We propose a weather prediction model in this article based on neural network and fuzzy inference system (NFIS-WPM), and then apply it to predict daily fuzzy precipitation given meteorological premises for testing. The model consists of two parts: th...
Nowadays, the Global Navigation Satellite Systems (GNSS) have become an effective atmospheric observing technique to remotely sense precipitable water vapor (PWV) mainly due to their high spatiotemporal resolutions. In this study, from an investigati...
How to predict precipitation accurately and efficiently is the key and difficult problem in the field of weather forecasting. At present, we can obtain accurate meteorological data through many high-precision weather sensors and use them to forecast...
Precipitation is a critical input for hydrologic simulation and prediction, and is widely used for agriculture, water resources management, and prediction of flood and drought, among other activities. Traditional precipitation prediction researches o...
Precipitation is a vital component of the regional water resource circulation system. Accurate and efficient precipitation prediction is especially important in the context of global warming, as it can help explore the regional precipitation pattern...
Radar precipitation nowcasting remains challenging because a model must not only represent the overall motion trends of large-scale precipitation systems, but also capture the fine-grained structural variations of localized strong echo regions while...
Due to the complexity and uncertainty of meteorological systems, traditional precipitation forecasting methods have certain limitations. Therefore, based on the common characteristics of meteorological data, a precipitation forecasting model named Mu...
This study investigated biased prediction of cumulative precipitation, using a variety of patterns of histories of cumulative precipitation, to explore how such biased prediction could delay evacuation or evacuation orders. The irrationality in predi...
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...
Typhoon is one of the most frequent meteorological phenomena that covers most of central-eastern China during the summer. Typhoon-induced precipitation is one of the most important water resources, but it often leads to severe flood disasters. Accura...
Southeast Australia is frequently impacted by drought, requiring monitoring of how the various factors influencing drought change over time. Precipitation and temperature trends were analysed for Canberra, Australia, revealing decreasing autumn preci...
Many natural disasters in South America are linked to meteorological phenomena. Therefore, forecasting and monitoring climatic events are fundamental issues for society and various sectors of the economy. In the last decades, machine learning models...
The performance of monthly prediction of extreme precipitation from the BCC-CPSv3-S2Sv2 model over the Yellow River Basin (YRB) using historical hindcast data from 2008 to 2022 was evaluated in this study, mainly from three aspects: overall performan...
Quantitative precipitation prediction is essential for managing water-related disasters, including floods, landslides, tsunamis, and droughts. Recent advances in data-driven approaches using deep learning techniques provide improved precipitation now...
This study employs precipitation data sets from historical trials on 20 CMIP6 global climate models and four shared socioeconomic pathway scenario trials (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) to predict trends in extreme precipitation changes...
Accurate precipitation prediction is of great significance for regional flood control and disaster mitigation. This study introduced a prediction model based on the least square support vector machine (LSSVM) optimized by the genetic algorithm (GA)....
Accurate precipitation intensity forecasting is crucial for effective flood management and early warning systems. This study evaluates the performances of convolutional neural network (CNN) and long short-term memory (LSTM) models in predicting hourl...
Meteorological disasters are the result of the interaction of multiple factors and multiple systems. In order to improve the accuracy of prediction, it is necessary not only to consider the characteristics and cycles of each subsystem, but also to st...
Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning technique—a formulation of gradient-boosted trees&m...
Boreal summer precipitation over the Tibetan Plateau (TP) is difficult to predict in current climate models and has become a challenging issue. To address this issue, a new analog-based correction method has been developed. Our analysis reveals a sub...
A covariance decomposition method is applied to a monthly global precipitation dataset to decompose the interannual variability in the seasonal mean time series into an unpredictable component related to “weather noise” and to a potential...
Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind sp...
This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5° × 0.5°) together with climate indices. The model is based on principal component analyses (PCA) and artificial neu...
It is a challenge to improve the skill of seasonal precipitation prediction, because there are many factors affecting summer precipitation in China, which are found on different time scales and have complex interactions with each other. For these rea...
