On the performance of the new NWP nowcasting system at the Danish Meteorological Institute during a heavy rain period
- 188 Downloads
At the Danish Meteorological Institute, the NWP nowcasting system has been enhanced to include assimilation of 2D precipitation rates derived from weather radar observations. The assimilation is performed using a nudging-based technique. Here the rain rates are used to estimate the changes in the vertical profile of horizontal divergence needed to induce the observed rain rate. Verification of precipitation forecasts for a 17-day period in August 2010 based on the NWP nowcasting system is presented and compared to a reference without assimilation of precipitation data. In Denmark, this period was particularly rainy, with several heavy precipitation events. Three of these events are studied in detail. The verification is mainly based on scatter plots and fractions skill scores, which give scale-dependant indicators of the spatial skill of the forecasts. The study shows that the inclusion of precipitation observations has a positive impact on the spatial skill of the forecasts. This positive impact is the largest in the first hour, and then gradually decreases. On the average, the forecasts with assimilation of precipitation are skilful after 4 h on scales down to a few tens of kilometers. For the events studied, the assimilation improves the forecasted frequencies of heavy and light precipitation relative to the control, while there is some tendency to overpredict intermediate precipitation levels.
KeywordsLead Time Numerical Weather Prediction Rain Rate Precipitation Intensity Radar Observation
This work was supported by the HydroCast project (hydrocast.dhigroup.com) funded partly by the Danish Council for Strategic Research under the Programme Commission on Sustainable Energy and Environment, and the OMOVAST project, funded partly by the Danish Ministry of Environment, under the Programme for Development and Demonstration Projects. The authors would like to express our gratitude for the constructive reviewer feedback. The first author would like to acknowledge DMI for funding the work which was carried out at the institute.
- Battan LJ (1973) Radar observation of the atmosphere, vol 2. The University of Chicago Press, Chicago, pp 297–302Google Scholar
- Courtier P, Andersson E, Heckley W, Vasiljevic D, Hamrud M, Hollingsworth A, Rabier F, Fisher M, Pailleux J (1998) The ecmwf implementation of three-dimensional variational assimilation (3d-var). I: formulation. Q J R Meteorol Soc 124(550):1783–1807Google Scholar
- Hong SY, Dudhia J (2012) Next-generation numerical weather prediction: bridging parameterization, explicit clouds, and large eddies. Bull Am Meteorol Soc 93(1):ES6–ES9Google Scholar
- Jensen DG, Petersen C, Rasmussen MR (2014) Assimilation of radar-based nowcast into a HIRLAM NWP model. Meteorol ApplGoogle Scholar
- Lilly DK (1990) Numerical prediction of thunderstorms—has its time come? Q J R Meteorol Soc 116(494):779–798Google Scholar
- Ninomiya K, Taira R, Ueno M, Kurihara K, Kudo T (1987) Mesoscale very short-range numerical prediction with dynamical initialization including condensation heating. ESA, Mesoscale Analysis and Forecasting, SP-282, pp 611–616Google Scholar
- Roebber PJ, Schultz DM, Colle BA, Stensrud DJ (2004) Toward improved prediction: high-resolution and ensemble modeling systems in operations. Weather Forecast 19(5)Google Scholar
- Sass BH, Petersen C (2002) Short range atmospheric forecasts using a nudging procedure to combine analyses of cloud and precipitation with a numerical forecast model. DMIGoogle Scholar
- Skamarock WC (2004) Evaluating mesoscale nwp models using kinetic energy spectra. Mon Weather Rev 132(12)Google Scholar
- Unden P, Rontu L, Järvinen H, Lynch P, Calvo J, Cats G, Cuxart J, Eerola K, Fortelius C, Garcia-Moya JA et al (2002) Hirlam-5 scientific documentation. http://www.hirlam.org