Original paper
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 references
published: Aug 6, 2021
published online: Jun 24, 2021
manuscript accepted: Apr 2, 2021
final revised version received: Jan 27, 2021
manuscript revision requested: Jan 27, 2021
manuscript received: Dec 17, 2020
Open Access (paper may be downloaded free of charge)
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.
Keywords
nowcasting • bias correction • seamless • pySteps • blending