Related papers: Global Climate Model Bias Correction Using Deep Le…
Large bias exists in shortwave cloud radiative effect (SWCRE) of general circulation models (GCMs), attributed mainly to the combined effect of cloud fraction and water contents, whose representations in models remain challenging. Here we…
To analyze climate change mitigation strategies, economists rely on simplified climate models - climate emulators. We propose a generic and transparent calibration and evaluation strategy for these climate emulators that is based on Coupled…
Reduced precision floating point arithmetic is now routinely deployed in numerical weather forecasting over short timescales. However the applicability of these reduced precision techniques to longer timescale climate simulations -…
Accurate and high-resolution Earth system model (ESM) simulations are essential to assess the ecological and socio-economic impacts of anthropogenic climate change, but are computationally too expensive to be run at sufficiently high…
With broad applications in various public services like aviation management and urban disaster warning, numerical precipitation prediction plays a crucial role in weather forecast. However, constrained by the limitation of observation and…
Climate change projections for boreal winter precipitation in Tropical America has beenaddressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model…
Long-term, high-fidelity simulation of slow-changing physical systems, such as the ocean and climate, presents a fundamental challenge in scientific computing. Traditional autoregressive machine learning models often fail in these tasks as…
Weather and climate simulations produce petabytes of high-resolution data that are later analyzed by researchers in order to understand climate change or severe weather. We propose a new method of compressing this multidimensional weather…
Numerical Weather Prediction (NWP) models that integrate coupled physical equations forward in time are the traditional tools for simulating atmospheric processes and forecasting weather. With recent advancements in deep learning, AI-based…
The IPCC AR6 assessment of the impacts and risks associated with projected climate changes for the 21st century is both alarming and ambiguous. According to computer projections, the global surface may warm from 1.3 to 8.0 {\deg}C by 2100,…
In light of growing threats posed by climate change in general and sea level rise (SLR) in particular, the necessity for computationally efficient means to estimate and analyze potential coastal flood hazards has become increasingly…
Total column water vapor is an important factor for the weather and climate. This study apply deep learning based multiple regression to map the TCWV with elements that can improve spatiotemporal prediction. In this study, we predict the…
Accurately predicting long-term rainfall is challenging. Global climate indices, such as the El Ni\~no-Southern Oscillation, are standard input features for machine learning. However, a significant gap persists regarding local-scale indices…
We propose a neural network approach to produce probabilistic weather forecasts from a deterministic numerical weather prediction. Our approach is applied to operational surface temperature outputs from the Global Deterministic Prediction…
We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise…
Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General…
Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of…
We may anticipate that climate change will bring changes to the intensity and variability of near surface winds, either through local effects or by altering the large-scale flow. The impact of climate change on European wind resources has…
Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in…
Observed changes such as increasing global temperatures and the intensification of the global water cycle in the 20th century are also robust results of coupled general circulation models. In spite of this success model-to-model variability…