NASA / SPoRT Update
Coordination Call: Friday 4/16/10
• Agenda items:
• MODIS/GOES Hybrid
• UAH’s CI and CTC data files to NOAA/HWT/EFP
• Lightning – Data transfer, Psuedo-GLM code,
Training
• Lightning Warning Algorithm
• MODIS/AMSR-E SST Composite
transitioning unique NASA data and research technologies to operations
MODIS-GOES
Hybrid
Work done by Gary Jedlovec, Matt Smith, Kevin Fuell
•
Running in real time,
but currently working
out the data ingest and
bowtie correction
logistics for MODIS.
• Need to remap
MODIS to 2km IR to
more accurately
portray ABI channels
• Need to examine
timing discontinuities
at edges and possible
limb-correction
• Product introduced to
WFO Partners in
March: They expressed
high interest.
MODIS 1km / GOES 4km IR over SW U.S. at 1845Z (2 MODIS swathes)
transitioning unique NASA data and research technologies to operations
UAH’s CI and CTC data files to
NOAA/HWT/EFP
Work by Kevin Fuell, Jon Case, John Walker (UAH), Chris Siewert, Greg Grosshans
• NASA/SPoRT asked to assist with the transfer of AWG Convective
Initiation and a Cloud Top Cooling product for use during Spring
Experiment 2010
• AWG CI product is by University of Alabama Huntsville
• SPoRT already sending LMA data in similar fashion
• SPoRT worked w/ UAH to make N-AWIPS ready file
• HWT has tested a file and will receive automated script from SPoRT
and UAH
GEMPAK via
SPoRT
transitioning unique NASA data and research technologies to operations
NAWIPS via
HWT
Real-time
KSC
Data
Work done by Geoffrey Stano, NASA Lightning Group
• Lightning group has obtained real-time LDAR data
o First successful ingest into AWIPS
o Currently at 1 km and 1 min resolution
oFinalize ingest to WFO Melbourne, FL during SPoRT
visit April 23
o Efforts support Spring
Experiment
Pseudo-GLM from
KSC LDAR II
transitioning unique NASA data and research technologies to operations
Data
Transition
Work done by Geoffrey Stano, Kristin Kuhlman
• Finalizing data feeds to Spring Experiment
o 3 total lightning networks
 KSC, North Alabama, Washington DC
oSPoRT personnel to participate in Experiment
• Methodology for Pseudo GLM has been transferred
•Providing WES case studies for in-active lightning days
o May 3 and 6, 2009
transitioning unique NASA data and research technologies to operations
Lightning Training
Work done by Geoffrey Stano, Eric Bruning
• Train forecasters on the use of the Pseudo GLM product
o Intended to be viewed before arriving in Norman
Followed by in-person
training on first day
o Educate forecasters on
potential of GLM era
o Provide 2 case examples
o Working to incorporate into
the NWS Learning
Management System
o
transitioning unique NASA data and research technologies to operations
WRF-based Lightning Algorithm
Work done by Bill McCaul, Jon Case, Scott Dembeck
•Ongoing work to use model microphysics to forecast lightning threat (graupel flux,
vertically integrated ice)
• NSSL WRF has been configured to output Flash Rate Density using hourly max fields. See :
•http://www.nssl.noaa.gov/wrf/
o Lightning Threat 1 = based solely on graupel flux
o Lightning Threat 2 = based solely on Vert. Integ. Ice
o Lightning Threat 3 = combined use of Threats 1 & 2
o Lightning Threat fields will be evaluated
at Experimental Forecast Program (EFP) 2010
Tie in with the Proving Ground
•Data assimilation from GLM to improve
microphysics and storm environment
•Better initial characterization of
microphysical fields and therefore
improved convective forecasts in the very
short term (1-6 hours)
•Request to use Lightning Threat Algorithm
in the simulated WRF-ABI case event
transitioning unique NASA data and research technologies to operations
Pseudo
ABI
Composite
SST
Product
Work done by Gary Jedlovec, Frank LaFontaine, Jaclyn Shafer
Application
•weather forecast community requires
continuous spatial coverage of SST for
model initialization
•marine weather users benefit from
high resolution SST data
Cloud cover cause a significant problem!
MODIS /AMSR-E product relevant to ABI
Composite fields of high resolution
satellite derived SSTs provide a good
way to provide this data
Compositing fills in cloudy or data void
regions with observations from the
previous day(s) satellite passes
Fixed time replacement preserves
diurnal cycle captured with
foursnapshots from polar orbiting data
NOAA/GLERL Ice Mask
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
Problem
The regional weather forecast community requires continuous spatial fields of
surface parameters for model initialization. Composite fields of high
resolution satellite derived SSTs may provide a good way to provide this
data. However, persistent cloud patterns can cause product latency and
reduced accuracy.
