Evaluation aerosol CCI satellite retrievals MACC assimilations Reading 2012.

Slides:



Advertisements
Similar presentations
GEMS-Aerosol WP_AER_4: Evaluation of the model and analysis Lead Partners: NUIG & CNRS-LOA Partners: DWD, RMIB, MPI-M, CEA- IPSL-LSCE,ECMWF, DLR (at no.
Advertisements

Page 1© Crown copyright 2004 AER sub-project: report to GEMS plenary Olivier Boucher GEMS - Kick-off meeting July 2005.
02/04/2009 University of Manchester ADIENT meeting 5 – Gareth Thomas, Maria Frontoso 1 WP 1.3: Satellite data in support of ADIENT aircraft flights Gareth.
MODIS Atmosphere Solar Reflectance Issues 1. Aqua VNIR focal plane empirical re-registration status Ralf Bennartz, Bob Holz, Steve Platnick 2 1 U. Wisconsin,
Characterization of radiance uncertainties for SeaWiFS and Modis-Aqua Introduction The spectral remote sensing reflectance is arguably the most important.
Gregory Leptoukh, David Lary, Suhung Shen, Christopher Lynnes What’s in a day?
Aerosol radiative effects from satellites Gareth Thomas Nicky Chalmers, Caroline Poulsen, Ellie Highwood, Don Grainger Gareth Thomas - NCEO/CEOI-ST Joint.
European Geosciences Union General Assembly 2006 Vienna, Austria, 02 – 07 April 2006 Paper’s objectives: 1. Contribute to the validation of MODIS aerosol.
Jianglong Zhang 1, Jeffrey S. Reid 2, James R. Campbell 2, Edward J. Hyer 2, Travis D. Toth, Matthew Christensen 1, and Xiaodong Zhang 3 1 University of.
GOES-R AWG Product Validation Tool Development Aerosol Optical Depth/Suspended Matter and Aerosol Particle Size Mi Zhou (IMSG) Pubu Ciren (DELL) Hongqing.
Evaluating Remote Sensing Data Or How to Avoid Making Great Discoveries by Misinterpreting Data Richard Kleidman ARSET-AQ Applied Remote Sensing Education.
PACE AEROSOL CAL/VAL Cameron McNaughton Golder Associates Ltd.
Aerosol-cci: WP2220: Cloud mask comparison Gerrit de Leeuw.
Aerosol Climate Change Initiative Stratospheric activities around the Aerosol_CCI project C. Bingen, C. Robert, A. Bourrassa & Aerosol_CCI Team F. Vanhellemont,
Imperial College - 19 Feb DESERT DUST SATELLITE RETRIEVAL INTERCOMPARISON Elisa Carboni 1, G.Thomas 1, A.Sayer 1, C.Poulsen 2, D.Grainger 1, R.Siddans.
AERONET in the context of aerosol remote sensing from space and aerosol global modeling Stefan Kinne MPI-Meteorology, Hamburg Germany.
Experiences Developing a Semantic Representation of Product Quality, Bias, and Uncertainty for a Satellite Data Product Patrick West 1, Gregory Leptoukh.
Combining Data Assimilation with an Algorithm to Improve the Consistency of VIIRS Chlorophyll: Toward a Multidecadal, Multisensor Global Record NASA ROSES.
VALIDATION OF SUOMI NPP/VIIRS OPERATIONAL AEROSOL PRODUCTS THROUGH MULTI-SENSOR INTERCOMPARISONS Huang, J. I. Laszlo, S. Kondragunta,
MPI-Meteorology Hamburg, Germany Evaluation of year 2004 monthly GlobAER aerosol products Stefan Kinne.
Thomas Holzer-Popp (DLR), Stefan Kinne (MPI-M) & the Aerosol_cci team aerosol_cci.
EGU Vienna, Matthias Jerg on behalf of Cloud CCI Deutscher Wetterdienst The ESA Cloud CCI project.
Earth Observation Science Recent and potential scientific achievements of the (A)ATSR Series - and some possibilities for synergy with MERIS David Llewellyn-Jones.
AMFIC: Aerosol retrieval Gerrit de Leeuw, FMI/UHEL/TNO Pekka Kolmonen, FMI Anu-Maija Sundström, UHEL Larisa Sogacheva, UHEL Juha-Pekka Luntama, FMI Sini.
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.
Advances in Applying Satellite Remote Sensing to the AQHI Randall Martin, Dalhousie and Harvard-Smithsonian Aaron van Donkelaar, Akhila Padmanabhan, Dalhousie.
General Objective: Conduct R&D activities to improve the quality of SST products used by MERSEA modeling and assimilation centers and produce global, Atlantic.
MAPSS and AeroStat: integrated analysis of aerosol measurements using Level 2 Data and Point Data in Giovanni Maksym Petrenko Charles Ichoku (with the.
Applications of Satellite Remote Sensing to Estimate Global Ambient Fine Particulate Matter Concentrations Randall Martin, Dalhousie and Harvard-Smithsonian.
Variational Assimilation of MODIS AOD using GSI and WRF/Chem Zhiquan Liu NCAR/NESL/MMM Quanhua (Mark) Liu (JCSDA), Hui-Chuan Lin (NCAR),
