Resources and Application of the
Virtual Lab
Dr. Bernadette Connell
CIRA/NOAA-RAMMT
March 2005
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
Winds from GOES
Cloud motion from Visible and IR
and Water Vapor Tracking
1. Determine “tracers”
2. Determine the track of the “tracers” in 2
successive images
3. Assign height
4. Check wind vectors and height assignments
against ancillary data (other derived wind
vectors, observations, model output
CIRA & NOAA/NESDIS/RAMM
Winds from GOES
Initial processing
• Imagery registration
• Screen out ‘difficult’ features:
For IR and visible imagery screen out clear pixels, multideck cloud scenes, and coastal features.
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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).
CIRA & NOAA/NESDIS/RAMM
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)
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
Visible cloud drift winds
CIRA & NOAA/NESDIS/RAMM
IR cloud drift winds
NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds
CIRA & NOAA/NESDIS/RAMM
NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds
Water vapor winds
http://cimss.ssec.wisc.edu/tropic/tropic.html
http://www.orbit.nesdis.noaa.gov/smcd/opdb/goes/winds/CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
QuickSCAT example from descending passes
NOAA Marine Observing Systems Team
QuickSCAT example from ascending passes
http://manati.orbit.nesdis.noaa.gov/quikscat/
NOAA Marine Observing Systems Team
Winds from SSM/I
• Algorithm developed by
Goodberlet et al.
– utilizes variations in surface emissivity
over the ocean due to different
roughness from wind
WS=147.90+1.0969*TB19v-0.4555*TB22v-1.7600*TB37v
+0.7860*TB37h
where, TB is the radiometric brightness temperature at the frequencies
and polarizations indicated.
All data where TB37v-TB37h < 50 or TB19h > 165 are rain flagged.
NOAA Marine Observing Systems Team
CIRA & NOAA/NESDIS/RAMM
SSM/I winds from ascending passes
NOAA Marine Observing Systems Team
SSM/I winds from descending passes
http://manati.orbit.nesdis.noaa.gov/doc/ssmiwinds.html
NOAA Marine Observing Systems Team
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
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
NOAA/ NESDIS ORAD/MAST
NOAA/ NESDIS ORAD/MAST
SST Anomaly
http://www.osdpd.noaa.gov/OSDPD/OSDPD_high_prod.html
NOAA/ NESDIS OSDPD
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
CIRA & NOAA/NESDIS/RAMM
NOAA/NESDIS/ORA Hydrology Team
Example: Hydroestimator Product
http://www.orbit.nesdis.noaa.gov/smcd/emb/ff
http://www.cira.colostate.edu/ramm/sica/main.html CIRA & NOAA/NESDIS/RAMM
Precipitation products from
microwave
•
•
•
•
•
Precipitation absorption and scattering characteristics
Microwave spectrum
Total Precipitable Water (TPW)
Cloud Liquid Water (CLW)
Rain Rate (RR)
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
Precipitation – Cloud Water and Ice
(key interactions and potential uses)
Frequencies
Microwave
Potential Uses
Processes
AMSU SSM/I
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
89 GHz
85 GHz
Scattering by ice cloud
Oceanic cloud water
and rainfall
Oceanic cloud water
and rainfall
Non-raining clouds
over the ocean
Land and ocean rainfall
Polar Satellite Products for the Operational Forecaster – COMET CD
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
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 = b0{ln[Ts - TB2] - b1ln[Ts - TB1] - b2}
L = a0{ln[Ts - TB2] - a1ln[Ts - 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
CIRA & NOAA/NESDIS/RAMM
NOAA/NESDIS/ARAD Microwave Sensing Research Team Website
Total Precipitable Water (TPW)
CIRA & NOAA/NESDIS/RAMM
NOAA/NESDIS/ARAD Microwave Sensing Research Team Website
Cloud Liquid Water (CLW)
CIRA & NOAA/NESDIS/RAMM
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
CIRA & NOAA/NESDIS/RAMM
NOAA/NESDIS/ARAD Microwave Sensing Research Team Website
Rain Rate (RR)
http://orbit-net.nesdis.noaa.gov/arad2/microwave.html
CIRA & NOAA/NESDIS/RAMM
http://amsu.cira.colostate.edu/
Meteorological Parameters
Summary of Key Interactions and Potential Uses
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/water boundaries
Soil moisture/wetness
Surface vegetation
Ocean surface wind speed
Snow and ice cover
–Land vs. water
–Different land types
–Differenc ocean surfaces
Scattering by snow and ice
Polar Satellite Products for the Operational Forecaster – COMET CD
CIRA & NOAA/NESDIS/RAMM
AMSU Products
• Microwave Surface and Precipitation Products System
(MSPPS)
http://www.osdpd.noaa.gov/PSB/IMAGES/MSPPS_day2.html
http://www.orbit.nesdis.noaa.gov/corp/scsb/mspps/main.html
• CIRA’s AMSU Website
http://amsu.cira.colostate.edu/
• NOAA/NESDIS AMSU Retrievals for Climate Applications
http://www.orbit.nesdis.noaa.gov/smcd/spb/amsu/noaa16/amsuclimate/
CIRA & NOAA/NESDIS/RAMM
..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/
CIRA & NOAA/NESDIS/RAMM
Vegetation Health
NOAA/NESDIS Office of Research and Applications
CIRA & NOAA/NESDIS/RAMM
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, 78:1121-1133.
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.gov/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/ssmiwinds.html
QuikSCAT http://manati.orbit.nesdis.noaa.gov/quikscat/
AVHRR SST
Strong, A. E, and McClain, E. P., 1984: Improved Ocean Surface Temperatures from Space – Comparison with Drifting
Buoys. Bulletin American Meteorological Society, 65(2): 138-142.
NOAA/NESDIS OSDPD
http://www.osdpd.noaa.gov/OSDPD/OSDPD_high_prod.html
NOAA/NESDIS MAST
http://www.orbit.nesdis.noaa.gov/sod/orad/mast_index.html
Precipitation Products
NOAA/NESDIS/ORA Hydrology Team http://www.orbit.nesdis.noaa.gov/smcd/emb/ff
CIRA Central America Page:
http://www.cira.colostate.edu/ramm/sica/main.html
CIRA & NOAA/NESDIS/RAMM
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/corp/scsb/mspps/main.html
CIRA’s 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
CIMSS Wildfire ABBA site
http://www.cira.colostate.edu/ramm/sica/main.html
http://cimss.ssec.wisc.edu/goes/burn/wfabba.html
Vegetation health
NOAA/NESDIS Office of Research and Applications
http://www.orbit.nesdis.noaa.gov/smcd/emb/vci/
CIRA & NOAA/NESDIS/RAMM