2.39 A NEW METHOD FOR THE NOWCASTING OF STRATIFORM PRECIPITATION USING RADAR DATA AND THE HORIZONTAL WIND FIELD OF THE GERMAN LOKALMODELL (LM).

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1 2.39 A NEW METHOD FOR THE NOWCASTING OF STRATIFORM PRECIPITATION USING RADAR DATA AND THE HORIZONTAL WIND FIELD OF THE GERMAN LOKALMODELL (LM). Tanja Winterrath * Deutscher Wetterdienst, Offenbach am Main, Germany 1. ABSTRACT A new module for the nowcasting of precipitation based on radar measurements and simulated wind fields has been developed. The Germany radar composite is tracked with the non-divergent horizontal wind field based on the forecast of the Lokalmodell, the operational numerical weather prediction model used at the German Weather Service. In contrast to linear extrapolation techniques, the new module allows a tracking of the precipitation field that is nonlinear in space and variable in time. First results show good agreement with observational data. 2. INTRODUCTION Flooding along with damage of properties has become a common phenomenon, favored by the straightening of watercourses and the development and sealing of flood plains. The time- and spaceresolved forecast of the precipitation input into catchment areas is of fundamental importance for an accurate prediction of flood events. Therefore, the nowcasting of severe precipitation is one of the major tasks the weather services are facing. Several radar nowcasting methods exist at weather services. They comprise forecasting of severe weather, thunderstorm and tornado warnings as well as general precipitation forecasts based on * Corresponding author address: Tanja Winterrath, Deutscher Wetterdienst, AP 2003, Kaiserleistr. 29/35, Offenbach am Main, Germany, Tanja.Winterrath@dwd.de radar measurements, combined with information taken from numerical weather prediction (NWP) and satellite observations. Precipitation nowcasting modules at the German Weather Service (Deutscher Wetterdienst, DWD) that are based on radar measurements comprise two modules, a nowcasting tool for convective precipitation and severe weather warning as well as a linear extrapolation routine mainly for stratiform precipitation. Both algorithms recognize patterns within consecutive preceding radar observations, derive displacement vectors, and produce a nowcast by spatial linear extrapolation of the actual radar data. This method has been shown to produce good results for the nowcasting of precipitation up to the next two hours. While on the one hand the displacement vectors are implicitly derived from the radar data and thus get the information on the extrapolation from the past, on the other hand these methods are restricted to linear extrapolation. The aim of this work is to extend the extrapolation of radar-derived precipitation to the timeframe of a few hours. Here, we present a new module for the nowcasting of stratiform precipitation. It is part of a hierarchy of different methods extending from the linear extrapolation of radar data to NWP model forecasts. The basic approach is to advect the radarderived precipitation pattern of the Germany Composite with the horizontal wind field taken from the Lokalmodell (LM; Doms and Schättler, 1999; Steppeler et al., 2003) or the Lokalmodell-Kürzestfrist (LMK; Doms and Förstner, 2004), respectively. The divergence of the horizontal wind field is eliminated to guarantee an area-preserving advection and therewith to prevent the build-up of new unrealistic precipitation pattern. The optimum pressure level to extract the

2 wind field from has to be chosen based on investigations with a test data set. 3. MODULE INPUT Input data for the nowcasting module comprise measured radar data as well as model results from NWP. 3.1 Radar Data The radar network of the German weather service comprises 16 radar stations each with a radial precipitation scan measurement range of 125 km covering Germany. The precipitation scans, i.e., the scans with the lowermost possible elevation angle at each radar station, are pooled to form one composite. equations are solved on a terrain-following grid with a horizontal resolution of 7 km or 2.8 km in the operational forecast (LM) and the nowcast version (LMK), respectively. Horizontal wind components are calculated as prognostic variables and provided in defined pressure levels. The pressure level (or a combination of several) that is best suited for the displacement of the radar pattern is derived in test studies. While already the 3d wind field in this formulation is not completely free of divergence, the 2d horizontal wind field in addition can be largely influenced by vertical transport, especially convection. Therefore it is necessary to eliminate the divergence in the horizontal wind field before applying it to the tracking of precipitation data (see 4.3). Radar reflectivities are converted into precipitation rates using an elaborated Z-R-relationship. Absolute calibration of the radar data is performed by combining rain gauge data with radar measurements applying an optimized algorithm (Bartels et al., 2005). This time-critical calibration will be performed online using hourly ombrometer data leading to the so-called quasi-gauge adjusted precipitation data. In this paper, we present model results using quantitative but not yet quasi-gauge adjusted precipitation data. Several approaches are applied to correct false pixel values, however, in order not to eliminate any true precipitation data, these filters are too defensive for a complete removal of clutter. Therefore an additional algorithm has been developed and applied in the current project. Further details can be found in Section Wind Field To derive the displacement vectors used to track the radar-derived precipitation, wind fields are taken from the forecast of the Lokalmodell (LM) that is used for operational numerical weather forecasts at DWD. The LM is a non-hydrostatic limited-area numerical weather prediction model designed for applications in the meso-β and meso-γ scale. Nonhydrostatic, full compressible hydro-thermodynamical 4. MODULE DESCRIPTION 4.1 Tracking The aim of this project is to develop a module that is capable of forecasting the movement of precipitation fields within the next few hours. To date linear extrapolation schemes applied at DWD are restricted to a maximum nowcast interval of 1 to 2 hours. These modules calculate the extrapolation vectors via recognition of patterns in consecutive preceding radar observations. The new module presented here uses the forecasted horizontal wind field from the LM to advect the radar data of the precipitation scan to its new position. This method allows the usage of displacement vectors that are variable in time and space and are thus thought to better describe the movement of precipitation fields over a longer nowcasting time period. On the other hand, these vectors have to be taken from an external source here the wind field from the LM assuming that the wind field is representative for the displacement of the ground precipitation field derived from the radar observations. The time-critical run time for a one hour precipitation forecast lies in the order of a few minutes with the modification of the wind field being calculated in advance. The sequence of the precipitation forecast is as follows:

3 Reading of wind field from a designated pressure level: The optimal pressure level or a combination of several will be retrieved from a test study of several cases covering different weather situations. Elimination of divergences from wind field (see 4.3): Divergence and convergence provoke the creation of new minima or maxima, whereas the non-divergent wind field guarantees an areaconserving and therefore more realistic transport of the radar data. Reading of (quasi-gauge adjusted) radar-derived precipitation data. Complete elimination of clutter (see 4.2): Because of numerical diffusion clutter pixel are smeared and therefore might be mistakenly taken as convective cells if not eliminated. Determination of the timestep for the advection scheme according to Courant-Friedrich-Lévy (CFL) criterion: The CFL criterion is based on the maximum wind speed within the time period and the length of a grid cell. Linear interpolation of wind fields on respective point in time in every timestep of the advection scheme Advection of ground precipitation data with 2D Euler advection scheme (Bott, 1989, 1992, 1993). Output of shifted radar data as precipitation rate as well as hourly precipitation sum. 4.2 Clutter Filter As the advection of precipitation cannot be completely free of numerical diffusion, clutter pixel, i.e., small areas with high radar reflectivity caused by moving obstacles reflecting the radar beam, e.g., the wings of windmills, have to be removed completely before applying the advection routine in order to avoid a misinterpretation of smoothed clutter pixel fields in the forecast. For this reason, an effective algorithm is applied that guarantees the complete removal of clutter pixels while conserving the reliable precipitation data. A pixel is defined as clutter, if more than 85% of the pixels within the 31 pixel x 31 pixel surrounding square have a value of less than 45% of the center pixel. In the module each pixel of the radar grid is checked according to the above mentioned criterion to mark the pixel as clutter. The marked pixels are then replaced by the mean of all data pixels (excluding the marked clutter pixels) within the defined square. Thus, the clutter field is either equalized to the overlying precipitation field or set to zero, if no precipitation is present. 4.3 Wind Divergence For the advection of the radar precipitation data the horizontal wind field of the LM forecast is used. As divergence and convergence in the wind field lead to stretching and compression of the precipitation field and subsequently to the formation of unrealistic minima or maxima, the divergence of the wind field has to be eliminated in order to guarantee an areapreserving advection. The basic approach is the minimization of the following equation (e.g., Sherman, 1978): 2 E( u, v, λ) = α u v λ x A u v ( u u ) + α ( v v ) + + dxdy, y (1) where u, u 0, v, v 0 are the non-divergent and the original wind field components, α u,v are the Gauß precision moduli, and λ is the Lagrange multiplier. Equation (1) is solved iteratively. As a result of the usage of a non-divergent wind field the advection of precipitation patterns is areaconserving, however not necessarily shapeconserving, as the wind field does not have to be uniform. This procedure ensures that no new maxima or minima develop. 5. RESULTS The method takes profit out of the fact that clutter pixel do not appear in contiguous areas but rather as pixel clouds with small spatial density that may or may not go along with precipitation patterns. In the following chapter we present exemplary results for 18 th August 2004 for the time interval between 8pm and 12pm. The wind field was taken from a pressure level of 500 hpa, which produced the best results (expressed as True Skill Statistics) in this

