Gregory Leptoukh, David Lary, Suhung Shen, Christopher Lynnes What’s in a day?

Slides:



Advertisements
Similar presentations
MODIS Atmosphere Solar Reflectance Issues 1. Aqua VNIR focal plane empirical re-registration status Ralf Bennartz, Bob Holz, Steve Platnick 2 1 U. Wisconsin,
Advertisements

Some Basic Concepts of Remote Sensing
Diurnal Variability of Aerosols Observed by Ground-based Networks Qian Tan (USRA), Mian Chin (GSFC), Jack Summers (EPA), Tom Eck (GSFC), Hongbin Yu (UMD),
Power Generation from Renewable Energy Sources
Prof. John Hearnshaw ASTR211: COORDINATES AND TIME Coordinates and time Sections 9 – 17.
Analysis and Mitigation of Atmospheric Crosstalk Kyle Hilburn Remote Sensing Systems May 21, 2015.
MODIS Regional and Global Cloud Variability Brent C. Maddux 1,2 Steve Platnick 3, Steven A. Ackerman 1,2, Paul Menzel 1, Kathy Strabala 1, Richard Frey.
Temporal and Spatial Variations of Sea Surface Temperature and Chlorophyll a in Coastal Waters of North Carolina Team Members: Brittany Maybin Yao Messan.
Fundamentals of Satellite Remote Sensing NASA ARSET- AQ Introduction to Remote Sensing and Air Quality Applications Winter 2014 Webinar Series ARSET -
1 Improved Sea Surface Temperature (SST) Analyses for Climate NOAA’s National Climatic Data Center Asheville, NC Thomas M. Smith Richard W. Reynolds Kenneth.
Spatial & Temporal Distribution of Clouds as Observed by MODIS onboard the Terra and Aqua Satellites  MODIS atmosphere products –Examples from Aqua Cloud.
Measurement of the Aerosol Optical Depth in Moscow city, Russia during the wildfire in summer 2010 DAMBAR AIR.
Proxy Data and VHF/Optical Comparisons Monte Bateman GLM Proxy Data Designer.
Jae H. Kim 1, Sunmi Na 1, and Mike Newchurch 2 1; Department of Atmospheric Science, Pusan Nat’l University 2; Department of Atmospheric Science, University.
Visualization, Exploration, and Model Comparison of NASA Air Quality Remote Sensing data via Giovanni Ana I. Prados, Gregory Leptoukh, Arun Gopalan, and.
AeroStat: Online Platform for the Statistical Intercomparison of Aerosols Gregory Leptoukh, NASA/GSFC (P.I.) Christopher Lynnes, NASA/GSFC (Co-I.) Robert.
Chapter 4: How Satellite Data Complement Ground-Based Monitor Data 3:15 – 3:45.
Evaluation aerosol CCI satellite retrievals MACC assimilations Reading 2012.
VALIDATION OF SUOMI NPP/VIIRS OPERATIONAL AEROSOL PRODUCTS THROUGH MULTI-SENSOR INTERCOMPARISONS Huang, J. I. Laszlo, S. Kondragunta,
Giovanni for AQ Gregory Leptoukh NASA Goddard Space Flight Center Goddard Earth Sciences Data and Information Services Center (GES DISC)
MODIS Sea-Surface Temperatures for GHRSST-PP Robert H. Evans & Peter J. Minnett Otis Brown, Erica Key, Goshka Szczodrak, Kay Kilpatrick, Warner Baringer,
Z. P. Szewczyk GIST 25, Oct Recent Field Campaigns with CERES Instruments Z. Peter Szewczyk Kory J. Priestley Lou Smith Remote Sensing of Clouds.
IAAR Seminar 21 May 2013 AOD trends over megacities based on space monitoring using MODIS and MISR Pinhas Alpert 1,2, Olga Shvainshtein 1 and Pavel Kishcha.
The MEaSUREs PAR Project Robert Frouin Scripps Institution of Oceanography La Jolla, CA _______________________________________ OCRT Meeting, 4-6 may 2009,
AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.
Régis Borde Polar Winds EUMETRAIN Polar satellite week 2012 Régis Borde
GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.
An Introduction to Remote Sensing NASA Remote Sensing Training Geo Latin America and Caribbean Water Cycle capacity Building Workshop Colombia, November.
MAPSS and AeroStat: integrated analysis of aerosol measurements using Level 2 Data and Point Data in Giovanni Maksym Petrenko Charles Ichoku (with the.
An Estimate of Contrail Coverage over the Contiguous United States David Duda, Konstantin Khlopenkov, Thad Chee SSAI, Hampton, VA Patrick Minnis NASA LaRC,
September 4, 2003MODIS Ocean Data Products Workshop, Oregon State University1 Goddard Earth Sciences (GES) Distributed Active Archive Center (DAAC) MODIS.
1 of 26 Characterization of Atmospheric Aerosols using Integrated Multi-Sensor Earth Observations Presented by Ratish Menon (Roll Number ) PhD.
