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The Bering10K Regional Ocean Modeling System (ROMS) model is a high-resolution (10-km) regional ocean model that has been used over the past decade to investigate relationships between the physical environment and the eastern Bering Sea shelf ecosystem in both research and management contexts. Extensive validation for this model has been conducted previously, particularly focused on bottom temperature, a key physical driver shaping ecosystem dynamics in this region. However, previous observations of bottom temperature were primarily limited to the summer months. Recent deployments of pop-up floats capable of overwinter measurements now allow us to extend the previous validation to other seasons. Here, we characterize bottom temperature on the southeastern Bering Sea shelf across time scales by combining data from our new pop-up floats with several existing temperature datasets. We then use this combination of data to systematically assess the skill of the Bering10K ROMS model in capturing these features, focusing on spatial variability in skill metrics and the potential processes leading to these patterns. We confirm that the model captures shelf-wide patterns in bottom temperature well, including mean patterns as well as both seasonal and interannual variability. However, a few areas of potential improvement were also identified: underestimated surface mixing in the model leads to delayed destratification across the middle and outer shelves, the position of the inner front may be offset slightly in the model, and bathymetric smoothing leads to poor representation near the shelf break and potentially underestimated flow onto the shelf through shelf break canyons. Overall, this paper presents the most detailed spatiotemporal analysis of this model's skill in simulating bottom temperature across the eastern Bering Sea shelf to date and supplies a benchmark analysis framework that can be used for planned regional model transitions and improvements over the coming years. [ABSTRACT FROM AUTHOR]

Phytoplankton are responsible for releasing half of the world's oxygen and for removing large amounts of carbon dioxide from surface waters. Despite many studies on the topic conducted in the past decades, we are still far from a good understanding of ongoing rapid changes in the Arctic Ocean and how they will affect phytoplankton and the whole ecosystem. An example is the difference in net primary production modelling estimates, which differ twice globally and fifty times when only the Arctic region is considered. Here, we aim to improve the quality of Greenland Sea primary production estimates, by testing different versions of primary production model against in situ data and then calculating regional estimates and trends for 1998-2022 for those performing best. As a baseline, we chose the commonly used global primary production model and tested it with different combinations of empirical relationships and input data. Local empirical relationships were taken from measurements by the literature and derived from the unpublished data of Institute of Oceanology of Polish Academy of Sciences across the Fram Strait. For validation, we took historical net primary production 14C data from literature and added to it our own gross primary production O2 measurements. Field data showed good agreement between primary production measured with 14C and O2 evolution methods. From all the model setups, those including local chlorophyll a profile and local absorption spectrum best reproduced in situ data. Our modelled regional annual primary production estimates are equal to 346 TgC/year for the Nordic Seas region and 342 TgC/year for the Greenland Sea sector of the Arctic defined as 45°W-15°E, 66°33′N-90°N. These values are higher than those previously reported. Monthly values show a seasonal cycle with less monthly variability than previously reported. No significant increase or decrease in primary production was observed when studying regionally averaged trends. The accuracy of the selected here model setups to reproduce the field data in terms of Root Mean Square Difference is better than in the related Arctic studies. The improved primary production estimates strengthen researchers' ability to assess carbon flux and understand biogeochemical processes in the Greenland Sea. [ABSTRACT FROM AUTHOR]

Groundwater models tend to become increasingly detailed to accommodate increasing data availability and higher accuracy demands from stakeholders. As runtimes increase almost quadratically with the number of model cells, this makes the models ever more computationally demanding. This high computational demand introduces challenges for the history‐matching (calibration) process as this is an algorithmic process that needs hundreds or thousands of model‐runs to obtain the model sensitivities needed to estimate parameters. Model runs may take hours or days to complete which in fact, is often a reason to discard the history‐matching all together. As a solution, we present a practical approach to use sub‐modeling in combination with parallelization for automatic history‐matching. Therefore a large model is subdivided into smaller models to carry out the sensitivity simulations. With a realistic case the method is elaborated, after which the method is demonstrated in the history‐matching of the transient Dutch National Groundwater Flow model. In this manner the model, which consisted of over 12 million model cells, could be optimized using 416 sub‐models and altogether 2,188 parameters in 1 week. This would take years to complete in a conventional way. Plain Language Summary: This article describes an innovative methodology to derive sensitivities to populate a Jacobian matrix for high dimensional parameter optimization problems in groundwater flow modeling. The method efficiently uses submodels to carry‐out those sensitivity simulations in parallel. These consume most of the computational time in conventional inverse modeling. As a result it became possible to optimize thousands of parameters for a demanding groundwater flow model within acceptable time. Key Points: An illustration of a practical implementation using submodels for history matching of computationally demanding groundwater flow modelsParameter sensitivities that feed a Jacobian matrix can be derived with acceptable accuracy from partly overlapping submodelsThe methodology has been applied to optimize 2,188 parameters within the Dutch National Groundwater Flow Model within a limited time frame [ABSTRACT FROM AUTHOR]

The Bering10K Regional Ocean Modeling System (ROMS) model is a high-resolution (10-km) regional ocean model that has been used over the past decade to investigate relationships between the physical environment and the eastern Bering Sea shelf ecosystem in both research and management contexts. Extensive validation for this model has been conducted previously, particularly focused on bottom temperature, a key physical driver shaping ecosystem dynamics in this region. However, previous observations of bottom temperature were primarily limited to the summer months. Recent deployments of pop-up floats capable of overwinter measurements now allow us to extend the previous validation to other seasons. Here, we characterize bottom temperature on the southeastern Bering Sea shelf across time scales by combining data from our new pop-up floats with several existing temperature datasets. We then use this combination of data to systematically assess the skill of the Bering10K ROMS model in capturing these features, focusing on spatial variability in skill metrics and the potential processes leading to these patterns. We confirm that the model captures shelf-wide patterns in bottom temperature well, including mean patterns as well as both seasonal and interannual variability. However, a few areas of potential improvement were also identified: underestimated surface mixing in the model leads to delayed destratification across the middle and outer shelves, the position of the inner front may be offset slightly in the model, and bathymetric smoothing leads to poor representation near the shelf break and potentially underestimated flow onto the shelf through shelf break canyons. Overall, this paper presents the most detailed spatiotemporal analysis of this model’s skill in simulating bottom temperature across the eastern Bering Sea shelf to date and supplies a benchmark analysis framework that can be used for planned regional model transitions and improvements over the coming years.

Phytoplankton are responsible for releasing half of the world’s oxygen and for removing large amounts of carbon dioxide from surface waters. Despite many studies on the topic conducted in the past decades, we are still far from a good understanding of ongoing rapid changes in the Arctic Ocean and how they will affect phytoplankton and the whole ecosystem. An example is the difference in net primary production modelling estimates, which differ twice globally and fifty times when only the Arctic region is considered. Here, we aim to improve the quality of Greenland Sea primary production estimates, by testing different versions of primary production model against in situ data and then calculating regional estimates and trends for 1998-2022 for those performing best. As a baseline, we chose the commonly used global primary production model and tested it with different combinations of empirical relationships and input data. Local empirical relationships were taken from measurements by the literature and derived from the unpublished data of Institute of Oceanology of Polish Academy of Sciences across the Fram Strait. For validation, we took historical net primary production 14C data from literature and added to it our own gross primary production O2 measurements. Field data showed good agreement between primary production measured with 14C and O2 evolution methods. From all the model setups, those including local chlorophyll a profile and local absorption spectrum best reproduced in situ data. Our modelled regional annual primary production estimates are equal to 346 TgC/year for the Nordic Seas region and 342 TgC/year for the Greenland Sea sector of the Arctic defined as 45°W-15°E, 66°33′N-90°N. These values are higher than those previously reported. Monthly values show a seasonal cycle with less monthly variability than previously reported. No significant increase or decrease in primary production was observed when studying regionally averaged trends. The accuracy of the selected here model setups to reproduce the field data in terms of Root Mean Square Difference is better than in the related Arctic studies. The improved primary production estimates strengthen researchers’ ability to assess carbon flux and understand biogeochemical processes in the Greenland Sea.

