Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities

Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applic...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - (2018) vom: 20. Aug.
1. Verfasser: Kumpf, Alexander (VerfasserIn)
Weitere Verfasser: Rautenhaus, Marc, Riemer, Michael, Westermann, Rudiger
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2018
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
LEADER 01000caa a22002652 4500
001 NLM287753026
003 DE-627
005 20240229161925.0
007 cr uuu---uuuuu
008 231225s2018 xx |||||o 00| ||eng c
024 7 |a 10.1109/TVCG.2018.2864901  |2 doi 
028 5 2 |a pubmed24n1308.xml 
035 |a (DE-627)NLM287753026 
035 |a (NLM)30136957 
040 |a DE-627  |b ger  |c DE-627  |e rakwb 
041 |a eng 
100 1 |a Kumpf, Alexander  |e verfasserin  |4 aut 
245 1 0 |a Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities 
264 1 |c 2018 
336 |a Text  |b txt  |2 rdacontent 
337 |a ƒaComputermedien  |b c  |2 rdamedia 
338 |a ƒa Online-Ressource  |b cr  |2 rdacarrier 
500 |a Date Revised 27.02.2024 
500 |a published: Print-Electronic 
500 |a Citation Status Publisher 
520 |a Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improve future forecasts. We-a team of visualization scientists and meteorologists-present a visual analysis framework to improve upon current practice of ESA. We support the user in selecting regions to compute a meaningful target forecast quantity by embedding correlation-based grid-point clustering to obtain statistically coherent regions. The evolution of sensitivity features computed via ESA are then traced through time, by integrating a quantitative measure of feature matching into optical-flow-based feature assignment, and displayed by means of a swipe-path showing the geo-spatial evolution of the sensitivities. Visualization of the internal correlation structure of computed features guides the user towards those features robustly predicting a certain weather event. We demonstrate the use of our method by application to real-world 2D and 3D cases that occurred during the 2016 NAWDEX field campaign, showing the interactive generation of hypothesis chains to explore how atmospheric processes sensitive to each other are interrelated 
650 4 |a Journal Article 
700 1 |a Rautenhaus, Marc  |e verfasserin  |4 aut 
700 1 |a Riemer, Michael  |e verfasserin  |4 aut 
700 1 |a Westermann, Rudiger  |e verfasserin  |4 aut 
773 0 8 |i Enthalten in  |t IEEE transactions on visualization and computer graphics  |d 1996  |g (2018) vom: 20. Aug.  |w (DE-627)NLM098269445  |x 1941-0506  |7 nnns 
773 1 8 |g year:2018  |g day:20  |g month:08 
856 4 0 |u http://dx.doi.org/10.1109/TVCG.2018.2864901  |3 Volltext 
912 |a GBV_USEFLAG_A 
912 |a SYSFLAG_A 
912 |a GBV_NLM 
912 |a GBV_ILN_350 
951 |a AR 
952 |j 2018  |b 20  |c 08