Fog is a meteorological phenomenon that reduces visibility and poses safety threats to road, maritime and aeronautical traffic. Visibility observations are commonly obtained manually or by sensors that are expensive and usually limited to critical locations such as airports. Fog is difficult to accurately forecast since several factors play a role in its formation and dissipation. Furthermore,...
High-resolution simulation models (of order kms or less) can deliver highly accurate low-level winds and their climatologies. The problem is that one cannot afford to run simulations at very high resolution over extended spatial domains for long periods because the computational power needed is prohibitive.
Instead, we propose using neural networks to downscale low-resolution wind-field...
For about two decades, operational postprocessing of NWP output for short-range and medium-range forecasts at Deutscher Wetterdienst (DWD) has been mainly based on linear/logistic regression Model Output Statistics (MOS) approaches. However, Artificial Intelligence (AI) has nowadays become a central key for many modern applications and research areas, and it also gains more and more importance...
The Improved Observation Usage in NWP (iOBS) project aims to contribute to improved weather forecast quality from existing and emerging observation systems by combining world-leading NWP with future generation e-infrastructure. The project targets to produce effective assimilation of diverse observations in regional high-resolution NWP models. The observations include, among other sources,...
Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the parameters of a predictive distribution are estimated from a training period. We propose a flexible alternative based on neural networks that can incorporate nonlinear...
In this talk, we will demonstrate the performance of a neural network approach in predicting the daily average concentration of NO2, PM10 and PM2.5 in different countries. The neural network is trained with archived observations where meteorological parameters and chemical transport predictions serve as inputs. The archived and forecasted chemical transport predictions are obtained through the...
This study explores the adaptation of state-of-the-art deep learning architectures for video frame prediction in the context of weather and climate applications. As a case study, we attempt to predict surface temperature fields over Europe for up to 24 hours based on meteorological analyses. Initial results have been achieved with a PredNet and a GAN architecture. To facilitate the massive...
Over the last decade, extreme events (i.e., storms, floods, droughts) are more and more frequent and/or intensive. 85% of the world's population is affected by these events. 70% of the world economy is dependent on meteorology according to the United Nations. 75% of the most vulnerable countries has no or little reliable, accurate and effective weather information. Indeed, some National...
Weather forecasts as provided by numerical weather prediction (NWP) models such as Harmonie-Arome, which is used by KNMI, are often (too) deterministic. Because of uncertainty in the forecast, it is preferable to have a full probability distribution instead.
Current statistical post-processing methods for providing a probabilistic forecast are not capable of using full spatial patterns from...
Using artificial intelligence to correct weather related vehicle data
Hellweg, M.(1) ; Stiller, C.(1)
(1) Institute for Measurement and Control Systems, Karlsruhe Institute of Technology (KIT)
The project “Fleet-Weather-Map” aims to use vehicles as mobile weather stations. With the vehicular data the improvement of spatial and temporal resolution of weather forecasts and...
