Abstract
AbstractIncreased human activity in the Arctic calls for accurate and reliable weather predictions. This study presents an intercomparison of operational and/or high-resolution models in an attempt to establish a baseline for present-day Arctic short-range forecast capabilities for near-surface weather (pressure, wind speed, temperature, precipitation, and total cloud cover) during winter. One global model [the high-resolution version of the ECMWF Integrated Forecasting System (IFS-HRES)], and three high-resolution, limited-area models [Applications of Research to Operations at Mesoscale (AROME)-Arctic, Canadian Arctic Prediction System (CAPS), and AROME with Météo-France setup (MF-AROME)] are evaluated. As part of the model intercomparison, several aspects of the impact of observation errors and representativeness on the verification are discussed. The results show how the forecasts differ in their spatial details and how forecast accuracy varies with region, parameter, lead time, weather, and forecast system, and they confirm many findings from mid- or lower latitudes. While some weaknesses are unique or more pronounced in some of the systems, several common model deficiencies are found, such as forecasting temperature during cloud-free, calm weather; a cold bias in windy conditions; the distinction between freezing and melting conditions; underestimation of solid precipitation; less skillful wind speed forecasts over land than over ocean; and difficulties with small-scale spatial variability. The added value of high-resolution limited area models is most pronounced for wind speed and temperature in regions with complex terrain and coastlines. However, forecast errors grow faster in the high-resolution models. This study also shows that observation errors and representativeness can account for a substantial part of the difference between forecast and observations in standard verification.
Highlights
The Arctic is experiencing rapid changes in its harsh climate and environment, for example, the observed annual averaged near-surface temperatures at Svalbard are increasing at between 1.048 and 1.768C decade21 (Hanssen-Bauer et al 2019)
If we assume that the precipitation will be rain when the temperature exceeds 118C (Jennings et al 2018), we find that the forecasts suggest that 70% (AROME-Arctic), 16% (IFS-HRES), 5% (CAPS), and 43% (MF-AROME) of the precipitation fell as rain at the observation sites
The forecast systems differ in model formulation, resolution, initialization methods and lateral boundary forcing (Table 1)
Summary
The Arctic is experiencing rapid changes in its harsh climate and environment, for example, the observed annual averaged near-surface temperatures at Svalbard are increasing at between 1.048 and 1.768C decade (Hanssen-Bauer et al 2019). The difference between a forecast value (grid box average from an NWP system) and a point observation can be decomposed into model, observation, interpolation, and representativeness errors (Kanamitsu and DeHaan 2011) The latter three components are nonnegligible for verification studies, in particular in the Arctic environment characterized by spatiotemporal sparseness and uncertainty in the observations (Casati et al 2017). AROME-Arctic and MF-AROME are both configurations of the same model system but use different parameterizations in the turbulence representation and for shallow convection, and in addition a sea ice model is used in AROME-Arctic Despite their differences, they all provide short-range forecasts for a common domain covering northern Scandinavia, the Barents Sea, and Svalbard (Fig. 1) during YOPP SOP-NH1. March (ranked as the fifteenthlowest value of all March months) was not as extreme in terms of NAO as February
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
