Martin Benko *, Marián Jurašek, Ján Ka ák, André Simon, Jozef Vivoda Slovak Hydrometeorological Institute, SHMÚ, Slovak Republic

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1 7.6 DEVELOPMENT OF NOWCASTING TECHNIQUES AT SHMÚ Martin Benko *, Marián Jurašek, Ján Ka ák, André Simon, Jozef Vivoda Slovak Hydrometeorological Institute, SHMÚ, Slovak Republic 1. INTRODUCTION Early attempts to develop nowcasting software at Slovak Hydrometeorological Institute (SHMÚ, further in the text) based on processing and extrapolation of satellite and radar imagery appeared already in the eighties of the previous century (Ka ák, 1994). However, the technology and quality of the measurements at that time did not allow wide application of these methods in the forecasting praxis. The present standard of observation and the level of computational technology give better conditions to build an advanced network for nowcasting. Since 1997, the use of DWSR92c type Doppler radar at Malý Javorník station brought considerable improvement of radar observations and enabled to detect severe convective phenomena of small scales and rapid development (e.g. pulse storms, supercells). Since 2005, there is second Doppler radar RDR250-GC manufactured by Radtec with Enigma III digital signal processor by Gamic in the operational service, situated at Kojšovská ho a (eastern part of Slovakia). Progress of the latest years concerns also satellite meteorology (data received from the Meteosat Second Generation satellite), lightning observations and increasing number of surface observations from automatic stations. Further, SHMÚ computes since 1998 short range numerical forecasts of hydrostatic limited area model ALADIN for the domain of Slovakia (Derková et al., 2005). The number of outputs and information passed towards the forecaster requires objective methods and sophisticated algorithms to enable fast decisions. Corresponding author address: Martin Benko, Slovak Hydrometeorological Institute, Jeséniova 17, Bratislava, Slovakia Martin.Benko@shmu.sk The evaluation of the meteorological situation and comparison of outputs from different sources is still done mainly in subjective way and methods of nowcasting are only partially used in the present operational service at SHMÚ. Forecasts of mesoscale weather phenomena on very short time range are issued for certain customers: e.g. for civil defence, transportation, power industry and building industry. Investigations are also done on early detection and forecasting of floods in a project called POVAPSYS (Initial project, 2001). The tasks of this project concern also development of nowcasting techniques using all available observations at SHMÚ mentioned above in the text. The chapter 2 of this paper presents the software for tracking of the convective cells developed at SHMÚ. Chapter 3 gives an overview about research and radar detection of severe weather phenomena. Chapter 4 describes the use of the ALADIN SLOVAKIA model in very short range forecasting and in diagnostics of the thunderstorm environment. Last chapter discusses the strategy and possibilities of the future development of nowcasting at SHMÚ. 2. TRACKING OF CONVECTIVE CELLS SHMÚ participated in the international project CEI nowcasting, coordinated by ZAMG that developed common nowcasting software for central European countries (Zwatz-Meise, 2004 and Poredos et al., 2005). Department of remote sensing at SHMÚ contributed with its own algorithm used for tracking of convective cells based on satellite and radar imagery (Ka ák and Jurašek, 2004). The objectives of the algorithm are the detection of convective cells from radar and satellite measurements, monitoring of the cell trajectories and time extrapolation of the cell trajectories. The algorithm determines the cell centre by searching the

Fig. 1. Demonstration of the method for searching the trajectory of the detected cells. The square is floating around the cell to find its predecessors on the previous image.

2 Fig. 1. Demonstration of the method for searching the trajectory of the detected cells. The square is floating around the cell to find its predecessors on the previous image. The size of the square and the range of the square surroundings are tuning parameters. centre of gravity of the detected cell, where corresponding measured quantity (the radiative temperature from satellite or the radar reflectivity) substitutes the mass. The second step is searching of the precursor of the detected cell in the previous image by method of maximum correlation in defined surroundings of the cell and construction of the cell trajectory. The motion of the cells is recognized with a method of floating square. This process is based on computation of minimum sum of square difference of pixels in the floating square which contains values from current image and corresponding values of the previous image. The square is drifting around the central position of the cell in current image. The range of the drift is given by parameter of floating square surroundings (Fig.1). Each cell is automatically detected on previous image of the image sequence until its parameters decrease under detectable limits or until the computational memory is sufficient for the selected image sequence. Hence, each cell can be tracked almost during its whole lifetime. Finally, the algorithm extrapolates the trajectories for the future time step. The algorithm was originally developed by means of artificial data, later tested on real data measured by Doppler radar at Malý Javorník and implemented to operational service with parameters tuned on a set of data meteorological case studies. The operational use is adapted for 30 minute interval of the Meteosat satellite imagery and 15 minute interval of radar observations from Malý Javorník station. However, the software was also tested on Meteosat Rapid scan images (10 minutes interval) and on radar data from particular case studies with 7.5 minute interval between the measurements. Testing and operational use of the software showed that the reasonable validity of the forecasted tracks for single convective cells is around half an hour, for mesoscale convective systems, in some cases, it is about one hour. Except of the tracking and forecasting of the future position of the convective cells, the outputs help to recognize important signatures of severe thunderstorm development.