Predicting the air flow pattern with proper accuracy and speed inside a building with natural ventilation is one of the important study topics of building design due to the economic importance of energy consumption. For this purpose, in this study, a chamber with certain dimensions and with natural connection has been investigated as a study sample to predict the air flow pattern and speed distribution with the help of regional models. Two air inlet and outlet valves were considered for natural air ventilation. After researching the previous models, the three-dimensional zone model coupled with heat transfer and air flow calculations (ZAER) was chosen as the basis for comparing the modeling results. In the following, after zoning, to improve the flow coefficient factor model, which is assumed to be a constant number, it was assumed to be variable and the results were recorded in different states. The behavior of the model has been checked in the flow coefficients between 0.3 and 1 in order to obtain the most optimal coefficient for the number of suitable cells. Comparing the results of the research with the results of other models and computational fluid dynamics (CFD) showed that the three-dimensional behavior of the flow shows better compatibility with the experimental researches. [ABSTRACT FROM AUTHOR]

Abstract Introduction Patients with cancer often want to spend their final days at home. In Norway, most patients with cancer die in institutions. We hypothesized that full integration of oncology and palliative care services would result in more time spent at home during end-of-life. Methods A prospective non-randomized intervention trial was conducted in two rural regions of Mid-Norway. The hospitals’ oncology and palliative care outpatient clinics and surrounding communities participated. An intervention including information, education, and a standardized care pathway was developed and implemented. Adult non-curative patients with cancer were eligible. Proportion of last 90 days of life spent at home was the primary outcome. Results We included 129 patients in the intervention group (I) and 76 patients in the comparison group (C), of whom 82% of patients in I and 78% of patients in C died during follow-up. The mean proportion of last 90 days of life spent at home was 0.62 in I and 0.72 in C (p = 0.044), with 23% and 36% (p = 0.073), respectively, dying at home. A higher proportion died at home in both groups compared to pre-study level (12%). During the observation period the comparison region developed and implemented an alternative intervention to the study intervention, with the former more focused on end-of-life care. Conclusion A higher proportion of patients with cancer died at home in both groups compared to pre-study level. Patients with cancer in I did not spend more time at home during end-of-life compared to those in C. The study intervention focused on the whole disease trajectory, while the alternative intervention was more directed towards end-of-life care. “Simpler” and more focused interventions on end-of-life care may be relevant for future studies on integration of palliative care into oncology. Trial Registration ClinicalTrials.gov Identifier: NCT02170168.

Introduction: Patients with cancer often want to spend their final days at home. In Norway, most patients with cancer die in institutions. We hypothesized that full integration of oncology and palliative care services would result in more time spent at home during end-of-life. Methods: A prospective non-randomized intervention trial was conducted in two rural regions of Mid-Norway. The hospitals' oncology and palliative care outpatient clinics and surrounding communities participated. An intervention including information, education, and a standardized care pathway was developed and implemented. Adult non-curative patients with cancer were eligible. Proportion of last 90 days of life spent at home was the primary outcome. Results: We included 129 patients in the intervention group (I) and 76 patients in the comparison group (C), of whom 82% of patients in I and 78% of patients in C died during follow-up. The mean proportion of last 90 days of life spent at home was 0.62 in I and 0.72 in C (p = 0.044), with 23% and 36% (p = 0.073), respectively, dying at home. A higher proportion died at home in both groups compared to pre-study level (12%). During the observation period the comparison region developed and implemented an alternative intervention to the study intervention, with the former more focused on end-of-life care. Conclusion: A higher proportion of patients with cancer died at home in both groups compared to pre-study level. Patients with cancer in I did not spend more time at home during end-of-life compared to those in C. The study intervention focused on the whole disease trajectory, while the alternative intervention was more directed towards end-of-life care. "Simpler" and more focused interventions on end-of-life care may be relevant for future studies on integration of palliative care into oncology. Trial Registration: ClinicalTrials.gov Identifier: NCT02170168. Plain Language Summary: Palliative care is an important part of cancer care to improve patients' quality of life. To be cared for and die in the preferred place are quality markers in palliative care. Patients with cancer often want to spend their final days at home. In Norway, most patients with cancer die in institutions. We hypothesized that full integration of cancer and palliative care would result in more time spent at home during end-of-life. An intervention that included information, education, and a standardized care pathway was developed and implemented in a region of Mid-Norway (the intervention region, I). A similar region served as comparison region (C). Adult patients with cancer treated with non-curative intent were eligible. Altogether, 129 patients in I and 76 patients in C were included in the study, of whom 82% in I and 78% in C died during follow-up. The mean proportion of time spent at home last 90 days of life was 0.62 in I and 0.72 in C (p = 0.044), and 22.6% and 35.6% (p = 0.073) died at home, respectively. A higher proportion died at home in both groups compared to pre-study national levels (12%). During the study period, C developed and implemented an alternative intervention to the study intervention, with the former placing more focus on end-of-life care compared to the she study intervention that focused on the whole disease trajectory. This may explain why the intervention did not result in more time spent at home during end-of-life as compared to C. "Simpler" interventions directed towards the study's primary outcome may be relevant for future studies on integration of palliative care into oncology. [ABSTRACT FROM AUTHOR]

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The paper presents the results of work on the numerical hydrogeological model of the mines of the Legnica-Głogów Copper District (LGCD) in Poland. Due to the extensive impact on the rock mass and the multilayer depression cone caused by drainage, the model covers an area of 3,049 km2. The complicated, mutual hydraulic connections of the multi-aquifer hydrogeological system required the model to cover the area beyond the range of mining areas, and to separate 17 numerical layers. The research was carried out in the GMS software environment using Modflow computing modules. The model was updated successively, since its structure, parameters, and boundary conditions are subject to change. It was also additionally calibrated based on new data from measurements in piezometers and changes recorded in inflows to the excavations. The simulations made it possible to determine the exact water balance, with a particular emphasis on the Sieroszowice mining area, where sudden unexpected inflows were noted. Subsequently, two prognostic simulations were performed to estimate dynamic water inflows to mine workings in the years 2020 and 2025. [ABSTRACT FROM AUTHOR]

