Realizing User-Relevant Conceptual Model for the Ski Jump Venue of the Vancouver 2010 Winter Olympics

As was the case for most other Olympic competitions, providing weather guidance for the ski jump and Nordic combined events involved its own set of unique challenges. The extent of these challenges was brought to light before the Vancouver 2010 Winter Olympics during a series of outflow wind events in the 2008/2009 winter season. The interactions with the race officials during the difficult race conditions brought on by the outflows provided a new perspective on the service delivery requirements for the upcoming Olympic Games. In particular, the turbulent nature of the winds and its impact on the ski jump practice events that season highlighted the need of race officials for nowcasting advice at very short time scales (from 2 min to 1 h) and forecast products tailored to their decision-making process. These realizations resulted in last minute modifications to the monitoring strategy leading up to the Olympic Games and required forecasters’ conceptual models for flow within the Callaghan Valley to be downscaled further to reflect the evolution of turbulence at the ski jump site. The SNOW-V10 (Science of Nowcasting Olympic Weather for Vancouver 2010) team provided support for these efforts by supplying diagnostic case analyses of important events using numerical weather data and by enhancing the real-time monitoring capabilities at the ski jump venue.     

A New Integrated Weighted Model in SNOW-V10: Verification of Categorical Variables

This paper presents the verification results for nowcasts of seven categorical variables from an integrated weighted model (INTW) and the underlying numerical weather prediction (NWP) models. Nowcasting, or short range forecasting (0–6 h), over complex terrain with sufficient accuracy is highly desirable but a very challenging task. A weighting, evaluation, bias correction and integration system (WEBIS) for generating nowcasts by integrating NWP forecasts and high frequency observations was used during the Vancouver 2010 Olympic and Paralympic Winter Games as part of the Science of Nowcasting Olympic Weather for Vancouver 2010 (SNOW-V10) project. Forecast data from Canadian high-resolution deterministic NWP system with three nested grids (at 15-, 2.5- and 1-km horizontal grid-spacing) were selected as background gridded data for generating the integrated nowcasts. Seven forecast variables of temperature, relative humidity, wind speed, wind gust, visibility, ceiling and precipitation rate are treated as categorical variables for verifying the integrated weighted forecasts. By analyzing the verification of forecasts from INTW and the NWP models among 15 sites, the integrated weighted model was found to produce more accurate forecasts for the 7 selected forecast variables, regardless of location. This is based on the multi-categorical Heidke skill scores for the test period 12 February to 21 March 2010.     

An Experimental High-Resolution Forecast System During the Vancouver 2010 Winter Olympic and Paralympic Games

Environment Canada ran an experimental numerical weather prediction (NWP) system during the Vancouver 2010 Winter Olympic and Paralympic Games, consisting of nested high-resolution (down to 1-km horizontal grid-spacing) configurations of the GEM–LAM model, with improved geophysical fields, cloud microphysics and radiative transfer schemes, and several new diagnostic products such as density of falling snow, visibility, and peak wind gust strength. The performance of this experimental NWP system has been evaluated in these winter conditions over complex terrain using the enhanced mesoscale observing network in place during the Olympics. As compared to the forecasts from the operational regional 15-km GEM model, objective verification generally indicated significant added value of the higher-resolution models for near-surface meteorological variables (wind speed, air temperature, and dewpoint temperature) with the 1-km model providing the best forecast accuracy. Appreciable errors were noted in all models for the forecasts of wind direction and humidity near the surface. Subjective assessment of several cases also indicated that the experimental Olympic system was skillful at forecasting meteorological phenomena at high-resolution, both spatially and temporally, and provided enhanced guidance to the Olympic forecasters in terms of better timing of precipitation phase change, squall line passage, wind flow channeling, and visibility reduction due to fog and snow.     

Verification of an ENSO-Based Long-Range Prediction of Anomalous Weather Conditions During the Vancouver 2010 Olympics and Paralympics

A brief review of the anomalous weather conditions during the Vancouver 2010 Winter Olympic and Paralympic Games and the efforts to predict these anomalies based on some preceding El Niño–Southern Oscillation (ENSO) signals are presented. It is shown that the Olympic Games were held under extraordinarily warm conditions in February 2010, with monthly mean temperature anomalies of +2.2 °C in Vancouver and +2.8 °C in Whistler, ranking respectively as the highest and the second highest in the past 30 years (1981–2010). The warm conditions continued, but became less anomalous, in March 2010 for the Paralympic Games. While the precipitation amounts in the area remained near normal through this winter, the lack of snow due to warm conditions created numerous media headlines and practical problems for the alpine competitions. A statistical model was developed on the premise that February and March temperatures in the Vancouver area could be predicted using an ENSO signal with considerable lead time. This model successfully predicted the warmer-than-normal, lower-snowfall conditions for the Vancouver 2010 Winter Olympics and Paralympics.     