3 Fig. 2. Output of the nowcasting system used at SHMÚ and applied on radar observations of 9 May 2003, 18,30 UTC. The figure shows cluster of convective cells appearing in the western part of Slovakia (area of Slovakia is marked with brown colour). Traces show the analysed and predicted (extrapolated) directions of cell motion, with forecasts of the position of the cores of radar reflectivity up to half an hour in advance. The prevailing motion of the cells was from southwest to northeast. Note the rightward deviation of the cells in the south (bottom) part of the cluster. These cells evolved to high precipitation supercell storm that caused considerable damages due to severe wind and hails. This was experienced in cases with splitting of cells or deviation of the supercell propagation compared to adjacent thunderstorms (Fig. 2). Since 2003, the software is used in the operational service together with other applications developed by the group of CEI nowcasting. 3. RADAR DETECTION OF SEVERE WEATHER Radar detection and research of severe weather phenomena as downbursts, tornadoes, hail and flash floods are very closely related to the problem of nowcasting. Since 2002, additional measurements are provided with radar at Malý Javorník in order to increase the frequency and the resolution of radar data. As mentioned in the previous chapter, measurements at 240 km range and with 7.5 minute frequency were used for testing of the nowcasting software. Scanning at shorter ranges ( km) is applied to get higher resolution data for the study of fine-scale structures in radar reflectivity and Doppler velocity field. Additional radar observations enabled to study the impact of different scanning strategies, setups of parameters (e.g. Signal Quality Index filtering) or unfolding of the Doppler velocity. The scanning strategy and type of the measurement are selected during the tests according to current meteorological situation it is dependent mostly on the distance of the thunderstorm from the radar site. For example, detection of the outflow from near thunderstorms

Fig. 3. Radar reflectivity (on the left) and radial Doppler velocity (on the right figure) as measured by radar at Malý Javorník station on 13 May 2003, 13:26 UTC.

with Slovakia. The observations showed remarkable hook echo in the southern part of the supercell visible at low elevation scans of the radar.

4 Fig. 3. Radar reflectivity (on the left) and radial Doppler velocity (on the right figure) as measured by radar at Malý Javorník station on 13 May 2003, 13:26 UTC. The cell detected on the western (left) side of the both figures is a supercell that was slowly propagating from Austria (on the left side of the figure) towards northeast and later hit the borders with Slovakia. The observations showed remarkable hook echo in the southern part of the supercell visible at low elevation scans of the radar. The hook echo is associated with mesocyclonic vortex signature in the Doppler radar velocities. Tornado and hails appeared during this situation in Vienna (Austria). requires more scans at low antenna angles and increase of pulse repetition frequency against measurements at high ranges. It appeared that the increase in resolution of radar data was crucial to recognize severe weather potential of the thunderstorm in most of the cases. Above all the observations at ranges below 120 km and with resolution along the radar beam (gate width) equal or below 250 m were able to show important features in the Doppler radar velocity field as mesocyclone vortex signatures of supercells (Fig. 3). On standard measurements using 240 km range and resolution approximately 1.0 km it was mostly more difficult to identify important signatures of severe weather. These were mainly echoes in radar reflectivity, such as WER or Bow- echoes. However, signatures in the radar reflectivity fields at lower resolutions were sometimes found to be ambiguous and insufficient to identify mesocyclonic rotation of the storm or the presence of severe downdrafts. Several products were tested and evaluated on case studies with severe convection. Outputs from Velocity Azimuth Display algorithms were used to study the impact of wind shear on thunderstorm development and propagation. Generation of Vertically Integrated Liquid (VIL) products proved to be very useful in detection of severe events, while rapid decrease of this parameter was usually associated with severe downdrafts and torrential rainfall. As single research experiment, Doppler velocity measurements of the Malý Javorník radar were compared with almost identical types of data obtained from the radar of Hungarian Meteorological Service situated at Budapest L rinc. The data were used to compute the 2-D horizontal field of Doppler velocity vectors at 3 km height. The output was analysed on the case study of 9 May 2003 high precipitation supercell in southwestern Slovakia (Fig. 4). In this case, the horizontal Doppler velocity field from the two radars at 240 km range gave better information about the mesocyclonic rotation inside the evaluated thunderstorm as the radial Doppler velocity field obtained from single radar. Nevertheless, algorithms to detect vortex signature from single radar are under development as their operational application seems to be more feasible in the near future.

