Insights from Forecast Demonstration Projects and Research Development Projects, training workshops, and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Programme of the World Meteorological Organization. The objective was to advance, promote, and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management, and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models, blending of observations and models, and multimodel systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology, and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps.

Using the spatial verification Fractions Skill Score (FSS), the quality of forecasts accumulated during the testing of the precipitation nowcasting system of the Hydrometeorological Center of Russia in the warm season (May–September 2017) on the territory of the Central and Northwestern federal districts of Russia is assessed. The precipitation intensity fields based on the products of nine DMRL-C radars serve as input data for the forecast model and control data for quality assessments. FSS values are calculated on full and random samples with generalizations by distribution quartiles. The verification results are interpreted in terms of the usefulness (or utility) of forecasting precipitation categories and the practical predictability of these categories in the radar coverage areas. For median FSS estimates, it is shown that the practical predictability of precipitation intensity categories for the entire system of the radars used reaches the nowcasting limit (2.5 hours) only at the forecast of exceeding the thresholds of 0.5 and 0.75 mm/hour. For the thresholds of 1, 2, 3, and 4 mm/hour, the predictability does not exceed 120, 90, 60, and 30 minutes, respectively. Based on the behavior of a set of FSS curves depending on the spatial scale (in the range of 2–122 km), some systematic features and mutual differences of the radars used have been revealed: thus, according to the ranking results, the highest position in the experiments was occupied by the forecasts in the DMRL-C Voeikovo coverage area.

Specific features of nowcasting as a forecast of current weather, its needs for observational data, and main approaches to solving a forecast problem are discussed: from simple extrapolation to hydrodynamic models with assimilation of an extended set of high-resolution observations. Prospects of machine learning applications are assessed. Some peculiarities of the nowcasting of precipitation and wind, which are the parameters of particular interest for most of users, are considered. The methodological complexity of the seamless approach to the nowcasting problems, extremely limited deterministic predictability on convective spatiotemporal scales, and importance of using a probabilistic approach are noted. Some illustrations from the national nowcasting developments are presented.

Using the spatial verification Fractions Skill Score (FSS), the quality of forecasts accumulated during the testing of the precipitation nowcasting system of the Hydrometeorological Center of Russia in the warm season (May-September 2017) on the territory of the Central and Northwestern federal districts of Russia is assessed. The precipitation intensity fields based on the products of nine DMRL-C radars serve as input data for the forecast model and control data for quality assessments. FSS values are calculated on full and random samples with generalizations by distribution quartiles. The verification results are interpreted in terms of the usefulness (or utility) of forecasting precipitation categories and the practical predictability of these categories in the radar coverage areas. For median FSS estimates, it is shown that the practical predictability of precipitation intensity categories for the entire system of the radars used reaches the nowcasting limit (2.5 hour) only at the forecast of exceeding the thresholds of 0.5 and 0.75 mm/hour. For the thresholds of 1, 2, 3, and 4 mm/hour, the predictability does not exceed 120, 90, 60, and 30 minutes, respectively. Based on the behavior of a set of FSS curves depending on the spatial scale (in the range of 2-122 km), some systematic features and mutual differences of the radars used have been revealed: thus, according to the ranking results, the highest position in the experiments was occupied by the forecasts in the DMRL-C Voeikovo coverage area.

Comparative quality analysis of ensemble radar precipitation nowcasting based on test results for the warm (May–September 2020) and cold (November 2021–March 2022) seasons are presented. Composite precipitation intensity fields obtained from radar observations were used as control data for verification. In both periods, a slight but systematic advantage of forecasts of the mean ensemble field was revealed, which indicates the expediency of using ensembles of even a small volume. For all the skill scores used (except for the frequency bias), forecasts in the cold season turn out to be better than forecasts in the warm season, however, the sample sizes for verification in the cold season may be significantly lower than the corresponding sample sizes in the warm season. The problems of comparative quality analysis are discussed, which are caused, in particular, by the loss of spatial connectivity of the composite field during the cold season. Keywords: ensemble nowcasting of meteorological fields, radar precipitation estimates, composite precipitation field, point and spatial field forecast verification