Adaptive Blending of Model and Observations for Automated Short-Range Forecasting: Examples from the Vancouver 2010 Olympic and Paralympic Winter Games

An automated short-range forecasting system, adaptive blending of observations and model (ABOM), was tested in real time during the 2010 Vancouver Olympic and Paralympic Winter Games in British Columbia. Data at 1-min time resolution were available from a newly established, dense network of surface observation stations. Climatological data were not available at these new stations. This, combined with output from new high-resolution numerical models, provided a unique and exciting setting to test nowcasting systems in mountainous terrain during winter weather conditions. The ABOM method blends extrapolations in time of recent local observations with numerical weather predictions (NWP) model predictions to generate short-range point forecasts of surface variables out to 6 h. The relative weights of the model forecast and the observation extrapolation are based on performance over recent history. The average performance of ABOM nowcasts during February and March 2010 was evaluated using standard scores and thresholds important for Olympic events. Significant improvements over the model forecasts alone were obtained for continuous variables such as temperature, relative humidity and wind speed. The small improvements to forecasts of variables such as visibility and ceiling, subject to discontinuous changes, are attributed to the persistence component of ABOM.     

Weather observations on Whistler Mountain during five storms

A greater understanding of precipitation formation processes over complex terrain near the west coast of British Colombia will contribute to many relevant applications, such as climate studies, local hydrology, transportation, and winter sport competition. The phase of precipitation is difficult to determine because of the warm and moist weather conditions experienced during the wintertime in coastal mountain ranges. The goal of this study is to investigate the wide range of meteorological conditions that generated precipitation on Whistler Mountain from 4–12 March 2010 during the SNOW-V10 field campaign. During this time period, five different storms were documented in detail and were associated with noticeably different meteorological conditions in the vicinity of Whistler Mountain. New measurement techniques, along with the SNOW-V10 instrumentation, were used to obtain in situ observations during precipitation events along the Whistler mountainside. The results demonstrate a high variability of weather conditions ranging from the synoptic-scale to the macro-scale. These weather events were associated with a variation of precipitation along the mountainside, such as events associated with snow, snow pellets, and rain. Only two events associated with a rain–snow transition along the mountainside were observed, even though above-freezing temperatures along the mountainside were recorded 90 % of the time. On a smaller scale, these events were also associated with a high variability of snowflake types that were observed simultaneously near the top of Whistler Mountain. Overall, these detailed observations demonstrate the importance of understanding small-scale processes to improve observational techniques, short-term weather prediction, and longer-term climate projections over mountainous regions.     

Nowcasting with INCA During SNOW-V10

A Central-European nowcasting system which has been developed for use in mountainous terrain is tested in the Whistler/Vancouver area as part of the SNOW-V10 experiment. The integrated nowcasting through comprehensive analysis system provides hourly updated gridded forecasts of temperature, humidity, and wind, as well as precipitation forecasts which are updated every 15 min. It is based on numerical weather prediction (NWP) output and real-time surface weather station and radar data. Verification of temperature, relative humidity, and wind against surface stations shows that forecast errors are significantly reduced in the nowcasting range compared to those of the driving NWP model. The main contribution to the improvement comes from the implicit bias correction due to use of the latest observations. Relative humidity shows the longest lasting effect, with >50 % reduction of mean absolute error up to +4 h. For temperature and wind speed this percentage is reached after +2 and +3 h, respectively. Two cases of precipitation nowcasting are discussed and verified qualitatively.     

FTS (Fog To Snow) Conversion Process During the SNOW-V10 Project

The objective of this work is to understand how winter fog which occurred on Whistler Mountain on 3–4 March 2010 developed into a snow event by the means of the FTS (Fog To Snow) process. This event was documented using data collected during the Science of Nowcasting Winter Weather for Vancouver 2010 (SNOW-V10) project that was supported by the Fog Remote Sensing and Modelling (FRAM) project. The FTS resulted in a snow event at about 1,850?m altitude where the RND (Roundhouse) meteorological station was located. For both days, there was no large scale system that affected local fog formation and its development into snow. Patchy fog occurred in the early hours of both days and was based below 1,500?m. Clear skies at night likely resulted in cooling, the valley temperature (T) was about ?1°C in the early morning, and snow was on the ground. Winds were relatively calm (<1?m?s^?1). At the RND site, T was about ?3°C. Weather at RND was clear and sunny till noon. When fog moved over the mountain peak/near RND, light snow started and lasted for about 4–5?h and was not detected by precipitation sensors except the Ground Cloud Imaging Probe (GCIP) and Laser Precipitation Sensor (LPM). In this work, the FTS process is conceptually summarized. Because clear weather conditions over the high mountain tops can become hazardous with low visibilities and significant snow amounts (<1.0?mm?h^?1), such events are important and need to be predicted.     

