Bridging observations, theory and numerical simulation of the ocean using machine learning
Progress within physical oceanography has been concurrent with the increasing
sophistication of tools available for its study. The incorporation of machine learning (ML) …
sophistication of tools available for its study. The incorporation of machine learning (ML) …
Deep learning for post-processing ensemble weather forecasts
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme
weather events. This is typically accomplished with ensemble prediction systems, which …
weather events. This is typically accomplished with ensemble prediction systems, which …
[HTML][HTML] Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
In order to enable the transition towards renewable energy sources, probabilistic energy
forecasting is of critical importance for incorporating volatile power sources such as solar …
forecasting is of critical importance for incorporating volatile power sources such as solar …
Cloud cover bias correction in numerical weather models for solar energy monitoring and forecasting systems with kernel ridge regression
Abstract Prediction of Total Cloud Cover (TCDC) from numerical weather simulation models,
such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring …
such as Global Forecast System (GFS), can aid renewable energy engineers in monitoring …
Comparison of model output statistics and neural networks to postprocess wind gusts
Wind gust prediction plays an important role in warning strategies of national meteorological
services due to the high impact of its extreme values. However, forecasting wind gusts is …
services due to the high impact of its extreme values. However, forecasting wind gusts is …
[HTML][HTML] Artificial Intelligence and Numerical Weather Prediction Models: A Technical Survey
Can artificial intelligence (AI) models beat traditional numerical weather prediction (NWP)
models based on physical principles? The rapid advancement of AI, inherent computational …
models based on physical principles? The rapid advancement of AI, inherent computational …
Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer
F Khojasteh-Leylakoohi, R Mohit, N Khalili-Tanha… - Scientific reports, 2023 - nature.com
Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis.
Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and …
Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and …
D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts
Current practice in predicting future weather is the use of numerical weather prediction
(NWP) models to produce ensemble forecasts. Despite of enormous improvements over the …
(NWP) models to produce ensemble forecasts. Despite of enormous improvements over the …
On the generalization ability of data-driven models in the problem of total cloud cover retrieval
Total Cloud Cover (TCC) retrieval from ground-based optical imagery is a problem that has
been tackled by several generations of researchers. The number of human-designed …
been tackled by several generations of researchers. The number of human-designed …
ARPEGE cloud cover forecast postprocessing with convolutional neural network
F Dupuy, O Mestre, M Serrurier… - Weather and …, 2021 - journals.ametsoc.org
Cloud cover provides crucial information for many applications such as planning land
observation missions from space. It remains, however, a challenging variable to forecast …
observation missions from space. It remains, however, a challenging variable to forecast …