Atmospheric Prediction Research Articles

Revealing Circulation Patterns Responsible for Extreme Precipitation Events Over the Hai River Basin From Moisture Transport Perspective

AbstractIn July 2023, an extreme precipitation event (EPE) caused severe damage and nearly 100 fatalities in the Hai River basin, North China. Using a Lagrangian approach, we investigated the moisture transport regime and identified the circulation pattern responsible for the EPE. The primary moisture sources were the northwest Pacific (24%), southeast China (21%), and the South China Sea (20%). The circulation pattern, featuring the western Pacific subtropical high to the northeast, tropical cyclone KHANUN in the northwest Pacific, and a low‐pressure system southwest of the Hai River Basin, predominantly controlled the moisture transport. We discovered that an unprecedented EPE in 1963 exhibited a similar circulation pattern. By classifying historical EPEs into three categories based on seven atmospheric predictors, we identified circulation patterns associated with disaster‐prone events. The most frequent category is characterized by a circulation pattern with positive geopotential height anomalies in northeast Asia and negative anomalies to the south, extending from the northwest Pacific to the Hai River Basin. This classification provides insights into the atmospheric conditions conducive to severe EPEs in the region.

The international multi-system OSEs/OSSEs by the UN Ocean Decade Project SynObs and its early results

“Synergistic Observing Network for Ocean Prediction (SynObs)” was launched in 2022 as a project of the United Nations Decade of Ocean Science for Sustainable Development to evaluate the importance of ocean observation systems and co-design the future evolution of the ocean observing network. SynObs is currently leading the flagship OSEs/OSSEs, an internationally coordinated activity in which observing system experiments (OSEs) and observing system simulation experiments (OSSEs) are conducted using a variety of ocean and coupled atmosphere–ocean prediction systems to evaluate ocean observation impacts consistent across most prediction systems. The flagship OSEs/OSSEs comprises the ocean prediction (OP) OSEs for high-resolution ocean predictions, the subseasonal-to-seasonal (S2S) OSEs for long-term lead-time coupled ocean–atmosphere predictions, and the OP OSSEs for evaluating new and future observing systems. SynObs plans to use the results of the flagship OSEs to contribute to the reports on the ocean observing network design made by international organizations and projects. Here, we introduce this initiative, and we report on some initial results. Some observation impacts consistent across four ocean prediction systems are found by a preliminary analysis of the analysis runs for the OP OSEs. For example, impacts of the altimetry data on the assimilated sea surface height (SSH) field are generally large in the westerly boundary current regions and around Antarctic Circumpolar Currents where SSH has large variability but are small in the tropical regions, despite the relatively large SSH variability there. The analysis also indicates the possibility that there are some characteristic differences in the observation impacts between low-resolution and eddy-resolving ocean prediction systems. Although OSE outputs of only four ocean prediction systems are available now, we will make further investigation, adding OSE outputs of other prediction systems that will be submitted in the near future.

Sensitivity of Dust Deposition for Parabolic Trough Collector Mirrors to Different Meteorological Drivers

This abstract presents a soiling forecasting tool (SFT) for assessing the deposition of dust on parabolic trough collector (PTC) mirrors and its cumulative impact on their reflectivity. The SFT was developed by the University of Patras in the frame of the Smart Solar System (S3) project (Horizon 2020 Solar-Era.net). An initial version of the model was presented at the SolarPACES 2022 conference, where the adaptation of the model to dust transport phenomena was demonstrated. The subsequent step in the evolution of the algorithm concerned the calibration of the SFT under different atmospheric conditions. In addition, the model was modified to incorporate meteorological and aerosol forecast data as inputs. The atmospheric predictions of dust and aerosol optical depth are from the global atmospheric forecasts of the Copernicus Atmosphere Monitoring Service. Regarding the meteorological data, three independent sources are used. Those are the Norwegian Meteorological Institute's meteorological forecasts (YR), the METAR meteorological observations from the closest airport, and lastly, the data from the automatic weather station at the location of the PTC system at the KEAN Soft Drinks Ltd factory in Limassol, Cyprus. The aim of this paper is to assess the impact of the three distinct meteorological input sources on the modelled reflectivity and its comparison to measurements taken during the experimental campaign.

