Beitrag
Smartmet nowcast – Rapidly updating nowcasting system at Finnish Meteorological Institute
Hieta, Leila; Partio, Mikko; Laine, Marko; Tuomola, Marja-Liisa; Hohti, Harri; Perttula, Tuuli; Gregow, Erik; Ylhäisi, Jussi S.
Meteorologische Zeitschrift Vol. 30 No. 4 (2021), p. 369 - 377
27 Literaturangaben
veröffentlicht: Aug 6, 2021
Online veröffentlicht: Jun 24, 2021
Manuskript akzeptiert: Apr 2, 2021
finale Ms. Revision erhalten: Jan 27, 2021
Manuskript-Revision angefordert: Jan 27, 2021
Manuskript erhalten: Dec 17, 2020
Open Access (Arbeit kann kostenlos heruntergeladen werden)
Abstract
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.
Schlagworte
nowcasting • bias correction • seamless • pySteps • blending