Snowfall Data Research Articles

Development of an evaluation method for precipitation particle types by using disdrometer data

Abstract A method for evaluating precipitation particle types from the size-fall velocity data observed by disdrometers by combining an expectation-maximization (EM) algorithm and a self-organizing map (SOM) was developed. An EM algorithm was used to estimate the particle size-fall velocity relationships according to previous work, and the SOM map was used to classify the relationships into graupel or snow categories. The method was applied for snowfall data observed by the volume scanning video disdrometer at a site in Sapporo, Japan, and the daily graupel-to-solid precipitation ratio (G/S ratio) from 1 December, 2017 to 28 February, 2018 was obtained. The sensitivity of the results to different SOM configurations, SOM node groupings, and training datasets was also tested. The G/S ratio obtained from the simulation data of a meteorological model was compared as an example of the application of the product created by the new method.

Impact of regional climate change on apple yield in Himachal Pradesh, India

Climate change has become a major global as well as regional challenge because of its adverse effects on biodiversity and agriculture. Himachal Pradesh, a state in India, is situated in the western Indian Himalayan region, which predominantly features an agricultural economy, with apple being the major cash crop of the region. This paper presents the trend analysis of climatic parameters to delineate the effect of climate change on the apple yield in the region from 1991 to 2018. The temperature and rainfall data were obtained from the Indian metrological department, while snowfall data and ambient air quality data were collected from the database of NASA. The chill unit was calculated using the UTAH model. The climatic parameters were compared with apple yield to identify the impact of climate change on the crop. Therefore, apple yield reduction risk was calculated. The trend analysis indicated a temperature decrease during the monsoon months and a temperature increase during winters. The same analysis also indicated decreased snowfall by 0.375 kg/m2 and increased rainfall during the spring season by 23.2 mm, 19.6 mm, 10.4 mm (between mid-February and mid-April from 1985 to 2020). The chill units and apple production showed a positive correlation. In summary, our results indicated that climate change has reduced the apple yield by 11.48%.

Let It Snow: Intercomparison of Various Total and Snow Precipitation Data over the Tibetan Plateau

The Global Precipitation Measurement Mission (GPM) improved spaceborne precipitation data. The GPM dual-frequency precipitation radar (DPR) provides information on total precipitation (TP), snowfall precipitation (SF) and snowfall flags (surface snowfall flag (SSF) and phase near surface (PNS)), among other variables. Especially snowfall data were hardly validated. This study compares GPM DPR TP, SF and snowfall flags on the Tibetan Plateau (TiP) against TP and SF from six well-known model-based data sets used as ground truth: ERA 5, ERA 5 land, ERA Interim, MERRA 2, JRA 55 and HAR V2. The reanalysis data were checked for consistency. The results show overall high agreement in the cross-correlation with each other. The reanalysis data were compared to the GPM DPR snowfall flags, TP and SF. The intercomparison performs poorly for the GPM DPR snowfall flags (HSS = 0.06 for TP, HSS = 0.23 for SF), TP (HSS = 0.13) and SF (HSS = 0.31). Some studies proved temporal or spatial mismatches between spaceborne measurements and other data. We tested whether increasing the time lag of the reanalysis data (+/−three hours) or including the GPM DPR neighbor pixels (3 × 3 pixel window) improves the results. The intercomparison with the GPM DPR snowfall flags using the temporal adjustment improved the results significantly (HSS = 0.21 for TP, HSS = 0.41 for SF), whereas the spatial adjustment resulted only in small improvements (HSS = 0.12 for TP, HSS = 0.29 for SF). The intercomparison of the GPM DPR TP and SF was improved by temporal (HSS = 0.3 for TP, HSS = 0.48 for SF) and spatial adjustment (HSS = 0.35 for TP, HSS = 0.59 for SF).

LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments.

Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.

