Down to Earth Article

Weather and Forecasting, 2006

Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 Ͻ ratio Ͻ 9:1), average (9:1 Յ ratio Յ 15:1), and light (ratio Ͼ 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than Ϫ2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than Ϫ10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8-4.1 mm (0.11-0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

2022

In the Nepalese region of Mount Everest, snow data is fragmentary and unreliable, as in many mountainous regions of the world. To get a better understanding of solid precipitation and how it has evolved over the last decades, and thus to improve our knowledge of hydro-climatic variations, we crossed quantitative data (measured, spatialised and simulated) produced from work in hydrology, with qualitative data (field interviews) derived from work in human geography. Here we present an innovative comparison method based on three key factors: seasonality, trends and notable events. Although quantitative and qualitative data show a similar decrease in snowfall in the Upper Khumbu Valley, which is taken as a case study, there are discrepancies that highlight the uncertainties present in the two data sets. This method of crossing data from different disciplines proves very useful for mountain areas where meteorological stations and precise surveys are lacking, and where the wealth of local people’s knowledge is not sufficiently taken into account.

2013

The presence of frozen water in several forms is fundamental at high altitudes and provides the essential ingredient for the development of avalanches and glaciers. These interrelated phenomena, which contribute much to the distinctiveness of high mountain landscapes, offer a considerable challenge to the inhabitants, both plant and animal, of these regions. Snow and Ice Snowfall and new Snow Snow is precipitation in the solid form that originates from the freezing of water in the atmosphere. This leads to one of the great mysteries of nature: Why should snow fall in the form of delicate and varying lacy crystals rather than as frozen raindrops? The commonly held assumption that water must freeze at 0°C (32°F) is incorrect. The freezing temperature can range as low as 240°C (240°F), which, coincidentally, is the crossover point of the two temperature scales. Water that remains liquid when cooled below 0°C is referred to as supercooled water. The actual freezing point of water in the atmosphere depends not only on ambient temperature but also on water droplet size, droplet purity, and mechanical agitation. Smaller droplets are more resistant to freezing. Very small droplets may resist freezing to the 240°C value mentioned above. Dissolved salts will retard freezing, but certain particulates will enhance freezing (i.e., promote freezing at temperatures closer to 0°C) (Knight 1967; Hobbs 1974; Pruppacher and Klett 1987; Tabazedeh et al. 2002). Clouds form most readily when certain contaminants are present in the atmosphere. These contaminants can be divided into two classes, depending on their ability to promote either condensation or freezing. Condensation nuclei are hygroscopic materials that attract water, such as salt 5490033_CH0004.INDD 85 26/04/13 9:55 PM fIGure 4.1 The Wegener-Bergeron-Findeisen mechanism for snow crystal growth in the atmosphere. Greater saturation vapor pressure over liquid water than over ice causes supercooled droplets to evaporate and ice crystals to grow. (Image courtesy of l. r. Dexter and K. Birkeland.) fIGure 4.2 A classic example of the six-sided hexagonal crystal structure of ice I. (Image courtesy of K. libbricht.) 5490033_CH0004.INDD 86 26/04/13 9:55 PM 23 to 25 Needles 27 to 23 25 to 28 Hollow prismatic columns 23 to 18 28 to 212 Hexagonal plates 18 to 10 212 to 216 Dendritic, fern-like crystals 10 to 3 216 to 225 Hexagonal plates 3 to 213 225 to 250 Hollow prisms 213 to 258 source: Nakaya 1954. fIGure 4.3 A sample of the thousands of snow crystal photographs taken by Wilson Bentley. (Image courtesy of Wilson Bentley Digital Archives of the Jericho Historical Society <http://snowflakebentley.com>.)

2022

Table S1. Radiative and turbulent heat fluxes in the High Mountain Asian (HMA) glacier/snow-covered/debris surfaces. All radiative fluxes are in W m-2. R net corresponds to the sum of S net and L net. Glacier/Snow-covered site Region Period of observation Lat (°) Long (°) Elevation (m) Surface type Reference Rnet (W m-2) H (W m-2) LE (W m-2) Tibetan Plateau (TP)

The project focused on multi-scale and multidisciplinary aspects of seasonal snow cover in the landscape. The snow physics parameterization takes into account snow structure for use in the Global Circulation Models (GCM). The geographic classification of snow classes is based on snow types, snow layers and crystal types. Numerical experiments with a GCM are performed. The outputs of the largescale models are linked to regional models and compared with measured data. The correlations between snow and hydrology are studied. The economic aspects of the snow, e.g. in tourism and transport, are analyzed. Three years of working together was not enough to answer all the questions that we originally posed. We could explore the potential of a longer lasting cooperation and, with some specific steps, contributed to the exploration of the interrelations of landscape and snow in different spatial scales. Research of this kind should continue in future.