Convective Initiation: Shortterm Prediction

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Convective Initiation Short-term Prediction and
Climatology Research John R. Mecikalski1,
Kristopher M. Bedka2 Simon J. Paech1, Todd A.
Berendes1, Wayne M. Mackenzie1, Laci
Gambill1 1Atmospheric Science Department Universit
y of Alabama in Huntsville 2Cooperative
Institute for Meteorological Satellite
Studies University of Wisconsin-Madison Supported
by The NASA SPoRT Initiative NASA New
Investigator Program (2002)
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Overview UAH Contributions

• Diagnostics ALEXI land-surface (So, Aw, Rnet,
  ET) fields, ADAS surface Ta. All at 2-10 km
  resolution. Aviation Safety.
• Nowcasting (0-6 h) Convective initiation (CI),
  Lightning Initiation First Lightning CI
  Index for 2-6 h CI (based on satellite NWP
  model fields). Aviation Safety (ASAP).
• Short-term Prediction (6-24 h) Utilize
  diagnostics as satellite-based boundary
  conditions, ADAS populated by remote sensing data
  (satellite radar) toward a high-resolution
  (5-10 km) regional initialization for ARPS, WRF,
  etc.
• UAH Graduate students NWS SCEP, MS/Ph.D. studies
  that involve NWS interactions. Developing
  in-house nowcasting expertise.

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Overview UAH Contributions

• Diagnostics ALEXI land-surface (So, Aw, Rnet,
  ET) fields, ADAS surface Ta. All at 2-10 km
  resolution.
• Nowcasting (0-6 h) Convective initiation (CI),
  Lightning Initiation First Lightning CI
  Index for 2-6 h CI (based on satellite NWP
  model fields). Aviation Safety (ASAP).
• Short-term Prediction (6-24 h) Utilize
  diagnostics as satellite-based boundary
  conditions, ADAS populated by remote sensing data
  (satellite radar) toward a high-resolution
  (5-10 km) regional initialization for ARPS, WRF,
  etc.
• UAH Graduate students NWS SCEP, MS/Ph.D. studies
  that involve NWS interactions. Developing
  in-house nowcasting expertise.

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Outline

• Current capability Overview
• Background
• Error assessments
• Confidence analysis
• Incorporation of MODIS information
• Current new initiatives for SPoRT in 2006
• NWS transition and assessment
• Nighttime CI forecasting
• Lightning (event) forecasting

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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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Input Datasets for Convective Nowcasts/Diagnoses

• 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

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Convective Cloud Mask

• Foundation of the CI nowcast algorithm
  Calculate IR fields only where cumulus are
  present (10-30 of a domain)
• Utilizes a multispectral and textural 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 AMVs with sequences of
  10.7 ?m TB imagery to identify growing convective
  clouds, which represent a hazard to the aviation
  community

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CI Interest Fields for CI Nowcasting
from Roberts and Rutledge (2003)
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CI Nowcast Algorithm 4 May 2003
2000 UTC
CI Nowcast Pixels
These are 1 hour forecasted CI locations!

• Satellite-based CI indicators provided 30-45 min
  advanced notice of CI in E. and N. Central
  Kansas.
• PODs 50 at 1 km (FARs 40)
• NEW Linear Discriminant Analysis methods provide
  65 POD scores for 1-hour convective initiation.

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An Example over the Tropics CI

• An example of the CI nowcasting method over
  Central America
• Real-time
• Every 30 min during the day (nighttime coming
  soon)
• GOES (MODIS soon)
• RED/GREEN pixels have highest CI probability

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CI Interest Fields 8-GOES 2-MODIS
8.5-10.7 ?m (MODIS)
gt 0 C
8.5-10.7 ?m (MODIS)
gt 0 C
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Interest Field Importance POD/FAR
8.5-10.7 ?m (MODIS)
gt 0 C
gt 0 C
8.5-10.7 ?m (MODIS)

• Instantaneous 13.310.7 um Highest POD (84)
• Time-trend 13.310.7 um Lowest FAR (as low as
  38)
• Important for CI Lightning Initiation

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Use of MODIS in Convective Initiation Forecasts
MODIS 3.7-11.0 ?m
MODIS 8.5-11.0 ?m
Smaller ice particles/ higher numbers
0
0
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• The lower right shows the supercooled water in
  green.
• Proof of concept for determining the difference
  between supercooled water and glaciated towering
  cumulus clouds.

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CI/LI Linear Discriminant 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 t30 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 (t30 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
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CI/LI Linear Discriminant Analysis (LDA)

• Improved POD of 65 and FAR of 34
• A virtual-radar from satellite

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Outline

• Current capability Overview
• Background
• Error assessments
• Confidence analysis
• Incorporation of MODIS information
• Current new initiatives for SPoRT in 2006
• NWS transition and assessment
• Nighttime CI forecasting
• Lightning (event) forecasting

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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
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Detecting Convective Initiation at Night
Nighttime CI Southeast Oklahoma
SHV 257 - 344 UTC
Enhanced 10.7 ?m
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Detecting Convective Initiation at Night
What weve 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).
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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
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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
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Transition Activities

• NWSFO Huntsville (May 2005)
• NOAA WWB (September 2005)
• FAA AWRP (Spring 2005)
• FAA MIT-LL (now)

AWIPS
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Nowcasting plans for 2006
Theme 1 Formal NWS evaluation of 0-1 hour CI
product (that has been pushed to NWS Huntsville
since May 2005). Theme 2 First efforts at
30-min to 1.5 hour lightning initiation
nowcasting. Theme 3 Formation of CI Index
that is based on Climatology of CI fields
over Southeastern U.S., NWSFO region, etc. Theme
4 First developments of assimilation of
boundaries as defined by CI fields.
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Contact Information/Publications

• Contact Info
• Prof. John Mecikalski johnm_at_nsstc.uah.edu
• Kristopher Bedka krisb_at_ssec.wisc.edu
• Web Pages
• nsstc.uah.edu/johnm/ci_home (biscayne.ssec.wisc.ed
  u/johnm/CI_home/)
• http//www.ssec.wisc.edu/asap
• Publications
• Mecikalski, J. R. and K. M. Bedka, 2006
  Forecasting convective initiation by monitoring
  the evolution of moving cumulus in daytime GOES
  imagery. In Press. Mon. Wea. Rev. (IHOP Special
  Issue, January 2006).
• Bedka, K. M. and J. R. Mecikalski, 2005
  Applications of satellite-derived atmospheric
  motion vectors for estimating mesoscale flows. J.
  Appl. Meteor. 44, 1761-1772.
• 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.
• 6-conference talks/posters in 2005/06