Resources and Application of the Virtual Lab

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Resources and Application of the Virtual Lab

• Dr. Bernadette Connell
• CIRA/NOAA-RAMMT
• March 2005

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Outline

• Winds
• GOES - Cloud Motion (VIS and IR) and Waper Vapor
• POES Scatterometer
• Sea Surface Temperature (SST)
• GOES and POES
• Precipitation
• GOES IR, multi-channel
• POES microwave
• Sea ice, snow cover, land characterization,
  vegetation health, fire, sea level anomaly
• The Virtual Laboratory for Satellite Training and
  Data Utilization
• http//www.cira.colostate.edu/WMOVL/index.html

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Winds from GOESCloud motion from Visible and
IRand Water Vapor Tracking

• Determine tracers
• Determine the track of the tracers in 2
  successive images
• Assign height
• Check wind vectors and height assignments against
  ancillary data (other derived wind vectors,
  observations, model output

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Winds from GOES

• Initial processing
• Imagery registration
• Screen out difficult features
• For IR and visible imagery screen out clear
  pixels, multi-deck cloud scenes, and coastal
  features.

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WINDS from GOES

• Tracer Selection
• Tracking clouds
• Semitransparent clouds or subpixel clouds are
  often the best tracers for estimating cloud
  motion vectors.
• Isolate the coldest brightness temperature (BT)
  within a pixel array (for IR)
• Isolate the highest albedo within a pixel array
  (for visible)
• Compute local bidirectional gradients and compare
  with empirically determined thresholds to
  identify targets

Velden et al. 1997 Nieman et al. 1993
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WINDS from GOES

• Tracer Selection
• Tracking water vapor features
• Features exhibiting the strongest gradients may
  not be confined to the coldest BT (as in clouds)
• Identify targets by evaluating the bidirectional
  gradients surrounding each pixel and selecting
  the maximum values that exceeds determined
  thresholds.

Velden et al. 1997 Nieman et al. 1993
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WINDS from GOES

• Tracking Metric
• Search for the minimum in the sum of squares of
  radiance differences between the target and
  search arrays in two subsequent images at 30-min
  intervals
• Use the model guess forecast of the upper level
  wind to narrow the search areas.
• Derive two displacement vectors. If the vectors
  survive consistency checks, they become
  representative wind vectors.

Velden et al. 1997
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WINDS from GOES

• Height Assignment
• Infrared Window (IRW) good for opaque tracers
• Determine average BT for the coldest 20 of
  pixels in target area
• Match the BT value with a collocated model guess
  temperature profile to assign an initial pressure
  height
• H2O IRW intercept - good for semitransparent
  tracer
• Based on the fact that radiances from a single
  cloud deck vary linearly with cloud amount
• Compares measured radiances from the IR (10.7 um)
  and H2O (6.7 um) channels to calculate Plank
  blackbody radiances (uses profile estimates from
  model).

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WINDS from GOES

• Height Assignment
• CO2-IRW techniques good for semitransparent
  tracer
• Equate the measured and calculated ratios of CO2
  (13.3 um) and IRW (10.7 um) channel radiance
  differences between clear and cloudy scenes (also
  uses profile estimates from model)

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WINDS from GOES

• Height Assignment
• For cloud tracked winds from visible imagery,
  initial height assignments are based on
  collocated IRW
• When all initial wind vectors are calculated,
  reassess height assignments based on best fit
  with other information from conventional data,
  neighboring wind vectors (from both water vapor
  and cloud tracked winds), and numerical model
  output.

Velden et al. 1997
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Visible cloud drift winds
NOAA/NESDIS GOES Experimental High Density
Visible Cloud Drift Winds
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IR cloud drift winds
NOAA/NESDIS GOES Experimental High Density
Visible Cloud Drift Winds
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Water vapor winds
NOAA/NESDIS GOES Experimental High Density
Visible Cloud Drift Winds
http//cimss.ssec.wisc.edu/tropic/tropic.html
http//www.orbit.nesdis.noaa.gov/smcd/opdb/goes/w
inds/
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Winds from POES Scatterometer

• What is a Scatterometer?
• A scatterometer is a microwave radar sensor used
  to measure the reflection or scattering effect
  produced while scanning the surface of the earth
  from an aircraft or a satellite.