While the reduction in anthropogenic emissions due to Coronavirus disease 2019 (COVID‐19) lockdown in China and its impact on air quality have been reported extensively, its impact on ambient carbon dioxide (CO2) concentrations is still yet to be assessed. In this study, the impact of emission reductions on spatiotemporal changes of CO2 concentrations during the COVID‐19 pandemic was quantified in the Yangtze River Delta region (YRD), using high‐resolution dynamic emission inventory and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF‐VPRM). The simulated CO2 concentrations from dynamic emission inventory shows a better agreement with surface observations compared with the Open‐source Data Inventory for Anthropogenic CO2 and Emission Database for Global Atmospheric Research emission, providing confidence in the quantification of CO2 concentrations variations. Our results show that emission reductions during the COVID‐19 pandemic lead to a CO2 decrease by 4.6 ppmv (−1.1%) in Shanghai and 3.1 ppmv (−0.7%) in YRD region. For the column‐averaged CO2 concentrations (denoted as XCO2), it also decreases by 0.20 ppmv (−0.05%) in Shanghai and 0.15 ppmv (−0.04%) in YRD region. Furthermore, emission reductions from transportation and industry are major contributors to the decline in CO2 concentrations at the near surface, accounting for 45.8% (41.1%) and 34.9% (41.0%) in Shanghai (YRD). Our study deepens the understanding of the response of CO2 concentrations to different sectors, which is helpful for emission management and climate adaption policies. Plain Language Summary: Carbon dioxide (CO2) is the most important greenhouse gas in the atmosphere and has a profound impact on global climate change. It kept increasing over the last decades. Although previous studies have investigated the sources and sinks of air pollutants, the variations of CO2 concentrations at regional to national scales remains poorly understood owing to a lack of long‐term observations and limited modeling studies. High‐resolution CO2 emission inventory is in high demand in accurate CO2 simulations. This work integrates a high‐resolution dynamic emission inventory with WRF‐VPRM model to quantify the influence of reduced emissions from different sectors on the spatiotemporal changes of CO2 concentrations during the COVID‐19 pandemic. This modeling system can help to understand the response of CO2 concentrations to emissions and serves as a basis for atmospheric inversion of CO2 emissions. Key Points: High‐resolution CO2 dynamic emission inventory greatly improves CO2 simulations compared with Open‐source Data Inventory for Anthropogenic CO2 and Emission Database for Global Atmospheric ResearchAnthropogenic emission reductions cause CO2 concentrations decrease by 4.6 (3.1) ppmv in Shanghai (Yangtze River Delta) during the COVID‐19 pandemicIndustrial and transportation emissions mainly dominate the reduction in CO2 concentrations at the near surface and vertical altitude [ABSTRACT FROM AUTHOR]

As a phytotoxic pollutant, surface ozone (O3) not only affects plant physiology but also influences meteorological fields and air quality by altering leaf stomatal functions. Previous studies revealed strong feedbacks of O3-vegetation coupling in China but with large uncertainties due to the applications of varied O3 damage schemes and chemistry-vegetation models. In this study, we quantify the O3 vegetation damage and the consequent feedbacks to surface meteorology and air quality in China by coupling two O3 damage schemes (S2007 vs. L2013) into a fully coupled regional meteorology-chemistry model. With different schemes and damaging sensitivities, surface O3 is predicted to decrease summertime gross primary productivity by 5.5 %–21.4 % and transpiration by 5.4 %–23.2 % in China, in which the L2013 scheme yields 2.5–4 times of losses relative to the S2007 scheme. The damages to photosynthesis of sunlit leaves are ~2.6 times that of shaded leaves in the S2007 scheme but show limited differences in the L2013 scheme. Though with large discrepancies in offline responses, the two schemes yield similar magnitude of feedback to surface meteorology and O3 air quality. The O3-induced damage to transpiration increases national sensible heat by 3.2–6.0 W m-2 (8.9 % to 16.2 %) while reduces latent heat by 3.3–6.4 W m-2 (-5.6 % to -17.4 %), leading to a 0.2–0.51 °C increase in surface air temperature and a 2.2–3.9 % reduction in relative humidity. Meanwhile, surface O3 concentrations on average increase by 1.3–3.3 μg m-3 due to the inhibitions of stomatal uptake and the anomalous enhancement in isoprene emissions, the latter of which is attributed to the surface warming by O3-vegetaion coupling. Our results highlight the importance of O3 control in China due to its adverse effects on ecosystem functions, deterioration of global warming, and exacerbation of O3 pollution through the O3-vegetation coupling. [ABSTRACT FROM AUTHOR]

We combined domestic ground-based and satellite magnetic measurements to create a regional three-dimensional surface Spline (3DSS) gradient model of the main geomagnetic field over the Chinese continent. To improve the precision of the model, we considered the data gap between the ground and satellite data. We compared and analyzed the results of the Taylor polynomial, surface Spline, and CHAOS-6 (the CHAMP, Ørsted and SAC-C model of Earth’s magnetic field) gradient models. Results showed that the gradients in the south−north and east−west directions of the four models were consistent. The 3DSS model was able to express not only gradients at different altitudes, but also average gradients inside the research area. The two Spline models were able to capture more information on gradient anomalies than were the fitted models. Strong local anomalies were observed in northern Xinjiang, Beijing, and the junction area between Jiangsu and Zhejiang, and the total intensity F decreased whereas the altitude increased. The gradient decreased by 21.69% in the south−north direction and increased by 11.78% in the east−west direction. In addition, the altitude gradient turned from negative to positive while the altitude increased. The Spline model and the two fitted models differed mainly in the field sources they expressed and the modeling theory.

Unlike global and regional assessments, the spatio-temporal variability of air temperature and precipitation, caused by climate change, must be more useful when the assessment is made at the sub-regional to local scale. Thus, this study aims to assess the possible changes in air temperature and precipitation in patterns for the late 21st century relative to the present climate in Mozambique. The regional model, RegCM4, driven by the global model HadGEM2, was used to perform the downscaling process under two Representative Concentration Pathways (RCPs), moderate RCP4.5 and strong RCP8.5. The three experiments were analyzed in the baseline (1971–2000) and future (2070−2099) range at the subregional scale in Mozambique. In this study domain, the highest amounts of precipitation and the highest air temperatures are observed during the extended summer season. However, the central region is rather warmer and rainier than the northern- and southernmost regions. Hence, the regional model RegCM4 demonstrated agreement relative to the observed weather stations and interpolated dataset from the Climate Research Unit. The strong performance of RegCM4 is revealed by its more realistic local spatio-temporal climate features, tied to the topography and geographical location of the study domain. The future increases in mean annual air temperature are well simulated by the model but, the spatial distribution and magnitude differ between the RCPs and over each of the three regions throughout the country. The sharp hottest response at the end of 21st century occurs in the summer and spring seasons under RCP8.5, spatially over the central and northern region of the study domain, with a hot-spot in the southern region. There is a predominantly drier response in the annual mean precipitation but, during the summer season, a meridional dipolarization pattern is observed, with the wettest response being over the southernmost region and a drier response in the northern and central regions of Mozambique.

