GOES Cloud Products and Cloud Studies Height Techniques Introduction GOES Sounder Currently there are three techniques being used to generate cloud top.

Slides:



Advertisements
Similar presentations
The µm Band: A More Appropriate Window Band for the GOES-R ABI Than 11.2 µm? Daniel T. Lindsey, STAR/CoRP/RAMMB Wayne M. MacKenzie, Jr., Earth Resources.
Advertisements

A fast physical algorithm for hyperspectral sounding retrieval Zhenglong Li #, Jun Li #, Timothy J. and M. Paul Menzel # # Cooperative Institute.
Overview of GOES and MTSAT Platforms: Fire Monitoring Characteristics
Improving Severe Weather Forecasting: Hyperspectral IR Data and Low-level Inversions Justin M. Sieglaff Cooperative Institute for Meteorological Satellite.
Atmospheric Emission.
Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.
Sounders METR280 Satellite Meteorology/Climatology.
Geostationary Imaging Fourier Transform Spectrometer An Update of the GIFTS Program Geostationary Imaging Fourier Transform Spectrometer An Update of the.
Introduction and Methodology Daniel T. Lindsey*, NOAA/NESDIS/STAR/RAMMB Louie Grasso, Cooperative Institute for Research in the Atmosphere
Winds and height assignment 11 October 2006 CIMSS/UW, Madison,WI.
Extending HIRS High Cloud Trends with MODIS Donald P. Wylie Richard Frey Hong Zhang W. Paul Menzel 12 year trends Effects of orbit drift and ancillary.
Short Course on Satellite Meteorology 11 January 1998 Phoenix, Arizona Applications and Interpretation: Part 3 - Sounder Products and Applications Donald.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
Orbit Characteristics and View Angle Effects on the Global Cloud Field
Data Changes from GOES- 12/NOP Imagers Timothy J. Schmit NOAA/NESDIS/STAR (formerly ORA) SaTellite Applications and Research (STAR) Advanced Satellite.
GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.
New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.
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.
Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.
Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,
Cloud Top Properties Bryan A. Baum NASA Langley Research Center Paul Menzel NOAA Richard Frey, Hong Zhang CIMSS University of Wisconsin-Madison MODIS Science.
On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes Yinghui Liu 1, Jeffrey R. Key 2, Xuanji Wang 1, Tim Schmit 2, and Jun Li.
60 West – A Wisconsin Perspective Timothy J. Schmit Gary S. Wade NOAA/NESDIS/STAR Advanced Satellite Products Branch (ASPB) Madison, WI GOES-10:
Improvements of the Geostationary Operational Environmental Satellites (GOES)-R series for Climate Applications GOES-R data and products will support applications.
1 Using water vapor measurements from hyperspectral advanced IR sounder (AIRS) for tropical cyclone forecast Jun Hui Liu #, Jinlong and Tim.
Chian-Yi Liu 1,*, Jun Li 1, and Timothy J. Schmit 2 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS) / University of Wisconsin-Madison.
Andrew Heidinger and Michael Pavolonis
Status of improving the use of MODIS, AVHRR, and VIIRS polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.
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.
Jinlong Li 1, Jun Li 1, Christopher C. Schmidt 1, Timothy J. Schmit 2, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.
Linear Optimization as a Solution to Improve the Sky Cover Guess, Forecast Jordan Gerth Cooperative Institute for Meteorological Satellite Studies University.
High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),
Studies of Advanced Baseline Sounder (ABS) for Future GOES Jun Li + Timothy J. Allen Huang+ W. +CIMSS, UW-Madison.
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
Using MODIS and AIRS for cloud property characterization Jun W. Paul Menzel #, Steve Chian-Yi and Institute.
Satellites Storm “Since the early 1960s, virtually all areas of the atmospheric sciences have been revolutionized by the development and application of.
Validation of Satellite-derived Clear-sky Atmospheric Temperature Inversions in the Arctic Yinghui Liu 1, Jeffrey R. Key 2, Axel Schweiger 3, Jennifer.
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 At the NOAA/NESDIS Office of Satellite Data Processing and Distribution.
Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.
Satellite Data Assimilation Activities at CIMSS for FY2003 Robert M. Aune Advanced Satellite Products Team NOAA/NESDIS/ORA/ARAD Cooperative Institute for.
