Ensemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency’s (JMA) non-hydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index (SSHPI) to estimate peak surge heights along the central coast of Japan. We show that Pareto optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios while exemplifying a diversity of trade-off surge outcomes among different coastal places. For example, some of the Pareto optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in-depth evaluation of Pareto optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multi-scenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

The detection and quantification of global land change by satellite observations are the grand challenges to improve the understanding of global environmental change. In this study, we develop a new vegetation index, which can be used as a proxy of the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects and quantifies the global land change which has been reported by the previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is the important signal to detect and quantify global change in the terrestrial ecosystem.

Data assimilation methods in a coupled system, namely coupled data assimilation (CDA), have been attracting researchers’ interests to improve Earth system modeling. The CDA methods are classified into two; weakly coupled data assimilation (wCDA), which considers cross-domain interaction only in a model’s forecast phase, and strongly coupled data assimilation (sCDA), which additionally uses other domain’s information in an analysis phase. Although sCDA can theoretically provide better estimates than wCDA since sCDA fully uses a cross-domain covariance, the effectiveness of sCDA is still in debate. In this paper, we investigated the conditions under which sCDA is effective by applying Local Ensemble Transform Kalman Filter (LETKF) to the joint-Lorenz96 model. By continuously changing the magnitude of the cross-domain interaction of the joint-Lorenz96 model, we found that the superiority of sCDA against wCDA is particularly evident when the cross-domain interaction is large, albeit it does not contribute to increasing the chaoticity of the system. In addition, the performance of sCDA is quite sensitive to the LETKF’s hyperparameters (such as localization and inflation parameters) especially when the ensemble size is small, and the insufficient calibration of these parameters deteriorate the sCDA’s performance. Furthermore, sCDA is more vulnerable to model bias than wCDA; both cross-domain and intra-domain biases degrade the estimation skills.