Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 1: Initial trade-off:

Slides:

Advertisements

Similar presentations

Proposed new uses for the Ceilometer Network

Advertisements

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.

Marc Schröder et al., FUB BBC2 Workshop, De Bilt, 10.´04 Problems related to absorption dependent retrievals and their validation Marc Schröder 1, Rene.

 nm)  nm) PurposeSpatial Resolution (km) Ozone, SO 2, UV8 3251Ozone8 3403Aerosols, UV, and Volcanic Ash8 3883Aerosols, Clouds, UV and Volcanic.

Quantitative retrievals of NO 2 from GOME Lara Gunn 1, Martyn Chipperfield 1, Richard Siddans 2 and Brian Kerridge 2 School of Earth and Environment Institute.

Retrieval of smoke aerosol loading from remote sensing data Sean Raffuse and Rudolf Husar Center for Air Pollution Impact and Trends Analysis Washington.

Rutherford Appleton Laboratory 5th ADIENT Meeting 2 nd April 2009, Manchester University WP4.3.1 Comparisons of model simulations with global radiance.

Improvement and validation of OMI NO 2 observations over complex terrain A contribution to ACCENT-TROPOSAT-2, Task Group 3 Yipin Zhou, Dominik Brunner,

16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro.

Extracting Atmospheric and Surface Information from AVIRIS Spectra Vijay Natraj, Daniel Feldman, Xun Jiang, Jack Margolis and Yuk Yung California Institute.

A 21 F A 21 F Parameterization of Aerosol and Cirrus Cloud Effects on Reflected Sunlight Spectra Measured From Space: Application of the.

Page 1 1 of 21, 28th Review of Atmospheric Transmission Models, 6/14/2006 A Two Orders of Scattering Approach to Account for Polarization in Near Infrared.

Cloud Top Height Retrieval From MIPAS Jane Hurley, Anu Dudhia, Graham Ewen, Don Grainger Atmospheric, Oceanic and Planetary Physics, University of Oxford.

1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.

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.

An Integrated Approach to the Remote Sensing of Methane with SCIAMACHY, IASI and AATSR Georgina Miles, Richard Siddans, Alison Waterfall, Brian Kerridge.

Rutherford Appleton Laboratory Remote Sensing Group Ozone Profile Retrieval from MetOp R. Siddans, G. Miles, B. Latter A. Waterfall, B. Kerridge Acknowledgements:

Aircraft spiral on July 20, 2011 at 14 UTC Validation of GOES-R ABI Surface PM2.5 Concentrations using AIRNOW and Aircraft Data Shobha Kondragunta (NOAA),

1. The MPI MAX-DOAS inversion scheme 2. Cloud classification 3. Results: Aerosol OD: Correlation with AERONET Surface extinction: Correlation with Nephelometer.

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.

9:00 Welcome, introductions (JL) 9:15 Study overview (RS) Consolidation of Requirements by Application 9:40 Height-resolved aerosol (RS) 10:10 Cloud-characterisation.

Elena Spinei and George Mount Washington State University 1 CINDI workshop March 2010.

EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA EUMETSAT METEOROLOGICAL SATELLITE CONFERENCE 15/09/2013 – 20/09/2013, VIENNA.

 Assuming only absorbing trace gas abundance and AOD are retrieved, using CO 2 absorption band alone provides a DOF ~ 1.1, which is not enough to determine.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 2 plan R.Siddans PM1:

Rutherford Appleton Laboratory Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Overview of Phase II & Cloud Model Code.

TEMPO Simulation and Retrieval Tools and Algorithm Testing at SAO Xiong Liu 3 rd TEMPO Science Team Meeting Huntsville, Al, May 27,

Marc Schröder, FUB Tutorial, De Bilt, 10.´04 Photon path length distributions and detailed microphysical parameterisations Marc Schröder Institut für Weltraumwissenschaften,

1 Optimal Channel Selection. 2 Redundancy “Information Content” vs. “On the diagnosis of the strength of the measurements in an observing system through.

IGARSS 2011, July 24-29, Vancouver, Canada 1 A PRINCIPAL COMPONENT-BASED RADIATIVE TRANSFER MODEL AND ITS APPLICATION TO HYPERSPECTRAL REMOTE SENSING Xu.

The Second TEMPO Science Team Meeting Physical Basis of the Near-UV Aerosol Algorithm Omar Torres NASA Goddard Space Flight Center Atmospheric Chemistry.

Rutherford Appleton Laboratory Cloud Model for operational Retrievals from MSG SEVIRI PM2, RAL, 17 Feb 2009 Non-linear Simulations.

Satellite group MPI Mainz Investigating Global Long-term Data Sets of the Atmospheric H 2 O VCD and of Cloud Properties.

