Rapid progress in the field of machine-learning for weather prediction has led to the emergence of algorithms whose forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to improve the quality of forecasting services provided by operational centers, particularly given the speed at which machine-learning based models generate predictions. Despite the clear promise of these systems, questions remain about the ability of the current generation of machine-learning models to generate physically consistent predictions of the full suite of required forecast fields under all conditions. Answering these questions will require careful comparisons between the well-understood physically based models, current state-of-the-art machine-learning models, and the hybrid models that combine elements of these two archetypes. The Weather Prediction Model Intercomparison Project (WP-MIP) is a World Meteorological Organization-supported initiative whose initial goal is to create a centralized database of physically based, machine-learning, and hybrid model forecasts to enable a distributed assessment and evaluation effort. The first instance of WP-MIP focuses on global deterministic predictions using both center-specific and common initializations to facilitate sensitivity studies. Forecasts contributed by institutions across six continents will be used to develop AI-ready verification techniques that highlight the strengths and weaknesses of each class of prediction system, with the goal of establishing best-practice guidance to model developers and national weather centers. The broad engagement of the operational and forecast-evaluation communities in WP-MIP will ensure that the project results are highly relevant to the development and deployment of next-generation weather prediction systems.

Abstract. Models are universally challenged to accurately predict the coupled microphysical, turbulent and radiative processes within widespread, long-lived marine cold-air outbreak (CAO) cloud fields, which leads to biases and uncertainties in atmospheric predictions over all time scales. Here we assemble a suite of ground-based and satellite measurements to initialize and constrain large-eddy simulations (LES) of cloud field evolution with distance downwind from the marginal ice zone during a strong, highly supercooled and convective CAO observed during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE). Detailed LES results are compared with large-scale models run in single-column model (SCM) mode, providing an observation-constrained framework for large-scale model evaluation and future improvements. All models reproduce rapid cloud formation off the ice edge, and a monotonic ascent of downwind cloud-top heights that is well correlated with time-integrated surface heat fluxes. LES generally reproduce domain-mean observational targets using a modest test domain (25 x 25 km2), and a larger domain (125 x 125 km2) enables better reproducing the observed growth of convective cell sizes. In realistic mixed-phase LES compared with liquid-only simulations, ice processes lead to thinner, broken cloud decks and substantially reduced cloud radiative effects on top-of-atmosphere longwave fluxes. By contrast, mixed-phase SCM simulations generally underpredict the impact of ice on radiative fluxes, primarily owing to insufficient reduction of cloud cover. Results indicate that cellular cloud structure is qualitatively captured by LES, and thus LES could provide guidance to improvement of large-scale model physics schemes. Follow-on work will extend these results to larger domains, apply objective analysis of mesoscale structure, and include prognostic aerosol properties for droplet and heterogeneous ice formation.

The paper examines the impact of applying the parameter perturbations for schemes describing sub-grid scale processes in the SL-AV atmosphere model on characteristics of monthly mean atmospheric circulation in ensemble subseasonal forecasts. 25-year retrospective forecasts are computed for different seasons with different perturbation sets, and the results are compared with the forecasts without perturbations. It is shown that perturbing just three parameters reduces forecast errors of some variables as compared to unperturbed forecasts without introducing errors into integral characteristics.

For the territory of Northern Eurasia, a scheme for the statistical correction of surface air temperature forecasts has been developed for periods of 1-4 months on the basis of the SL-AV model using the MOS concept. For statistical correction of operational temperature forecasts, the regression parameters and EOF expansion coefficients obtained by cross-validation on historical forecasts were used. Due to the internal relationships of the model output data, the proposed scheme allows improving the skill of surface characteristic forecasts. A significant improvement in the skill of deterministic air temperature forecasts by using statistical correction is manifested in transition seasons. The scheme of statistical correction is constantly evolving. Further development of the statistical correction technology involves the use of neural networks and forecast indices of atmospheric circulation.

