Localized variational blending for nowcasting purposes.
Mesoscale models' improvement of recent years (like spin-up reduction, assimilation techniques, and assimilation of new observation) and increased computational resources justify a rapid update cycle (1 hour). Despite all these improvements, precipitation forecasts provided by these models are...
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2020-10-01
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Online Access: | http://dx.doi.org/10.1127/metz/2020/1003 |
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doaj-4f199711de93428b93d9ab8bea557a592020-11-25T03:43:54ZengBorntraegerMeteorologische Zeitschrift0941-29482020-10-0129324726110.1127/metz/2020/100393563Localized variational blending for nowcasting purposes.Aitor AtenciaAlexander KannYong WangFlorian MeierMesoscale models' improvement of recent years (like spin-up reduction, assimilation techniques, and assimilation of new observation) and increased computational resources justify a rapid update cycle (1 hour). Despite all these improvements, precipitation forecasts provided by these models are not able to beat the observation-based Lagrangian extrapolation nowcasting for the first forecast steps. In this paper, these two forecasting sources are merged by a new blending technique, more complex than the regular global weights. It takes advantage of a variational technique commonly used in building the analysis in data assimilation cycles. This methodology allows to keep the spatial correlation of the errors and to merge the forecast with locally different weights. The results show an improvement over the original sources of forecast in terms of deterministic, dichotomic and spatial scores.http://dx.doi.org/10.1127/metz/2020/1003variationalblendingprecipitationseamless |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aitor Atencia Alexander Kann Yong Wang Florian Meier |
spellingShingle |
Aitor Atencia Alexander Kann Yong Wang Florian Meier Localized variational blending for nowcasting purposes. Meteorologische Zeitschrift variational blending precipitation seamless |
author_facet |
Aitor Atencia Alexander Kann Yong Wang Florian Meier |
author_sort |
Aitor Atencia |
title |
Localized variational blending for nowcasting purposes. |
title_short |
Localized variational blending for nowcasting purposes. |
title_full |
Localized variational blending for nowcasting purposes. |
title_fullStr |
Localized variational blending for nowcasting purposes. |
title_full_unstemmed |
Localized variational blending for nowcasting purposes. |
title_sort |
localized variational blending for nowcasting purposes. |
publisher |
Borntraeger |
series |
Meteorologische Zeitschrift |
issn |
0941-2948 |
publishDate |
2020-10-01 |
description |
Mesoscale models' improvement of recent years (like spin-up reduction, assimilation techniques, and assimilation of new observation) and increased computational resources justify a rapid update cycle (1 hour). Despite all these improvements, precipitation forecasts provided by these models are not able to beat the observation-based Lagrangian extrapolation nowcasting for the first forecast steps. In this paper, these two forecasting sources are merged by a new blending technique, more complex than the regular global weights. It takes advantage of a variational technique commonly used in building the analysis in data assimilation cycles. This methodology allows to keep the spatial correlation of the errors and to merge the forecast with locally different weights. The results show an improvement over the original sources of forecast in terms of deterministic, dichotomic and spatial scores. |
topic |
variational blending precipitation seamless |
url |
http://dx.doi.org/10.1127/metz/2020/1003 |
work_keys_str_mv |
AT aitoratencia localizedvariationalblendingfornowcastingpurposes AT alexanderkann localizedvariationalblendingfornowcastingpurposes AT yongwang localizedvariationalblendingfornowcastingpurposes AT florianmeier localizedvariationalblendingfornowcastingpurposes |
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1724517678438154240 |