This study explores the use of gene expression programming (GEP) and artificial neural networks (ANNs) to estimate flood index values based on the unit flood response (UFR) method in two adjacent watersheds (Ardak and Kardeh) located in northeast Iran. The performances of the studied data-driven models were compared according to certain statistical measures such as Root Mean Square Error (RMSE). The findings indicate that GEP models were more accurate than ANNs (RMSE = 0.0986 vs. 0.1512 for Ardak and RMSE = 0.1024 vs. 0.1112 for Kardeh, respectively). Another advantage of the GEP models was providing an explicit relationship between flood index values and physical attributes. As flood index values derived via the UFR method were close to each other, flood contribution area maps were developed using a geographic information system (GIS) to consider uncertainty. Then, fusion algorithms including ordinary averaging, linear regression, and GEP were applied to develop a flexible regional model. [ABSTRACT FROM AUTHOR]

The article aims to evaluate the heterogeneous nature of the regional space of Romanian Banat, perceived as a developed region. The method is based on the complex paradigm of the concept of territorial disparities, which includes other concepts such as development, territory, spatial equity. The elementary disparities are analyzed by studying three categories of variables: social, economic and of life standard. In order to reveal the intraregional behavior the presented methodology used data from two census – 1992 and 2002, which permitted an analysis in two key moments: little after the change of political regime and respectively, after 1o years of transition. Global disparities are studied by calculating a global development index; according to it two types of areas were established: advantaged (above the regional average) and disadvantaged (under this average). The study of the territorial disparities – both elementary and global ones – and of their dynamics 1992-2002 led to the establishment of a territorial model.

The track forecasts of tropical cyclones (TC) in the western North Pacific (WNP) basin during 2021 typhoon season with five global models and four regional models are evaluated here. The results show that the average direct position errors (DPEs) of the global and regional models are approximately 80, 150, 200, 300, and 400 km at 24 h, 48 h, 72 h, 96 h, and 120 h lead-times, respectively. The European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (ECMWF-IFS) achieved the best track forecast performance at each lead among the five global models. Among the four regional models, The China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) attained the smallest DPEs within a 72 h lead, while The Hurricane Weather Research and Forecasting (HWRF) achieved the best track forecast performance at 96 h and 120 h leads. Most of the models produced an obvious westward systematic bias on track forecast from a 24 h to a 120 h lead. Further correlation and cluster analyses indicate that initial TC intensity and size and environmental steering flow can be regarded as good predictors for TC DPEs. TCs with a stronger initial intensity, a bigger initial size, and a larger environmental steering flow in general attain a smaller DPE, and the improvements may go up to 36% at short lead-time. [ABSTRACT FROM AUTHOR]

Unlike global and regional assessments, the spatio-temporal variability of air temperature and precipitation, caused by climate change, must be more useful when the assessment is made at the sub-regional to local scale. Thus, this study aims to assess the possible changes in air temperature and precipitation in patterns for the late 21st century relative to the present climate in Mozambique. The regional model, RegCM4, driven by the global model HadGEM2, was used to perform the downscaling process under two Representative Concentration Pathways (RCPs), moderate RCP4.5 and strong RCP8.5. The three experiments were analyzed in the baseline (1971–2000) and future (2070−2099) range at the subregional scale in Mozambique. In this study domain, the highest amounts of precipitation and the highest air temperatures are observed during the extended summer season. However, the central region is rather warmer and rainier than the northern- and southernmost regions. Hence, the regional model RegCM4 demonstrated agreement relative to the observed weather stations and interpolated dataset from the Climate Research Unit. The strong performance of RegCM4 is revealed by its more realistic local spatio-temporal climate features, tied to the topography and geographical location of the study domain. The future increases in mean annual air temperature are well simulated by the model but, the spatial distribution and magnitude differ between the RCPs and over each of the three regions throughout the country. The sharp hottest response at the end of 21st century occurs in the summer and spring seasons under RCP8.5, spatially over the central and northern region of the study domain, with a hot-spot in the southern region. There is a predominantly drier response in the annual mean precipitation but, during the summer season, a meridional dipolarization pattern is observed, with the wettest response being over the southernmost region and a drier response in the northern and central regions of Mozambique. [ABSTRACT FROM AUTHOR]

The choice of initial perturbation technique in regional ensemble prediction systems is important for forecasts of heavy rain events. Although the uncertainty at synoptic scale has a strong impact on the forecast performance of mesoscale phenomena, there is no established method for explicitly providing synoptic‐scale uncertainty when generating mesoscale initial perturbations. In this study, a new method of calculating mesoscale singular vectors (SVs) that reflect synoptic‐scale uncertainty by applying a regional targeting technique to a subspace of the model phase space spanned by global ensemble perturbations was developed. Comparison of the results of this new method with those of the original SVs showed that the new SVs with high growth rate corresponded to the original SVs with high growth rate, whereas the new SVs with relatively lower growth rate had a larger contribution from the global ensemble perturbations. The new SVs mitigated the tendency of the original SVs to localize over the southern sea of the computational domain, and the forecast errors were better captured by the new SVs than by the original SVs. Furthermore, the effect on forecast performance of the new SVs as initial perturbations was confirmed by using the operational regional ensemble prediction system at the Japan Meteorological Agency. It was found that the precipitation probability forecast performance was superior in the first half of the forecast period at all rainfall thresholds. [ABSTRACT FROM AUTHOR]

Due to the large number of finite element mesh generated, it is difficult to use full-scale model to simulate largesection underground engineering, especially considering the coupling effect. A regional model is attempted to achieve this simulation. A variable boundary condition method for hybrid regional model is proposed to realize the numerical simulation of large-section tunnel construction. Accordingly, the balance of initial ground stress under asymmetric boundary conditions achieves by applying boundary conditions step by step with secondary development of Dynaflowscripts, which is the key issue of variable boundary conditionmethod implementation. In this paper, Gongbei tunnel based on hybrid regional model involvingmulti-field coupling is simulated. Meanwhile, the variable boundary condition method for regional model is verified against model initialization and the ground deformation due to tunnel excavation is predicted via the proposed hybrid regional model. Compared with the monitoring data of actual engineering, the results indicated that the hybrid regional model has a good prediction effect. [ABSTRACT FROM AUTHOR]

High‐resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a two‐decade‐long high‐resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA‐Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA‐Interim data, especially over the NA regions with complex terrain. The spatial‐pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA‐Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year‐to‐year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high‐resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region. [ABSTRACT FROM AUTHOR]

Studying the impact of the environment on metal corrosion is of considerable significance for the safety assessment of buildings and the life prediction of equipment. We developed a new regional environmental corrosion model (RECM) to predict the atmospheric corrosion of Q235 carbon steel based on measured environmental data and corrosion rates obtained from one-year-long static coupon tests. The corrosion of metals varies depending on the environment; therefore, the ability of the model to distinguish such differences is crucial for accurately predicting corrosion. Herein, the regions in which the test sites were located were divided based on the basic principles of atmospheric corrosion. Furthermore, random forest was used to assess the importance of various environmental factors in the corrosion process within each region, which established a close relationship between corrosion and environmental conditions. Our results showed that the accuracy of the RECM is higher than that of the dose-response function of the ISO9223-2012 standard. The method of model construction can be realized automatically using a computer.