Impact of Assimilating Radar Solid Precipitation Data in the Canadian Precipitation Analysis System

Abstract The Canadian Precipitation Analysis (CaPA) system provides near-real-time precipitation analyses over Canada by combining observations with short-term numerical weather prediction forecasts. CaPA’s snowfall estimates suffer from the lack of accurate solid precipitation measurements to correct the first-guess estimate. Weather radars have the potential to add precipitation measurements to CaPA in all seasons but are not assimilated in winter due to radar snowfall estimate imprecision and lack of precipitation gauges for calibration. The main objective of this study is to assess the impact of assimilating Canadian dual-polarized radar-based snowfall data in CaPA to improve precipitation estimates. Two sets of experiments were conducted to evaluate the impact of including radar snowfall retrievals, one set using the high-resolution CaPA (HRDPA) with the currently operational quality control configuration and another increasing the number of assimilated surface observations by relaxing quality control. Experiments spanned two winter seasons (2021 and 2022) in central Canada, covering part of the entire CaPA domain. The results showed that the assimilation of radar-based snowfall data improved CaPA’s precipitation estimates 81.75% of the time for 0.5-mm precipitation thresholds. An increase in the probability of detection together with a decrease in the false alarm ratio suggested an improvement of the precipitation spatial distribution and estimation accuracy. Additionally, the results showed improvements for both precipitation mass and frequency biases for low precipitation amounts. For larger thresholds, the frequency bias was degraded. The results also indicated that the assimilation of dual-polarization radar data is beneficial for the two CaPA configurations tested in this study.

Characteristics Analysis and Prediction of Land Use Evolution in the Source Region of the Yangtze River and Yellow River Based on Improved FLUS Model

Climate change profoundly alters land use in alpine regions, and delving into the evolutionary characteristics of these changes is crucial for the sustainable development of regional land resources and the gradual enhancement of the ecological environment. Taking the source region of the Yangtze and Yellow River (SRYAYE) as a case study, we integrate permafrost and snowfall data into the Future Land Use Simulation model (FLUS). Analyzing historical land use, we predict and simulate the land use scenarios for 2030, 2035, and 2060 under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 climates, and conduct a detailed analysis of the scale, composition, and pattern of land use in this area. Scale. The results showed that ① the Kappa coefficient of the improved FLUS model was higher than 0.927, and that the overall accuracy of the simulation was increased by 2.64%; ② the area of forest land and the high-coverage grassland will increase in the future and the center of gravity will migrate to the west, and that the area of moderate and low-coverage grassland will slightly decrease but tend to become green to the west; and ③ the fragmentation degree of the SRYAYE is decreasing, and the influence of human activities on the landscape pattern is weaker than in the past.

Snowfall deposition in mountainous terrain: a statistical downscaling scheme from high-resolution model data on simulated topographies

One of the primary causes of non-uniform snowfall deposition on the ground in mountainous regions is the preferential deposition of snow, which results from the interaction of near-surface winds with topography and snow particles. However, producing high-resolution snowfall deposition patterns can be computationally expensive due to the need to run full atmospheric models. To address this, we developed two statistical downscaling schemes that can efficiently downscale near-surface, low-resolution snowfall data to fine-scale snow deposition accounting for the effect of preferential deposition in mountainous regions. Our approach relies on a comprehensive, model database generated using 3D wind fields from an atmospheric model and a preferential deposition model on several thousand simulated topographies covering a broad range in terrain characteristics. Both snowfall downscaling schemes rely on fine-scale topographic scaling parameters and low-resolution wind speed as input. While one scheme, referred to as the “wind scheme”, further necessitates fine-scale vertical wind components, a second scheme, referred to as the “aspect scheme”, does not require fine-scale temporal input. We achieve this by additionally downscaling near-surface vertical wind speed solely using topographic scaling parameters and low-resolution wind direction. We assess the performance of our downscaling schemes using an independent subset of the model database on simulated topographies, model data on actual terrain, and spatially measured new snow depth obtained through a photogrammetric drone survey following a snowfall event on previously snow-free ground. While the assessments show that our downscaling schemes perform well (relative errors ≤ ±3% with modeled and ≤ ±6% with measured snowfall deposition), they also demonstrate comparable results to benchmark downscaling models. However, our schemes notably outperform the benchmark models in representing fine-scale patterns. Our downscaling schemes possess several key features, including high computational efficiency, versatility enabled by the comprehensive model database, and independence from fine-scale temporal input data (aspect scheme), indicating their potential for widespread applicability. Therefore, our downscaling schemes for near-surface snowfall and vertical wind speed can be beneficial for various applications at fine grid resolutions such as in atmospheric and climate sciences, snow hydrology, glaciology, remote sensing, and avalanche sciences.