JPL web page http//winds.jpl.nasa.gov/aboutScat/
index.cfm
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Summary of determination of winds for QuikSCAT

• Microwave radar (13.4 GHz)
• Pulses hit the ocean surface and causes
  backscatter
• Rough ocean surface returns a strong signal
• Smooth ocean surface returns a weak signal
• Signal strength is related to wind speed
• 2 beams emitted 6 degrees apart help determine
  wind direction
• Able to detect wind speeds from 5 to 40 kts

VISIT Scatterometer session and JPL web site
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QuickSCAT example from descending passes
NOAA Marine Observing Systems Team
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QuickSCAT example from ascending passes
http//manati.orbit.nesdis.noaa.gov/quikscat/
NOAA Marine Observing Systems Team
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Winds from SSM/I

• Algorithm developed by
• Goodberlet et al.
• utilizes variations in surface emissivity
• over the ocean due to different
• roughness from wind
• WS147.901.0969TB19v-0.4555TB22v-1.7600TB37v
  0.7860TB37h
• where, TB is the radiometric brightness
  temperature at the frequencies and polarizations
  indicated.
• All data where TB37v-TB37h lt 50 or TB19h gt 165
  are rain flagged.

NOAA Marine Observing Systems Team
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SSM/I winds from ascending passes
NOAA Marine Observing Systems Team
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SSM/I winds from descending passes
http//manati.orbit.nesdis.noaa.gov/doc/ssmiwinds.
html
NOAA Marine Observing Systems Team
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Sea Surface Temperature (SST)

• AVHRR SST products primarily developed for NOAA's
  Coral Reef Watch (CRW) Program from satellite
  data for both monitoring and assessment of coral
  bleaching.
• SST anomalies (for monitoring El Nino/ La Nina)

NOAA/ NESDIS ORAD/MAST
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NESDIS SST Algorithms for AVHRR

• Day
• SST 1.0346 T11 2.5789 (T11- T12 ) - 283.21
• Night
• SST 1.0170 T11 0.9694 (T3.7- T12 ) - 276.58

NOAA/ NESDIS ORAD/MAST
Strong and McClain, 1984
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NOAA/ NESDIS ORAD/MAST
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NOAA/ NESDIS ORAD/MAST
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SST Anomaly
http//www.osdpd.noaa.gov/OSDPD/OSDPD_high_prod.ht
ml
NOAA/ NESDIS OSDPD
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Precipitation Products from GOES

• Hydroestimator
• Uses IR (10.7 um) brightness temperature to
  estimate precipitation estimates
• The relationship between BT and precipitation
  estimates was derived by statistical analysis
  between radar rainfall estimates and BT.
• GOES Multispectral Rainfall Algorithm (GMSRA)
• Uses all 5 GOES imager channels (vis, 3.9, 6.7,
  10.7, and 12.0 um)
• Calibrated with radar and rain gauge data

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Example Hydroestimator Product
NOAA/NESDIS/ORA Hydrology Team
http//www.orbit.nesdis.noaa.gov/smcd/emb/ff http
//www.cira.colostate.edu/ramm/sica/main.html
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Precipitation products from microwave

• Precipitation absorption and scattering
  characteristics
• Microwave spectrum
• Total Precipitable Water (TPW)
• Cloud Liquid Water (CLW)
• Rain Rate (RR)

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Precipitation Characteristics

• Dominant absorption by water
• Very little absorption by ice

• Scattering most prevalent at higher frequencies
• Ice scattering dominates at the higher frequency