Ierley's regional model of a single-gyre wind-driven circulation is an interesting compromise between the need to account for flow nonlinearity and its Sverdrupian behaviour far from the western area of the basin. The model assumes a priori that current intensification is westwards, but it can be easily reformulated, as the authors do, by setting the intensification region at the east. However, in this case the energetics of the flow exhibits an inconsistency that shows that westward intensification has no alternative. This is in accordance with all the previous conclusions on the subject but, for the first time, with the novelty of having taken into account also the nonlinear character of the flow outside the Sverdrupian area. [ABSTRACT FROM AUTHOR]

Global warming has a serious impact on access to water resources, especially in arid regions due to changes in rainfall and temperature. The Global Circulation Models (GCM) and the Regional Climate Models (RCM) have been considered as the main tools for assessing changes in climatic variables in the future. The difference in the spatial resolution of these models causes the different results in climate change assessment. The performance of the CanESM2 and REMO models was evaluated using statistical criteria in the Jazmourian Watershed. The results indicated that the CanESM2 has performed better than the REMO regional model for predicting climatic parameters. Climate parameter simulation based on the CanESM2 model showed that precipitation will decrease under the RCP scenarios (RCP2., RCP4.5, RCP8.5) by 19.23, 18.55, 14.55 mm, respectively at the Iranshahr Station and 8.31, 10.6, 15.72 mm, respectively at the Bam Station. The mean projected temperature based on the RCP scenarios showed that temperature will increase by 1.57, 2.15 and 3.1 ºC at the Iranshahr Station and 1.84, 2.31 and 3.35 ºC at Bam station under RCP2.6, RCP4.5 and RCP8.5, respectively. Generally, the finding of simulated precipitation and temperature in the Jazmourian Watershed showed that the long dry periods is more likely to occur in the future as compared to the historical period. Hence, knowing the trend of the changes in climatic variables can help managers and planners to provide required strategies under the future climate change conditions.

Although Theater activities inject a significant effect on outdoor public affairs, local urban planners do not sufficiently consider such impact while designing a new civic theater in Habiganj, a growing sub-urban district of Bangladesh. Incoherent theater buildings in such urban areas fail to engage people with theater at large, in consequence, the act of theater remains incomplete. The research was conducted through a semi structured interview, social demographic data analysis, case studies, technical aspects and literature survey providing the anthropomorphic image of a theater as an embodied space, elementary spaces of theater and affordable & inclusive theatrical form as major findings. A regional model is driven from the research for local theater (different types of performing spaces) practice that can guide future municipal theater planning for theater to sustain among society & people.

Analysis of wind generated waves due to the prevailing climate change induced by global warming is vital for benefit to the coastal community. In the present paper, the hindcasting of decadal variation namely (i) 1979–1992, (ii) 1993–2006, and (iii) 2007–2019 of wind-wave climate over seasonal and annual scale through 41 years starting from 1979 is carried out using ERA5 reanalysis wind-forcing with a daily temporal and 0.5° × 0.5° spatial resolution. The numerical simulations are executed over the computational domains (i) Global—Indian Ocean and (ii) Regional—south Gujarat to north Goa for evaluating the variability analysis of annual and seasonal mean significant wave heights. The evaluation framework is validated over three in-situ measurement locations with good correlation as compared to the simulated results. The results indicate that at this site the wind and wave activity is noted to increase during transition from 1979–1992 to 1993–2006, then a declination of mean significant wave height is noted during the span 2007–2019. Further, along with the spatial variation estimation, 27 locations along 15 m bathymetric contour at 25 km interval are selected for trend line study. All the 27 locations exhibits a similar kind of trend by variation of decreasing trend from 1979–2019 with variations 0.07–0.22 cm/year. This study highlights the significance in simulating the prevailing wind-wave changes at a regional level considering the impact of the climatology and seasonality changes for effective coastal management, environmental impact assessments and sustainable development. [ABSTRACT FROM AUTHOR]

Large-scale atmospheric information plays an important role in the regional model for the forecasts of weather such as tropical cyclone (TC). However, it is difficult to be fully represented in regional models due to domain size and a lack of observation data, particularly at sea used in regional data assimilation. Blending analysis has been developed and implemented in regional models to reintroduce large-scale information from global model to regional analysis. Research of the impact of this large-scale blending scheme for the Global / Regional Assimilation and PrEdiction System (GRAPES) model on TC forecasting is limited and this study attempts to further progress by examining the adaptivity of the blending scheme using the two-dimensional Discrete Cosine Transform (2D-DCT) filter on the model forecast of Typhoon Haima over Shenzhen, China in 2016 and considering various cut-off wavelengths. Results showed that the error of the 24-hour typhoon track forecast can be reduced to less than 25 km by applying the scale-dependent blending scheme, indicating that the blending analysis is effectively able to minimise the large-scale bias for the initial fields. The improvement of the wind forecast is more evident for u-wind component according to the reduced root mean square errors (RMSEs) by comparing the experiments with and without blending analysis. Furthermore, the higher equitable threat score (ETS) provided implications that the precipitation prediction skills were increased in the 24h forecast by improving the representation of the large-scale feature in the GRAPES analysis. Furthermore, significant differences of the track error forecast were found by applying the blending analysis with different cut-off wavelengths from 400 km to 1200 km and the track error can be reduced less than by 10 km with 400 km cut-off wavelength in the first 6h forecast. It highlighted that the blending scheme with dynamic cut-off wavelengths adapted to the development of different TC systems is necessary in order to optimally introduce and ingest the large-scale information from global model to the regional model for improving the TC forecast. In this paper, the methods and data applied in this study will be firstly introduced, before discussion of the results regarding the performance of the blending analysis and its impacts on the wind and precipitation forecast correspondingly, followed by the discussion of the effects of different blending scheme on TC forecasts and the conclusion section. [ABSTRACT FROM AUTHOR]

The article discusses theoretical and modeled framework practical aspects of supporting children with disabilities living in an industrial region. The main goal was to study the traditions and trends of providing socio-psychological, pedagogical and economic support to the above population with further development of theoretical foundations of rehabilitation and social adaptation of children with disabilities in the industrial region and framework justification of socio-pedagogical support program. social and pedagogical realities of one of the industrial regions of Ukraine. It is proved that the basis of the regional model of social and pedagogical support of children with disabilities is always the appropriate state model, as well as the general guidelines and traditions of its implementation adopted in this region. Such guidelines for the Donetsk industrial region are: understanding the problems of children with disabilities not as medical and pedagogical, but as socio-pedagogical; an integrated approach to the use and integration of great opportunities of the industrial region (strong socio-economic and pedagogical potentials and opportunities of all subjects of socio-pedagogical support of children with disabilities); targeted, comprehensive nature of socio-pedagogical support. The mechanism of implementation of the regional program of social and pedagogical support of children with disabilities in an industrial region has been modeled. Its structure includes sectors and areas of activity, the level of implementation (regional, city (district), educational institution), and its implementation consists in systematic activities for the implementation of socio-pedagogical diagnostics of the child and youth environment, identification of the goal and priority areas of activity at the regional level and development of strategic and tactical measures for the practical implementation of the regional action program. [ABSTRACT FROM AUTHOR]

The 16 April 2016 earthquake in Ecuador exposed the significant weaknesses concerning the methodological designs to compute—from an economic standpoint—the consequences of a natural hazard-related disaster for productive exchanges and the accumulation of capital in Ecuador. This study addressed one of these challenges with an innovative ex ante model to measure the partial and net short-term effects of a natural hazard-related catastrophe from an interregional perspective, with the 16 April 2016 earthquake serving as a case study. In general, the specified and estimated model follows the approach of the extended Miyazawa model, which endogenizes consumption demand in a standard input–output model with the subnational interrelations and resulting multipliers. Due to the country's limitations in its regional account records the input–output matrices for each province of Ecuador had to be estimated, which then allowed transactions carried out between any two sectors within or outside a given province to be identified by means of the RAS method. The estimations provide evidence that the net short-term impact on the national accounts was not significant, and under some of the simulated scenarios, based on the official information with respect to earthquake management, the impact may even have had a positive effect on the growth of the national product during 2016. [ABSTRACT FROM AUTHOR]

Abstract The development of a limited area model (LAM) capability for the nonhydrostatic Finite‐Volume Cubed‐Sphere (FV3) dynamical core is described and compared with a globally nested approach featuring two‐way feedback. Comparisons of the computational performance of the LAM relative to the two‐way nest reveal that the LAM configuration exhibits considerable improvement in efficiency. High‐resolution (i.e., 3‐km) LAM and nest configuration forecasts covering a 1‐month period show statistically comparable results for most parameters. Forecast differences between the two configurations primarily arise in the upper air temperature and height fields, which show a statistically significant increase in the magnitude of negative biases in geopotential height and upper‐air temperature using the LAM configuration relative to the nest at forecast lead times >24‐h. Precipitation forecasts over the full 60‐h forecast period are also evaluated and depict no statistically significant differences between the two configurations, with the nest configuration exhibiting slightly improved scores. Overall results suggest that while the FV3 LAM approach can introduce degradations into the forecast relative to the two‐way interactive nest at lead times >24‐h, these errors are generally small in magnitude and are accompanied by considerable improvement in computational efficiency.