Assessment and mapping of landslides in steep mountainous terrain using PS-InSAR: A case study of Karimabad Valley in Chitral

Assessing and mapping landslides in rugged, mountainous terrain presents considerable challenges due to the high cost, risk, and complexity involved. High-resolution assessments of landslides are essential for mitigating landslide-relevant damage and understanding landslide kinematics. Recent studies have utilized satellite-based Synthetic Aperture Radar Interferometry (InSAR) to achieve millimeter-scale detection of ground surface deformation. Among these InSAR techniques, Persistent Scatterers Interferometry (PS-InSAR) is particularly useful in identifying landslide-prone areas, offering a means to map and detect slope movements and characterize the stability of potentially unstable slopes. This study focuses on the slow-moving unstable hillslopes of Karimabad Valley in Chitral. We conducted an analysis using Sentinel-1 SLC data over a three-year period, obtained in interferometric wide swath mode with vertical-vertical polarization. Our results, derived from both ascending and descending satellite passes, reveal that the hillslopes of Karimabad Valley exhibit an average velocity of −25 to +28 mm/yr. Based on the deformation pattern identified, we detected seven deformation zones, with particular concern for the Beshgram village area. To better understand terrain steepness, four topographic profile lines (AA', BB', CC', and DD') were extracted and correlated with the deformation time series. Furthermore, the deformation time series were examined in relation to rainfall, earthquake, and snowfall data. These analyses highlight the complex interplay of multiple factors contributing to the observed variations in deformation, including the region's topography characterized by steep slopes, the freezing and thawing of snow, and seismic events.

Evaluation of multiple gridded snowfall datasets using gauge observations over high mountain Asia

Snowfall is an important component of the High Mountain Asia (HMA) cryosphere; its changes affect the regional energy and water balance. Snowfall data from numerous gridded products provide the basis for conducting climate-change studies; however, the applicability of these datasets in HMA has not been assessed comprehensively in terms of precipitation. This study comprehensively evaluates the applicability of the following widely used gridded datasets in HMA for the period 1991–2014: fifth generation of European ReAnalysis (ERA5), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR), Multi-Source Weather (MSWX), National Centers for Environmental Prediction (NCEP), and China Meteorological Forcing Dataset (CMFD). The accuracy features of each dataset were evaluated based on observations from 148 gauges at various temporal scales (i.e., annual, monthly, and daily), elevations, sub-regions, and snowfall intensities. The results show that CMFD performs better at multiple temporal scales (i.e., annual, cool season, monthly, and daily) than other snowfall data. In addition, the CMFD accuracy has good stability, even at different elevations. CMFD is advantageous regarding interpreting the frequency and amount of different levels of (even extreme) snowfall events. The slightly inferior MSWX also performs well at different timescales, spatial scales, and elevations, and constitutes an attractive alternative to CMFD. HAR captures better light and moderate snowfall, and is the most accurate of the three sets of gridded products that contain direct snowfall data. This study constitutes an important reference for selecting suitable snowfall datasets for future scientific research.

