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New Developments in GOES-12 and GOES-R Advanced Baseline

Imager Convective Initiation DetectionWayne 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

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OverviewOverview

Satellite CI methodologySatellite CI methodology History of transition from research History of transition from research

to operationsto operations Examples of near real-time CI Examples of near real-time CI

nowcast in AWIPS and N-AWIPSnowcast in AWIPS and N-AWIPS Researcher <-> End-user (NWS, Researcher <-> End-user (NWS,

FAA, SPC) iterative feedbackFAA, SPC) iterative feedback

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CI DefinitionCI Definition

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

-40°C

-25°C

-10°C+5°C

TimeX-Z Plane View

Satellite ViewTime

Satellite Observations of Convective InitiationSatellite Observations of Convective Initiation Rapid IR window cloud-top cooling can precede significant rainfall (> 35 Rapid IR window cloud-top cooling can precede significant rainfall (> 35

dBZ) by ~30-60 mins, imager-based (not sounder) dBZ) by ~30-60 mins, imager-based (not sounder) Roberts and Rutledge 2003 (WAF)Roberts and Rutledge 2003 (WAF) The first cloud-top ground strikes were most often observed at IR The first cloud-top ground strikes were most often observed at IR

window BTs of ~-40° C in a study over S. Africawindow BTs of ~-40° C in a study over S. Africa

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

UWCI: Algorithm logic – an overview1. Computing Box Averaged Brightness Temperature

• Example using the previous time (1545 UTC)

UWCI: Algorithm logic – an overview1. 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

UWCI Box-Average CI Nowcast Product Description

• 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

M. Pavolonis (NOAA/NESDIS) Day/Night Cloud Microphysical Typing

15-Min Cloud Top Cooling CI Nowcast Using 15-min Data

45-min Accumulated Cooling 45-min Accumulated CI Nowcast

Channel 9 BT at End of 45-min Period

Channel 9 BT 1 hour Later

SEVIRI Channel 9 IR Window BT

Lightning Initiation POD (133 LI cases): 76%Lightning Nowcast FAR (10214 pixels): 29%Lead time decreases with cloud glaciation

Product Validation

UWCI Algorithm UWCI Algorithm DescriptionDescription

Day/Night UW Cloud typing product (Pavolonis et al, Day/Night UW Cloud typing product (Pavolonis et al, uses 3.9, 6.7, 10.7, and 12.0/13.3 um channels)uses 3.9, 6.7, 10.7, and 12.0/13.3 um channels)

Monitor microphysical propertiesMonitor microphysical properties Infrared window (10.7 um) box-averaging conducted Infrared window (10.7 um) box-averaging conducted

(monitoring mean 10.7 um cooling rate over area)(monitoring mean 10.7 um cooling rate over area) This algorithm for convective initiation phase onlyThis algorithm for convective initiation phase only Two primary algorithm products are cloud top cooling Two primary algorithm products are cloud top cooling

(CTC) rate and CI nowcast(CTC) rate and CI nowcast http://cimss.ssec.wisc.edu/goes_r/proving-ground/GOES_CINowcast.htmlhttp://cimss.ssec.wisc.edu/goes_r/proving-ground/GOES_CINowcast.html

UWCI Data Flow OverviewUWCI Data Flow OverviewUWCI 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 from27 April – 1 June 2009

UWCI/ACCUMCTC -   60 minute accumulated cloud-top UWCI/ACCUMCTC -   60 minute accumulated cloud-top cooling ratecooling rate

UWCI/ACCUMCI   - 60 minute accumulated CI nowcastUWCI/ACCUMCI   - 60 minute accumulated CI nowcast UWCI/INSTCTC – Most recent single image CTCUWCI/INSTCTC – Most recent single image CTC UWCI/INSTCI - Most recent single image CI nowcast UWCI/INSTCI - Most recent single image CI nowcast

For the INSTCI field, there will be values ranging from 0 to 3:For the INSTCI field, there will be values ranging from 0 to 3: Value 0: No CI nowcastValue Value 0: No CI nowcastValue 1: "Pre-CI Cloud Growth" associated with growing liquid water 1: "Pre-CI Cloud Growth" associated with growing liquid water

cloudValue cloudValue 2: "CI Likely" associated with growing supercooled water or mixed 2: "CI Likely" associated with growing supercooled water or mixed

phase cloudValue phase cloudValue 3: "CI Occurring" associated with cloud that has recently transitioned 3: "CI Occurring" associated with cloud that has recently transitioned

