An Overview of the GOES-R Risk Reduction Program: Proving Ground Applications

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An Overview of the GOES-R Risk Reduction Program
Proving Ground Applications
GOES-R Proving Ground MeetingMay 15-16,
2008 Boulder, Colorado

• Mark DeMaria and Ingrid Guch
• NESDIS/StAR

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Outline

• The Algorithm Working Group
• Why was GOES-R Risk Reduction created?
• Summary of Current Projects
• Future Plans

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The GOES-R Algorithm Working Group

• Responsible for required algorithm developed
• Algorithms to be turned over to system contractor
• 16 application teams
• Run by NESDIS/StAR
• Replaced contractor responsibility in 2004

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AWG Product Maturity Estimates
Release 1 Set
Release 2 Set
Release 3 Set
Release 4 Set
Day 1 Products
High Maturity
Medium Maturity
Low Maturity

• Release 1 and 2 required for GOES-R launch
  readiness
• Products are grouped into four Product Generation
  capability releases releases 3 4 are options
• Prioritization methodology user prioritization,
  product precedence trees (MIT/LL, STAR), heavy
  hitters from latency requirements, and LIRD/MRD
  Prioritization Tiers

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What is GOES-R Risk Reduction (R3)?

• Product Assurance and Science support for the AWG
• AWG recommended actions (examples create proxy
  data, or further research needed to bring
  algorithms in-spec)
• Exploratory Algorithms, New Products and
  Applications
• Multisensor
• Multisatellite
• Data assimilation and nowcasting
• Results feed into AWG, SPSRB, other transition
  activities/testbeds
• GOES-R demonstrations and training
• Demonstrate new GOES-R capabilities to public and
  private sector users in an efficient, timely
  manner
• Most promising user applications feed into GOES-R
  Proving Ground
• Training leverages NESDIS Cooperative Institute
  heritage in Visit, Comet, SHyMet courses as well
  as NASA/SPoRT center

R3 complements AWG by conducting the higher-risk
activities that are needed for users to fully
exploit all GOES-R instruments and capabilities
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Why was R3 created?

• R3 was created by STAR (Menzel), GPO (Kirkner)
  and OSD (Davis) to create a program similar to
  the GIMPAP for GOES-R
• Started prior to AWG
• GIMPAP was the GOES I-M Product Assurance
  Program created because users were unable to
  benefit from new GOES-8 capabilities and
  something needed to be done so that GOES I-M were
  not also underutilized.
• GIMPAP still exists today as the GOES
  IMprovement and Product Assurance Program but is
  not expected to support GOES-R.

R3 creators did not wait until GOES-R was
underused to create a user readiness and product
assurance program. Using lessons learned from
GOES-8 underutilization and the success of GIMPAP
they were able to move forward with R3.
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GOES-7 to GOES-8 Improvements
Satellite GOES-7 GOES-8
Spacecraft bus Spin-stabilized 3-axis stabilized
Visible band (0.65 µm) 6-bit (1-64) _at_ 1 km 10-bit (1-1023) _at_ 1 km
Imager IR bands Only 2 or 3 bands _at_ 4 x 16 km (with sounder in multi-spectral imaging mode) 3.9, 6.7, 10.7, 12.7 µm 5 bands _at_ 2 x 4 km (depending on band) 3.9, 6.7, 10.7, and 12.0 µm
Temperature precision 0.25 K _at_ 300 K 0.15 K _at_ 300 K 1 K (absolute)
Navigation accuracy 3-10 km (absolute) 1.5 km (frame-to-frame) 2-4 km (absolute)
Temporal resolution CONUS (every 15 minutes) full-disk (every 3 hours) CONUS (every 15 minutes) full-disk (every 3 hours)
Rapid scan interval 3 minutes 1 minute (normal) 30 seconds (possible)
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Examples of GOES-8 Utilization Delays

