Transcript No Slide Title
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, multi deck 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
H 2 O – 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 H 2 O (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 NOAA/NESDIS GOES Experimental High Density 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
Water vapor winds http://cimss.ssec.wisc.edu/tropic/tropic.html
CIRA & NOAA/NESDIS/RAMM 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
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 CIRA & NOAA/NESDIS/RAMM 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
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.
CIRA & NOAA/NESDIS/RAMM NOAA Marine Observing Systems Team
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 T 11 + 2.5789 (T 11 - T 12 ) - 283.21 Night • SST = 1.0170 T 11 + 0.9694 (T 3.7
- T 12 ) - 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
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 AMSU SSM/I Microwave Processes Potential Uses 31 GHz 19 GHz 50 GHz 37 GHz 89 GHz 85 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 Scattering by ice cloud 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 = b 0 {ln[Ts - TB2] - b 1 ln[Ts - TB1] - b 2 } L = a 0 {ln[Ts - TB2] - a 1 ln[Ts - TB1] - a 2 } Ts: 2-meter air temperature over land or SST over ocean TB1: AMSU Channel (23.8 GHz) TB2: AMSU Channel (31.4 GHz) Coefficients a 0 , b 0 , a 1 , b 1 , a 2 , and b 2 are functions of the water vapor and cloud liquid water mass absorption coefficient, emissivity and optical thickness CIRA & NOAA/NESDIS/RAMM MSPPS Day-2 Algorithms Page
Total Precipitable Water (TPW) CIRA & NOAA/NESDIS/RAMM
Cloud Liquid Water (CLW) CIRA & NOAA/NESDIS/RAMM
Rain rate (RR) from AMSU-B
• Empirical / statistical algorithm RR = a 0 + a 1 IWP + a 2 IWP2 IWP = Ice Water Path derived from 89 GHz and 150 GHZ data a0, a1, and a2 are regression coefficients.
CIRA & NOAA/NESDIS/RAMM MSPPS Day-2 Algorithms Page
Rain Rate (RR) http://orbit-net.nesdis.noaa.gov/arad2/microwave.html
http://amsu.cira.colostate.edu/ CIRA & NOAA/NESDIS/RAMM
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 89 GHz 19, 37, 85 GHz 85 GHz Absorption and emission by cloud water Scattering by cloud ice Oceanic cloud water and rainfall 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 Polar Satellite Products for the Operational Forecaster – COMET CD Land/water boundaries Soil moisture/wetness Surface vegetation Ocean surface wind speed Snow and ice cover 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