Outline

1. Geostationary satellite-based methods for nowcasting convective initiation, total lightning flash rates, and convective cloud properties John R. Mecikalski1, Kristopher M. Bedka2 Simon J. Paech1, Todd A. Berendes1, Wayne M. Mackenzie1 1Atmospheric Science Department University of Alabama in Huntsville 2Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison Supported by: NASA New Investigator Program (2002) NASA ASAP Initiative

2. 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. 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. 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. Input Datasets for Convective Nowcasts/Diagnoses • Build relationships between GOES and NWS WSR-88D imagery: • Identified GOES IR TBand 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 TB • Other non-McIDAS data: • UAH Convective Cloud Mask to identify locations of cumulus clouds

6. 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. 50% shown “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 TB imagery to identify growing convective clouds, which represent a hazard to the aviation community

8. Oceanic Convective Cloud Growth Product 30 Minute • 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

9. 1000 900 800 700 600 500 400 300 200 100 mb 1/30th of vectors shown 1/5th 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 1/120th of vectors shown

10. CI Interest Fields for CI Nowcasting from Roberts and Rutledge (2003)

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

12. ASAP CI/LI Linear Determinant Analysis (LDA) • Remap GOES data to 1 km gridded radar reflectivity data • Correct for parallax effect by obtaining cloud height through matching the 10.7 m TB to standard atmospheric T profile • 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 • 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) • Collect database of IR interest fields (IFs) for these CI/LI pixels • Apply LDA: identify relative contribution of each IF toward an accurate nowcast • Test LDA equation on • independent cases to • assess skill of new method ddddddddddddddddddddddd

13. CI “Interest Fields”: 8 Total from GOES

14. “Interest Field” Importance: POD/FAR • 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. Detecting Convective Initiation at Night • Detection of convective initiation at night must address several • unique issues: • Restricted to 4 km data (unless MODIS is relied upon) • Visible data cannot be used to formulate cumulus mask • Highly-dense, GOES visible winds are unavailable for tracking • 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. Detecting Convective Initiation at Night Nighttime CI: Southeast Oklahoma SHV: 2:57 - 3:44 UTC Enhanced 10.7 m

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

18. Satellite-Lightning Relationships • Current Work: Develop relationships between IR TB/TB 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 Northern Alabama LMA Lightning Source Counts 2040-2050 UTC 2047 UTC 2147 UTC 2140-2150 UTC kkoooooooookkkkkkkkkkk

19. Meteosat Second Generation (MSG) 8.7 µm • 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. Nighttime CI event over Italy 9.7 µm

20. Hyperspectral: GOES-R Small wavenumber change results in significant changes in view:  Low-level water vapor  Surface temperature  Subtle cloud growth & microphysical changes 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

21. 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.