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Resources and Application of the Virtual Lab. Dr. Bernadette Connell CIRA/NOAA-RAMMT March 2005. 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

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Resources and application of the virtual lab l.jpg

Resources and Application of the Virtual Lab

Dr. Bernadette Connell

CIRA/NOAA-RAMMT

March 2005


Outline l.jpg
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


Winds from goes cloud motion from visible and ir and water vapor tracking l.jpg
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/winds/


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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

      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


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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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SST Anomaly

http://www.osdpd.noaa.gov/OSDPD/OSDPD_high_prod.html

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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Microwave Spectrum and 23 GHz Channel location COMET CD

Absorption and emission by water vapor at 23GHz:

Use: Oceanic precipitable water

Polar Satellite Products for the Operational Forecaster – COMET CD


Total precipitable water tpw and cloud liquid water clw over the ocean from amsu a l.jpg
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


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Total Precipitable Water (TPW) over the ocean from AMSU-A

NOAA/NESDIS/ARAD Microwave Sensing Research Team Website


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Cloud Liquid Water (CLW) over the ocean from AMSU-A

NOAA/NESDIS/ARAD Microwave Sensing Research Team Website


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Rain rate (RR) from AMSU-B over the ocean from AMSU-A

  • 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) over the ocean from AMSU-A

NOAA/NESDIS/ARAD Microwave Sensing Research Team Website

http://orbit-net.nesdis.noaa.gov/arad2/microwave.html

http://amsu.cira.colostate.edu/



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AMSU Products COMET CD

  • 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/


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..The rest of the links COMET CD

  • 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/


Vegetation health l.jpg
Vegetation Health COMET CD

NOAA/NESDIS Office of Research and Applications


References and links l.jpg
References and Links COMET CD

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


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References and Links continued COMET CD

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 sitehttp://www.cira.colostate.edu/ramm/sica/main.html

CIMSS Wildfire ABBA sitehttp://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/


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