Roundhouse (RND) Mountain Top Research Site: Measurements and Uncertainties for Winter Alpine Weather Conditions

The objective of this work is to better understand and summarize the mountain meteorological observations collected during the Science of Nowcasting Winter Weather for the Vancouver 2010 Olympics and Paralympics (SNOW-V10) project that was supported by the Fog Remote Sensing and Modeling (FRAM) project. The Roundhouse (RND) meteorological station was located 1,856 m above sea level that is subject to the winter extreme weather conditions. Below this site, there were three additional observation sites at 1,640, 1,320, and 774 m. These four stations provided some or all the following measurements at 1 min resolution: precipitation rate (PR) and amount, cloud/fog microphysics, 3D wind speed (horizontal wind speed, U _h; vertical air velocity, w _a), visibility (Vis), infrared (IR) and shortwave (SW) radiative fluxes, temperature (T) and relative humidity with respect to water (RH_w), and aerosol observations. In this work, comparisons are made to assess the uncertainties and variability for the measurements of Vis, RH_w, T, PR, and wind for various winter weather conditions. The ground-based cloud imaging probe (GCIP) measurements of snow particles using a profiling microwave radiometer (PMWR) data have also been shown to assess the icing conditions. Overall, the conclusions suggest that uncertainties in the measurements of Vis, PR, T, and RH can be as large as 50, >60, 50, and >20 %, respectively, and these numbers may increase depending on U _h, T, Vis, and PR magnitude. Variability of observations along the Whistler Mountain slope (~500 m) suggested that to verify the models, model space resolution should be better than 100 m and time scales better than 1 min. It is also concluded that differences between observed and model based parameters are strongly related to a model’s capability of accurate prediction of liquid water content (LWC), PR, and RH_w over complex topography.     

Precipitation Observations with NSSL’s X-band Polarimetric Radar during the SNOW-V10 Campaign

In support of SNOW-V10, the National Oceanic Administration/National Severe Storms Laboratory (NOAA/NSSL) mobile dual-polarized X-band (NO-XP) radar was deployed to Birch Bay State Park in Birch Bay, Washington from 3 January 2010 to 17 March 2010. In addition to being made available in real time for Science and NOWcasting of the Olympic Weather for Vancouver 2010 (SNOW-V10) operations, NO-XP data are used here to demonstrate the capabilities of easily deployable, polarimetric X-band radar systems, especially for regions where mountainous terrain results in partial beam blockage. A rainfall estimator based on specific attenuation is shown to mitigate the effects of partial beam blockage and provide potential improvement in rainfall estimation. The ability of polarimetric X-band radar to accurately detect melting layer (ML) height is also shown. A 16 h comparison of radar reflectivity (Z), differential reflectivity (Z _DR), and correlation coefficient (ρ_hv) measurements from NO-XP with vertically pointing Micro Rain Radar observations indicates that the two instruments provide ML height evolution that exhibit consistent temporal trends. Since even slight changes in the ML height in regions of mountainous terrain might result in a change in precipitation type measured at the surface, this shows that horizontally extensive information on ML height fluctuations, such as provided by the NO-XP, is useful in determining short term changes in expected precipitation type. Finally, range-height indicator (RHI) scans of NO-XP Z, Z _DR, and ρ_hv fields from SNOW-V10 are used to demonstrate the ability of polarimetric radar to diagnose microphysical processes (both above and below the ML) that otherwise remain unseen by conventional radar. Near-surface enhancements in Z _DR are attributed to either differential sedimentation or the preferential evaporation of smaller drops. Immediately above the ML, regions of high Z, low Z _DR, and high ρ_hv are believed to be associated with convective turrets containing heavily aggregated or rimed snow that supply water/ice mass that later result in enhanced regions of precipitation near the surface. Higher up, horizontally extensive regions of enhanced Z _DR are attributed to rapid dendritic growth and the onset of snow aggregation, a feature that has been widely observed with both S band and C band radars.     