Simulations of a Heavy Snowfall Event in Xinjiang via the WRF Model Coupled with Different Land Surface Parameterization Schemes

Frequent heavy snowfall in Xinjiang plays an important role in the land water cycle. In this study, 18 groups of simulation experiments are conducted on the heavy snowfall event in Xinjiang during 9–13 December of 2015 using the Weather Research and Forecasting (WRF) model. In these experiments, the combination of six land surface parameterization schemes (the Noah scheme, Noah-MP scheme, RUC scheme, CLM4 scheme, PX scheme, and TD scheme) with three microphysical parameterization schemes (the WSM6 scheme, Thompson scheme, and Lin scheme) are adopted, where the observed snowfall data are used for performance evaluation. Results show that the simulated snowfall intensity and snowfall range in different areas are very sensitive to the selection of the land surface scheme. The snowfall in southern Xinjiang is overestimated by almost all six schemes, where the Noah-MP scheme performs more reasonably than the others. The Noah scheme shows its advantage in northwestern Xinjiang. The three different microphysical schemes vary significantly in producing snowfall amount. The WSM6 scheme produced the largest snowfall amount, and the Lin scheme resulted in the smallest snowfall amount. In addition, the accumulated snowfall amounts above 10 mm are generally underestimated by all six land surface schemes, while the accumulated snowfall amounts below 10 mm are overestimated by most of the schemes. The Noah-MP scheme performs the best in the simulation of the snowfall amount in the whole region. However, the Noah scheme shows an advantage in areas with a large snowfall amount.

A Snowfall Detection Algorithm for Fengyun-3D Microwave Sounders with Differentiated Atmospheric Temperature Conditions

Precipitation in different phases has varying effects on runoff. However, monitoring surface snowfall poses a significant challenge, highlighting the importance of developing a snowfall detection algorithm. The objective of this study is develop a snowfall detection algorithm for the Microwave Temperature Sounder-2 (MWTS-II) and the Microwave Humidity Sounder-2 (MWHS-II) onboard the FY-3D satellite while considering the differentiated atmosphere temperature conditions. The results show that: (1) The brightness temperature (TB) of MWTS Channel 3 is well-suited for pre-classifying atmospheric temperatures, and significant differences in TB distribution exist between the two pre-classification subsets. (2) Among six machine classifiers examined, the random forest classifier exhibits favorable classification performance on both the validation set (accuracy: 0.76, recall: 0.76, F1 score: 0.75) and test set (accuracy: 0.80, recall: 0.44, F1 score: 0.44). (3) The application of the snowfall detection algorithm showcases a reasonable spatial distribution and outperforms the IMERG and ERA5 snowfall data.

The Effect of Local Weather Conditions on American Kestrel (Falco sparverius) Reproduction

ABSTRACT American Kestrel (Falco sparverius) populations have been declining in North America during recent decades and potential causes, including climate change, currently are under investigation. Many studies of migratory birds have demonstrated the effects of climate change, including changes in local weather conditions and the increase in extreme weather, but the relationship between local weather and kestrel ecology in the northeastern United States has received little attention. We examined the relationship of local weather conditions and kestrel breeding performance in northwestern New Jersey between 1996 and 2016. We also obtained temperature, rainfall, and snowfall data from weather stations within or adjacent to our study area. We did not detect significant changes in climate over the study period, but we did find significant associations between local weather conditions and kestrel breeding performance. Warmer temperatures during the breeding season were significantly correlated with earlier nesting, and early nesting had significant positive correlations with clutch size, hatching rate, nestling survival, and nesting success (raising at least one young to fledging). We did not detect a significant relationship between rainfall and nesting variables, but snowfall during autumn was strongly related to kestrel breeding performance the following year. Compared to years when there was no snowfall in our study area the preceding autumn, kestrels breeding in years following autumn snow had significantly smaller clutches, lower hatching rates, lower nestling survival rate, and lower nesting success. Kestrels in our region are partial migrants; first-year birds generally migrate to southern wintering grounds while older birds may remain in the region, presumably in response to local conditions. We suggest that substantial snow during autumn may drive a larger percentage of adults to migrate south, as indicated by the greater number of kestrels counted at representative autumn hawk watch sites during years with substantial autumn snow. Our study concludes that variations in local weather conditions throughout the year can affect the breeding performance of American Kestrels, whether or not they are associated with climate change.

