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The Year of Polar Prediction (YOPP) Final Summit What : One hundred eighty-seven scientists, stakeholders, and representatives from operational forecasting centers and international bodies assembled in person and online to review the accomplishments and impacts of YOPP and make recommendations on prediction-related priorities for future international polar research projects. Where : Montreal, Canada When : 29 August–1 September 2022

Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered “ideal” for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts–Miller–Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5–BMJ, WSM3 (the three-class version of the WSM scheme)–BMJ, and Ferrier microphysics in combination with the Grell–Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

A new version of the Global Ensemble Forecast System (GEFS, v11) is tested and compared with the operational version (v10) in a 2-yr parallel run. The breeding-based scheme with ensemble transformation and rescaling (ETR) used in the operational GEFS is replaced by the ensemble Kalman filter (EnKF) to generate initial ensemble perturbations. The global medium-range forecast model and the Global Forecast System (GFS) analysis used as the initial conditions are upgraded to the GFS 2015 implementation version. The horizontal resolution of GEFS increases from Eulerian T254 (~52 km) for the first 8 days of the forecast and T190 (~70 km) for the second 8 days to semi-Lagrangian T574 (~34 km) and T382 (~52 km), respectively. The sigma pressure hybrid vertical layers increase from 42 to 64 levels. The verification of geopotential height, temperature, and wind fields at selected levels shows that the new GEFS significantly outperforms the operational GEFS up to days 8–10 except for an increased warm bias over land in the extratropics. It is also found that the parallel system has better reliability in the short-range probability forecasts of precipitation during warm seasons, but no clear improvement in cold seasons. There is a significant degradation of TC track forecasts at days 6–7 during the 2012–14 TC seasons over the Atlantic and eastern Pacific. This degradation is most likely a sampling issue from a low number of TCs during these three TC seasons. The results for an extended verification period (2011–14) and the recent two hurricane seasons (2015 and 2016) are generally positive. The new GEFS became operational at NCEP on 2 December 2015.

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

The High-Resolution Rapid Refresh (HRRR) is a convection-allowing implementation of the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model with hourly data assimilation that covers the conterminous United States and Alaska and runs in real time at the NOAA/National Centers for Environmental Prediction (NCEP). Implemented operationally at NOAA/NCEP in 2014, the HRRR features 3-km horizontal grid spacing and frequent forecasts (hourly for CONUS and 3-hourly for Alaska). HRRR initialization is designed for optimal short-range forecast skill with a particular focus on the evolution of precipitating systems. Key components of the initialization are radar-reflectivity data assimilation, hybrid ensemble-variational assimilation of conventional weather observations, and a cloud analysis to initialize stratiform cloud layers. From this initial state, HRRR forecasts are produced out to 18 h every hour, and out to 48 h every 6 h, with boundary conditions provided by the Rapid Refresh system. Between 2014 and 2020, HRRR development was focused on reducing model bias errors and improving forecast realism and accuracy. Improved representation of the planetary boundary layer, subgrid-scale clouds, and land surface contributed extensively to overall HRRR improvements. The final version of the HRRR (HRRRv4), implemented in late 2020, also features hybrid data assimilation using flow-dependent covariances from a 3-km, 36-member ensemble (“HRRRDAS”) with explicit convective storms. HRRRv4 also includes prediction of wildfire smoke plumes. The HRRR provides a baseline capability for evaluating NOAA’s next-generation Rapid Refresh Forecast System, now under development.

Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Aerosols influence cloud and precipitation development in complex ways due to myriad feedbacks at a variety of scales from individual clouds through entire storm systems. This paper describes the implementation, testing, and results of a newly modified bulk microphysical parameterization with explicit cloud droplet nucleation and ice activation by aerosols. Idealized tests and a high-resolution, convection-permitting, continental-scale, 72-h simulation with five sensitivity experiments showed that increased aerosol number concentration results in more numerous cloud droplets of overall smaller size and delays precipitation development. Furthermore, the smaller droplet sizes cause the expected increased cloud albedo effect and more subtle longwave radiation effects. Although increased aerosols generally hindered the warm-rain processes, regions of mixed-phase clouds were impacted in slightly unexpected ways with more precipitation falling north of a synoptic-scale warm front. Aerosol impacts to regions of light precipitation, less than approximately 2.5 mm h−1, were far greater than impacts to regions with higher precipitation rates. Comparisons of model forecasts with five different aerosol states versus surface precipitation measurements revealed that even a large-scale storm system with nearly a thousand observing locations did not indicate which experiment produced a more correct final forecast, indicating a need for far longer-duration simulations due to the magnitude of both model forecast error and observational uncertainty. Last, since aerosols affect cloud and precipitation phase and amount, there are resulting implications to a variety of end-user applications such as surface sensible weather and aircraft icing.

