The present study evaluates the National Aeronautics Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset that provides statistically downscaled CMIP5 historical and future climate projections of the daily...
Previous studies on multi-model ensemble forecasting mainly focused on the weight allocation of each model, but did not discuss how to suppress the reduction of ensemble forecasting accuracy when adding poorer models. Based on a variant weight (VW) m...
Simulated historical extreme precipitation is evaluated for Coupled Model Intercomparison Project Phase 6 (CMIP6) models using precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). The indices of 33 Global...
The multi-model ensemble (MME) approaches are highly regarded in climate prediction and risk assessment for their capacity to integrate multiple global climate models (GCMs) and minimize uncertainties associated with individual models. However, the q...
Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasti...
With the development of in-situ monitoring techniques, the ensemble Kalman filter (EnKF) has become a popular data assimilation method due to its capability to jointly update model parameters and state variables in a sequential way, and to assess the...
Southwest Asia has different climate types including arid, semiarid, Mediterranean, and temperate regions. Due to the complex interactions among components of the Earth system, forecasting precipitation is a difficult task in such large regions. The...
Urban multimodal travel trajectory prediction is a core challenge in Intelligent Transportation Systems (ITSs). It requires modeling both spatiotemporal dependencies and dynamic interactions among different travel modes such as taxi, bike-sharing, an...
Given the substantial impacts that are expected due to climate change, it is crucial that accurate rainfall–runoff results are provided for various decision-making purposes. However, these modeling results often generate uncertainty or bias due to th...
Hydrologic models are an approximation of reality, and thus, are not able to perfectly simulate observed streamflow because of various sources of uncertainty. On the other hand, skillful operational hydrologic forecasts are vital in water resources e...
It is a grand challenge to realize robust rainfall-runoff prediction for a changing climate through conceptual hydrological models. Although multi-model ensemble (MME) is considered useful in improving the robustness of hydrological prediction, it ha...
Large ensembles of climate models are increasingly available either as ensembles of opportunity or perturbed physics ensembles, providing a wealth of additional data that is potentially useful for improving adaptation strategies to climate change. In...
Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA)...
This study introduces an unequally weighted technique for Multi-model Ensemble (MME) forecasting for western North Pacific Tropical Cyclone (TC) tracks. Weights are calculated by partial least square regression, and members are selected by paired t-t...
Petroleum is a significant source of global energy supply, and accurate and efficient anomaly diagnosis of energy consumption in oilfields plays a crucial role in controlling operational costs and ensuring environmental sustainability. This study uti...
The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS...
Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for...
In order to accurately evaluate the operating status of secondary equipment in smart substations, this paper establishes a secondary equipment status evaluation index system and proposes a secondary equipment status evaluation method based on multi-m...
In this study, we devised a constraint method, called multi-model ensemble pattern (MEP), to estimate the refractive index structure parameter (
Climate change presents considerable challenges to hydrological stability by modifying precipitation patterns and exacerbating the frequency and intensity of extreme rainfall events. This research evaluates the prospective alterations in rainfall qua...
The Maritime Continent (MC) is a critical region with unique geographical conditions and significant monsoon activities that plays a vital role in global climate variation. In this study, the weekly prediction of precipitation over the MC during bore...
The climate is changing and its impacts on agriculture are a major concern worldwide. The impact of precipitation will influence crop yield and water management. Estimation of such impacts using inputs from the General Circulation Models (GCMs) for f...
This study conducts a comprehensive analysis of the impacts of climate change on South Korea’s climate and hydrology, utilizing a Multi-Model Ensemble (MME) approach with thirteen Climate Model Intercomparison Project Phase 5 (CMIP5) models und...
Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and...
Understanding how medicinal plant distributions shift in response to climate change is essential for developing forward-looking conservation strategies. Cibotium barometz (L.) J. Sm., a tree fern from the family Dicksoniaceae, is not only ecologicall...
With the rapid development of meteorological models, numerical weather prediction is increasingly used in flood forecasting and reservoir regulation, but its forecasting ability is limited by the large amount of uncertainty from meteorological system...
This study proposes an alternative method to estimate the potential predictability without assuming the perfect model. A theoretical consideration relates a maximum possible value of the initial-value error to the covariance between analysis and bias...
A multimodel superensemble technique was applied to improve the accuracy of a model predicting the subseasonal surface air temperatures in East Asia. Superensemble forecast data based on seven subseasonal-to-seasonal (S2S) forecast models were produc...
While weighted multi-model approaches are widely used to improve predictive capability, hydrological models (HMs) and their weighted combinations that perform well under past conditions may not guarantee robustness under future climate scenarios. Fur...
The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By dia...
The quality of precipitation forecasting is critical for more accurate hydrological forecasts, especially flood forecasting. The use of numerical weather prediction (NWP) models has attracted much attention due to their impact on increasing the flood...
Southeast Europe is considered one of the most vulnerable regions in the context of climate change, and projected future summer warming is expected to exceed global rates significantly. Despite the importance of this problem, there have been few stud...
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 th...
This study investigated the strength and limitations of two widely used multi-model averaging frameworks—Bayesian model averaging (BMA) and reliability ensemble averaging (REA), in post-processing runoff projections derived from coupled hydrological...
Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers o...
Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived us...
This work aims to examine the effect of bias correction (BC) methods on the development of Intensity–Duration–Frequency (IDF) curves under climate change at multiple temporal scales. Daily outputs from a 9-member CORDEX-CA GCM-RCM multi-m...
In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based...
Bayesian model averaging (BMA) is a popular method using the advantages of forecast ensemble to enhance the reliability and accuracy of predictions. The inherent assumptions of the classical BMA has led to different variants. However, there is not a...
This study assesses the performance of historical rainfall data from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the spatial and temporal rainfall variability over North Africa. Datasets from Climatic Research Unit (CRU)...
This research assesses four Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) concerning precipitation quantiles across Korea, utilizing the CMIP6 multi-model ensemble comprising 23 General Circulation Models along...
More than half of South Korea’s land area is covered by forests, which significantly increases the potential for extensive damage in the event of a forest fire. The majority of forest fires in South Korea are caused by humans. Over the past dec...
With the significant increase in solar power generation’s proportion in power systems, the uncertainty of its power output poses increasingly severe challenges to grid operation. In recent years, solar forecasting models have achieved remarkabl...
Hybrid drought prediction models were developed for areas with limited monitoring gauges using the APEC Climate Center Multi-Model Ensemble seasonal climate forecast and machine learning models of Extra-Trees and Adaboost. The models provide spatiall...
With global temperatures steadily rising, understanding the impacts of warming on regional climates has become crucial, particularly for countries like India, where climate sensitivity has significant socio-economic implications. This study assesses...
With evidence suggesting that climate change is resulting in changes within the hydrologic cycle, the ability to robustly model hydroclimatic response is critical. This paper assesses how extreme runoff—1:2- and 1:30-year return period (RP) eve...
Streamflow is a very important component of the hydrological cycle, and variation in the streamflow can be an indication of hydrological disaster. Thus, the accurate quantification of streamflow variation is a core concern in water resources engineer...
Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in...
Surface incident shortwave radiation (SSR) is crucial for understanding the Earth’s climate change issues. Simulations from general circulation models (GCMs) are one of the most practical ways to produce long-term global SSR products. Although...
Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detectio...
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