Reduced Road Injuries While Commuting Due to Heavy Snowfall and Ensuing Modal Shifts Among Junior High School Students in Japan.

BackgroundModal shifts in transport may reduce overall road injuries. Cyclist junior high school students are at a high risk of road injuries while commuting in Japan, and injuries among junior high school students could be reduced if the cyclists switch to other transport modes.MethodsWe estimated the change in the incidence of road deaths and serious injuries while commuting in months with heavy snowfall, when cyclists are likely to switch to other transport modes. Using police data on the monthly number of road injuries while commuting among junior high school students in Japan between 2004 and 2013 and corresponding population statistics and snowfall data, we calculated the monthly injury rate (number of deaths and serious injuries divided by population) at the prefecture level. We conducted Poisson regression analysis to estimate the change in the rate in months with a snowfall of ≥100 cm, compared to months without snowfall.ResultsA total of 3,164 deaths and serious injuries occurred during 2004 to 2013. The injury rate among cyclists was almost zero in months with a snowfall of ≥100 cm. That among cyclists and pedestrians in these months was reduced by 68% (95% confidence interval, 43–82%).ConclusionIn months with heavy snowfall, road injuries while commuting were reduced due to the near-elimination of cycling injuries among junior high school students in Japan. Switching from cycling to other transport modes would reduce overall road injuries among this population, and inducing modal shifts can be an important tool for road safety.

Evaluation of the Impact Caused by the Snowfall after Storm Filomena on the Arboreal Masses of Madrid

Following a copious and famous snowfall in Madrid city (Spain) and its surrounding area, the tree masses were analysed to assess the impact of this snowfall. In this way, this paper proposes an approach towards urban forest impact inventory mapping as a consequence of the snowfall of January 2021, and the subsequent atmospheric stability, which kept surface temperatures in Madrid close to −10 °C. The study has been carried out using snowfall data from 1920 to 2020 and images from the Sentinel, Landsat and MODIS satellites. The results obtained by means of image changes in the NDVI reveal a clear impact on the trees, with 11% of the winter vegetation cover of the municipality being affected. Especially significant has been the damage detected both in the forest areas in the city and in the parks, gardens or urban roads.

Climatology of Lake-Effect Snow Days Along the Southern Shore of Lake Michigan: What Is the Sensitivity to Environmental Factors and Snowband Morphology?

The Laurentian Great Lakes have substantial influences on regional climatology, particularly with impactful lake-effect snow events. This study examines the snowfall, cloud-inferred snow band morphology, and environment of lake-effect snow days along the southern shore of Lake Michigan for the 1997–2017 period. Suitable days for study were identified based on the presence of lake-effect clouds assessed in a previous study and extended through 2017, combined with an independent classification of likely lake-effect snow days based on independent snowfall data and weather map assessments. The primary goals are to identify lake-effect snow days and evaluate the snowfall distribution and modes of variability, the sensitivity to thermodynamic and flow characteristics within the upstream sounding at Green Bay, WI, and the influences of snowband morphology. Over 300 lake-effect days are identified during the study period, with peak mean snowfall within the lake belt extending from southwest Michigan to northern Indiana. Although multiple lake-effect morphological types are often observed on the same day, the most common snow band morphology is wind parallel bands. Relative to days with wind parallel bands, the shoreline band morphology is more common with a reduced lower-tropospheric zonal wind component within the upstream sounding at Green Bay, WI, as well as higher sea-level pressure and 500-hPa geopotential height anomalies to the north of the Great Lakes. Snowfall is sensitive to band morphology, with higher snowfall for shoreline band structures than for wind parallel bands, especially due south of Lake Michigan. Snowfall is also sensitive to thermodynamic and flow properties, with a greater sensitivity to temperature in southwest Michigan and to flow properties in northwest Indiana.

