1. WSN05 Toulouse, France, 5-9 September 2005 Geostationary satellite-based methods for nowcasting convective initiation, total lightning flash rates, and convective cloud properties John R. Mecikalski 1, Kristopher M. Bedka 2 Simon J. Paech 1, Todd A. Berendes 1, Wayne M. Mackenzie 1 1 Atmospheric Science Department University of Alabama in Huntsville 2 Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Supported by: NASA New Investigator Program (2002) NASA ASAP Initiative

2. WSN05 Toulouse, France, 5-9 September 2005 Outline Current capability: Overview Progress toward improvements Error assessments Confidence analysis Assessment of “interest field” importance; convective regimes Current & new initiatives Nighttime convective initiation Lightning (event) forecasting Examples with MSG data over Europe (MODIS) Demonstration with hyperspectral data

3. WSN05 Toulouse, France, 5-9 September 2005 How this began… Which cumulus will become a thunderstorm? GEO satellite seems to be well-suited to address this question. What methods are available? What changes to current, globally-developed codes are needed?  Who can benefit from this research?  What user groups are interested (e.g., 0-2 h nowcasting)

4. WSN05 Toulouse, France, 5-9 September 2005 Where are we now … Applying CI algorithm over U.S., Central America & Caribbean Validation & Confidence analysis Satellite CI climatologies/CI Index: 1-6 h Work with new instruments Data assimilation possibilities

5. WSN05 Toulouse, France, 5-9 September 2005 Build relationships between GOES and NWS WSR-88D imagery: - Identified GOES IR T B and multi-spectral technique thresholds and time trends present before convective storms begin to precipitate - Leveraged upon documented satellite studies of convection/cirrus clouds [Ackerman (1996), Schmetz et al. (1997), Roberts and Rutledge (2003)] - After pre-CI signatures are established, test on other independent cases to assess algorithm performance Use McIDAS to acquire data, generally NOT for processing: GOES-12 1 km visible and 4-8 km infrared imagery every 15 minutes UW-CIMSS visible/IR “Mesoscale” Atmospheric Motion Vectors (AMVs) WSR-88D base reflectivity mosaic used for real-time validation NWP model temperature data for AMV assignment to cumulus cloud pixels … based on relationship between NWP temp profile and cumulus 10.7  m T B Other non-McIDAS data: UAH Convective Cloud Mask to identify locations of cumulus clouds Input Datasets for Convective Nowcasts/Diagnoses

6. WSN05 Toulouse, France, 5-9 September 2005 Convective Cloud Mask Foundation of the CI nowcast algorithm: Calculate IR fields only where cumulus are present (only 10-30% of a domain on average)…greatly reduces CPU requirements Utilizes a multispectral region clustering technique for classifying all scene types (land, water, stratus/fog, cumulus, cirrus) in a GOES image Identifies 5 types of convectively-induced clouds: low cumulus, mid-level cumulus, deep cumulus, thick cirrus ice cloud/cumulonimbus tops, thin cirrus anvil ice cloud

7. WSN05 Toulouse, France, 5-9 September 2005 “Mesoscale” Atmospheric Motion Vector Algorithm “Operational Settings” New Mesoscale AMVs (only 20% shown) We can combine mesoscale AMV’s with sequences of 10.7  m T B imagery to identify growing convective clouds, which represent a hazard to the aviation community 50% shown

8. WSN05 Toulouse, France, 5-9 September 2005 Oceanic Convective Cloud Growth Product Satellite AMVs are used to track clouds in sequential images and compute cloud-top cooling rates Rapid cloud-top cooling induced by convective cloud growth likely correlate well with vigorous updrafts and strong CIT 30 Minute

9. WSN05 Toulouse, France, 5-9 September 2005 1000900800700600500400300200100 mb 1/120 th of vectors shown 1/30 th of vectors shown 1/5 th of vectors shown Oceanic Satellite Atmospheric Motion Vectors Meso-scale satellite AMVs provide detailed depictions of flow near convective cloud features Validation of AMVs using ACARS and wind profiler data is a future ASAP effort

10. WSN05 Toulouse, France, 5-9 September 2005 CI Interest FieldCritical Value 10.7 µm T B (1 score)< 0° C 10.7 µm T B Time Trend (2 scores) < -4° C/15 mins ∆T B /30 mins < ∆T B /15 mins Timing of 10.7 µm T B drop below 0° C (1 score)Within prior 30 mins 6.5 - 10.7 µm difference (1 score)-35° C to -10° C 13.3 - 10.7 µm difference (1 score)-25° C to -5° C 6.5 - 10.7 µm Time Trend (1 score)> 3° C/15 mins 13.3 - 10.7 µm Time Trend (1 score)> 3° C/15 mins CI Interest Fields for CI Nowcasting from Roberts and Rutledge (2003)

11. WSN05 Toulouse, France, 5-9 September 2005 2000 UTC 2030 UTC 2100 UTC CI Nowcast Algorithm: 4 May 2003 Satellite-based CI indicators provided 30-45 min advanced notice of CI in E. and N. Cent. KS CI Nowcast Pixels These are forecasted CI locations!