Matthew Lagor Remote Sensing Stability Indices and Derived Product Imagery from the GOES Sounder
Summary Remote Sensing Seminar Summary Remote Sensing Seminar Lectures in Maratea Paul Menzel NOAA/NESDIS/ORA May 2003.
High impact weather nowcasting and short-range forecasting using advanced IR soundings Jun Li Cooperative Institute for Meteorological.
PRELIMINARY VALIDATION OF IAPP MOISTURE RETRIEVALS USING DOE ARM MEASUREMENTS Wayne Feltz, Thomas Achtor, Jun Li and Harold Woolf Cooperative Institute.
Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
USING GOES-R TO HELP MONITOR UPPER LEVEL SO2
Winds in the Polar Regions from MODIS: Atmospheric Considerations
Tim Schmit Advanced Satellite Products Brach
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
Hyperspectral IR Clear/Cloudy
GOES-R Hyperspectral Environmental Suite (HES) Requirements
Geostationary Sounders
Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.
HIRS Observations of a Decline in High Clouds since 1995 February 2002
Michael J. Jun Li#, Daniel K. Zhou%, and Timothy J.
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
METR280 Satellite Meteorology/Climatology
AIRS/GEO Infrared Intercalibration
GOES -12 Imager April 4, 2002 GOES-12 Imager - pre-launch info - radiances - products Timothy J. Schmit et al.
Generation of Simulated GIFTS Datasets
Front page of the realtime GOES-12 site, showing all of the latest Sounder spectral bands (18 infrared and 1 visible) over the central and Eastern US All.
Presentation transcript:

GOES Cloud Products and Cloud Studies Height Techniques Introduction GOES Sounder Currently there are three techniques being used to generate cloud top pressure (CTP) and effective cloud amount (ECA) from GOES data at the University of Wisconsin – Madison. They are the: IR Window (or IR Look Up)Technique H2O/IRW Intercept Technique CO2 Absorption Technique. Each is briefly described below and a sample CTP derived image is shown for each case. For a more thorough explanation of these techniques please see Nieman, et al (1993). IR Window The IR Window Technique is based on a simple assumption that the all clouds have an ECA of 1.0 (or are infinitely thick), and therefore the radiating temperature of the cloud, Tb c, in the 11.0 µm region corresponding atmospheric temperature at the top of the cloud are The Geostationary Operational Environmental Satellite (GOES) series of satellite platforms includes two Infrared (IR) radiometers, the Imager and the Sounder instruments. Radiance information has been available for approximately ten years. From 1994 through the present GOES Sounder data are available hourly over the CONtinental United States (CONUS) and the immediate surrounding waters at approximately 10 km spatial resolution. In 2001 the launch of GOES-12 offered a slightly different suite of spectral bands at approximately four km horizontal resolution. This new Imager instrument replaced the 12.0 µm IR “dirty” window band with a 13.3 µm “CO 2 absorption” band. The Sounder instrument has not modified its suite of 18 IR bands and 1 Visible band since the launch of GOES-8 in Due to its length of service (ten plus years to date), routine data availability (hourly information for the Sounder and thirty minute hemispherical coverage for the Imager), and spatial resolution (10 and 4 km at the sub-point, respectively for the Sounder and Imager) it is now possible to examine cloud trends over that period of time. To some extent this has already been done for the GOES Sounder and will be shown over several “regions” within the GOES Sounder coverage. Additional diurnal of characteristics of high clouds will also be shown. Monthly Trends Long Term Studies Fig. 4. Sample GOES Imager CTP DPI.Fig. 5. Sample GOES Sounder CTP DPI. Anthony J. Schreiner 1, Timothy J. Schmit 2, W. Paul Menzel 2, Jun Li 1, James A. Jung 1, Steven A. Ackerman 1, Wayne F. Feltz 1, Robert M. Aune 2 1 Cooperative Institute for Meteorological Satellite Studies (CIMSS) 2 NOAA/NESDIS/ORA Advanced Satellite Products Team (ASPT) Madison, Wisconsin GOES Imager H 2 0/IRW Intercept The H2O/IRW Intercept technique height assignment is predicated on the fact that the radiances for two spectral bands vary linearly with cloud amount. Thus a plot of H 2 O (6.5 µm or 6.7 µm) radiances versus IRW (10.7 µm or 11.0 µm) radiances in a field of varying cloud