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This paper presents the 1000 ensemble flood simulations using ensemble rainfalls simulated by 4D LETKF. The number of ensemble rainfall members is large as 1000 compared to the operational rainfall products of two-digit numbers to avoid sampling errors in the three-dimensional meteorological simulation based on chaotic theory. Using the large data set, 1000 ensemble rainfall–runoff for dam catchments and high-resolution inundation simulations of large area are carried out focusing on the Kumagawa river catchment. Herewith, the comparisons were carried out with 21-member ensemble rainfalls of an operational forecast by Japan Meteorological Agency and 100-member 4D-LETKF ensemble rainfalls simulated independent of 1000-member 4D-LETKF. At the same time, the accuracy of selective 100-member ensembles out of 1000 members is investigated. As a result, although many previous research works show a large number of ensemble simulations are necessary for three-dimensional meteorological field, the number could be reduced in the catchment-average rainfall–runoff and 2.5-dimensional inundation simulations given that the rainfall prediction has a certain level of accuracy since improving the discharge prediction accuracy with lower dimension is sometimes possible by adjusting the horizontally/vertically integrated model parameters determined by topography and soil characteristics in advance against the observed rainfall. Also, the 1000 ensembles could be classified into several patterns in horizontally accumulated 2D rainfall field. Likewise, the flood flow moves toward the low elevation area and river; thus, the resultant 2.5-dimensional flood field does not show much variety as three-dimensional meteorological simulation. The paper summarizes these studies. [ABSTRACT FROM AUTHOR]

This study presents two machine learning models, namely, the light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), for the first time for predicting flash flood susceptibility (FFS) in the Wadi System (Hurghada, Egypt). A flood inventory map with 445 flash flood sites was produced and randomly divided into two groups for training (70%) and testing (30%). Fourteen flood controlling factors were selected and evaluated for their relative importance in flood occurrence prediction. The performance of the two models was assessed using various indexes in comparison to the common random forest (RF) method. The results show areas under the receiver operating characteristic curves (AUROC) of above 97% for all models and that LightGBM outperforms other models in terms of classification metrics and processing time. The developed FFS maps demonstrate that highly populated areas are the most susceptible to flash floods. The present study proves that the employed algorithms (LightGBM and CatBoost) can be efficiently used for FFS mapping. [ABSTRACT FROM AUTHOR]

With the increasing of extreme rainfall frequency, landslides accompanied by mudslides often lead to serious casualties and property damage. On 3rd July 2021, a debris flow occurred in Izu Mountain area, Shizuoka County, Japan, and then resulted in 26 deaths and 131 houses destroyed, where houses were mainly built along the banks of the creek. In order to analyse the landslide state and distribution, a two-dimensional debris flow dynamic model (Massflow) was selected to simulate the process of the landslide-generated debris flow. When the model results are considered together with remote sensing images, the volume distribution of the unstable landslide is also able to be determined. The results show that (1) the affected areas are mainly concentrated at the outfall of the gully and on both sides of the streets. (2) The pore pressure ratio is an important factor affecting the damage range of this debris flow. (3) The increase of the pore pressure ratio in the landslide make the movement distance of debris flow increase significantly. [ABSTRACT FROM AUTHOR]

A clustering and classification method using a Gaussian mixture model (GMM) is used to summarize and simplify meteorological data from a numerical weather prediction (NWP) model. Each horizontal grid in the integration domain of the NWP model is characterized by a feature vector, which consists of a multivariable with multiple pressure levels. All horizontal grids at every forecast time are classified based on the GMM clustering. The classification results show that grids are clustered into air masses or disturbances with the same meteorological characteristics. This paper describes application of the proposed classification method as a framework to verify the forecast of surface solar irradiance from the NWP model. Satellite observation data are used as the reference so that verification can be performed over the integration domain of the NWP model for each air mass or disturbance that moves and changes shape over time. The mean square error (MSE) is decomposed into the square of the mean error and the MSE between variables centered on zero, the square root of which is called the centered root mean square error (CRMSE). The analyses are performed for forecast data over a 2 day forecast horizon. The change in mean error is not significant until the second day, whereas the CRMSE is maintained only during the first day. Each air mass has a different forecast error structure. The proposed framework clarifies the structure of the forecast error of the surface solar irradiance. Key Points: A method is proposed for simplifying data from a numerical weather prediction model by classification with a Gaussian mixture modelThe classification results show clusters correspond to air mass and disturbance where atmospheric features are homogeneousClassification results enable verification of surface solar irradiance forecasts from the NWP model for each air mass [ABSTRACT FROM AUTHOR]

