The governmental meteorological offices of Estonia, Finland, Latvia, Norway, and Sweden maintain joint production of regional forecasts based on numerical weather prediction (NWP). The collaboration is organized in a continuing project called Meteorological Cooperation on Operational NWP (MetCoOp). The operational products contribute to weather data for daily use and form the base for official weather warnings in the participating countries. The forecasts are distributed to the public, to specific users such as the national road authorities, and to other governmental institutes. MetCoOp operates two NWP suites: 1) The MetCoOp Ensemble Prediction system (MEPS) is a lagged ensemble of 30 members that is updated hourly by running five members out to the 66-h lead time, whereas 2) the MetCoOp Nowcasting System (MNWC) is a deterministic run out to the lead time of 12 h. The suites share a common modeling domain that covers Scandinavia, Finland, and Baltic countries with 2.5-km horizontal grid spacing. There is an additional postprocessing suite, the MetCoOp Ensemble Calibration System (MECaS), that uses the raw MEPS forecast as input. MECaS produces a statistically calibrated ensemble forecast of near-surface parameters in a 1-km grid. The convection-permitting grids and frequent update cycles create added value on top of global forecasts that are received at no finer than 8-km horizontal grid spacing and no more than two-four times per day.

Extreme weather events, such as heavy precipitation, strong winds, and convective storms, pose significant challenges to societies. Accurate forecasting of these events at high spatial and temporal resolutions, including uncertainty estimates, is crucial for effective disaster preparedness and mitigation.In this work, we present recent developments in the EPS (Ensemble Prediction System) aspects of the Destination Earth On-Demand Extremes Digital Twin (DE_330_MF), which offers a highly configurable, on-demand workflow capable of detecting extreme weather events and triggering high-resolution forecasting at subkilometer scales. These features are valuable in supporting decision-makers in impact sectors such as hydrology, air quality, and energy. We showcase and evaluate the performance of the ensemble forecasting capabilities of this workflow with respect to prediction skill and uncertainty estimates.We assess the workflow's performance for a selection of European extreme weather events relative to kilometer-scale forecasting systems like DINI-EPS, which is operationally deployed in the UWC West Consortium (Denmark, Ireland, the Netherlands, and Iceland). The subkilometer results from these investigations generally demonstrate skillful performance compared to the coarser models, providing potential added value for national meteorological services and decision-makers.

A regional reanalysis has been produced for a domain covering entire Europe from 1984 to 2021. The reanalysis is produced as part of the Copernicus Climate Change Service. The Service provides the high-resolution deterministic Copernicus European Regional Reanalysis (CERRA), run at a horizontal resolution of 5.5 km, a 10-member ensemble run at 11-km resolution as well as an offline surface analysis, CERRA-Land. CERRA-EDA uses an ensemble data assimilation (EDA) technique to perturb the initial condition of the different members. Apart from the horizontal resolution the CERRA and CERRA-EDA setups are the same; for example, the same data assimilation scheme, same physics parameterization as well as the same vertical levels. These new systems are built from HARMONIE cy40 version, including some back-phased physics from a newer model version (cy42). Conventional observations, satellite-based radiances, atmospheric motion vector winds and bending angle from radio occultation observations are used. In addition, ground-based zenith total delay (ZTD) from global navigational satellite systems (GNSS) and local surface observations, rescued from historical archives at the local National Meteorological Services, are used. Another new feature is the construction of the background error statistics for the data assimilation. Information from the ensemble run, CERRA-EDA, is used in the derivation of the background error statistics for the high-resolution CERRA runs. These background error statistics are updated every second day. By doing so, daily environment variation is taken into account as well as all variations over the 37 years of production. The reanalyses and reforecasts from CERRA show an added value compared to the global ERA5 for almost all variables at the surface level. This becomes particularly clear when selecting smaller areas with complex terrain where the high resolution is beneficial. In the free atmosphere it is primarily the analyses and short forecasts, 3-6 hours, that give an added value.

High-resolution, limited-area forecasting is strongly affected by errors in the initial atmospheric state, lateral boundary conditions (LBCs), and physical parameterizations used by numerical weather prediction (NWP) models. These errors need to be accounted for through the introduction of uncertainty in an ensemble prediction system (EPS). One approach to account for model error is to use a stochastically perturbed parameterizations (SPPs) scheme. A first version of the SPP scheme of HARMONIE EPS (HarmonEPS) has been tested, with promising improvements in ensemble spread. However, it introduced systematic biases and deteriorated skill scores for some variables. Here, we investigate the performance of an updated version of the HarmonEPS SPP scheme, which includes (i) the use of uniform distributions, (ii) the correlation of stochastic patterns between key SPP parameters, and (iii) the introduction of four additional parameters, in the microphysics and mass-flux schemes. Two five-parameter SPP-based setups are compared against initial and (iv) 13-26 January 2021, and (v) 20 May-2 June 2021. We find that SPP-based experiments show better probabilistic metrics for near-surface and cloud-related variables than the non-SPP experiments. The SPP-based ensembles show increased spatial spread (as indicated by dFSS), while maintaining similar spatial skill (as indicated by eFSS) with the nonSPP experiment. In addition, the systematic bias in the ensemble members of the previous SPP iteration has been alleviated with the use of uniform distributions. Finally, the use of microphysical and mass-flux perturbations improves the ensemble scores for cloud-related variables, precipitation, and visibility.