Predictability of midlatitude Rossby‐wave packets

AbstractRossby‐wave packets (RWPs) along the midlatitude jet are a fundamental ingredient of extratropical dynamics. RWPs have been linked to enhanced atmospheric predictability and the occurrence of extreme weather events. We here investigate the predictability of Northern Hemispheric RWPs as physical entities, defined by enhanced values of the Rossby‐wave envelope field. Using a catalogue of RWPs identified and tracked in reanalysis data, we analyze RWP predictability in a 19‐year period of NOAA GEFSv12 reforecasts. Our analysis adopts the so‐called distance and amplitude score (DAS), a verification metric that avoids both the double‐penalty issue of field‐based verification of coherent features and the complications of an object‐based approach. Applied to the envelope field, forecast errors defined by this metric asymptote towards saturation, but do not completely reach saturation within the 10 days lead time available to this study. The growth rate of the median DAS is highest initially and decreases with lead time. This is a nontrivial result, because the underlying envelope field largely de‐emphasizes phase information, but still exhibits very similar error‐growth characteristics to fields that contain the full phase information. Variations in RWP predictability are dominated by the stage of the RWP life cycle, with higher predictability found for the propagation stage than the onset and decay stages. In addition, RWP predictability exhibits a seasonal cycle, with higher predictability in winter than in summer. Controlling for seasonality and the stage of the life cycle, we find that (i) high‐amplitude RWPs exhibit higher predictability than low‐amplitude RWPs up to 6 days lead time and (ii) there is a general pattern of relatively high predictability over Eurasia. Finally, predictability of the propagating stage is higher if forecasts are initialized after RWP onset than if initialized before onset. In this sense, RWP onset acts as a predictability barrier to the subsequent propagation stage.

Integrating Flow Field Dynamics and Chemical Atmosphere Predictions for Enhanced Sulfur Corrosion Risk Assessment in Power Boilers.

In this work, we attempt to explain the phenomenon of sulfur corrosion of power boiler water walls under the conditions of large fluctuations in carbon monoxide concentrations. To assess the conditions required for corrosion formation, a criterion based on the chemical and flow field parameters of the flue gas is proposed. The formulated sulfur corrosion criterion is based on the mixture fraction variance and the turbulence time scale. Numerical modeling of coal combustion in a 250 MW power boiler is performed using ANSYS. Two cases of combustion in a boiler are analyzed, with the first simulating the boiler operated using classic high-swirl burners and the second one accounting for boiler operation with modified low-swirl burners. Calculations of pulverized coal combustion are performed using the standard k-ε turbulence model and the combustion described by the mixture fraction. The simulation results reveal that the low-swirl burner is characterized by higher values of the mixture fraction variance and a higher frequency of fluctuation of the velocity field, which is strongly related to an increased corrosion rate. The study outcomes show the validity of using the criterion of the mixture fraction variance and velocity field fluctuations to determine the areas at risk of sulfur corrosion.

Predictability Limit of the 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis

AbstractThe traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the perturbations. Eliminating small scales from the perturbations also yields similar forecast improvements. Extending the length of the optimization window, we find forecast improvement to about 23 days, suggesting atmospheric predictability at the upper end of recent estimates.

AI-Driven Forecasting for Morning Fog Expansion (Sea of Clouds)