The application of the Fraction Skill Score (FSS) to the radar nowcasting of precipitation fields is considered. The main feature of the method is that the quality is estimated not at the points (or cells) of the fields, but in their neighborhoods. Verification of field forecasts acquires a probabilistic character, due to which the well-known “double penalty” danger is eliminated when advancing from coarse computational grids to finer ones. Moreover, the method makes it possible to distinguish such range of scales within which the tested model generates forecasts that are acceptable or useful for both weather forecasters and third-party consumers of forecast products. The features and advantages of the FSS are demonstrated using the data of radar precipitation nowcasting in the warm and cold seasons of 2017–2018. An information archive of observation and forecast fields in the coverage areas of nine DMRL-C radars on the territory of the Central and Northwestern federal districts was used. Due to the large time spent to calculate the skill score, the possibility of obtaining summary estimates based on random samples was tested. Based on the output tabular and graphical verification products, meaningful general and partial conclusions are formulated that are stratified by seasons, radars, thresholds for exceeding the precipitation intensity, and the forecast lead time. Keywords: precipitation field nowcasting, radar observations, spatial forecast verification, neighborhood verification method, Fractions Skill Score (FSS)

Statistical analysis was performed using methods of the extreme value theory for spatial objects and specified situations identified for object-oriented verification of precipitation regions with substantial and maximal areas. We made an estimation of the effect of missing values at field points and of different observation-forecast pairs construction on volumes and on statistical characteristics of samples retrieved for spatial verification purposes. We used spatial quantile functions and geographical representations in regular coordinates to illustrate particular aspects of composite fields built on about three dozen radars' data over the European territory of Russia. Keywords: spatial forecast verification, radar precipitation nowcasting, extreme value theory, missing data, conditional verification sampling, spatial quantiles

The assessments of nowcasting of large precipitation areas accumulated in the last few years at the Hydrometeorological Research Center of the Russian Federation are presented in two parts complemented by a discussion of methodological problems in the first part and application problems in the second part of the paper. The division is largely due to the sharp distinction between the theoretical modeling of extremes with a relatively free choice of assumptions and the statistical analysis of the distribution "tails" in rapidly "impoverishing" samples. The contrast between these parts is exacerbated by the responsibility we attribute to the statistical inference relating to extreme and, as a rule, dangerous events. The first part deals with the description of two classical models of the extreme value theory for independent one-dimensional random variables ("block maxima") and for threshold exceedances in stationary time series ("peaks over threshold"). The article explores problems arising from violation of the theoretical results and carries a brief overview of the methods of addressing such problems when extremes are modeled using real data, including those from the field of meteorology. Special attention is given to the distributions with "heavy" tails. Methods and formulas for estimating important characteristics, including the parameters of limiting distributions, are discussed that are borrowed from the references in the documentation of computational mathematical packages of the R language repository. Keywords: precipitation nowcasting, extreme value theory, statistical modeling of extremes, heavy distribution tails, mathematical packages for fitting extreme value distributions

The generalized Pareto distribution was used to model the distribution of precipitation area sizes that were observed and predicted for the coverage zones of individual radars by the Hydrometeorological Research Center of the Russian Federation's nowcasting scheme in 2017-2018. Various methods for estimating the distribution parameters and confidence intervals were tested. The main attention was paid to the estimates of the shape parameter that determines the behavior of the distribution tail. The generalized assessment of the nowcasting quality is built on the intersection ratio of the corresponding confidence intervals. It is shown that for most cases the Pareto threshold of 625 points, which is equivalent to a square of 5050 km, separates objects of larger sizes that are satisfactorily modeled by the heavy-tailed distribution and which are quite acceptably (on "climatological" average) predicted by the precipitation nowcasting system for specific periods of the year. Keywords: precipitation nowcasting, spatial verification, radar precipitation estimates, statistical analysis of threshold exceedances, mathematical packages for extreme value analysis