Polar Satellite Products for the Operational
Forecaster COMET CD
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Precipitation Characteristics
Brightness temperature increases rapidly over
the ocean as cloud water increases for low
rain rates.
A mixture of snow, ice, and rain are the main
cause of scattering and result in a decrease in
BT within actively raining regions (over land
and ocean).
Polar Satellite Products for the Operational
Forecaster COMET CD
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Precipitation Cloud Water and Ice (key interactions and potential uses) Precipitation Cloud Water and Ice (key interactions and potential uses) Precipitation Cloud Water and Ice (key interactions and potential uses)
Frequencies AMSU SSM/I Microwave Processes Potential Uses
31 GHz 19 GHz 50 GHz 37 GHz 89 GHz 85 GHz Absorption and emission by cloud water large drops high water content medium drops moderate water content small drops low water content Oceanic cloud water and rainfall Oceanic cloud water and rainfall Non-raining clouds over the ocean
89 GHz 85 GHz Scattering by ice cloud Land and ocean rainfall
Polar Satellite Products for the Operational
Forecaster COMET CD
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Microwave Spectrum and 23 GHz Channel location
Absorption and emission by water vapor at
23GHz Use Oceanic precipitable water
Polar Satellite Products for the Operational
Forecaster COMET CD
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Total Precipitable Water (TPW) and Cloud Liquid
Water (CLW) over the ocean from AMSU-A

• TPW and CLW are derived from vertically
  integrated water vapor (V) and the vertically
  integrated liquid cloud water (L)
• V b0lnTs - TB2 - b1lnTs - TB1 - b2
• L a0lnTs - TB2 - a1lnTs - TB1 - a2
• Ts 2-meter air temperature over land or SST
  over ocean
• TB1 AMSU Channel (23.8 GHz)
• TB2 AMSU Channel (31.4 GHz)
• Coefficients a0, b0, a1, b1, a2, and b2 are
  functions of the water vapor and cloud liquid
  water mass absorption coefficient, emissivity
  and optical thickness

MSPPS Day-2 Algorithms Page
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Total Precipitable Water (TPW)
NOAA/NESDIS/ARAD Microwave Sensing Research Team
Website
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Cloud Liquid Water (CLW)
NOAA/NESDIS/ARAD Microwave Sensing Research Team
Website
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Rain rate (RR) from AMSU-B

• Empirical / statistical algorithm
• RR a0 a1 IWP a2 IWP2
• IWP Ice Water Path derived from 89 GHz and 150
  GHZ data
• a0, a1, and a2 are regression coefficients.

MSPPS Day-2 Algorithms Page
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Rain Rate (RR)
NOAA/NESDIS/ARAD Microwave Sensing Research Team
Website
http//orbit-net.nesdis.noaa.gov/arad2/microwave.h
tml http//amsu.cira.colostate.edu/
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Meteorological Parameters Summary of Key Interactions and Potential Uses Meteorological Parameters Summary of Key Interactions and Potential Uses Meteorological Parameters Summary of Key Interactions and Potential Uses Meteorological Parameters Summary of Key Interactions and Potential Uses
Frequencies AMSU SSMI Frequencies AMSU SSMI Microwave Processes Potential Uses
23 GHz 22GHz Absorption and emission by water vapor Oceanic precipitable water
31, 50, 89 GHz 19, 37, 85 GHz Absorption and emission by cloud water Oceanic cloud water and rainfall
89 GHz 85 GHz Scattering by cloud ice Land and ocean rainfall
31, 50, 89 GHz 19, 37, 85 GHz Variations in surface emissivity Land vs. water Different land types Differenc ocean surfaces Scattering by snow and ice Land/water boundaries Soil moisture/wetness Surface vegetation Ocean surface wind speed Snow and ice cover
Polar Satellite Products for the Operational
Forecaster COMET CD
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AMSU Products

• Microwave Surface and Precipitation Products
  System (MSPPS) http//www.osdpd.noaa.gov/PSB/IMAGE
  S/MSPPS_day2.html
• http//www.orbit.nesdis.noaa.gov/corp/scsb/mspps/
  main.html
• CIRAs AMSU Website
• http//amsu.cira.colostate.edu/
• NOAA/NESDIS AMSU Retrievals for Climate
  Applications
• http//www.orbit.nesdis.noaa.gov/smcd/spb/amsu/no
  aa16/amsuclimate/