Satellite observations of AOD and fires for Air Quality applications Edward Hyer Naval Research Laboratory AQAST June, Madison, Wisconsin 15 June.
Rossana Dragani ECMWF Comparison of L3 CCI Ozone, Aerosols, and GHG data with models outputs using the CMF.
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.
NASA Ocean Color Research Team Meeting, Silver Spring, Maryland 5-7 May 2014 II. Objectives Establish a high-quality long-term observational time series.
QA filtering of individual pixels to enable a more accurate validation of aerosol products Maksym Petrenko Presented at MODIS Collection 7 and beyond Retreat.
Provenance in Earth Science Gregory Leptoukh NASA GSFC.
Fog- and cloud-induced aerosol modification observed by the Aerosol Robotic Network (AERONET) Thomas F. Eck (Code 618 NASA GSFC) and Brent N. Holben (Code.
Aerosol_cci ECV. Aerosol_cci > Thomas Holzer-Popp > ESA Living Planet Symposium, Bergen, 1 July 2010 slide 2 Major improvements over precursor AOD datasets.
Satellite Aerosol Validation Pawan Gupta NASA ARSET- AQ – GEPD & SESARM, Atlanta, GA September 1-3, 2015.
1 N. Christina Hsu, Deputy NPP Project Scientist Recent Update on MODIS C6 Deep Blue Aerosol Products and Beyond N. Christina Hsu, Corey Bettenhausen,
Characterization of Aerosol Data Quality from MODIS for Coastal Regions Jacob Anderson Mentor: Gregory Leptoukh.
Uncertainty in aerosol retrievals: interaction with the community Adam Povey 1, Thomas Holzer-Popp 2, Gareth Thomas 3, Don Grainger 1, Gerrit de Leeuw.
Estimating PM 2.5 from MODIS and MISR AOD Aaron van Donkelaar and Randall Martin March 2009.
High Resolution MODIS Aerosols Observations over Cities: Long Term Trends and Air Quality.
NGAC verification NGAC verification is comparing NGAC forecast (current AOT only) with observations from ground-based and satellite measurements and with.
SCIAMACHY TOA Reflectance Correction Effects on Aerosol Optical Depth Retrieval W. Di Nicolantonio, A. Cacciari, S. Scarpanti, G. Ballista, E. Morisi,
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.
GEWEX Aerosol Assessment Panel members Sundar Christopher, Rich Ferrare, Paul Ginoux, Stefan Kinne, Jeff Reid, Paul Stackhouse Program Lead : Hal Maring,
Intercomparison of satellite retrieved aerosol optical depth over ocean G. Myhre, F. Stordal, M. Johnsrud, A. Ignatov, M.I. Mishchenko, I.V. Geogdzhayev,
Analysis of satellite imagery to map burned areas in Sub-Saharan Africa CARBOAFRICA conference “Africa and Carbon Cycle: the CarboAfrica project” Accra.
Satellites Model Validation Parameterizations Parameterizations Climate Sensitivity Climate Sensitivity Underlying mechanisms Underlying mechanisms CURRENT.
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.
Global Air Pollution Inferred from Satellite Remote Sensing Randall Martin, Dalhousie and Harvard-Smithsonian with contributions from Aaron van Donkelaar,
Ocean Sciences The oceans cover 3/4 of the Earth’s surface. They provide the thermal memory for the global climate system, and are a major reservoir of.
MPI-Meteorology Hamburg, Germany Evaluation of year 2004 monthly GlobAER aerosol products Stefan Kinne.
MODIS Atmosphere Group Summary Summary of modifications and enhancements in collection 5 Summary of modifications and enhancements in collection 5 Impacts.
Aerosol Climate Time Series Evaluation in ESA Aerosol_cci Thomas Popp, Gerrit de Leeuw, Simon Pinnock, Miriam Kosmale, Larisa Sogachewa, Pekka Kolmonen,
Use of Near-Real-Time Data for the Global System
Algorithm Theoretical Basis Document GlobAlbedo Aerosol Retrieval
SEVIRI Solar Channel Calibration system
sun- (/sky-) photometer ground-networks
Evaluating Remote Sensing Data
OVERVIEW OF THE AEROSTAT PROJECT
Datasets, applications, plans
Comparison of AATSR SST with other sensors
G. Myhre, F. Stordal, M. Johnsrud, A. Ignatov, M. I. Mishchenko, I. V
Global Climatology of Aerosol Optical Depth
Assessment of Satellite Ocean Color Products of the Coast of Martha’s Vineyard using AERONET-Ocean Color Measurements Hui Feng1, Heidi Sosik2 , and Tim.
Presentation transcript:

evaluation aerosol CCI satellite retrievals MACC assimilations Reading 2012

the participants / the task AATSR F v142 AATSR O v202/v2q2 AATSR S v040/v031 MERIS A v21 MERIS B v11 MERIS E 802 PARASOL v30 MODIS_aqua c5.1 MODIS_terra c5.1 SEAWIFS MISR v31 FBOV  AOD (MODIS) FMNG  AOD & fine-fr evaluate for the year 2008 daily global AOD and Angstrom data of –aerosol-CCI retrievals –MACC assimilations compare to –AERONET / MAN –MODIS (AOD)

the scoring concept rescale all data to a common 1x1 lat/lon grid –establish 1x1 gridded AERONET daily data) identify local data pairs (satellite/model test data versus AERONET reference data) require 10+ days with 10+ sites (spatial test) or 10+ sites with 10+ temporal records (time test) to evaluate bias and correlation in any region both, a valid spatial score and a valid temporal score, are required for a valid regional score

scoring steps establish regional scores (1: best, 0: poor) –step1regional spatial score for each day (rank) correlation and (rank) bias –step2regional temporal score for each 1x1 (rank) correlation and (rank) biast –step3combined regional score total score = bias * spat.corr * temp.corr –step4combine regional to one global score ‘single score’ from regional total scores investigated properties: AOD550, Angstrom

error / bias evaluation explore –total error = (1 - |score, total |) –bias error= (1 - |score, bias |) –spatial error= (1 - score, space ) –temporal error= (1 - score, time ) score, total = score, bias * score, space * score, time –bias general direction ( + > test-data / - < test-data ) absolute bias (IQ test-data minus IQ reference) test vs AERONET:AOD, Angstrom test vs MODIS, terra:AOD

AOD scores vs AERONET global land ocean MODIS A/T AATSR S v FMNG FBOV AATSR O v MISR AATSR F v AATSR S v MERIS ESA MERIS B v SEAWIFS PARASOL.64 MERIS A based on a 10+ sample statistics absolute larger score is better +/- sign for overall bias

