Spectral Band Selection for the Advanced Baseline Imager (ABI)

Slides:



Advertisements
Similar presentations
Overview of GOES and MTSAT Platforms: Fire Monitoring Characteristics
Advertisements

1 Satellite Imagery Interpretation. 2 The SKY Biggest lab in the world. Available to everyone. We view from below. Satellite views from above.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
1 GOES Users’ Conference October 1, 2002 GOES Users’ Conference October 1, 2002 John (Jack) J. Kelly, Jr. National Weather Service Infusion of Satellite.
GOES Cloud Products and Cloud Studies Height Techniques Introduction GOES Sounder Currently there are three techniques being used to generate cloud top.
Review of Remote Sensing Fundaments IV Infrared at High Spectral Resolution – Basic Principal & Limitations Allen Huang Cooperative Institute for Meteorological.
ATS 351 Lecture 8 Satellites
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
Climate, Meteorology and Atmospheric Chemistry.
The Advanced Baseline Imager (ABI) Timothy J. Schmit NOAA/NESDIS/ORA Advanced Satellite Products Team (ASPT) in Madison, Wisconsin in collaboration with.
The Advanced Baseline Imager (ABI) Timothy J. Schmit NOAA/NESDIS/ORA Advanced Satellite Products Team (ASPT) in Madison, Wisconsin in collaboration with.
Satellite basics Estelle de Coning South African Weather Service
Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere
THE GOES-R SERIES ADVANCED BASELINE IMAGER (ABI) UW-Madison Timothy J. Schmit NOAA/NESDIS/ORA Advanced Satellite Products Team (ASPT) James J Gurka NOAA/NESDIS/OSD.
Spectral Band Selection for the Advanced Baseline Imager (ABI) Tim Schmit Paul Menzel September 1999 National Oceanic and Atmospheric Administration NESDIS/ORA.
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
UNCLASSIFIED Navy Applications of GOES-R Richard Crout, PhD Naval Meteorology and Oceanography Command Satellite Programs Presented to 3rd GOES-R Conference.
Gene Legg Office of Satellite Data Processing and Distribution Asia Pacific Satellite Data Exchange and Utilization Meeting Honolulu, HI, September 20-22,
GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.
Thanks also to… Tom Wrublewski, NOAA Liaison Office Steve Kirkner, GOES Program Office Scott Bachmeier, CIMSS Ed Miller, NOAA Liaison Office Eric Chipman,
PLANS FOR THE GOES-R SERIES AND COMPARING THE ADVANCED BASELINE IMAGER (ABI) TO METEOSAT-8 UW-Madison James J Gurka, Gerald J Dittberner NOAA/NESDIS/OSD.
1 The Next Generation GOES Instruments: Status and Potential Impact Jim Gurka Team Lead: Geostationary Spacecraft Requirements NOAA/NESDIS/OSD.
SATELLITE METEOROLOGY BASICS satellite orbits EM spectrum
USING OF METEOSAT SECOND GENERATION HIGH RESOLUTION VISIBLE DATA FOR THE IMPOVEMENT OF THE RAPID DEVELOPPING THUNDERSTORM PRODUCT Oleksiy Kryvobok Ukrainian.
Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,
1 The Next Generation GOES Instruments: Status and Recommendations From the GOES Users’ Conference James J. Gurka Timothy J. Schmit NOAA/NESDIS.
Towards Operational Satellite-based Detection and Short Term Nowcasting of Volcanic Ash* *There are research applications as well. Michael Pavolonis*,
Improvements of the Geostationary Operational Environmental Satellites (GOES)-R series for Climate Applications GOES-R data and products will support applications.
Andrew Heidinger and Michael Pavolonis
Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 The GOES-14 Science Test Timothy Schmit (GOVERNMENT PRINCIPAL INVESTIGATOR)
Jinlong Li 1, Jun Li 1, Timothy J. Schmit 2, Fang Wang 1, James J. Gurka 3, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Using CALIPSO to Explore the Sensitivity to Cirrus Height in the Infrared.
Bill Campbell, Brian Gockel, and Jim Heil (NOAA/NWS) Marge Ripley and Jean-Jacques Bedet (NOAA/NESDIS/GOES-R) Improved Temporal Resolution GOES-R Sectorized.
High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),
R. T. Pinker, H. Wang, R. Hollmann, and H. Gadhavi Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Use of.
Preparing for GOES-R: old tools with new perspectives Bernadette Connell, CIRA CSU, Fort Collins, Colorado, USA ABSTRACT Creating.
Studies of Advanced Baseline Sounder (ABS) for Future GOES Jun Li + Timothy J. Allen Huang+ W. +CIMSS, UW-Madison.
Future GOES Satellite Product Upgrades Donald G. Gray Office of Systems Development NOAA/NESDIS, Washington, DC Satellite Direct Readout Users Conference.
GOES-R Aviation Weather Applications Frederick R. Mosher NWS/NCEP Aviation Weather Center.
METEOSAT SECOND GENERATION FROM FIRST TO SECOND GENERATION METEOSAT
DIRECT READOUT APPLICATIONS USING ATOVS ANTHONY L. REALE NOAA/NESDIS OFFICE OF RESEARCH AND APPLICATIONS.
Real-time Display of Simulated GOES-R (ABI) Experimental Products Donald W. Hillger NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
1 Recommendations From the GOES Users’ Conference Jim Gurka Gerald Dittberner NOAA/NESDIS/OSD Advanced Systems Planning Division.
1 GLIMPSING THE FIRST PRODUCTS FROM VIIRS Dr. Wayne Esaias NASA GSFC Thomas F. Lee Jeffrey Hawkins Arunas Kuciauskas Kim Richardson Jeremy Solbrig Naval.
Matthew Lagor Remote Sensing Stability Indices and Derived Product Imagery from the GOES Sounder
4 th Workshop on Hyperspectral Science of UW-Madison MURI, GIFTS, and GOES-R Hyperspectral Applications for Aviation Advanced Satellite Aviation-weather.
Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
Climate, Meteorology and Atmospheric Chemistry.
Atmospheric Applications of Multi- and Hyperspectral Remote Sensing
GOES-R ABI and Himawari-8 AHI Training using SIFT
GOES-R ABI and Himawari-8 AHI Training using SIFT
Best practices for RGB compositing of multi-spectral imagery
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
AmeriGEOSS Week. GEONETCast. Americas and
USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
GOES visible (or “sun-lit”) image
Preparation for use of the GOES-R Advanced Baseline Imager (ABI)
Who We Are SSEC (Space Science and Engineering Center) is part of the Graduate School of the University of Wisconsin-Madison (UW). SSEC hosts CIMSS (Cooperative.
ABI Visible/Near-IR Bands
GOES-R Hyperspectral Environmental Suite (HES) Requirements
Geostationary Sounders
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
Meteosat Second Generation
In the past thirty five years NOAA, with help from NASA, has established a remote sensing capability on polar and geostationary platforms that has proven.
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Presentation transcript:

Spectral Band Selection for the Advanced Baseline Imager (ABI) Tim Schmit Paul Menzel, Hal Woolf, Mat Gunshor, Bryan Baum, Chris Sisko, Allen Huang, Gary Wade, Scott Bachmeier, Liam Gumley, Kathy Strabala etc. February 2000 National Oceanic and Atmospheric Administration NESDIS/ORA Advanced Satellite Products Team Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison

Why ABI?: A Continuing Evolution To keep pace with the growing needs for GOES data and products, NOAA must continue to evolve its geostationary remote sensing capabilities. The Advanced Baseline Imager (ABI) follows this evolutionary path. ABI enhances the current capabilities and addresses unmet NWS requirements.

What’s the Advance Baseline Imager: Spatial resolution Visible (0.64 mm) 0.5 km (14 mrad) All other bands 2 km (56 mrad) Spatial coverage Full disk 4 per hour (every 15 min) CONUS (3000 x 5000 km) 12 per hour (every 5 min) Operation during eclipse Yes Lifetime Mean Mission life 8.4 years Noise NEdT (except 13.3 mm) 0.1K @ 300K Data rate < 15 Megabits-per second (Mbps)

When’s the ABI: GOES-__ (L) - 3 May 2000 launch GOES-__ (M) - early 2002 (estimate) - spectrally wider water vapor - higher spatial resolution water vapor - additional 13.3 m band - no 12 m band GOES-__ (N, O, P) GOES-__ (Q) - 2008 (estimate) w/ ABI GOES-__ (R) - 2010