Fig. 4. Experimental calculation of 2-D Doppler velocity field and composite radar reflectivity from Malý Javorník and Budapest L rinc radar measured on 9 May 2003, 19,22 UTC, at 3 km AGL.

5 Fig. 4. Experimental calculation of 2-D Doppler velocity field and composite radar reflectivity from Malý Javorník and Budapest L rinc radar measured on 9 May 2003, 19,22 UTC, at 3 km AGL. White arrow shows the position of the mesocyclone of the supercell. The starting point of the velocity vector is marked by thick spot. Example in the bottom right part of the figure denotes a vector corresponding to westerly wind with speed of 20 m/s. 4. NUMERICAL WEATHER PREDICTION Analyses and forecasts of the spectral limited area model ALADIN (Radnóti et al., 1995) are very important in studying severe weather in Slovakia, though, operationally are used more in short range and very short range forecasting as in nowcasting. The operational version of ALADIN SLOVAKIA model has 9.0 km horizontal grid resolution and 37 vertical levels (using the hybrid vertical coordinate system developed by Simmons and Burridge, 1981) and hydrostatic two-time level semi-implicit semilagrangian dynamics. The research is concentrated on high resolution modelling. The hydrostatic high resolution ALADIN DADA model is based on the same dynamics and almost identical physical parameterization as the operational model but it is running with 2.5 km resolution. Besides that, SHMÚ runs the so called dynamical adaptation of the ALADIN SLOVAKIA model with 2.5 km resolution as well. This model uses initial and boundary conditions from the operational model and it is used to produce very short (half an hour or one hour) forecasts of the wind field, mean sea level pressure and temperature without applying physical parameterization. Very recently, non-hydrostatic version of the ALADIN model was tested again with 2.5 km resolution. In these preliminary tests the model used non-hydrostatic dynamics of the ALADIN model but physical parameterization of its hydrostatic version. Both operational and research runs were very successful in predicting severe downslope windstorm in the region of High and Low Tatras on 19 November, 2004 (Simon and Vivoda, 2005). Nevertheless, only runs with high resolution were able to localize and estimate the maximum of the wind gusts during this storm (almost 60 m/s measured on southern slopes of

Fig. 5. Forecast of wind gusts at 10 m height (left) and vertical cross section of the potential temperature and vertical velocity field (right) computed by high resolution model ALADIN DADA.

Note the two principal areas of extreme wind: one in the central and right part of the picture (lee side of High Tatra Mountains), the second in the bottom left part (region of Low Tatras).

6 Fig. 5. Forecast of wind gusts at 10 m height (left) and vertical cross section of the potential temperature and vertical velocity field (right) computed by high resolution model ALADIN DADA. On the left picture the colours represent the magnitudes of the wind gusts starting with 24 m/s (green) up to 55 m/s (maximum forecasted value, red). Note the two principal areas of extreme wind: one in the central and right part of the picture (lee side of High Tatra Mountains), the second in the bottom left part (region of Low Tatras). The dashed AB line shows the sense of the vertical cross section on the right figure. High Tatras). Moreover, outputs of both hydrostatic ALADIN DADA and non-hydrostatic ALADIN model show the effect of hydraulic jump appearing as deformation of potential temperature field accompanied by strong downslope and upslope vertical motions (Fig. 5). Such results are necessary for the forecasters to recognize the character of this severe event which only rarely occurs in the vicinity of Slovak mountains. For the time being, the computationally cheap dynamical adaptation of the ALADIN SLOVAKIA model seems as a sufficient tool for very short range forecasting of nonconvective windstorms. In cases with thunderstorms, stability indices and parameters of wind shear computed from the ALADIN SLOVAKIA model are used to analyse the properties of the convective environment. To estimate both positive and negative buoyancy CAPE (Convective Available Potential Energy), CIN (Convective Inhibition) and DCAPE (Downdraft Convective Available Potential Energy) are computed (following the textbook of Emanuel, 1994). Parameters as SREH (Storm to Relative Environmental Helicity) and Bulk Richardson Number were evaluated as possible precursors of updraft rotation within convective storms (see, e.g. Davies-Jones et al., 1990 or Rasmussen et al., 1998). Storm motion vector was computed as density averaged wind between 0 and 6 km layer AGL. Helicity was compared in certain case studies with values obtained from combination of VAD algorithm wind profile and density profile from model analysis. Experiences with parameters as SREH or EHI (Energy Helicity Index) indicate a good predictability of organized convection on squall lines or cold fronts, associated with severe windstorms. This was the case of the convective windstorm that appeared on 19 June 2004 in central part of Slovakia (district Žiar nad Hronom) where the localization of the maximum values of predicted SREH (18 hour forecast) was in good agreement with the position of the event (Fig. 6). However, the results can be very sensitive on the setup of physical parameterization used by the model (e.g. parameterization of turbulent transport or convection).