Abstract This study attempted to forecast the morning fog expansion (MFE), commonly referred to as the “sea of clouds,” utilizing an artificial intelligence (AI) algorithm. The radiation fog phenomenon that contributes to the sea of clouds is caused by various weather conditions. Hence, the MFE was predicted using datasets from public meteorological observations and a mesoscale numerical model (MSM). In this study, a machine learning technique, the gradient-boosting method, was adopted as the AI algorithm. The Miyoshi basin in Japan, renowned for its MFE, was selected as the experimental region. Training models were developed using datasets from October to December 2018–21. Subsequently, these models were applied to forecast MFE in 2022. The model employing the upper-atmospheric prediction data from the MSM demonstrated the highest robustness and accuracy among the proposed models. For untrained data in the fog season during 2022, the model was confirmed to be sufficiently reliable for forecasting MFE, with a high hit rate of 0.935, a low Brier score of 0.119, and a high area under the curve (AUC) of 0.944. Furthermore, the analysis of the importance of the features elucidated that the meteorological factors, such as synoptic-scale weak wind, temperatures close to the dewpoint temperature, and the absence of middle-level cloud cover at midnight, strongly contribute to the MFE. Therefore, the incorporation of upper-level meteorological elements improves the forecast accuracy for MFE. Significance Statement An AI-driven forecasting model for predicting morning fog expansion (MFE), sea of clouds, which often affects local livelihoods, was constructed. Fog forecasting machine learning techniques were utilized in the Japanese region famous for the morning fog. This study revealed that more accurate forecasting models incorporate numerically predicted weather elements sourced from the public routine system rather than real-time observed weather elements. Notably, the upper-level wind speed reflecting synoptic-scale dynamics, surface dewpoint depression, and middle-level cloud cover play significant roles in governing MFE. Therefore, incorporating upper-level meteorological elements into the features to machine learning is crucial for improving the forecasting accuracy of MFE.

Indian Ocean Dipole (IOD) forecasts based on convolutional neural network with sea level pressure precursor

Abstract Forecasting the Indian Ocean Dipole (IOD) is crucial because of its significant impact on regional and global climates. While traditional dynamic and empirical models suffer from systematic errors due to nonlinear processes, convolutional neural networks (CNN) are nonlinear in nature and have demonstrated remarkable El Niño Southern Oscillation (ENSO) and IOD forecasting skills based on oceanic predictors, particularly sea surface temperature and heat content. However, it is difficult to measure heat content and easily introduces uncertainties, prompting the need to explore atmospheric predictors for IOD forecasts. Based on sensitivity prediction experiments, we identified the sea level pressure (SLP) signal as a crucial predictor, which forecasts IOD at a 7 month lead. In addition, the CNN model improves monthly forecasting accuracy while reducing errors by 13.43%. Utilizing the heatmap analysis, we elucidated that the multi-seasonal predictability of the IOD primarily originates from mid-latitude climate variability. Besides ENSO signals in the Pacific Ocean, our study highlights the significant impact of remote climate forcing in the South Indian Ocean, tropical North Indian Ocean, and Northwest Pacific Ocean on IOD forecasts. By introducing the SLP precursor and extratropical zones into IOD forecasts, our study offers fresh insights into the underlying dynamics of IOD evolution.

Winter Targeted Observing Periods during the Year of Polar Prediction in the Southern Hemisphere (YOPP-SH)

Abstract The Year of Polar Prediction in the Southern Hemisphere (YOPP-SH) held seven targeted observing periods (TOPs) during the 2022 austral winter to enhance atmospheric predictability over the Southern Ocean and Antarctica. The TOPs of 5–10-day duration each featured the release of additional radiosonde balloons, more than doubling the routine sounding program at the 24 participating stations run by 14 nations, together with process-oriented observations at selected sites. These extra sounding data are evaluated for their impact on forecast skill via data denial experiments with the goal of refining the observing system to improve numerical weather prediction for winter conditions. Extensive observations focusing on clouds and precipitation primarily during atmospheric river (AR) events are being applied to refine model microphysical parameterizations for the ubiquitous mixed-phase clouds that frequently impact coastal Antarctica. Process studies are being facilitated by high-time-resolution series of observations and forecast model output via the YOPP Model Intercomparison and Improvement Project (YOPPsiteMIIP). Parallel investigations are broadening the scope and impact of the YOPP-SH winter TOPs. Studies of the Antarctic tourist industry’s use of weather services show the scope for much greater awareness of the availability of forecast products and the skill they exhibit. The Sea Ice Prediction Network South (SIPN South) analysis of predictions of the sea ice growth period reveals that the forecast skill is superior to the sea ice retreat phase.