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.

Retrieval of Methane Distributions from IASI

Intercomparison of OMI NO 2 and HCHO air mass factor calculations: recommendations and best practices A. Lorente, S. Döerner, A. Hilboll, H. Yu and K.

IASI CH 4 Operational Retrieval Feasibility - Optimal Estimation Method Task 1+3: Updates Richard Siddans, Jane Hurley PM2, webex 10 October 2014.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Initial trade-off: Cloud-characterisation.

Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.

BBHRP Assessment Part 2: Cirrus Radiative Flux Study Using Radar/Lidar/AERI Derived Cloud Properties David Tobin, Lori Borg, David Turner, Robert Holz,

Trace gas algorithms for TEMPO G. Gonzalez Abad 1, X. Liu 1, C. Miller 1, H. Wang 1, C. Nowlan 2 and K. Chance 1 1 Harvard-Smithsonian Center for Astrophysics.

S5P cloud products Sebastián Gimeno García, Ronny Lutz, Diego Loyola German S5P Verification Meeting 1 Bremen, November Chart 1>

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,

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

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC.

Rutherford Appleton Laboratory CAMELOT Observation Techniques and Mission Concepts for Atmospheric Chemistry Task 4: Assessment of Cloud Contamination.

Evaluation of model simulations with satellite observed NO 2 columns and surface observations & Some new results from OMI N. Blond, LISA/KNMI P. van Velthoven,

The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.

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

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Height-resolved aerosol.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: H 2 O retrieval from IASI.

Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.

UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Initial trade-off: Height-resolved.

GOME2 Error Study WP 260: Slant Column Errors due to neglicence of BRDF PM3 02/2002 Slant Column Errors due to Assumption of.

University of Oxford EUMETSAT Satellite Conference 2004 Aerosol Retrieval Algorithm for Meteosat Second Generation Sam Dean, Steven Marsh and Don Grainger.

1 uni-bremen 04/2002 GOME2 Error Assessment Study Error Budget, Error Mitigation and Proposal for Future Work Mark Weber, Rüdiger.

© 2014 RAL Space Study for the joint use of IASI, AMSU and MHS for OEM retrievals of temperature, humidity and ozone D. Gerber 1, R. Siddans 1, T. Hultberg.

Challenge the future Corresponding author: Delft University of Technology Collocated OMI DOMINO and MODIS Aqua aerosol products.

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: FP, 25 April 2014, ESTEC.

What Are the Implications of Optical Closure Using Measurements from the Two Column Aerosol Project? J.D. Fast 1, L.K. Berg 1, E. Kassianov 1, D. Chand.

1 Deriving cloud parameters for O 3 profile retrieval Zhaonan Cai 1, Xiong Liu 1, Kai Yang 2, Kelly Chance 1 1 SAO 2 UMD 4 th TEMPO Science Team Meeting,

Investigating Cloud Inhomogeneity using CRM simulations.

Carbon monoxide from shortwave infrared measurements of TROPOMI: Algorithm, Product and Plans Jochen Landgraf, Ilse Aben, Otto Hasekamp, Tobias Borsdorff,

Intercomparison of SCIAMACHY NO2, the Chimère air-quality model and

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

An Improved Retrieval of Tropospheric Nitrogen Dioxide from GOME

Intercomparison of SCIAMACHY NO2, the Chimère air-quality model and

Presentation transcript:

Rutherford Appleton Laboratory Requirements Consolidation of the Near-Infrared Channel of the GMES-Sentinel-5 UVNS Instrument: Task 1: Initial trade-off: Cloud-characterisation for uv-vis R.Siddans PM1: RAL, 9 July 2013

Overview Initial assessment based on work in the Eumesat Study for then proposed MTG UVN sounder: R. Siddans, B.G. Latter, B.J. Kerridge, Study to Consolidate the UVS Mission Requirements for the Oxygen A-band (Eumetsat Contract No. EUM/CO/05/1411/SAT), 2007 This provided basis for later studies re height resolved aerosol (ACOR, CAPACITY, CAMELOT), but also studied cloud/trace-gas application in some detail. Will form basis of new work in T2 of this study. Basis of approach is to Define realistic cloud senarios & simulate measurements Perform imager and A-band spectrometer cloud retrievals Determine implied errors in the uv-vis trace gases by quantifying air- mass-factor errors stemming from the retrieved cloud representation Various instrument resolutions tested

Assessment approach UVS mission – tropospheric trace gas retrievals O 3 (trop), BrO, NO 2, CH 2 O give rise to optically thin absorption signatures: DOAS is applicable: Slant column is fitted to measured spectra Vertical column estimated by dividing by air-mass factor (AMF) S m which is calculated using an RTM (assuming scattering profile) Errors in modelled scattering only enter via the AMF calculation –Simulate errors due to cloud/aerosol by evaluating error in AMF

