Postprocessing ensemble forecasts of vertical temperature profiles
<p>Weather forecasts from ensemble prediction systems (EPS) are improved by statistical models trained on past EPS forecasts and their atmospheric observations. Recently these corrections have moved from being univariate to multivariate. The focus has been on (quasi-)horizontal atmospheric var...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-05-01
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| Series: | Advances in Statistical Climatology, Meteorology and Oceanography |
| Online Access: | https://www.adv-stat-clim-meteorol-oceanogr.net/6/45/2020/ascmo-6-45-2020.pdf |
| Summary: | <p>Weather forecasts from ensemble prediction systems (EPS)
are improved by statistical models trained on past EPS forecasts and
their atmospheric observations. Recently these corrections have moved
from being univariate to multivariate. The focus has been on
(quasi-)horizontal atmospheric variables. This paper extends the
correction methods to EPS forecasts of vertical profiles in two steps. First univariate distributional regression methods correct the
probability distributions separately at each vertical level. In the second step copula coupling re-installs the
dependence among neighboring levels by using the rank order structure
of the EPS forecasts. The method is applied to EPS data from the European Centre for Medium-Range Weather Forecasts (ECMWF) at model
levels interpolated to four locations in Germany, from which
radiosondes are released to measure profiles of temperature and other
variables four times a day. A winter case study and a summer case study,
respectively, exemplify that univariate postprocessing fails to
preserve stable layers, which are crucial for many atmospheric
processes. Quantile resampling and a resampling that preserves the
relative distance between individual EPS members improve the
calibration of the raw forecasts of the temperature profiles as shown
by rank histograms. They also improve the multivariate metrics of
energy score and variogram score and retain the stable layers.
Improvements take place over all times of the day and all
seasons. They are largest within the atmospheric boundary layer and
for shorter lead times.</p> |
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| ISSN: | 2364-3579 2364-3587 |