A long-range forecast system based on the improved version of the SLAV072L96 global atmosphere model has been verified at the Hydrometcenter of Russia. The model has the horizontal resolution of 0.9° × 0.72° in longitude and latitude and 96 vertical levels and includes modern parametrizations for the subgrid-scale processes in the atmosphere and active soil layer. Main features and particularities of this model version are presented along with a brief description of ensemble long-range meteorological forecast technology using this model. Some verification scores for long-range forecasts based on the archive data from ERA5 reanalysis for 1991–2015 and on the data for 2023 are presented.

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags the lower latitudes. So far there have been relatively few efforts to evaluate processes in Numerical Weather Prediction systems using in-situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic, compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data but also by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The YOPP site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing Merged Observatory Data Files (MODFs) and Merged Model Data Files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (SOP1), February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variance across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. Diagnostic analysis using surface fluxes suggests that this is at least partly related to insufficient thermal representation of the land-surface in the models, which all use a single layer snow model.

An algorithm for stochastic perturbation of the semi-Lagrangian trajectories is implemented in the ensemble weather prediction system based on the global atmosphere model SL-AV20 with a horizontal resolution of approximately 20 km, 51 vertical levels, and Local Ensemble Transform Kalman Filter (LETKF). The combined use of methods for stochastic perturbation of trajectories and the parameters and tendencies of subgrid-scale processes parameterizations allows to generate ensembles with a larger spread compared to ensembles without stochastic perturbations of trajectories. An improvement in probabilistic estimates of the ensemble forecasts for various variables is shown. The comparison of two versions of ensemble prediction system is presented.

SL-AV is a global atmosphere model used for operational medium-range and long-range forecasts. Following previous successful implementation of single precision in some parts of the model dynamics solver, such computations are introduced into some parts of parameterization block of the model. Also, some memory optimizations are implemented. As a result, the elapsed time needed to compute 24-h forecast for SL-AV version with 10 km horizontal resolution and 104 vertical levels is reduced by 22

The main advantages of ensemble prediction systems and probabilistic climate forecasts as compared to deterministic ones are considered. Quality assessment has been performed for retrospective probabilistic forecasts of air temperature and precipitation on subseasonal timescales (the forecasts that were obtained with a new version of the SLAV072L96 global semi-Lagrangian atmospheric model developed in the Hydrometcenter of Russia and the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences). Skill scores of probabilistic forecasts indicate the presence of a useful signal in the context of the forecasts of two extreme gradations of air temperature and precipitation at weekly and monthly integration intervals. It is concluded that the use of probabilistic approaches makes it possible to expand the time interval of the “usefulness” of forecasts from a week to a month, as well as to obtain estimates of uncertainty of a forecast and its potential economic value. The results of the study are expected to be used in the operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre, as well as in the preparation of consensus forecasts during sessions of regional climate forums.

The physical background and main types of climate forecasts issued by the world meteorological centers are considered. It is emphasized that modern forecasting systems have an integrated nature and combine not only data assimilation systems and global numerical atmosphere-ocean-land models, but also support infrastructure for providing forecast products to users. The features of the forecast system of the Hydrometcenter of Russia/North Eurasia Climate Centre (NEACC) and other world meteorological centers are compared. It is noted that, unlike other centers, the forecast system of the Hydrometeorological Center of Russia/NEACC is based on the integrated use of synoptic, statistical, and hydrodynamic methods. It has been revealed that new trends and directions in the development of the forecast system are associated with the emergence of a new version of the SL-AV global semi-Lagrangian finite-difference atmosphere model of the Hydrometcenter of Russia and the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS) with an expanded (up to 61 members) forecast ensemble, as well as with using the INR RAS climate model (INM-CM5) and additional applications designed for interaction with various economic sectors. The findings may be useful in determining a vector of future research aimed at developing the Russian climate prediction system.

