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Bibliographie complète 313 ressources
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Abstract Spatial analog techniques consist in identifying locations whose historical climate is similar to the anticipated future climate at a reference location. In the process of identifying analogs, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this study, six a priori suitable metrics are described (the standardized Euclidean distance, the Kolmogorov–Smirnov statistic, the nearest-neighbor distance, the Zech–Aslan energy statistic, the Friedman–Rafsky runs statistic, and the Kullback–Leibler divergence) and criteria are proposed and investigated in an attempt to identify the best metric for selecting spatial analogs. The case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM, version 4.2.3), from which three annual indicators (total precipitation, heating degree-days, and cooling degree-days) are calculated over 30-yr periods (1971–2000 and 2041–70). It is found that the six metrics identify comparable analog regions at a relatively large scale but that best analogs may differ substantially. For best analogs, it is shown that the uncertainty stemming from the metric choice does not generally exceed that stemming from the simulation or model choice. On the basis of the set of criteria considered in this study, the Zech–Aslan energy statistic stands out as the most recommended metric for analog studies, whereas the Friedman–Rafsky runs statistic is the least recommended.
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Abstract Accurate forecasting of precipitation phase and intensity was critical information for many of the Olympic venue managers during the Vancouver 2010 Olympic and Paralympic Winter Games. Precipitation forecasting was complicated because of the complex terrain and warm coastal weather conditions in the Whistler area of British Columbia, Canada. The goal of this study is to analyze the processes impacting precipitation phase and intensity during a winter weather storm associated with rain and snow over complex terrain. The storm occurred during the second day of the Olympics when the downhill ski event was scheduled. At 0000 UTC 14 February, 2 h after the onset of precipitation, a rapid cooling was observed at the surface instrumentation sites. Precipitation was reported for 8 h, which coincided with the creation of a nearly 0°C isothermal layer, as well as a shift of the valley flow from up valley to down valley. Widespread snow was reported on Whistler Mountain with periods of rain at the mountain base despite the expectation derived from synoptic-scale models (15-km grid spacing) that the strong warm advection would maintain temperatures above freezing. Various model predictions are compared with observations, and the processes influencing the temperature, wind, and precipitation types are discussed. Overall, this case study provided a well-observed scenario of winter storms associated with rain and snow over complex terrain.
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Abstract Accurate snowfall measurements are critical for a wide variety of research fields, including snowpack monitoring, climate variability, and hydrological applications. It has been recognized that systematic errors in snowfall measurements are often observed as a result of the gauge geometry and the weather conditions. The goal of this study is to understand better the scatter in the snowfall precipitation rate measured by a gauge. To address this issue, field observations and numerical simulations were carried out. First, a theoretical study using finite-element modeling was used to simulate the flow around the gauge. The snowflake trajectories were investigated using a Lagrangian model, and the derived flow field was used to compute a theoretical collection efficiency for different types of snowflakes. Second, field observations were undertaken to determine how different types, shapes, and sizes of snowflakes are collected inside a Geonor, Inc., precipitation gauge. The results show that the collection efficiency is influenced by the type of snowflakes as well as by their size distribution. Different types of snowflakes, which fall at different terminal velocities, interact differently with the airflow around the gauge. Fast-falling snowflakes are more efficiently collected by the gauge than slow-falling ones. The correction factor used to correct the data for the wind speed is improved by adding a parameter for each type of snowflake. The results show that accurate measure of snow depends on the wind speed as well as the type of snowflake observed during a snowstorm.