Numerical simulations of snowfall events: Sensitivity analysis of physical parameterizations

Abstract

Accurate estimation of snowfall episodes several hours or even days in advance is essential to minimize risks to transport and other human activities. Every year, these episodes cause severe traffic problems on the northwestern Iberian Peninsula. In order to analyze the influence of different parameterization schemes, 15 snowfall days were analyzed with the Weather Research and Forecasting (WRF) model, defining three nested domains with resolutions of 27, 9, and 3 km. We implemented four microphysical parameterizations (WRF Single‐Moment 6‐class scheme, Goddard, Thompson, and Morrison) and two planetary boundary layer schemes (Yonsei University and Mellor‐Yamada‐Janjic), yielding eight distinct combinations. To validate model estimates, a network of 97 precipitation gauges was used, together with dichotomous data of snowfall presence/absence from snowplow requests to the emergency service of Spain and observatories of the Spanish Meteorological Agency. The results indicate that the most accurate setting of WRF for the study area was that using the Thompson microphysical parameterization and Mellor‐Yamada‐Janjic scheme, although the Thompson and Yonsei University combination had greater accuracy in determining the temporal distribution of precipitation over 1 day. Combining the eight deterministic members in an ensemble average improved results considerably. Further, the root mean square difference decreased markedly using a multiple linear regression as postprocessing. In addition, our method was able to provide mean ensemble precipitation and maximum expected precipitation,which can be very useful in the management of water resources. Finally, we developed an application that allows determination of the risk of snowfall above a certain threshold.

Aircraft Icing: In‐Cloud Measurements and Sensitivity to Physical Parameterizations

“…In many of the events, we found standard deviations similar in orders of magnitude to the ensemble mean. These results are consistent with the demonstrated sensitivity of the two variables to PBL and microphysical parameterizations (Fernández‐González et al, ).…”

“…The selected parameterization schemes were as follows: Dudhia (1989) for shortwave radiation; the Rapid Radiative Transfer model (Mlawer et al, 1997) for longwave radiation; Eta surface layer described by Janjic (1996), and Noah Land Surface Model (Chen & Dudhia, 2001), which is a four-layer soil temperature and moisture model with canopy moisture and snow cover estimation. This configuration was based on the results of studies treating winter situations (Fernández-González et al, 2015;Evans et al, 2012;Yuan et al, 2012). The effectiveness of cumulus parameterizations has been demonstrated using low-resolution models when resolutions better than 10-15 km were not needed (Arakawa, 2004).…”

Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees

“…To our knowledge this is still to be tested. In the absence of this information, we decided to use the settings shown on Table 2, based on information from previous work in the region and other mountain areas using the WRF model [56][57][58]. For a detailed description of the settings see the WRF User's Guide [59].…”

Case Studies of Low‐Visibility Forecasting in Falling Snow With WRF Model

“…To evaluate the performance of different parameterizations, the following five skill scores were calculated: the probability of detection (POD), false alarm ratio (FAR), missing rate (MR), critical success index (CSI, also known as the threat score), and BIAS using the traditional 2 × 2 contingency table that displays all possible combinations of forecast and observed yes/no binary events (Table ). This methodology has also been used for validating WRF microphysical parameterizations (Fernández‐González et al, ).…”