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..The rest of the links

• Sea ice, snow cover, and (land characterization)
• http//orbit-net.nesdis.noaa.gov/arad2/MSPPS/
• Sea level anomaly
• http//ibis.grdl.noaa.gov/SAT/near_rt/topex_2day.
  html
• Fire
• http//www.cira.colostate.edu/ramm/sica/main.html
  
• http//cimss.ssec.wisc.edu/goes/burn/wfabba.html
  
• Vegetation health
• http//www.orbit.nesdis.noaa.gov/smcd/emb/vci/

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Vegetation Health
NOAA/NESDIS Office of Research and Applications
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References and Links

• The Virtual Laboratory for Satellite Training and
  Data Utilization
• http//www.cira.colostate.edu/WMOVL/index.html
• GOES Winds
• Nieman, S. J., J. Schmetz, and W. P. Menzel,
  1993 A Comparison of Several Techniques to
  Assign Heights to Cloud Tracers. Journal of
  Applied Meteorology, 32 1559-1568.
• Nieman, S. J., W. P. Menzel, C. M. Hayden, D.
  Gray, S. T. Wanzong, C.S. Veldon, and J. Daniels,
  1997 Fully Automated Cloud-Drift Winds in
  NESDIS Operations. Bulletin of the American
  Meteorological Society, 781121-1133.
• Velden. C. S., T. L. Olander, and S. Wanzong,
  1998 The Impact of Multispectral GOES-8 Wind
  Information on Atlantic Tropical Cyclone Track
  Forecasts in 1995 Part I Dataset Methodology,
  Description, and Case Analysis. Monthly Weather
  Review, 126 1202-1218.
• NOAA/NESDIS GOES Experimental High Density
  Visible Cloud Drift Winds
• http//www.orbit.nesdis.noaa.gov/smcd/opdb/goes/
  winds/
• University of Wisconsin Cooperative Institute
  for Meteorological Satellite Studies Tropical
  Cyclone Web page
• http//cimss.ssec.wisc.edu/tropic/tropic.html
• SSM/I and QuikSCAT Winds
• Goodberlet, M. A., Swift, C. T. and Wilkerson, J.
  C., Remote Sensing of Ocean Surface Winds With
  the Special Sensor Microwave/Imager, Journal of
  Geophysical Research,94, 14574-14555, 1989
• NASA Jet Propulsion Laboratory, California
  Institute of Technology http//winds.jpl.nasa.go
  v/aboutScat/index.cfm
• VISIT Training Session QuikSCAT
  http//www.cira.colostate.edu/ramm/visit/quikscat.
  html
• NOAA Marine Observing Systems Team Web page
  SSMI http//manati.orbit.nesdis.noaa.gov/doc/ssmiw
  inds.html
• 
  QuikSCAT
  http//manati.orbit.nesdis.noaa.gov/quikscat/
• AVHRR SST

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References and Links continued

• Precipitation Products continued
• CD produced by the COMET program (see
  meted.ucar.edu)
• Polar Satellite Products for the Operational
  Forecaster
• NOAA/NESDIS/ARAD Microwave Sensing Research Team
  - Microwave Surface and Precipitation Products
  System (MSPPS) Day-2 Algorithms Page
• http//www.osdpd.noaa.gov/PSB/IMAGES/MSPPS_day2
  .html
• http//www.orbit.nesdis.noaa.gov/c
  orp/scsb/mspps/main.html
• CIRAs AMSU Website http//amsu.cira.colostate.
  edu/
• Sea ice, snow cover, and (land characterization)
• NOAA/NESDIS/ARAD Microwave Sensing Research Team
  - Microwave Surface and Precipitation Products
  System
• http//www.orbit.nesdis.noaa.gov/corp/scsb/mspps
  /main.html
• Sea level anomaly
• NOAA/NESDIS Oceanic Research and Applications
  Division - Laboratory for Satellite Altimetry
• http//ibis.grdl.noaa.gov/SAT/near_rt/topex_2day.
  html
• Fire
• CIRA Central America web site http//www.cira.col
  ostate.edu/ramm/sica/main.html
• CIMSS Wildfire ABBA site http//cimss.ssec.wisc.
  edu/goes/burn/wfabba.html