OZONE PRODUCTS S5P Verification Workshop 20/05/2015 MPIC, Mainz.

Slides:

Advertisements

Similar presentations

KNMI Ozone Satellite Observations

Advertisements

WP 5 : Clouds & Aerosols L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute (KNMI) SCIAvisie Meeting, KNMI, De Bilt, Absorbing.

Results from the OMPS Nadir Instruments on Suomi NPP Satellite

DIRECT TROPOSPHERIC OZONE RETRIEVALS FROM SATELLITE ULTRAVIOLET RADIANCES Alexander D. Frolov, University of Maryland Robert D. Hudson, University of.

15% 1. ABSTRACT We show results from joint TES-OMI retrievals for May, We combine TES and OMI data by linear updates from the spectral residuals.

CPI International UV/Vis Limb Workshop Bremen, April Development of Generalized Limb Scattering Retrieval Algorithms Jerry Lumpe & Ed Cólon.

Xiong Liu Harvard-Smithsonian Center for Astrophysics December 20, 2004 Direct Tropospheric Ozone Retrieval from GOME.

Xiong Liu Harvard-Smithsonian Center for Astrophysics Kelly Chance, Christopher Sioris, Robert Spurr, Thomas Kurosu, Randall Martin,

Xiong Liu, Kelly Chance, and Thomas Kurosu Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA The 36 th COSPAR Scientific Assembly Beijing,

KNMI, The Netherlands * SCIAMACHY validation workshop, Bremen, 6 Dec ‘04 Large scale validation of SCIAMACHY nadir reflectance Gijs van.

Page 1 1 of 20, EGU General Assembly, Apr 21, 2009 Vijay Natraj (Caltech), Hartmut Bösch (University of Leicester), Rob Spurr (RT Solutions), Yuk Yung.

1 Global Observations of Sulfur Dioxide from GOME Xiong Liu 1, Kelly Chance 1, Neil Moore 2, Randall V. Martin 1,2, and Dylan Jones 3 1 Harvard-Smithsonian.

Predictability study using the Environment Canada Chemical Data Assimilation System Jean de Grandpré Yves J. Rochon Richard Ménard Air Quality Research.

Development of GEMS Cloud Data Processing Algorithm Yong-Sang Choi 1, Bo-Ram Kim 1, Heeje Cho 2, Myong-Hwan Ahn 1 (Former COMS PI), and Jhoon Kim 3 (GEMS.

Status S5p TROPOMI Pepijn Veefkind.

WP 3: Absorbing Aerosol Index (AAI) WP 10: Level-1 validation L.G. Tilstra 1, I. Aben 2, and P. Stammes 1 1 Royal Netherlands Meteorological Institute.

Cloud algorithms and applications for TEMPO Joanna Joiner, Alexander Vasilkov, Nick Krotkov, Sergey Marchenko, Eun-Su Yang, Sunny Choi (NASA GSFC)

Quality of the official SCIAMACHY Absorbing Aerosol Index (AAI) level-2 product L.G. Tilstra and P. Stammes Royal Netherlands Meteorological Institute.

M. Van Roozendael, AMFIC Final Meeting, 23 Oct 2009, Beijing, China1 MAXDOAS measurements in Beijing M. Van Roozendael 1, K. Clémer 1, C. Fayt 1, C. Hermans.

Slide 1 Retrospective analysis of ozone at ECMWF Rossana Dragani ECMWF Acknowledgements to: D. Tan, A. Inness, E. Hólm, and D. Dee R. Dragani, SPARC/IOC/IGACO,

Page 1 Validation by Model Assimilation and/or Satellite Intercomparison - ESRIN 9–13 December 2002 Monitoring of near-real-time SCIAMACHY, MIPAS, and.

Tropospheric NO2 Ronald van der A, Michel Van Roozendael, Isabelle De Smedt, Ruud Dirksen, Folkert Boersma KNMI and BIRA-IASB Beijing, October 2008.

A. Bracher, L. N. Lamsal, M. Weber, J. P. Burrows University of Bremen, FB 1, Institute of Environmental Physics, P O Box , D Bremen, Germany.

S5P tropospheric ozone product: Convective Cloud Differential method First German S5P Verification Meeting Bremen, November 2013 Pieter Valks DLR,

Validation workshop, Frascati, 13 December 2002Page 1 SCIAMACHY products quality and recommendations Based on presentations and discussions during this.

S5P Ozone Profile (including Troposphere) verification: RAL Algorithm R.Siddans, G.Miles, B.Latter S5P Verification Workshop, MPIC, Mainz th May.

ESA Frascati 31 Oct 1 Determination of the light availability in ocean water utilizing Vibrational Raman Scattering.

Assessment of SBUV Profile Algorithm Using High Vertical Resolution Sensors Assessment of SBUV Profile Algorithm Using High Vertical Resolution Sensors.

