Ken Cook – SOO NWS Wichita, KS (ICT) An Assessment of Using the Mean Field Bias Correction to Improve Precipitation Estimates Ken Cook and Maggie Schoonover NOAA/National Weather Service Office 2142 South Tyler Road Wichita, KS Phone: (316) Fax: (316)

Ken Cook – SOO NWS Wichita, KS (ICT) Outline Introduction Prompted Use Methodology of Analysis Assessment –Examination Results –Case Studies –Challenges –Advantages Ken Cook – SOO NWS Wichita, KS (ICT)

What is the Mean Field Bias? A statistical analysis between the gauge observations and the radar bin that matches that gauge (NPair) Performed hourly Uses a minimum of 10 NPairs –If 10 cannot be ascertained during the current hour, then looks back in time until 10 is reached –User adaptable parameter Applies this statistical analysis (one number) to the entire coverage area Software part of Multi-sensor Precipitation Estimator (MPE)

Ken Cook – SOO NWS Wichita, KS (ICT) Why Use It? June 8 th, 2005 case 2-3X observed precipitation Latest in a number of cases where precipitation estimates were less than desirable Saw media partners using this bad data Needed to improve forecasters confidence in radar estimated precipitation Improve service/warning meteorology

Ken Cook – SOO NWS Wichita, KS (ICT) Once Implemented Results were instantly improved Noticed some underestimation during various events How much have we improved? How can we make the system better? –Local training/learning –National science sharing/improve development

Ken Cook – SOO NWS Wichita, KS (ICT) Methodology Radar Data (ICT) –12Z STP level III data from NCDC –Nexrad Exporter (create shapefiles) –ArcGIS 9, create rasters Observation Data –Gathered 12Z Rain Gauge (Tipping Buckets/COOP) reports –Imported into Microsoft Access –Loaded then added XY Data in ArcGIS 9 Compared Datasets for the period March through June 2006 Resulted in ~ 400 G-R Observation Cases Gauge data assigned a “bin” value –.45 gauge value assigned to the.3 to.6 radar “bin”

Ken Cook – SOO NWS Wichita, KS (ICT) Results Slight underestimation evident

Ken Cook – SOO NWS Wichita, KS (ICT) Results

Ken Cook – SOO NWS Wichita, KS (ICT) Results

Ken Cook – SOO NWS Wichita, KS (ICT) Results There seems to be a clear signal of slight underestimation More noticeable as –Event grows in size –Amount of precipitation increases

Ken Cook – SOO NWS Wichita, KS (ICT) Case Studies 32 Gauge Observations Light Rain Event Average Gauge Observation.14 inches Highest Gauge Observation.22 inches Estimates were outstanding Hourly MFB Calculations Very Consistent

Ken Cook – SOO NWS Wichita, KS (ICT) Case Studies 43 Gauge Observations Moderate Rain Event Average Gauge Observation 1.10 inches Highest Gauge Observation 2.85 inches Poorest estimation of the cases Largest number of observations for one case (largest coverage) Highest average precipitation Hourly MFB Calculations somewhat less stable

Ken Cook – SOO NWS Wichita, KS (ICT) Challenges - How to Improve the MFB Use proper overlays in MPE to inspect suspect gauges Inspect gauge table in MPE Take out bad gauges from MPE ingest filter Inspect MPE Local Bias for areas that may be over/underestimating as compared to Mean Field Bias

Ken Cook – SOO NWS Wichita, KS (ICT) Advantages - Using the MFB Better Precipitation Estimations No change to Z/R relationship necessary Updated hourly Reacts to a warm rain process with no interaction Improved credibility & customer service

Ken Cook – SOO NWS Wichita, KS (ICT) Future Continued assessment –Alter adaptable parameters? –Alter npairs? Science sharing with developers other users –Incorporate differences from second/third runs of bias? –Assume 1:1 bias after no precipitation? Goal: Improve performance Thank you – Questions??

Ken Cook – SOO NWS Wichita, KS (ICT) Resources Cook, Kenneth (SOO – ICT), 2006: WFO Wichita Science and Training Intranet Page ( –Training Materials Also Available Hunter, S. M., 1996: WSR-88D rainfall estimation: Capabilities, limitations, and potential improvements. National Weather Digest 20 (4), WHFS Field Support Group Web Site (