Spatial and Temporal Variation Characteristics of Snowfall in the Haihe River Basin from 1960 to 2016

The spatio-temporal variation of precipitation under global warming had been a research hotspot. Snowfall is an important part of precipitation, and its variabilities and trends in different regions have received great attention. In this paper, the Haihe River Basin is used as a case, and we employ the K-means clustering method to divide the basin into four sub-regions. The double temperature threshold method in the form of the exponential equation is used in this study to identify precipitation phase states, based on daily temperature, snowfall, and precipitation data from 43 meteorological stations in and around the Haihe River Basin from 1960 to 1979. Then, daily snowfall data from 1960 to 2016 are established, and the spatial and temporal variation of snowfall in the Haihe River Basin are analyzed according to the snowfall levels as determined by the national meteorological department. The results evalueted in four different zones show that (1) the snowfall at each meteorological station can be effectively estimated at an annual scale through the exponential equation, for which the correlation coefficient of each division is above 0.95, and the relative error is within 5%. (2) Except for the average snowfall and light snowfall, the snowfall and snowfall days of moderate snow, heavy snow, and snowstorm in each division are in the order of Zones III > IV > I > II. (3) The snowfall and the number of snowfall days at different levels both show a decreasing trend, except for the increasing trend of snowfall in Zone I. (4) The interannual variation trend in the snowfall at the different levels are not obvious, except for Zone III, which shows a significant decreasing trend.

A Bayesian approach towards daily pan-Arctic sea ice freeboard estimates from combined CryoSat-2 and Sentinel-3 satellite observations

Abstract. Observations of sea ice freeboard from satellite radar altimeters are crucial in the derivation of sea ice thickness estimates, which in turn provide information on sea ice forecasts, volume budgets, and productivity rates. Current spatio-temporal resolution of radar freeboard is limited as 30 d are required in order to generate pan-Arctic coverage from CryoSat-2 and 27 d are required from Sentinel-3 satellites. This therefore hinders our ability to understand physical processes that drive sea ice thickness variability on sub-monthly timescales. In this study we exploit the consistency between CryoSat-2, Sentinel-3A, and Sentinel-3B radar freeboards in order to produce daily gridded pan-Arctic freeboard estimates between December 2018 and April 2019. We use the Bayesian inference approach of Gaussian process regression to learn functional mappings between radar freeboard observations in space and time and to subsequently retrieve pan-Arctic freeboard as well as uncertainty estimates. We also employ an empirical Bayesian approach towards learning the free (hyper)parameters of the model, which allows us to derive daily estimates related to radar freeboard spatial and temporal correlation length scales. The estimated daily radar freeboard predictions are, on average across the 2018–2019 season, equivalent to CryoSat-2 and Sentinel-3 freeboards to within 1 mm (standard deviations <6 cm), and cross-validation experiments show that errors in predictions are, on average, ≤ 4 mm across the same period. We also demonstrate the improved temporal variability of a pan-Arctic daily product by comparing time series of the predicted freeboards, with 31 d running means from CryoSat-2 and Sentinel-3 freeboards, across nine sectors of the Arctic, as well as making comparisons with daily ERA5 snowfall data. Pearson correlations between daily radar freeboard anomalies and snowfall are as high as +0.52 over first-year ice and +0.41 over multi-year ice, suggesting that the estimated daily fields are able to capture real physical radar freeboard variability at sub-weekly timescales.

Evaluation of the CMORPH high-resolution precipitation product for hydrological applications over South Korea

The use of a high-resolution satellite-based precipitation product on a global-scale is attractive to hydrological applications. However, it should be systematically evaluated from various perspectives as the quality property is dependent on the geographical and topographical features of a region. This study aims to comprehensively assess the Climate Prediction Center morphing technique (CMORPH) precipitation product over South Korea. Two evaluation approaches, the general evaluation using statistical metrics and the detection evaluation (to measure skill in detecting precipitation and non-precipitation) using categorical metrics, are employed based on an 18-year long-term period of record. As a result, the CMORPH product tended to underestimate precipitation over South Korea, and the level of the underestimation varied with the seasons and regions. The overall quality was adequate for hydrological applications that require precipitation data at the annual-to-daily resolution but not at hourly resolution. Skill in detecting hourly precipitation in a storm event was 60% of the rain-gauge data, and the accuracies of total volume and peak value were 45% and 40%, respectively. The quality in the coastal regions and islands was not as good as in inland areas at low altitudes. The accuracy in a wet season was better than that in a dry/winter season. Notably, the CMORPH precipitation product was not suitable for snowfall data. Ultimately, the CMORPH product at hourly resolution needs a correction process using the local measurement systems for enhancing the quality property over South Korea.

