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

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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).

  9. 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)

  10. 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

  11. Visible cloud drift winds NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds

  12. IR cloud drift winds NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds

  13. 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/

  14. 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

  15. 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

  16. QuickSCAT example from descending passes NOAA Marine Observing Systems Team

  17. QuickSCAT example from ascending passes http://manati.orbit.nesdis.noaa.gov/quikscat/ NOAA Marine Observing Systems Team

  18. 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

  19. SSM/I winds from ascending passes NOAA Marine Observing Systems Team

  20. SSM/I winds from descending passes http://manati.orbit.nesdis.noaa.gov/doc/ssmiwinds.html NOAA Marine Observing Systems Team

  21. 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

  22. 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

  23. NOAA/ NESDIS ORAD/MAST

  24. NOAA/ NESDIS ORAD/MAST

  25. SST Anomaly http://www.osdpd.noaa.gov/OSDPD/OSDPD_high_prod.html NOAA/ NESDIS OSDPD

  26. 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

  27. 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

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

  29. 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

  30. 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

  31. Polar Satellite Products for the Operational Forecaster – COMET CD

  32. 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

  33. 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

  34. Total Precipitable Water (TPW) NOAA/NESDIS/ARAD Microwave Sensing Research Team Website

  35. Cloud Liquid Water (CLW) NOAA/NESDIS/ARAD Microwave Sensing Research Team Website

  36. 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

  37. Rain Rate (RR) NOAA/NESDIS/ARAD Microwave Sensing Research Team Website http://orbit-net.nesdis.noaa.gov/arad2/microwave.html http://amsu.cira.colostate.edu/

  38. Polar Satellite Products for the Operational Forecaster – COMET CD

  39. 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/

  40. ..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/

  41. Vegetation Health NOAA/NESDIS Office of Research and Applications

  42. 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

  43. 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 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/