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New Developments in GOES-12 and GOES-R Advanced Baseline Imager Convective Initiation Detection

New Developments in GOES-12 and GOES-R Advanced Baseline Imager Convective Initiation Detection. Wayne F. Feltz*, Kristopher Bedka^, Lee Cronce*, and Jordan Gerth* John Mecikalski # and Wayne Mackenzie # *Cooperative Institute for Meteorological Satellite Studies (CIMSS)

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New Developments in GOES-12 and GOES-R Advanced Baseline Imager Convective Initiation Detection

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  1. New Developments in GOES-12 and GOES-R Advanced Baseline Imager Convective Initiation Detection Wayne F. Feltz*, Kristopher Bedka^, Lee Cronce*, and Jordan Gerth* John Mecikalski# and Wayne Mackenzie# *Cooperative Institute for Meteorological Satellite Studies (CIMSS) ^Science Systems and Applications, Inc, Hampton, VA #University of Alabama - Huntsville UW-Madison

  2. Overview • Satellite CI methodology • History of transition from research to operations • Examples of near real-time CI nowcast in AWIPS and N-AWIPS • Researcher <-> End-user (NWS, FAA, SPC) iterative feedback

  3. CI Definition • Roberts and Rutledge (2003) and Mecikalski and Bedka (2006) define Convective Initiation as the “first occurrence of a 35 dBZ radar reflectivity .” 3

  4. -40°C -25°C -10°C +5°C Time X-Z Plane View Time Satellite View

  5. Satellite Observations of Convective Initiation • Rapid IR window cloud-top cooling can precede significant rainfall (> 35 dBZ) by ~30-60 mins, imager-based (not sounder) • Roberts and Rutledge 2003 (WAF) • The first cloud-top ground strikes were most often observed at IR window BTs of ~-40° C in a study over S. Africa

  6. GOES-12 IR Window Cloud Typing (Pavolonis/Heidinger – NOAA)

  7. UWCI: Algorithm logic – an overview 1. Computing Box Averaged Brightness Temperature • Example using the previous time (1545 UTC)

  8. UWCI: Algorithm logic – an overview 1. Computing Box Averaged Brightness Temperature • Algorithm loops through every pixel within an image • For each pixel, all pixels within a 7x7 pixel box centered upon the pixel of interest are investigated • For pixels within the 7x7 pixel box that are classified by the GOES Cloud Typing product as water, supercooled water, mixed phase, thick ice or cirrus; their 11 micron BTs are summed and then averaged to determine the box-averaged 11 micron BT. • The box averaged BT is assigned to the center pixel of the 7x7 pixel box

  9. UWCI Box-Average CI Nowcast Product Description M. Pavolonis (NOAA/NESDIS) Day/Night Cloud Microphysical Typing SEVIRI Channel 9 IR Window BT • Current and future imagers are operating in 5-min rapid scan, so cumulus do not move very far between images • For 50 CI cases over CONUS, average cloud movement=5 km/5 mins, 1 SD=2 km/5 mins • One can disregard cloud motions by time-differencing “box-averaged” cloud top properties to determine CI • Compute mean IRW BT for cloud categories identified by day/night cloud type product over 7x7 and 14x14 pixel SEVIRI IR pixel boxes • Employ rules to eliminate situations where cirrus anvil moves into box with developing cumulus • Use 13 km NWP stability analysis to minimize CI nowcast false alarm from cooling of non-convective cloud • Compute cloud-top cooling rate, minimum threshold at -4 K/15 mins • Use cloud typing information with cooling rates to further minimize false alarms and develop confidence indicators for convective initiation • Filter out isolated noisy nowcast pixels • Cat 1: Cooling liquid water clouds • Cat 2: Cooling supercooled/mixed phase • Cat 3: Cooling with recent transition to thick ice cloud • Though the method is optimized for 5-minute imagery, the examples and validation to the left show results when applied to 15-minute SEVIRI imagery 15-Min Cloud Top Cooling CI Nowcast Using 15-min Data Channel 9 BT at End of 45-min Period 45-min Accumulated Cooling 45-min Accumulated CI Nowcast Channel 9 BT 1 hour Later Product Validation Lightning Initiation POD (133 LI cases): 76% Lightning Nowcast FAR (10214 pixels): 29% Lead time decreases with cloud glaciation

