Convective Initiation, Motion, and Climatological

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Convective Initiation, Motion, and
Climatological fields for Flood, Lightning, and
Short-term Prediction over Mesoamerica 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, SERVIR
SPoRT Initiatives
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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
• CI climatology, motion and flood forecasting
• Plans for Mesoamerica

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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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Where are we now
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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 (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 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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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

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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
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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

• Satellite-based CI indicators provided 30-45 min
  advanced notice of CI in E. and N. Cent. KS

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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 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 Interest Fields 8 Total from GOES
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Interest Field Importance POD/FAR

• 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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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
kkoooooooookkkkkkkkkkk
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Use of MODIS
MODIS 3.7-11.0 ?m
MODIS 8.5-11.0 ?m
Smaller ice particles/ higher numbers
0
0
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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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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
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An Example over Mesoamerica 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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An Example over Mesoamerica 6 October
2005 30-60 min forecast of CI 1 km resolution
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Plans for Mesoamerica

• Convective Initiation (CI), Nocturnal CI, and
  Lightning-Event Forecasting
• Heavy Rainfall, Flood and related Hazard
  Forecasting.
• Improved understanding of Convective Processes
  and Characterization in the
• Tropics the Development of Satellite
  Climatological Data Sets.
• Basic Research utilizing the NSSTC/UAH/NASA
  STORMnet Testbed.

Four Main Themes

• Mapping of convective storm initiation, both in a
  0-2 h prediction mode,
• and from a climatological perspective
• Mapping of the frequency of occurrence of CI
  across Mesoamerica
• Storm path and motion climatologies
• Classification of convective storm intensity (in
  terms of lightning,
• rainfall potential, or other derived index)
  Convective initiation index.

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Plans for Mesoamerica
Theme 1 Mapping of convective storm initiation,
both in a 0-2 h prediction mode, and from a
climatological perspective
This will involve a) Running the CI algorithm
routinely over the 3 Mesoamerican regions, and
archiving fields b) Developing a CI
Climatology based on composited satellite
fields c) Mapping these CI Climatology fields to
topography and other land-surface characteristic
s Computer/Data Requirements a) Dual-processor
computer with large disk storage (tormenta as
already supplied by SERVIR) b) Consistent feed
of 15-30 min GOES data (MODIS as
well) Personnel 25 FTE
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Plans for Mesoamerica
Theme 2 Mapping of the frequency of occurrence
of CI across Mesoamerica
This will involve a) Running the CI algorithm
routinely over the 3 Mesoamerican regions, and
archiving fields b) Developing a CI
Climatology based on composited satellite
fields c) Mapping these CI Climatology fields to
topography and other land-surface characteristic
s Computer/Data Requirements a) Dual-processor
computer with large disk storage b) Archive of
1-2 years of GOES-based CI fields. Personnel
25-50 FTE (Graduate student for 1-2 years)
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Plans for Mesoamerica
Theme 3 Storm path and motion climatologies
This will involve a) Running the CI algorithm
routinely over the 3 Mesoamerican regions, and
archiving fields b) Developing a CI
Climatology based on composited satellite
fields c) Building statistical relationships
between storm-motion, satellite wind versus
time of year, season and location in
Mesoamerica Computer/Data Requirements a)
Large disk storage b) Archive of 1-2 years of
GOES-based CI fields. Personnel 1-Graduate
student for 2-years
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Plans for Mesoamerica
Theme 4 Classification of convective storm
intensity (in terms of lightning, rainfall
potential, or other derived index) Convective
initiation index.
This will involve a) Running the CI algorithm
routinely over the 3 Mesoamerican regions, and
relating to environmental conditions over
region b) Integrating satellite fields with
land-surface (energy and water fluxes),
soil moisture, topography, land-use fields c)
Combining CI Climatology with real-time CI
fields d) Training with UAH-NASA Lightning
Mapping Array data and dual- polarmetric radar
(the ARMOR in Huntsville) Computer/Data
Requirements a) Dual-processor computer with
large disk storage b) Consistent feed of 15-30
min GOES data (MODIS as well) archive c)
Lightning and radar data on time scale of GOES
satellite data Personnel 75 FTE (or 1-Graduate
student for 2-3 years)
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Contact Information/Publications
Contact Info Prof. John Mecikalski
johnm_at_nsstc.uah.edu Kristopher Bedka
krisb_at_ssec.wisc.edu UW-CIMSS ASAP Web
Page nsstc.uah.edu/johnm/ci_home
(biscayne.ssec.wisc.edu/johnm/CI_home/) http//ww
w.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.