APOLLO_NG A new cloud retrieval for the CAMS radiation service

Slides:

Advertisements

Similar presentations

Gert König-Langlo, DataCite Meeting 2010 The World Radiation Monitoring Center and the Baseline Surface Radiation Network.

Advertisements

Virtual Institute of Energy Meteorology Earth Observations and Energy Management Expert Meeting August 2006 M. Schroedter-Homscheidt German Aerospace.

15 June 2005 Workshop on climatic analysis and mapping for agriculture Frost mapping with NOAA- AVHRR data G. Antolini, V. Marletto ISTITUTO DI BIOMETEOROLOGIA.

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

Daytime Cloud Shadow Detection With MODIS Denis Grljusic Philipps University Marburg, Germany Kathy Strabala, Liam Gumley CIMSS Paul Menzel NOAA / NESDIS.

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

Xiaolei Niu and R. T. Pinker Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Radiative Flux Estimates from.

An Evaluation of the Surface Radiation Budget Over North America for a Suite of Regional Climate Models Student: Marko Markovic Director: Colin Jones Université.

An optimal estimation based retrieval method adapted to SEVIRI infra-red measurements M. Stengel (1), R. Bennartz (2), J. Schulz (3), A. Walther (2,4),

An overview of CM SAF cloud retrieval methods Karl-Göran Karlsson SMHI, Sweden Outline: How do we observe clouds from space? Which cloud properties can.

Quantifying aerosol direct radiative effect with MISR observations Yang Chen, Qinbin Li, Ralph Kahn Jet Propulsion Laboratory California Institute of Technology,

Brief Overview of CM-SAF & Possible use of the Data for NCMPs.

Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.

KMA NMSC Abstract Operational COMS(Communication, Ocean and Meteorological Satellite) Cloud Detection(CLD) algorithm shows that fog and low-level clouds.

Quick Review of Remote Sensing Basic Theory Paolo Antonelli CIMSS University of Wisconsin-Madison Benevento, June 2007.

CRN Workshop, March 3-5, An Attempt to Evaluate Satellite LST Using SURFRAD Data Yunyue Yu a, Jeffrey L. Privette b, Mitch Goldberg a a NOAA/NESDIS/StAR.

Meteorolojik Uzaktan Algılamaya Giriş Erdem Erdi Uzaktan Algılama Şube Müdürlüğü 7-8 Mayıs 2012, İzmir.

Meteosat MVIRI surface radiation dataset

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.

Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.

OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Scattering by Clouds & Applications.

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

Challenges with AVHRR retrieval at high-latitudes ESA LTDP meeting DLR, Munich April 2015 Karl-Göran Karlsson SMHI, Norrköping, Sweden Öystein Godöy.

Evaluation and applications of a new satellite-based surface solar radiation data set for climate analysis Jörg Trentmann1, Richard Müller1, Christine.

New Products from combined MODIS/AIRS Jun Li, Chian-Yi Liu, Allen Huang, Xuebao Wu, and Liam Gumley Cooperative Institute for Meteorological Satellite.

Satellite-derived Sea Surface Temperatures Corey Farley Remote Sensing May 8, 2002.

AGU 2002 Fall Meeting NASA Langley Research Center / Atmospheric Sciences Validation of GOES-8 Derived Cloud Properties Over the Southeastern Pacific J.

Land Surface Analysis SAF: Contributions to NWP Isabel F. Trigo.

USING OF METEOSAT SECOND GENERATION HIGH RESOLUTION VISIBLE DATA FOR THE IMPOVEMENT OF THE RAPID DEVELOPPING THUNDERSTORM PRODUCT Oleksiy Kryvobok Ukrainian.

APOLLO Cloud Properties Challenges and Requirements after 28 Years of Experience at DLR Lars Klüser, Niels Killius and Gerhard Gesell Chart.

Improvements of the Geostationary Operational Environmental Satellites (GOES)-R series for Climate Applications GOES-R data and products will support applications.