  10. UWCI Algorithm Description • Day/Night UW Cloud typing product (Pavolonis et al, uses 3.9, 6.7, 10.7, and 12.0/13.3 um channels) • Monitor microphysical properties • Infrared window (10.7 um) box-averaging conducted (monitoring mean 10.7 um cooling rate over area) • This algorithm for convective initiation phase only • Two primary algorithm products are cloud top cooling (CTC) rate and CI nowcast • http://cimss.ssec.wisc.edu/goes_r/proving-ground/GOES_CINowcast.html

  11. UWCI Data Flow Overview UWCI Algorithm (Fortran 90) – Processing time 1-2 minutes CIMSS McIDAS ADDE Server NSSL N-AWIPS Image -> SPC Regridded Overlays WDSS-II and Google Earth Over 2000 GOES images processed and delivered from 27 April – 1 June 2009

  12. UWCI ADDE Products • UWCI/ACCUMCTC -   60 minute accumulated cloud-top cooling rate • UWCI/ACCUMCI   - 60 minute accumulated CI nowcast • UWCI/INSTCTC – Most recent single image CTC • UWCI/INSTCI - Most recent single image CI nowcast • For the INSTCI field, there will be values ranging from 0 to 3: • Value 0: No CI nowcastValue • 1: "Pre-CI Cloud Growth" associated with growing liquid water cloudValue • 2: "CI Likely" associated with growing supercooled water or mixed phase cloudValue • 3: "CI Occurring" associated with cloud that has recently transitioned to a thick ice cloud top

  13. 20090429 Dryline CI CaseSPC HWT Proving Ground 20090429 2015 UTC Instantaneous CI Nowcast CI Occurring CI Likely CI Possible

  14. 29 April 2009 CI nowcast at 2015 UTC KAMA 2018 UTC Base Reflectivity CI likely Non-detection due to cirrus CI occurring KAMA 2035 UTC Base Reflectivity KAMA 2103 UTC Base Reflectivity First echo >= 35 dBZ, 17 minutes after nowcast

  15. 20090506 0545 UTC Nocturnal Instantaneous CI Nowcast SD/NE Border Warm Frontal 0545 UTC 0546 UTC 0606 UTC 0615 UTC

  16. KFSD 0546 UTC Base Reflectivity CI nowcast at 0545 UTC 6 May 2009 Nothing on radar at this time KFSD 0601 UTC Base Reflectivity KFSD 0616 UTC Base Reflectivity First echo >= 35 dBZ, 16 minutes after nowcast

  17. University of Wisconsin Convective Initiation (UWCI) • High-level algorithm overview • Compute IR-window brightness temperature cloud top cooling rates for growing convective clouds using a box-average approach • Combine cloud-top cooling information with cloud-top microphysical (phase/cloud type) transitions for convective initiation nowcasts Example from June 17, 2009 over northern KS First UWCI cooling rate signal precedes NEXRAD 35 dBz signal by 37 minutes 1545 UTC – first cloud top cooling signal 1610 UTC - Continued cooling signal First NEXRAD 35+ dBz echo at 1622 UTC NEXRAD at 1735 UTC 1732UTC - Severe t-storm

  18. Hastings, NE NEXRAD Radar Reflectivity from 06/17/2009 Radar Reflectivity at 1544 UTC Radar Reflectivity at 1618 UTC No echo on radar Reflectivity echo >35 dBZ First signs of convection on radar 33 min after significant cooling detected First significant cooling (<-4K/15min) at 1545 UTC Radar Reflectivity at 1826 UTC Resulting strong convection ~3.5 hrs after significant cooling detection

  19. AWIPS CI/CTC Interaction with Sullivan (MKE) NWS Office CI likely CI occurring Lightning 05:02 UTC 04:30 UTC 06:30 UTC Forecaster generated screen captures from AWIPS at MKE

  20. "The UWCI performed very well in Iowa last night!  These thunderstorms fired up along an existing boundary and are coincident with the leading edge of 700mb moisture transport and weak 850mb warm air advection.” - Marcia Cronce NWS Forecaster AWIPS CI/CTC Interaction with Sullivan (MKE) NWS Office 25