•Previous work developed a SST composite product
with MODIS data to provide high-resolution SST data
over limited regions (Haines et al. 2007)
•Impact of MODIS SSTs (versus RTG) on fluxes of heat /
moisture and subsequent weather forecasts was
significant in coastal regions (Lacasse et al. 2008; Case
et al. 2009)
•Regions of high latency reduced accuracy and impact
Single image
12
17
MODIS Composite
22
27
Just finished a collaborative project with PO.DAAC at JPL
Developed an enhanced SST composite product – reduced latency
•MODIS and AMSR-E, PO.DAAC L2P data stream, latency weighted compositing algorithm
Demonstrate improvement and impact on forecasts
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
Approach overview
• Increased SST data availability – transition from direct broadcast to more
complete data source
• Bring in AMSR-E SST data for coverage in persistent cloud regions
• SST compositing algorithm changes – latency, error, and resolution
weighted product
• Use near real-time L2P data stream (JPL) for MODIS and AMSR-E passes
more passes
– expand product coverage
– pixel by pixel quality estimates and bias
– slight additional delay in
data access – tolerable
– better cloud / rain detection
– AMSR-E data coarse resolution with
no data near coast
Coverage of Enhanced MODIS/AMSR-E SST Product
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
Approach details
• A collection of MODIS and AMSR-E SST values corresponding to the last 7
days is obtained for the JPL PO.DAAC for each pixel in a product region (at
1 km resolution), for the four Terra / Aqua overpass times
–
–
MODIS proximity flags 4 and 5, bias adjustment
AMSR-E proximity flag 4
• Apply latency weighted compositing scheme to the collection at each
point in the 1 km resolution output file
where SSTcp (I,j) is a composite SST value at a point (I,j),
SST(I,j,k) is a SST collection (k is an index corresponding to date of data in collection),
L(k) is the latency (in days) of a particular SST value in the collection, and
Wt(d,k) is a data weight factor where d corresponds to either MODIS (Wt=1.0), AMSR-E
(Wt=0.20), or some other value for another instrument source.
• The inverse latency formula uses all data in the collection and allows
more recent data to have a greater influence on the composite
• The reduced AMSR-E weight factor (Wt) accounts for the large footprint
compared to MODIS
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
• MODIS alone produces a high-resolution (1km) SST composite but
with some latency issues and gaps
• AMSR-E alone reduces latency in the SST composite with coarser
resolution data, but not near land
• Enhanced MODIS / AMSR-E SST composite a blend of both
• Product available 4x a day corresponding to Terra (day and night)
and Aqua (day and night)
6-4-07 - Aqua
MODIS Day
6-4-07 - Aqua
AMSR-E Day
MODIS Composite SST
AMSR-E Composite SST
6-4-07 – Aqua Day
MODIS/AMSR-E
MODIS/AMSR-E +
OSTIA or NESDIS POES/GOES SST product (with a Wt=0.20) used to fill in where neither
MODIS nor AMSR-E coverage is complete (a few coastal areas)
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
Validation
4 regions, 4 seasons, 4 times a day
60-70 fixed buoys (coastal and gradient regions), 90-100 drifting buoys
(more open ocean) per time - bias and rms
• Enhanced (black) much improved
over MODIS only (red) at all times –
due to reduced latency
• Drifting buoy (solid lines) biases
smaller than fixed (dashed)
• Biases generally <0.20 C, RMS
<0.50 C
• Trend in MODIS only due to latency
– no trend in enhanced SST
composite bias
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
Forecast impact
High resolution SSTs have had a
positive impact on a forecast
applications
Enhanced cyclonic flow
associated with SST gradient
Hurricane WRF
forecasts with high
resolution SST data
show improvements
in intensity versus
RTG model runs for
Hurricane Ike
(Courtesy of Dr. Craig
Mattocks – UNC).
T.S. Claudette, 03z 17 Aug 09
4-h forecast, 10-m wind & MSLP diff
WRF EMS 6h forecast by Mobile NWS WFO (using high reso
SSTs instead of RTG) for T.S. Claudette (17Aug2009) better
depicted location of landfall
Impact of high resolution lake temperatures on lake effect
snow in WRF EMS
transitioning unique NASA data and research technologies to operations
Pseudo ABI Composite SST Product
- GOES-R PG Application
• Current product available in WRF EMS and selected WFOs
– 1km resolution (2km in WRF EMS), 4 times a day
– Planning to adapt product to GOES ABI resolution
–
–
–
–
1-3 hourly, 2 km resolution
compare to current POES / GOES once daily product (Maturi)
monitor accuracy
Eventual dissemination to WFOs, etc.
• Will collaborate with AWG on application and use
• Positive impact on regional weather forecasts
o Real-time data available from NASA
/ SPoRT (GRIB2) –
ftp://ftp.nsstc.org/outgoing/lafonta/sst/grib
2/conus/
o Also available from JPL PO.DAAC in
GRIB2 and netCDF (May 2010)
o SPoRT –
http://weather.msfc.nasa.gov/sport/modis/
sst_comparison.html
transitioning unique NASA data and research technologies to operations