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Polar lows exhibit features with very sharp weather contrasts. In weather forecasting, a small misplacement of areas with hazardously high wind speeds can have fatal impacts for people living in polar regions. Therefore, a novel application of spatial verification methods for objective metrics of size, shape, and location of areas with hazardous weather is tested. To separate the effect of errors in polar low location and direction of motion from errors relative to the polar low centre, surface wind fields from the limited‐area weather forecasting model Applications of Research to Operations at Mesoscale‐Arctic and Copernicus Climate Change Service Arctic Regional Reanalysis are centred at the polar low centre and rotated according to the direction of background flow surrounding the polar low. Then the possibilities of the features‐based verification methods SAL (structure, amplitude, location) and MODE (Method for Object‐based Diagnostic Evaluation) are explored using a test case from October 2019. The study demonstrates that the methodology can provide valuable information about forecast performance. MODE is a flexible method with metrics that focus on characteristics of individual objects and can be adapted to questions at hand. For example, a measure of storm eye size was added. SAL, on the other hand, provides effective summary metrics for the full domain and proved particularly useful for evaluation of the overall distribution of wind speed. To evaluate the number of correctly or incorrectly identified areas with harsh weather rather than their details about their shape, contingency scores are more suitable. Applied to a larger dataset, this methodology can assess performance as a function of forecast length, as well as geographical area, and the type of polar low. The methodology can also be applied to other types of low‐pressure systems, such as extratropical cyclones. [ABSTRACT FROM AUTHOR]

Greenland rainfall has come into focus as a climate change indicator and from a variety of emerging cryospheric impacts. This study first evaluates rainfall in five state‐of‐the‐art numerical prediction systems (NPSs) (CARRA, ERA5, NHM‐SMAP, RACMO, MAR) using in situ rainfall data from two regions spanning from land onto the ice sheet. The new EU Copernicus Climate Change Service (C3S) Arctic Regional ReAnalysis (CARRA), with a relatively fine (2.5 km) horizontal grid spacing and extensive within‐model‐domain observational initialization, has the lowest average bias and highest explained variance relative to the field data. ERA5 inland wet bias versus CARRA is consistent with the field data and other research and is presumably due to more ERA5 topographic smoothing. A CARRA climatology 1991–2021 has rainfall increasing by more than one‐third for the ice sheet and its peripheral ice masses. CARRA and in situ data illuminate extreme (above 300 mm per day) local rainfall episodes. A detailed examination CARRA data reveals the interplay of mass conservation that splits flow around southern Greenland and condensational buoyancy generation that maintains along‐flow updraft 'rapids' 2 km above sea level, which produce rain bands within an atmospheric river interacting with Greenland. CARRA resolves gravity wave oscillations that initiate as a result of buoyancy offshore, which then amplify from terrain‐forced uplift. In a detailed case study, CARRA resolves orographic intensification of rainfall by up to a factor of four, which is consistent with the field data. [ABSTRACT FROM AUTHOR]

The impact of Arctic conventional and satellite observations on regional short‐range weather forecasts is assessed using observing‐system experiments, in which observations are removed (denied) when creating the initial conditions of the forecasts. The experiments are conducted with the AROME‐Arctic regional mesoscale numerical weather prediction system, using as lateral boundary conditions (LBCs) observing‐system experiments performed at the European Centre for Medium‐Range Forecasts (ECMWF) with the global forecasting system. This allows the assessment of the relative impacts of observations on forecast skill through regional data assimilation (DA), through LBCs, and the total impact due to the denial of observations in both the regional and global forecasting systems. The study is conducted during the first and second Special Observing Periods of the Year Of Polar Prediction. The total impact on the upper‐air forecasts is dominated by the impact of observations through their assimilation in the LBCs, while for the winter period the impact on surface fields is dominated by the regional DA. The latter is significant up to 36 hr, while the former impact can last throughout the verified forecast range (48 hr). The use of observations in the LBCs has both significantly positive and significantly negative impacts. In terms of total impact on forecast skill, conventional observations, followed by infrared radiances, have the largest impact on all upper‐air parameters, except for humidity. For upper‐air humidity forecasts, the microwave radiances have the largest impact. In terms of observation impact through regional DA, conventional observations play the largest role for upper‐air temperature and geopotential, microwave for upper‐air humidity, and atmospheric motion vectors and Infrared Atmospheric Sounding Interferometer (IASI) for wind forecast. Regional DA of conventional observations also contributes the most to improvements of surface fields, except for 10‐m winds, for which the microwave temperature‐sensitive radiances are the most important. [ABSTRACT FROM AUTHOR]

Challenges related to the design and use of a convection‐permitting ensemble (CPEPS) are discussed. In particular the scale‐dependent predictability of precipitation and the use of a CPEPS as well as its potential added value over global ensemble prediction systems (EPS) are investigated. Forecasts of precipitation from the operational CPEPS in Finland, Norway and Sweden (MEPS) are used for the investigations. It is found that predictability for scales smaller than ∼60 km is lost rapidly within the first 6 h of the forecast with the smallest predictable scale growing more slowly to ∼100 km over the following 18–24 h. However, there is large case‐to‐case variability and the ensemble perturbations fail to become fully saturated, especially in winter, suggesting a weakness in the design of the ensemble. The added value of CPEPS over deterministic forecasts and coarser resolution EPSs is discussed with summary statistics and case‐studies. It is shown that the added value varies between seasons and lead times. For precipitation there is an added value for both severe precipitation events and for precipitation/no precipitation decisions. The added value is higher in summer compared to winter and for shorter lead times compared to longer lead times. [ABSTRACT FROM AUTHOR]