Forecast Errors and Uncertainties in Atmospheric Rivers
| Autor: | David S. Richardson, Ryan D. Torn, Carolyn A. Reynolds, James D. Doyle, F. Martin Ralph, David A. Lavers, N. Bruce Ingleby, Vijay Tallapragada, Mark J. Rodwell, Aneesh C. Subramanian, Florian Pappenberger |
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| Rok vydání: | 2020 |
| Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Meteorology 0207 environmental engineering Sample (statistics) 02 engineering and technology Numerical weather prediction 01 natural sciences 13. Climate action Key (cryptography) Environmental science Observational study 020701 environmental engineering 0105 earth and related environmental sciences |
| Zdroj: | Weather and Forecasting. 35:1447-1458 |
| ISSN: | 1520-0434 0882-8156 |
| Popis: | A key aim of observational campaigns is to sample atmosphere–ocean phenomena to improve understanding of these phenomena, and in turn, numerical weather prediction. In early 2018 and 2019, the Atmospheric River Reconnaissance (AR Recon) campaign released dropsondes and radiosondes into atmospheric rivers (ARs) over the northeast Pacific Ocean to collect unique observations of temperature, winds, and moisture in ARs. These narrow regions of water vapor transport in the atmosphere—like rivers in the sky—can be associated with extreme precipitation and flooding events in the midlatitudes. This study uses the dropsonde observations collected during the AR Recon campaign and the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) to evaluate forecasts of ARs. Results show that ECMWF IFS forecasts 1) were colder than observations by up to 0.6 K throughout the troposphere; 2) have a dry bias in the lower troposphere, which along with weaker winds below 950 hPa, resulted in weaker horizontal water vapor fluxes in the 950–1000-hPa layer; and 3) exhibit an underdispersiveness in the water vapor flux that largely arises from model representativeness errors associated with dropsondes. Four U.S. West Coast radiosonde sites confirm the IFS cold bias throughout winter. These issues are likely to affect the model’s hydrological cycle and hence precipitation forecasts. |
| Databáze: | OpenAIRE |
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