Abstract This study explores how the Indian Summer Monsoon rainfall simulations are affected by the mathematical representations of the planetary boundary and microphysics parameterizations at a gray zone resolution. The monsoon is simulated with the Weather Research and Forecast model at a 9 km horizontal resolution over South–East Asia for three different years, chosen to represent early, normal, and delayed monsoon onset. Changing the boundary layer from a hybrid to a local scheme dramatically impacts the monsoon simulation, reducing around 40% in total rainfall. In contrast, changing the microphysics scheme has a less pronounced impact (5%) on the precipitation but still induces regional variations. To assess how physical parameterization changes affect South Asia's rainfall, we evaluate their impact on both the energy and moisture budgets over the subcontinent. It is revealed that local changes in evaporation do not directly drive changes in precipitation. Instead, changes in the regional distribution of the energy sources and sinks modify the atmospheric circulation, which affects the distribution of rainfall. In particular, the boundary layer changes can substantially increase the latent heat flux, strengthening the monsoon. In the tropics, overturning circulation that exports energy is tied to a net inflow of water. Thus, the intensification of the monsoonal circulation results in an enhanced water inflow and increased rainfall.

The development of a limited area model (LAM) capability for the nonhydrostatic Finite‐Volume Cubed‐Sphere (FV3) dynamical core is described and compared with a globally nested approach featuring two‐way feedback. Comparisons of the computational performance of the LAM relative to the two‐way nest reveal that the LAM configuration exhibits considerable improvement in efficiency. High‐resolution (i.e., 3‐km) LAM and nest configuration forecasts covering a 1‐month period show statistically comparable results for most parameters. Forecast differences between the two configurations primarily arise in the upper air temperature and height fields, which show a statistically significant increase in the magnitude of negative biases in geopotential height and upper‐air temperature using the LAM configuration relative to the nest at forecast lead times >24‐h. Precipitation forecasts over the full 60‐h forecast period are also evaluated and depict no statistically significant differences between the two configurations, with the nest configuration exhibiting slightly improved scores. Overall results suggest that while the FV3 LAM approach can introduce degradations into the forecast relative to the two‐way interactive nest at lead times >24‐h, these errors are generally small in magnitude and are accompanied by considerable improvement in computational efficiency. Plain Language Summary: In this study, we describe and evaluate a new limited area model (LAM) capability for the Finite‐Volume Cubed‐Sphere dynamical core. This capability provides a way to run the dynamical core over any region without the need for simultaneous integration of a global model counterpart, thus saving considerable computational resources. However, this framework is susceptible to errors from lateral boundaries, which are provided by an external model at a coarse temporal frequency compared to a global model employing a nest with two‐way feedback. Short range forecasts at 3‐km grid spacing over the contiguous United States show generally similar performance between the limited area and nest configurations for a month‐long period in 2019, with the nest showing slightly better forecast scores near the end of the studied forecast length of 60 h. These results suggest that use of the LAM performs similarly to the more sophisticated, and computationally expensive, two‐way nest configuration for convection‐allowing, short range forecasts to 60 h. Key Points: A limited area modeling capability has been developed for the Finite‐Volume Cubed‐Sphere dynamical coreThis capability was evaluated for a month‐long period against a similarly configured two‐way nest driven by a global modelThe limited area model is statistically comparable to the two‐way nest for the first 24 h, with minor degradation by 48–60 h [ABSTRACT FROM AUTHOR]

This study explores how the Indian Summer Monsoon rainfall simulations are affected by the mathematical representations of the planetary boundary and microphysics parameterizations at a gray zone resolution. The monsoon is simulated with the Weather Research and Forecast model at a 9 km horizontal resolution over South–East Asia for three different years, chosen to represent early, normal, and delayed monsoon onset. Changing the boundary layer from a hybrid to a local scheme dramatically impacts the monsoon simulation, reducing around 40% in total rainfall. In contrast, changing the microphysics scheme has a less pronounced impact (5%) on the precipitation but still induces regional variations. To assess how physical parameterization changes affect South Asia's rainfall, we evaluate their impact on both the energy and moisture budgets over the subcontinent. It is revealed that local changes in evaporation do not directly drive changes in precipitation. Instead, changes in the regional distribution of the energy sources and sinks modify the atmospheric circulation, which affects the distribution of rainfall. In particular, the boundary layer changes can substantially increase the latent heat flux, strengthening the monsoon. In the tropics, overturning circulation that exports energy is tied to a net inflow of water. Thus, the intensification of the monsoonal circulation results in an enhanced water inflow and increased rainfall. Plain Language Summary: The high resolution atmospheric model can produce realistic monsoon simulations, and such simulations are highly sensitive to the physical parameterizations used in the model. Current research reveals how cloud microphysics and planetary boundary layer can severely affect the precipitation patterns over India. In this study, a monsoon is simulated using the Weather Research and Forecast model at a 9 km horizontal resolution over south Asia during 2007, 2008, and 2015, representing an early, normal, and delayed monsoon onset. It has been found that changing the boundary layer scheme from hybrid to local scheme has a dramatic impact on simulation, with a reduction of around 40% in total rainfall. Simulations with a hybrid scheme capture most of the circulations and precipitations patterns, but the local scheme leads to an overall weaker monsoonal strength. Different microphysics parameterizations show similar total rainfall distributions (5% variations) but exhibit regional variabilities. Key Points: Weather Research Forecast (WRF) can accurately reproduce the monsoon precipitation distribution but exhibit sensitivity to physical processesChanges to planetary boundary layer parameterizations cause around 40% change in seasonal precipitationIncreased heating intensifies the overturning circulation, exports more energy, and thus import more rainfall [ABSTRACT FROM AUTHOR]

