Transcript Document

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
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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
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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)
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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 (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
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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
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50% shown
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
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200 100 mb
ththof
thof
1/30
vectors
shown
1/5
1/120
of
vectors
vectors
shown
shown
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CI Interest Fields for CI Nowcasting
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
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
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
2030 UTC
These are forecasted CI locations!
2100 UTC
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ASAP CI/LI Linear Determinant Analysis (LDA)
1)
Remap GOES data to 1 km gridded radar reflectivity data
•
2)
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
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
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CI “Interest Fields”: 8 Total from GOES
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
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
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“Interest Field” Importance: POD/FAR
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
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
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Detecting Convective Initiation at Night
Detection of convective initiation at night must address several
unique issues:
a)
b)
c)
d)
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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Toulouse, France, 5-9 September 2005
Detecting Convective Initiation at Night
Nighttime CI: Southeast
Oklahoma
SHV: 2:57 - 3:44 UTC
Enhanced 10.7 m
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Detecting Convective Initiation at Night
What we’ve learned so far:
6.5-3.9 m
10.7-3.9
13.3-3.9 m
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
2047 UTC
2147 UTC
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kkoooooooookkkkkkkkkkk
2040-2050 UTC
2140-2150 UTC
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
LEFT
8.508-10.98
m Band
Difference:
Red (’s >0)
= Ice
Small wavenumber change
results in significant changes
in view:
 Low-level water vapor
 Surface temperature
 Subtle cloud growth
& microphysical changes
Comparison
between the 10.98
m (right) and
11.00 m (far
right) bands:
22 UTC 6.12.2002
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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.
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