AOD score detail vs AERONET global land ocean FBOV –total score –seasonal score –bias score –spatial score FMNG –total score –seasonal score –bias score –spatial score events

Angstrom scores vs AERONET global land ocean AATSR S v FBOV FMNG AATSR S v AATSR F v AATSR O v MISR MERIS ESA PARASOL.44 MERIS A based on a 10+ sample statistics absolute larger score is better +/- sign for overall bias

ANG score detail vs AERONET global land ocean FBOV –total score –seasonal score –bias score –spatial score FMNG –total score –seasonal score –bias score –spatial score events

assimilation data performance regional error assessment –vs AERONET AOD –vs MODIS-AOD –vs AERONET Angstrom

ECMWF AOD FBOV (single assim.) FMNG (dual assim.)

FBOV AOD performance total error –bias –spatial –temporal bias –tendency –absolute

FMNG AOD performance total error –bias –spatial –temporal bias –tendency –absolute

2ass … minus … 1 ass  improvement deteriation 

FBOV AOD performance total error –bias –spatial –temporal bias –tendency –absolute

FMNG AOD performance total error –bias –spatial –temporal bias –tendency –absolute

2ass … minus … 1 ass  improvement deteriation 

ECMWF Angstrom FBOV (single assim.) FMNG (dual assim.)

FBOV ANG performance total error –bias –spatial –temporal bias –tendency –absolute

FNMG ANG performance total error –bias –spatial –temporal bias –tendency –absolute

2ass … minus … 1 ass  improvement deteriation 

satellite data performance regional error assessment –vs AERONET AOD –vs MODIS-AOD

the participants / the task AATSR F v142 AATSR O v202/v2q2 AATSR S v040/v031 MERIS A v21 MERIS B v11 MERIS E 802 PARASOL v30 MODIS_aqua c5.1 MODIS_terra c5.1 SEAWIFS MISR v31 evaluate daily global ‘CCI-aerosol’ retrievals –for the entire year 2008 AOD 550nm Angstrom (if offered) versus AERONET / MAN performance in context of established retrievals

data content AOD year 2008 MISR MODIS TERRA PARASOL MERIS-ESA MERIS-BAER MERIS-ALAMO ATSR-SU ATSR ORAC ATSR ADV

data volume AOD 1/1/2008 MODIS AQUA MISR PARASOL MERIS-ESA MERIS-BAER MERIS-ALAMO ATSR-SU ATSR ORAC ATSR ADV MODIS TERRA

MODIS TERRA AOD performance total error –bias –spatial –temporal bias –tendency –albsolute

MODIS AQUA AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MISR AOD performance total error –bias –spatial –temporal bias –tendency –absolute

POLDER AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS ESA AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS BEAR AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS ALAMO AOD performance total error –bias –spatial –temporal bias –tendency –absolute

AATSR SU40 AOD performance total error –bias –spatial –temporal bias –tendency –absolute

AATSR SU31 AOD performance total error –bias –spatial –temporal bias –tendency –absolute

AATSR O202 AOD performance total error –bias –spatial –temporal bias –tendency –absolute

AATSR O202iq AOD performance total error –bias –spatial –temporal bias –tendency –absolute effectively ‘identical to O202

AATSR ADV142 AOD performance total error –bias –spatial –temporal bias –tendency –absolute

AATSR SU40 AOD performance total error –bias –spatial –temporal bias –tendency –absolute MODIS terra

AATSR O202 AOD performance total error –bias –spatial –temporal bias –tendency –absolute MODIS terra

AATSR ADV142 AOD performance total error –bias –spatial –temporal bias –tendency –absolute MODIS terra

POLDER AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS ESA AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS BEAR AOD performance total error –bias –spatial –temporal bias –tendency –absolute

MERIS ALAMO AOD performance total error –bias –spatial –temporal bias –tendency –absolute