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Downloaded 10/05/21 10:23 PM UTC 960 WEATHER and FORECASTING VOLUME 34 AUGUST 2019 K Ø LTZOW ET AL. 959 An NWP Model Intercomparison of Surface Weather Parameters in the European Arctic during the Year of Polar Prediction Special Observing Period Northern Hemisphere 1 MORTEN KØLTZOW Norwegian Meteorological Institute, Oslo, Norway BARBARA CASATI Environment and Climate Change Canada, Dorval, Quebec, Canada ERIC BAZILE Météo France, Toulouse, France THOMAS HAIDEN ECMWF, Reading, United Kingdom TERESA VALKONEN Norwegian Meteorological Institute, Oslo, Norway (Manuscript received 11 January 2019, in final form 24 May 2019) ABSTRACT Increased 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 dif- ficulties 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. Denotes content that is immediately available upon publica- tion as open access. This article is licensed under a Creative Commons Attribution 4.0 license (http://creativecommons.org/ Corresponding author: Morten Køltzow, [email protected] licenses/by/4.0/). DOI: 10.1175/WAF-D-19-0003.1 Ó 2019 American Meteorological Society Unauthenticated | Downloaded 10/05/21 10:23 PM UTC 960 WEATHER AND FORECASTING VOLUME 34 1. Introduction field observations and used as a tool to better un- derstand the investigated phenomena. For example, The Arctic is experiencing rapid changes in its harsh polar lows have received substantial attention (e.g., climate and environment, for example, the observed an- Kristjánsson et al. 2011), but remain a challenge in op- nual averaged near-surface temperatures at Svalbard are 2 erational forecasting because of their rapid growth and now increasing at between 1.048 and 1.768Cdecade 1 mesoscale nature (e.g., Spengler et al. 2017). Arctic cy- (Hanssen-Bauer et al. 2019). Anticipated increases in clones (e.g., Yamagami et al. 2018), and sudden strato- ship traffic, resource exploitation, tourism, and other spheric warming events (e.g., Jung and Leutbecher 2007; activities (WMO 2017) call for accurate and reliable Karpechko 2018) have also been the subject of recent weather predictions for safe and efficient operations. Arctic forecast skill evaluations. Other examples of Despite improved Arctic forecast skill in recent decades high-impact weather that have been studied are severe (Bauer et al. 2016; Jung and Leutbecher 2007), Jung et al. precipitation events at Svalbard (Hansen et al. 2014; (2016) argues that existing numerical weather prediction Serreze et al. 2015) and maritime icing on vessels (Sultana (NWP) systems do not meet existing user requirements. et al. 2018; Samuelsen 2018). Furthermore, forecast errors in the Arctic are larger than The difference between a forecast value (grid box at lower latitudes (e.g., Nordeng et al. 2007; Bauer et al. average from an NWP system) and a point observation 2016; Gascard et al. 2017). Nordeng et al. (2007) argue can be decomposed into model, observation, interpola- that the main reasons for this are the sparse conventional tion, and representativeness errors (Kanamitsu and observational network and the small spatial scales of DeHaan 2011). The latter three components are many (high impact) Arctic-specific weather phenomena. nonnegligible for verification studies, in particular in NWP systems are also often developed and tuned with a the Arctic environment characterized by spatiotem- focus on mid and lower-latitude weather. poral sparseness and uncertainty in the observations Arctic verification studies of global model systems (Casati et al. 2017). The observation uncertainty has often use model analyses as truth, given the relative been neglected in verification practices for several sparseness of observations. However, this introduces decades. As forecast capabilities improve, however, uncertainty in the interpretation because a higher a larger part of the forecast–observation difference is spread between analyses compared to lower latitudes is due to observational uncertainty and representativeness found since the analyses are less constrained by obser- mismatch rather than to model errors alone, in partic- vations and are closer to their inherent model clima- ular for short-range forecasts. tology (Jung and Matsueda 2016; Bauer et al. 2016). The Year of Polar Prediction (YOPP), with extra avail- Bauer et al. (2016) found that verifying near-surface ability of observations and model simulations (Jung et al. temperatures in the Arctic against observations gave 2016), is a great opportunity to improve our understand- substantially larger errors compared to verifying against ing of forecast capabilities in the Arctic. In this study we model analyses. To establish the state of the art on compare three high-resolution regional NWP systems Arctic forecast capabilities, more verification of near- and one global NWP system during the YOPP Special surface parameters, including snow and sea ice charac- Observing Period Northern Hemisphere 1 (SOP-NH1, 1 teristics, are needed (Jung et al. 2016). February–31 March 2018) with focus on surface weather The use of regional models can, compared with global parameters. In addition, issues related to observational models, improve forecast accuracy by the use of opti- uncertainty are discussed to improve our interpretation mized physics for the targeted area and finer horizontal of the verification results. and vertical resolution (Jung et al. 2016). However, The NWP systems are briefly described in section 2, operational convection permitting resolution models together with the observations and weather during YOPP have just recently started to appear for the Arctic do- SOP-NH1. The models are compared in terms of objective main. Müller et al. (2017) and Yang et al. (2018) de- verification scores in section 3, including a discussion on scribe added value from operational high-resolution some aspects of observation errors and representativeness HIRLAM–ALADIN Research on Mesoscale Opera- issues. In section 4, two cases of high-impact weather are tional NWP in Euromed (HARMONIE)–Applications discussed before we summarize main findings in section 5. of Research to Operations at Mesoscale (AROME) runs in the Arctic compared to coarser resolution sys- 2. NWP systems, observations, and weather tems. Furthermore, specific Arctic weather phenom- a. NWP systems ena, often connected to high-impact weather, have been studied in both global and regional high- The NWP systems included in the comparison are resolution models. Models have been compared with the high-resolution version of the global ECMWF Unauthenticated | Downloaded 10/05/21 10:23 PM UTC AUGUST 2019 K Ø LTZOW ET AL. 961 Integrated Forecasting System (IFS-HRES) with 9-km Over the open ocean, we utilize near-real-time data grid spacing (Buizza et al. 2017) and the three regional from the global Advanced Scatterometer (ASCAT) convection permitting NWP systems: AROME-Arctic coastal wind product on a 12.5-km grid (Verhoefetal. with 2.5-km grid spacing (Müller et al. 2017; Bengtsson 2012). The ASCAT wind products, provided by the et al. 2017), the Canadian Arctic Prediction System EUMETSAT Ocean and Sea Ice Satellite Application (CAPS) with 3-km grid spacing (G. C. Smith et al. 2019, Facility (OSI SAF), include a thorough quality control. unpublished manuscript), and AROME with Météo- We utilized only the data with the highest-quality flags. France setup (MF-AROME) with 2.5-km grid spacing For the model comparison, the ASCAT data were re- (Seity et al. 2011). Apart from spatial resolution, the projected on the intercomparison domain, and NWP four forecast systems differ in their model formulations, model data were regridded onto a 12.5-km grid corre- initialization methods, and in lateral and surface forcing sponding to the grid spacing of the ASCAT data. (details in Table 1). IFS-HRES and AROME-Arctic c. Weather during YOPP SOP-NH1 forecasts are taken from daily operational
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