Simon Tschannett*, B. Bica, B. Chimani, M. Dorninger, W. Gepp, S. Schneider, R. Steinacker University of Vienna Vienna, Austria

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1 7.31 VERA AS AN OPERATIONAL NOWCASTING TOOL Simon Tschannett*, B. Bica, B. Chimani, M. Dorninger, W. Gepp, S. Schneider, R. Steinacker University of Vienna Vienna, Austria M. Kerschbaum Austro Control, Aviation Weather Service Vienna, Austria 1. INTRODUCTION VERA (Vienna Enhanced Resolution Analysis, is a system of objective high resolution analyses of meteorological fields over complex terrain in 2D with a grid point distance of down to 1 km. VERA covers the lower Meso- to upper Microscale and has been developed at the Department of Meteorology and Geophysics of the University of Vienna during the last few years. The operational real time VERA suite (20, 10 and 4km grid point distance), including real time model comparison fields, has been used in the Austrian aviation weather service Austro Control successfully for nowcasting in the Alpine region for several years. Examples of the application of the VERA-system for nowcasting at Austro Control are presented. This includes an event with unusually high gusts at Klagenfurt airport south of the Alps and an experimental 4 km wind analysis in the region of Vienna. 2. VERA The VERA analysis scheme is based on the variational principle and does not need any first guess fields, so it is NWP model independent and can therefore also be used as an unbiased reference for real time model validation (at the moment this is done * Corresponding author address: Simon Tschannett, University of Vienna, Dept. of Meteorology and Geophysics, UZA II, Althanstrasse 14, 1090 Vienna, Austria; Simon.Tschannett@univie.ac.at for the LM (Lokalmodel of the German Weather Servcie), ALADIN-Vienna (Central Institute of Meteorology and Geodynamcis, Austria) and IFS of the ECMWF, for more information see also extended abstract 8.08 by Dorninger et al.) The basic philosophy of the Vienna Enhanced Resolution Analysis VERA is to use physical a priori knowledge (the so-called Fingerprints, Steinacker et al. (2005)) about typical atmospheric structures in the atmospheric boundary layer and lower troposphere over complex terrain for downscaling purposes, as the station density in the Alpine region is partially sparse. This yields a more realistic and detailed analysis of the meteorological fields such as pressure, temperature and wind fields (see also extended abstract 3.04 by Bica et al. and Bica et al. (2005)). The VERA analysis method (Steinacker et al. (2000)) is based on the variational principle applied to higher order spatial derivatives, which are computed from overlapping finite elements. For a scalar quantity R the functional J (a weighted sum of squared spatial derivatives on two dimensions) is minimised: i 2 [ SDi ( R) ] dσ Min with R = RS crt J ( R) γ + = σ i (1) where γ i stands for the weight of the different (i) spatial derivatives SD i and σ denotes the area of the finite elements used for the derivation. This method minimises the curvature and/or gradient of scalar fields and the kinematic quantities of vector fields, respectively. It is equivalent to the penalty function of thin-plate smoothing splines; the cost function, defined as the sum of the squared differences between abserved and analysed values, is assumed to be zero, which means that data points are on the

2 resulting function. This is why VERA includes a sophisticated quality control tool, which is one of the fundamentals of the VERA method, as a good data quality is absolutely necessary to obtain meteorologically and physically reliable analyses fields. The analysed variable R can be assumed to be composed of a synoptic part R S and a part R T that is due to orographic influence. c denotes a weighting factor that is to be determined in the course of the analysis process. c is variable on the analysis field. One task is to make some assumption on the structure of the fingerprint R T. In contrast to first guess or background fields that are commonly used for conventional interpolation methods, our fingerprint is a physical model that is based on known properties of meteorological fields over complex terrain. The modification of the atmosphere by a mountain massif has been split up into two different physical processes so far: into thermal effects due to differential heating or cooling of the atmosphere over mountains (e.g. thermal low or thermal high pressure zones) and into dynamic influences. Currently, efforts are being made to introduce further model-based fingerprints to the VERA analysis, such as a fingerprint for upslope precipitation, a radar fingerprint, an urban heat island fingerprint or a coastline fingerprint. 3. OPERATIONAL NOWCASTING Nowcasting is especially important for aviation weather services, as e.g. reliable warnings and forecasts for capacity planning are needed. In the following two chapters examples of the usage of VERA are presented. 3.1 High gust event During the 9 th of September 2003 a cold front Klagenfurt Figure 1: VERA-Analysis on 11 September 2003, 1100 UTC, of reduced pressure (isolines, hpa, spacing 1hPa, thick lines indicating 5 hpa steps), wind (arrows, m/s) and equivalent potential temperature (colour, C, spacing 2 C, ranging from 30 C to 58 C). Stations used in the analysis indicated as crosses (pressure), diamonds (equivalent potential temperature) and squares (wind). Cold front approaching Klagenfurt (red arrow) from NW.

