1. Estimation of Rainfall Areal Reduction Factors Using NEXRAD Data Francisco Olivera, Janghwoan Choi and Dongkyun Kim Texas A&M University – Department of Civil Engineering Funded by the Texas Department of Transportation 2006 AWRA GIS-Water Resources IV 8-10 May 2006 – Houston, Texas

2. Purpose of the Project Characterize:  rainfall intensity distribution,  storm shape, and  storm orientation

3. Areal Precipitation Rainfall intensity is not uniformly distributed. Rain gauges measure precipitation at points, but precipitation over areas has to be estimated (Weather Bureau 1957, 1958, 1958, 1959, 1960, 1964; Rodriguez-Iturbe and Mejia 1974; Frederick et al. 1977; NWS 1980; Omolayo 1993; Srikathan 1995; Bacchi and Ranzi 1996; Siriwardena and Weinmann 1996; Sivapalan and Bloschl 1998; Asquith and Famiglietti 2000; De Michele et al. 2001; Durrans et al. 2002; among others). ARFs are used to convert point precipitation (P P ) into areal average precipitation (P A ).

4. Areal Reduction Factors (ARF) According to TP-29 (Weather Bureau 1957, 1958, 1958, 1959, 1960, 1964 )

5. ARF Estimation Methods Geographically-fixed (used in TP-29) Concurrent Areal Annual Maxima Station Annual Maxima Geographically-fixed rain gage network 25 28 35 31 35 41 35 41 37 38

6. ARF Estimation Methods Average precipitation within the area Maximum precipitation within the area ARF = Spatially-distributed precipitation data allows us to implement other methods for estimating ARFs that capture the storm anisotropy. Storm-centered ARF For each storm:

7. Data

8. Precipitation data Type: NEXRAD Multisensor Precipitation Estimator (MPE) Period: Years 2003 and 2004 Area: West Gulf River Forecasting Center Time resolution: 1 hour Spatial resolution: 4km x 4km (approx.)

9. Study Area The WGRFC mesh has 165,750 cells. After clipping out Texas (including a 50 km buffer), the mesh had 56,420 cells. More than 2.9 billion precipitation values

10. NEXRAD: Sources of error Vertical Profile Reflectivity (VPR) effect  Related to how well the radar can see the precipitation near the surface.  x 2 of overestimation and x 10 of underestimation.  It is a major source of error. Microphysical parameters  Related to the different Z-R relationship for different types of storms (convective, tropical, stratiform) Radar calibration  Site specific (corrected for all radar sites in the USA now) Sampling errors  Arbitrary/random errors (cancels out for lumped model) Truncation error  Related to the numerical processing of the values (magnitude of 0.1mm, important for long low intensity events)

11. Reliability of NEXRAD Data When comparing NEXRAD precipitation data to gauged precipitation data:  NEXRAD adjustments are not site specific.  16-km 2 areal precipitation is not the same as point precipitation.  Rain gauges are not perfect.

12. Methodology

13. Precipitation Annual Maxima Storm durations of 1, 3, 6, 12 and 24 hours. NEXRAD data Annual maxima Concurrent rainfall

14. Storm Identification 21 x 21 cell window (i.e., 84 km × 84 km) around the “center cell”. Calculations proceed only if the “center cell” has the maximum concurrent precipitation depth. 18,531 storms were identified.

15. Optimum Ellipse For a given area, the ellipse that comprises the maximum precipitation volume was selected. For determining the “optimum ellipse”, the shape aspect and orientation were changed systematically. After determining the optimum ellipse, the same procedure was applied for a new area value.

16. Optimum Ellipse Values Location Duration (hours) Rainfall depth (mm) (center) Area (km 2 ) Rainfall depth (mm) (ellipse) ARF Aspect Orientation For each optimum ellipse:

17. Results

18. Climate Regions TWDB (1967), The Climate and Physiography of Texas, Texas Water Development Board, Report 53, 27 p. Points represent 1-hour storms.

19. Region / Season / Depth Probability distribution: ARF

20. Region

21. Season

22. Depth

23. Storm Aspect Summer Winter

24. Storm Orientation Summer Winter

25. Conclusions

26. Season made a difference in all cases except for a few in regions 1 and 6. Region made a difference in all cases except for 4 and 5 in summer. Depth made a stronger difference in summer than in winter storms. However, because the database was very limited in the depth range (i.e., only low return period values were considered), conclusions cannot be definite. Storm aspect values of around 2 were the most frequent for both seasons and all regions. SW-NE storm orientations are predominant in winter storms, and less pronounced in summer storms. ARF vary within a wide range, because it depends on each storm characteristics

27. Questions?