Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting
Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated wit...
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2017
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01677055_v36_n7_p135_Diehl http://hdl.handle.net/20.500.12110/paper_01677055_v36_n7_p135_Diehl |
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paper:paper_01677055_v36_n7_p135_Diehl2023-06-08T15:16:48Z Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Picture/Image Generation—Viewing algorithms I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.3.8 [Computer Graphics]: Applications—Probabilistic Weather Forecasting Computer graphics Decision making Forecasting Numerical methods Quality control Regression analysis Uncertainty analysis Visualization Analysis capabilities Descriptors Forecast uncertainty Interaction techniques Probabilistic forecasts Probabilistic weather forecasting Statistical information Viewing algorithms Weather forecasting Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements. © 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01677055_v36_n7_p135_Diehl http://hdl.handle.net/20.500.12110/paper_01677055_v36_n7_p135_Diehl |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Picture/Image Generation—Viewing algorithms I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.3.8 [Computer Graphics]: Applications—Probabilistic Weather Forecasting Computer graphics Decision making Forecasting Numerical methods Quality control Regression analysis Uncertainty analysis Visualization Analysis capabilities Descriptors Forecast uncertainty Interaction techniques Probabilistic forecasts Probabilistic weather forecasting Statistical information Viewing algorithms Weather forecasting |
| spellingShingle |
Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Picture/Image Generation—Viewing algorithms I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.3.8 [Computer Graphics]: Applications—Probabilistic Weather Forecasting Computer graphics Decision making Forecasting Numerical methods Quality control Regression analysis Uncertainty analysis Visualization Analysis capabilities Descriptors Forecast uncertainty Interaction techniques Probabilistic forecasts Probabilistic weather forecasting Statistical information Viewing algorithms Weather forecasting Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| topic_facet |
Categories and Subject Descriptors (according to ACM CCS) I.3.3 [Computer Graphics]: Picture/Image Generation—Viewing algorithms I.3.6 [Computer Graphics]: Methodology and Techniques—Interaction techniques I.3.8 [Computer Graphics]: Applications—Probabilistic Weather Forecasting Computer graphics Decision making Forecasting Numerical methods Quality control Regression analysis Uncertainty analysis Visualization Analysis capabilities Descriptors Forecast uncertainty Interaction techniques Probabilistic forecasts Probabilistic weather forecasting Statistical information Viewing algorithms Weather forecasting |
| description |
Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this method are essentially automated and provide limited analysis capabilities. In this paper, we propose a novel approach that breaks down the automatic process using the experience and knowledge of the users and creates a new interactive visual workflow. Our approach allows forecasters to study probabilistic forecasts, their inner analogs and observations, their associated spatial errors, and additional statistical information by means of coordinated and linked views. We designed the presented solution following a participatory methodology together with domain experts. Several meteorologists with different backgrounds validated the approach. Two case studies illustrate the capabilities of our solution. It successfully facilitates the analysis of uncertainty and systematic model biases for improved decision-making and process-quality measurements. © 2017 The Author(s) Computer Graphics Forum © 2017 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. |
| title |
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| title_short |
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| title_full |
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| title_fullStr |
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| title_full_unstemmed |
Albero: A Visual Analytics Approach for Probabilistic Weather Forecasting |
| title_sort |
albero: a visual analytics approach for probabilistic weather forecasting |
| publishDate |
2017 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01677055_v36_n7_p135_Diehl http://hdl.handle.net/20.500.12110/paper_01677055_v36_n7_p135_Diehl |
| _version_ |
1768546205324804096 |