The aim of this article is to describe the reference configuration of the convection-permitting numerical weather prediction (NWP) model HARMONIE-AROME, which is used for operational short-range weather forecasts in Denmark, Estonia, Finland, Iceland, Ireland, Lithuania, the Netherlands, Norway, Spain, and Sweden. It is developed, maintained, and validated as part of the shared ALADIN–HIRLAM system by a collaboration of 26 countries in Europe and northern Africa on short-range mesoscale NWP. HARMONIE–AROME is based on the model AROME developed within the ALADIN consortium. Along with the joint modeling framework, AROME was implemented and utilized in both northern and southern European conditions by the above listed countries, and this activity has led to extensive updates to the model’s physical parameterizations. In this paper the authors present the differences in model dynamics and physical parameterizations compared with AROME, as well as important configuration choices of the reference, such as lateral boundary conditions, model levels, horizontal resolution, model time step, as well as topography, physiography, and aerosol databases used. Separate documentation will be provided for the atmospheric and surface data-assimilation algorithms and observation types used, as well as a separate description of the ensemble prediction system based on HARMONIE–AROME, which is called HarmonEPS.

In this study a 1-yr dataset of a convective-scale atmospheric prediction system of the European Arctic (AROME-Arctic) is compared with the ECMWF’s medium-range forecasting, ensemble forecasting, and reanalysis systems, by using surface and radiosonde observations of wind and temperature. The focus is on the characteristics of the model systems in the very short-term forecast range (6–15 h), but without a specific focus on lead-time dependencies. In general, AROME-Arctic adds value to the representation of the surface characteristics. The atmospheric boundary layer thickness, during stable conditions, is overestimated in the global models, presumably because of a too diffusive turbulence scheme. Instead, AROME-Arctic shows a realistic mean thickness compared to the radiosonde observations. All models behave similarly for the upper-air verification and surprisingly, as well, in forecasting the location of a polar low in the short-range forecasts. However, when comparing with the largest wind speeds from ocean surface winds and at coastal synoptic weather stations during landfall of a polar low, AROME-Arctic shows the most realistic values. In addition to the model intercomparison, the limitation of the representation of sea ice and ocean surface characteristics on kilometer scales are discussed in detail. This major challenge is illustrated by showing the rapid drift and development of sea ice leads during a cold-air outbreak. As well, the available sea surface temperature products and a high-resolution ocean model result are compared qualitatively. New developments of satellite products, ocean–sea ice prediction models, or parameterizations, tailored toward high-resolution atmospheric Arctic prediction, are necessary to overcome this limitation.

Postprocessing ensemble weather predictions to correct systematic errors has become a standard practice in research and operations. However, only a few recent studies have focused on ensemble postprocessing of wind gust forecasts, despite its importance for severe weather warnings. Here, we provide a comprehensive review and systematic comparison of eight statistical and machine learning methods for probabilistic wind gust forecasting via ensemble postprocessing that can be divided in three groups: state-of-the-art postprocessing techniques from statistics [ensemble model output statistics (EMOS), member-by-member postprocessing, isotonic distributional regression], established machine learning methods (gradient-boosting extended EMOS, quantile regression forests), and neural network–based approaches (distributional regression network, Bernstein quantile network, histogram estimation network). The methods are systematically compared using 6 years of data from a high-resolution, convection-permitting ensemble prediction system that was run operationally at the German weather service, and hourly observations at 175 surface weather stations in Germany. While all postprocessing methods yield calibrated forecasts and are able to correct the systematic errors of the raw ensemble predictions, incorporating information from additional meteorological predictor variables beyond wind gusts leads to significant improvements in forecast skill. In particular, we propose a flexible framework of locally adaptive neural networks with different probabilistic forecast types as output, which not only significantly outperform all benchmark postprocessing methods but also learn physically consistent relations associated with the diurnal cycle, especially the evening transition of the planetary boundary layer.