MODIS OCEAN QA Browse Imagery (MQABI Browse Tool) NASA Goddard Space Flight Center Sept 4, 2003
Why We Care or Why We Go to Sea.
Evaluation aerosol CCI retrievals Reading the participants / the task AATSR F v142 AATSR O v202/v2q2 AATSR S v040/v031 MERIS A v21 MERIS B v11 MERIS.
DEVELOPING HIGH RESOLUTION AOD IMAGING COMPATIBLE WITH WEATHER FORECAST MODEL OUTPUTS FOR PM2.5 ESTIMATION Daniel Vidal, Lina Cordero, Dr. Barry Gross.
MODLAND Volumes and Loads Status MODIS Land Science Team Workshop July 15, 2003 Robert Wolfe MODIS Land Team Support Group NASA GSFC Code 922, Raytheon.
NASA and Earth Science Applied Sciences Program
2005 ARM Science Team Meeting, March 14-18, Daytona Beach, Florida Canada Centre for Remote Sensing - Centre canadien de télédétection Geomatics Canada.
QA filtering of individual pixels to enable a more accurate validation of aerosol products Maksym Petrenko Presented at MODIS Collection 7 and beyond Retreat.
NASA Earth Observing System Visualization Tools ARSET - AQ Applied Remote SEnsing Training – Air Quality A project of NASA Applied Sciences Introduction.
Provenance in Earth Science Gregory Leptoukh NASA GSFC.
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
DEPARTMENT OF GEOMATIC ENGINEERING MERIS land surface albedo: Production and Validation Jan-Peter Muller *Professor of Image Understanding and Remote Sensing.
As components of the GOES-R ABI Air Quality products, a multi-channel algorithm similar to MODIS/VIIRS for NOAA’s next generation geostationary satellite.
1 AOD to PM2.5 to AQC – An excel sheet exercise ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan Gupta NASA.
VIIRS Product Evaluation at the Ocean PEATE Frederick S. Patt Gene C. Feldman IGARSS 2010 July 27, 2010.
Satellite Basics MAC Smog Blog Training CATHALAC, Panama, Sept 11-12, 2008 Jill Engel-Cox & Erica Zell Battelle Memorial Institute
MISR Geo-registration Overview Brian E. Rheingans Jet Propulsion Laboratory, California Institute of Technology AMS Short Course on Exploring and Using.
0 0 Robert Wolfe NASA GSFC, Greenbelt, MD GSFC Hydrospheric and Biospheric Sciences Laboratory, Terrestrial Information System Branch (614.5) Carbon Cycle.
An Observationally-Constrained Global Dust Aerosol Optical Depth (AOD) DAVID A. RIDLEY 1, COLETTE L. HEALD 1, JASPER F. KOK 2, CHUN ZHAO 3 1. CIVIL AND.
MODIS Cryosphere Science Data Product Metrics Prepared by the ESDIS SOO Metrics Team for the Cryosphere Science Data Review January 11-12, 2006.
GEWEX Aerosol Assessment Panel members Sundar Christopher, Rich Ferrare, Paul Ginoux, Stefan Kinne, Jeff Reid, Paul Stackhouse Program Lead : Hal Maring,
INTEGRATING SATELLITE AND MONITORING DATA TO RETROSPECTIVELY ESTIMATE MONTHLY PM 2.5 CONCENTRATIONS IN THE EASTERN U.S. Christopher J. Paciorek 1 and Yang.
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.
MODIS Science Team Meeting Columbia, MD - April 29-May 1, 2014 MODIS VI Product Suite Status Kamel Didan & Armando Barreto The University of Arizona.
Overview: MODLAND Production Status, Schedule and Time Series Issues (C4 to C5 Transition) MODIS Land Collection 5 Workshop Jan. 17, 2007 Robert Wolfe.
OMI L2G and L3 Status Peter Leonard – OSST June 21, 2006.
Data acquisition From satellites with the MODIS instrument.
New Aerosol Models for Ocean Color Retrievals Zia Ahmad NASA-Ocean Biology Processing Group (OBPG) MODIS Meeting May 18-20, 2011.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
Spatial & Temporal Distribution of Cloud Properties observed by MODIS: Preliminary Level-3 Results from the Collection 5 Reprocessing Michael D. King,
NASA Langley Research Center / Atmospheric Sciences CERES Instantaneous Clear-sky and Monthly Averaged Radiance and Flux Product Overview David Young NASA.
A comparison of AMSR-E and AATSR SST time-series A preliminary investigation into the effects of using cloud-cleared SST data as opposed to all-sky SST.
Air Quality Satellite Products from 3D-AQS and Giovanni Ana I. Prados (UMBC/JCET)
MERRA Data Access and Services
Basic Concepts of Remote Sensing
OVERVIEW OF THE AEROSTAT PROJECT
Presentation transcript:

