Quantifying the Location Error of Precipitation Nowcasts
In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the chan...
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| Format: | Article |
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Wiley
2020-01-01
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| Series: | Advances in Meteorology |
| Online Access: | http://dx.doi.org/10.1155/2020/8841913 |
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| author | Arthur Costa Tomaz de Souza Georgy Ayzel Maik Heistermann |
| author_facet | Arthur Costa Tomaz de Souza Georgy Ayzel Maik Heistermann |
| author_sort | Arthur Costa Tomaz de Souza |
| collection | DOAJ |
| description | In precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models. |
| format | Article |
| id | doaj-art-7661573f02bf4f9eb196a8f02ba228b1 |
| institution | Kabale University |
| issn | 1687-9309 1687-9317 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Meteorology |
| spelling | doaj-art-7661573f02bf4f9eb196a8f02ba228b12025-02-03T01:03:57ZengWileyAdvances in Meteorology1687-93091687-93172020-01-01202010.1155/2020/88419138841913Quantifying the Location Error of Precipitation NowcastsArthur Costa Tomaz de Souza0Georgy Ayzel1Maik Heistermann2University of Potsdam, Institute of Environmental Science and Geography, Potsdam 14476, GermanyUniversity of Potsdam, Institute of Environmental Science and Geography, Potsdam 14476, GermanyUniversity of Potsdam, Institute of Environmental Science and Geography, Potsdam 14476, GermanyIn precipitation nowcasting, it is common to track the motion of precipitation in a sequence of weather radar images and to extrapolate this motion into the future. The total error of such a prediction consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow isolating the extent of location errors, making it difficult to specifically improve nowcast models with regard to location prediction. In this paper, we introduce a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time Δt ahead of the forecast time t corresponds to the Euclidean distance between the observed and the predicted feature locations at t + Δt. Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the German Weather Service. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion from t − 1 to t (LK-Lin1) and t − 4 to t (LK-Lin4) and the other two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear (DIS-Lin1) and Semi-Lagrangian extrapolation (DIS-Rot1). Of those four models, DIS-Lin1 and LK-Lin4 turned out to be the most skillful with regard to the prediction of feature location, while we also found that the model skill dramatically depends on the sinuosity of the observed tracks. The dataset of 376,125 detected feature tracks in 2016 is openly available to foster the improvement of location prediction in extrapolation-based nowcasting models.http://dx.doi.org/10.1155/2020/8841913 |
| spellingShingle | Arthur Costa Tomaz de Souza Georgy Ayzel Maik Heistermann Quantifying the Location Error of Precipitation Nowcasts Advances in Meteorology |
| title | Quantifying the Location Error of Precipitation Nowcasts |
| title_full | Quantifying the Location Error of Precipitation Nowcasts |
| title_fullStr | Quantifying the Location Error of Precipitation Nowcasts |
| title_full_unstemmed | Quantifying the Location Error of Precipitation Nowcasts |
| title_short | Quantifying the Location Error of Precipitation Nowcasts |
| title_sort | quantifying the location error of precipitation nowcasts |
| url | http://dx.doi.org/10.1155/2020/8841913 |
| work_keys_str_mv | AT arthurcostatomazdesouza quantifyingthelocationerrorofprecipitationnowcasts AT georgyayzel quantifyingthelocationerrorofprecipitationnowcasts AT maikheistermann quantifyingthelocationerrorofprecipitationnowcasts |