A dual state-parameter updating scheme using the particle filter and high-spatial-resolution remotely sensed snow depths to improve snow simulation

Snow varies widely in space and time over high mountain regions. Accurately representing the spatial distribution of snow water equivalent (SWE) is critically important for improving our understanding of snow accumulation and melt processes. Despite its importance, in situ observations are lacking in poorly gauged regions such as the headwater region of the Yangtze River (HRYR). Traditional remotely sensed retrievals are highly uncertain due to the effect of cloudiness (e.g., optical remote sensing-derived snow cover area) and the coarse spatial resolution (e.g., passive microwave remote sensing-derived snow depth). Hydrological modeling is a powerful way to understand the snow processes, but uncertainty still exists due to model deficiency and errors of forcing data. Assimilating high-spatial-resolution remotely sensed snow data into a snowmelt model may be potentially valuable for more accurate snow predictions. In this study, a high-spatial-resolution remotely sensed snow depth data set (500 m, derived by integrating snow cover area, land surface temperature, and passive microwave brightness temperature products) was assimilated into a snowmelt model within a particle filter (PF) assimilation framework, which updates both model state variables and parameters simultaneously. After assimilation, the time series of basin-averaged SWE estimation showed an appreciable improvement with respect to the simulation with the traditional calibration (TC) method, in terms of the Nash-Sutcliffe efficiency (NSE) coefficient increased by ~ 10–20% and root‐mean‐square error (RMSE) decreased by ~ 7–18%. The PF approach also greatly improved the spatial distribution of SWE estimation with RMSE decreased by ~ 15–30%. The SWE estimation from the PF was also comparable or even better than that from another assimilation scheme, namely, the direct insertion. Comparison with in situ snowfall data indicated that the simulated snowfall from the PF outperformed the TC, with RMSE decreased by ~ 15–32% and correlation coefficient increased by ~ 58–83%. Furthermore, the evolution of parameters suggested the applicability of the PF method with spatially variable parameters. With spatiotemporally variable parameters in the PF, the snow model could perfectly simulate the actual snow distribution particularly over high elevation regions where the average temperature was lower than 0 °C, while fixed parameters in the TC cannot simulate the variable snow distribution. The proposed data assimilation framework has large potential of improving the accuracy of snow prediction across poorly gauged high mountain areas.

Development of a heavy snowfall alarm model using a Markov chain for disaster prevention to greenhouses

Global climate change has, in recent years, increased the frequency and intensity of meteorological disasters. In particular, damage to greenhouses caused by heavy snowfall continues to occur on farms in South Korea. Because heavy snowfall occurs over a relatively long time compared to other sudden meteorological disasters, if an appropriate warning system for heavy snowfall events is in place then damage to greenhouses can be prevented. However, the existing snowfall warning system in South Korea consists only of a simple alert that is unable to anticipate future risks to an area caused by heavy snowfall. In this study, the aim was to develop a stochastic alarm model to minimise the damage to greenhouses caused by heavy snowfall events. A Markov chain was designed to construct the alarm model using snowfall data from nationwide weather stations as well as from greenhouse standards with various safety criteria. The snow depth data were divided into several sections using a cluster analysis, and the failure probabilities of the greenhouses were derived for specific time interval according to current snow depth. This method considers the weather characteristics of each region as well as various greenhouse standards because the risks of heavy snowfall vary depending on location and type of the installation. Using the alarm model developed in this study, it is possible to predict and therefore manage the negative impacts of heavy snowfall events on greenhouses.