CIRA & NOAA/NESDIS/RAMM 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

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 NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds

CIRA & NOAA/NESDIS/RAMM 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:

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 VISIT Scatterometer session and JPL web site

QuickSCAT example from descending passes NOAA Marine Observing Systems Team

QuickSCAT example from ascending passes NOAA Marine Observing Systems Team

CIRA & NOAA/NESDIS/RAMM 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= *TB19v *TB22v *TB37v *TB37h where, TB is the radiometric brightness temperature at the frequencies and polarizations indicated. All data where TB37v-TB37h 165 are rain flagged. NOAA Marine Observing Systems Team

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

SSM/I winds from descending passes NOAA Marine Observing Systems Team

CIRA & NOAA/NESDIS/RAMM 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 = T (T 11 - T 12 ) Night SST = T (T T 12 ) Strong and McClain, 1984 NOAA/ NESDIS ORAD/MAST

SST Anomaly NOAA/ NESDIS OSDPD

CIRA & NOAA/NESDIS/RAMM 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 NOAA/NESDIS/ORA Hydrology Team

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 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

CIRA & NOAA/NESDIS/RAMM Precipitation Characteristics Polar Satellite Products for the Operational Forecaster – COMET CD 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).

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 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 GHzScattering by ice cloudLand and ocean rainfall Polar Satellite Products for the Operational Forecaster – COMET CD

CIRA & NOAA/NESDIS/RAMM Microwave Spectrum and 23 GHz Channel location Polar Satellite Products for the Operational Forecaster – COMET CD Absorption and emission by water vapor at 23GHz: Use: Oceanic precipitable water

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 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 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

CIRA & NOAA/NESDIS/RAMM NOAA/NESDIS/ARAD Microwave Sensing Research Team Website Rain Rate (RR)

CIRA & NOAA/NESDIS/RAMM Meteorological Parameters Summary of Key Interactions and Potential Uses Frequencies AMSU SSMI Microwave ProcessesPotential Uses 23 GHz22GHzAbsorption 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 GHz85 GHzScattering by cloud iceLand 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 cover Polar Satellite Products for the Operational Forecaster – COMET CD

CIRA & NOAA/NESDIS/RAMM AMSU Products Microwave Surface and Precipitation Products System (MSPPS) CIRA’s AMSU Website NOAA/NESDIS AMSU Retrievals for Climate Applications

CIRA & NOAA/NESDIS/RAMM..The rest of the links Sea ice, snow cover, and (land characterization) Sea level anomaly Fire Vegetation health

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 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: 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: 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: NOAA/NESDIS GOES Experimental High Density Visible Cloud Drift Winds University of Wisconsin – Cooperative Institute for Meteorological Satellite Studies Tropical Cyclone Web page 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, , 1989 NASA Jet Propulsion Laboratory, California Institute of Technology VISIT Training Session: QuikSCAT NOAA Marine Observing Systems Team Web page: SSMI 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): NOAA/NESDIS OSDPDhttp:// NOAA/NESDIS MASThttp:// Precipitation Products NOAA/NESDIS/ORA Hydrology Team CIRA Central America Page:

CIRA & NOAA/NESDIS/RAMM 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 CIRA’s AMSU Website Sea ice, snow cover, and (land characterization) NOAA/NESDIS/ARAD Microwave Sensing Research Team - Microwave Surface and Precipitation Products System Sea level anomaly NOAA/NESDIS Oceanic Research and Applications Division - Laboratory for Satellite Altimetry Fire CIRA Central America web site CIMSS Wildfire ABBA site Vegetation health NOAA/NESDIS Office of Research and Applications References and Links continued