Understanding of the characteristics of variation in surface solar irradiance on time scales shorter than several hours has been limited because ground-based observation stations are located coarsely. However, satellite observation data can be used to bridge this gap. We propose an approach for predicting characteristics of a time series of surface solar irradiance in a 121-min time window for areas without ground-based measurement systems. Time series features—mean, standard deviation, and sample entropy—are used to represent the characteristics of variation in surface solar irradiance quantitatively. We examine cloud properties over the area to design prediction models of these time series features. Cloud properties averaged over the defined domain and texture features that represent characteristics of the spatial distribution of clouds are used as measures of cloud features. Predictors for time series features, where explanatory variables are cloud features, are constructed employing the random-forest regression method. The performance test for predictions indicates that the mean and standard deviation can be predicted with higher prediction skill, whereas the predictor for sample entropy has lower prediction skill. The importance of cloud features for predictors and partial dependence of the predictors on explanatory variables are also analyzed. Cloud optical thickness (COT) and cloud fraction (CFR) were important for predicting the mean. Two texture features—contrast and local homogeneity (LHM)—and COT were important for predicting the standard deviation, and COT, LHM, and CFR were important for predicting the sample entropy. These results indicate which satellite-derived cloud field properties are useful for predicting time series features of surface solar irradiance. [ABSTRACT FROM AUTHOR]

This study presents an application of self-organising maps (SOMs) to downscaling medium-range ensemble forecasts and probabilistic prediction of local precipitation in Japan. SOM was applied to analyse and connect the relationship between atmospheric patterns over Japan and local high-resolution precipitation data. Multiple SOM was simultaneously employed on four variables derived from the JRA-55 reanalysis over the area of study (south-western Japan), and a two-dimensional lattice of weather patterns (WPs) was obtained. Weekly ensemble forecasts can be downscaled to local precipitation using the obtained multiple SOM. The downscaled precipitation is derived by the five SOM lattices based on the WPs of the global model ensemble forecasts for a particular day in 2009-2011. Because this method effectively handles the stochastic uncertainties from the large number of ensemble members, a probabilistic local precipitation is easily and quickly obtained from the ensemble forecasts. This downscaling of ensemble forecasts provides results better than those from a 20-km global spectral model (i.e. capturing the relatively detailed precipitation distribution over the region). To capture the effect of the detailed pattern differences in each SOM node, a statistical model is additionally concreted for each SOM node. The predictability skill of the ensemble forecasts is significantly improved under the neural network-statistics hybrid-downscaling technique, which then brings a much better skill score than the traditional method. It is expected that the results of this study will provide better guidance to the user community and contribute to the future development of dam-management models. [ABSTRACT FROM AUTHOR]

The email and affiliation of co-author Daisuke NOHARA is wrong in the original article. Now the email has been corrected to "noharad@kajima.com" and the affiliation has been corrected to "Sustainable Society Laboratory, KAJIMA Technical Research Institute, Tobitakyu 2-19-1, Chofu 182-0036, Japan". [ABSTRACT FROM AUTHOR]

Anomalous weather patterns (WPs) in relation to heavy precipitation events during the baiu season in Japan are investigated using a nonlinear classification technique known as the self-organizing map (SOM). The analysis is performed on daily time scales using the Japanese 55-year Reanalysis Project (JRA-55) to determine the role of circulation and atmospheric moisture on extreme events and to investigate interannual and interdecadal variations for possible linkages with global-scale climate variability. SOM is simultaneously employed on four atmospheric variables over East Asia that are related to baiu front variability, whereby anomalous WPs that dominated during the 1958-2011 period are obtained. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of heavy precipitation events. Each WP is associated with regional variations in the probability of extreme precipitation events. On interannual time scales, El Niño-Southern Oscillation (ENSO) affects the frequency of the WPs in relation to the heavy rainfall events. The warm phase of ENSO results in an increased frequency of a WP that provides a southwesterly intrusion of high equivalent potential temperature at low levels, while the cold phase provides southeastern intrusion. In addition, the results of this analysis suggest that interdecadal variability of frequency for heavy rainfall events corresponds to changes in frequency distributions of WPs and are not due to one particular WP. [ABSTRACT FROM AUTHOR]

The degree of reversibility in the behavior of El Niño-Southern Oscillation (ENSO) in response to CO2 concentration changes is examined using the air-sea coupled Community Climate System Model, version 4. The model is run under simple and idealized climate change scenarios where atmospheric CO2 concentration is gradually increased to four times the preindustrial level and then gradually reduced at a similar rate along this trajectory. While the simulated ENSO amplitude is reduced when CO2 concentration increases, the amplitude is enhanced when CO2 decreases. This enhancement occurs with a relative El Niño-like warming of mean sea surface temperature. Most of the El Niño-like warming is attributed to a weakened cold water upwelling in the equatorial eastern Pacific. This weakened cooling is a result of a vertical ocean temperature gradient that is reduced in relation to a lag between subsurface temperature warming and surface temperature change. From these findings, a statistical parameterization expressing ENSO variability is developed in terms of the global-mean surface temperature and the vertical gradient of the global-mean temperature in the upper ocean. Plugging this parameterization into a statistical model of ENSO and combining the latter with a statistical spatial pattern of the ENSO-related temperature anomaly enables the reconstruction of surface temperature variability over North America. This approach can be useful when one assesses future climate changes under various CO2 emission scenarios using very simple models whose outputs are limited to a small number of global-mean variables. [ABSTRACT FROM AUTHOR]

