Extended Abstract

Blending a probabilistic nowcasting method with a high resolutionensemble for convective precipitation forecastsKirstin Kober¹, George C. Craig¹², Christian Keil¹¹Meteorologisches Institut der Ludwig-Maximilians Universität München, Germany²Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre,Oberpfaffenhofen, Germanyemail: kirstin.kober@lmu.de1. IntroductionNowcasting and NWP are two different methods to address the challenge ofaccurately forecasting convective precipitation for short lead times. It is known thatnowcasting methods that are based on extrapolation of observations perform quite well forvery short term prediction (0-1 hour) as in this time frame the motion of convection canadequately be described by advection. For longer lead times, the skill of nowcastsdecreases quite rapidly as the temporal evolution of the precipitation field can not berepresented. High-resolution NWP is able to resolve convection. NWP models cansimulate the temporal evolution of the precipitation field, but have difficulties with therepresentation of the initial state of the atmosphere. Due to the strengths and weaknessesof both methods, a skillfull forecast over lead times from 0 to 8 hours has to be acombination of both methods. The inherent uncertainty of the methods and ofmeteorological situations requires a probabilistic approach.2. Data and MethodsThe forecasts based on radar observations (2 km resolution) are calculated with theradar tracking algorithm Rad-TRAM (Kober and Tafferner 2009). This algorithm isextended with a module based on the Local Lagrangian method (Germann and Zawadzki2005) to calculate the probability of exceeding the threshold of 19 dBZ for different leadtimes up to 8 hours (Kober et al. 2010). Therefore, the fraction of pixels above thethreshold is calculated in a search area with lead time dependent size. This value is shiftedto consider the motion of the field with the displacement vector calculated in the originalalgorithm, again depending on the lead time.For the forecasts based on NWP, an experimental ensemble based on COSMO-DEmodel is used (2.8 km resolution). The model is started daily at 0 UTC with 24 hourforecasts. The COSMO-DE-EPS (Gebhardt et al. 2010) considers uncertainties of thelateral boundary conditions and the model physics and consists of 20 members. Based onthe fields of synthetic radar reflectivities in 850 hPa, probabilistic forecasts are derived withthree approaches. First, as traditionally applied on ensembles, the fraction of membersabove the threshold at each grid point is calculated. Second, each member is treated as adeterministic solution and the neighbourhood method (Theis et al. 2005) is applied. Here,the variability of the solution in time and space is considered and the fraction of pixelsabove the threshold in a predefined search area is calculated around each grid point.Third, the mean of the 20 neighbourhood members is calculated. This means, this methodconsiders three sources of uncertainty: the imperfectness of the model, the lateral boundaryconditions and possible timing and location errors. Finally, 22 different probabilisticforecasts are available at each forecast time.The probabilistic model forecasts are calibrated with the reliability diagram statisticsmethod (Zhu et al. 1996) for each of the three methods separately. This results in lowerprobability maxima for all three methods and more similar forecasts. The reliability com--302-