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Multiple record-breaking rainfall events were observed along the Western Ghats (WG) during the recent monsoon seasons (2019–2021). Rainfall amounts of up to > 200 mm/day (Extreme rainfall, ER) were recorded especially over the Mumbai region (19.07 N, 72.8 E) causing flooding, landslides, damage to infrastructure and loss of life. Thus, to enhance the resilience of this region by providing early warning for flooding, the National Center for Medium-Range Weather Forecasting Unified model's regional forecasting system (NCUM-reg) provides rainfall forecasts up to 3 days (72-h), which are utilized in the integrated flood warning system hydrological model. This study focuses on evaluating the performance of NCUM-reg forecasts during ER events. For this purpose, we have systematically performed verification of regional model operational forecasts using the suite of observations (rain gauge, satellite) and newly generated NCMRWF's regional reanalysis, Indian Monsoon Data Assimilation and Analysis (IMDAA). Key findings indicate that NCUM-reg model with explicit convection is performing well in representing the synoptic and dynamic features of the ER events similar to those observed. Quantitative assessment of the forecasts shows the strength of in-situ observations. In addition, the results summarize the importance of continuous and quality-controlled observations and stress the need for collective efforts of observations and new verification metrics (like process-oriented diagnostics) to enhance our understanding and as well as the model's ability in forecasting such events. [ABSTRACT FROM AUTHOR]

While there is huge demand for regional forecasts, information needed for selection of the most appropriate temporal and spatial scales is not available. The objective of this study is to demonstrate the basis of forecasting monthly mean rainfall over homogeneous regions by analyzing the forecasting skill and source of predictability. Reforecasts generated at the National Center for Medium Range Weather Forecasting (NCMRWF) for the period 1993–2015 using the coupled Unified Model are used in this study. Analysis of the forecasting skill over increasingly large lead times, averaging periods and spatial scales, is carried out to compare the skill at different time‐scales and to highlight the effect of spatial averaging over regions of coherent rainfall characteristics. Analysis of probabilistic forecasts is carried out to further demonstrate the usefulness of monthly mean forecasts. The influence of forcings on rainfall is studied both in model and in observations to understand the model's skill in representing interannual variability of monthly mean rainfall. Multiple regression analyses carried out for rainfall using climate indices as independent variables shows that the extent of forcings can largely explain the high variability of rainfall during the onset and withdrawal phase compared to the peak phase of monsoons. ENSO‐related subsidence is found to influence mainly the southern peninsular region, while tropical sea surface temperatures (SSTs) in the Indian Ocean are found to influence rainfall over northwest and central India by forcing circulation patterns typically associated with circumglobal teleconnections (CGTs) which are strongest during the month of June. Interestingly, the influence of CGTs on rainfall in the northeast is opposite to its influence on other homogeneous regions, which explains the contrast in influence of the North Indian Ocean SSTs on rainfall over the northeast and over All India. The model representation of influence of forcings and strength of teleconnections is better for specific region–month pairs, which is seen to influence the monthly variations in skill of forecasting rainfall over homogeneous regions. [ABSTRACT FROM AUTHOR]

The paper systemizes problems of state regulation over regional development. An ideal model of a multiregional system is described. The factors impeding the effective regulation over regional development are analyzed: the existence of different institutional models of a region that are fundamentally irreducible to each other and the lack of their proper coordination; the existence of limits of structural paradigms that affect the feasibility of the selected target plants; the presence of powerful mechanisms for self-stabilization (regional development constants) for an insufficiently expressed focus of particular solutions on the generalizing gradient of regional development; the not always achieved compliance between federal and regional interests, etc. Approaches estimating the effects of state socioeconomic policy on regional development are formulated. A concept for coordinating macroeconomic and regional long-term solutions based on the transfer of defining scenario conditions according to the territorial hierarchy of the economy is proposed. A general scheme and structure of a four-level system of hierarchical calculations that is presented comprehensively combines the contours of interlevel and horizontal information flows of the input, defining, and calculated rates. For Russia's eastern regions, a detail of a roadmap is presented that shows transfer of macroeconomic scenario conditions to the level of federal subjects and some results of its testing. [ABSTRACT FROM AUTHOR]