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

Optical properties Satellite observation ? T,H 2 O… From dust microphysical properties to dust hyperspectral infrared remote sensing Clémence Pierangelo.

Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal.

J AMS Annual Meeting - 16SATMET New Automated Methods for Detecting Volcanic Ash and Retrieving Its Properties from Infrared Radiances Michael.

1 « TReSS » ATMOSPHERIC OPTICAL REMOTE SENSING TRANSPORTABLE-MOBILE PLATFORM Pierre H. Flamant, Claude Loth Juan Cuesta, Dimitri Edouard, Florian Lapouge*

Jinlong Li 1, Jun Li 1, Christopher C. Schmidt 1, Timothy J. Schmit 2, and W. Paul Menzel 2 1 Cooperative Institute for Meteorological Satellite Studies.

Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.

Dipl.-Geogr. Markus TumD Wessling German Aerospace CenterGermany German Remote Sensing Data CenterTel Land Surface DynamicsFax.

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.

Cloud optical properties: modeling and sensitivity study Ping Yang Texas A&M University May 28,2003 Madison, Wisconsin.

R. T. Pinker, H. Wang, R. Hollmann, and H. Gadhavi Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Use of.

Clouds & Radiation: Climate data vs. model results A tribute to ISCCP Ehrhard Raschke, University of Hamburg Stefan Kinne, MPI-Meteorology Hamburg 25 years.

Use of a high-resolution cloud climate data set for validation of Rossby Centre climate simulations Presentation at the EUMETSAT Meteorological Satellite.

Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.

Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.

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.

May 15, 2002MURI Hyperspectral Workshop1 Cloud and Aerosol Products From GIFTS/IOMI Gary Jedlovec and Sundar Christopher NASA Global Hydrology and Climate.

EUMETSAT, May 2014 Coordination Group for Meteorological Satellites - CGMS Terms of Reference CGMS-ICWG CGMS International Clouds Working Group by EUMETSAT.

Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.

Coordination Group for Meteorological Satellites - CGMS Korea Meteorological Administration, May 2015 KMA Progress on COMS Cloud Detection Presented to.

Copernicus Atmosphere Monitoring Service CAMS General Assembly, Athens, June 2016 Marion Schroedter-Homscheidt (DLR) with Armines (L. Wald, M. Lefèvre),

Masaya Takahashi Meteorological Satellite Center,

CLAVR-x in CSPP Andrew Heidinger, NOAA/NESDIS/STAR, Madison WI

Australian report for Atmosphere theme

Dust detection methods applied to MODIS and VIIRS

Best practices for RGB compositing of multi-spectral imagery

Precipitation Classification and Analysis from AMSU

Meteorological Satellite Center Japan Meteorological Agency

Vicarious calibration by liquid cloud target

Comparison of GOME-2 and OMI surface UV products

Jian Wang, Ph.D IMCS Rutgers University

German Aerospace Center – Space Administration

AVHRR operational cloud masks intercomparison

The World Radiation Monitoring Center

Zhang Hailong, Xin Xiaozhou, Liu Qinhuo

+ = Climate Responses to Biomass Burning Aerosols over South Africa

Generation of Simulated GIFTS Datasets

Studying the cloud radiative effect using a new, 35yr spanning dataset of cloud properties and radiative fluxes inferred from global satellite observations.

Satellite-based climate data records of the surface solar radiation Jörg Trentmann, Uwe Pfeifroth, Steffen Kothe, Vivien Priemer.

Presentation transcript:

APOLLO_NG A new cloud retrieval for the CAMS radiation service Niels Killius1, Lars Klüser1, Shengyin Li1, Marion Schroedter-Homscheidt1 , Philippe Blanc2 1German Aerospace Center (DLR), Wessling, Germany 2MINES Paristech / Armines, Sophia-Antipolis, France

Outline Copernicus atmospheric monitoring service (CAMS) and its radiation service APOLLO/APOLLO_NG How can APOLLO_NG improve CAMS radiation service?