• GOES-8 was first of GOES-Next satellites
• Became operational in 1994
• NWS AWIPS deployment not completed until 1999
• 5 year delay for display of full resolution data
• RAMSDIS program and ORA/CIMSS/CIRA internet sites
  provided interim solution/capability
• Channel 2 for tropical cyclone fixes began in
  1998 at NHC
• 4 year delay in utilization of new channel
• Sounder products in AWIPS began in 2000
• 6 year delay is use of new sounder
• Auto-triggering of rapid scan began in Jan of
  2000
• 6 year delay in full utilization of new scanning
• 1-minute imagery still not used by NWS
• Some GOES data still not assimilated in NCEP
  models

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Channel 2 Aids Tropical Cyclone Center Location
at Night
IR Channel 4
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Channel 2 Center Fixing Technique Used at NHC
Beginning in 1998
1995 1996
1997
None of these storm forecasts benefited from the
new GOES Channel 2 data!
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Rapid Scan and Super-Rapid Scan GOES Operations
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New Programs to Improve GOES-Next Utilization

• GOES Improved Measurement and Product Assurance
  Plan (GIMPAP) implemented in 1995
• 3 million annual budget
• Divided into applied research and operational
  transition components in 2001
• Virtual Institute for Satellite Integration
  Training (VISIT) program started in 1999
• Live teletraining to NWS forecasters on-line
  recorded versions
• 17,000 training certificates since 1999
• Satellite Hydrology and Meteorology (SHyMet)
  training class in 2006
• On-line and instructor-led of satellite training
  for NWS interns
• Joint Center for Satellite Data Assimilation
  formed in 2001

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Vision for GOES-R3

• Capable, informed users
• Flexible inventive providers
• Knowledge brokers that recognize new connections
  between capabilities and needs
• Champions of new opportunities

GOES-R demonstrations and training Exploratory
Algorithms, New Products and Applications Produc
t Assurance and Science support for the AWG
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GOES-R3 Exploratory Algorithm Development
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GOES-R3 Team

• CICS aerosol, OLR, snow and precipitation
• CIMSS soundings, winds, clouds
• CIRA New severe weather, tropical cyclone and
  hazard
• products, training
• JCSDA assimilation of high temporal resolution
  data over land
• OAR clouds, soundings, assimilation
• STAR all activities except space weather
  (includes partners in SciTECH and at CREST)
• NSSTC _at_ NASA lightning, nowcasting
• NWS space weather

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R3 Technical Advisory Committee

• Chair, Co-Chair
• Ingrid Guch and Mark DeMaria, NESDIS/StAR
• Committee Members
• Dave Benner, NESDIS/OSDPD
• Mitch Goldberg, NESDIS/StAR
• Steve Goodman, NESDIS/StAR
• Jim Gurka, NESDIS/GPO
• Allen Huang, UW/CIMSS
• Jeff Key, NESDIS/StAR
• Steve Koch, OAR/ESRL
• Paul Menzel, UW/CIMSS
• Jim Purdom, CSU/CIRA
• Peter Romanov, UM/CICS
• Tim Schmit, NESDIS/StAR
• Kevin Schrab, NOAA/NWS
• Bill Smith, UMBC
• Chris Velden, UW/CIMSS

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GOES-R Risk Reduction FY schedule
FY09 GOES-R3 priorities announced along with
procedures for applications, reviews and
selections
Funding decisions with TAC guidance announced 10
days after FY09 R3 budget is known. If budget
information appears far away (CR), tentative
estimates are given instead.
FY08 approved funding arrives to teams and work
begins. Input from participants, AWG, GOES-R
Program Office and TAC collected for FY09
priorities
GOES-R3 review held
GOES-R3 management updates GOES-R3 Plan. PIs
submit revised FY08 proposals per TAC guidance.
Grant applications should be at NESDIS 75 days
prior to start date if possible.
PIs prepare For GOES-R3 review
May July 2008 is reserved for input from AWG
and others for GOES-R3 FY09 proposal guidance
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Post-Launch Programs

• Product assurance, development and improvements
  need to continue
• Current GOES model
• GIMPAP program for new ideas and product
  assurance
• Product Systems Development and Integration
  (PSDI) program for operational transition
• GOES Ground Systems for hardware
• JCSDA Systems Development and Integration (JSDI)
  program for upgrades and improvements to data
  assimilation
• ShyMet/VisitView/COMET for training needs
• GOES-R model????

