Transcript Satellite Based Nowcasting of Convection Initiation and Data Assimilation

Satellite Based Nowcasting of Convection
Initiation and Data Assimilation
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
• Convective Initiation research, validation & transition activities
• NWS Products
• Soil Moisture Initialization Research (data assimilation)
• New Research, with NASA Data (lightning, MAMVs)
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Ambient Environment:
Freezing level
(i.e. tropical vs. midlatitude)
CAPE (also, its shape)
Models/
Sounders
Cumulus:
Cloud-top T
Cloud growth rate
Cloud glaciation
Freezing level:
 warm rain process
 ice microphysics
Interactions with ambient
clouds (pre-existing
cirrus anvils)
Satellite: VIS & IR
CI
LMA & NLDN
What are
the
factors?
Lightning:
Courtesy, NCAR RAP
Type (CG, IC, CC)
Amount
Polarity
Altitude in clouds
with respect to
anvil
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Where We Are Now
• Applying CI algorithm over U.S., Central America & Caribbean
• SATCAST/Flat ADAS to NWS HUN & MKX
• SATCAST to NOAA/NESDIS & SPC
• Validation & Confidence analysis
• Satellite CI climatologies/CI Index: 1-6 h
• Work with new instruments
• Hydrological applications
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CI Nowcast Validation
•
Like any satellite-based weather decision-support product, false alarms do occur with
the SATCAST nowcasting product
– VIS/IR Satellite observations only provide a “view from the top” and cannot
retrieve in-cloud dynamics or thermodynamics, which can greatly influence
cumulus evolution
– Cloud tracking errors using MESO AMVs
•
An objective quantitative validation of the SATCAST nowcast product is challenging
for several reasons
1) Parallax viewing effect causes difficulty in matching satellite observations with
radar observed precipitation fields
2) Objective synchronization of current satellite cloud growth trends with future
radar observations
3) Satellite navigation issues
Parallax Shift
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SATELLITE AMV WOULD
INCORRECTLY FORECAST
FUTURE RADAR ECHO
LOCATION
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PC
1
2
3
4
5
6
7
Eigenvalue
396.87
122.21
42.57
11.59
8.93
2.66
1.89
% Variance
67.64
20.83
7.26
1.98
1.52
0.454
0.322
Correlations
IF1
IF2
IF3
IF5
IF6
IF7
IF8
IF1
1.000
0.8581
-0.9388
-0.2411
-0.4403
0.1405
0.0914
IF2
—
1.000
-0.8690
-0.3304
-0.4989
0.2246
0.3405
IF3
—
—
1.000
0.2818
0.5143
-0.1358
-0.0892
IF5
—
—
—
1.000
0.5976
-0.9323
-0.7743
IF6
—
—
—
—
1.000
-0.5029
-0.3654
IF7
—
—
—
—
—
1.000
0.7826
IF8
—
—
—
—
—
—
1.000
ExVar
PC#1
PC#2
PC#3
PC#4
PC#5
PC#6
PC#7
23.745
10.425
7.712
-1.057
-39.710
-6.859
-7.123
12.318
1.758
5.807
-28.687
6.427
34.038
-5.950
-33.025
-11.616
-1.729
-11.201
-28.009
7.016
-9.345
-7.552
22.017
-17.353
-17.045
-4.775
-5.932
33.245
-16.327
22.970
34.192
5.090
4.812
2.323
-0.824
4.048
-17.277
15.330
10.407
-9.567
14.112
39.048
2.986
-13.936
17.877
-26.514
6.700
-29.721
4.465
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CI Nowcast Validation
Conditional POD skill scores
Interest
Field
IF1
IF2
IF3
IF4
IF5
IF6
IF7
IF8
IF1
IF2
IF3
IF4
IF5
IF6
IF7
IF8
Mean
0.751
0.700
0.883
0.449
0.424
0.449
0.217
0.212
0.216
0.231
0.470
0.477
0.295
0.157
0.542
0.555
0.635
0.339
0.175
0.375
0.730
0.511
0.539
0.319
0.167
0.529
0.422
0.604
0.414
0.412
0.255
0.134
0.411
0.327
0.430
0.481
Max:
50.83%
53.51%
34.33%
18.85%
40.69%
44.46%
43.99%
35.80%
53.51%
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CI Nowcast Validation
Conditional FAR skill scores
Interest
Field
IF1
IF2
IF3
IF4
IF5
IF6
IF7
IF8
IF1
0.670
IF2
0.607
0.828
IF3
0.364
0.339
0.364
IF4
0.164
0.164
0.162
0.179
IF5
0.430
0.447
0.239
0.114
0.554
IF6
0.505
0.611
0.277
0.129
0.394
0.753
IF7
0.457
0.498
0.255
0.121
0.523
0.415
0.591
IF8
0.355
0.352
0.193
0.095
0.398
0.319
0.402
0.463
Min:
Mean
44.42%
48.08%
27.42%
14.10%
38.74%
42.56%
40.80%
32.21%
14.10%
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SATCAST Algorithm
“Interest Field” Importance
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
• Deep convection, dry upper troposphere.
• Best for high CAPE environments, and strong updrafts.