Gregory Leptoukh, David Lary, Suhung Shen, Christopher Lynnes What’s in a day?

Why Level 3 products? Level 2 data is difficult to work with because of: –Formats –Volume –Number of files Level 3 data are easy to use … but might lead to wrong conclusions if not being careful Level 3 products are mostly used by modelers, application users, climate scientists 5/17/2015Gregory Leptoukh2

5/17/2015Gregory Leptoukh3 MODIS vs. MODIS MODIS-Terra vs. MODIS-Aqua: Map of AOD temporal correlation, 2008

AOD MODIS Terra vs. Aqua in Pacific Over the datelineAway from the dateline R 2 = 0.45 RMS = 0.05 R 2 = 0.72 RMS = /17/20154Gregory Leptoukh

AOD Aqua MODIS vs MISR correlation map 5/17/20155Gregory Leptoukh

5/17/2015Gregory Leptoukh6 MODIS vs. MISR on Terra MODIS-Terra vs. MISR-Terra: Map of AOD temporal correlation

5/17/2015Gregory Leptoukh7 MODIS Cloud Top Pressure MODIS-Terra vs. MODIS-Aqua: Map of CTP temporal correlation, Jan 1–16, 2008

5/17/2015Gregory Leptoukh8 MODIS Terra & Aqua vs. AIRS Cloud Top Pressure AIRS vs. MODIS AquaAIRS vs. MODIS Terra MODIS Aqua vs. MODIS Terra Correlation maps for Jan 1 – 16, 2008

Terra vs. Aqua MODIS AOD correlation: 16-day periods for 4 seasons 5/17/20159Gregory Leptoukh Winter Summer Spring Fall

MODIS Atmos. Data day definition Level 3 daily products are generated by binning Level 2 data belonging to one day onto a certain spatial grid according to a dataday definition. The latter is different for different sensors and even for the same sensor but by different groups. MODIS Atmospheric products (from MODIS L3 ATBD): The Daily L3 product contains statistics computed from a set of L2 MODIS granules (HDF files) that span a 24-hour (00:00:00 to 23:59:59 UTC) interval. In the case where a L2 parameter is only computed during the daytime, then only daytime files are read to compute the L3 statistics. 5/17/2015Gregory Leptoukh10

Start Location of Aqua Orbit Vary Daily 5/17/201511Gregory Leptoukh MODIS Aqua Level 3 coverage for diff. days in the 16-day cycle

Aqua Terra Orbit track from: Aqua Terra Later in a Day Early in a Day 00:10 00:30 23:45 23:30 03:05 01:45 21:55 21:40 23:20 23:00 00:00 01:25 Orbit Time Difference for Terra and Aqua

