Light Snowfall Research Articles

Anthropogenic influence has significantly affected snowfall changes in Eurasia

It has been widely reported that anthropogenic influences are detectable with high confidence in global warming. However, whether human activities have an impact on snowfall changes is still unclear. Here we based on phase 6 of the Coupled Model Intercomparison Project (CMIP6) multi-forcing dataset and the regularized optimal fingerprint method, the detection and attribution of various grades of snowfall (including annual snowfall (>0.1 mm/day), light snowfall (<2.5 mm/day), intense snowfall (>5 mm/day), and their corresponding days) changes in Eurasia (20°-90°N, 10°W-180°E) were carried out. Results show that anthropogenic activity forcing (ANT) and greenhouse gas forcing (GHG) well reproduced the spatial-temporal characteristics of snowfall indices. The ANT influence is robustly detected in the decreased trend of snowfall days (snowday), light snowfall, and light snowfall days (light_day) at the 90% confidence interval, clearly separated from the natural forcing. Moreover, the GHG signals are detectable for decreases in the three snowfall indices, which only could be distinguished from the natural and aerosol forcings for snowday and light snowfall. Thus, anthropogenic activities may considerably account for the decreases in snowday, light snowfall, and light_day across Eurasia, wherein the changes in the first two indices dominated by GHG emissions. However, human influence detections fail for intense snowfall, and it is hard to detect on regional scales, except for North Asia. Finally, by the end of this century (2081–2100), the observation-constrained projections based on the detection and attribution analysis under two SSPs (new Shared Socioeconomic Pathway) scenarios exhibit that the scaled snowday, light snowfall, and light_day are expected to decrease by approximately 13.9 days (28.3 days), 24.8% (48.7%), and 4.3 days (8.8 days) under the SSP2–4.5 (SSP5–8.5) scenario with reference to the current climate (1995–2014). Our study highlights the need to improve climate model performance in simulating extreme snowfall to clear whether and to what extent human influence impacts it.

Quantifying the snowfall variations in the Third Pole region from 1980 to 2020

Snowfall is critical in the mountainous cryosphere cycle and is vulnerable to climate change. Due to data-scarcity and strong surface heterogeneity, its information is not sufficiently revealed across the Third Pole (TP) under warming. Accordingly, this study investigated the spatiotemporal variability of snowfall and its entropy information across the TP from 1980 to 2020 based on a new high-accuracy snowfall dataset (TPHiS), which was estimated from a long-term high-resolution precipitation dataset for the TP (TPHiPr) and High Asia Refined analysis version 2 reanalysis data (HARv2) using a threshold temperature method. The results indicated that the correlation coefficient, mean bias, and root-mean-square error of the daily TPHiS were 0.77, 0.03 mm/daily, and 0.18 mm/daily, respectively, compared against in-situ observation, illustrating that this dataset performs better than other snowfall datasets. The annual mean snowfall measured 259.38 mm across the TP and experienced a slight negative trend (−0.77 mm/decade, p > 0.05). A significantly positive trend in annual snowfall was observed in the intersection of the West Kunlun, Karakoram, and Western Inner Tibet, with elevations over 5500 m. In contrast, a significantly negative trend prevailed in the Pamir, Hengduan Shan, and West and Central Himalaya intersections, especially at elevations varied from 3000 to 4500 m. Compared with other seasons, a significantly negative trend in snowfall was observed in summer (−1.94 mm/decade, p < 0.05). Change in snowfall frequency entropy was more significant than that in snowfall amount entropy. A significantly positive trend in light snowfall frequency dominated the augmentation in snowfall frequency, causing a negative trend in snowfall frequency entropy, while a substantial reduction in snowfall frequency associated with the decline in light snowfall frequency induced a positive trend in snowfall frequency entropy. The outcomes provide a finer snowfall dataset for regional cryospheric research and might inform sustainable water resource management in alpines.

Genetic differentiation and evolution of broad-leaved evergreen shrub and tree varieties of Daphniphyllum macropodum (Daphniphyllaceae).