CAMS and its radiation service CAMS services: Air quality & composition, climate forcing, eimissions & surface fluxes, … CAMS radiation service: provides UV information, solar radiation (clear sky + total sky) Free of charge + well documented / easy to recompute

How to compute solar radiation – Heliosat-4 method

Heliosat-4 cloud properties APOLLO (Avhrr Processing scheme Over Land, cLouds and Ocean, Saunders & Kriebel, 1983)  uses AVHRR heritage channels (0.6, 0.8, 1.6/3.9, 10.8, 12µm) Five different cloud tests (Dynamic visible test, infrared gross temperature test, shortwave reflectance ratio, spatial coherence test, brightness temperature difference) Cloud mask decision: bit adding scheme of fixed thresholds

APOLLO_NG: a probabilistic cloud scheme Uses the same five cloud tests as APOLLO Probability instead of binary threshold Start cloud probability is subsequently updated with cloud test results

Which cloud probability threshold fits best to the old world? Count number of pixels for which APOLLO and APOLLO_NG agree in cloud/no cloud decision Number of agreeing pixels is highest for an APOLLO_NG cloud probability threshold of 40% in most cases. Date Time of observations (UTC) 2008-01-15 02:15 – 21:45 2008-04-15 02:45 – 21:15 2008-06-13 2008-06-17 2008-06-18 2008-06-22 02:15 – 14:00 2008-07-03 02:30 – 21:45 2008-08-15 02:30 – 21:30 2008-10-15

Comparison APOLLO_NG vs GFSC cloud product Three golden days out of Cloud Retrieval Evaluation Workshops (CREW) dataset: 13th June, 22nd June and 3rd July 2008

Comparison: cold desert surface 15. January 2008 08:00 UTC RGB composite Cloud mask comparison pthresh = 40% Old APOLLO Cloudfree Cloudy APOLLO_NG

Comparison with ground measurements APOLLO_NG vs original APOLLO cloud properties as HS-4 input Comparison with ground measurements (Plataforma solar de Almería(PSA) & BSRN stations IZA, LIN, PAY, CAM, CAR, SBO, TAM) Date Time of observations (UTC) 2005-08-08 02:15-21:45 2008-01-15 02:15 – 21:45 2008-04-15 02:45 – 21:15 2008-06-13 2008-06-17 2008-06-18 2008-06-22 02:15 – 14:00 2008-07-03 02:30 – 21:45 2008-08-15 02:30 – 21:30 2008-10-15 2009-01-04 2013-07-15 2013-10-15

References Hoyer-Klick, C., Lefèvre, M., Schroedter-Homscheidt, M., Wald, L., USER’S GUIDE to the MACC-RAD Services on solar energy radiation resources, MACC III project report D57.5, version v4.0, public report accessible via http://www.atmosphere.copernicus.eu, 2015 Klüser, L., Killius, N., & Gesell, G. (2015). APOLLO_NG–a probabilistic interpretation of the APOLLO legacy for AVHRR heritage channels. Atmospheric Measurement Techniques, 8(10), 4155-4170. König-Langlo, G. , Sieger, R. , Schmithüsen, H. , Bücker, A. , Richter, F. and Dutton E.G. 2013 The Baseline Surface Radiation Network and its World Radiation Monitoring Centre at the Alfred Wegener Institute. Qu, Z., Oumbe, A., Blanc, P., Espinar, B., Gschwind, G., Lefèvre, M., ... & Klueser, L. (2016). Fast radiative transfer parameterisation for assessing the surface solar irradiance: The Heliosat-4 method. Meteorol. Z. Roebeling, R., B. Baum, R. Bennartz, U. Hamann, A. Heidinger, A. Thoss, and A. Walther (2012): Evaluating and Improving Cloud Parameter Retrievals. Bulletin of the American Meteorological Society, 94, ES41–ES44, http://journals.ametsoc.org/doi/full/10.1175/BAMS-D-12-00041.1 Saunders, R. W., & Kriebel, K. T. (1988). An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9(1), 123-150.

Thank you