The Upper Indus Basin (UIB) and the Karakoram Range are the subject of ongoing hydro-glaciological studies to investigate possible glacier mass balance shifts due to climatic change. Because of the high altitude and remote location, the Karakoram Ran...
Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do n...
Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting....
It has become increasingly difficult in recent years to predict precipitation scientifically and accurately due to the dual effects of human activities and climatic conditions. This paper focuses on four aspects to improve precipitation prediction ac...
The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the Nort...
Interactions between clouds, aerosol, and precipitation are crucial aspects of weather and climate. The simple Koren–Feingold conceptual model is important for providing deeper insight into the complex aerosol–cloud–precipitation sy...
Previous studies suggested that spring precipitation over the tropical western Pacific Ocean can influence the development of El Niño–Southern Oscillation (ENSO). To identify crucial precipitation patterns for post-spring ENSO evolution,...
Ensemble precipitation forecasts (EPFs) can help to extend lead times and provide reliable probabilistic forecasts, which have been widely applied for streamflow predictions by driving hydrological models. Nonetheless, inherent biases and under-dispe...
Droughts in southeastern Australia can profoundly affect the water supply to Sydney, Australia’s largest city. Increasing population, a warming climate, land surface changes and expanded agricultural use increase water demand and reduce catchme...
Short-term precipitation forecasting is a core task in meteorological science, aiming to achieve accurate predictions by modeling the spatiotemporal evolution of radar echo sequences, thereby supporting meteorological services and disaster warning sy...
The role of local (wind speed, potential evaporation, vapor pressure, air temperature, and precipitation amount) and regional parameters (teleconnection indices such as Indian Ocean Dipole (IOD), Bivariate ENSO index (BEST), North Atlantic Oscillatio...
The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships b...
Based on C-band weather radar and ground precipitation data from the Helan Mountain area in Yinchuan between 2017 to 2020, we evaluated the forecasting performances of 15 mainstream deep learning models used in recent years, including recurrent-based...
Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particula...
Convolution-based recurrent neural networks and convolutional neural networks have been used extensively in spatiotemporal prediction. However, these methods tend to concentrate on fixed-scale spatiotemporal state transitions and disregard the comple...
In this study, the viability of three metaheuristic regression techniques, CatBoost (CB), random forest (RF) and extreme gradient tree boosting (XGBoost, XGB), is investigated for the prediction of monthly streamflow considering satellite precipitati...
Accurate precipitation forecasting on the Qinghai-Tibet Plateau (QTP) remains a significant challenge due to complex terrain and nonlinear atmospheric dynamics. This study evaluates an XGBoost-SHAP framework for 24 h precipitation forecasting at Maqu...
Precipitation isoscapes have provided supporting data for numerous studies of water stable isotopes, alleviating the lack of observation data. However, the applicability of simulation data from global models to specific regional contexts remains a su...
Nanoscale Cu-rich precipitates (CRPs) play a crucial role in the irradiation embrittlement of reactor pressure vessels (RPVs), and binary Fe-Cu alloys serve as practical models to study the evolution of these precipitates. This study investigates the...
Global warming has recently intensified research interest in renewable polymer chemistry, with significant attention directed towards lignin nanoparticle (LNP) synthesis. Despite progress, LNP industrial application faces challenges: (1) reliance on...
The number of numerical weather prediction (NWP) models is on the rise, and they are commonly used for ensemble precipitation forecast (EPF) and ensemble streamflow prediction (ESP). This study evaluated the reliabilities of two well-behaved NWP cent...
The Eastern inland of Syria has a Mediterranean climate in the north and a tropical desert climate in the south, which results in a dry south and wet north climate feature, especially in winter. The circulation dynamics analysis of 16 winter strong p...
Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Mu...
The development of reliable predictive models for soil behavior represents a crucial advancement in geotechnical engineering, particularly for optimizing material compositions and reducing experimental uncertainties. Traditional experimental approach...
This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfal...
Reduction of area (RA) measurement in a hot ductility test is widely used to define the susceptibility of surface crack of cast steel, but the test is complex because it entails processes such as specimen fabrication, heat treatment, tensile testing,...
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