Tree form evolution is an important ecological specialization for woody species, but its evolutionary process with adaptation is poorly understood, especially on the microevolutionary scale. Daphniphyllum macropodum comprises two varieties: a tree variety growing in a warm temperate climate with light snowfall and a shrub variety growing in a cool temperate climate with heavy snowfall in Japan. Chloroplast DNA variations and genome-wide single-nucleotide polymorphisms across D. macropodum populations and D. teijsmannii as an outgroup were used to reveal the evolutionary process of the shrub variety. Population genetic analysis indicated that the two varieties diverged but were weakly differentiated. Approximate Bayesian computation analysis supported a scenario that assumed migration between the tree variety and the southern populations of the shrub variety. We found migration between the two varieties where the distributions of the two varieties are in contact, and it is concordant with higher tree height in the southern populations of the shrub variety than the northern populations. The genetic divergence between the two varieties was associated with snowfall. The heavy snowfall climate is considered to have developed since the middle Quaternary in this region. The estimated divergence time between the two varieties suggests that the evolution of the two varieties may be concordant with such paleoclimatic change.

Examining the Effect of Weather Conditions on On-Street Parking Variables

In this study, the effect of weather conditions on on-street parking variables is examined. In this context, data sets consist of 17 variables such as parking capacity, employee personnel, off-duty personnel, turn-over rate, first-hour parking price, daily parking price, number of transactions, number of cash transactions, average cash parking duration, 0-15 min parking duration, average parking duration, number of subscription, parking subscription price, daily transactions of subscription, subscription parking duration and income on the on-street parking spaces in the Anatolian Side of Istanbul for two years (2017-2018). In this study, turnover rate, parking duration, cash parking duration, and parking income variables are investigated by multiple regression analysis with dummy variables. Daily general weather data during this period are classified into 11 categories as sunny, cloudy, partly cloudy, light rain, rain, short-term heavy rainfall, heavy rainfall, light snow, light snowfall, snow, and a heavy snowstorm- as dummy variables. Based on the estimation results, it is found that both sunny and light snow + light snowfall + snow + heavy snowstorm weather variables decrease parking duration by 0.528 and 1.293 min, respectively. Moreover, results also indicated that the same variables increase cash parking duration by 1.21 and 3.29 min, respectively. Both sunny and cloudy + partly cloudy weather variables increase parking income by 719 TL and 580 TL, respectively. Light snow + light snowfall + snow + heavy snowstorm weather variable decreases parking income by 1143 TL.

Marine Cold‐Air Outbreak Snowfall in the North Atlantic: A CloudSat Perspective

AbstractThis study analyzes the influence of marine cold‐air outbreaks (MCAO) on snowfall and cloud properties in the North Atlantic Ocean using CloudSat observations. Comparing reanalysis‐determined MCAO conditions (low‐level instability) against “non‐CAO” conditions, we find that MCAO conditions are associated with predominantly light snowfall rates (&lt;2 mm day−1 liquid water equivalent) whereas non‐CAO conditions are more frequently associated with higher snowfall rates. Near cold‐air sources, such as sea ice or cold continents, MCAO‐forced snowfall rates tend to be more frequent and more intense. Additionally, 76% of snowing clouds identified during MCAO conditions are shallow (mean cloud top height &lt;3 km) stratocumulus, whereas 44% (43%) of clouds in non‐CAO conditions are deeper nimbostratus (stratocumulus). With greater boundary layer instability (stronger MCAO conditions), CloudSat observes higher cloud‐top heights, reflecting a deepening boundary layer and the presence of two distinct cloud modes during MCAO conditions.

Assessing Snow Water Retrievals over Ocean from Coincident Spaceborne Radar Measurements

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Ze) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars.

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall.