The Impact of Weather Forecasts of Various Lead Times on Snowmaking Decisions Made for the 2010 Vancouver Olympic Winter Games

The Vancouver 2010 Winter Olympics were held from 12 to 28 February 2010, and the Paralympic events followed 2 weeks later. During the Games, the weather posed a grave threat to the viability of one venue and created significant complications for the event schedule at others. Forecasts of weather with lead times ranging from minutes to days helped organizers minimize disruptions to sporting events and helped ensure all medal events were successfully completed. Of comparable importance, however, were the scenarios and forecasts of probable weather for the winter in advance of the Games. Forecasts of mild conditions at the time of the Games helped the Games' organizers mitigate what would have been very serious potential consequences for at least one venue. Snowmaking was one strategy employed well in advance of the Games to prepare for the expected conditions. This short study will focus on how operational decisions were made by the Games' organizers on the basis of both climatological and snowmaking forecasts during the pre-Games winter. An attempt will be made to quantify, economically, the value of some of the snowmaking forecasts made for the Games' operators. The results obtained indicate that although the economic value of the snowmaking forecast was difficult to determine, the Games' organizers valued the forecast information greatly. This suggests that further development of probabilistic forecasts for applications like pre-Games snowmaking would be worthwhile.     

The role of meteorological forcing and snow model complexity in winter glacier mass balance estimation,Columbia River basin,Canada

Glaciers are commonly located in mountainous terrain subject to highly variable meteorological conditions. High resolution meteorological (HRM) data simulated by atmospheric models can complement meteorological station observations in order to assess changes in glacier energy fluxes and mass balance. We examine the performance of two snow models, SnowModel and Alpine3D, forced by different meteorological data for winter mass balance simulations at four glaciers in the Canadian portion of the Columbia Basin. The Weather Research and Forecasting model (WRF) with resolution of 1 km and the North American Land Data Assimilation System with ~12 km resolution, provide HRM data for the two snow models. Evaluation is based on the ability of the snow models to simulate snow depth at both point locations (automated snow weather stations) and over the entire glacier surface (airborne LiDAR [Light Detection and Ranging] surveys) during the 2015/2016 winter accumulation. When forced with HRM data, both models can reproduce snow depth to within ±15% of observed values. Both models underestimate winter mass balance when forced by HRM data. When driven with WRF data, SnowModel underestimates winter mass balance integrated over the glacier area by 1 and 10%, whilst Alpine3D underestimates winter mass balance by 12 and 22% compared with LiDAR and stake measurements, respectively. The overall results show that SnowModel forced by WRF simulated winter mass balance the best.     

Data time step to estimate snowpack accumulation at select United States meteorological stations

When estimating the water balance for a cold region watershed, that is one that receive a substantial portion of its annual precipitation as snow, accumulation and other winter hydrological processes must be considered. For many of theses watersheds, all but the most fundamental meteorological data (temperature and precipitation), are either not measured or not measured at a reasonable time step. Of particular importance are wind data, as wind influences underestimates of precipitation due to wind undercatch and losses of snow from the snowpack, specifically, snowpack sublimation, and the occurrence and magnitude of blowing snow. Estimating snow accumulation to yield snowmelt amounts requires summing of gauged precipitation and gauge undercatch, and subtracting minus snowpack sublimation and blowing snow transport. The first two components are computed on a daily time step, while the latter two are computed on an hourly time step. From five National Weather Service meteorological stations (Pullman WA, Rawlins WY, Leadville CO, Rhinelander WI, Syracuse NY), the variations in computed snowpack mass losses minus undercatch using data at different time intervals show that at most sites it is difficult to use monthly time steps for computations derived using hourly or daily data. At the relative dry and cold Leadville, Colorado site the computations were transferable between time steps. Copyright © 2007 John Wiley & Sons, Ltd.     

A New Integrated Weighted Model in SNOW-V10: Verification of Continuous Variables

This paper presents the verification results of nowcasts of four continuous variables generated from an integrated weighted model and underlying Numerical Weather Prediction (NWP) models. Real-time monitoring of fast changing weather conditions and the provision of short term forecasts, or nowcasts, in complex terrain within coastal regions is challenging to do with sufficient accuracy. A recently developed weighting, evaluation, bias correction and integration system was used in the Science of Nowcasting Olympic Weather for Vancouver 2010 project to generate integrated weighted forecasts (INTW) out to 6 h. INTW forecasts were generated with in situ observation data and background gridded forecasting data from Canadian high-resolution deterministic NWP system with three nested grids at 15-, 2.5- and 1-km horizontal grid-spacing configurations. In this paper, the four variables of temperature, relative humidity, wind speed and wind gust are treated as continuous variables for verifying the INTW forecasts. Fifteen sites were selected for the comparison of the model performances. The results of the study show that integrating surface observation data with the NWP forecasts produce better statistical scores than using either the NWP forecasts or an objective analysis of observed data alone. Overall, integrated observation and NWP forecasts improved forecast accuracy for the four continuous variables. The mean absolute errors from the INTW forecasts for the entire test period (12 February to 21 March 2010) are smaller than those from NWP forecasts with three configurations. The INTW is the best and most consistent performer among all models regardless of location and variable analyzed.     