Retrieval of Ozone Profiles from GOME (and SCIAMACHY, and OMI, and GOME2 ) Roeland van Oss Ronald van der A and Johan de Haan, Robert Voors, Robert Spurr.

POLAR MULTI-SENSOR AEROSOL PROPERTIES OVER LAND Michael Grzegorski, Rosemary Munro, Gabriele Poli, Andriy Holdak and Ruediger Lang.

Sentinel-5 precursor: TROPOMI Cloud slicing retrieval: Program development and testing with SCIAMACHY/GOME-2 WFDOAS data Kai-Uwe Eichmann, Mark Weber,

Validation of SCIAMACHY total ozone: ESA/DLR V5(W) and IUP WFDOAS V2(W) M. Weber, S. Dikty, J. P.Burrows, M. Coldewey-Egbers (1), V. E. Fioletov (2), S.

NASA/GSFC Tropospheric Ozone Residual M. Schoeberl NASA/GSFC M. Schoeberl NASA/GSFC.

Evaluation of OMPS sensor and EV SDR performance Conclusions: Sensors are performing well Calibrations are very good; no sign of degradation Comparisons.

OMPS Products Applications Craig Long NOAA/NWS/NCEP Climate Prediction Center SUOMI NPP SDR Product Review -- 23/24 October NCWCP Auditorium.

A Long Term Data Record of the Ozone Vertical Distribution IN43B-1150 by Richard McPeters 1, Stacey Frith 2, and Val Soika 3 1) NASA GSFC

Evaluation of OMI total column ozone with four different algorithms SAO OE, NASA TOMS, KNMI OE/DOAS Juseon Bak 1, Jae H. Kim 1, Xiong Liu 2 1 Pusan National.

Ozone time series and trends Various groups compute trends in different ways. One goal of the workshop is to be able to compare time series and trends.

Status of the Development of a Tropospheric Ozone Product from OMI Measurements Jack Fishman 1, Jerald R. Ziemke 2,3, Sushil Chandra 2,3, Amy E. Wozniak.

Retrieval of Vertical Columns of Sulfur Dioxide from SCIAMACHY and OMI: Air Mass Factor Algorithm Development, Validation, and Error Analysis Chulkyu Lee.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Status/ plans R.Siddans.

Rutherford Appleton Laboratory Remote Sensing Group Tropospheric ozone retrieval from uv/vis spectrometery RAL Space - Remote Sensing Group Richard Siddans,

C. Lerot 1, M. Koukouli 2, T. Danckaert 1, D. Balis 2, and M. Van Roozendael 1 1 BIRA-IASB, Belgium 2 LAP/AUTH, Greece S5P L2 Verification Meeting – 19-20/05/2015.

TOMS Ozone Retrieval Sensitivity to Assumption of Lambertian Cloud Surface Part 1. Scattering Phase Function Xiong Liu, 1 Mike Newchurch, 1,2 Robert Loughman.

1 Monitoring Tropospheric Ozone from Ozone Monitoring Instrument (OMI) Xiong Liu 1,2,3, Pawan K. Bhartia 3, Kelly Chance 2, Thomas P. Kurosu 2, Robert.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Study Overview R.Siddans.

Recent Solar Irradiance Data From SBUV/2 and OMI Matthew DeLand and Sergey Marchenko Science Systems and Applications, Inc. (SSAI) SOLID WP-2 Workshop.

H 2 O retrieval from S5 NIR K. Weigel, M. Reuter, S. Noël, H. Bovensmann, and J. P. Burrows University of Bremen, Institute of Environmental Physics

S5P tropical tropospheric ozone product based on convective cloud differential method Klaus-Peter Heue, Pieter Valks, Diego Loyola, Deutsches Zentrum für.

1 Improving SO 2 AMFs: Comparison of different approaches P. Hedelt, P. Valks, D. Loyola Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR) Institut.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Requirement: Provide information to air quality decision makers and improve.

Kelly Chance Harvard-Smithsonian Center for Astrophysics Xiong Liu, Christopher Sioris, Robert Spurr, Thomas Kurosu, Randall Martin,

1 Xiong Liu Harvard-Smithsonian Center for Astrophysics K.V. Chance, C.E. Sioris, R.J.D. Spurr, T.P. Kurosu, R.V. Martin, M.J. Newchurch,

March 21, ‘06 comp. May 5, ‘06 comp Summary ~4% swath angle dependent difference Up to 9% difference over clouds Differences correlate with snow/ice.

USE OF GEOS-CHEM BY SMITHSONIAN ASTROPHYSICAL OBSERVATORY AND DALHOUSIE UNIVERSITY Randall Martin Mid-July SAO Halifax, Nova Scotia.