Ice on road surfaces can lead to a significant decrease in the friction coefficient, thus endangering driving safety. However, there are still no studies that provide exact friction coefficient values for pavements covered in ice, which is detrimental to both road design and the selection of winter road maintenance measures. Therefore, this article presents an experimental method to determine the friction coefficient of icy road surfaces in the winter. A British portable tester (BPT), also known as a pendulum friction coefficient meter, was employed for the experiment. The experiment was divided into the following five steps: the preparation of the equipment, the calculation and analysis of the snowfall, equipment calibration, friction coefficient determination, and data analysis. The accuracy of the final experiment is directly affected by the equipment accuracy, which is described in detail. Moreover, this article suggests a method for calculating the ice thickness for corresponding amounts of snowfall. The results illustrate that even patchy ice formed by very light snowfall may lead to a significant decrease in the friction coefficient of the pavement, thus endangering driving safety. Additionally, the friction coefficient is at its peak when the ice thickness reaches 5 mm, meaning protection measures should be taken to avoid the formation of such ice.

HONEY BEE CLEANSING FLIGHTS…JUST CLEANSING?

The first major cleansing flight during the winter in a honey bee apiary was witnessed on February 6, 2021. A light snowfall and freezing rain prior to the flight allowed delineation of the distribution of distances from the aggregation of hives of fecal depositions and immobilized honey bees fallen to the ground. Five transects were surveyed radiating from the aggregation of four overwintered hives to collect the data. The frequency distributions of these events were fit to Gamma probability density functions and then a test of the following hypothesis was constructed. Are cleansing flights solely comprised of local defecation by workers and then return to the hive? Or do workers also commence foraging in suboptimal air temperatures for flight? We found evidence that cleansing flights are comprised of two behaviors, local deposition of feces and long-distance foraging. This is based upon a significant difference (P < 0.001) between the mean distance from the hives of fecal deposition (3.01 ± 0.36(se)) and immobilized bees on the ground (9.49 ± 1.21(se)).

Daily snowfall events on the Eurasian continent: CMIP6 models evaluation and projection

AbstractThis study evaluated the performance of 23 models participating in Phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating snowfall variation across the Eurasian continent. The results show that the CMIP6 multi‐model ensemble (MME) can reasonably capture the climate means of snowfall indices for 1995–2014 period and has high pattern correlations with observations. However, most of CMIP6 models generally overestimate snowfall amounts, snowfall days and snowfall duration, and the overestimation is much clearer in spring. The performance of these models in simulating snowfall events with different grades (including light snowfall, moderate snowfall, heavy snowfall and snowstorm) becomes gradually poorer with higher snowfall levels. On the whole, the comprehensive model ranks implied that NorESM2‐MM, NorESM2‐LM and CESM2‐WACCM models showed the best performances. In addition, the CMIP6 MME outperforms individual models in terms of both spatial–temporal features and interannual variability; thus, the MME is used to project the snowfall change in the 21st century. Further evaluations show that by the end of this century, snowfall will decrease in most of Eurasia with an average value of 16.7% (31.3%) under the SSP2‐4.5 (SSP5‐8.5) scenarios, and snowfall days will decrease by 12.6 days (25.0 days); the snowfall duration will shorten further due to the first date of snowfall occurrence will postpone by 12.7 days (23.0 days) and the last date further advance by 15.0 days (26.7 days). Additionally, heavy snowfall, snowstorms and their number of days will increase over part of Northern Asia, the Tibetan Plateau and Central Asia, where the risk of snowfall extremes will increase. However, large inter‐model and scenario uncertainties also exist in the CMIP6 model projection of snowfall events, which requires further improvement.

Changes in the spatial–temporal characteristics of daily snowfall events over the Eurasian continent from 1980 to 2019

AbstractThe observed spatial–temporal variations in daily snowfall over the Eurasian continent from 1980 to 2019 are explored in this study. The results show that the snowfall amount and its number of days are mainly centred in the high latitudes and high terrain regions of the Eurasian continent (such as North Asia), while they are relatively small in Southern Europe, Central Asia and East Asia. During the past decades, a decreasing trend can be observed for snowfall amounts and the numbers of days over most regions of Eurasia, while increases are clear in terms of the mean intensity. In response to climate warming in recent decades, the first date of snowfall occurrence postponed and the ending date advanced across the Eurasian continent. Furthermore, the change characteristics of the different grades of snowfall are also investigated across this region, including light snowfall, moderate snowfall, heavy snowfall and snowstorm. The light snowfall and number of days show a decreasing tendency, which mainly results in a decrease in the total snowfall amount and number of days. However, the amounts of heavy snowfall and snowstorms and their numbers of days are significantly increased, implying that the Eurasian continent has experienced increased severe snowfall events during recent decades.

Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100 kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5 mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

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 &gt; IV &gt; I &gt; 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.

Extreme Lake-Effect Snow from a GPM Microwave Imager Perspective: Observational Analysis and Precipitation Retrieval Evaluation.

This study focuses on the ability of the Global Precipitation Measurement (GPM) passive microwave sensors to detect and provide quantitative precipitation estimates (QPE) for extreme lake-effect snowfall events over the U.S. lower Great Lakes region. GPM Microwave Imager (GMI) high-frequency channels can clearly detect intense shallow convective snowfall events. However, GMI Goddard Profiling (GPROF) QPE retrievals produce inconsistent results when compared with the Multi-Radar Multi-Sensor (MRMS) ground-based radar reference dataset. While GPROF retrievals adequately capture intense snowfall rates and spatial patterns of one event, GPROF systematically underestimates intense snowfall rates in another event. Furthermore, GPROF produces abundant light snowfall rates that do not accord with MRMS observations. Ad hoc precipitation-rate thresholds are suggested to partially mitigate GPROF's overproduction of light snowfall rates. The sensitivity and retrieval efficiency of GPROF to key parameters (2-m temperature, total precipitable water, and background surface type) used to constrain the GPROF a priori retrieval database are investigated. Results demonstrate that typical lake-effect snow environmental and surface conditions, especially coastal surfaces, are underpopulated in the database and adversely affect GPROF retrievals. For the two presented case studies, using a snow-cover a priori database in the locations originally deemed as coastline improves retrieval. This study suggests that it is particularly important to have more accurate GPROF surface classifications and better representativeness of the a priori databases to improve intense lake-effect snow detection and retrieval performance.

Comparison of the Causes of High-Frequency Heavy and Light Snowfall on Interannual Timescales over Northeast China

This study investigates and compares the reasons for high-frequency heavy and light snowfall in winter on interannual timescales over northeast China (NEC) during 1961–2017. Results indicate that the frequency and its variability are strong over southeastern NEC for heavy snowfall but over northern NEC for light snowfall. Analysis of the annual cycle shows that the maximum frequency of heavy snowfall occurs in November and March due to more warm–wet air masses and increased atmospheric instability, and that of light snowfall occurs in December–January due to drier conditions and increased atmospheric stability. The frequency of heavy snowfall exhibits an increasing trend which partly results from the warming trend in NEC, while that of light snowfall shows a decreasing trend. High-frequency heavy snowfall is associated with a positive North Atlantic Oscillation (NAO), warmer regional air temperatures, an increased water vapor budget associated with an anomalous anticyclone occupying the Kuril Islands, and relatively unstable atmospheric layers. High-frequency light snowfall is associated with a strengthened East Asian winter monsoon, colder regional air temperatures, a decreased water vapor budget, and relatively stable atmospheric layers. High-frequency heavy and light snowfall are both related to eastward-propagating quasi-stationary waves over Eurasia, but with different features. The waves of the former are located in midlatitude Eurasia and related to the positive phase of the NAO. The waves of the latter exhibit two pathways, located in midlatitude and northern Eurasia, respectively. The northern one can be partially attributed to a weak polar vortex. In addition, higher sea surface temperatures of the Kuroshio Extension may contribute to high-frequency heavy snowfall.

