A Technique for the Verification of Precipitation Forecasts and Its Application to a Problem of Predictability
A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called...
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| Veröffentlicht in: | Monthly weather review 2018-05, Vol.146 (5), p.1303-1318 |
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| creator | Han, Fan Szunyogh, Istvan |
| description | A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2. |
| doi_str_mv | 10.1175/MWR-D-17-0040.1 |
| format | Article |
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An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2.</description><identifier>ISSN: 0027-0644</identifier><identifier>EISSN: 1520-0493</identifier><identifier>DOI: 10.1175/MWR-D-17-0040.1</identifier><language>eng</language><publisher>Washington: American Meteorological Society</publisher><subject>Adaptation ; Algorithms ; Amplitude ; Amplitudes ; Dependence ; Dynamic height ; Errors ; Forecast verification ; Geopotential ; Geopotential height ; Image processing ; Lead time ; Medical imaging ; Morphing ; Potential vorticity ; Precipitation ; Precipitation forecasting ; Quality ; Similarity ; State variable ; Storms ; Vorticity ; Weather forecasting ; Winter storms</subject><ispartof>Monthly weather review, 2018-05, Vol.146 (5), p.1303-1318</ispartof><rights>Copyright American Meteorological Society May 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c376t-e41a767e9214ecb2a01f5d9154e6db3e5882f7d18dcbad08c4f87cf9d292045d3</citedby><cites>FETCH-LOGICAL-c376t-e41a767e9214ecb2a01f5d9154e6db3e5882f7d18dcbad08c4f87cf9d292045d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,3668,27901,27902</link.rule.ids></links><search><creatorcontrib>Han, Fan</creatorcontrib><creatorcontrib>Szunyogh, Istvan</creatorcontrib><title>A Technique for the Verification of Precipitation Forecasts and Its Application to a Problem of Predictability</title><title>Monthly weather review</title><description>A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2.</description><subject>Adaptation</subject><subject>Algorithms</subject><subject>Amplitude</subject><subject>Amplitudes</subject><subject>Dependence</subject><subject>Dynamic height</subject><subject>Errors</subject><subject>Forecast verification</subject><subject>Geopotential</subject><subject>Geopotential height</subject><subject>Image processing</subject><subject>Lead time</subject><subject>Medical imaging</subject><subject>Morphing</subject><subject>Potential vorticity</subject><subject>Precipitation</subject><subject>Precipitation forecasting</subject><subject>Quality</subject><subject>Similarity</subject><subject>State variable</subject><subject>Storms</subject><subject>Vorticity</subject><subject>Weather forecasting</subject><subject>Winter 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Technique for the Verification of Precipitation Forecasts and Its Application to a Problem of Predictability</title><author>Han, Fan ; Szunyogh, Istvan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c376t-e41a767e9214ecb2a01f5d9154e6db3e5882f7d18dcbad08c4f87cf9d292045d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adaptation</topic><topic>Algorithms</topic><topic>Amplitude</topic><topic>Amplitudes</topic><topic>Dependence</topic><topic>Dynamic height</topic><topic>Errors</topic><topic>Forecast verification</topic><topic>Geopotential</topic><topic>Geopotential height</topic><topic>Image processing</topic><topic>Lead time</topic><topic>Medical imaging</topic><topic>Morphing</topic><topic>Potential vorticity</topic><topic>Precipitation</topic><topic>Precipitation forecasting</topic><topic>Quality</topic><topic>Similarity</topic><topic>State variable</topic><topic>Storms</topic><topic>Vorticity</topic><topic>Weather forecasting</topic><topic>Winter storms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Han, Fan</creatorcontrib><creatorcontrib>Szunyogh, Istvan</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>STEM Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase 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review</jtitle><date>2018-05-01</date><risdate>2018</risdate><volume>146</volume><issue>5</issue><spage>1303</spage><epage>1318</epage><pages>1303-1318</pages><issn>0027-0644</issn><eissn>1520-0493</eissn><abstract>A new morphing-based technique is proposed for the verification of precipitation forecasts for which the location error can be described by a spatial shift. An adaptation of the structural similarity index measure (SSIM) of image processing to the precipitation forecast verification problem, called the amplitude and structural similarity index (ASSIM), is also introduced. ASSIM is used to measure both the convergence of the new morphing algorithm, which is an iterative scheme, and the amplitude and structure component of the forecast error. The behavior of the proposed technique, which could also be applied to other forecast parameters with sharp gradients (e.g., potential vorticity), is illustrated with idealized and realistic examples. One of these examples examines the predictability of the location of precipitation events associated with winter storms. It is found that the functional dependence of the average magnitude of the location error on the forecast lead time is qualitatively similar to that of the root-mean-square error of the fields of the conventional atmospheric state variables (e.g., geopotential height). Quantitatively, the average magnitude of the estimated location error is about 40 km at initial time, 110 km at day 1, 250 km at day 3, and 750 km at week 1, and it eventually saturates at about week 2.</abstract><cop>Washington</cop><pub>American Meteorological Society</pub><doi>10.1175/MWR-D-17-0040.1</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
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| source | American Meteorological Society; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Adaptation Algorithms Amplitude Amplitudes Dependence Dynamic height Errors Forecast verification Geopotential Geopotential height Image processing Lead time Medical imaging Morphing Potential vorticity Precipitation Precipitation forecasting Quality Similarity State variable Storms Vorticity Weather forecasting Winter storms |
| title | A Technique for the Verification of Precipitation Forecasts and Its Application to a Problem of Predictability |
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