PRELIMINARY RESULTS OF THE COMPARISON OF TWO ADVECTION METHODS

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1 2.28 PRELIMINARY RESULTS OF THE COMPARISON OF TWO ADVECTION METHODS Virginia Poli*, PierPaolo Alberoni, Tiziana Paccagnella and Davide Cesari ARPA SIM, Viale Silvani 6, Bologna, Italy 1. DESCRIPTION OF ARPA SIM NOWCASTING ALGORITHM Nowcasting algorithm developed at ARPA SIM (Emilia Romagna region, Italy) uses an extrapolation technique, such as TREC method (Rinehart and Garvey, 1978). This family of algorithms starts from an analysis of a series of radar reflectivity fields in order to identify areas of precipitation and determine the motion field which allows the tracking of coherent structures from an image to the next one. Motion of individual rainfall areas is extrapolated through two different methods: a simple translation and a semi lagrangian advection scheme. Selected methodology is based on a multi scale recursive cross correlation analysis, where different targets are tracked at different scales examinated. In the first step of procedure two subsequent reflectivity patterns in a time gap of 15 minutes are chosen and are both divided in three different layers bounded respectively by 0.2, 30 and 50 dbz. For the first layer, for each of the two data fields, an area of reflectivity centered on radar image is considered. The one from field at time t0 is shifted around that one of field at time t1=t0+15 minutes in order to determine in what position and in what direction cross correlation coefficient is maximum. Translations between selected areas (Figure 1) are performed with an angular step of 30 and with a radial step of 1 Km (with an upper limit of 20 Km). Fig. 1 Examples of translations between selected areas of two radar scans. * Corresponding author address: Virg in ia Pol i, SIM, Viale Si lvani 6, 40122, Bologna, e- mail : vpol i@arpa.emr. i t ARPAI ta l y ; Best cross correlation coefficient determines components of steering vector for this layer which is assumed to represent the average motion of the whole reflectivity pattern. To reduce the variability of this vector in time and to avoid abrupt spatial variations in reflectivity field, a weighted average of motion vector estimated is made. Comparison of averaged vectors components with the istantaneous one shows that trend of values of motion vector is more homogeneous (Figure 2). Fig. 2 Trend of motion vector components during an event. In order to identify steering vector for the second and the third reflectivity layers, radar scans are divided in regular grids, with increasing resolution corresponding to an increasing reflectivity. Each box of the grid of the first scan is compared to all possible box in a range of 20 Km in the second scan (Figure 3) until maximum cross correlation coefficient is derived. Also in this case values calculated are used to retrieve components of motion vectors. Fig. 3 Examples of some translations for gridded radar scans. Finally all of the components of vectors calculated are collected and are combined in order to define a motion vector for each pixel of radar image (Figures 4 and 5). Up to this point some algorithm limits can be stressed. In fact results are affected by dimensions of selected area for cross

2 correlation coefficient estimation, by grid resolution used in 2nd and 3rd layer, by values chosen as threshold for reflectivity ranges. field. Furthermore semi lagrangian advection smoothes reflectivity peaks when forecast time increases. 2. CASE STUDIES ANALYSIS The outcome of the two methods comes from the study of convective and stratiform episodes in Po valley. Results are compared to observations in order to define forecast performance indexes such as CSI, FAR, POD and BIAS. Analysis concerns only pixels with reflectivity greater than 30 dbz , 7 March Fig. 4 Motion vectors calculated for each reflectivity level (different color means different level). First case study deals with a stratiform event characterized by a low reflectivity due to persistent precipitations during almost the whole day. Near radar, bright band is present, i. e. precipitation is snow. Data are from San Pietro Capofiume radar. Figure 6 shows reflectivity pattern of 10:15 GMT with superimposed motion field calculated between 10:00 and 10:15 GMT, while Figures 7 and 8 display results for semi lagrangian advection and translation. Fig. 6 Motion field. Fig. 5 Motion vectors defined for each pixel (not all the vectors are represented). But the most important restriction is due to the assumption that within 15 minutes the major features of tracked system are not substantially modified that is: physical development of radar pattern are not taken into account. It is well known that this hypotesis is no longer verified especially in strong convective situation. In this case, because procedure just described is quite sensible to small variations which could happen in observed field, the tracking algorithm could produce unstable outcomes. Obtained motion field gives different results depending on exploited approach to forecast reflectivity pattern. Translation preserves every characteristic of reflectivity field whose deformation is allowed by semi lagrangian advection scheme. Actually deformation itself mirrores variations in the steering wind Fig. 7 Advected field for 5 different forecast times.

3 Observed Forecasted YES NO YES A B NO C D Table 1 Contingency table. Fig. 8 Translated field for 5 different forecast times. Given the contingency table (Table 1) we define CSI (Critical Success Index) as A CSI = 1 A B C This index is calculated in order to verify if forecasts maded are good (CSI=1 means perfect forecast). For each of 5 forecast times can be noted that CSI value increases if the number of pixel examinated is large (Figure 9), but decreases with increasing forecast time (Figure 10). In scatterplot of Figure 11, CSI evaluated for semi lagrangian advection is plotted versus that evaluated for translation for the whole event. Assessing results from these images it is evident that semi lagrangian advection not always gives better results, especially semi lagrangian advection works better than translation until 1 hour forecast. Fig. 9 CSI values depending on number of pixels with reflectivity greater than 30 dbz.

4 Fig. 10 CSI values extimated during the entire event. Fig. 11 Relationship between CSI calculated with semi lagrangian advection (x axis) and translation (y axis).

5 , 9 May In this case study precipitations occurred during the afternoon until the end of day and produced reflectivities locally high due to the development of convective cells. Data are from Gattatico radar. Motion field is that of Figure 5. Images presented are of the same kind of those of previous case. Most meaningful difference in the comparison of examinated events is in evaluated CSI indexes. Actually stratiform case gives better results than convective one. The reason of that resides in nature of event itself. In a strong convective situation the rate of increase or decrease of observed area is very high and this can cause errors in producing forecasts. Moreover the number of analysed pixels is very different. Convective events are spatially limited while those stratiform tend to cover the whole radar area. Fig. 12 CSI values depending on number of pixels with reflectivity greater than 30 dbz.

6 Fig. 13 CSI values extimated during the entire event. Fig. 14 Relationship between CSI calculated with semi lagrangian advection (x axis) and translation (y axis).

7 3. CONCLUSIONS Starting from a sequence of radar images we defined a method to nowcast reflectivity fields. We have shown obtained results of two cases over Emilia Romagna region where we examined the skill of radar nowcast. Outcomes are dependent by nature of the event. In order to verify method reliability we made a comparison with a simple nowcasting approach. Better results provided by translation of reflectivity pattern in many of cases analysed is partly due to the fact that semi lagrangian advection smoothes reflectivity peaks while, as seen before, translation keeps reflectivity characteristics intact. Reflectivity values for semi lagrangian method decrease with forecast time recovering under the threshold fixed for the analysis: because of the smaller quantity of pixels examinated outcomes are worst of those waited for. Acknowledgments This work is partially supported by the European Union under the INTERREG IIIB CADSES programme RISK AWARE, contract number 3B064. References Rinehart, R. E., and T. Garvey, 1978: Three dimensional storm motion detection by conventional weather radar. Nature, 273,