Observations of late winter Canadian tundra snow cover properties

Tundra snow cover is important to monitor as it influences local, regional, and global‐scale surface water balance, energy fluxes, as well as ecosystem and permafrost dynamics. Observations are already showing a decrease in spring snow cover duration at high latitudes, but the impact of changing winter season temperature and precipitation on variables such as snow water equivalent (SWE) is less clear. A multi‐year project was initiated in 2004 with the objective to quantify tundra snow cover properties over multiple years at a scale appropriate for comparison with satellite passive microwave remote sensing data and regional climate and hydrological models. Data collected over seven late winter field campaigns (2004 to 2010) show the patterns of snow depth and SWE are strongly influenced by terrain characteristics. Despite the spatial heterogeneity of snow cover, several inter‐annual consistencies were identified. A regional average density of 0.293 g/cm³ was derived and shown to have little difference with individual site densities when deriving SWE from snow depth measurements. The inter‐annual patterns of SWE show that despite variability in meteorological forcing, there were many consistent ratios between the SWE on flat tundra and the SWE on lakes, plateaus, and slopes. A summary of representative inter‐annual snow stratigraphy from different terrain categories is also presented. © 2013 Her Majesty the Queen in Right of Canada. Hydrological Processes. © 2013 John Wiley & Sons, Ltd.     

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.     

Ensemble rainfall forecasting with numerical weather prediction and radar‐based nowcasting models

The overall objective of this study is to improve the forecasting accuracy of the precipitation in the Singapore region by means of both rainfall forecasting and nowcasting. Numerical Weather Predication (NWP) and radar‐based rainfall nowcasting are two important sources for quantitative precipitation forecast. In this paper, an attempt to combine rainfall prediction from a high‐resolution mesoscale weather model and a radar‐based rainfall model was performed. Two rainfall forecasting methods were selected and examined: (i) the weather research and forecasting model (WRF); and (ii) a translation model (TM). The WRF model, at a high spatial resolution, was run over the domain of interest using the Global Forecast System data as initializing fields. Some heavy rainfall events were selected from data record and used to test the forecast capability of WRF and TM. Results obtained from TM and WRF were then combined together to form an ensemble rainfall forecasting model, by assigning weights of 0.7 and 0.3 weights to TM and WRF, respectively. This paper presented results from WRF and TM, and the resulting ensemble rainfall forecasting; comparisons with station data were conducted as well. It was shown that results from WRF are very useful as advisory of anticipated heavy rainfall events, whereas those from TM, which used information of rain cells already appearing on the radar screen, were more accurate for rainfall nowcasting as expected. The ensemble rainfall forecasting compares reasonably well with the station observation data. Copyright © 2012 John Wiley & Sons, Ltd.     

Distributed snow accumulation and ablation modeling in the American River basin

A spatially distributed snow model procedure for estimating snow melt, snow water equivalent and snow cover area is formulated and tested with data from the American River basin in California’s Sierra Nevada. An adaptation of the operational National Weather Service snow accumulation and ablation model is used for each model grid cell forced by spatially distributed precipitation and temperature data. The model was implemented with 6-hourly time steps on 1 km² grid cells for the snow season of 1999–2003. Temperature is spatially interpolated using the prevailing lapse rate and digital terrain elevation data. Precipitation is spatially interpolated using regional climatological analyses obtained from PRISM. Parameters that control snow melt are distributed using ground surface aspect. The model simulations are compared with data from 12 snow-sensors located in the basin and the daily 500-m snow cover extent product from the MODIS/Terra satellite mission. The results show that the distribution of snow pack over the area is generally captured. The snow pack quantity compared to snow gauges is well estimated in high elevations with increasing uncertainty in the snow pack at lower elevations. Sensitivity and uncertainty analyses indicate that the significant input uncertainty for precipitation and temperature is primarily responsible for model errors in lower elevations and near the snow line. The model is suitable for producing spatially resolved realistic snow pack simulations when forced with operationally available observed or predicted data.