Related papers: Cosmic-Ray-Constrained LSTM Model for Geomagnetic …

The Gravity Recovery and Climate Experiment (GRACE) satellite mission, spanning from 2002 to 2017, has provided a valuable dataset for monitoring variations in Earth's gravity field, enabling diverse applications in geophysics and…

We present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms within disturbance storm time (Dst) amplitudes ranging from Dst =-64 nT to Dst=-…

With COVID-19 affecting every country globally and changing everyday life, the ability to forecast the spread of the disease is more important than any previous epidemic. The conventional methods of disease-spread modeling, compartmental…

Coronal holes (CHs) are the source of high-speed streams (HSSs) in the solar wind, whose interaction with the slow solar wind creates corotating interaction regions (CIRs) in the heliosphere. Whenever the CIRs hit the Earth, they can cause…

Many systems used by society are extremely vulnerable to space weather events such as solar flares and geomagnetic storms which could potentially cause catastrophic damage. In recent years, many works have emerged to provide early warning…

Intense geomagnetic storms are characterized by a minimum value of the Dst index at or below -100 nT. It is well known that these storms are caused by the southward magnetic fields in coronal mass ejections (CMEs) and corotating interaction…

Forecasting the geomagnetic effects of solar coronal mass ejections (CMEs) is currently an unsolved problem. CMEs, responsible for the largest values of the north-south component of the interplanetary magnetic field, are the key driver of…

Although space weather events may not directly affect human life, they have the potential to inflict significant harm upon our communities. Harmful space weather events can trigger atmospheric changes that result in physical and economic…

Accurate estimation of photometric redshifts (photo-$z$s) is crucial for cosmological surveys. Various methods have been developed for this purpose, such as template fitting methods and machine learning techniques, each with its own…

Precisely forecasting wind speed is essential for wind power producers and grid operators. However, this task is challenging due to the stochasticity of wind speed. To accurately predict short-term wind speed under uncertainties, this paper…

This work addresses the challenge of short-term precipitation forecasting by applying Convolutional Long Short-Term Memory (ConvLSTM) neural networks to weather radar data from the Royal Netherlands Meteorological Institute (KNMI). The…

This paper presents a physics-informed framework that integrates graph convolutional networks (GCN) with long short-term memory (LSTM) architecture to forecast microstructure evolution over long time horizons in both 2D and 3D with…

In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to…

We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a gamma-class flare within the next 24 hours. We consider three gamma classes, namely >=M5.0 class, >=M class, and >=C class, and…

The forecasting of local GIC effects has largely relied on the forecasting of dB/dt as a proxy and, to date, little attention has been paid to directly forecasting the geoelectric field or GICs themselves. We approach this problem with…

Prediction of the effects of coronal mass ejections (CMEs) on Earth strongly depends on knowledge of the interplanetary magnetic field southward component, Bz. Predicting the strength and duration of Bz inside a CME with sufficient accuracy…

With the highly demand of large-scale and real-time weather service for public, a refinement of short-time cloudage prediction has become an essential part of the weather forecast productions. To provide a weather-service-compliant cloudage…

Accurate climate forecasting is vital for Bangladesh, a region highly susceptible to climate change impacts on temperature and rainfall. Existing models often struggle to capture long-range dependencies and complex temporal patterns in…

The safe and stable operation of power systems is greatly challenged by the high variability and randomness of wind power in large-scale wind-power-integrated grids. Wind power forecasting is an effective solution to tackle this issue, with…

Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution (${\gtrsim}50$ km) than is optimal for accurately resolving important physical processes. Such processes are…