1. AQUARadar Identification of temporally stable Z-R relationships using measurements of micro-rain radars M. Clemens (1), G. Peters (1), J. Seltmann (2), P. Winkler (2) (1) Meteorological Institute, University of Hamburg, Germany; (2) Meteor. Obs. Hohenpeißenberg, DWD ERAD 2008 Helsinki

2. AQUARadar ERAD 2008 Helsinki Motivation Approach Experimental setup Estimation of mean Z-R relations Estimation of differentiated Z-R relations (modes) Outlook Identification of temporally stable Z-R relationsOutline

3. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsMotivation There is a need for adapted Z-R relations for improved radar precipitation estimation MRR measurements of DSDs show sudden changes in their structure Classification of rain events into single subdivided periods in terms of changes in the “spectral behaviour” leads to Z-R relations with significantly reduced scatter Due to vertical changes of DSDs it is meaningful to estimate the relation directly in the radar volume A concept for optimum and automatic choice of modes has been developed.

4. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsApproach Many authors (e.g., Amitai 2000; Tokay and Short 1996; Rosenfeld et al. 1995; Nzeukou et al. 2004) classified Z-R relations by the existence or non-existence of a brightband signature as a criteria for stratiform or convective rainfall. The corresponding Z-R relations show significant differences indicating higher reflectivities in stratiform cases as compared to the convective ones, for a given rain rate. For operationally practical purposes, we have pursued a more statistical approach. We focus on the identification of temporally stable Z-R relationships (modes) by use of a simple statistical correlation analysis technique.

5. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsExperimental setup Micro Rain Radar (MRR) Transmit frequency: 24.1 GHz Doppler resolution: 0.191 ms -1 Range resolution: 100 m Height range: 300 m - 3000m Measuring cycle time: 10 s

6. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations 4th Aug. 2006

7. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsEstimation of mean Z-R relations fit on logarithmic scale: expected value of r at a given z 0: transformed coefficients: single estimates of r: standard error as sum of individual error and the error estimating the mean: residual variance:

8. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsEstimation of mean Z-R relations … assumption of linearity … assumption of homoscedasticity Regression model does NOT fit … σ 2 rz =4.78

9. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsDifferentiated Z-R relations Retrospective detection of the change points at unknown times is made by use of the correlation structure between the r and z time series Be careful … Magnitude of the coefficient does not proof the existence or absence of a functional relationship between both. Correlation coefficient is not a satisfactory descriptive measure as it can be influenced by single extreme values. But … If a relation between both variables is evident, it is an adequate indicator for the stability of such an assumed function. running correlation + regression analysis ρ(z,r) in overlapping windows width of each time window covers 15 minutes (90 measurements) shift of the window for each new coefficient is 10 seconds or one measurement Additionally … 99%-confidence limit as well as the critical correlation as a function of the significance level and the number of measurements

10. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsDifferentiated Z-R relations duration [min.] σ 2 rz a*b*ab 1590.55-10.800.491572.04 21791.01-11.080.541071.84 3390.48-12.260.611051.65 4 731.47-21.000.843141.19

11. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsDifferentiated Z-R relations

12. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsDifferentiated Z-R relations

13. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsDifferentiated Z-R relations

14. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations Micro-rain radar drop size measurements from a field campaign were used to obtain relationships between radar reflectivity factor and rain rate. The detection of transition points in the linear relation between reflectivity and rain rate in log-log space was performed by use of running correlation analysis. Rapid changes of the relation parameters were characterized by sharp decreases in the correlation function. In comparison to a mean relation derived for the whole example, the scatter of the rain rates versus reflectivities is remarkably reduced. The regression coefficients a* and b* turn out to be highly dependent within the analysed modes of the Z-R relation. Conclusions

15. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relationsOutlook Thank you!!! Testing Z-R nowcast procedure on the basis of the correlation function with a constant window width of 15 minutes. Identify the spatial pattern in volumetric radar data using statistical pattern recognition method. 1.pass the analysed Z-R function (intersection volume) to the identified pattern (or all patterns with same statistical or physical characteristics) 2.track the identified pattern with the impressed Z-R relation using tracking method

16. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations

17. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations

18. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations

19. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations

20. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations

21. AQUARadarERAD 2008 Helsinki Identification of temporally stable Z-R relations