Renewable energy is key for the development of African countries, and knowing the best
location for the implementation of solar and wind energy projects is important within this …

Multi‐model ensembles (MMEs) are used to improve the forecasts of thermospheric neutral
densities. A variety of algorithms for constructing the model weights for the MMEs are …

Seasonal hydrological forecasts at high spatial and temporal resolution can help manage
water resources and mitigate impacts of extreme events but are dependent on skillful and …

Bridging the gap between weather forecasting and climate prediction, subseasonal to
seasonal (S2S) forecasts are of great importance yet currently of relatively poor quality …

Estuarine floods are one of the most harmful and complex extreme events occurring in
coastal environments. To predict the associated effects, characterize areas of risk, and …

Climate ensembles utilize outputs from multiple climate models to estimate future climate
patterns. These multi-model ensembles generally outperform individual climate models. In …

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 …

Assessing the capability of sub-seasonal rainfall forecast of dynamic model and proposing
correction method is quite an important topic in current climate research field. From the …

One of the key attributes of an ensemble prediction system (EPS) is the spread among the
members. It plays a crucial role in conveying the uncertainty associated with the forecasted …

Accurate and reliable predictions for bus arrival in public transport networks are essential for
delivering an attractive service. This paper presents a multi-model approach for bus arrival …