As China’s accession to the World Trade Organization takes longer and longer, it has gained a place in the international market. At present, in the international market, China ranks first in the trading volume of agricultural and sideline products, especially in the development of foreign trade in Southeast Asia, Europe, and Africa. The development momentum of the edible fungus industry continues to increase in China, becoming a leader in the emerging agricultural industry. Based on the influence of the edible fungus industry in rural areas, according to the survey and record of data valuation, the specific index data content from 2005 to 2019 was taken as an example, and a three-tier research system was proposed using var3D model. Respectively, it is the relationship between the three types of rural basic indicators, economic indicators, and industrial development indicators, verifying the role of the edible fungus industry in driving the development of beautiful new rural construction and enhancing economic development. Results show that the targeted distribution of edible fungi across the country and the construction of beautiful villages have a high degree of direct correlation, and its steep performance trend is obvious. In the analysis of the equilibrium degree of the edible fungi industry driving the local rural economic development, the edible fungi continues to strengthen the advantages of local industries have used its industrial power to further improve the intensity and integrity of local rural construction and promote the development of beautiful new rural areas. The development of the edible fungus industry has also led to the enthusiasm and source power of the construction of beautiful new villages. The development of beautiful new villages will help to enhance the gain effect of the edible fungus industry and form a new model of the beautiful new rural circular economy. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate temperature and precipitation trends over South America (SA), simulated by the regional Eta model with 20-km horizontal resolution nested to three global models: The Canadian Earth System Model Second Generation (CanESM2-ES), The Met Office Hadley Centre Global Environmental Model - Earth System (HadGEM2-ES) and the Model for Interdisciplinary Research on Climate version 5 (MIROC5). The simulated trends are compared to observed trends (OBS) using six (four) extreme temperature (precipitation) indices recommended by the Expert Team on Climate Change Detection and Indices. In both simulations and OBS, extreme temperature trends over Amazon (AMZ) and Northeast Brazil (NEB) are higher than over South East SA (SESA) and West SA (WSA). Also, trends based on maximum temperatures are, in general, smaller than those based on minimum temperatures. The three downscaling simulations reproduce these features. The temperature simulations of the Eta-CanESM2 (Eta-MIROC5) show the largest (smallest) trends among the three simulations. The Eta-MIROC5, performs better than the other two simulations, reproducing the signals and the magnitudes of the extreme temperature trends, and some cooling trends over SESA, as shown in the observation. Precipitation trends from the three simulations do not compare well with observations, such as temperature trends. However, Eta-MIROC5 presents the best simulations, showing increase in the magnitude of the precipitation extremes over most of SA. The results from the three simulations presented here will be further used for the assessment of climate change projections in the SA region.

"Good management of water resources requires a good allocation of their availability, especially in public irrigated schemes in Tunisia. This paper contributes to a better reallocation of available water resources at the farm and regional levels. A case study was discussed in the Kalâa Kebira region, in the center-east of Tunisia. Regional models based on aggregation and the possibility of water transfer between two irrigated schemes was tested. The results show that a good seasonal allocation is possible with a total regional exchange of 9.60% m3 of water available between these two schemes. This reallocation is beneficial at the regional level, recording an increase of 2.12% in agricultural income and less beneficial, except for farms that are less competitive, in terms of use of water resources. This reallocation also allows for cultural diversity and specification of agricultural farms. Competitiveness in the water use, diversification and specification of agricultural production systems help to preserve natural resources but they also help to satisfy demand of the regional market.Good management of water resources requires a good allocation of their availability, especially in public irrigated schemes in Tunisia. This paper contributes to a better reallocation of available water resources at the farm and regional levels. A case study was discussed in the Kalâa Kebira region, in the center-east of Tunisia. Regional models based on aggregation and the possibility of water transfer between two irrigated schemes was tested. The results show that a good seasonal allocation is possible with a total regional exchange of 9.60% m3 of water available between these two schemes. This reallocation is beneficial at the regional level, recording an increase of 2.12% in agricultural income and less beneficial, except for farms that are less competitive, in terms of use of water resources. This reallocation also allows for cultural diversity and specification of agricultural farms. Competitiveness in the water use, diversification and specification of agricultural production systems help to preserve natural resources but they also help to satisfy demand of the regional market."

When assessing seismic risk at a city scale, local site conditions can significantly modify the destructive potential of an earthquake. This was the case in Mexico City in 1985 and 2017, which is located on a sedimentary valley. For that reason, characterization and consideration of these site effects, in addition to assessing the probability of occurrence of a destructive earthquake in a given region, are crucial. For this purpose, a tri-dimensional model was developed using a numerical code that permits to carry out such kind of studies. The code is based on the spectral element method allowing solving the waves propagation problem in tri-dimensional solid media. The seismic phenomenon is simulated in its entirety: from the seismic source to the site using a representative model of the complexity of the wave path. However, this introduces uncertainties that must be quantified, controlled and reduced by validating the results of the numerical tool with measurements on a real study site. This work aims to study numerically the effect of the geometric structure of soil and subsoil on the prediction of ground motion at the regional scale. The first obtained results show the importance of the in situ measures on the regional-scale simulations. [ABSTRACT FROM AUTHOR]

The regional model‐based Mesoscale Ensemble Prediction System (MEPS) has been operational since June 2019 at the Japan Meteorological Agency (JMA). The primary objective of the newly operational MEPS is to provide uncertainty information for JMA's operational regional model, Mesoscale Model (MSM), which provides information to support disaster prevention and aviation safety. This article describes MEPS in detail and discusses issues to be addressed in the future. For effective evaluation of uncertainties in MSM, the forecast model in MEPS is configured in the same way as that in MSM, except for the initial and lateral boundary conditions. Initial perturbations for all 20 ensemble runs are generated by a linear combination of singular vectors (SVs) with three different spatial and temporal resolutions, with the aim of capturing multi‐scale uncertainties in the initial conditions simultaneously. The SVs from a global model are also used as lateral boundary perturbations to ensure consistency between the initial and boundary conditions of each ensemble member. The verification results showed that MEPS achieved the expected performance of an ensemble prediction system: the ensemble mean outperformed the control forecast with a good spread–skill relationship; moreover, the skill scores of probabilistic precipitation forecasts were evaluated as valid for rainfall of up to 30 mm·(3 hr)−1. In an additional experiment conducted without using the two smaller‐scale initial perturbations, the skill was substantially reduced compared with that of the original MEPS, especially for larger precipitation thresholds. Therefore, the smaller‐scale perturbations were essential to capture uncertainties associated with local heavy rainfall events. [ABSTRACT FROM AUTHOR]

The article considers the importance of research and functioning of creative industries and substantiates the stages of integrated assessment of their development. One of the methodological approaches to the study of creative industries is considered namely correlation-regression analysis. The necessity of application of multiple correlation-regression analysis for estimation of the interdependence of certain creative indicators with their creative parameters is opened and the regional model of development of creative industries is constructed. The study is based on calculations and formulas, the values of which are used in the construction of a matrix of paired correlation coefficients and other important components. Based on the study, regression statistics and analysis of variance. The paper provides all possible indicators for the development of an organizational and economic mechanisms for building regional models of creative industries. [ABSTRACT FROM AUTHOR]

The purpose of this study was to obtain the regional model of erosion according to the specific climatic, adaptive, and other conditions of the Toroq watershed located in the east north of Khorasan Razavi province. To conduct this research, first, the homogeneous units were prepared using slope maps, lithology, land use, and erosion forms in a Geographic Information System environment. Then, to optimize the number of homogeneous units, the cluster analysis method was used in Statistical Product and Service Solutions (SPSS) software. The diagnostic analysis confirmed the accuracy of cluster analysis inhomogeneous regions. Field operations were carried out in homogeneous units with the establishment of a rainfall simulator and also the application of 30-min rainfall intensity with a return period of 10 years. Also, the collected soil samples were analyzed in the laboratory. After performing statistical analyses in the SPSS environment, the variables affecting erosion were determined and prioritized. Then, through the use of multivariate linear regression and step-by-step and interpolation methods, the equations for estimating the amount of erosion were determined. Finally, the multivariate linear model of plot erosion was prepared using the step-by-step method using two variables of plot slope and land use. The model was selected for estimating erosion after examining different validation methods based on less RE and less RMSE, higher R, low significance coefficient (Sig < 0.05), and also fewer inputs. [ABSTRACT FROM AUTHOR]

