Why Are Radar Data so Difficult to Assimilate Skillfully?
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| Název: | Why Are Radar Data so Difficult to Assimilate Skillfully? |
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| Autoři: | Fabry, Frédéric, Meunier, Véronique |
| Zdroj: | Monthly Weather Review. 148:2819-2836 |
| Informace o vydavateli: | American Meteorological Society, 2020. |
| Rok vydání: | 2020 |
| Témata: | Convective-scale processes, Numerical weather prediction/forecasting, 13. Climate action, Data assimilation, Instability, Radars/Radar observations, Nowcasting, 01 natural sciences, 0105 earth and related environmental sciences |
| Popis: | Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently. |
| Druh dokumentu: | Article |
| Jazyk: | English |
| ISSN: | 1520-0493 0027-0644 |
| DOI: | 10.1175/mwr-d-19-0374.1 |
| Přístupová URL adresa: | http://escholarship.mcgill.ca/downloads/08612t271 https://journals.ametsoc.org/view/journals/mwre/148/7/mwrD190374.xml https://escholarship.mcgill.ca/concern/articles/wh246x58q https://escholarship.mcgill.ca/downloads/08612t271 https://ui.adsabs.harvard.edu/abs/2020MWRv..148.2819F/abstract https://journals.ametsoc.org/mwr/article/148/7/2819/348554/Why-Are-Radar-Data-so-Difficult-to-Assimilate https://journals.ametsoc.org/mwr/article-abstract/148/7/2819/348554/Why-Are-Radar-Data-so-Difficult-to-Assimilate |
| Přístupové číslo: | edsair.doi.dedup.....e335d5d06cdd731454b12085d123b7eb |
| Databáze: | OpenAIRE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Although radar is our most useful tool for monitoring severe weather, the benefits of assimilating its data are often short lived. To understand why, we documented the assimilation requirements, the data characteristics, and the common practices that could hinder optimum data assimilation by traditional approaches. Within storms, radars provide dense measurements of a few highly variable storm outcomes (precipitation and wind) in atmospherically unstable conditions. However, statistical relationships between errors of observed and unobserved quantities often become nonlinear because the errors in these areas tend to become large rapidly. Beyond precipitating areas lie large regions for which radars provide limited new information, yet whose properties will soon shape the outcome of future storms. For those areas, any innovation must consequently be projected from sometimes distant precipitating areas. Thus, radar data assimilation must contend with a double need at odds with many traditional assimilation implementations: correcting in-storm properties with complex errors while projecting information at unusually far distances outside precipitating areas. To further complicate the issue, other data properties and practices, such as assimilating reflectivity in logarithmic units, are not optimal to correct all state variables. Therefore, many characteristics of radar measurements and common practices of their assimilation are incompatible with necessary conditions for successful data assimilation. Facing these dataset-specific challenges may force us to consider new approaches that use the available information differently. |
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| ISSN: | 15200493 00270644 |
| DOI: | 10.1175/mwr-d-19-0374.1 |