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The paper justifies the possibility of improving the dynamical-statistical method of predicting monthly average surface air temperature through the application more advanced hydrodynamic models and statistical methods for operational use by the Hydrometeorological Center of Russia. A technology for monthly forecasting of surface air temperature anomalies is presented. The technology is based on using both the results of an improved 15-day medium-range weather element forecast scheme and the outcome of integrating the SL-AV model over a 16–30 day interval. Quality assessments of the forecasts obtained in real-time mode for 326 stations located in Russia are provided. The advantages of the proposed approach, particularly evident in cases of significant air temperature anomalies, are demonstrated. The obtained results are expected to be used in the technology for issuing long-range forecasts by the Hydrometeorological Center of Russia. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The issues related to the verification of ensemble seasonal forecasts obtained using a new technology implemented on the basis of the INM-CM5 Earth system model are considered. The forecast objects are global gridded () fields of ensemble mean anomalies and probabilities of three gradations of the anomalies (below normal, normal, above normal) for each of the five characteristics: 500 hPa geopotential height (), mean sea level pressure (SLP), 850 hPa air temperature (), surface air temperature (), and precipitation (Prec) for six periods of time averaging (at monthly intervals for the 1st, 2nd, 3rd, 4th months and at two seasonal intervals: season 1 (the 1st–3rd months) and season 2 (the 2nd–4th months)). It is noted that forecast skill scores vary greatly depending on a region, a season, and a meteorological parameter. The best results for all parameters were obtained for the tropics, where the main sources of long-term predictability of the atmosphere are located. In extratropical latitudes, the quality of forecasts, especially at the monthly intervals, decreases and approaches the level of climate forecasts. The skill of precipitation forecasts is significantly inferior to the quality of forecasts of other meteorological parameters. It is noted that the success in ensemble seasonal probabilistic and deterministic forecasting of the main meteorological elements with the INM-CM5 model, both on a global scale and in individual regions, is comparable with the skill scores of similar forecasts of the foreign meteorological centers participating in the LC MME-WMO project. The introduction of the new climate model into the scientific and operational practice of the Hydrometcenter of Russia/North Eurasia Climate Centre will contribute to improving the quality of monthly and seasonal forecasts and developing specialized climate services. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The problem of predictability of atmospheric processes such as blocking in the Northern Hemisphere on intraseasonal timescales is considered using the operational version of the semi-Lagrangian atmosphere model for long-rahge forecasting (SL-AV) of the Hydrometcenter of Russia, as well as the U.K. Met Office coupled atmosphere–ocean model (UKMO) and the reanalysis of the European Center for Medium-range Weather Forecasts (ERA5). It is shown that beyond the first forecast week, the quality of deterministic forecasts drops sharply, and the forecast error grows rapidly. The use of probabilistic formulations makes it possible to extend the time interval of the skillful forecast from a week to a month. The dependence of the forecast quality on initial data of the model, as well as on spatiotemporal scales of blocking systems, is demonstrated by the case study (the summer of 2010 in the European part of Russia). Some specific features of the verification of forecasts of rare events, such as blocking, are noted. The results can be used for preparing long-range meteoroloical forecasts on intraseasonal timescales. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The ability to forecast air temperature and precipitation at monthly-seasonal time scale in the Amur River basin is assessed. The climatic characteristics of actual monthly values of air temperature and total precipitation are compared with those based on the semi-Lagrangian global finite-difference model (SL-AV) of general atmospheric circulation for the Amur basin. It is shown that there is high correlation between total precipitation for 1–2 months and water discharge in the Amur River in next months during the warm half-year. The SL-AV model forecasts of air temperature and precipitation in the Amur basin are verified. It is demonstrated that the accuracy of prediction of total precipitation averaged over the Amur basin is higher as compared with that for initial gridded forecasts. The accuracy of such forecasts is rather high for July and August being critical months for the flood formation. The procedure for the statistical correction of monthly and seasonal forecasts of air temperature and precipitation based on the SL-AV model and the regression scheme for the Amur runoff prediction are proposed. [ABSTRACT FROM AUTHOR]

The monthly and seasonal anomalies of temperature and precipitation over the Arctic are considered depending on the global and regional patterns of atmospheric circulation. Climate 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. [ABSTRACT FROM AUTHOR]