Flight Tests of the Novel TechSat Satellite Ozone Meter Algorithms and Measurement Processing Results M. Guelman, F. Ortenberg, B. Wolfson Asher Space.

Impact of OMI data on assimilated ozone Kris Wargan, I. Stajner, M. Sienkiewicz, S. Pawson, L. Froidevaux, N. Livesey, and P. K. Bhartia   Data and approach.

SADDU Meeting, March 2009, SRON, Utrecht1 Recent progress on nadir UV-Vis retrievals at BIRA M. Van Roozendael 1 C. Lerot 1, I. De Smedt 1, N. Theys.

GOME2 Error Study WP 150: Basic SNR column retrieval PM2 10/2001 SNR Basic Column Retrieval R. De Beek, M. Weber,

Iodine Monoxide (IO) Nadir Scientific Data Product

DOAS workshop 2015, Brussels, July 2015

Cloud Trends and Anomalies Observed by MISR

Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC Height-resolved aerosol R.Siddans.

Johan de Haan Pepijn Veefkind

Randall Martin, Daniel Jacob, Jennifer Logan, Paul Palmer

Sentinel 5 Precursor Ozone Column products

Authors: B.Kerridge, R.Siddans, J.Reburn, B.Latter and V.Jay

Retrieval of SO2 Vertical Columns from SCIAMACHY and OMI: Air Mass Factor Algorithm Development and Validation Chulkyu Lee, Aaron van Dokelaar, Gray O’Byrne:

The new Pandora total ozone algorithm including the retrieval of

Randall Martin Mid-July

Presentation transcript:

OZONE PRODUCTS S5P Verification Workshop 20/05/2015 MPIC, Mainz

Verification: work completed Total Ozone – Simulated retrieval comparsions completed (RSS/polarisation/viewing geometry/albedo) – Offline Prototype algorithm speed increased using LUT – Offline algorithm extensively compared to other products Tropical Tropospheric Ozone – CCD prototype and verification algorithms products compared to other data products – Cloud slicing prototype compared to sondes Ozone Profile – Linear (error mapping) and non-linear (iterative) retrieval simulations nearly completed

Improved Performance of Offline Total Ozone Algorithm Look-up table version of GODFIT No systematic dependence introduced by the method. The full reprocessing has been achieved within 3 weeks (the processing is more than 10 times faster compared to the online algorithm). No Soft calibration applied for OMI!! Motivation: Full reprocessing of the OMI L1 data set at the end of year 1. Concept: All online RT calculations are replaced by interpolation through pre- calculated tables of radiances. Specs: o 9 dimensions: 3 angles, T°, Scene altitude and albedo, month, latitude, total O 3 o Jacobians computed from the radiance LUT itself. o Final size: ~370 Gbytes. o Has been generated in 20 days using 32 cores (2.66GHz) LUT – online difference (%)

Offline Total Ozone Comparisons to other reference total ozone products OMI_GODFIT - OMI_TOMS OMI_GODFIT - OMI_DOAS OMI-GODFIT - SBUV

5 Tropospheric ozone CCD comparisons WFDOAS Prototype Verification Mean diff= -2.4 DU STDEV=0.8 DU Difference

Ozone Profiles Linear simulations (error mapping)Non-linear iterative simulations

Verification: outstanding tasks Total Ozone – Comparison of NRT(DOAS) and offline (direct fit) using real data (GOME-2/OMI) – Look at consistency between total column and integrated total ozone from profile – Some differences remain in modelling of Raman scattering between LIDORT and DISAMAR Tropical Tropospheric Ozone – Make comparisons with ozone profiles (Bremen) up to 14km (CCD) – Comparisons using 1 month of TCO3 from real data processed by S5P total column algorithms (CCD/Cloud Slicing) – Use of operational total ozone for Cloud Slicing Ozone Profile – Comparisons of retrievals using real data and identification of metrics for comparison. Considering using a model for cross-comparison. – Close off linear and non-linear simulation retrieval work by direct comparison to prototype

Comparisons of retrievals with real data GOME 2 data will be used for comparison of products based on real measurements – GOME-2 MetOp-B (minimal degradation, full spectral coverage) – Action on all groups to identify what would be good day(s)/cases to process – ~1 day per season (inc. ozone hole case) Consider use of a model (MACC) for select days for cross- comparison, particularly for ozone profiles 1 month of total column product based on real measurements will be provided to the tropical tropospheric groups

Summary points for feeding back to WG 1.It would be very useful to have the S5P NPP cloud product for Band 3, in addition to that for NIR/SWIR bands. Cloud fraction/mask algorithm can do this but might be dependent on processing resources (and storage). 2.AMT paper possibilities being considered by each product/group. Some limitations as much has been published already for some algorithms. Opportunity for including verification results in prototype algorithm papers. 3.Individual slides for ATMOS conference to be provided by each product/group.