ABI - 8 Range (m) Center # Wavelengths Description Primary Use 1 0.52 – 0.72 0.62 Visible Daytime clouds, fog 8 1.3 – 1.9 1.6 Near IR Daytime clouds/snow, water/ice clouds 2 3.8 – 4.0 3.9 Shortwave IR Nighttime low clouds, fog, fire detection 3 6.5 – 7.0 6.75 Water Vapor 1 Upper tropospheric flow, winds 6 7.0 – 7.5 7.25 Water Vapor 2 Mid tropospheric flow, winds 4 10.2 – 11.2 10.7 IR Window 3 Clouds, low-level water vapor, fog, winds 5 11.5 – 12.5 12.0 IR Window 4 Low-level water vapor, volcanic ash 7 13.2 – 13.8 13.5 Carbon Dioxide Cloud-top parameters, heights for winds

ABI - 12 Range (m) Center # Wavelengths Description Primary Use 1 0.59 – 0.69 0.64 Visible Daytime clouds, fog - 0.81 ­ 0.91 0.86 Solar window Day clouds, NDVI, fog, aerosol, ocean studies - 1.36 - 1.39 1.375 Near IR Daytime thin cirrus detection 2 1.58 – 1.64 1.61 Near IR Daytime clouds/snow, water/ice clouds 3 3.8 – 4.0 3.9 Shortwave IR Nighttime low clouds, fog, fire detection 4 5.7 – 6.6 6.15 Water Vapor 1 Upper tropospheric flow, winds 5 6.8 – 7.2 7.0 Water Vapor 2 Mid tropospheric flow, winds 6 8.3 – 8.7 8.5 IR Window 1 Sulfuric acid aerosols, cloud phase, sfc 7 10.1 – 10.6 10.35 IR Window 2 Cloud particle size, sfc properties 8 10.8 – 11.6 11.2 IR Window 3 Clouds, low-level water vapor, fog, winds 9 11.8 – 12.8 12.3 IR Window 4 Low-level water vapor, volcanic ash 10 13.0 – 13.6 13.3 Carbon Dioxide Cloud-top parameters, heights for winds

ABI-8 (IR only) Channels

ABI-12 (IR only) Channels

ABI-12 (top bars) and ABI-8 (bottom bars) IR Channels

Weighting Functions for the IR Channels Units: m and K Weighting Functions for the IR Channels

ABI High Spatial Resolution Visible (0 ABI High Spatial Resolution Visible (0.64 m) Based on GOES Imager Ch 1

ABI Channel 2 (1. 61 um) Based on AVHRR/3 and MODIS Example of MAS 0 ABI Channel 2 (1.61 um) Based on AVHRR/3 and MODIS Example of MAS 0.66, 1.61, and 1.88 um

ABI Channel 2 (1.61 um) Example of MAS 0.66 and 1.61 um convective cumulonimbus surrounded by lower-level water clouds (King et al., JAOT, August 1996)

ABI Channel 2 (1.61 um) AVHRR/3a Snow cover over western Canada for 1999 March 11. Left: AVHRR channel-3a (1.6 µm) showing mostly solar energy reflected from low clouds. Center: Principle Component Image 3 (containing primarily input from AVHRR channel-3a) discriminating between snow (white) and cloud (dark). Right: AVHRR channel-1 (0.6 µm) verifying snow cover by texture, but not discriminating between snow and cloud.

ABI Channel 2 (1.61 um) Snow detection example from VIRS Clouds Thin Cirrus Snow

ABI Channel 2 (1. 61 um) spectral width was narrowed (was 1. 3 to 1 ABI Channel 2 (1.61 um) spectral width was narrowed (was 1.3 to 1.9 um)

1.88 m is helpful for contrail detection Examples from MAS (Chs 2, 10, 16)

ABI Channel 3 (3.9 m) Based on GOES Imager Ch 2 useful for fog, snow, and cloud detection

ABI Channel 3 (3.9 m) useful for fire and smoke detection GOES-8 imagery on 9 May 1998 at 15:45 UTC fire detection using the 3.9 and 11 m visible imagery showing smoke