  21. Algorithm weaknesses were noted during the Spring 2009 Storm Prediction Center Hazardous Weather Testbed Field Experiment • The weaknesses are currently being addressed by UW/CIMSS scientists • Areas of improvement • Decrease false alarms associated with thin cirrus moving across scenes • Decrease false alarms associated with rapid anvil expansion • Increase POD and/or POD lead-time by updating to latest GOES-R AWG Cloud Mask and Cloud Type algorithms (at the same time monitor POD and FAR to ensure latest version of cloud algorithms do not adversely impact algorithm) • Two examples of improvements • Lower FAR from thin cirrus movement across a scene • Increase POD lead-time for storms using latest cloud mask/cloud typing algorithms UWCI – Ongoing Algorithm Improvement

  22. CI: False Cooling Rates Due to Anvil Expansion and Overriding Cirrus Expanding Cloud Edges Thin Cirrus

  23. UWCI – Decreased FAR with thin cirrus False alarms over the Central Plains at 1915 UTC May 20, 2009 associated with thin cirrus

  24. UWCI – Decreased FAR with thin cirrus False alarms eliminated with improved UWCI algorithm over the Central Plains at 1915 UTC May 20, 2009 associated with thin cirrus

  25. UWCI – Increased Signal Lead-Time Missed CTC signal at 1945 UTC 01 May 2009 over north central Texas; not captured until 2002 UTC Also note false alarms over SE NM/central TX

  26. UWCI – Increased Signal Lead-Time CTC signal at 1945 UTC 01 May 2009 over north central Texas is now captured; 15 minutes sooner than old version of UWCI algorithm Better lead-time due to better cloud detection with latest version of GOES-R AWG Cloud Mask (and Cloud Typing algorithm); able to calculate a 1945 UTC – 1932 UTC cloud-top cooling rate because cloud was detected at 1932 UTC with new version of cloud mask; not the case with previous version of cloud mask False alarms over SE NM/central TX have been eliminated

  27. UWCI Algorithm(Very new methodology, still learning) Advantages Computationally fast Day/night independent The code is flexible and operates on both rapid-scan (5-min) and operational (15 to 30-min) scanning patterns Heritage – Algorithm already being used/evaluated by operational centers (SPC, SAB, local NWSFOs) using current GOES imager Product offers up to 30-45 minute lead-time before significant radar echoes/cloud to ground lightning is present Disadvantages Algorithm does not operate in mostly cloudy/cloudy scenes or in areas dominated by ice clouds (including thin cirrus debris cloud) Algorithm performs more as a diagnostic than prognostic tool in areas of air mass driven convection (e.g.-southeastern US) Thin cirrus moving across scene may result in false alarms 32

  28. Exploring the Use of Object Tracking for CI Nowcasting • UW-CIMSS and the University of Alabama in Huntsville (UAH) are working toward development of object-based methods for CI nowcasting in the GOES-R ABI era, using current GOES-12 and MSG SEVIRI as proxies for GOES-R • UW-CIMSS is experimenting with the Warning Decision Support System-Integrated Information (WDSS-II, Lakshmanan et al. (J. Tech., 2009), (WAF, 2007)) to compute cloud-top cooling rates, which can be used with cloud-top microphysical trends to produce CI nowcasts • Radar reflectivity can be remapped to the satellite resolution/projection and carried along with the satellite-derived objects for reliable product validation. • This capability is not available with current pixel-based CI nowcast methods which causes significant difficulty in evaluating current product accuracy over large scenes and numerous cases GOES-12 Example MSG SEVIRI Example

  29. Why do we need GOES-R ABI for convective initiation nowcasts? • Temporal resolution of imager needs to be 5 minutes or less operationally to match convective initiation time scale! (30 second tendency information can and will also be used) • Improvement in spatial resolution of infrared imagery from 4 to 2 km (this a factor of 4 improvement!) will improve cloud detection and box averaging cooling rate signal • Additional spectral channels will improve microphysical cloud typing and sensitivity of thin cirrus detection

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