Right: Observation of column maxima radar reflectivity measured by radar at Malý Javorník on 19 June 2004 20,45 UTC.

7 Fig. 6. Left: Forecast of the Storm to Relative Helicity (in colour) and storm motion vector (wind barbs) as density averaged wind in 0-6 km layer AGL. The ALADIN SLOVAKIA model forecast is based on 19 June UTC and it is valid for 19 June UTC. Right: Observation of column maxima radar reflectivity measured by radar at Malý Javorník on 19 June ,45 UTC. The arrow shows the position of the convective windstorm that hit the city of Žiar nad Hronom. 5. FUTURE DEVELOPMENT 6. REFERENCES The aim of present activities at SHMÚ in the field of nowcasting and very short range forecasting is to establish basic technology and methods to forecast small scale (and sometimes severe) weather events. Tracking of convective cells or diagnostics of the thunderstorm environment are only first steps towards more complex system that should collect and evaluate all available data and observations useful for nowcasting. First tests and preliminary results show that the predictability of mesoscale weather phenomena can be much improved using high resolution numerical models. The numerical weather prediction department at SHMÚ participates on international development of data assimilation and non-hydrostatic dynamics of the model ALADIN that is intended to be used in the mesoscale model called AROME (planned to be operational since 2008). Besides technical development, building of specialized nowcasting department is planned in the near future. This task will require appropriate working strategy and an efficient system of spreading and verification of forecasts and warnings on severe weather. Davies-Jones, R., Burgess, D., Foster, M., 1990: Test of helicity as a tornado forecast parameter. Preprints, 16 th Conf. on Severe Local Storms, Kananaskis Park, AB, Canada, Amer. Meteor. Soc., Derková, M., Belluš, M., Mašek, J., Vivoda, J., 2005: New operational ALADIN setup at SHMI, ALADIN Newsletters, 27, Emanuel, K. A., 1994: Atmospheric convection, Oxford University Press, 580 pp. Initial project, POVAPSYS, Flood Warning and Forecasting System of the Slovak Republic, 2001: SHMÚ, report, 136 pp. Ka ák, J., 1994: Meteotrend and nowcasting. Österreichische Beiträge zu Meteorologie und Geophysik, 10, Ka ák, J., Jurašek, M., 2004: Tracking algorithm for Meteosat data as a CEI nowcasting tool. The 2004 EUMETSAT Meteorological Satellite Conference Proceedings. Darmstadt, 360

8 Poredos, A., Strelec, N., Drvar, D., Zwatz-Meise, V., Jann, A., Horvath, A., Kanak, J., 2005: Development of common nowcasting tools in central European national weather services, WWRP Symposium on Nowcasting and Very Short Range Forecasting, abstract Radnóti, G., Ajjaji, R., Bubnová, R., Caian, M., Cordoneanu, E., et al., 1995: The spectral limited-area model Arpègè-Aladin. In: PWPR report series n.7 WMO TD n. 699, Rasmussen, E. N., Blanchard, D. O., A Baseline Climatology of Sounding-Derived Supercell and Tornado Forecast Parameters. Wea. Forecasting, 13, Simmons, A. J., Burridge, D. M., 1981: An Energy and Angular Momentum Conserving Vertical Finite- Difference Scheme and Hybrid Vertical Coordinates. Mon. Weather. Rev., 109, Simon, A., Vivoda, J., 2005: Case of extreme wind occurrence at High Tatras on 19 th November 2004, ALADIN Newsletter, 27, Zwatz-Meise, V., 2004: CEI Nowcasting: A common nowcasting system within five countries of Central Europe. The 2004 EUMETSAT Meteorological Satellite Conference Proceedings. Darmstadt., 206

Downslope windstorm in High Tatras 19 November high resolution study by André Simon, Jozef Vivoda SHMÚ

Downslope windstorm in High Tatras 19 November 2004 high resolution study by André Simon, Jozef Vivoda SHMÚ Impact of the 19 November windstorm Southern part of High- and Low Tatras: 120 km2 of forest