4 the measured radar data for the four succeeding hours each combined with the actual wind forecast. At this time the cold front passed from the southwest to the northeast connected to the precipitation band seen in the radar data. While some structures seem to be shifted to the northeast, others dissolve or newly develop. Figure 1: Germany composite of the radar network of the DWD, qualitative precipitation heights given in color coding; white areas contain no data. Wind barbs give the non-divergent wind field based on the 500 hpa forecast of the Lokalmodell. particular case, however no general conclusion on the optimal wind field can be drawn from this single investigation at this time. 5.1 Weather Situation An upper trough was moving from the eastern Atlantic towards the European continent. A corresponding surface low was moving from the UK to southern Scandinavia. In the evening a fast moving cold front with embedded thunderstorms crossed Germany from the southwest to the northeast. Figure 1 shows the Germany composite of radarderived ground precipitation on 18 th August 2004 at 8 pm. The total size of the data file shown is 900 pixels x 900 pixels that is equivalent to 900 km x 900 km. The colored area defines the dimensions of the composite of the German radar network with the violet areas representing no precipitation and the color coding representing the (not gauge adjusted) amount of precipitation. The white areas lie outside of the range of the radar network and thus do not contain any data. Note that the plotted data is effectively corrected from clutter, while small structures of the precipitation field are still visible. Overlying is the horizontal non-divergent wind field in 500 hpa based on the latest LM forecast, i.e., the eight hour forecast of the 12 pm run. One can see a precipitation band over the western part of Germany. Figure 2 (a-d) show The plots show a defined frontal precipitation field and a second area southwest with slightly lower precipitation. During the forecast period this second precipitation structure dissolves whereas the frontal structure changes its shape and location. 5.2 Forecast Figure 3 (a-d) show the hourly forecasted precipitation. Along with the measured precipitation field the white background mask defining the border of the radar network is also shifted with the wind field. Due to this procedure all areas of the radar network are whitened that lie at the end point of displacement vectors originating outside of the network area, i.e. areas for which no forecast can be performed. Due to the transport of the radar measurements with a non-divergent wind field the method described here is area-conserving. Therefore, neither the dissolution of the western precipitation field nor the development of new cells can be predicted. This has to be kept in mind when comparing the results of the forecast module with the measured radar data. The eastward shifting of the large precipitation field stretching approximately north-south in the beginning of the forecast interval is represented by the module. In addition, the slight turning and bending of the precipitation band is indicated in the module results, although to a lesser extent. Also shown in the upper left corners of Figures 3 (a-d) are the True Skill Statistics (TSS), i.e. a score used for verification of binary statements yes and no. It is defined as the difference between the Probability of Detection (POD) and the cases that caused false alarm divided by all no cases (see Table 1 for the elements of the contingency table):

5 Figure 2 (a-d): Radar precipitation measurements and forecasted non-divergent wind field on 18 th August 2004 at (a) 9, (b) 10, (c) 11, and (d) 12 pm. Figure 3 (a-d): Forecasted precipitation data for (a) 9, (b) 10, (c) 11, and (d) 12 pm.

6 TSS = d c (2) h g with TSS = 1 or 100% being the ideal forecast. The TSS for the shown example are 76%, 61%, 47%, and 42% after 1, 2, 3, and 4 hours, respectively. Overall, the results are a promising step in the development of this forecasting module. Fore Real No Yes Sum No a b e Yes c d f Sum g h N Table 1: Contingency table for binary event precipitation yes or no. 6. CONCLUSIONS AND OUTLOOK A new method for the nowcasting of precipitation based on the precipitation measurements of the radar network of the German Weather Service and a horizontal wind field based on the forecast of the Lokalmodell was presented. To avoid the smearing of clutter pixel and the subsequent misinterpretation of these structures as convective cells, a method for the efficient elimination of clutter pixel has been developed and applied. The transport of the radar data is performed by applying the 2d advection scheme by Bott (1989, 1992, 1993). As displacement vectors the horizontal wind field from a defined pressure level of the forecast of the Lokalmodell is taken. To guarantee an area-preserving shifting of the radar data, the divergence is eliminated from the wind field. Future plans comprise a thorough verification of the newly developed module against measurement data as well as against existing nowcasting schemes. In case of a successful verification the module will be introduced into operational routine. ACKNOWLEDGEMENTS The author thanks C. Podlasly, E. Weigl, T. M. Böhm, K. Helmert, J. Förstner, W. Peyinghaus, and the colleagues in Project 3 of the Aktionsprogramm 2003 for helpful discussions. REFERENCES Bartels, H. et al., 2005: Projekt RADOLAN., Final Report, German Weather Service, 111 pp. Bott, A., 1989: A positive definite advection scheme obtained by nonlinear renormalization of the advective fluxes. Mon. Wea. Rev., 117, Bott, A., 1992: Monotone flux limitations in the areapreserving flux-form algorithm. Mon. Wea. Rev., 120, Bott, A., 1993: The monotone area-preserving fluxform advection algorithm: Reducing the time-splitting error in two-dimensional flow fields., Mon. Wea. Rev., 121, Doms, G., and J. Förstner, 2004: Development of a kilometer-scale NWP-system: LMK., COSMO Newsletter, 4, Doms, G., and U. Schättler, 1999: The nonhydrostatic limited-area model LM (Lokal-Modell) of DWD Part I: Scientific Documentation., German Weather Service, 134 pp. Sherman, C. A., 1978: A mass-consistent model for wind fields over complex terrain., J. Appl. Meteor., 17, Steppeler, J., G. Doms, U. Schättler, H. W. Blitzer, A. Gassmann, U. Damrath, and G. Gregoric: 2003: Meso-gamma scale forecasts using the nonhydrostatic model LM., Meteorol. Atmos. Phys., 82, The work is part of the Aktionsprogramm 2003 at the German Weather Service.

Romanian Contribution in Quantitative Precipitation Forecasts Project

3 Working Group on Physical Aspects 29 Romanian Contribution in Quantitative Precipitation Forecasts Project Rodica Dumitrache, Victor Pescaru, Liliana Velea, Cosmin Barbu National Meteorological Administration,