Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute
Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI) to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The system produces an hourly-updated seamless 10‑...
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| Format: | Article |
| Language: | English |
| Published: |
Borntraeger
2021-08-01
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| Series: | Meteorologische Zeitschrift |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1127/metz/2021/1070 |
| _version_ | 1819130324534165504 |
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| author | Leila Hieta Mikko Partio Marko Laine Marja-Liisa Tuomola Harri Hohti Tuuli Perttula Erik Gregow Jussi S. Ylhäisi |
| author_facet | Leila Hieta Mikko Partio Marko Laine Marja-Liisa Tuomola Harri Hohti Tuuli Perttula Erik Gregow Jussi S. Ylhäisi |
| author_sort | Leila Hieta |
| collection | DOAJ |
| description | Rapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI) to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The system produces an hourly-updated seamless 10‑day forecast over the Scandinavian forecast domain by combining several information sources, which are 1) radar-based FMI‑PPN nowcast 2) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 3) 10‑day operational forecast. The combination of the parallel information sources is done using an optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable. Prior to this combination, each of these individual forecast sources are postprocessed in a multitude of ways. To MNWC model analysis and forecast fields of temperature, relative humidity and wind speed, a simple bias correction scheme based on recent forecast error information is applied whereas ensemble nowcasts from FMI‑PPN are non-uniformly weighted using the non- member as the baseline. The Smartmet nowcasting system improves the quality of short range forecasts, reduces the delay of forecast production and frees the time of on-duty forecaster to other responsibilities. |
| first_indexed | 2024-12-22T08:57:48Z |
| format | Article |
| id | doaj.art-f312a72a83f34deeb997f601cdaee75c |
| institution | Directory Open Access Journal |
| issn | 0941-2948 |
| language | English |
| last_indexed | 2024-12-22T08:57:48Z |
| publishDate | 2021-08-01 |
| publisher | Borntraeger |
| record_format | Article |
| series | Meteorologische Zeitschrift |
| spelling | doaj.art-f312a72a83f34deeb997f601cdaee75c2022-12-21T18:31:48ZengBorntraegerMeteorologische Zeitschrift0941-29482021-08-0130436937710.1127/metz/2021/107099458Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological InstituteLeila HietaMikko PartioMarko LaineMarja-Liisa TuomolaHarri HohtiTuuli PerttulaErik GregowJussi S. YlhäisiRapidly updating nowcasting system, Smartmet nowcast, has been developed at Finnish Meteorological Institute (FMI) to operationally produce accurate and timely short range forecasts and a detailed description of the present weather to the end-users. The system produces an hourly-updated seamless 10‑day forecast over the Scandinavian forecast domain by combining several information sources, which are 1) radar-based FMI‑PPN nowcast 2) Rapidly-updating high-resolution numerical weather prediction (NWP) MetCoOp nowcast (MNWC) forecast 3) 10‑day operational forecast. The combination of the parallel information sources is done using an optical-flow based image morphing method, which provides visually seamless forecasts for each forecast variable. Prior to this combination, each of these individual forecast sources are postprocessed in a multitude of ways. To MNWC model analysis and forecast fields of temperature, relative humidity and wind speed, a simple bias correction scheme based on recent forecast error information is applied whereas ensemble nowcasts from FMI‑PPN are non-uniformly weighted using the non- member as the baseline. The Smartmet nowcasting system improves the quality of short range forecasts, reduces the delay of forecast production and frees the time of on-duty forecaster to other responsibilities.http://dx.doi.org/10.1127/metz/2021/1070nowcastingbias correctionseamlesspystepsblending |
| spellingShingle | Leila Hieta Mikko Partio Marko Laine Marja-Liisa Tuomola Harri Hohti Tuuli Perttula Erik Gregow Jussi S. Ylhäisi Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute Meteorologische Zeitschrift nowcasting bias correction seamless pysteps blending |
| title | Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute |
| title_full | Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute |
| title_fullStr | Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute |
| title_full_unstemmed | Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute |
| title_short | Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute |
| title_sort | smartmet nowcast rapidly updating nowcasting system at finnish meteorological institute |
| topic | nowcasting bias correction seamless pysteps blending |
| url | http://dx.doi.org/10.1127/metz/2021/1070 |
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