This paper develops the quasi‐analytic Bayesian analysis of the generalized least squares (GLS) (B‐GLS) model introduced by Reis et al. (2005, https://doi.org/10.1029/2004WR003445) into an operational and statistically comprehensive GLS regional hydrologic regression methodology to estimate flood quantiles, regional shape parameters, low flows, and other statistics with spatially correlated flow records. New GLS regression diagnostic statistics include a Bayesian plausibility value, pseudo adjusted R2, pseudo analysis of variance table, and two diagnostic error variance ratios. Traditional leverage and influence are extended to identify rogue observations, address lack of fit, and support gauge network design and region‐of‐influence regression. Formulas are derived for the Bayesian computation of estimators, standard errors, and diagnostic statistics. The use of B‐GLS and the new diagnostic statistics are illustrated with a regional log‐space skew regression analysis for the Piedmont region in the Southeastern U.S. A comparison of ordinary, weighted, and GLS analyses documents the advantages of the Bayesian estimator over the method‐of‐moment estimator of model error variance introduced by Stedinger and Tasker (1985, https://doi.org/10.1029/WR021i009p01421). Of the three types of analyses, only GLS considers the covariance among concurrent flows. The example demonstrates that GLS regional skewness models can be highly accurate when correctly analyzed: The B‐GLS average variance of prediction is 0.090 for South Carolina (92 stations), whereas a traditional ordinary least squares analysis published by the U.S. Geological Survey yielded 0.193 (Feaster & Tasker, 2002, https://doi.org/10.3133/wri024140). B‐GLS provides a statistical valid framework for the rigorous analysis of spatially correlated hydrologic data, allowing for the estimation of parameters and their actual precision and computation of several diagnostic statistics, as well as correctly attributing variability to the three key sources: time sampling error, model error, and signal. Key Points: A comprehensive statistical framework for regional hydrologic regression is presented with spatially correlated flow data and varying record lengthsFramework correctly attributes variability to sampling errors in computed statistic, variability explained by the model, and model errorNew diagnostics includes Bayesian plausibility value, pseudo adjusted R2, pseudo ANOVA, and Bayesian metrics for leverage and influence [ABSTRACT FROM AUTHOR]

Extensive nitrogen loads at the soil surface exceed plant uptake and soil biochemical capacity, and therefore lead to nitrogen accumulation in the deep vadose zone. Studies have shown that stored nitrogen in the vadose zone can eventually reach the water table and affect the quality of groundwater resources. Recently, global scale models have been implemented to quantify nitrate storage and nitrate travel time in the vadose zone. These global models are simplistic and relatively easy to implement and therefore facilitate analysis of the considered transport processes at a regional scale with no further requirements. However, the suitability of applying these models at a regional scale has not been tested. Here, we evaluate, for the first time, the performance and utility of global scale models at the regional scale. Applied to the Loess Plateau of China, we compare estimates of groundwater recharge and nitrate storage derived from global scale models with results from a regional scale approach utilizing the Richards and advection‐dispersion equations. The estimated nitrate storage was compared to nitrate observations collected in the deep vadose zone (>50 m) at five sites across the Loess Plateau. Although both models predict similar spatial patterns of nitrate storage, the recharge fluxes were three times smaller and the nitrate storage was two times higher compared with the regional model. The results suggest that global scale models are a potentially useful screening tool, but require refinement for local scale applications. [ABSTRACT FROM AUTHOR]

In this paper, we consider a hybrid method of downscaling of the GCM‑generated meteorological fields to the characteristic spatial resolution which is usually used for modeling of a single mountain glacier mass balance. The main purpose of the study is to develop a reliable forecasting method to evaluate future state of moun‑ tain glaciation under changing climatic conditions. The method consists of two stages. In the first or dynamical stage, we use results of calculations of the regional numerical model HadRM3P for the Black Sea‑Caspian region with a spatial resolution of 25 km [22]. Initial conditions for the HadRM3P were provided by the GCM devel‑ oped in the Institute of Numerical Mathematics of RAS (INMCM4) [18]. Calculations were carried out for two time periods: the present climate (1971–2000) and climate in the late 21st century (2071–2100) according to the scenario of greenhouse gas emissions RCP 8.5. On the second stage of downscaling, further regionalization is achieved by projecting of RCM‑generated data to the high‑resolution (25 m) digital altitude model in a domain enclosing a target glacier. Altitude gradients of the surface air temperature and precipitation were derived from the model data. Further on, both were corrected using data of observations. Incoming shortwave radiation was calculated in the mass balance model separately, taking into account characteristics of the slope, i.e. exposition and shading of each cell. Then, the method was tested for glaciers Marukh (Western Caucasus) and Jankuat (Central Caucasus), both for the present‑day and for future climates. At the end of the 21st century, the air tem‑ perature rise predicted for the summer months was calculated to be about 5–6 °C, and the result for the winter to be minus 2–3 °C. Change in annual precipitation is not significant, less than 10%. Increase in the total short‑ wave radiation will be about 5%. These changes will result in the fact that the snow line will be higher than the body of the glacier itself. This will inevitably cause degradation of the glacier and its gradual disappearance. The main contribution to the glacier shrinking and disappearance will be made by air temperature rise, because it will affect the change in the ratio of the areas of ablation and accumulation. Besides, a rise of temperature will increase the average melting season duration. These are, of cause, preliminary results just to illustrate how the downscaling method works. We did not take into account dynamic effects and gradual reducing of the glaciated area. In future, we plan to couple mass balance and dynamical models [17] and to adjoin them with downscaled climate change in order to account for transient glacier changes.

Abstract Several blending methods have been developed in dynamic downscaling and rapid cycled data assimilation. Blending the large‐scale part of the global model (GM) analysis or forecast has led to improvement in regional model (RM) simulations. However, in previous studies the blended waveband of the GM has generally been determined using a fixed, arbitrarily chosen cutoff wave number. Here we introduce a new dynamic blending (DB) scheme with a dynamic cutoff wave number computed according to the spectral characteristics of GM forecast quality and the spectral distribution of errors in the RM. The DB scheme is described and applied to eight‐day summertime and seven‐day wintertime cycled Weather Research and Forecasting Model forecasts over a regional domain in the continental United States. The scheme can determine a cutoff wave number with significant temporal variations. The temporal variation results from the error growth property of the RM and has a clear diurnal oscillation, suggesting that fewer (more) GM waves should be introduced into the RM at noon (night). The cutoff wave number difference between the two periods indicates seasonal variation of the cutoff wave number with larger day‐to‐day change in winter. Comparison among no blending experiment, two fixed wave number blending experiments, and two DB experiments with and without vertically varying cutoff wave number suggests that the DB scheme with vertically averaged but temporally varying cutoff wave number results in less model bias and less disturbance to the RM dynamic balance. By reducing the forecast background error, the DB scheme can potentially provide improved first guess for a rapid‐update‐cycle weather forecast system.