3 Klagenfurt Figure 2: VERA-ALADIN model comparison on 11 September 2003, 1100 UTC. Analysed reduced pressure (isolines, hpa, spacing 1hPa, thick lines indicating 5 hpa steps). Difference field (colour, hpa, spacing 2 hpa, ranging from -4 hpa to +8 hpa) Stations used in the analysis indicated as crosses. Cold front approaching Klagenfurt (red arrow) from NW. Yellow areas show that pressure in the analysis is 4 hpa higher than forecast by the model. approached the Alps from NW (Fig. 1). ECMWF and ALADIN model predicted a pressure gradient over the alpine ridge of about 5hPa. Following a rule of thumb used by bench forecasters at Klagenfurt airport, the expected gusts in knots can be calculated by taking 5 times the pressure difference north to south of the Alps. This leads to an estimation of gusts with 25 knots. VERA-NWP model comparison indicated already at 10 UTC and much better at 11 UTC (Fig.2) a pressure gradient of about 8 hpa, which is much stronger than predicted. The forecasters were alerted and warned of gusts with 40 knots. Observed gust values at 11:20 UTC were 42 knots, which are very unusual at this airport. 3.2 Experimental 4km wind analyses The high temporal and spatial resolution (every 10 minutes, 4km grid point distance) of the Vienna region wind analysis (Fig. 3) makes it possible to decide well in advance if and when to change the pattern of the runway usage at the airport. This allows detailed capacity planning and can therefore be of high economic value. To get this high spatial resolution, the wind data of the mesonet (red barbs in Fig.3) is interpolated to and analysed on a 4km grid. The analysed values have to be smoothed in such a degree (radius of smoothing is 5 grid points) that subscale effects are suppressed and only the synoptic part of the wind field is

4 Figure 3: VERA Vienna region 4km wind analysis on 22 June 2005, 1440 UTC. Red wind barbs indicating observed values at TAWES stations, black wind barbs analysed 4km wind field. Blue lines: Danube river and lake Neusieldersee. Red dotted line: borders, in the centre of the picture: border of the city of Vienna. Black sphere indicating Vienna airport. displayed. This prevents rough fields to be created by the high sampling rate and by small scale and local wind patterns. This leads to an easily readable wind field chart for real time application at the Viennese airport. 4. SUMMARY AND CONCLUSIONS The VERA Analysis method can easily be used as an operational analysis and nowcasting tool since it is computationally inexpensive and it can be applied to real time observational data. VERA-analyses fields and model comparison being available 20 minutes after the full hour allow reacting quickly on newly developing and potentially dangerous weather features and hazardous conditions. The analyses fields are especially valuable as e.g. gradients are stronger and more realistic compared to NWP models. Moreover, as 3 different NWP models (LM, ALADIN, IFS) are available for real time comparison, the most accurate one for the particular event can be chosen for further forecasts. This all helps to get a deeper understanding of ongoing weather events and NWP model results. VERA can therefore also be used for the identification of suspicious regions and for the refinement of the forecast concerning regionalisation and timing. New insights on the diagnostics and prognostics can be found. For the future the development and testing of a 4D analysis tool is planned. First tests in 2D and 3D with this new and flexible approach already show promising results.

5 5. REFERENCES Bica, B., T. Knabl, M. Dorninger, R. Steinacker, C. Lotteraner, S. Schneider, B. Chimani, W. Gepp, M. Ratheiser, S. Tschannett, 2005: Thermally and Dynamically Induced Pressure Features over Complex Terrain from High Resolution Analyses. Submitted to: J. Appl. Meteor. Bica, B., S. Schneider, S. Tschannett, M.Dorninger, M. Ratheiser, R. Steinacker, 2005: High Resolution Analysis and Nowcasting over Complex Terrain by using Physical a priori Knowledge. WWRP International Symposium on Nowcasting and Very Short range Forecasting, Toulouse, France. Extended Abstract 3.04 Dorninger, M., R. Steinacker, T. Gorgas, B. Chimani, S. Tschannett, B.Bica, 2005: Real-Time Verification of Mesoscale Model Products over Complex Terrain. WWRP International Symposium on Nowcasting and Very Short range Forecasting, Toulouse, France. Extended Abstract 8.08 Steinacker R., C. Häberli and W. Pöttschacher, 2000: A Transparent Method for the Analysis and Quality Evaluation of Irregularly Distributed and Noisy Observational Data. Mon. Wea. Rev.,128, Steinacker, R., M. Ratheiser, B. Bica, B. Chimani, M. Dorninger, W. Gepp, C. Lotteraner, S. Schneider and S. Tschannett, 2005: A mesoscale data analysis and downscaling method over complex terrain. Submitted to: Mon. Wea. Rev.