Real-Time Assimilation of Observations of Key Prognostic Variables Including the Development of Advanced Observation Operators (RAPTOR)

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1 Real-Time Assimilation of Observations of Key Prognostic Variables Including the Development of Advanced Observation Operators (RAPTOR) Volker Wulfmeyer and Hans-Stefan Bauer Institute of Physics and Meteorology (IPM), University of Hohenheim (UHOH) Garbenstrasse 30, Stuttgart, Germany 1. Introduction Since precipitation strongly affects many aspects of our livelihood, efforts to improve quantitative precipitation forecasting (QPF) have high priority in meteorological research. Whereas precipitation developing along frontal systems driven by large-scale processes is usually reasonably represented in today s models, the representation of convection not directly associated with fronts, e.g. prefrontal convergence lines or even convection triggered only by boundary layer processes or the underlying topography, is much more complex and depends on processes not explicitly resolved in even highresolution mesoscale models. In mountainous terrain, the collection of precipitation in narrow valleys leads to higher risks and shorter lead times of flood events. Therefore, the correct timing, location and intensity of precipitation forecasts are even more important. Furthermore, the processes leading to precipitation are more complex in orographic terrain. According to the WWRP working group on Mesoscale Weather Forecasting Research (MWFR; WMO 2010), the combination of mesoscale models operated with a resolution on the order of 1 km with sophisticated data assimilation and state-of-the-art observations is most promising to advance the scientific understanding in quantitative precipitation forecasting. 2. Status of the spatial project RAPTOR RAPTOR contributed to the improvement of short-range QPF with the development of observation operators for present and future observing systems as different lidar systems or GPS slant total delay (STD). Furthermore, process studies were performed to improve our understanding of convection in southwest Germany (e.g. Schwitalla et al., 2011). During the last 12 months, we focused on the following major topics for which examples are shown on the following pages. Pre-processing and first tests to assimilate radar radial velocity and reflectivity from the French and German radar networks. Implementation of the capability to assimilate observations of the Joint D-PHASE/COPS data set (Dorninger et al., 2009) with the WRF-Var system. Comparison of two COPS IOPs where the model performance is quite different no matter of the initialization with or without data assimilation. Set-up and automatization of a rapid update cycle (RUC) for the WRF system For the investigations, the mesoscale Weather Research and Forecasting (WRF) model (Skamarock et al., 2008) was applied. The selected model domain is the same as that suggested by Schwitalla et al (2011) and is shown in Figure 1. The forecasts were performed with 3.6 km horizontal resolution on a 550 x 550 grid point domain with 50 vertical levels up to 50 hpa. The domain size was selected to allow the evolution of the complete life cycle of investigated convective events in the domain not affected by resolution changes by e.g. nest boundaries. Following the suggestions of e.g. Kain et al. (2008), we switched off the convection scheme for the simulations. 1

2 Figure 1: Model domain selected for the simulations. The dimensions are 550x550 grid points in longitude and latitude and 50 vertical levels up tol 50 hpa. The selected horizontal resolution is 3.6 km in longitude and latitude. One important focus during the recent 12 months was set on the pre-processing of radar radial velocity and radar reflectivity from the German and French radar networks. This was done in close cooperation with DWD where the BUFR data was decoded and stored into NetCDF files and, in case of the German network, quality information were provided in extra files. For the French radar network, quality information was provided by Météo France. At IPM, a preprocessing system was developed to read the radar data and the corresponding quality information. Furthermore, the data was filtered and thinned before used for the assimilation. This filtering and thinning was optimized with preliminary short assimilation experiments. Figure 2 shows an example. The upper row shows filtered raw data of reflectivity on the left panel and the thinned data set before it is included into the assimilation on the right panel. The lower row shows the same for radar radial velocity. 2