LLAP Band Structure and Intense Lake-Effect Snowfall Downwind of Lake Ontario: Insights from the OWLeS 7–9 January 2014 Event

AbstractModeling and observational studies stemming from the 2013–14 Ontario Winter Lake-Effect Systems (OWLeS) field campaign have yielded much insight into the structure and development of long-lake-axis-parallel (LLAP) lake-effect systems over Lake Ontario. This study uses airborne single- and dual-Doppler radar data obtained during two University of Wyoming King Air flights, as well as a high-resolution numerical model simulation, to examine and contrast two distinctly different LLAP band structures observed within a highly persistent lake-effect system on 7–9 January 2014. On 7 January, a very cold air mass accompanied by strong westerly winds and weak capping aloft resulted in a deep, intense LLAP band that produced heavy snowfall well inland. In contrast, weaker winds, weaker surface heat fluxes, and stronger capping aloft resulted in a weaker LLAP band on 9 January. This band was blocked along the downwind shore and produced only light snowfall closer to the shoreline. Although the two structures examined here represent opposite ends of a spectrum of LLAP bands, both cases reveal a well-organized mesoscale secondary circulation composed of two counterrotating horizontal vortices positioned on either side of a narrow updraft within the band. In both cases, this circulation traces back to a shallow, baroclinic land-breeze front originating along a bulge in the lake’s southern shoreline. As the band extends downstream and the low-level baroclinity weakens, buoyancy increases within the band—driven in part by cloud latent heating—leading to band intensification and a deeper, stronger, and more symmetric secondary circulation over the lake.

If Sakhalin spruce or Sakhalin fir die in windbreaks on seasonally frozen soils, what species can be recommended?

Soil of the Tokachi plain of eastern Hokkaido freezes during the winter due to low temperatures and light snowfall. Deciduous and fast-growing Japanese larch, Larix kaempferi, is often planted in arable land windbreaks to prevent wind damage to cultivation plants. Japanese larch is more resistant to winter desiccation than evergreen trees. However,windbreaks made of Japanese larch are disliked by many farmers because they shed a lot of branches and branchlets every year; if not completely removed from cultivation fields, branches and branchlets may damage agricultural tractors. Therefore, some farmers favor planting evergreen trees like Sakhalin spruce, Picea glehnii, or Sakhalin fir, Abies sachalinensis, both indigenous to Hokkaido. However, the disadvantage of these evergreen trees is that they are susceptible to winter desiccation. In settings where spruce and fir do not survive winter desiccation,planting of the evergreen Korean nut pine, Pinus koraiensis, is recommended. Although not native to Hokkaido, this species is less likely to suffer damage from winter desiccation than the other two evergreen species.

Metric Learning for Approximation of Microwave Channel Error Covariance: Application for Satellite Retrieval of Drizzle and Light Snowfall

Improved microwave retrieval of land and atmospheric state variables requires proper weighting of the information content of radiometric channels through their error covariance matrix. Inspired by recent advances in metric learning techniques, a new framework is proposed for a formal approximation of the channel error covariance. The idea is tested for the detection of precipitation and its phase over oceans, using coincidences of passive/active data from the Global Precipitation Measurement (GPM) and CloudSat satellites. The initial results demonstrate that the presented approach cannot only capture the known laws of radiative transfer equations, but also the surrogate signatures that can arise due to the co-occurrence of precipitation and other radiometrically active land-atmospheric state variables. In particular, the results demonstrate high precision (low error) for the low-frequency channels of 10–37 GHz in the detection of both rain and snowfall over oceans. Using the optimal estimate of the channel error covariance through the multi-frequency ${k}$ -nearest neighbor (kNN) classification approach, without any ancillary data, it is demonstrated that the probability of passive microwave detection of snowfall (0.97) can be higher than that of the rainfall (0.88), when drizzle and light snowfall are the dominant form of precipitation. This improvement is hypothesized to be largely related to the information content of the low-frequency channels of 10–37 GHz that can capture the co-occurrence of snowfall with an increased cloud liquid water content, sea ice, and wind-induced changes of surface emissivity.