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Projects from FY08 AWG Science Support
CIMSS ABI forward models 110.0 CIMSS Motion
vectors 90.0 CIMSS Ozone retrievals
40.0 CIMSS Sounding algorithms
70.0 CIMSS Cal/Val methods 60.0 CIMSS Fire
algorithms 85.0 CIMSS Turbulence
algorithms 90.0 CIMSS Cloud optical
parameters 50.0 CIMSS Ice algorithms
40.0 CIMSS Visualization methods
25.0 CIRA Severe weather detection algorithms
46.0 CIRA Tropical cyclone algorithms
40.0 CIRA Hazard detection algorithms
60.0 SEC Space weather products 300.0 NASA GLM
algorithms 400.0 OAR Water vapor algorithms
30.0 StAR Aerosol detection 140.0 StAR SW
flux 75.0 StAR Particle shape for aerosol
retrieval 75.0CICS LW radiation budget
80.0 CICS NDVI algorithms 29.0 CICS Rainfall
algorithms 85.0 CICS Rainfall climatology
60.0 StAR Aerosol imagery 90.0 CREST Nowcasti
ng 30.0 StAR General support 354.0 Total
2554 K
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Projects from FY08 Exploratory Algorithms,
Products and Applications
JCSDA Data assimilation 450.0 CIMSS High time
resolution assimilation 90.0 CIMSS Hurricane
secondary eyewall product 45.0 CIMSS New
sounding products 70.0 CIMSS Nearcasting
applications 40.0 CIMSS Ozone products and
proxy data 40.0 CIMSS Visualization
40.0 CIRA Advanced assimilation methods
79.0 CIRA New severe weather products
40.0 CIRA New tropical cyclone products
47.0 SEC Space weather products 150.0 NASA New
lightning applications 110.0 OAR Water vapor
products 70.0 StAR Air quality applications
70.0 CREST Nowcasting 80.0 StAR General
support 80.0 Total 1546.0 K
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Projects from FY08 training and demonstrations
CIMSS GOES-R Education and Public Outreach
14.5 CIMSS Nearcasting 20.0 CIMSS
Visualization 10.0 CIRA National and
International Training 35.0 CREST Nowcasting 4
0.0 StAR General Support 30.5 Total 15
0.0 K
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GOES-R3 key activities Green active for 08Red
halted in 07blue Coming soon
Activity STAR, CREST, SciTECH CIMSS CIRA CICS CIOSS JCSDA OAR COMET SEC NSSTC
Cal/Val . . .
Images/ Aerosols/Air Quality . . . . .
Clouds . . . .
Soundings . . . .
Winds . . .
Surface . .
Precip . . . . .
Ocean
Radiation Budget . .
Ozone .
Space Env .
Assimilation . . . . . .
Training Outreach . . . .
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Current Project Examples

• Convective Nowcasting/Nearcasting
• CUNY, CIMSS (2), CICS
• Tropical cyclones
• CIRA/StAR
• Fire detection
• CIRA, CIMSS, CICS
• Land surface
• CICS/StAR NDVI
• Supplementary data for product evaluation
• CICS/StAR (Cloudsat), ESRL (GPS moisture)
• Additional products
• OLR, Space Weather, Lightning, proxy ABI imagery
• More examples
• http//www.orbit.nesdis.noaa.gov/star/goesr/

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Comparison of Satellite-Based (pre)Nowcasting
Algorithms over the New York City Area
Students Mr. Bernard Mhando Ms. Nasim Nourozi
(Graduate Students), Department of Civil
Engineering, City College of New York at
CUNY Supervisors Dr. Shayesteh Mahani, Dr. Brian
Vant-Hull, Dr. Arnold Gruber, and Dr. Reza
Khanbilvardi, NOAA-CREST at the City College of
New York of CUNY NOAA collaborators Mamoudou Ba
(NWS), Stephan Smith (NWS), Robert Kuligowski
(NESDIS), Robert Rabin (NSSL) Meteo-France
Collaborators Stephane Senesi, Frederic
Autrones, Yann Guillou
RDT contours Hydro-Estimator
RDT contours Radar Rainfall

