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NMQ/Q2 Reanalysis in the southeastern United States

NMQ/Q2 Reanalysis in the southeastern United States. Scott E. Stevens – CICS-NC, NC State University, NCDC Brian R. Nelson – NCDC Carrie L. Langston – CIMMS, University of Oklahoma, NSSL Ryan Boyles – NC State Climate Office, NC State University. Outline. Motivation

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NMQ/Q2 Reanalysis in the southeastern United States

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  1. NMQ/Q2 Reanalysis in the southeastern United States Scott E. Stevens – CICS-NC, NC State University, NCDC Brian R. Nelson – NCDC Carrie L. Langston – CIMMS, University of Oklahoma, NSSL Ryan Boyles – NC State Climate Office, NC State University

  2. Outline • Motivation • Why is this product needed? • Product Introduction • Process description • Generated fields • Pilot Project • Sample Output • Next Steps and Long-Term Goals

  3. Objective • NOAA does not maintain a high-resolution precipitation product • Impacts modeling, forecasting, flood guidance, etc. • Currently available products have limitations • Spatial Resolution • Temporal Frequency • Accuracy • NMQ/Q2 represents a significant step in precipitation estimates

  4. Current State of Precipitation Estimates • Global Precipitation Climatology Project (GPCP) • 1 day x 100 km • TRMM 3B42 • 3 hours x 25 km • MPE • 1 hour x approx. 4 km • Gauge-based precipitation estimates • 1 hour – 1 day x approx. 20 km

  5. NMQ/Q2: Next Generation QPE • Developed at the National Severe Storms Laboratory (NSSL) in Norman, OK • Research/Development arm of NOAA • Multi-sensor estimate of precipitation • Primarily surface radar-based • Incorporates RUC model data and gauge networks • Represents significant step in resolution • 0.01 degree (1 km spatial resolution) • 5-minute temporal resolution

  6. MPE Resolution Courtesy: NC State Climate Office

  7. NMQ/Q2 Resolution

  8. Q2 Pilot Project (1998-2010)

  9. Q2 Process • WDSS-II (Warning Decision Support System – Integrated Information) • Used for pre-processing • Performs QC and cartesian mapping on raw Level 2 data • NMQ (National Mosaic and Multi-Sensor QPE) • Performs analysis on gridded data to generate value-added products (bright band, hail statistics, VPR) • Q2 (Next-generation QPE) • Final step of NMQ process, generates precip estimates

  10. Q2 Output • Provides precipitation estimates at 1-km resolution every five minutes • Three-dimensional radar reflectivity • Various value-added products • Echo Tops • Bright band heights • Vertically integrated liquid, etc.

  11. Sample Output

  12. Sample Output

  13. The Next Steps • Immediate next step is assessment of output • Comparison with other data sources • Gauge data sets (Co-Op, CRN, etc.) • TRMM/GPCP data • MPE • Performance in different scenarios • Night vs. Day • Mountains vs. Plains • Coastal vs. Inland • Convective vs. Stratiform • Incorporate gauge calibration and terrain adjustment

  14. Long-Term Challenge • Ultimate goal is expansion from pilot domain to cover ConUS • Existing data for pilot domain currently taking up approximately 20 TB • Took a few weeks to move out of NCDC archive to Renci • Processing took approximately 12 weeks of computing time • There are approx. 140 NEXRAD sites in ConUS • Over 200 TB of data to be moved and processed • Output size much smaller, approx. 60-80 GB for QPE

  15. Applications • If product is made operational • High-resolution precipitation estimates at near real-time • Flash flood guidance at 1 km resolution • Short-term hydrology forecasting • Research Applications • Endless possibilities for climatological studies • Orographic influence • Tropical cyclone precipitation contribution • Contributions from individual storm events

  16. The Goal • Archived Precipitation Estimates • Full reanalysis of all US NEXRAD data for period of record • Put in NCDC archive as climate data record for future research in other applications • Using reanalysis mode to provide feedback to NSSL • Can be quickly reprocessed under different circumstances • Identify strengths and limitations to improve product

  17. Acknowledgments • Katherine Willingham – CIMMS/NSSL for technical support with WDSS-II • Howard Lander at UNC RENCI for support with computing cluster • Steve Ansari at NCDC for visualization support • NC State Climate Office at NCSU for input on this presentation

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