Role of Eurasian Snow Cover in Linking Winter‐Spring Eurasian Coldness to the Autumn Arctic Sea Ice Retreat

AbstractAn anomalous “north‐south” dipole mode of the snow water equivalent (SWE) persisting from winter to spring is detected over the Eurasian mid‐to‐high latitudes in this study. Using observational data sets and numerical experiments of the Community Atmospheric Model (5.0), we show that this mode contributes to prolonged winter‐springtime coldness in midlatitude Eurasia and is closely linked to the declining November Arctic sea ice concentration. The decline in the sea ice concentration over the Barents‐Laptev Seas can induce a teleconnection pattern over the mid‐to‐high latitudes in the following winter, accompanied by an anomalous ridge over the Ural Mountains and an anomalous trough over Europe and East Asia. Such changes in the large‐scale circulation lead to more cold surges and heavy snowfall in the midlatitudes and light snowfall in the high latitudes, forming an anomalous north‐south dipole mode of the SWE, which further reduces the temperature through thermodynamic feedback. Due to seasonal memory, this SWE pattern can persist into the following spring and can lead to springtime midlatitude coldness via thermodynamic and dynamic processes. For the thermodynamic process, the anomalous SWE condition can lead to anomalous wet soil, reduced incoming surface solar radiation, and cooling air in the midlatitudes. This phenomenon induces an enhanced Siberian High and a deepened East Asian trough via the snow‐Siberian high‐feedback mechanism, which favors a cold spring in northern East Asia. Further analysis suggests that an empirical seasonal prediction model based on the SWE can reasonably predict East Asian spring temperature anomalies.

Possible impact of North Atlantic warming on the decadal change in the dominant modes of winter Eurasian snow water equivalent during 1979–2015

An east–west dipole mode of winter Eurasian snow water equivalent (SWE) is found during the period of 1979–2015. It accounts for about 23.4% of the total variance, and displayed a significant decadal change in the early-2000s. The basin warming footprint of the North Atlantic likely exerted an influence on this decadal change, and the observation-based evidence is reproduced by numerical experiments using the Community Atmosphere Model (CAM3.1). A basin-wide warming of North Atlantic sea surface temperature induced atmospheric anomalies by exciting a stationary Rossby wave train, which prorogates from the subtropical North Atlantic to the mid-to-high latitudes of the Eurasian continent. Along with the Rossby wave train, an enhanced upper-level ridge occurs over the Ural Mountain, and a deepened upper-level trough appears over the eastern Siberian Plateau, which promotes heavy snowfall over the eastern Siberian Plateau and light snowfall to its west. Thus, it is plausible that the North Atlantic warming plays a role in exciting the Rossby wave train to modulate the decadal change in the east–west dipole SWE mode of the extratropical Eurasian continent. The possible moisture transport paths associated with the decadal change in the east–west dipole SWE mode are also discussed.

A hybrid snowfall detection method from satellite passive microwave measurements and global forecast weather models

Despite significant progress made in snowfall estimation from space, methods utilizing passive microwave measurements continue to be plagued by low detectability compared to those that estimate rainfall. This article presents a hybrid snowfall detection algorithm that combines the output from a statistical algorithm utilizing satellite passive microwave measurements with the output from a statistical algorithm trained with in situ data that uses meteorological variables derived from a global forecast model as predictors. The satellite algorithm computes the probability of snowfall over land using logistic regression and the principal components of the high‐frequency brightness‐temperature measurements at AMSU/MHS and ATMS channel frequencies 89 GHz and above. In a separate investigation, analysis of modelled data derived from NOAA's Global Forecast System (GFS) showed that cloud thickness and relative humidity at 1 to 3 km height were the best predictors of snowfall occurrence. A statistical logistical regression model that combined cloud thickness, relative humidity and vertical velocity was selected among statistically significant variants as the one with the highest overall classification accuracy. Next, the weather‐based and satellite model outputs were combined in a weighting scheme to produce a final probability of snowfall output, which was then used to classify a weather event as “snowing” or “not snowing” based on an a priori threshold probability. Statistical analysis indicated that a scheme with equal weights applied to the weather‐based and satellite model significantly improved satellite snowfall detection. Example applications of the hybrid algorithm over continental USA demonstrated the improvement for a major snowfall event and for an event dominated by lighter snowfall.