The automatic identification of objects associated with various extreme weather events (EWE) on seasonal and intraseasonal timescales is done based on surface air temperature and precipitation datasets (NCEP/NCAR daily reanalysis fields for the Northern Hemisphere). Some features of the spatial and temporal variability of the extreme characteristics of temperature and precipitation regimes are considered in the context of climate change. An inventory of extreme events is carried out for the Northern Hemisphere in 1981–2019 depending on the spatial extent, duration, and intensity of EWEs. The years with the most striking events are noted, and a brief description of their specific features is given. The results will be used to analyze the EWE predictability in the context of the verification of long-range weather forecasts. Keywords: extreme weather events, climate change, identification of extreme events, long-range weather forecasts

The study presents the new system for operational short-range numerical weather prediction with the grid spacing of 1 km for the Moscow region that considers the features of urbanized surface and is based on the COSMO-Ru1M model configuration. This system is implemented in the Hydrometcenter of Russia. The results of the trial testing of the optimum model configuration for the Moscow region using observations from the dense network of weather stations and MTP-5 temperature profilers are presented. High prediction capabilities of the new forecasting system are demonstrated. The approaches to the minimization of the time of calculations for the technology chain implemented at the Roshydromet supercomputer are described. A case study of modeling with the grid spacing of 500 m versus 1000 m for summer convective weather events in the Moscow region has been analyzed.

The Extreme Forecast Index (EFI) calculations are performed using the ECMWF 2-m air temperature forecasts produced in the framework of the Subseasonal to Seasonal (S2S) Prediction Project. Four computation schemes are implemented using empirical and theoretical distributions of heat wave characteristics as well as the one-dimensional test statistics of histograms and linear interpolation formulas. Case studies (for different initial dates and regions) characterized by the significant air temperature anomalies in Northern Eurasia are performed using traditional forecast skill scores and the spatial verification methods to evaluate the efficiency of the proposed schemes for different threshold values of EFI. It is shown that the forecast quality can be considered satisfactory in most cases. The dependence of forecast skill on the intensity, spatial scales, and duration of temperature anomalies is revealed. Further studies should be carried out using larger samples based on several hydrodynamic models and the multimodel approach.

The paper presents the summary and results of long-term and multi-faceted experience of international scientific and technical cooperation of Hydrometeorological Center of Russia in the field of hydrometeorology and environmental monitoring within the framework of WMO programs, which indicates its high efficiency in performing a wide range of works at a high scientific and technical level. Keywords: World Meteorological Organization, major WMO programs, representatives of Hydrometeorological Center of Russia in WMO

The COSMO-Ru1Mp prototype of the numerical weather prediction system for the Moscow region with a grid spacing of 1 km and with the included and adapted TERRA_URB parameterization module for the urban areas is implemented. The module is provided with necessary information on the urban development and on the sources of anthropogenic heat based on the adaptation of open data. The module is interfaced with the COSMO-Ru operational system for the regional numerical weather prediction developed in the Hydrometcenter of Russia. The primary testing of the COSMO-Ru1Mp is performed, including daily forecasts based on operational data. The advantage of this forecast system and the prospects of its further development are revealed.

The monthly and seasonal anomalies of temperature and precipitation over the Arctic are considered depending on the global and regional patterns of atmospheric circulation. Ñlimate indices are used to identify patterns. The composite analysis allowed identifying the geographic regions where the influence of atmospheric circulation modes on temperature and precipitation is statistically significant. The contingency of atmospheric circulation patterns in the Northern Hemisphere was statistically estimated. Special attention is paid to the case studies where the extreme episodes of circulation indices are associated with the significant anomalies of air temperature and precipitation. The potential is demonstrated of the numerical simulation of extreme episodes on monthly and seasonal timescales with the global semi-Lagrangian model SL-AV developed in the Institute of Numerical Mathematics jointly with the Hydrometcenter of Russia.