NOAA/CREST is assisting the GOES AWG in the
selection of a operational thunderstorm
nowcasting algorithm. Currently EeMETSETs
Rapidly Developing Thunderstorm (RDT) and NESDIS
HydroNowcaster (HN) algorithms are under
investigation. But the first step in nowcasting
is to select the features to be extrapolated into
the near future. HN uses rainfall output created
by the Hydro-Estimator (HE) as the features of
interest RDT uses convective cells and then
calculates trends. Since RDT does not actually
perform a nowcast, it is best to compare the
pre-nowcast features used by each algorithm.
RDT Detects convective cells by
temperature growth rates and spatial
gradients at the cell peripheries.
Uses single channel thermal IR (BT)
to track and characterize
cells. HE Estimates rainfall based on BT
thresholds modified by local BT
statistics and NWF stability and
water vapor.
Temporal information is not used.
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Nearcasts - Filling the information gap between
Observations and NWP using Dynamic Projections of
GOES Moisture Products
How Lagrangian Nearcasts work Instead of
interpolating randomly spaced moisture
observations to a fixed grid (which smoothes the
data) and then calculating moisture changes at
the fixed grid points, the Lagrangian
approach interpolated wind data to locations of
GOES multi-layer moisture observations and then
moves the full-resolution observed data to new
locations, using the primitive equations to
dynamically changing wind fields with long (10
minute) time steps. . The moved moisture
obs values from each level are then
both 1) Converted back to
images for display and for use as
predicted DPIs, and 2) Combined
(subtracted) to identify meso-scale
areas where convective
instability will develop (where
low-level moisture becomes capped by dry
air aloft), even after convective clouds
appear.
13 April 2006 2100 UTC 900-700 hPa GOES PW 0
Hour Ob Locations
13 April 2006 2100 UTC 900-700 hPa GOES PW 3
Hour Nearcast Image

• Nearcast of Vertical Moisture Gradient
  (Convective Instability)
• (900-700 hPa GOES PW -700-500 hPa GOES PW)
• 3 Hour Nearcast valid 0000UTC

Verification
Updated Hourly - Full-resolution 10 km data - 10
minute time steps
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Synthetic 2 km GOES-R ABI WV Imagery (W. Feltz)

• Waves are evident in all three 2 km ABI WV
  channels, with wave spatial patterns being far
  clearer than current GOES-12
• 3 ABI WV channels could provide information on
  mountain wave amplitude, as they detect peak
  signal from differing heights

Observed GOES-12 Band 3 (6.5 micron)
Simulated ABI Band 8 (6.2 micron)
Simulated ABI Band 9 (7.0 micron)
Simulated ABI Band 10 (7.3 micron)
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Cloud Top Cooling Using 5-Min Rapid Scan MSG
SEVIRI Data
South African Region Using 15-Min SEVIRI Data
Lightning Initiation Nowcast Validation
Free State, SA
Lesotho
Box-Avg Method POD40, FAR22 AMV Based
Method POD27, FAR35
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Mesoscale Convective System Climatology using
Geostationary Infrared ImageryDr. Daniel Vila
Ralph Ferraro Robert Kuliwoski

• Use the cloud tracking / nowcasting methodology
  selected by the GOES-R Algorithm Working Group
  (AWG) for application to the ABI to track
  mesoscale convective systems (MCSs) in SEVIRI
  data through their entire life cycle.
• Analyze the MCS life cycle events to find a
  priori indicators that are useful for forecasting
  changes in MCS intensity.
• This example shows the use of SEVIRI channel 9
  (10.8 µm) data to track MCS over South America.
  One of the nowcasting methodologies is being
  tested for a period of time up to three hours.

