1. Unlocking the Scientific Value of NEXRAD Weather Radar Data Witold F. Krajewski with Anton Kruger, Ramon Lawrence, Allen A. Bradley, and Grzegorz J. Ciach

2. Two Issues: Hydrologic use of NEXRAD data: (NSF funded NEXRAD Hydro-ITR project)Hydrologic use of NEXRAD data: (NSF funded NEXRAD Hydro-ITR project) Probabilistic QPE (OHD initiative towards ensemble based hydrologic prediction)Probabilistic QPE (OHD initiative towards ensemble based hydrologic prediction)

3. NEXRAD Hydro-ITR Project The University of Iowa (Lead)The University of Iowa (Lead) –W.F. Krajewski (PI) –A.A. Bradley, A. Kruger, R.E. Lawrence Princeton UniversityPrinceton University –J.A. Smith –M. Steiner, M.L.Baeck National Climatic Data CenterNational Climatic Data Center –S.A. Delgreco –S. Ansari UCAR/Unidata Program CenterUCAR/Unidata Program Center –M. K. Ramamurthy –W.J. Weber

4. Project Premise Rainfall is a key component of the hydrologic cycleRainfall is a key component of the hydrologic cycle NEXRAD data have potential to provide surface rainfall estimatesNEXRAD data have potential to provide surface rainfall estimates Reality: NEXRAD data are severely underutilized in the hydrologic sciencesReality: NEXRAD data are severely underutilized in the hydrologic sciences

5. Why? These are significant obstacles – often show-stoppers. Weather radar operationsWeather radar operations Radar data quality controlRadar data quality control Formatting and data handlingFormatting and data handling Radar-rainfall algorithmsRadar-rainfall algorithms Current methods of accessing NEXRAD data require considerable expertise in:

6. Project Goal …to provide the science (hydrologic) community with ready access to the vast archives and real- time information collected by the national network of NEXRAD radars. Again The main focus is on radar-rainfall data for use in hydrology, hydrometeorology, and water resources.

7. What Does This Mean? “Find all the 2002 storms over the Ralston Creek watershed with mean areal precipitation greater than X mm, and with a spatial extent of more than Z km 2, with a duration of less than N hours. I want the data in GeoTIFF” Rather than saying: “Get the Level II data for the KDVN Iowa NEXRAD (KDVN) for the 16 July 2002 severe weather outbreak. Show a 2 km CAPPI of reflectivity and cross-section of Doppler velocity” a hydrologist wants to say

8. Hydrology Centered View Basin-centered –Name, USGS HUC, etc. Precipitation –MAP, Rain amount, … not Reflectivity Z Georeferencing –Location, spatial extent Data Format –GeoTIFF, NetCDF => use in GIS Find “Find all the 2002 storms over the Ralston Creek watershed with mean areal precipitation greater than X mm, and with a spatial extent of more than Z km 2, with a duration of less than N hours. I want the data in GeoTIFF” Encode expertise in software system

9. IT Issues –Open –Open source vs. commercial software, Java –Data formats: NetCDF & HDF –Front end/client & back end /server –Linux vs. Windows –XML, XML Schema, OWL –Metadata standards (Federal, USGS) –Interfaces with DLESE, NSDL, THREDDS –LDM/IDD –Web services: SOAP, XML-RPC –Relational Databases –Compatibility with (ESRI) GIS

10. Compute Engine Metadata Archive CUAHSI HIS Data Archive Unidata Extreme Events University A Runoff Model Data Archive Metadata Archive NCDC Injects NEXRAD Data University B Internet Request

11. User/Client’s View User/Client Program Library Get data Connect and query Get URIs HTTP Data Archive Metadata Archive NCDC Metadata Archive CUAHSI HIS “Find all the 2002 storms over the Ralston Creek watershed with mean areal precipitation greater than X mm, and with a spatial extent of more than Z km 2, with a duration of less than N hours. I want the data in GeoTIFF”

12. Concept: Metadata Metadata –Data about data –Descriptive statistics Areal coverage, Maximum, Minimum, AP present, Associated Hydrologic Units, Anything else Key Ideas –Simple, easy to compute –Do not have to be definitive –Building blocks for other metadata

13. Rainfall Algorithms NWS Precipitation Processing SystemNWS Precipitation Processing System Anomalous propagation and ground clutter echo detection and removalAnomalous propagation and ground clutter echo detection and removal Range-dependent bias adjustmentRange-dependent bias adjustment Reflectivity vs. rainfall rate relationshipReflectivity vs. rainfall rate relationship Coordinate conversionCoordinate conversion Advection correctionAdvection correction Accumulation calculationAccumulation calculation Multiple radar mosaicingMultiple radar mosaicing Combining with rain gauge dataCombining with rain gauge data Uncertainty quantificationUncertainty quantification Etc., etc….Etc., etc…. Embedded expertise Embedded expertise Will range from simple to complex Will range from simple to complex

14. PPS PQPE Project The University of IowaThe University of Iowa –W.F. Krajewski –Grzegorz J. Ciach, Gabriele Villarini National Weather Service OHDNational Weather Service OHD –David Kitzmiller –Richard Fulton NSSLNSSL –Alexander Ryzhkov –Dusan Zrnič Hydrologic Research CenterHydrologic Research Center –Konstantine P. Georgakakos

15. Product-Error Driven Approach Collect reliable data on the relation between different radar-rainfall (RR) products and the corresponding True Rainfall;Collect reliable data on the relation between different radar-rainfall (RR) products and the corresponding True Rainfall; Create a flexible model of this relation and apply it to the PQPE product generator;Create a flexible model of this relation and apply it to the PQPE product generator; Develop empirically based generalizations of the model for different situations.Develop empirically based generalizations of the model for different situations. Combined effects of all error sources!

16. Ground Reference Error Filtering Assume that, for given spatio-temporal resolution and radar-range, we have available:Assume that, for given spatio-temporal resolution and radar-range, we have available: –Large sample of corresponding (R r,R g ) pairs; –Detailed information about spatial rainfall variability in this sample. Can we retrieve a good estimate of the verification distribution (R r, R a )?Can we retrieve a good estimate of the verification distribution (R r, R a )?

17. ARS Micronet

18. Oklahoma PicoNet

19. G/R Quantiles: Hourly Scale Hot All Warm Cold Radar-Rainfall (mm) Conditional Multiplicative Error 10% 25% 50% 75% 90%

20. Model Fitting: Hot Season Conditional Multiplicative Standard Deviation Radar-Rainfall (mm)

21. Temporal Correlation of the Random Component (hourly scale) Correlation Coefficient Lag (minutes) ColdWarm Hot All

22. Conclusions & Recommendations Guiding principles for solving the PQPE problem: Nested clusters of double gauges strategically located to represent different rain regimes of the country Nested clusters of double gauges strategically located to represent different rain regimes of the country Cluster configuration designed for specific purpose (e.g. statistical characterization of rainfall, minimum RMS, spatial dependence of errors, etc.) Cluster configuration designed for specific purpose (e.g. statistical characterization of rainfall, minimum RMS, spatial dependence of errors, etc.) Development of inference methodologies and transferability studies Development of inference methodologies and transferability studies Large sample (5-10 years) Large sample (5-10 years) High quality of data = double gauge setup! High quality of data = double gauge setup!

24. Thank You! The End