Exploring the Origin of the Two-Week Predictability Limit: A Revisit of Lorenz’s Predictability Studies in the 1960s

The 1960s was an exciting era for atmospheric predictability research: a finite predictability of the atmosphere was uncovered using Lorenz’s models and the well-acknowledged predictability limit of two weeks was estimated using a general circulation model (GCM). Here, we delve into details regarding how a correlation between the two-week predictability limit and a doubling time of five days was established, recognize Lorenz’s pioneering work, and suggest non-impossibility for predictability beyond two weeks. We reevaluate the outcomes of three different approaches—dynamical, empirical, and dynamical-empirical—presented in Lorenz’s and Charney et al.’s papers from the 1960s. Using the intrinsic characteristics of the irregular solutions found in Lorenz’s studies and the dynamical approach, a doubling time of five days was estimated using the Mintz–Arakawa model and extrapolated to propose a predictability limit of approximately two weeks. This limit is now termed “Predictability Limit Hypothesis”, drawing a parallel to Moore’s Law, to recognize the combined direct and indirect influences of Lorenz, Mintz, and Arakawa under Charney’s leadership. The concept serves as a bridge between the hypothetical predictability limit and practical model capabilities, suggesting that long-range simulations are not entirely constrained by the two-week predictability hypothesis. These clarifications provide further support to the exploration of extended-range predictions using both partial differential equation (PDE)-physics-based and Artificial Intelligence (AI)—powered approaches.

Onboard guidance and control algorithms for increased autonomous aerobraking capabilities at Mars

Aerobraking represents a valuable option for interplanetary missions thanks to its capability of reducing the orbital semi-major axis through multiple atmospheric passages, thus with limited fuel consumption. However, due to structural and thermal loads the spacecraft undergoes, mission risks and ground operations costs increase. This study aims to tackle this aerobraking drawback by enhancing its autonomy. The main activities required for aerobraking execution, including atmospheric prediction, orbital state estimation, and orbit control, are tailored for onboard execution. Regarding orbit control, two different methodologies, differentiated by working principle and prediction horizon breadth, are investigated. The first plans orbit control manoeuvres based on the prediction of the atmospheric conditions for a single upcoming pericentre, while the second schedules and optimizes the manoeuvres considering the recent atmospheric conditions experienced by the spacecraft and the altitude trend of multiple upcoming pericentres. The whole operational concept is tested within a simulated environment, with the primary objective of accurately reproducing the intense atmospheric variations of the Martian atmosphere, which represents the most challenging aspect during aerobraking execution. The numerical testing, involving the simulation of different aerobraking regimes under varied conditions, shows that the natural dynamics of the orbital motion can be exploited to minimize both fuel consumption for orbit control manoeuvres and spacecraft load variations, thus enabling more efficient aerobraking and more stable flight conditions, even with the limited predictive capabilities that characterize onboard resources. Both control logics permit the aerobraking execution without exceeding the maximum allowed thermal limits, hence showing their effectiveness. Furthermore, a Monte Carlo analysis conducted on a complete aerobraking simulation reveals a 25.5% reduction in propellant consumption and a 9.4% reduction in heat rate variability when the orbit control decision process uses information relative to multiple future pericentres.

