The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Probabilistic Weather Prediction with an Analog Ensemble
Open accessAbstract
This study explores an analog-based method to generate an ensemble [analog ensemble (AnEn)] in which the probability distribution of the future state of the atmosphere is estimated with a set of past observations that correspond to the best analogs of a deterministic numerical weather prediction (NWP). An analog for a given location and forecast lead time is defined as a past prediction, from the same model, that has similar values for selected features of the current model forecast. The AnEn is evaluated for 0–48-h probabilistic predictions of 10-m wind speed and 2-m temperature over the contiguous United States and against observations provided by 550 surface stations, over the 23 April–31 July 2011 period. The AnEn is generated from the Environment Canada (EC) deterministic Global Environmental Multiscale (GEM) model and a 12–15-month-long training period of forecasts and observations. The skill and value of AnEn predictions are compared with forecasts from a state-of-the-science NWP ensemble system, the 21-member Regional Ensemble Prediction System (REPS). The AnEn exhibits high statistical consistency and reliability and the ability to capture the flow-dependent behavior of errors, and it has equal or superior skill and value compared to forecasts generated via logistic regression (LR) applied to both the deterministic GEM (as in AnEn) and REPS [ensemble model output statistics (EMOS)]. The real-time computational cost of AnEn and LR is lower than EMOS.
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Cites
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Self-citationForecast combinations: An over 50-year review
Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2022.11.005 · Cited by 280
WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A., Gueymard, C. A., Thompson, G., Eidhammer, T., & Deng, A. (2016) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-14-00279.1 · Cited by 268
Outlook for Exploiting Artificial Intelligence in the Earth and Environmental Sciences
Boukabara, S.-A., Krasnopolsky, V., Penny, S. G., Stewart, J. Q., McGovern, A., Hall, D., Ten Hoeve, J. E., Hickey, J., Allen Huang, H.-L., Williams, J. K., et al. (2021) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-20-0031.1 · Cited by 87
Metadata
- Authors
- Delle Monache, L., Eckel, F. A., Rife, D. L., Nagarajan, B., & Searight, K.
- Venue
- Monthly Weather Review
- Publisher
- American Meteorological Society
- Published
- 2013-09-25
- Volume
- 141
- Issue
- 10
- Pages
- 3498-3516
- ISSN
- 0027-0644 (ISSN-L)
- 1520-0493
- OpenAlex
- W2170860720
- Cited by
- 339
- References
- 47
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- bronze
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