ABI Channel 4 (6.15 m) Based on MSG/ SEVIRI Used together with ABI Ch 5 Units: m and K

ABI Channel 5 (7.0 m) Based on GOES Sounder Ch 11 Used together with ABI Ch 4

Current Phase Discrimination Daytime phase discrimination is most effective for fairly thick clouds

Future Phase Discrimination With the addition of the 8.5 m, phase discrimination is improved for both day/night time clouds for non-opaque clouds

ABI Channel 6 (8.5 m) Based on MODIS used with ABI Chs 7, 8, 9, 10 to separate water from ice clouds

Ice / Water Clouds Separate in 8.6 - 11um vs 11 - 12 um BT plots

similar to MSG (8.7) and proposed VOLCAM (8.6) ABI Channel 6 (8.5 m) Based on MODIS used with ABI Chs 8 & 9 to improve volcanic cloud detection H2SO4 similar to MSG (8.7) and proposed VOLCAM (8.6)

ABI Channel 6 (8.5 m) - volcanic cloud detection can be improved by detecting sulfuric acid aerosols (Realmuto et al., JGR, July 1997) - microphysical properties of clouds can be determined. This includes a more accurate and consistent delineation of ice from water clouds during the day or night - international commonality is furthered as MSG carries a similar channel (8.5 to 8.9 m) as well as MODIS and GLI - thin cirrus can be detected in conjunction with the 11 m. This will improve other products derived from the split window (SST or low-level moisture) by avoiding cloud contamination - SST estimates can be improved by a better atmospheric correction in relatively dry atmospheres - surface properties can be observed in conjunction with the 10.35 m channel.

ABI Channel 6 (8.5 m) 10 km too coarse for volcanic ash detection ~10 km resolution (sounder) ~4 km resolution (imager) Split Window Differences

ABI-12 (top bars) and MSG/SEVIRI (bottom bars) Channels

ABI and MSG/SEVIRI Weighting Functions

ABI and MSG Weighting Functions

ABI Channel 7 (10. 35 m) Examples of MAS 0. 66, 10. 5-11. 0, and 8 ABI Channel 7 (10.35 m) Examples of MAS 0.66, 10.5-11.0, and 8.6-10.5 um reveal utility of new IR window for seeing through clouds to ice floes

Based on HIS data, ABI Chs 7, 8, & 9 useful for effective radius Cloud particle size emerges in high resolution IR window spectra Based on HIS data, ABI Chs 7, 8, & 9 useful for effective radius

ABI Channel 7 (10.35 m) - microphysical properties of clouds can be determined. This includes a more accurate determination of cloud particle size during the day or night - cloud particle size is revealed along with cloud liquid water content. - particle size may be related to the “enhanced V” signature - surface properties can be observed in conjunction with the 8.6, 11.2, and 12.3 m bands - low level moisture determinations are enhanced with more split windows

ABI Channel 8 (11.2 m) Based on GOES Sounder Ch 8

Channel 9 (12.3 m) Based on GOES Imager Ch 5 used with ABI Ch 8 for low atm moisture, volcanic ash, and SST

ABI Channel 10 (13.3 m) Based on GOES Sounder Ch 5 used with ABI Ch 8 for cloud heights Clear Low Cloud High Cloud

ABI-8 (Threshold) ; ABI-12 (Goal) In order of priority: 8.5, 10.35, 0.86 and 1.375 m. The 0.86 m channel: - provides synergy with the AVHRR/3; - determining vegetation amount, aerosols and ocean/land studies; - also enables localized vegetation stress monitoring, fire danger monitoring, and albedo retrieval. The 1.38 m channel: - is modeled on the band on MODIS; - does not see into the lower troposphere due to water vapor sensitivity and thus it provides excellent daytime sensitivity to very thin cirrus.

