Improving Medium‐Range Forecasts of Rain‐on‐Snow Events in Prealpine Areas. Issue 9 (6th September 2019) - Fehlmann, Michael; Gascón, Estíbaliz; Rohrer, Mario; Schwarb, Manfred; Stoffel, Markus
Abstract: Rain‐on‐snow (RoS) events have caused severe floods in mountainous catchments in the recent past. Challenges in forecasting such events are uncertainties in meteorological input variables, the accurate estimation of snow cover and deficits in process understanding during runoff formation. Here, we evaluate the potential of the European Centre for Medium‐Range Weather Forecasts Integrated Forecasting System (ECMWF IFS) to forecast RoS disposition (i.e., minimum rainfall amounts, initial snow cover, and meltwater contribution) several days ahead. We thereby evaluate forecasts of rain and snowfall with disdrometer observations and show that ensemble‐based forecasts have larger potential than the high‐resolution forecast of ECMWF IFS. Then, we use ECMWF IFS weather forecasts as input to a conceptual hydrological model, which is calibrated using estimates of snow‐covered area (SCA), snow water equivalent (SWE), and discharge observations. We show that the forecast skill of this model chain is reasonably high with respect to SCA and SWE, even several days ahead. However, a number of RoS events are missed in the forecast, mainly due to an underestimation of rainfall amounts. These misses can be reduced by lowering the rainfall amount threshold for the forecast as compared to the analysis, being accompanied by only a moderate increase in false alarm rates. In contrast, the forecast of RoS disposition is found to be less sensitive to thresholds of initial snow cover and meltwater contribution. We conclude that valuable disposition warnings for RoS events can be issued several days ahead, and we illustrate this idea with a case study. Key Points: ECMWF model forecasts are used as input to a hydrological model to forecast rain‐on‐snow events The disposition of rain‐on‐snow events can be forecasted already several days ahead Misses are reduced by lowering the rainfall amount threshold for the forecast as compared to the analysis