Improved freezing rain forecast using machine learning

Freezing rain is one of the most damaging weather phenomena in winter or early spring in many parts of the world, affecting traffic, power lines and agriculture. Thus, reliable and computationally efficient prediction of its occurrence is urgently needed in weather forecast operations. However, there are different thermodynamic processes that can lead to freezing rain, resulting in unsatisfactory forecasting performance of the state-of-the-art Numerical Weather Prediction (NWP) models. Here a data-driven forecasting method for freezing rain using machine learning technologies is proposed. Observations of weather phenomenon collected from 2 515 national weather stations of China for winter of 2016–2019 and the corresponding atmospheric predictors derived from ERA5 reanalysis are used. The prediction function is constructed based on the classification and regression tree, and the predicting variables include temporal and vertical profiles of fundamental thermodynamic and kinematic parameters from 500 hPa to 1000 hPa, with a total dimension of 2 304. The LightGBM (Light Gradient Boosting Machine) framework is adopted to train our prediction model and an algorithm-level approach of modifying the loss function is used to address the imbalance of classes to improve forecasting skill. Results show that the data-driven prediction model, namely DDFR (data driven forecast of freezing rain), out-performs the benchmark NWP, i.e., ECMWF IFS product. It's improvements in terms of TS score range from 120% to 258% depending on different forecast leading times, which range from 0 to 12 h. In addition, DDFR is applied in an operational NWP model of China. The problem of domain adaptation is tackled and transfer learning method is employed to adapt the original DDFR to this NWP model. The effectiveness of such adaptation has been demonstrated by its performance on both training and testing datasets.

An explainable machine learning technique to forecast lightning density over North-Eastern India

Increasing lightning fatalities over India is a concerning subject. Especially, it is pretty crucial over North-Eastern part of the country where lightning is extremely frequent. Given the complex nature of the problem, machine learning can be an excellent option in such forecasting scenarios. However, such dynamic processes seek proper transparency of the model. The current work attempts to devise a model for short range prediction (one month ahead) of lightning density based on primary atmospheric parameters from satellite data with a lead time of one month over North –Eastern and Eastern part of the country. Random Forest regression seems to outperform other models explored, with a R2 of 0.86 and an MAE of 0.0071. The interpretation of the model output using SHAP index reveals that 2 m temperature at previous two months and CAPE and K-index at previous month has a positive impact on the output of the model whereas, instantaneous surface heat flux of previous month and two month prior K-index has an inhibiting effect on model's output. The use of machine learning techniques for atmospheric predictions without the shed of the black box can be of importance to the scientific community. Such studies especially over lightning prone tropical regions can be crucial in meteorological forecasting applications.

Maritime free space optical communications field test and link budget statistics

During sub-optimal weather, a free-space optical (FSO) link range degrades depending on attenuation (atmospheric extinction) and turbulence effects. The ability to predict the system level performance can be exceedingly challenging as the atmospheric variability in a maritime link can be large and difficult to model. Link budget estimation for FSO systems often takes a nominal view of atmospheric conditions; here, we use statistical atmospheric predictions specific to a geographic area of interest to enable performance trades to be evaluated through link budget analysis. We compare these models to field-collected data to show the utility of the statistical atmospheric analysis in predicting FSO link performance for specific parts of the world. We have performed shore-to-ship FSO communications field tests at 10 Gb/s with links reaching out to a horizon limit over 40 km away in times of moderate extinction to clear weather. We provide further analysis by describing the expected performance of the link using statistical probabilities via cumulative distribution functions of both extinction and turbulence. The atmospheric variability can be determined for nearly any region of interest through the implementation of numerical weather prediction data to calculate the atmospheric performance drivers. These conditions are specifically evaluated for the 2017 Trident Warrior field test off the coast of San Diego, California, USA.

Hybrid multilayer perceptron and convolutional neural network model to predict extreme regional precipitation dominated by the large-scale atmospheric circulation

Recent advances in deep learning have provided tools that enable meteorologists to predict extreme precipitation using massive atmospheric data. However, individual models are constrained by imbalanced samples and prone to false alarms owing to the rarity of extreme precipitation events. In this study, a novel ensemble learning model, that is, hybrid multilayer perceptron and convolutional neural network (MLP-CNN) model is proposed for the binary prediction of extreme precipitation in Central-Eastern China (CEC), with a daily time horizon. The MLP-CNN model achieves an overall accuracy of 86% in predicting extreme and non-extreme precipitation days using the anomalous fields of two large-scale atmospheric predictors, i.e., geopotential height at 500 hPa and vertically integrated water vapor transport. Subsequently, we employ the MLP-CNN to predict extreme precipitation with a 1–15 day leadtime. The performance of MLP-CNN tends to decrease with increasing leading time of circulation anomalies. However, 1–2 days of advance forecasting can be considered a reference for predicting the occurrence probabilities of extreme precipitation. Finally, based on various evaluation metrics, MLP-CNN outperforms the independent predictions from MLP, CNN, and two other machine learning models (i.e., random forest and support vector machine). Overall, in scenarios where samples are limited, the utilization of hybrid models presents an opportunity for optimizing predictions for extreme precipitation.