amount will CO 2 Absorption The CO2 Absorption technique uses a ration of deviations in observed cloudy radiances from corresponding clear air radiances for IRW and CO2 bands (for the GOES- 12 Imager and two adjacent CO2 bands for the GOES Sounder). The clear and cloudy radiance differences are determined Summary & Future Work Diurnal Trends the same. This technique works well for thick clouds, but is less than perfect for semi-transparent clouds. be nearly linear. These data are used in conjunction with forward calculations of radiance for both spectral bands for opaque clouds at different levels of the atmosphere specified by a numerical weather prediction of temperature and humidity. The intersection of measured and calculated radiances will occur at clear sky radiances and opaque cloud radiances (i.e. the height of the cloud). from observations with GOES and radiative transfer calculations. It assumes the emissivities are the same for both bands so the ratio of the clear and cloudy radiances differences yields a solution for the CTP within the field of view. The observed differences are compared to a series of radiative transfer calculations with different cloud pressures; the CTP belongs to the calculation that best satisfies the observations. All cloud trends results are based on GOES Sounder radiance data using the CO 2 Absorption and IR Window Techniques, and High cloudiness is defined as 300 hPa to 100 hPa (Schreiner, et al. 2001). BandFrequencySome Uses 114.7Retrieval, Ozone 214.4Retrieval, Ozone 314.0Retrieval, Ozone, Clouds 413.7Retrieval, Ozone, Clouds 513.4Retrieval, Clouds, Cloud-Clearing 612.7Retrieval, Clouds 712.0Retrieval, Clouds, Cloud-Clearing 811.0Retrieval, Clouds, Snow, Cloud-Clearing 99.7Ozone 107.5Retrieval 117.0Retrieval Retrieval, Ozone 134.6Retrieval, Ozone 144.5Retrieval, Ozone Retrieval, Ozone 164.1Retrieval, Ozone, Snow 174.0Snow, Cloud-Clearing 183.8Snow BandFrequencySome Uses 1VisClouds 23.9Snow, fire, fog 36.7Winds (36.5Winds)* 410.7Retrieval, Clouds, Snow, Cloud-Clearing 512.0Retrieval, Clouds, Cloud-Clearing* (613.3Clouds)* * For GOES-12 Band 3 has been modified, Band 5 eliminated, and Band 6 added. Spatial Resolution4 km Temporal Resolution30 min Spatial Resolution10 km Temporal Resolution60 min Diurnal Change of Effective Cloud Amount over Central Plains for High Clouds Only Winter 1997/98 (#obs. 9,400) Time (LST) Frequency of Occurrence Thin (0<ECA<50) Thick (50<ECA  95) Opaque (95<ECA  100) “Central Plains” includes 31N to 45N and 92W to 107W. “High Clouds” is defined as layer from 300 to 100 hPa Spring 1998 (#obs. 9,167) Time (LST) Summer 1998 (#obs. 12,267)Fall 1998 (#obs. 10,058) Winter 1998/99 (#obs. 7,305)Spring 1999 (#obs. 8,420) Summer 1999 (#obs. 18,526)Fall 1999 (#obs. 4,658) Time (LST) Frequency of Occurrence Given the long-term availability of GOES radiance data and the techniques for generating cloud product information it is now possible to examine long term trends on various time scales. Differing climatic regimes can be investigated. From the GOES Sounder approximately ten years of CONUS data is available (approximately 10 km & 1 hr resolution). From the GOES-12 Imager approximately three years of hemispheric data is available (approximately 4 km & 30 min resolution). Recent results show a strong annual trend of high clouds over the CONUS using GOES Sounder radiance information. Annual max of high clouds during the late summer/early fall over the CONUS is driven by the increased frequency of high clouds in the Gulf of Mexico region. Diurnal trends can be seen in both the effective cloud amount for high clouds. It is now possible to determine the change in diurnal cloud trends using either the GOES Sounder or Imager or both radiance information. Verification During the Atlantic THORPEX Regional Campaign (ATReC, Dec. 2003) cloud top information was measured using a Cloud Physics Lidar. Comparisons of lidar measured cloud top height (black) to the GOES- 12 Imager Cloud Product (red) and GOES-12 Sounder Cloud Product (blue) along a 5 December 2003 flight track. References Nieman, S.J., J. Schmetz and W.P. Menzel, 1993: A comparison of several techniques to assign heights to cloud tracers. J. Appl. Meteor., 32, Schreiner, A.J., T.J. Schmit and W.P. Menzel, 2001: Observations and trends of clouds based on GOES sounder data. J. Geophys. Res., 106,