Based on the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset, the relationships between the climatological states and transition processes of simulated ENSO are investigated. The air--sea coupled system of the observed ENSO can remain in the weak cold event for up to 2 yr, whereas those of the warm events tend to turn rapidly into a cold phase. Therefore, the authors separately investigate the simulated transition process of a warm-phase and a cold-phase ENSO in the CMIP3 models. Some of the models reproduce the features of the observed transition process of El Niño/La Niña, whereas most models fail to concurrently reproduce the process during both phases. In the CMIP3 models, four climate models simulate well the rapid transition from El Niño to La Niña. The intensity of a rapid transition of El Niño is mainly related to the intensity of the simulated climatological precipitation over the western--central Pacific (WCP). The models that have strong WCP precipitation can simulate the rapid termination of the equatorial zonal wind in the WCP, which tends to result in the termination of El Niño phase. This relationship is not applicable for the La Niña transition phase. The simulation of La Niña persistency is related to the reflection of off-equatorial Rossby waves at the western boundary of the Pacific and the seasonal evolution of the climatological precipitation in the WCP. Differences in the transition processes between El Niño and La Niña events are fundamentally due to the nonlinear atmospheric (convective) response to SST, which originates from the distribution of climatological SST and its seasonal changes. The results of the present study indicate that a realistic simulation of the climatological state and its seasonality in the WCP are important to be able to simulate the observed transition process of the ENSO. [ABSTRACT FROM AUTHOR]

The article presents a study on the multiplicity of N-Glycan structures of bovine 18kDa lactophorin. It mentions the use of heparin affinity chromatography in purifying bovine 18 kDa lactophorin from cow's milk whey. It states that lactophorin is a heat-stable phosphoglycoprotein called as milk glycosylation-dependent cell adhesion molecule1.

This study investigates the projections of river discharge for 24 major rivers in the world during the twenty-first century simulated by 19 coupled atmosphere–ocean general circulation models based on the Special Report on Emissions Scenarios A1B scenario. To reduce model bias and uncertainty, a weighted ensemble mean (WEM) is used for multimodel projections. Although it is difficult to reproduce the present river discharge in any single model, the WEM results produce more accurate reproduction for most rivers, except those affected by anthropogenic water usage. At the end of the twenty-first century, the annual mean precipitation, evaporation, and runoff increase in high latitudes of the Northern Hemisphere, southern to eastern Asia, and central Africa. In contrast, they decrease in the Mediterranean region, southern Africa, southern North America, and Central America. Although the geographical distribution of the changes in precipitation and runoff tends to coincide with that in the river discharge, it should be emphasized that the change in runoff at the upstream region affects the river flow in the downstream region. In high-latitude rivers (Amur, Lena, MacKenzie, Ob, Yenisei, and Yukon), the discharge increases, and the peak timing shifts earlier because of an earlier snowmelt caused by global warming. Discharge tends to decrease for the rivers in Europe to the Mediterranean region (Danube, Euphrates, and Rhine), and southern United States (Rio Grande). [ABSTRACT FROM AUTHOR]

Refolding of reduced and denatured Streptomyces griseus trypsin (SGT) was investigated. In the standard buffer of 50 mM Tris-HCl, the refolding yield of 1 μg/ml of SGT did not exceed 15%. This low yield was assumed to be due mainly to autoproteolysis and/or aggregation occurring concurrently with refolding. On the basis of this assumption, SGT was immobilized on agarose gel in order to suppress such intermolecular interactions, and various refolding media were examined for their ability to minimize misfolding. As a result, 1M Tris, 1M diethanolamine, and 1 M triethanolamine were chosen, and their application to the solution system increased the refolding yield considerably, to ca. 45%. A further dramatic increase in yield, to 85%, was observed when a mutant Streptomyces subtilisin inhibitor (SSI, C71SM73KC101S), engineered as a temporary inhibitor of SGT, was added to the solution system to suppress autoproteolysis during refolding. The application of a temporary inhibitor may be greatly effective in not only improvement of yield but also selection of media for the refolding of protease. [ABSTRACT FROM PUBLISHER]

This study shows the application of self-organizing maps (SOMs) to probabilistic forecasts of wind power generation and ramps in Japan. SOMs are applied to atmospheric variables obtained from the Japanese 55-year atmospheric Reanalysis JRA-55 over the region, thus deriving classified weather patterns (WPs). Probabilistic relationships are established between the synoptic-scale atmospheric variables over East Japan and the generation of regionally integrated wind power in East Japan. Medium-range probabilistic wind power predictions are derived by SOM as analog ensembles based on the WPs of the multi-center ensemble forecasts. As this analog approach handles stochastic uncertainties effectively, probabilistic wind power forecasts are rapidly generated from a very large number of forecast ensembles. The use of a multi-model ensemble provides better results than a one-forecast model. The hybrid ensemble forecasts further improve the probabilistic predictability skill of wind power generation compared with non-hybrid methods. It is expected that long-term wind forecasts will provide better guidance to transmission grid operators. The advantage of this method is that it can include an interpretative analysis of meteorological factors for variations in renewable energy. [ABSTRACT FROM AUTHOR]