12. WSN05 Toulouse, France, 5-9 September 2005 ddddddddddddddddddddddd 1)Remap GOES data to 1 km gridded radar reflectivity data Correct for parallax effect by obtaining cloud height through matching the 10.7  m T B to standard atmospheric T profile 2)Identify radar/lightning pixels that have undergone CI/LI at t+30 mins Advect pixels forward using low-level satellite wind field to find their approximate location 30 mins later 3)Determine what has occurred between imagery at time t, t-15, & t-30 mins to force CI/LI to occur in the future (t+30 mins) 4)Collect database of IR interest fields (IFs) for these CI/LI pixels 5)Apply LDA: identify relative contribution of each IF toward an accurate nowcast Test LDA equation on independent cases to assess skill of new method ASAP CI/LI Linear Determinant Analysis (LDA)

13. WSN05 Toulouse, France, 5-9 September 2005 CI “Interest Fields”: 8 Total from GOES CI Interest FieldCritical Value 10.7 µm T B (1 score)< 0° C 10.7 µm T B Time Trend (2 scores) < -4° C/15 mins ∆T B /30 mins < ∆T B /15 mins Timing of 10.7 µm T B drop below 0° C (1 score)Within prior 30 mins 6.5 - 10.7 µm difference (1 score)-35° C to -10° C 13.3 - 10.7 µm difference (1 score)-25° C to -5° C 6.5 - 10.7 µm Time Trend (1 score)> 3° C/15 mins 13.3 - 10.7 µm Time Trend (1 score)> 3° C/15 mins

14. WSN05 Toulouse, France, 5-9 September 2005 “Interest Field” Importance: POD/FAR CI Interest FieldCritical Value 10.7 µm T B (1 score)< 0° C 10.7 µm T B Time Trend (2 scores) < -4° C/15 mins ∆T B /30 mins < ∆T B /15 mins Timing of 10.7 µm T B drop below 0° C (1 score)Within prior 30 mins 6.5 - 10.7 µm difference (1 score)-35° C to -10° C 13.3 - 10.7 µm difference (1 score)-25° C to -5° C 6.5 - 10.7 µm Time Trend (1 score)> 3° C/15 mins 13.3 - 10.7 µm Time Trend (1 score)> 3° C/15 mins Instantaneous 13.3–10.7 um:Highest POD (82%) Time-trend 13.3–10.7 um:Lowest FAR (as low as 38%) Important for CI & Lightning Initiation

15. WSN05 Toulouse, France, 5-9 September 2005 Detecting Convective Initiation at Night Detection of convective initiation at night must address several unique issues: a)Restricted to 4 km data (unless MODIS is relied upon) b)Visible data cannot be used to formulate cumulus mask c)Highly-dense, GOES visible winds are unavailable for tracking d)Forcing for convection often elevated and difficult to detect (e.g., low-level jets, bores, elevated boundaries) However, the advantages are: a)Ability to use ~3.9  m channel (near-infrared) data (!!!) b)More “interest fields” become available for assessing cumulus cloud development Therefore, new work is toward expanding CI detection for nocturnal conditions, and/or where lower resolution may be preferred (i.e. over large oceanic regions). Wayne Mackenzie, MS student

16. WSN05 Toulouse, France, 5-9 September 2005 Detecting Convective Initiation at Night Nighttime CI: Southeast Oklahoma Enhanced 10.7  m SHV: 2:57 - 3:44 UTC

17. WSN05 Toulouse, France, 5-9 September 2005 Detecting Convective Initiation at Night What we’ve learned so far: 10.7-3.9  m channel difference (Ellrod “fog” product) 13.3-3.9  m 6.5-3.9  m Evaluation is being done in light of the forcing for the convection (e.g., low-level jets, QG).

18. WSN05 Toulouse, France, 5-9 September 2005 kkoooooooookkkkkkkkkkk Satellite-Lightning Relationships Current Work: Develop relationships between IR T B /T B trends and lightning source counts/flash densities toward nowcasting (0-2 hr) future lightning occurrence * Supported by the NASA New Investigator Program Award #:NAG5-12536 2047 UTC 2147 UTC Northern Alabama LMA Lightning Source Counts 2040-2050 UTC 2140-2150 UTC

19. WSN05 Toulouse, France, 5-9 September 2005 Use of MODIS MODIS 3.7-11.0  mMODIS 8.5-11.0  m Smaller ice particles/ higher numbers 00

20. WSN05 Toulouse, France, 5-9 September 2005 The Meteosat Second Generation (MSG) satellite system could be used effectively for CI nowcasting within this algorithm: 12 spectral bands; ~3 km resolution, 2 water vapor channels centered on two different central wavelengths. 8.7 µm, 9.7 µm, ~1.5 µm channels Plus many of the GOES capabilities. Meteosat Second Generation (MSG) Nighttime CI event over Italy 9.7 µm 8.7 µm

21. WSN05 Toulouse, France, 5-9 September 2005 Hyperspectral: GOES-R LEFT 8.508-10.98  m Band Difference: Red (  ’s >0) = Ice Comparison between the 10.98  m (right) and 11.00  m (far right) bands: 22 UTC 6.12.2002 Small wavenumber change results in significant changes in view:  Low-level water vapor  Surface temperature  Subtle cloud growth & microphysical changes

22. WSN05 Toulouse, France, 5-9 September 2005 Contact Information/Publications Contact Info: Prof. John Mecikalski: johnm@nsstc.uah.edu Kristopher Bedka: krisb@ssec.wisc.edu UW-CIMSS ASAP Web Page: nsstc.uah.edu/johnm/ci_home (biscayne.ssec.wisc.edu/~johnm/CI_home/) http://www.ssec.wisc.edu/asap Publications: Mecikalski, J. R. and K. M. Bedka, 2005: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. In Press. Mon. Wea. Rev. (IHOP Special Issue, ~Late 2005). Bedka, K. M. and J. R. Mecikalski, 2005: Applications of satellite-derived atmospheric motion vectors for estimating mesoscale flows. In Press. J. Appl. Meteor. Mecikalski, J. R., K. M. Bedka, and S. J. Paech, 2005: A statistical evaluation of GOES cloud-top properties for predicting convective initiation. In preparation. Mon. Wea. Rev.