cira & noaa/nesdis/ramm resources and application of the virtual lab dr. bernadette connell...

CIRA & NOAA/NESDIS/RAMM

Resources and Application of the Virtual Lab

Dr. Bernadette Connell

CIRA/NOAA-RAMMT

March 2005


OutlineWinds

– 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 Utilizationhttp://www.cira.colostate.edu/WMOVL/index.html

http://www.cira.colostate.edu/WMOVL/index.html



Winds from GOESCloud motion from Visible and IR

and Water Vapor Tracking1. 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


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.


WINDS from GOESTracer 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


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


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


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).


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)


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


Visible cloud drift winds

NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds


IR cloud drift winds

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/

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http://cimss.ssec.wisc.edu/tropic/tropic.html

http://www.orbit.nesdis.noaa.gov/smcd/opdb/goes/winds/


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


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

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

http://manati.orbit.nesdis.noaa.gov/quikscat/


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.


SSM/I winds from ascending passes


SSM/I winds from descending passes

http://manati.orbit.nesdis.noaa.gov/doc/ssmiwinds.html NOAA Marine Observing Systems Team

http://manati.orbit.nesdis.noaa.gov/doc/ssmiwinds.html


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


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

Strong and McClain, 1984NOAA/ NESDIS ORAD/MAST

NOAA/ NESDIS ORAD/MAST

SST Anomaly

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

NOAA/ NESDIS OSDPD

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


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


Example: Hydroestimator Product

http://www.orbit.nesdis.noaa.gov/smcd/emb/ffhttp://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)


Precipitation Characteristics

Polar Satellite Products for the Operational Forecaster – COMET CD

• Dominant absorption by water • Very little absorption by ice

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


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).


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


Non-raining clouds over the ocean

89 GHz 85 GHz Scattering by ice cloud Land and ocean rainfall



Microwave Spectrum and 23 GHz Channel location


Absorption and emission by water vapor at 23GHz:

Use: Oceanic precipitable water


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 oceanTB1: 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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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


http://orbit-net.nesdis.noaa.gov/arad2/microwave.html http://amsu.cira.colostate.edu/

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Meteorological Parameters

Summary of Key Interactions and Potential UsesFrequencies

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


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 coverPolar Satellite Products for the Operational Forecaster – COMET CD


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/


..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/


Vegetation Health

NOAA/NESDIS Office of Research and Applications


References and LinksThe Virtual Laboratory for Satellite Training and Data Utilization


GOES WindsNieman, 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 pagehttp://cimss.ssec.wisc.edu/tropic/tropic.html

SSM/I and QuikSCAT WindsGoodberlet, 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, 1989NASA 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 SSTStrong, 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 ProductsNOAA/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


Precipitation Products continuedCD 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 anomalyNOAA/NESDIS Oceanic Research and Applications Division - Laboratory for Satellite Altimetry

http://ibis.grdl.noaa.gov/SAT/near_rt/topex_2day.html

FireCIRA Central America web site http://www.cira.colostate.edu/ramm/sica/main.html CIMSS Wildfire ABBA site http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

Vegetation healthNOAA/NESDIS Office of Research and Applications

http://www.orbit.nesdis.noaa.gov/smcd/emb/vci/

References and Links continued

cira & noaa/nesdis/ramm resources and application of the virtual lab dr. bernadette connell...

Documents

cira noaanesdisramm

model slide

cira noaanesdisramm

model output slide

systems team slide

initial wind vectors

derived wind vectors

neighboring wind vectors