150 Results Found

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrologi...

Disastrous floods are destructive and likely to cause widespread economic losses. An understanding of flood forecasting and its potential forecast uncertainty is essential for water resource managers. Reliable forecasting may provide future streamflo...

To facilitate wind power integration for the electric power grid operated by the Inner Mongolia Electric Power Corporation—a major electric power grid in China—a high-resolution (of 2.7 km grid intervals) mesoscale ensemble prediction sys...

Precise multi-time scales prediction of groundwater level is essential for water resources planning and management. However, credible and reliable predicting results are hard to achieve even to extensively applied artificial intelligence (AI) models...

In order to address the significant prediction errors resulting from the substantial fluctuations in agricultural product prices and the non-linear features, this paper proposes a hybrid forecasting model based on variational mode decomposition (VMD)...

To address the challenges posed by the volatility and uncertainty of wind power generation, this study presents a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD...

Zenith Tropospheric Delay (ZTD) is a significant source of atmospheric error in the Global Navigation Satellite System (GNSS). Developing a high-accuracy ZTD prediction model is essential for both GNSS positioning and GNSS meteorology. To address the...

The use of ensemble models to forecast the dispersion and transport of airborne volcanic ash in operational contexts is increasingly being explored. The ensemble members are usually constructed to represent a priori uncertainty estimates in meteorolo...

The Brazilian Northeast (BNE) is located in the tropical region of Brazil. It is bounded by the Atlantic Ocean, and its climate and vegetation are strongly affected by continental plateaus. The plateaus keep the humid air masses to the east and are r...

The feasibility of a hurricane initialization framework based on the Gridpoint Statistical Interpolation (GSI)-based four-dimensional ensemble-variational (GSI-4DEnVar) hybrid data assimilation system for the Hurricane Weather Research and Forecastin...

Accurate prediction of PM2.5 concentration for half a day can provide valuable guidance for urban air pollution prevention and daily travel planning. In this paper, combining adaptive variational mode decomposition (AVMD) and multivariate temporal gr...

In an ensemble prediction system, each member performs differently from each other for individual cases. To adaptively (not only statistically) calibrate or post-process raw ensemble forecasts and produce more reliable and accurate forecast products...

Surface wind speed forecast from an operational WRF Ensemble Prediction System (WEPS) was verified, and the system-bias representations of the WEPS were investigated. Results indicated that error characteristics of the ensemble 10-m wind speed foreca...

Many ensemble-based data assimilation (DA) methods use observation space localization to mitigate the sampling errors due to the insufficient ensemble members. Observation space localization is simpler and more timesaving than model space localizatio...

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...

The non-stationarity, nonlinearity and complexity of the PM2.5 series have caused difficulties in PM2.5 prediction. To improve prediction accuracy, many forecasting methods have been developed. However, these methods usually do not consider the impor...

Hybrid data assimilation (DA) methods have received extensive attention in the field of numerical weather prediction. In this study, a hybrid gain data assimilation (HGDA) method that combined the gain matrices of ensemble and variational methods was...

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-ter...

Based on the precipitation data of an ensemble forecast from the European Centre for Medium-Range Weather Forecasts, we establish a clustering model named EOF_AP by using the empirical orthogonal function decomposition and the affinity propagation cl...

A hybrid ensemble adjustment Kalman filter—three-dimensional ensemble—variational (EAKF-En3DVar) system is developed to assimilate conventional and radar data, and is applied to a convective case in Colorado and Kansas, USA. The system is based on th...

This study proposes a four-module “decomposition–forecasting–ensemble–correction” framework to improve the accuracy of complex coal price forecasts. The framework combines Variational Mode Decomposition (VMD), adaptive A...

Wind energy is a commonly utilized renewable energy source, due to its merits of extensive distribution and rich reserves. However, as wind speed fluctuates violently and uncertainly at all times, wind power integration may affect the security and st...

A novel ensemble algorithm based on kernel density estimation (KDE) is proposed to forecast distributed generation (DG) from renewable energy sources (RES). The proposed method relies solely on publicly available historical input variables (e.g., met...