Max Time diff. for Terra (calendar day) 5/17/2015Gregory Leptoukh13

5/17/2015Gregory Leptoukh14

5/17/2015Gregory Leptoukh15 Artifact

Data day definitions Level 3 daily products are generated by binning Level 2 data belonging to one day onto a certain spatial grid according to a dataday definition. The latter is different for different sensors, and even for the same sensor but as selected by different Science Teams. 1.Calendar: all granules between 00:00 – 24:00 UTC: MODIS Atmospheric products, OMI L2G 2.Spatial (pixel-based): uses local date/time and ensures spatial continuity. TOMS, AVHRR, AIRS, OMI, MODIS Ocean, SeaWiFS More flavors: 1.Calendar (EqCT): 24 hours centered at the Equatorial Crossing Time at 180 deg longitude, Intermediate case 2.Spatial (with ∆t = 1.6 h to eliminate multiple overlaps at high lat.) 3.MISR: full 14 or 15 orbits depending on a day in the 16-day cycle 5/17/2015Gregory Leptoukh16

Spatial (local time) Data Day definition Each data set contains information for 24 hours of local time, e.g., 1:30 p.m. The gridding starts at the dateline and progresses westward, as does the satellite. Parts of scan lines that cross the dateline are included in the current date data set or the next, depending on which day is at the local time/day at that longitude. For Aqua, the p.m. orbit starts at roughly 1:30 Z on the day and ends on roughly 1:30 Z of the following day. 5/17/2015Gregory Leptoukh17

AIRS local time (from L. Iredell, GSFC) 5/17/2015Gregory Leptoukh18

AIRS UTC time (from L. Iredell) 5/17/2015Gregory Leptoukh19

5/17/201520Gregory Leptoukh Max time diff. between Terra and Aqua Calendar UTC (MODIS) dataday Spatial dataday The artifact around the dateline disappears. In other areas, results are exactly the same for the (-7, 18) latitude belt. At higher latitudes, the additional restriction for one orbit time around the local time produces different results for two dataday definitions.

Removing the artifact in aerosol scattering angle correlation Calendar dataday Spatial dataday 5/17/2015Gregory Leptoukh21 Artifact: difference between calendar and spatial dataday defs.

Removing the artifact in 16-day AOD correlation Calendar dataday Spatial dataday 5/17/2015Gregory Leptoukh22 Artifact: difference between calendar and spatial dataday defs.

Caveats and Options Granule-based vs. L2 pixel-based: –Applying local-time approach to granules (not pixels) improves consistency between Terra and Aqua but –doesn’t remove the artifact completely UTC begin and end time of a Level 3 day will be different for Terra and Aqua Level 3 products – it reflects the actual measurement local time Limiting orbit overlap to +- one around the local time: –where strong diurnal or other temporal changes occur, we know that all the observations averaged occurred with in a narrow and clearly defined local time window. 5/17/2015Gregory Leptoukh23

Conclusions and recommendations It’s the sampling… and packaging of L2 Ability to compare daily Level 3 products from different sensors depends on the dataday definition The calendar UTC (MODIS) dataday definition leads to artifacts around the dateline due to ∆t between measurements reaching up to 23 hours Spatial (local-time-pixel-based) dataday definition insures consistently small ∆t between measurements from different satellites, thus removing artifacts 5/17/2015Gregory Leptoukh24

BACKUPS 5/17/2015Gregory Leptoukh25

5/17/2015Gregory Leptoukh26 A TOMS L3 day: the ensemble of all L2 ground pixels with pixel centers that have the same local calendar date on the ground. Pixels to the east of the 180° meridian get assigned to data-day N, whereas the pixels to the west of the meridian correspond to data-day N+1.

Max time diff. Terra: day starts at 10:30 pm 5/17/201527Gregory Leptoukh

Max time diff. Aqua: day starts at 1:30 am 5/17/201528Gregory Leptoukh

5/17/2015Gregory Leptoukh29 Max Local time diff. for spatial DD def. with additional orbit filtering

5/17/2015Gregory Leptoukh30 MISR data day: a day in a 16-day cycle Full orbits: either 14 or 15 in a dataday