Satellite Based Nowcasting of Convection

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Satellite Based Nowcasting of Convection Initiatio
n 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 What are the factors?
LMA NLDN
Lightning Type (CG, IC, CC) Amount Polarity Altit
ude in clouds with respect to anvil
Courtesy, NCAR RAP
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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
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SATELLITE AMV WOULD INCORRECTLY FORECAST FUTURE
RADAR ECHO LOCATION
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(No Transcript)
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CI Nowcast Validation
Conditional POD skill scores
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CI Nowcast Validation
Conditional FAR skill scores
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SATCAST Algorithm
Interest Field Importance

• 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

• 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

• Use of 8.5-11 3.7-11 ?m from MODIS have been
  considered

• Instantaneous 13.310.7 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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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
2040-2050 UTC
2047 UTC
2147 UTC
2140-2150 UTC
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CI Climatology Research
GOES CI Interest Fields 21 July 2005 (afternoon)
Details -topography -main updrafts
for LI
1 km Resolution
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GOES-R Risk Reduction
Semi-Transparent Cirrus
Java-based Hydra Visualization Tool
Deep Cu/Ice Cloud
8.5-11 µm Difference
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/Mid-Height Cu
Clear Sky/Small Cu
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GOES-R Risk Reduction
Preliminary MSG CI Nowcasting Criteria

• 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
GOES Convective Cloud Mask
MODIS Convective Cloud Mask
Visible Channel
GOES Convective Cloud Mask
MODIS Convective Cloud Mask
Visible Channel
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MODIS/GOES Convective Cloud Mask Validation
MSG Convective Cloud Mask
MODIS Convective Cloud Mask
Visible Channel
In preparation for GOES-R
Visible Channel
MSG Convective Cloud Mask
MODIS 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
  AMSRMODIS 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