The unusually long cold spell and the snowstorm Filomena in Spain in January 2021

In early January 2021, Spain was affected by two extreme events – an unusually long cold spell and a heavy snowfall event associated with extratropical cyclone Filomena. For example, up to 50 cm of snow fell in Madrid and the surrounding areas in 4 days. Already during 9 days prior to the snowfall event, anomalously cold temperatures at 850 hPa and night frosts prevailed over large parts of Spain. During this period, anomalously cold and dry air was transported towards Spain from central Europe and even from the Barents Sea. The storm Filomena, which was responsible for major parts of the snowfall event, developed from a precursor low-pressure system over the central North Atlantic. Filomena intensified due to interaction with an upper-level potential vorticity (PV) trough, which was the result of anticyclonic wave breaking over Europe. In turn, this wave breaking was related to an intense surface anticyclone and upper-level ridge, whose formation was strongly influenced by a warm conveyor belt outflow of a cyclone off the coast of Newfoundland. The most intense snowfall occurred on 09 January and was associated with a sharp air mass boundary with an equivalent potential temperature difference at 850 hPa across Spain exceeding 20 K. Overall, the combination of pre-existing cold surface temperatures, the optimal position of the air mass boundary, and the dynamical forcing for ascent induced by Filomena and its associated upper-level trough were all essential – and in parts physically independent – ingredients for this extreme snowfall event to occur.

Using and Evaluating the Efficiency of GRIMs Model in Making Some Simulations of Weather Variables in Three Countries in Middle East: A Snowfall Case Study

Predicting weather by numerical models have been used extensively in research works for Middle East, mostly for dust storms, rain showers, and flash floods with a less deal of interest on snow precipitation. In this study, the Global/Regional Integrated Model System (GRIMs) that was developed in South Korea was used to predict a rare snowfall event occurred in three countries in Middle East (Syria, Jordan and Iraq) located between (25-65 oE; 12-42 oN) in year 2008. The main aim of this study was to test GRIMs efficiency, which would be used for the first time in Middle East, to make predictions of weather parameters such as pressure, temperature, and relative humidity especially in the selected area. In addition, the study would investigate the conditions that caused the snowfall event. GRIMs model was installed, compiled, and run on a Linux platform by using NCEP-NCAR reanalysis dataset as initial conditions on 0.5 × 0.5 grid resolution to make simulations for three days at intervals of three hours. The output of the model was evaluated by making comparisons with actual data obtained from the GFS Agency dataset and the model showed its efficiency. The snowfall event was synoptically discussed in details. It was found that the snowfall event was a result of fast succession systems of a strong cold high pressure and then a deep warm low pressure. The high instability in the region had led to form large cumuliform clouds with snow precipitation as a rare event in very long period.

Vertical Structure of a Snowfall Event Based on Observations From the Aircraft and Mountain Station in Beijing

An aircraft platform, ground-based disdrometer, cloud radar, radiometer, and automatic station were combined to study a snowfall case (16:30–21:00 observed by ground cloud radar) on the Yangqing Mountains in Beijing. Comparing the variation of ice habit and number concentration at aircraft altitude (2.9–3.2 km) and ground, we discussed the ice growth mechanisms in the Beijing Mountains. Results indicated that the snowfall was steady but not strong with reflectivity less than 20dBZ, and cloud top altitude less than 4.5 km. The number concentrations for both liquid and ice crystals at aircraft altitude and ground were very similar, both dominated by small particles at diameters of 0.1–1.2 mm, and the proportion of mean number concentrations at small diameters both in the aircraft and on the ground was large, peaking at 44 L−1 mm−1 and 8826 L−1 mm−1 respectively, and decreased rapidly as the diameter increased. There was no mixed phase in clouds with little liquid water. Particles were relatively regular, and were transparent with dendritic and disk-hexagonal shapes. The ice crystals and snowflakes were mainly grown by the deposition and aggregation, rarely by the riming process, and no secondary ice formation was observed.

Weather Reasearch and Forecasting Model Simulation of a Snowfall Event in Southern Brazil

This study evaluates the reliability of the Weather Research and Forecasting (WRF) to simulate a snowfall event in the south of Brazil. The event in August 2013 was considered one of the most intense in recent years in the region with the highest topographic elevations between the states of Rio Grande do Sul (RS) and Santa Catarina (SC). The Snowfall in the mountain region of RS and SC was associated with the configuration involving a polar anticyclone and the intensification of an extratropical cyclone over the Atlantic Ocean. The WRF simulation results demonstrated the model's viability to predict the event, but without the magnitude representation of the phenomenon. The WRF simulation underestimated the results for the accumulated and area of the snowfall region, which may be linked to overestimations of surface and vertical air temperature and liquid water precipitation. These results were attributed to the choice of WRF Single–moment 6–class (WSM6) microphysics and in the Noah Land Surface Model scheme. Despite these limitations, WRF has proved to be an important tool for predicting the spatial and temporal distribution of snowfall and precipitation in the higher regions of southern Brazil.