“Short-term load forecasting (STLF)” is increasingly significant because of the extensive use of distributed energy resources, the incorporation of intermitted RES, and the implementation of DSM. This paper provides a novel ensemble forec...

The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics assoc...

In this paper, we describe and analyze a climatology of the Hot-Dry-Windy Index (HDW), with the goal of providing fire-weather forecasters with information about the daily and seasonal variability of the index. The 30-year climatology (1981–201...

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a h...

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper eval...

In order to systematically understand the operational forecast performance of current numerical, statistical, and ensemble models for O3 in Beijing–Tianjin–Hebei and surrounding regions, a comprehensive evaluation was conducted for the 30...

The data assimilation method to improve the sea fog forecast over the Yellow Sea is usually three-dimensional variational assimilation (3DVAR), whereas ensemble Kalman filter (EnKF) has not yet been applied to this weather phenomenon. In this paper,...

Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. Thi...

Data complexity directly affects the dynamics of complex systems, which in turn influences the accuracy and robustness of forecasting models. However, the load data exhibit complex features such as self-similarity, long-term memory, randomness, and c...

The coasts of the Italian peninsula have been recently affected by frequent damaging hydrological events driven by intense rainfall and deluges. The internal climatic mechanisms driving rainfall variability that generate these hydrological events in...

Air pollution poses a significant global challenge, impacting human health and environmental sustainability worldwide. Accurate air quality forecasting is essential for effective mitigation strategies, particularly in rapidly urbanizing regions. This...

This paper describes storm surge simulations made for Sandy (2012) for the Metropolitan New York (NYC) area using the Advanced Circulation (ADCIRC) model forced by the Weather Research and Forecasting (WRF) model. The atmospheric forecast uncertainty...

The North Atlantic Oscillation (NAO) is the most significant mode of the atmosphere in the North Atlantic, and it plays an important role in regulating the local weather and climate and even those of the entire Northern Hemisphere. Therefore, it is v...

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multis...

The aim of this study is to joint assimilate the ozone product from the satellite Atmospheric Infrared Sounder (AIRS) and bogus data using the four-dimensional ensemble-variational (4DEnVar) method, and demonstrate the potential benefits of this init...

An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as...

With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting m...

In this paper, we propose a Four-Dimensional Variational (4D-Var) data assimilation framework for wind energy potential estimation. The framework is defined as follows: we choose a numerical model which can provide forecasts of wind speeds then, an e...

Detection of an active fire in an image scene relies on an accurate estimation of the background temperature of the scene, which must be compared to the observed temperature, to decide on the presence of fire. The expected background temperature of a...

This study examines the impacts of assimilating ocean-surface winds derived from the NASA Cyclone Global Navigation Satellite System (CYGNSS) on improving the short-range numerical simulations and forecasts of landfalling hurricanes using the NCEP op...

This paper selects the case of the Atlantic hurricane “Michael” in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecast...

Temporal domain generalization is crucial for the temporal forecasting of sensor data due to the non-stationary and evolving nature of most sensor-generated time series. However, temporal dynamics vary in scale, semantics, and structure, leading to d...

In this study, we conducted experiments to assess the forecasting capabilities for tropical cyclone (TC) genesis over the east sea of Vietnam using the ensemble-based data assimilation system (EPS-DA) by WRF-LETKF. These experiments covered forecast...

The National Center for Atmospheric Research (NCAR) recently updated the comprehensive wind power forecasting system in collaboration with Xcel Energy addressing users’ needs and requirements by enhancing and expanding integration between numer...

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting...

This study investigates the assimilation impact of rapid-scan (RS) atmospheric motion vectors (AMVs) derived from the geostationary satellite Himawari-8 on tropical cyclone (TC) forecasts. Forecast experiments for three TCs in 2016 in the western Nor...

The four-dimensional variational data assimilation (4DVar) method is one of the most popular techniques used in numerical weather prediction. Nevertheless, the needs of the adjoint model and the linearization of the forecast model largely limit the w...