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A Multi-Radar, Multi-Sensor-based Hail Climatology for the CONUS: 2000-2011 Derek Rosseau, Kiel Ortega, Anthony Reinhart, and Holly Obermeier OU/CIMMS & NOAA/OAR/NSSL Introduction This project uses data from Mul4-Year Reanalysis of Remotely Sensed Storms (MYRORSS) for years 2000-2011. MYRORSS was a project that involved reprocessing the archive of WSR-88D level-II data for the con4guous United States MYRORSS data combines WSR-88D radar data with RUC/RAP model analyses and produces Mul4-Radar Mul4-Sensor (MRMS) grids, such as Maximum Expected Size of Hail (MESH). Quality Control (QC) Analyze daily accumulations of MESH. Errors in reflectivity data result in erroneous MESH values. The bad data were removed, if possible, then reprocessed the day. Once a full year was reprocessed, the data was accumulated. Accumulations ran through a series of smoothers to clean up the field. Results Issues Future Work Persistent QC problems (e.g. coastal interaction, radar spikes) affect the hail days per year climatology Cannot manually remove some errors due to other precipitation in the area Incorrect MESH values due to coastal interac4on lead to noise in the climatology This poster was prepared by Derek Rosseau with funding provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA-University of Oklahoma Coopera4ve Agreement #NA11OAR4320072, U.S. Department of Commerce. The statements, findings, conclusions, and recommenda4ons are those of the author(s) and do not necessarily reflect the views of NOAA or the U.S. Department of Commerce. 12 year Accumula4on Maximum MESH (unsmoothed) 12 year Accumula4on Maximum MESH (smoothed) Days per Year Any Hail Days per Year Severe Hail January Any Hail February Any Hail March Any Hail April Any Hail May Any Hail June Any Hail July Any Hail August Any Hail September Any Hail October Any Hail November Any Hail December Any Hail Radar spikes due to terrain: another common QC issue Explore variability of hail es4mates per given near-storm environment Develop a climatology that es4mates the likelihood of severe hail using coarser grid spacing Compare MESH to other MRMS grids to see if any major differences in a radar-derived hail climatology exist between products Raw MESH fields were accumulated with a mul4ple hypothesis technique. Accumula4ons were smoothed using mul4ple dila4on and erosion filters, preserving smaller tracks while elimina4ng noise, and finished with a Gaussian filter.

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Page 1: Derek Rosseau, Kiel Ortega, Anthony Reinhart, and Holly ... · A Multi-Radar, Multi-Sensor-based Hail Climatology for the CONUS: 2000-2011 Derek Rosseau, Kiel Ortega, Anthony Reinhart,

A Multi-Radar, Multi-Sensor-based Hail Climatology for the CONUS: 2000-2011 Derek Rosseau, Kiel Ortega, Anthony Reinhart, and Holly Obermeier

OU/CIMMS & NOAA/OAR/NSSL

Introduction •  ThisprojectusesdatafromMul4-YearReanalysisofRemotelySensedStorms(MYRORSS)

foryears2000-2011.•  MYRORSSwasaprojectthatinvolvedreprocessingthearchiveofWSR-88Dlevel-IIdata

forthecon4guousUnitedStates•  MYRORSSdatacombinesWSR-88DradardatawithRUC/RAPmodelanalysesand

producesMul4-RadarMul4-Sensor(MRMS)grids,suchasMaximumExpectedSizeofHail(MESH).

Quality Control (QC)

•  Analyze daily accumulations of MESH. •  Errors in reflectivity data result in erroneous MESH values. •  The bad data were removed, if possible, then reprocessed the day. •  Once a full year was reprocessed, the data was accumulated. •  Accumulations ran through a series of smoothers to clean up the field.

Results

Issues Future Work

•  Persistent QC problems (e.g. coastal interaction, radar spikes) affect the hail days per year climatology •  Cannot manually remove some errors due to other precipitation in the area

IncorrectMESHvaluesduetocoastalinterac4onleadtonoiseintheclimatology

ThisposterwaspreparedbyDerekRosseauwithfundingprovidedbyNOAA/OfficeofOceanicandAtmosphericResearchunderNOAA-UniversityofOklahomaCoopera4veAgreement#NA11OAR4320072,U.S.DepartmentofCommerce.Thestatements,findings,conclusions,andrecommenda4onsarethoseoftheauthor(s)anddonotnecessarilyreflecttheviewsofNOAAortheU.S.DepartmentofCommerce.

12yearAccumula4onMaximumMESH(unsmoothed)

12yearAccumula4onMaximumMESH(smoothed)

DaysperYearAnyHail

DaysperYearSevereHail

JanuaryAnyHail FebruaryAnyHail MarchAnyHail AprilAnyHail MayAnyHail JuneAnyHail

JulyAnyHail AugustAnyHail SeptemberAnyHail OctoberAnyHail NovemberAnyHail DecemberAnyHail

Radarspikesduetoterrain:anothercommonQCissue

•  Explorevariabilityofhailes4matespergivennear-stormenvironment

•  Developaclimatologythates4matesthelikelihoodofseverehailusingcoarsergridspacing

•  CompareMESHtootherMRMSgridstoseeifanymajordifferencesinaradar-derivedhailclimatologyexistbetweenproducts

•  RawMESHfieldswereaccumulatedwithamul4plehypothesistechnique.Accumula4onsweresmoothedusingmul4pledila4onanderosionfilters,preservingsmallertrackswhileelimina4ngnoise,andfinishedwithaGaussianfilter.