3 Figure 2: Example of filtered raw observations (left) and thinned observations (right) for data from the three German radar sites Feldberg, Neuheilenbach and Türkheim. The upper row shows reflectivity and the lower row radial velocity. The capability of the WRF-Var system to assimilate radar radial velocity and reflectivity was implemented by Xiao and Sun (2007). For radial velocity, the three wind components are projected to the radar beam position by the following forward operator = + + Here, x, y and z denotes the location of the radar system, x i, y i and z i are the location of the radar observation and r i is the distance between the radar location and the observation relative to the center of the earth. The terminal velocity v t describes the fall speed of the rain drops and is defined by =5.4. With the surface pressure p s, the model pressure p and the rain water mixing ratio q r at the observation location. Assimilation of radar reflectivity allows, depending on the applied forward operator, the adjustment of rain and cloud water content or solid hydrometeors. In the WRF-Var system, the forward operator of Sun and Crook (1997) is implemented. = Here, ρ air is the dry air density and q r the rain water mixing ratio. Currently, German radar data are thinned similar to an approach of Xiao et al. (2008). Radial velocities are filtered, so that only observations with a variance of the surrounding observations smaller than 60 m 2 /s 2 are accepted. Otherwise, they are set to missing value. French radar data is filtered and thinned following an approach of Montmerle and Faccani (2009) on a 10x10 km 2 grid. The data thinning and radar reflectivities follow the same procedure as mentioned for radial winds, except that data are rejected if the variance exceeds 150 dbz 2. Another extension of the observation pre-processing of the WRF-Var system was that the JDC dataset (Dorninger et al., 2009), collected for COPS, can now also be incorporated into the assimilation. This largely increases the number of surface observations as compared to the number of GTS stations from the MARS archive applied so far. Figure 3 e.g. compares the station locations for 10 m wind observations in

4 Figure 3: Comparison of station number and location measuring 10 m wind velocity. Left: GTS data from the MARS archive and the same complemented by the JDC data set (right). The shell scripting framework automatizing our WRF simulations including assimilation was further extended to allow the setup of a rapid update cycle where the assimilation window and the length of the cycle can be freely selected by the used. Figure 4 illustrates our RUC system. Figure 4: Setup of the rapid update a shell scripting framework. cycle (RUC) for the WRF-Var system. The whole process was automatized in After the setup of the RUC system, tests were performed to find the optimal length of the assimilation window. To do so, forecasts with different lengths were done and the performance of the following free forecast was judged with observations. Figure 5 shows an example of the wind velocity at 06 UTC, July 20 th 2007 at 5000m above sea level for a 1-hour assimilation cycle (left) and a 3-hour assimilation cycle (right). It is clearly seen, that the 1-hour RUC analysis shows a much larger variability compared to the 3- hour RUC cycle. The larger amount of noise included by the former degrades the quality of the forecast. 4

5 Figure 5: Wind velocity [m/s] at an altitude of 5000 m for RUC intervals of 1 hour (left) and 3 hours. For COPS IOP 9c (20 July 2007), several assimilation experiments were done to test the influence of the new developments on the forecast performance. As an example, Figure 6 compares the model reflectivity for two time steps during COPS IOP 9c of a CONTROL simulation without a re-assimilation of observations in the WRF domain (upper row) and an ASSIM experiment, where all available observations (that can be assimilated), including the JDC data set an radial velocities and reflectivities from radar, are applied. The comparison is done with a radar composite of the German Meteorological Service (middle row). 5

6 Figure 6: Simulated and observed radar reflectivity for two time steps during COPS IOP 9c (20 July 2007). Upper panel: Simulation without re-assimilation in the WRF domain (CONTROL) Middle panel: Radar composite of DWD. Lower panel: Simulation with a re-assimilation of observations in the WRF domain (including JDC and radar). The results are not perfect, but promising, since the location and timing of convection is improved in many regions. However, the future optimization of the system might lead to further improvements. Another task of RAPTOR, the investigation of the capability of WRF to represent single synoptic situations, was also continued during recent months. Here, the model performance for two different COPS IOPs was compared when the same types of observations were assimilated. Interestingly but not unexpected, the results show that the model performance strongly depends on the forcing of the synoptic situation. In case of strong large-scale forcing (IOP 9c), the model performance is clearly improved by the assimilation. In case of the weaker forcing during COPS IOP 4b (20 June 2007), the assimilation, on the other hand, only slightly improved the situation as 6