The Effects of Ice Habit on Simulated Orographic Snowfall

Many factors are at play in determining the amount and distribution of mountain snowfall that is predicted by weather models; among them is the influence of assumed ice habit on snowfall distribution. Ice habit is necessarily greatly simplified in microphysics schemes and uncertainty remains in how best to model ice processes. In this study we simulate a Sierra Nevada snowfall event driven by an extratropical cyclone in February 2014. We have simulated the storm with four fixed habit types as well as with an ice habit scheme that is variable in time and space. In contrast to some previous studies, we found substantially smaller sensitivity of total accumulated precipitation amount and negligible changes in spatial distribution to the ice habit specification. The reason for smaller sensitivity seems to be linked to strong aggregation of ice crystals in the model. Nonetheless, while changes in total accumulated precipitation were small, changes in accumulated ice hydrometeors were larger. The variable-habit simulation produced up to 37% more ice precipitation than any of the fixed-habit simulations with an average increase of 14%. The variable-habit simulation led to a maximization of ice growth in the atmosphere and, subsequently, ice accumulation at the surface. This result points to the potential importance of accounting for the time and space variation of ice crystal properties in simulations of orographic precipitation.

Periodic oscillations of atmospheric electric field during snowfall in the Tokyo metropolitan area

We report the first observations of periodic oscillations of an atmospheric electric field simultaneously derived by field mills at four observation sites at a distance of 50–65 km in metropolitan Tokyo. Oscillations were detected during a snowfall event on 23–24 November, 2016. The main period of the oscillations of the atmospheric electric field at CHB was 78 min, which was similar to those at other sites. The periods of 39.0, 54.6, and 78.0 min observed at Chiba (CHB) were similar to those observed by W-band cloud radar (FALCON-I) reflectivity below a height of 5 km. High coherence of the 78-min period between the atmospheric electric field at CHB and the X-band phased array weather radar reflectivity suggest that the periodic oscillations of the atmospheric electric field during snowfall were caused by vertically convective cells in snow clouds with a radius of 60 km centered on CHB.

Diagnostic analysis of a regional heavy snowfall event over the Tibetan Plateau using NCEP reanalysis data and WRF

Snowstorms frequently occur in spring over the heterogeneous underlying surface of the Tibetan Plateau, causing both economic and societal damage. What the intensity of factors triggering snowstorms remains poorly understood. This study quantitatively diagnoses water vapor, the thermodynamic and dynamic conditions of a large-scale heavy snowfall event over the Tibetan Plateau using reanalysis data. Here we show, a cold vortex, the Southern Branch Trough and a meridional shear line are favorable synoptic systems. The snowfall is characterized by low-layer (− 8.3 × 10−7 g s−1 hPa−1 cm−2) and whole-layer (− 4.5 × 10−4 g s−1 cm−2) moisture convergence, low-level atmospheric convergence and high-level divergence (± 3 × 10−4 s−1), low-level positive vorticity (4.8 × 10−4 s−1) and strong vertical velocity (− 4 Pa s−1). Although the convectively-stable stratification acted to suppress snowfall, the abundant water vapor and strong orographic uplift of Himalayas and the downhill wind speed convergence overcome this to trigger the heavy snowfall event witnessed in March 2017. These diagnostic results are well consistent with those from WRF simulation. Our study acknowledges the importance of WRF in diagnostic analysis, deepens the understanding of evolution mechanisms and provides theoretical references for accurate forecasting of such events over the Tibetan Plateau. It would aid the development of effective strategies for sustainable livestock, and the mitigation and prevention of snow disasters in this region.

Impacts of Erratic Snowfall on Apple Orchards in Kashmir Valley, India

Kashmir Valley has been witnessing erratic snowfall events in recent autumns which severely impacted apple orchards and harvests. Here, we combine remotely sensed data and field observations to map snowfall distribution and snow depths during the recent snowfall events in November 2018 and November 2019. Besides, we used ERA-5 reanalysis climate datasets to investigate the causes of these erratic snowfall events, pointing to an early arrival of Western Disturbances (WD) to the area. Analysis of these untimely snowfall episodes indicates that snow depths varied from 5–122 cm and 31–152 cm during the 2018 and 2019 snowfall events, respectively. In turn, satellite data analysis reveals that the apple orchards cover roughly 9.8% (1329 km2) of the entire surface of Kashmir Valley, out of which 32.6% were mildly to severely damaged by snow. The areas in South Kashmir suffered the most from the untimely snowfall with an area affected estimated to ~264 km2, followed by North Kashmir (~151 km2) and Central Kashmir (18 km2). The snowfall caused substantial harvest losses in orchards ranging from 4–50% with an average of ~35%. The geopotential analysis from the ERA-5 dataset provides insights into the synoptic weather patterns leading to the snowfall events and point to a trough in the high-troposphere (200 mb), along with a col at lower levels (850 mb) over the Kashmir Valley from November 2–5, 2018. The lower levels (850 mb) experienced intense cyclonic circulation which favored advection of moisture from the Arabian Sea during the November 6–7, 2019, snowfall event. The large economic losses related to early arrival of WD led to a virtual grounding of the horticultural sector in 2018 and 2019. Therefore, more baseline research is critically needed along with a comprehensive evaluation of the suitability of horticulture as an economically viable sector that is being promoted over the Kashmir region, also under climate change.