrid Approach Integrating Remote Sensing and Analytical Hierarchy Process
by Rana Muhammad Amir Latif and Jinliao He
Atmosphere 2025, 16(1), 22; https://doi.org/10.3390/atmos16010022 - 28 Dec 2024
Cited by 2 | Viewed by 7101
Abstract
Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We [...] Read more.
Flood events pose significant risks to infrastructure and populations worldwide, particularly in Punjab, Pakistan, where critical infrastructure must remain operational during adverse conditions. This study aims to predict flood-prone areas in Punjab and assess the vulnerability of critical infrastructures within these zones. We developed a robust Flood Susceptibility Model (FSM) utilizing the Maximum Likelihood Classification (MLC) model and Analytical Hierarchy Process (AHP) incorporating 11 flood-influencing factors, including “Topographic Wetness Index (TWI), elevation, slope, precipitation (rain, snow, hail, sleet), rainfall, distance to rivers and roads, soil type, drainage density, Land Use/Land Cover (LULC), and the Normalized Difference Vegetation Index (NDVI)”. The model, trained on a dataset of 850 training points, 70% for training and 30% for validation, achieved a high accuracy (AUC = 90%), highlighting the effectiveness of the chosen approach. The Flood Susceptibility Map (FSM) classified high- and very high-risk zones collectively covering approximately 61.77% of the study area, underscoring significant flood vulnerability across Punjab. The Sentinel-1A data with Vertical-Horizontal (VH) polarization was employed to delineate flood extents in the heavily impacted cities of Dera Ghazi Khan and Rajanpur. This study underscores the value of integrating Multi-Criteria Decision Analysis (MCDA), remote sensing, and Geographic Information Systems (GIS) for generating detailed flood susceptibility maps that are potentially applicable to other global flood-prone regions. Full article
(This article belongs to the Special Issue The Water Cycle and Climate Change (3rd Edition))
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15 pages, 4075 KB  
Article
Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania
by Egidijus Rimkus, Edvinas Stonevičius, Indrė Gečaitė, Viktorija Mačiulytė and Donatas Valiukas
Atmosphere 2024, 15(11), 1349; https://doi.org/10.3390/atmos15111349 - 10 Nov 2024
Viewed by 1705
Abstract
This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated [...] Read more.
This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid. Full article
(This article belongs to the Section Meteorology)
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21 pages, 4678 KB  
Article
Quantifying the Impact of Rain-on-Snow Induced Flooding in the Western United States
by Brennan Lynn Bean and Emma Watts
Water 2024, 16(19), 2826; https://doi.org/10.3390/w16192826 - 4 Oct 2024
Viewed by 1941
Abstract
The potentially destructive flooding resulting from rain-on-snow (ROS) events motivates efforts to better incorporate these events and their residual effects into flood-related infrastructure design. This paper examines relationships between measured streamflow surges at streamgages across the Western United States and the meteorological conditions [...] Read more.
The potentially destructive flooding resulting from rain-on-snow (ROS) events motivates efforts to better incorporate these events and their residual effects into flood-related infrastructure design. This paper examines relationships between measured streamflow surges at streamgages across the Western United States and the meteorological conditions preceding them at SNOTEL stations within the same water catchment. Relevant stream surges are identified using a peak detection algorithm via time series analysis, which are then labeled ROS- or non-ROS-induced based on the preceding meteorological conditions. Both empirical and model-derived differences between ROS- and non-ROS-induced stream surges are then explored, which suggest that ROS-induced stream surges are 3–20 percent larger than non-ROS-induced stream surges. Quantifying the difference between ROS and non-ROS-induced stream surges promises to aid the improvement of flood-related infrastructure design (such as culverts) to better guard against extreme flooding events in locations subject to ROS. Full article
(This article belongs to the Section Hydrology)
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20 pages, 20182 KB  
Article
Use of Indices Applied to Remote Sensing for Establishing Winter–Spring Cropping Areas in the Republic of Kazakhstan
by Asset Arystanov, Natalya Karabkina, Janay Sagin, Marat Nurguzhin, Rebecca King and Roza Bekseitova
Sustainability 2024, 16(17), 7548; https://doi.org/10.3390/su16177548 - 31 Aug 2024
Cited by 2 | Viewed by 1822
Abstract
Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government [...] Read more.
