Name
Title | ||
|---|---|---|
A Learning-Based Approach For Uncertainty Analysis In Numerical Weather Prediction Models |
Abstract | ||
|---|---|---|
This paper demonstrates the use of machine learning techniques to study the uncertainty in numerical weather prediction models due to the interaction of multiple physical processes. We aim to address the following problems: (1) estimation of systematic model errors in output quantities of interest at future times and (2) identification of specific physical processes that contribute most to the forecast uncertainty in the quantity of interest under specified meteorological conditions. To address these problems, we employ simple machine learning algorithms and perform numerical experiments with Weather Research and Forecasting (WRF) model and the results show a reduction of forecast errors by an order of magnitude. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22747-0_10 | COMPUTATIONAL SCIENCE - ICCS 2019, PT IV |
Keywords | Field | DocType |
Numerical weather prediction, Structural uncertainty, Model errors, Machine learning | Mathematical optimization,Numerical weather prediction models,Simple machine,Computer science,Weather Research and Forecasting Model,Uncertainty analysis,Artificial intelligence,Machine learning,Numerical weather prediction | Conference |
Volume | ISSN | Citations |
11539 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Azam S. Zavar Moosavi | 1 | 14 | 4.12 |
| Vishwas Rao | 2 | 17 | 4.39 |
| Adrian Sandu | 3 | 325 | 58.93 |