The new VarEPS‐monthly forecasting system: A first step towards seamless prediction

Abstract

ABSTRACT:A combined medium-range and monthly-forecasting forecasting system is now operational at the European Centre for Medium-Range Weather Forecasts. Previously, these two systems were run separately. The new combined system provides skillful predictions of small-scale, severe-weather events in the early forecast range, accurate large-scale forecast guidance up to day 15 twice a day, and large-scale guidance up to day 32 once a week. In addition, the daily medium-range forecasts starting at 0000 utc are now coupled to an ocean general-circulation model after day 10 and persisted sea-surface temperature (SST) anomalies instead of persisted SSTs are applied when the atmospheric model is run in uncoupled mode. Average results indicate that the monthly forecasting scores are slightly higher in the Extratropics with this new combined system. In particular, the new system seems to produce better monthly forecasts for extreme events such as the 2003 heatwave over Europe or the 2007 wet summer over England. However, the monthly forecasting scores in the Tropics are slightly lower than with the previous system, most likely because of the lack of ocean-atmosphere coupling during the first ten days of the forecasts. This demonstrates that ocean-atmosphere coupling may be needed in medium-range forecasting and future plans include coupling the atmosphere to the ocean model from day 0. Average results based on 30 cases indicate that the medium-range forecasts benefit from the introduction of the ocean-atmosphere coupling after day 10, most especially the prediction of low-level temperature in the Tropics.

Influence of the Eurasian snow on the negative North Atlantic Oscillation in subseasonal forecasts of the cold winter 2009/2010

“…Other forcings or internal variability may govern the evolution of the observed NAO, but our argument is that the thick snowpack contributes to the negative phase maintenance. This is further supported by additional analysis of the operational monthly forecasts carried out in December 2009 with the ECMWF Variable Resolution Ensemble Prediction System (VAREPS; Vitart et al 2008). These runs are very similar to our SNOWGLACE runs in that they use the same model cycle and land surface module, but they are launched weekly and are of shorter duration (32 days) with a large ensemble size (51 members).…”

On the forecast skill of a convection-permitting ensemble

“…For many applications, e.g., for large-scale extreme events, such as the central Europe flooding event of 2013, the best solution will be a combination of both systems: the coarser ensembles with longer forecast range for (pre)warnings and the convectionpermitting ensemble for the detailed specification of the expected event. Regarding different time and length scales in that way could lead to the generation of seamless forecast products (e.g., Drobinski et al, 2014;Vitart et al, 2008).…”

CFSv2-based sub-seasonal precipitation and temperature forecast skill over the contiguous United States

“…The medium-range weather forecast is strongly influenced by atmospheric initial conditions (Vitart et al, 2008), while the seasonal climate forecast depends on slowly evolving components of the climate system (e.g., sea surface temperature and soil moisture) (Troccoli, 2010). However, since the subseasonal timescale is usually too long to be favored by the atmospheric initial conditions (Vitart, 2004) and too short to be strongly influenced by the variability of the ocean, making skillful sub-seasonal forecasts is particularly difficult and thus far has less progress than the medium-range weather forecasts and seasonal climate forecasts.…”

“…Previous studies have evaluated the potential of sub-seasonal to seasonal forecasts for heat wave forecasting (e.g., Hudson et al, 2011a;White et al, 2014), hydrological forecasting (e.g., Orth and Seneviratne, 2013;Yuan et al, 2014), water resources management (e.g., Sankarasubramanian et al, 2009), hydropower production management (e.g., Garcia-Morales and Dubus, 2007), and crop yield predic-tion (e.g., Hansen et al, 2006;Zinyengere et al, 2011). Due to the improvement of numerical models, prediction techniques, and computing resources, there is an increasing focus on sub-seasonal forecasts (e.g., Toth et al, 2007;Vitart et al, 2008;Brunet et al, 2010;Hudson et al, 2011bHudson et al, , 2013Robertson et al, 2014).…”