Distance (km) IWC 1km regridded Cloud Scenarios Cloud scenarios based on ground-based radar/lidar data, analyed by CloudNet project (Hogan et al) Wind fields used to synthesise orbit x-sections from station data provided as fn of time. Similar data now available from CloudSat/CALIPSO DARDAR project (Hogan and Delanoe)

Imager retrieval Based on ORAC, currently used on ATSR, AVHRR, MODIS, SEVIRI etc: GRAPE, GlobAerosl, CCI-Clouds, CCI-Aerosol, Eumetsat OCA etc: Uses optimal estimation & retieves Optical depth at 0.55 µm Effective radius. Cloud-top height. Assumes single, homogenous cloud/aerosol layer of particular type (liquid/ice cloud, maritime / continental / biomass… aerosol) Cost function can be used to identify type or where single layer assumption not valid (possibly)

Imager retrieval ORAC currently uses 0.55, 0.67, 0.87, 1.6, 11 and 12µm only vis/near-ir for aerosol Extended here to 9 "FDHSI" channels for both cloud & aerosol 0.55, 0.64, 0.809, 1.63, 3.92, 8.71, 10.8, 11.9 and 13.4 µm Added aerosol layer height (from ir) to state vector ORAC Based on RT look-up-tables Here based on full on-line RT with RAL FM2D to ensure consistency with measurement simulations & facilitate use of additional channels Here each scene is analysed with 4 particle models: Liquid cloud, ice cloud, desert aerosol, maritime aerosol The retrieval with lowest cost function is selected

Imager results true Retrieved (cloud/aerosol only) Cost/type Single layer cloud / aerosol fits quite well where possible Mixed layer cloud gives high cost Generally cost selects correct type Aerosol noisy (k ext & r eff )

A-band retrieval scheme Extinction coefficient profile retrieval as aerosol assessment Here take scatter type and r eff from imager retrieval Assume spatial resolution 10km cf imager 1km Divide scenes into cloudy & clear fraction Cloudmask: Threshold reflectance > 0.2 In each fraction find type from imager retrieval which is associated with most measured photons. Then take radiance weighted mean k ext and r eff for this type as imager representation of cloud/clear fraction. A-band retrieval run assuming imager type on whole scene if homogenous or "cloudy" fraction if mixed.

A-band retrieval scheme Standard scheme represents cloud as a scattering profile (CSP) 2nd scheme implemented: emulates the approach GOME Operational total column Cloud as reflecting surface (CRS approach) Assume cloud is Lambertian surface at elevated atlitude Assume cloud fraction and type from imager (operationally fraction from PMDs, type assumed) Retrieve Cloud top height A priori 5±10 km. Cloud reflectance A priori 0.05±1 only applied to 0.6 nm resolution measurements

true imager A-band retrieval results 0.6 nm resolution 1D measurements 3D measurements 1D, SNR=250

0.06nm resolution true imager A-band retrieval results 0.6nm resolution Cloud representation reasonable even from low resolution A-band but improves with resolution.

true imager A-band retrieval results 3 cm -1 resolution 2km retrieval 1km retrieval 1km, SNR=250 At 1cm -1 much better representation of thin ice cloud + cost OK SNR 2500 better than 250

AMFs AMFs are computed for 3 column amounts Total Tropospheric (0-12km) Boundary- layer (0-2km) Sub-column AMFs would be used in combination with external info to constrain other layer or profile shape (e.g. model or other wavelengths) Calculated by perturbing absorber amount & taking ratio of apparent optical depth to actual, vertical optical depth of absorber AMFs are first at 1km spatial resolution from True field Retrieval representation of field (using cloud fraction for sub- pixel representation) AMF of 10km pixel is given by radiance weighted mean

Imager OK for cloud free & simple cloud A-band CRS improves results for cloudy conditions

Profile retrieval improves over CRS at 0.6 nm resolution

0.18 nm slightly better than 0.6 nm

0.06 nm ~ similar 0.18nm

Conclusions Realisitic end to end simulations of modelling scattering profile for uv- vis retrievals based on imager and A-band retrievals conducted in Eumetsat A-band study These indicate Imager retrieval functions well for simple cloud layers AMF adequate in cloud-free (aerosol) & simple cloud cases A-band cloud-as-reflecting-surface improves AMFs A-band scattering profile retrieval improves further A-band instrument needed to mitigate scattering profile errors High resolution, low error instrument demonstrates superior cloud profile retrieval, however AMFs do not improve significantly For application to characterise AMFs for uv, then A-band with nm resolution, signal to noise ~250 (moderate reqs on other instrumental error sources).