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GOES-R Tropical Cyclone Applications

• Emphasis on genesis and intensity change
• Synthetic data
• Lili (02) and Wilma (05) model runs
• Simulated data
• MSG proxy for ABI
• World Wide Lightning Locator Network (WWLLN)
  proxy for GLM

12 hourly lightning strikes from the WWLLN 31
Aug 12 UTC to 6 Sept 18 UTC 2007 Hurricane Felix
indicated by the blue dot
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Synthetic Fire Hotspots for GOES-R(CIRA)

• Hot spots are added as multiples of 400m x 400m
  RAMS model grid cells (grid 4) .
• Synthetic 3.9 and 10.35 µm GOES-R ABI images are
  produced for grid 4.
• An approximate GOES-R ABI point spread function
  is applied to grid 4 synthetic imagery.
• GOES-R ABI imagery results from application of
  the point spread function.
• Synthetic GOES-R ABI brightness temperatures are
  mapped on GOES-R ABI Mcidas template.

Point spread function for GOES-R ABI at 3.9 µm
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GOES ABI Fire Algorithm Output for Central
America proxy dataset
Central American Case Study Fires vary over
time, clouds included Proxy data by CIRA, fire
algorithm by CIMSS
Date April 23, 2004 Times
1500 2055UTC
CIRA Model Simulated ABI 3.9 µm band
CIMSS ABI WF_ABBA Fire Mask Product
Solar Block-Out Zone 1715 1835 UTC
Experimental ABI WF_ABBA Fire Legend
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The number of true fires and percentage of true
fires for modified algorithm are overall higher
than the old algorithm
PI Zhanqing Li (CICS/UMD)
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NDVI from geostationary satellites
Multiple daily observations from geo satellites
help to effectively reduce cloud contamination in
daily and weekly NDVI products
cloud
cloud
cloud
cloud
NDVI map
NDVI map
NDVI map
Instantaneous map
Daily max NDVI composite map
Weekly max NDVI composite map
June 23, 2007
Image time 13.15 UTC
June 23-30, 2007
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NDVI time series polar vs geo
Higher frequency of observations from geo
satellites allows for better characterization of
seasonal vegetation dynamics as compared to polar
orbiting satellites
Red weekly MSG SEVIRI NDVI Blue weekly NOAA
AVHRR NDVI
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Utilization of A-Train Data to Better Understand
Liquid Cloud/Warm Rain Signatures from GOES-R
ABIChen, Li, Kuligowski and Ferraro
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ESRL/GSD ProjectGOES moisture evaluation with GPS
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GLM Studies with Ground-based Networks
Time series of lightning flash rate (right
ordinateblue) and the time derivative (DFRDT
left ordinate, green) of lightning flash rate
with the mean DFRDT and standard deviation of the
DFRDT indicated (standard deviation indicated by
red line). Time of tornado is indicated by the
triangles and lightning jumps by dots on green
trend line.
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Synergy between Operational Polar-Orbiting and
Geostationary Satellite OLR Products
NOAA/MetOp/NPOESS - HIRS, IASI, CrIS, ERBS/CERES
Geostationary
FY-2C
GERBMet-8/9
GMS
Met-5
GOES-E
MTSAT
GOES-W
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Determining the initial speed of a coronal mass
ejection and/or shock to estimate their arrival
time at Earth
Source FY07 2Q space weather report from
http//www.orbit.nesdis.noaa.gov/star/goesr/
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CIMSS Proxy Dataset Activities
Proxy ABI Band 14 (11.2 ?m)
GOES-12 Band 4 (10.8 ?m)
Comparison of observed and simulated IR
brightness temperatures for the full disk
domain, which contained 6-km horizontal
resolution.
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CIMSS Proxy Dataset Activities
16-panel proxy ABI image for the 2-km CONUS
domain, which contained 2-km horizontal
resolution.
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CIRA Simulated ABI full-disk IR imageryfor
band-9 (6.95 um) from MSG
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Summary

• GOES-R Risk Reduction program
• AWG science support
• New product and exploratory research
• Proxy data and products
• Training and user readiness
• Conduit for Proving Ground demonstration products