We developed a global semi-implicit two-time-level semi-Lagrangian model for the atmospheric adiabatic primitive equations discretized through finite-differences, on locally refined grids. The principal aim of the technique is to have high resolution over a region of interest through successive grid refinements of a relatively coarse global uniform mesh, and in this way, to obtain accurate forecasts over this region at much lower computational costs. We tested the method through experiments with standard test cases for global atmospheric dynamical cores and also with real meteorological data. For a few days of integration, we obtained forecasts over the region of interest with similar accuracy to predictions computed on very fine globally uniform meshes, in a very efficient method. For longer periods, unresolved features outside the refined areas eventually reach the region of interest and affect the quality of the results. Our conclusion is that the use of locally refined meshes, combined with a stable semi-Lagrangian semi-implicit method, provides an effective way to compute accurate short range regional predictions. [ABSTRACT FROM AUTHOR]

Because of heterogeneity across regions, economic policy measures are increasingly targeted at the regional level and, therefore, require regional forecasts. The data available to compute regional forecasts are usually a pseudo panel of a limited number of observations over time and a large number of regions strongly interacting with each other. Traditional time-series techniques applied to distinct time series of regional data are probably a suboptimal forecasting strategy. Although both linear and nonlinear models have been applied and evaluated to forecast socioeconomic variables, spatial interactions among regions are often ignored. This article evaluates the ability of spatial error and spatial lag models to correct for misspecifications due to neglected spatial autocorrelation in the data. The empirical application on short-term forecasts of employment in 326 West German regions shows that the superimposed spatial structure that is required for the estimation of spatial models improves the forecasting performance of nonspatial models. [ABSTRACT FROM AUTHOR]

This article analyzes neural neural networks (ANNs) as a method to compute employment forecasts at a regional level The empirical application is based on employment data collected for 327 West German regions over a period of fourteen years. First, the authors compare ANNs to models commonly used in panel data analysis. Second, they verify, in the case of panel data, whether the common practice of combining forecasts of the computed models is able to produce more reliable forecasts. The technique currently employed by the German authorities to compute such regional employment forecasts is comparable to a simple naïve no-change model. For this reason, ANNs are also compared to this undemanding technique. [ABSTRACT FROM AUTHOR]

Pyrenopeziza brassicae, cause of light leaf spot of oilseed rape, has a complex polycyclic life cycle. It can be difficult to control light leaf spot in winter oilseed rape in the UK since it is not easy to optimise fungicide application timing. Early autumn infections are usually symptomless and recognisable lesions do not develop until the epidemic has progressed further by the spring. Light leaf spot often has a patchy distribution in winter oilseed rape crops and estimation of disease incidence can be difficult. There is evidence that epidemics are initiated primarily by ascospores produced from apothecia that survive the summer inter-crop period on infected debris. Subsequent development of the epidemic during the winter and spring is maintained by rain-splashed conidia that spread light leaf spot from initial foci. Understanding the relative roles of ascospores and conidia in the light leaf spot life cycle is crucial for forecasting epidemic severity and developing control strategies. The current web-based regional forecast system provides an autumn forecast of the incidence of light leaf spot that can be expected the following spring. This is based on survey data which assesses the occurrence of disease the previous July, and weather factors, such as deviations from summer mean temperature and winter rainfall. The forecast can be updated throughout the autumn and winter and includes crop-specific elements so that growers can adjust risks by inputting information about cultivar, sowing date and fungicide use. Crop-specific forecasts can be confirmed by assessing the incidence of light leaf spot. Such assessments will become easier when immunodiagnostic methods for detection of the disease become available. Incorporation of information on spore biology (e.g. apothecial maturation, ascospore release and infection conditions) is considered as a component of the interactive, continuously updated, crop-specific, web-based forecasts which are needed in the future. [ABSTRACT FROM AUTHOR]

System-based combination weights for series r/step-length h incorporate relative accuracy information from other forecast step-lengths for r and from other series for step-length h. Such weights are examined utilizing the West and Fullerton (1996) data set—4275 ex ante employment forecasts from structural simultaneous equation econometric models for 19 metropolitan areas at 10 quarterly step-lengths and a parallel set of 4275 ARIMA forecasts. The system-based weights yielded combined forecasts of higher average accuracy and lower risk of large inaccuracy than seven alternative strategies: (1) averaging; (2) relative MSE weights; (3) outperformance (per cent best) weights; (4) Bates and Granger (1969) optimal weights with a convexity constraint imposed; (5) unconstrained optimal weights; (6) select a `best' method (ex ante) by series and; (7) experiment in the Bischoff's (1989) sense and select either method (2) or (6) based on the outcome of e experiment. Accuracy gains of the system-based combination were concentrated at step-lengths two to five. Although alternative (5) was generally outperformed. none of the six other alternatives was systematically most accurate when evaluated relative to each other. This contrasts with Bischoff's (1989) results that held promise for an empirically applicable guideline to determine whether or not to combine. [ABSTRACT FROM AUTHOR]