Unlocking the Scientific Value of NEXRAD Weather Radar Data

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) • Probabilistic QPE (OHD initiative towards ensemble based hydrologic prediction)

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

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

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

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? 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 “Find all the 2002 storms over the Ralston Creek watershed with mean arealprecipitation greater than X mm, and with a spatial extent of more than Z km2, with a duration of less than N hours. I want the data in GeoTIFF”

8. Hydrology Centered View “Find all the 2002 storms over the Ralston Creek watershed with mean arealprecipitation greater than X mm, and with a spatial extent of more than Z km2, with a duration of less than N hours. I want the data in GeoTIFF” • Basin-centered • Name, USGS HUC, etc. • Precipitation • MAP, Rain amount, … not Reflectivity Z • Georeferencing • Location, spatial extent • Data Format • GeoTIFF, NetCDF => use in GIS Encode expertise in software system

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

11. CUAHSI HIS NCDC Data Archive Metadata Archive Program Library Metadata Archive User/Client’s View Connect and query “Find all the 2002 storms over the Ralston Creek watershed with mean arealprecipitation greater than X mm, and with a spatial extent of more than Z km2, with a duration of less than N hours. I want the data in GeoTIFF” User/Client Get URIs Get data HTTP

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 • Embedded expertise • Will range from simple to complex • NWS Precipitation Processing System • Anomalous propagation and ground clutter echo detection and removal • Range-dependent bias adjustment • Reflectivity vs. rainfall rate relationship • Coordinate conversion • Advection correction • Accumulation calculation • Multiple radar mosaicing • Combining with rain gauge data • Uncertainty quantification • Etc., etc….

14. PPS PQPE Project • The University of Iowa • W.F. Krajewski • Grzegorz J. Ciach, Gabriele Villarini • National Weather Service OHD • David Kitzmiller • Richard Fulton • NSSL • Alexander Ryzhkov • Dusan Zrnič • Hydrologic 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; • 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. Combined effects of all error sources!

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

17. ARS Micronet

18. Oklahoma PicoNet

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

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

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

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 • 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 • Large sample (5-10 years) • High quality of data = double gauge setup!

24. Thank You! The End