to a thick ice cloud topto a thick ice cloud top

20090429 2015 UTCInstantaneous CI Nowcast

CI PossiblePossible

CI OccurringCI Occurring

CI LikelyCI Likely

20090429 Dryline CI Case20090429 Dryline CI CaseSPC HWT Proving GroundSPC HWT Proving Ground

KAMA 2103 UTC Base Reflectivity

KAMA 2018 UTC Base Reflectivity

CI nowcast at 2015 UTC CI likely

CI occurring

Non-detection due to cirrus

KAMA 2035 UTC Base Reflectivity

First echo >= 35 dBZ, 17 minutes after nowcast

29 April 2009

SD/NE BorderWarm Frontal 0545 UTC 0546 UTC

0615 UTC0606 UTC

20090506 0545 UTC Nocturnal Instantaneous CI Nowcast

KFSD 0616 UTC Base Reflectivity

6 May 2009CI nowcast at 0545 UTC

KFSD 0546 UTC Base Reflectivity

Nothing on radar at this time

KFSD 0601 UTC Base Reflectivity

First echo >= 35 dBZ, 16 minutes after nowcast

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 -Continuedcooling signal

1732UTC -Severet-storm

First NEXRAD 35+ dBz echo at 1622 UTC NEXRAD at 1735 UTC

Hastings, NE NEXRAD Radar Reflectivity from 06/17/2009

First significant cooling (<-4K/15min) at 1545 UTC

No echo on radar Reflectivity echo >35 dBZ

First signs of convection on radar 33 min after significant cooling detected

Resulting strong convection ~3.5 hrs after significant cooling detection

Radar Reflectivity at 1544 UTC

Radar Reflectivity at 1618 UTC

Radar Reflectivity at 1826 UTC

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AWIPS CI/CTC Interaction with Sullivan AWIPS CI/CTC Interaction with Sullivan (MKE) NWS Office(MKE) NWS Office

04:30 UTC

06:30 UTC

05:02 UTC

Forecaster generated screen captures from AWIPS at MKE

CI likely CI occurring

Lightning

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"The UWCI performed very well in Iowa last night!  These "The UWCI performed very well in Iowa last night!  These thunderstorms fired up along an existing boundary and are thunderstorms fired up along an existing boundary and are coincident with the leading edge of 700mb moisture coincident with the leading edge of 700mb moisture transport and weak 850mb warm air advection.” transport and weak 850mb warm air advection.”

- Marcia Cronce NWS Forecaster- Marcia Cronce NWS Forecaster

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AWIPS CI/CTC Interaction with Sullivan (MKE) NWS Office

UWCI – Ongoing Algorithm UWCI – Ongoing Algorithm ImprovementImprovement

• 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

Expanding Cloud Edges

Thin Cirrus

CI: False Cooling Rates Due to Anvil Expansion and Overriding Cirrus

UWCI – Decreased FAR with thin UWCI – Decreased FAR with thin cirruscirrus

False alarms over the Central Plains at 1915 UTC May 20, 2009 associated with thin cirrus

UWCI – Decreased FAR with thin cirrusUWCI – 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

UWCI – Increased Signal Lead-TimeUWCI – 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

UWCI – Increased Signal Lead-TimeUWCI – 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

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UWCI AlgorithmUWCI Algorithm(Very new methodology, still learning)(Very new methodology, still learning)

AdvantagesAdvantages– Computationally fastComputationally fast– Day/night independentDay/night independent– The code is flexible and operates on both rapid-scan (5-min) and The code is flexible and operates on both rapid-scan (5-min) and

operational (15 to 30-min) scanning patternsoperational (15 to 30-min) scanning patterns– Heritage – Algorithm already being used/evaluated by operational Heritage – Algorithm already being used/evaluated by operational

centers (SPC, SAB, local NWSFOs) using current GOES imagercenters (SPC, SAB, local NWSFOs) using current GOES imager– Product offers up to 30-45 minute lead-time before significant radar Product offers up to 30-45 minute lead-time before significant radar

echoes/cloud to ground lightning is presentechoes/cloud to ground lightning is present

DisadvantagesDisadvantages– Algorithm does not operate in mostly cloudy/cloudy scenes or in Algorithm does not operate in mostly cloudy/cloudy scenes or in

areas dominated by ice clouds (including thin cirrus debris cloud)areas dominated by ice clouds (including thin cirrus debris cloud)– Algorithm performs more as a diagnostic than prognostic tool in areas Algorithm performs more as a diagnostic than prognostic tool in areas

of air mass driven convection (e.g.-southeastern US)of air mass driven convection (e.g.-southeastern US)– Thin cirrus moving across scene may result in false alarmsThin cirrus moving across scene may result in false alarms

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

MSG SEVIRI Example

GOES-12 Example

Why do we need GOES-R ABI for Why do we need GOES-R ABI for convective initiation nowcasts?convective initiation nowcasts?

Temporal resolution of imager needs to be 5 minutes Temporal resolution of imager needs to be 5 minutes or less operationally to match convective initiation time or less operationally to match convective initiation time scale! (30 second tendency information can and will scale! (30 second tendency information can and will also be used) also be used)

Improvement in spatial resolution of infrared imagery Improvement in spatial resolution of infrared imagery from 4 to 2 km (this a factor of 4 improvement!) will from 4 to 2 km (this a factor of 4 improvement!) will improve cloud detection and box averaging cooling improve cloud detection and box averaging cooling rate signalrate signal

Additional spectral channels will improve microphysical Additional spectral channels will improve microphysical cloud typing and sensitivity of thin cirrus detectioncloud typing and sensitivity of thin cirrus detection