Synoptic-scale drivers of fire weather in Greece

The identification of the large-scale atmospheric circulation patterns which are associated with extreme fire weather is of great importance for developing early warning systems, management strategies, and for increasing awareness and preparedness of all the involved entities, including both the public and practitioners. Such a forecasting approach is currently missing in Greece and many other countries. Furthermore, considering climate projections over the Mediterranean, which indicate an environment more conducive to wildfire activity, the need for timely forecasting of extreme fire weather becomes increasingly urgent. Here, we present an alternative fire weather forecasting framework using ERA5 reanalysis data of atmospheric variables and fire weather indices of the Canadian Forest Fire Weather Index System (CFFWIS) during the period June–October from 1979 to 2019. Within this framework, we define the critical fire weather patterns (CFWPs) of Greece associated with different levels of fire weather severity by applying Self-Organizing-Maps (SOMs) on mid-tropospheric geopotential height. We quantify the fire weather conditions associated with each CFWP. Using a set of CFFWIS indices and key fire weather variables, our SOM-based analysis reveals five distinct CFWPs linked to different levels and characteristics of fire weather severity. The lowest fire weather severity is associated with lower than average geopotential heights, and anomalous cold and moist weather. The highest fire weather severity is associated with higher than average geopotential heights, and anomalous hot, dry, and windy conditions, suggesting the potential for wind-driven wildfires. Our analysis yields elevated fire weather severity linked to a CFWP, when hot and dry conditions are accompanied by atmospheric instability, suggesting the potential for plume-driven wildfires and the potential for pyroconvection. The main advantage of this forecasting framework is that it could be used for providing valuable information regarding the upcoming fire weather conditions even up to 7–12 days in advance depending on the atmospheric predictability.

Physics-Inspired Adaptions to Low-Parameter Neural Network Weather Forecast Systems

Abstract Recently, there has been a surge of research on data-driven weather forecasting systems, especially applications based on convolutional neural networks (CNNs). These are usually trained on atmospheric data represented on regular latitude–longitude grids, neglecting the curvature of Earth. We assess the benefit of replacing the standard convolution operations with an adapted convolution operation that takes into account the geometry of the underlying data (SphereNet convolution), specifically near the poles. Additionally, we assess the effect of including the information that the two hemispheres of Earth have “flipped” properties—for example, cyclones circulating in opposite directions—into the structure of the network. Both approaches are examples of physics-informed machine learning. The methods are tested on the WeatherBench dataset, at a resolution of ∼1.4°, which is higher than many previous studies on CNNs for weather forecasting. For most lead times up to day +10 for 500-hPa geopotential and 850-hPa temperature, we find that using SphereNet convolution or including hemisphere-specific information individually leads to improvement in forecast skill. Combining the two methods typically gives the highest forecast skill. Our version of SphereNet is implemented flexibly and scales well to high-resolution datasets but is still significantly more expensive than a standard convolution operation. Finally, we analyze cases with high forecast error. These occur mainly in winter and are relatively consistent across different training realizations of the networks, pointing to flow-dependent atmospheric predictability.

Physical Insights From the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks

AbstractNorth Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable in the world's oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25‐year leadtimes. Layer‐wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream‐North Atlantic Current region for accurate predictions. Additionally, CESM1‐trained NNs successfully predict the phasing of multidecadal variability in an observational data set, suggesting consistency in physical processes driving NASST variability between CESM1 and observations.

Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting

Analysis of Chaos Fluctuations in Atmospheric Prediction, Fluid Mechanics and Power System Load Forecasting

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble 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 1,000 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) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index 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. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. 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 multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.