WSN05 Toulouse, France, 5-9 September 2005 Geostationary satellite-based methods for nowcasting convective initiation, total lightning flash rates, and

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. Mecikalski1, Kristopher M. Bedka2

Simon J. Paech1, Todd A. Berendes1, Wayne M. Mackenzie1

1Atmospheric Science DepartmentUniversity of Alabama in Huntsville

2Cooperative Institute for Meteorological Satellite StudiesUniversity of Wisconsin-Madison

Supported by:Supported by:

NASA New Investigator Program (2002)NASA New Investigator Program (2002)NASA ASAP InitiativeNASA ASAP Initiative

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Outline

• Current capability: Overview• Progress toward improvements

Error assessmentsConfidence analysisAssessment of “interest field” importance; convective regimes

• Current & new initiativesNighttime convective initiationLightning (event) forecastingExamples with MSG data over Europe (MODIS)Demonstration with hyperspectral data

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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)

••

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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

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• Build relationships between GOES and NWS WSR-88D imagery:

- Identified GOES IR TB 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 TB

Other non-McIDAS data:• UAH Convective Cloud Mask to identify locations of cumulus clouds

Input Datasets for Convective Nowcasts/Diagnoses

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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

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“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

50% shown

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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

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1000 900 800 700 600 500 400 300 200 100 mb

1/120th of vectors shown1/30th of vectors shown1/5th 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

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CI Interest Field Critical Value

10.7 µm TB (1 score) < 0° C

10.7 µm TB Time Trend (2 scores)< -4° C/15 mins

∆TB/30 mins < ∆TB/15 mins

Timing of 10.7 µm TB drop below 0° C (1 score) Within prior 30 mins

6.5 - 10.7 µm difference (1 score) -35° C to -10° C


6.5 - 10.7 µm Time Trend (1 score) > 3° C/15 mins


CI Interest Fields for CI Nowcasting

from Roberts and Rutledge (2003)

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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!

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Toulouse, France, 5-9 September 2005ddddddddddddddddddddddd

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 TB 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 relativecontribution of each IF toward an accurate nowcast• Test LDA equation on

independent cases toassess skill of new method

ASAP CI/LI Linear Determinant Analysis (LDA)

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CI “Interest Fields”: 8 Total from GOESCI Interest Field Critical Value

10.7 µm TB (1 score) < 0° C








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“Interest Field” Importance: POD/FARCI Interest Field Critical Value

10.7 µm TB (1 score) < 0° C








• 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

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Detecting Convective Initiation at Night

Detection of convective initiation at night must address severalunique issues:

a) Restricted to 4 km data (unless MODIS is relied upon)b) Visible data cannot be used to formulate cumulus maskc) Highly-dense, GOES visible winds are unavailable for trackingd) 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 fornocturnal conditions, and/or where lower resolution may bepreferred (i.e. over large oceanic regions).

Wayne Mackenzie, MS student

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Nighttime CI: SoutheastOklahoma

Enhanced 10.7 m

SHV: 2:57 - 3:44 UTC

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What we’ve learned so far:

10.7-3.9 m channel difference (Ellrod “fog” product)13.3-3.9 m6.5-3.9 m

Evaluation is being done in light of the forcing for the convection (e.g., low-level jets, QG).

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Toulouse, France, 5-9 September 2005 kkoooooooookkkkkkkkkkk

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

2047 UTC

2147 UTC

Northern Alabama LMA Lightning Source Counts

2040-2050 UTC

2140-2150 UTC

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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

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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 changeresults in significant changesin view:

Low-level water vapor Surface temperature Subtle cloud growth

& microphysical changes

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Contact Information/Publications

Contact Info:

Prof. John Mecikalski: [email protected]

Kristopher Bedka: [email protected]

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.

Documents

WSN05 Toulouse, France, 5-9 September 2005 Geostationary satellite-based methods for nowcasting convective initiation, total lightning flash rates, and