Although the quality of weather forecasts in the polar regions is improving, forecast skill there still lags behind lower latitudes. So far there have been relatively few efforts to evaluate processes in numerical weather prediction systems using in situ and remote sensing datasets from meteorological observatories in the terrestrial Arctic and Antarctic compared to the mid-latitudes. Progress has been limited both by the heterogeneous nature of observatory and forecast data and by limited availability of the parameters needed to perform process-oriented evaluation in multi-model forecast archives. The Year of Polar Prediction (YOPP) site Model Inter-comparison Project (YOPPsiteMIP) is addressing this gap by producing merged observatory data files (MODFs) and merged model data files (MMDFs), bringing together observations and forecast data at polar meteorological observatories in a format designed to facilitate process-oriented evaluation. An evaluation of forecast performance was performed at seven Arctic sites, focussing on the first YOPP Special Observing Period in the Northern Hemisphere (NH-SOP1) in February and March 2018. It demonstrated that although the characteristics of forecast skill vary between the different sites and systems, an underestimation in boundary layer temperature variability across models, which goes hand in hand with an inability to capture cold extremes, is a common issue at several sites. It is found that many models tend to underestimate the sensitivity of the 2 m air temperature (T2m) and the surface skin temperature to variations in radiative forcing, and the reasons for this are discussed.

The paper considers a version of SLAV atmosphere model for medium-range weather prediction. The main focus of the paper is on a new software for handling input-output operations with the file system, that has been implemented into SLAV model. The use of this software has reduced the elapsed time for ten-day weather forecast by about 15 % . This result is obtained for two technologies: the deterministic weather forecast with high spatial resolution, and the ensemble technology with a twice coarser horizontal and vertical resolution.

This study evaluates the simulation of wintertime (15 October, 2019, to 15 March, 2020) statistics of the central Arctic near-surface atmosphere and surface energy budget observed during the MOSAiC campaign with short-term forecasts from 7 state-of-the-art operational and experimental forecast systems. Five of these systems are fully coupled ocean-sea ice-atmosphere models. Forecast systems need to simultaneously simulate the impact of radiative effects, turbulence, and precipitation processes on the surface energy budget and near-surface atmospheric conditions in order to produce useful forecasts of the Arctic system. This study focuses on processes unique to the Arctic, such as, the representation of liquid-bearing clouds at cold temperatures and the representation of a persistent stable boundary layer. It is found that contemporary models still struggle to maintain liquid water in clouds at cold temperatures. Given the simple balance between net longwave radiation, sensible heat flux, and conductive ground flux in the wintertime Arctic surface energy balance, a bias in one of these components manifests as a compensating bias in other terms. This study highlights the different manifestations of model bias and the potential implications on other terms. Three general types of challenges are found within the models evaluated: representing the radiative impact of clouds, representing the interaction of atmospheric heat fluxes with sub-surface fluxes (i.e., snow and ice properties), and representing the relationship between stability and turbulent heat fluxes.

Summation-by-Parts Finite Differences (SBP-FD) is an approach for approximation of differential operators satisfying a discrete analogue of integration by parts analytic property. SBP-FD allows one to build provably stable high-order spatial approximations. SBP-FD methods are widely used for approximation of partial differential equations in multiblock domains with logically-rectangular curvilinear mesh inside. The gnomonic cubed-sphere grid is an example of such a domain. However, applications of the SBP-FD approximation for the cubed-sphere grid in meteorological context are not a widespread research area.We present a SBP-FD based shallow water model using a non-staggered grid. The model is total-energy conserving, mass-conservative and has discrete analogues of other mimetic properties such as curl-free gradient property. The shallow water model is tested with the commonly used Williamson test suite supplemented with the Galewsky barotropic instability case. High-order convergence is shown for tests with analytic solutions. The SBP-FD shallow water model is more accurate than low-order mimetic finite-element counterparts, but slightly less accurate than high-order finite-volume, spectral-elements and discontinuous Galerkin schemes.(c) 2022 Elsevier Inc. All rights reserved.