• Winter-time, Midlatitudes
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SATCAST Algorithm
“Interest Field” Importance
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
• Moist upper troposphere, “warm-top” convection. Shallow convection.
• Low CAPE environments (tropical, cold-upper atmosphere). f(Physics)
• Optimal in Tropics during summer.
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SATCAST Algorithm
“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
• Use of 8.5-11 & 3.7-11 m from MODIS have been considered
• Instantaneous 13.3–10.7 um:
Highest POD (88%)
• Cloud-top freezing transition:
Lowest FAR (as low as 15%)
• Important for CI & Lightning Initiation
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NWS Transition Activities
SATCAST in AWIPS
U. Wisconsin - CIMSS collaboration
Web Survey 2007
NESDIS Operations
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transitioning unique NASA data and research technologies to the NWS
Outline
• Convective Initiation research, validation & transition activities
• NWS Products
• Soil Moisture Initialization Research (data assimilation)
• New Research, with NASA Data (lightning, MAMVs)
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NWS Transition Activities
Flat ADAS for Surface Analyses
Flat ADAS
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NWS Transition Activities
Mesoscale AMVs: 2007-2008
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NWS Transition Activities
CI “Trends of Trends”: 2007-2008
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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
kkoooooooookkkkkkkkkkk
2040-2050 UTC
2140-2150 UTC
CI Climatology Research
Details: GOES CI Interest Fields: 21 July 2005 (afternoon)
-topography
-main updrafts
1 km Resolution
…for LI
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GOES-R Risk Reduction
Java-based Hydra Visualization Tool
8.5-11 μm Difference
Deep Cu/Ice Cloud
Semi-Transparent
Cirrus
8.5-11 m Difference vs IR Window TB
IR Window TB
Multispectral cloud properties are used to classify
cumulus and identify clouds in a pre-CI state
Cirrus Edge/MidHeight Cu
Clear Sky/Small Cu
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GOES-R Risk Reduction
Preliminary MSG CI Nowcasting Criteria
CI Interest Field
Critical Value
10.8 µm TB
<0K
10.8 µm TB Time Trend
< -4 K/15 mins
TB/30 mins > TB/15 mins
Timing of 10.8 µm TB drop below 0° C
Within prior 30 mins
8.5-10.8 µm TB Difference
<0K
12.0-10.8 µm TB Difference*
-3 to 0 K
CAPE
> 500 J/kg
Microphysical information from 1.6 reflectance is used to improve the
convective cloud mask and negative 8.7-10.8 m differencing values are used
to identify cumulus with liquid water tops

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MODIS/GOES Convective Cloud
Mask Validation
a)
Visible Channel
GOES Convective MODIS Convective
Cloud
Mask
Cloud Mask
b)
c)
d)
e)
f)
Visible Channel GOES Convective MODIS Convective
Cloud Mask
Cloud Mask
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MODIS/GOES Convective Cloud
Mask Validation
Visible Channel
a)
MSG Convective MODIS Convective
b)Cloud Mask
c)Cloud Mask
In preparation for GOES-R
d)
Visible Channel
e)
f)
MSG Convective MODIS
Cloud Mask
Convective Cloud
Mask
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1 Aug 2004 Soil Moisture Differences
ALEXI - EDAS
0-10 cm
10-40 cm
• The largest differences between
ALEXI and EDAS soil moisture occur
over the eastern half of the study
domain
• The 40-100 cm soil layer shows that
ALEXI soil moisture is wetter across a
majority of the domain
40-100 cm
100-200 cm
• The drier conditions in the 100-200
cm soil layer are once again in a region
where vegetation is not able to extract
water from the soil. The wetter
conditions in SE OK are located within
vegetation types which can extract
water from this layer.
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ALEXI and EDAS Comparisons
The retrieval of ALEXI soil moisture is compared to soil moisture observations
from the Eta Data Assimilation System (EDAS) for each of the composite periods.
The EDAS soil moisture show substantial dry biases, with the largest bias
occurring during observed wet soil moisture conditions (high fPET).
With respect to all observations during this period, the ALEXI soil moisture retrieval
produces soil moisture estimates that exhibit a much lower RMSE than EDAS.
RMSE = 0.059 or 19.7% fAW
RMSE = 0.095 or 31.7% fAW
ALEXI
EDAS
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Future Work
• Continue AWG GOES-R Risk Reduction
• Further Satellite based lightning initiation research using GOES, MODIS
& MSG
• Thru NASA SERVIR, provide more hydrologic information from SATCAST
• Using MODIS NDVI product and topography maps, improve nowcasting
0-3 hours using vegetation and topography to determine areas where CI
may occur. [John Walker, UAH]
• CloudSat, MODIS & QuikSCAT for convective momentum fluxes and
mesoscale AMV assimilation [Chris Jewett, UAH]
• Peer-reviewed papers
• Additional soil moisture assimilation work: AMSR+MODIS via ALEXI
[Chris Hain, UAH]; with the USDA
• Convective Climatologies [2 UAH Graduate Students]
• Collaboration with SPC [late 2007] & NOAA NESDIS
• Continued SATCAST validation + transfer of MAMVs to NWS Huntsville
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