Farmers in Kazakhstan face unreliable water resources. This includes water scarcity in the summer, high fluctuations in precipitation levels, and an increase in extreme weather events such as snow, rain, floods, and droughts. Wheat production is regulated and subsidized by the Kazakh government to strengthen food security. The proper monitoring of crop production is vital to government agencies, as well as insurance and banking structures. These organizations offer subsidies through different levels support. Some farmers already use farmland soil monitoring combined with adaptive combinations of different crops. These include winter–spring plowing crop programs. Winter wheat crops are generally more adaptive and may survive summer droughts. Kazakhstan is a large country with large plots of farmland, which are complicated to monitor. Therefore, it would be reasonable to adapt more efficient technologies and methodologies, such as remote sensing. This research work presents a method for identifying winter wheat crops in the foothills of South Kazakhstan by employing multi-temporal Sentinel-2 data. Here, the researchers adapted and applied a Plowed Land Index, derived from the Brightness Index. The methodology encompasses satellite data processing, the computation of Plowed Land Index values for the swift recognition of plowed fields and the demarcation of winter wheat crop sowing regions, along with a comparative analysis of the acquired data with ground surveys. Full article
(This article belongs to the Special Issue Farmers’ Adaptation to Climate Change and Sustainable Development)
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22 pages, 33778 KB  
Article
Synthetic Aperture Radar Monitoring of Snow in a Reindeer-Grazing Landscape
by Ida Carlsson, Gunhild Rosqvist, Jenny Marika Wennbom and Ian A. Brown
Remote Sens. 2024, 16(13), 2329; https://doi.org/10.3390/rs16132329 - 26 Jun 2024
Cited by 1 | Viewed by 1609
Abstract
Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led [...] Read more.
Snow cover and runoff play an important role in the Arctic environment, which is increasingly affected by climate change. Over the past 30 years, winter temperatures in northern Sweden have risen by 2 °C, accompanied by an increase in precipitation. This has led to a higher incidence of thaw–freeze and rain-on-snow events. Snow properties, such as the snow depth and longevity, and the timing of snowmelt in spring significantly impact the alpine tundra vegetation. The emergent vegetation at the edge of the snow patches during spring and summer constitutes an essential nutrient supply for reindeer. We have used Sentinel-1 synthetic aperture radar (SAR) to determine the onset of the surface melt and the end of the snow cover in the core reindeer grazing area of the Laevás Sámi reindeer-herding community in northern Sweden. Using SAR data from March to August during the period 2017 to 2021, the start of the surface melt is identified by detecting the season’s backscatter minimum. The end of the snow cover is determined using a threshold approach. A comparison between the results of the analysis of the end of the snow cover from Sentinel-1 and in situ measurements, for the years 2017 to 2020, derived from an automatic weather station located in Laevásvággi reveals a 2- to 10-day difference in the snow-free ground conditions, which indicates that the method can be used to investigate when the ground is free of snow. VH data are preferred to VV data due to the former’s lower sensitivity to temporary wetting events. The outcomes from the season backscatter minimum demonstrate a distinct 25-day difference in the start of the runoff between the 5 investigated years. The backscatter minimum and threshold-based method used here serves as a valuable complement to global snowmelt monitoring. Full article
(This article belongs to the Section Ecological Remote Sensing)
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21 pages, 14485 KB  
Article
Analysis of Dual-Polarimetric Radar Observations of Precipitation Phase during Snowstorm Events in Jiangsu Province, China
by Lei Wang, Yi Wang, Mei Liu, Wei Chen and Chiqin Li
Atmosphere 2024, 15(3), 321; https://doi.org/10.3390/atmos15030321 - 4 Mar 2024
Viewed by 1745
Abstract
Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were [...] Read more.
Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. Full article
(This article belongs to the Special Issue Data Assimilation for Predicting Hurricane, Typhoon and Storm)
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