Statistical downscaling of temperature and humidity for snow-quality risk forecasts for Beijing 2022 Winter Olympics

<p>High-quality snow is critical for the Winter Olympic Games. Snow quality is very sensitive to the changes of meteorological elements, especially temperature and humidity. Forecasts on snow quality can provide information for snow maintenance and require high-resolution meteorological forecasts. However, the complex terrain of the mountainous areas where the winter sports are often carried out could result in complex local meteorological fields, which makes it difficult to forecast. Zhangjiakou Competition Zone is one of the three competition zones for the Beijing 2022 Winter Olympics and includes two districts: Genting snow park and Guyangshu Ski Resort. Taking Genting snow park as an example, there is a difference of about 350m in altitude in Genting snow park which covers an area of about 2×2km<sup>2</sup>, and there is an average difference of about 3℃ in hourly temperature and about 10% in hourly relative humidity at noon.</p><p>Short-term forecasts in the past Winter Olympic Games were usually based on mesoscale NWP models with a horizontal resolution of up to 1×1km. Due to the limitation of boundary layer parameterization schemes, some small-scale air processes affected by local topography cannot be caught in mesoscale models. Some MOS methods can correct the systematic bias of the models but are unable to deal with the non-systematic errors caused by these small-scale processes.</p><p>The purposes of this study were to develop statistical downscaling methods for the temperature and humidity forecasts, which are required in the snow-quality risk classification for the Zhangjiakou Competition Zone of the Beijing 2022 Winter Olympics.  </p><p>Hourly data during 2018-2021 from 20 meteorological stations and ERA5-Land reanalysis in the study area were used for the calibration and validation of models. A decaying average method which is similar to the Kalman Filter method was applied to develop the downscaling models. To evaluate the efficiency of the models on snow-quality risk forecasts. A classification model of snow-quality risk developed by the Climate Centre of Hebei Province was applied. Snow-quality risk classification model was developed based on the four years’ meteorological and snow-quality observations in the study area, in which the risk of snow quality was classified into 4 levels: zero-risk, low-risk, medium-risk and high-risk based on the input temperature and humidity. The downscaled prediction fell into 3 cases: (1) the predicted risk level equal to the observed risk level (Accuracy); (2) the predicted risk level lower than the observed risk level (Miss); (3) the predicted risk level higher than the observed risk level (False-Alarm).</p><p>The results showed that: (1) the downscaling models can decrease the RMSE of the ERA5 by ~13% for the temperature and by ~14% for the dewpoint temperature; (2) the accuracy of the snow-quality risk classification increased from 72% to 76% on average comparing the inputs of ERA5 and the downscaled temperature and humidity. For the stations with high elevation, the ratio of False-Alarm decreased by ~13%. Further research will focus on improving the statistical model by calibrating the model for different locations and different circulation patterns.</p>