Example of 0.93 um (Band 10 on FY-1C)

ABI and NWS REQUIREMENTS: (ABI channels in order of increasing wavelength) Visible (1): Daytime cloud imaging, snow and ice cover, severe thunderstorm detection, cloud drift winds, precipitation estimates, fog, flash floods, winter storms Near IR (2): Daytime automated discrimination of clouds from snow for estimating total cloud cover, discrimination of water clouds from ice clouds (in aviation), detection of smoldering fires Shortwave IR window (3): Nighttime detection of low clouds and fog detection when used with the IR and “dirty” IR windows; identification of fires; daytime detection of cloud over snow, fog IR water vapor 1 (4): Delineates broad scale mid-tropospheric patterns, mid-tropospheric water vapor drift winds (used for numerical model initialization and hurricane track prediction) IR water vapor 2 (5): Delineates upper-tropospheric water vapor-drift winds (used for numerical model initialization), broad scale patterns corresponding with jet stream cores

IR window 1 (6): Determination of cloud phase (ice or water) IR window 2 (7): Determination of cloud particle size, discrimination of surface water and ice in cloudy scenes IR window 3 (8): Continuous day and night detection of cloud cover and cloud top heights, low-level water vapor, fog, when used with the “dirty” IR window 4, precipitation estimates, surface temperatures, hurricane winds, winter storms IR window 4 (9): Determination of low-level water vapor when used with IR window, detection of volcanic ash, and estimation of SST IR carbon dioxide (10): Used with the IR window to determine cloud top heights and cloud parameters above 12,000 feet complementing ASOS and providing cloud information to forecasters and numerical models; parameters include sky cover, heights of cloud tops, and cloud opacity

Simulated Water Vapor Images The following figure demonstrates the simulation comparisons of water vapor images of current GOES-8, GOES-12, and two proposed ABI water vapor bands. High spectral resolution data from the NAST (NPOES Atmospheric Sounder Testbed) were convoluted with the various GOES spectral response functions (SRF) to derive the field of brightness temperatures. The ABI water vapor band (5.7-6.6 m) is very similar to the current GOES. Additional low-level water vapor information is provided by the new band of 6.8-7.2 m. The mean brightness temperature difference between the GOES-8 and ABI 6.15 m is approximately the same size (2 K) as between GOES-8 and the spectrally wider GOES-12 water vapor band.

SRF bandpass images derived from NAST-I. GOES-8 GOES-12 ABI ABI These water vapor images were simulated from NAST-I 2.6 km resolution data collected during WALLOPS 99 field experiment (September 13, 1998).

Units: m and K

11 m brightness temperatures similar to existing instrument Units: m and K 11 m brightness temperatures similar to existing instrument

Summary ABI 12 channels address NWS requirements for cloud, moisture, and surface products. Original ABI ( 8) channels have been adjusted: experience with new channels/applications on the MAS (MODIS Airborne Simulator) suggested new channels, additional window channels will be on future NPOESS instruments, some channels were modified to conform to other sensors that will be in-orbit in the same time frame, channel selection continues on an evolutionary path. A second visible channel was added (as a goal) for NDVI and aerosol detection. A second near IR channel was added (as a goal) for daytime thin cirrus detection. IR Window brightness temperatures will be similar to those on existing instruments.

What if we could have more bands? 9.6 m -- total ozone 14.2 m -- better cloud heights 4.57 m -- better TPW 0.47 m -- aerosols particle size (over land)

More information can be found at http://cimss.ssec.wisc.edu/goes/abi/ http://cimss.ssec.wisc.edu/modis1/modis1.html MODIS MAS http://cimss.ssec.wisc.edu/nast/index.html http://cimss.ssec.wisc.edu/goes/goes.html Real-time Sounder page GOES Gallery Biomass Burning http://www2.ncdc.noaa.gov/docs/klm/html/c3/sec3-0.htm NOAA KLM User's Guide http://www.eumetsat.de/en/ MSG..System..MSG..Payload..Spectral bands..Spectral bands

Acronyms ABI -- Advanced Baseline Imager AGS -- Advanced Geostationary Studies AVHRR -- Advanced Very High Resolution Radiometer CIMSS -- Cooperative Institute for Meteorological Satellite Studies GLI -- Global Imager HIS -- High-resolution Interferometer Sounder MAS -- MODIS Airborne Simulator MODIS -- MODerate-resolution Imaging Spectrometer MSG -- Meteosat Second Generation NWS -- National Weather Service GOES -- Geostationary Operational Environmental Satellite SEVIRI -- Spinning Enhanced Visible and Infra Red Imager VIRS -- Visible Infrared Scanner

ABI and MSG Weighting Functions