A multimodel real-time system for global probabilistic subseasonal forecasts of precipitation and temperature
| Autor: | Andrew W. Robertson, Jing Yuan, Michael K. Tippett, Rémi Cousin, Kyle Hall, Nachiketa Acharya, Bohar Singh, Ángel G. Muñoz, Dan Collins, Emerson LaJoie, Johnna Infanti |
|---|---|
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Weather and Forecasting. |
| ISSN: | 1520-0434 0882-8156 |
| DOI: | 10.1175/waf-d-22-0160.1 |
| Popis: | A global multimodel probabilistic subseasonal forecast system for precipitation and near-surface temperature is developed based on three NOAA ensemble prediction systems that make their forecasts available publicly in real time as part of the Subseasonal eXperiment (SubX). The weekly and biweekly ensemble means of precipitation and temperature of each model are individually calibrated at each gridpoint using extended logistic regression, prior to forming equal-weighted multimodel ensemble (MME) probabilistic forecasts. Reforecast skill of weeks 3–4 precipitation and temperature is assessed in terms of the cross-validated ranked probability skill score (RPSS) and reliability diagram. The multimodel reforecasts are shown to be well-calibrated for both variables. Precipitation is moderately skillful over many tropical land regions, including Latin America, Sub-Saharan Africa and SE Asia, and over subtropical South America, Africa, and Australia. Near surface temperature skill is considerably higher than for precipitation and extends into the extratropics as well. The multimodel RPSS skill of both precipitation and temperature is shown to exceed that of any of the constituent models over Indonesia, South Asia, South America and East Africa, in all seasons. An example real-time weeks 3–4 global forecast for 13–26 November 2021 is illustrated and shown to bear the hallmarks of the combined influences of a moderate Madden-Julian Oscillation event as well as weak-moderate ongoing La Niña event. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|