7 compared to the simulation without assimilation. This is illustrated with Figure 7, comparing the model simulations with and without assimilation with observations of the Vienna Enhanced Resolution Analysis (VERA; Steinacker et al., 2006) for one time step during COPS IOP 4b. Figure 7: Precipitation [mm/hr] and 10 m wind field for a CONTROL simulation without data assimilation (upper left panel) and an ASSIM simulation with data assimilation (upper right panel) for one time step during COPS IOP 4b (20 June 2007). The lower panel shows the Vienna Enhanced Resolution Analysis (VERA) for the same time step. A closer comparison of the 10 m wind field revealed that even with the assimilation, the model is not able to capture the correct near surface circulation with the consequencee that convection is triggered at wrong locations. 3. Plans for the rest of the year 2011 Until the end of 2011, the work will be continued to optimize the RUC system and the preparation of radar data. References Dorninger M., T. Gorgas, T. Schwitalla, M. Arpagaus, M. Rotach, and V. Wulfmeyer, 2009: Joint D- PHASE - COPS data set (JDC data set). Technical report. Available at Kain, J. S., S. J. Weiss, D. R. Bright, M. E. Baldwin, J. J. Levit, G. W. Carbin, C. S. Schwartz, M. L. Weisman, K. K. Droegemeier, D. B. Weber, and K. W. Thomas, 2008: Some practical considerations regarding horizontal resolution in the first generation of operational convection- allowing NWP. Wea. Forecasting, 23,

8 Montmerle, T. and C. Faccani, 2009: Mesoscale Assimilation of Radial Velocities from Doppler Radars in a Preoperational Framework. Mon. Wea. Rev., 137, Schwitalla, T., H.-S. Bauer, V. Wulfmeyer, and F. Aoshima, 2011: High-resolution simu-lation over central Europe: Assimilation experiments with WRF 3DVAR during COPS IOP9c. Q. J. R. Meteorol. Soc. 137, , DOI: /qj.721. Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, M. Duda, X-Y Huang, W. Wang, and J.G. Powers, 2008: A Description of the Advanced Research WRF Version 3. NCAR Technical Note TN-475+STR, 113pp. Steinacker, R., M. Ratheiser, B. Bica, B. Chimani, M. Dorninger, W. Gepp, C. Lotteraner, S. Schneider, and S. Tschannett, 2006: Downscaling Meteorological information over complex terrain with the fingerprint technique by using a priori knowledge. Mon Wea Rev, 134, Sun, J. and N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54, Xiao, Q. and J. Sun, 2007: Multiple-Radar Data Assimilation and Short-Range Quantitative Precipitation Forecasting of a Squall Line Observed during IHOP_2002. Mon. Wea. Rev., 135, Xiao, Q., E. Lim, X. Zhang, J. Sun and Z. Liu, 2008: Doppler Radar Data Assimilation with WRF 3D-Var: IHOP Retrospective Studies. 9 th WRF users workshop, Boulder/CO. WMO. 2010: WWRP Strategic Plan Available online: /arep/wwrp/new/documents/final_wwrp_sp_6_oct.pdf. Wulfmeyer, V., A. Behrendt, Ch. Kottmeier, U. Corsmeier, C. Barthlott, G.C. Craig, M. Hagen, D. Althausen, F. Aoshima, M. Arpagaus, H.-S. Bauer, L. Bennett, A. Blyth, C. Bran-dau, C. Champollion, S. Crewell, G. Dick, P. Di Girolamo, M. Dorninger, Y. Dufournet, R. Eigenmann, R. Engelmann, C. Flamant, T. Foken, T. Gorgas, M. Grzeschik, J. Handwerker, C. Hauck, H. Höller, W. Junkermann, N. Kalthoff, C. Kiemle, S. Klink, M. König, L. Krauss, C.N. Long, F. Madonna, S. Mobbs, B. Neininger, S. Pal, G. Peters, G. Pigeon, E. Richard, M.W. Rotach, H. Russchenberg, T. Schwitalla, V. Smith, R. Steinacker, J. Trentmann, D.D. Turner, J. van Baelen, S. Vogt, H. Volkert, T. Weckwerth, H. Wernli, A. Wieser, M. Wirth, 2011: The Convective and Orographically Induced Precipitation Study (COPS): The Scientific Strategy, the Field Phase, and First Highlights. COPS Special Issue of the Q. J. R. Meteorol. Soc. 137, 3-30, DOI: /qj