The effect of considering correlated errors in AMV (Atmospheric Motion Vectors) satellite observations of wind in the local ensemble transform Kalman filter data assimilation system is studied. It is customary to use a diagonal covariance matrix of errors in observations taking part in assimilation. When assimilating satellite data with correlated errors, the data are thinned, and the values of diagonal elements of the error covariance matrix are often overestimated. This is accompanied by the loss of useful information about the correlation between the errors. The present study uses a different method: the elements of the error covariance matrix for AMV satellite observations are simulated using a second-order autoregressive function. It is shown that such approach reduces the root-mean-square error in initial data for a numerical weather prediction model, in particular on small scales, and improves the forecast quality. It is found that the application of the non-diagonal AMV observation error covariance matrix increases the accuracy of analysis and forecast fields.

Dynamics of the stratosphere and ozone layer are among important sources of atmospheric circulation predictability at subseasonal-to-seasonal time scales. The simulation of the stratospheric dynamics with the SL-AV atmospheric general circulation model for the SLAV072L96 seasonal weather prediction configuration is analyzed. The configuration is currently under preoperational testing at the Hydrometcenter of Russia. The model simulates both winter and summer averaged distributions of zonal wind and temperature close to the reanalysis data. The quasi-biennial oscillation of equatorial zonal wind is simulated with a realistic period and amplitude. There is a significant reduction of errors as compared with the previous stratosphere-resolving SL-AV model configuration. It is shown that the stratospheric process simulation enhancement is largely due to the reduction of systematic errors in the simulation of troposphere dynamics. The work on the inclusion of the CHARM photochemical model in the SL-AV model is described. The results of first experiments with the coupled model are given.

The effect of considering correlated errors in AMV (Atmospheric Motion Vectors) satellite observations of wind in the local ensemble transform Kalman filter data assimilation system is studied. It is customary to use a diagonal covariance matrix of errors in observations taking part in assimilation. When assimilating satellite data with correlated errors, the data are thinned, and the values of diagonal elements of the error covariance matrix are often overestimated. This is accompanied by the loss of useful information about the correlation between the errors. The present study uses a different method: the elements of the error covariance matrix for AMV satellite observations are simulated using a second-order autoregressive function. It is shown that such approach reduces the root-mean-square error in initial data for a numerical weather prediction model, in particular on small scales, and improves the forecast quality. It is found that the application of the non-diagonal AMV observation error covariance matrix increases the accuracy of analysis and forecast fields.

The implementation of the soil moisture analysis algorithm based on screen-level observations of air temperature and relative humidity for the INM RAS-MSU multilayer active soil layer model is presented. This soil model is a part of the global atmospheric model SL-AV. Methodical experiments on tuning of the simplified extended Kalman filter algorithm for considered atmospheric modeling system are carried out. It is found that the use of soil moisture analysis as initial data allows increasing the accuracy of short- and medium-range forecasts for screen-level temperature and relative humidity during summer season at the part of Asia, the European part of Russia, and the southeastern part of North America. The recommendations obtained in this work can be used for tuning the simplified extended Kalman filter algorithm to prepare initial data for long-range weather forecasts.

This paper presents the results of applying an optimal interpolation method to assimilate meteorological observation data obtained by using ground-based weather stations and temperature profilers of the Atmosphere JUC (Joint Use Center) at the Institute of Atmospheric Optics SB RAS to calculate a numerical prediction with high horizontal resolution (1km) of the parameters of the atmospheric boundary layer for the next 24 hours.

In situ soil moisture and temperature observations are used to evaluate soil moisture and temperature analysis of the global atmospheric model SL-AV for the summer season of 2014 across Western Europe. It is shown that the seasonal temperature analysis trends are reproduced better than the seasonal trends of soil moisture. Numerical experiments with the offline land surface scheme demonstrated that the implementation of the Mualem-van Genuchten water retention curve has the advantage for describing the processes of soil moisture transfer in comparison with Brooks-Corey parameterization. The last one is used in the basic version of the model. The Mualem-van Genuchten function provides better computational stability of the soil model in case of soils with high coarse grain content. An important contribution to the soil moisture error in the analysis and forecast is caused by specifying an inaccurate profile of the soil granulometric composition. It indicates a need to involve state-of-the-art databases for soil characteristics