Abstract. This paper presents a new version of HCLIM, a regional climate modelling system based on the ALADIN-HIRLAM numerical weather prediction (NWP) system. HCLIM uses atmospheric physics packages from three NWP model configurations, HARMONIE-AROME, ALARO and ALADIN, which are designed for use at different horizontal resolutions. The main focus of HCLIM is convection permitting climate modelling, i.e. developing the climate version of HARMONIE-AROME. In HCLIM, the ALADIN and ALARO physics packages are used for coarser resolutions where convection needs to be parameterized. Here we describe the structure, development and performance of the current recommended HCLIM version, cycle 38. HCLIM38 is a new system for regional climate modelling and it is being used in a number of national and international projects over different domains and climates, ranging from equatorial to polar regions. Our initial evaluation indicates that HCLIM38 is applicable in different conditions and provides satisfactory results without additional region-specific tuning. HCLIM is developed by a consortium of national meteorological institutes in close collaboration with the ALADIN-HIRLAM NWP model development. While the current HCLIM cycle has considerable differences in model setup compared to the NWP version (primarily in the description of the surface), it is planned for the next cycle release that the two versions will use a very similar setup. This will ensure a feasible and timely climate model development and updates in the future and provide evaluation of long-term model biases to both NWP and climate model developers.

The single-column version of the shared ALADIN-HIRLAM numerical weather prediction system, called MUSC, was developed by Météo-France in the 2000s and has a growing user-base in both HIRLAM and ALADIN countries. Tools to derive the required input, to run the experiments and to handle outputs of experiments carried out using MUSC have been developed within the HARMONIE-AROME canonical model configuration of the ALADIN-HIRLAM system are described within and constitute a large portion of this paper. The paper also illustrates the usefulness of the single-column approach for testing and developing HARMONIE-AROME physical parametrizations related to cloud microphysics and radiative transfer. Study cases concerning these physical parametrizations have been included for illustration purposes.

HarmonEPS is the limited-area, short-range, convection-permitting ensemble prediction system developed and maintained by the HIRLAM consortium as part of the shared ALADIN-HIRLAM system. HarmonEPS is the ensemble realization of HARMONIE-AROME, used for operational short-range forecasting in HIRLAM countries. HarmonEPS contains a range of perturbation methodologies to account for uncertainties in the initial conditions, forecast model, surface, and lateral boundary conditions. This paper describes the state of the system at the version labeled cycle 40 and highlights some directions for further development. The different perturbation methods available are evaluated and compared where appropriate. Several institutes have operational or preoperational implementations of HarmonEPS, such as MEPS (Finland, Norway, and Sweden), COMEPS (Denmark), IREPS (Ireland), KEPS (the Netherlands), AEMET-gamma SREPS (Spain), and RMI-EPS (Belgium), and these systems are briefly described and compared with the ensemble prediction system (IFS ENS) from the European Centre for Medium-Range Weather Forecasts (ECMWF).

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.

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.

Since October 2013 a convective-scale weather prediction model has been used operationally to provide short-term forecasts covering large parts of the Nordic region. The model is now operated by a bilateral cooperative effort [Meteorological Cooperation on Operational Numerical Weather Prediction (MetCoOp)] between the Norwegian Meteorological Institute and the Swedish Meteorological and Hydrological Institute. The core of the model is based on the convection-permitting Applications of Research to Operations at Mesoscale (AROME) model developed by Meteo-France. In this paper the specific modifications and updates that have been made to suit advanced high-resolution weather forecasts over the Nordic regions are described. This includes modifications in the surface drag description, microphysics, snow assimilation, as well as an update of the ecosystem and surface parameter description. Novel observation types are introduced in the operational runs, including ground-based Global Navigation Satellite System (GNSS) observations and radar reflectivity data from the Norwegian and Swedish radar networks. After almost two years' worth of experience with the AROME-MetCoOp model, the model's sensitivities to the use of specific parameterization settings are characterized and the forecast skills demonstrating the benefit as compared with the global European Centre for Medium-Range Weather Forecasts' Integrated Forecasting System (ECMWF-IFS) are evaluated. Furthermore, case studies are provided to demonstrate the ability of the model to capture extreme precipitation and wind events.

∗Royal Netherlands Meteorological Institute (KNMI), P.O. Box 201, 3730 AE De Bilt, The Netherlands. 1Danish Meteorological Institute, Lyngbyvej 100, DK-2100 Copenhagen, Denmark. 2Sveriges Meteorologiska och Hydrologiska Institut S-60176 Norrköping, Sweden. EDMFm is an eddy di usivity mass ux (EDMF) scheme developed at KNMI and applied in the Harmonie and RACMO model. Recently it is tested against EDKF for one summer and one winter month in an extensive veri cation experiment set up by Xiaohua Yang.

ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright (C) 2011 Royal Meteorological Society

ERA‐40 is a re‐analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re‐analysis period, with assimilable data provided by a succession of satellite‐borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean‐buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA‐40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA‐40. This benefited from many of the changes introduced into operational forecasting since the mid‐1990s, when the systems used for the 15‐year ECMWF re‐analysis (ERA‐15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re‐analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.

AbstractAtmospheric (re‐)analysis is a powerful tool to create data sets that provide a good description of the atmosphere including the hydrological cycle. Atmospheric analysis systems use a numerical weather prediction model to propagate the state of the atmosphere in time. Observations, which are irregular in space and time, are inserted into the system in such a way that an optimal blend is obtained between the background fields (forecast from e.g. 6 hours before) and the observations. Observations range from traditional surface observations and radiosondes, to commercial aircraft observations, cloud track winds from satellites and profiling measurements from infrared and microwave instruments in space. Re‐analyses are performed to benefit from the most recent model improvements, data assimilation improvements and increased computer power and to obtain a time series that does not suffer from system changes as in operational numerical weather prediction. Current re‐analyses have a spatial resolution of the order of 1 degree with a temporal sampling interval of 3 to 6 hours. Re‐analysis products have the advantage of being projected on a global numerical grid and have no gaps.The ECMWF 40‐year Re‐Analysis is described as an example. Its realism is discussed by comparing with observations of precipitation, vertically integrated water vapor, moisture convergence and surface fluxes over land and ocean. It is concluded that re‐analysis products are very useful to study the hydrological cycle in the atmosphere. The synoptic variability is particularly good although biases may exist in some of the parameters.

The BALTEX regional reassimilation project uses meteorological data assimilation for quantifying the climatic energy and water cycles over the catchment basin of the Baltic Sea during the course of one annual cycle, Sep. 1999-Oct. 2000. This report presents the data assimilation system used, the available products, and a sample of preliminary results. The latter demonstrate that the system is capable of simulating the essential features of the energy and water cycles of the Baltic drainage basin. We find this encouraging, because the model has not been tuned to reproduce these cycles, but mainly to predict the atmospheric state.

A 3-dimensional variational data assimilation (3D-Var) scheme for the HIgh Resolution Limited Area Model (HIRLAM) forecasting system is described. The HIRLAM 3D-Var is based on the minimisation of a cost function that consists of one term, J(b), which measures the distance between the resulting analysis and a background field, in general a short-range forecast, and another term. J(o), which measures the distance between the analysis and the observations. This paper is concerned with J(o) and the handling of observations, while the companion Paper by Gustafsson et al. (2001) is concerned with the general 3D-Var formulation and with the J(b) term. Individual system components. such as the screening of observations and the observation operators, and other issues, such as the parallelisation strategy for the computer code, are described. The functionality of the observation quality control is investigated and the 3D-Var system is validated through data assimilation and forecast experiments. Results from assimilation and forecast experiments indicate that the 3D-Var assimilation system performs significantly better than two currently used HIRLAM systems. which are based on statistical interpolation. The use of all significant level data from multilevel observation reports is shown to be one factor contributing to the superiority of the 3D-Var system. Other contributing factors are most probably the formulation of the analysis as a single global problem, the use of non-separable structure functions and the variational quality control, which accounts for non-Gaussian observation errors.

Cloud parameters and surface radiative fluxes predicted by regional atmospheric models are directly compared with observations for a 10-day period in late summer 1995 characterized by predominantly large-scale synoptic conditions. Observations of total cloud cover and vertical cloud structure are inferred from measurements with a ground-based network of Lidar ceilometers and IR-radiometers and from satellite observations on a 100 kilometer scale. Ground-based observations show that at altitudes below 3 km, implying liquid water clouds, there is a considerable portion of optically non-opaque clouds. Vertical distributions of cloud temperatures simultaneously inferred from the ground-based infrared radiometer network and from satellite can only be reconciled if the occurrence of optically thin cloud structures at mid- and high tropospheric levels is assumed to be frequent. Results of three regional atmospheric models, i.e. the GKSS-REMO, SMHI-HIRLAM, and KNMI-RACMO, are quantitatively compared with the observations. The main finding is that all models predict too much cloud amount at low altitude below 900 hPa, which is then compensated by an underestimation of cloud amount around 800 hPa. This is likely to be related with the finding that all models tend to underestimate the planetary boundary layer height. All models overpredict the high-level cloud amount albeit it is difficult to quantify to what extent due to the frequent presence of optically thin clouds. Whereas reasonably alike in cloud parameters, the models differ considerably in radiative fluxes. One model links a well matching incoming solar radiation to a radiatively transparent atmosphere over a too cool surface, another model underpredicts incoming solar radiation at the surface due to a too strong cloud feedback to radiation, the last model represents all surface radiative fluxes quite well on average, but underestimates the sensitivity of atmospheric transmissivity to cloud amount.