Resources and Application of the Virtual Lab Dr

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 • 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 • CO 2 -IRW techniques – good for semitransparent tracer – Equate the measured and calculated ratios of CO 2 (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

NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds Water vapor winds http: //cimss. ssec. wisc. edu/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/about. Scat/index. cfm CIRA & NOAA/NESDIS/RAMM

Summary of determination of winds for Quik. SCAT 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

Quick. SCAT example from descending passes NOAA Marine Observing Systems Team

Quick. SCAT 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*TB 19 v-0. 4555*TB 22 v-1. 7600*TB 37 v +0. 7860*TB 37 h where, TB is the radiometric brightness temperature at the frequencies and polarizations indicated. All data where TB 37 v-TB 37 h < 50 or TB 19 h > 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 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 Mc. Clain, 1984 CIRA & NOAA/NESDIS/RAMM

NOAA/ NESDIS ORAD/MAST

NOAA/ NESDIS ORAD/MAST

SST Anomaly http: //www. osdpd. noaa. gov/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 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 Processes Potential Uses 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 Non-raining clouds over the ocean Land 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 23 GHz: 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 - TB 2] - b 1 ln[Ts - TB 1] - b 2} L = a 0{ln[Ts - TB 2] - a 1 ln[Ts - TB 1] - a 2} Ts: 2 -meter air temperature over land or SST over ocean TB 1: AMSU Channel (23. 8 GHz) TB 2: 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 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 = a 0 + a 1 IWP + a 2 IWP = Ice Water Path derived from 89 GHz and 150 GHZ data a 0, a 1, and a 2 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/arad 2/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 22 GHz 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 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_day 2. 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/noaa 16/amsuclimate/ CIRA & NOAA/NESDIS/RAMM

. . The rest of the links • Sea ice, snow cover, and (land characterization) http: //orbit-net. nesdis. noaa. gov/arad 2/MSPPS/ • Sea level anomaly http: //ibis. grdl. noaa. gov/SAT/near_rt/topex_2 day. 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. html SSM/I and Quik. SCAT 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/about. Scat/index. cfm VISIT Training Session: Quik. SCAT 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 Quik. SCAT http: //manati. orbit. nesdis. noaa. gov/quikscat/ AVHRR SST Strong, A. E, and Mc. Clain, 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_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_day 2. 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_2 day. 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