David Santek, Rick Kohrs, Nick Bearson, Matthew Lazarra McIDAS Users’ Group Meeting 26 October 2010.

Slides:



Advertisements
Similar presentations
GOES: Geostationary Orbiting Environmental Satellite Satellite (~36,000 km altitude) period ( 24 hours for each orbit) Always above same location. Must.
Advertisements

Rainfall estimation for food security in Africa, using the Meteosat Second Generation (MSG) satellite. Robin Chadwick.
GOFC/GOLD - Fire Requirements for Fire Observations.
SNPP VIIRS green vegetation fraction products and application in numerical weather prediction Zhangyan Jiang 1,2, Weizhong Zheng 3,4, Junchang Ju 1,2,
Lightning Imager and its Level 2 products Jochen Grandell Remote Sensing and Products Division.
Robert DeMaria.  Motivation  Objective  Data  Center-Fixing Method  Evaluation Method  Results  Conclusion.
McIDAS-Lite Dave Santek Program Manager Space Science & Engineering Center University of Wisconsin-Madison 5 December 2002.
Proxy ABI datasets relevant for fire detection that are derived from MODIS data Scott S. Lindstrom, 1 Christopher C. Schmidt 2, Elaine M. Prins 2, Jay.
The Climate Prediction Center Rainfall Estimation Algorithm Version 2 Tim Love -- RSIS/CPC.
ADVANCED FIRE INFORMATION SYSTEM AFIS I AFIS is the 1 st satellite based, near real time fire information system developed to fulfill the needs of both.
Green Vegetation Fraction (GVF) derived from the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the SNPP satellite Zhangyan Jiang 1,2,
Satellites and instruments How RS works. This section More reflection Sensors / instruments and how they work.
Satellite Imagery Meteorology 101 Lab 9 December 1, 2009.
GOES-13 Science Team Report SST Images and Analyses Eileen Maturi, STAR/SOCD, Camp Springs, MD Andy Harris, CICS, University of Maryland, MD Chris Merchant,
1 Remote Sensing of the Ocean and Atmosphere: John Wilkin Sea Surface Temperature IMCS Building Room 214C
Ice Sheet Motion Speckle tracking. Velocity measurement techniques Day 1 Day 24 Feature Retracking 100 m Day 1 Day m Day 1 Day m InSAR Speckle.
Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 NOAA Operational Geostationary Sea Surface Temperature Products from NOAA.
Cooperative Institute for Meteorological Satellite Studies University of Wisconsin - Madison Steve Ackerman Director, Cooperative Institute for Meteorological.
Fire Products Training Workshop in Partnership with BAAQMD Santa Clara, CA September 10 – 12, 2013 Applied Remote SEnsing Training (ARSET) – Air Quality.
Geography 1010 Remote Sensing. Outline Last Lecture –Electromagnetic energy. –Spectral Signatures. Today’s Lecture –Spectral Signatures. –Satellite Remote.
Douglas Spangenberg, SSAI, Hampton, VA Rabi Palikonda, SSAI, Hampton, VA Louis Nguyen, NASA LaRC, Hampton VA MUG Meeting Sep
1 GOES-R AWG Hydrology Algorithm Team: Rainfall Probability June 14, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.
COMT 3911 Satellite Basics COMT 391 Wireless. COMT 3912 Satellite Components Satellite Subsystems –Telemetry, Tracking, and Control –Electrical Power.
Yimin Ji - Page 1 October 5, 2010 Global Precipitation Measurement (GPM) mission Precipitation Processing System (PPS) Yimin Ji NASA/GSFC,
MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,
25 June 2009 Dawn Conway, AMSR-E TLSCF Lead Software Engineer AMSR-E Team Leader Science Computing Facility.
Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
SATELLITE METEOROLOGY BASICS satellite orbits EM spectrum
Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.
 Introduction to Remote Sensing Example Applications and Principles  Exploring Images with MultiSpec User Interface and Band Combinations  Questions…
Cloud-top Relief Spatial Displacement Adjustments of GOES-R Images Shayesteh Mahani CREST & CE Dept. at the City College (CCNY) of the City University.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
Investigating the use of Deep Convective Clouds (DCCs) to monitor on-orbit performance of the Geostationary Lightning Mapper (GLM) using Lightning Imaging.
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.
POLAR MULTI-SENSOR AEROSOL PROPERTIES OVER LAND Michael Grzegorski, Rosemary Munro, Gabriele Poli, Andriy Holdak and Ruediger Lang.
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.
Studies of Advanced Baseline Sounder (ABS) for Future GOES Jun Li + Timothy J. Allen Huang+ W. +CIMSS, UW-Madison.
Real-time Generation of Winds and Sea Ice Motion from MODIS Jeff Key 1, Dave Santek 2, Chris Velden 2 1 Office of Research and Applications, NOAA/NESDIS,
Environmental Remote Sensing GEOG 2021 Lecture 8 Observing platforms & systems and revision.
Applying Pixel Values to Digital Images
Ocean Dynamics Algorithm GOES-R AWG Eileen Maturi, NOAA/NESDIS/STAR/SOCD, Igor Appel, STAR/IMSG, Andy Harris, CICS, Univ of Maryland AMS 92 nd Annual Meeting,
Yi Chao Jet Propulsion Laboratory, California Institute of Technology
Data Processing Flow Chart Start NDVI, EVI2 are calculated and Rank SDS are incorporated Integrity Data Check: Is the data correct? Data: Download a) AVHRR.
McIDAS-X Software Development and Demonstration Rick Kohrs and Jay Heinzelman 7 May 2012.
McIDAS-X Software Development and Demonstration Dave Santek and Jay Heinzelman 2 June 2009 PDA Animated Weather (PAW) Status by Russ Dengel.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
Assimilating Cloudy Infrared Brightness Temperatures in High-Resolution Numerical Models Using Ensemble Data Assimilation Jason A. Otkin and Rebecca Cintineo.
Quick Review - Fronts. Quick Review - Clouds Using Satellite and Radar Imagery to Find Weather Features.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
McIDAS-X Software Development and Demonstration Dave Santek and Jay Heinzelman 25 October 2010.
Orbits and Sensors Multispectral Sensors. Satellite Orbits Orbital parameters can be tuned to produce particular, useful orbits Geostationary Sun synchronous.
CIMSS Board of Directors Meeting 12 December 2003 Personnel: John Mecikalski (Principal Investigator) and Kristopher Bedka Objective: Develop methods to.
SNPP data access for agricultural monitoring
Satellite Derived Mid- Upper Level Winds
RENISH THOMAS (GPM) Global-Precipitation- Mapper
GEOGRAPHIC INFORMATION SYSTEMS & RS INTERVIEW QUESTIONS ANSWERS
Satellite Meteorology
Verifying the DCC methodology calibration transfer
ABI Visible/Near-IR Bands
Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.
Hyperspectral Wind Retrievals Dave Santek Chris Velden CIMSS Madison, Wisconsin 5th Workshop on Hyperspectral Science 8 June 2005.
Matthew A. Lazzara1, Linda M
Hanlie XU, Xiuqing HU, Chunqiang Wu, Tianhang Yang, Na Xu
AIRS/GEO Infrared Intercalibration
Calibration and Validation of Microwave Humidity Sounder onboard FY-3D Satellite Yang Guo, Songyan Gu NSMC/CMA Mar
Spectroscopy Workshop
Department of Geography
Geographic Features in Satellite Imagery
Presentation transcript:

David Santek, Rick Kohrs, Nick Bearson, Matthew Lazarra McIDAS Users’ Group Meeting 26 October 2010

Outline Project overview IMGPARM COMP_ALLBAND Current results

Project Overview Combine data from polar and geostationary satellites to track clouds in high latitudes Geostationary satellites are good up to 50º-60º latitude Polar orbiting satellites are good poleward of 70º latitude This technique is designed to ‘fill the gap’

Project Issues Accounting for inter-satellite calibration differences Correcting for parallax in viewing the cloud tracers from different satellites and instruments Using the pixel time within the composite that corresponds to the viewing by each satellites when computing cloud velocity

IMGPARM: Create multi-band files BandDescription 1Grayscale values (brightness temperature) 2Time difference from nominal time (sec) 3Distance from satellite subpoint (km) 4Pixel area (km 2 ) 5McIDAS Satellite Sensor number (SS) 6Wavelength 7Parallax distance (km*10) 8Parallax direction (degrees)

COMP_ALLBAND All IMGPARM files are remapped with REMAP2 into a common projection COMP_ALLBAND then combines many IMGPARM files into a single file Choose highest resolution pixel Ensure pixels are in time range Retain all IMGPARM bands

Example composite Infrared composite Satellite ID number

Pixel Time (Difference from a nominal time) Blue: 0 to 15 minutes before nominal time Green: 0 to 15 minutes after nominal time

Pixel Distance (km) from Subpoint

Pixel Area (km 2 )

Parallax Distance (km)

Satellite-derived winds Arctic for 21 October 2010 Triplets of ½ hourly composites Composite building delayed by 3 hours Wind flags (blue) at all levels Potential targets (green) not tracked due to overlapping satellite data

Summary IMGPARM, REMAP2, COMP_ALLBAND process: 3-hour delay before running process Up to 60 input files every 15 minutes Each polar region takes about 3 minutes ADDE dataset (images and winds) DATALOC ADD LEOGEO LEOGEO.SSEC.WISC.EDU Composite with winds overlayed (LEO-GEO): IMGPARM and COMP_ALLBAND will be in McIDAS-XRD