Satellite Remote Sensing of Volcanic Ash

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Satellite Remote Sensing of Volcanic Ash"

Transcription

1 Marco Fulle Satellite Remote Sensing of Volcanic Ash Michael Pavolonis NOAA/NESDIS/STAR SCOPE Nowcasting 1 Meeting November 19 22,

2 Outline Getty Images Volcanic ash satellite remote sensing challenges Photo: Photo/Jose Luis Pos Overview of advanced volcanic cloud remote sensing techniques Integrating passive satellite remote sensing products with other tools

3 Volcanic ash satellite remote sensing challenges

4 1). Ash dominated volcanic plumes Semitransparent clouds dominated by volcanic ash. Lightning is usually not present in these clouds. 2). Ice topped umbrella clouds These cloud are mostly observed during a major eruption. A spectral based volcanic ash signal is usually initially absent because the ash is encased in ice and/or the cloud is opaque. Lightning is often present in these clouds. 3). SO 2 clouds Sulfur dioxide clouds (SO 2 gas is invisible to the eye) that may or may not contain volcanic ash. Some eruptions produce large amounts of SO 2 and very little ash and vice versa. Nadeau and Dalton (2009)

5 1). Ash dominated volcanic plumes Semitransparent clouds dominated by volcanic ash. Lightning is usually not present in these clouds. Most important sensor attributes Cloud Tracking/Identification At least two channels in the μm window μm and/or 3.9 μm channel(s) High temporal refresh High spatial resolution Global coverage Large viewing angle and/or high spectral resolution Determining Cloud Properties Lidar and/or multiple IR window channels + IR CO 2 absorption channels and/or multi angle measurements High temporal refresh High spatial resolution Global coverage Large viewing angle and/or high spectral resolution No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

6 2). Ice topped umbrella clouds These cloud are mostly observed during a major eruption. A spectral based volcanic ash signal is usually initially absent because the ash is encased in ice and/or the cloud is opaque. Lightning is often present in these clouds. Cloud Tracking/Identification Measurements at UV and/or visible wavelengths High temporal refresh (at least 15 min; 5 min or less is much better) High spatial resolution Global coverage Lightning detection capability Most important sensor attributes Determining Cloud Properties Active sensor (lidar or radar) and/or multiple IR window channels + IR CO 2 absorption channels and/or multi angle measurements High temporal refresh High spatial resolution Global coverage No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

7 3). SO 2 clouds Sulfur dioxide clouds (SO 2 gas is invisible to the eye) that may or may not contain volcanic ash. Some eruptions produce large amounts of SO 2 and very little ash and vice versa. Nadeau and Dalton (2009) Cloud Tracking/Identification Most important sensor attributes High spectral resolution measurements in the nm range and/or 7 9 μm range High temporal refresh Global coverage Determining Cloud Properties High spectral resolution measurements in the nm range and/or 7 9 μm range High temporal refresh Global coverage No planned or current sensor has all of these attributes (e.g. best spectral and spatial info is generally available on LEO sensors; high temporal refresh is achieved with GEO sensors)

8 Summary of Primary Challenges No single sensor is ideally suited for detecting and characterizing all types of volcanic clouds in a timely manner Basic cloud retrieval challenges: multiple cloud layers (results in artifacts) with the same and/or different compositions, uncertainty in microphysical parameters (particle shape, index of refraction, etc ) Mitigating measurement errors and artifacts (calibration, noise, stray light, sensor degradation, striping, navigation errors, etc ) Interaction with operational users developing product displays, communicating product uncertainty and caveats A multi sensor approach is best, but more difficult to design and implement

9 Overview of advanced volcanic cloud remote sensing techniques

10 Possible attributes of advanced approaches Explicitly accounts for surface temperature, surface emissivity, atmospheric temperature, and major background absorbing gases (H 2 O, CO 2, O 3 ) Retrieves ash cloud temperature/pressure/height (uses absorption channels) Fully automated Utilizes a flexible (in terms of input and output) mathematical model that provides uncertainty estimates (e.g. optimal estimation) Does not rely on negative um BTD Utilizes all relevant spectral information Utilizes spatial and temporal information Multi sensor based approach **Volcanic cloud retrieval algorithms should be consistent with best practices established by the greater meteorological remote sensing community

11 Francis et al., 2012 Pavolonis, 2010 and Pavolonis et al., 2013 Also see Clarisse et al., 2010 and Watts et al., 2011 for examples of optimal estimation schemes

12 Benefits of Optimal Estimation and CO 2 Absorption Channels Unless the cloud is really optically thin, the results are not particularly sensitive to the first guess Tri spectral optimal estimation schemes that robustly account for background conditions have demonstrated skill in retrieving ash cloud loading and height Pavolonis et al., 2013

13 Benefits of Optimal Estimation and CO 2 Absorption Channels Francis et al., 2012 Tri spectral optimal estimation schemes that robustly account for background conditions have demonstrated skill in retrieving ash cloud loading and height

14 Prata and Prata (2012) assembled a multi sensor (ground and aircraft based) mass loading validation data set and validated their split window approach. Is this data set available for other algorithm developers to use? The establishment of common validation data sets is an important steps towards harmonization. Prata and Prata, 2012

15 Hyperspectral IR algorithms can now differentiate different aerosol types with good skill Clarisse et al., 2013

16 Reliance on manual analysis of satellite images from a single sensor means that volcanic ash advisories and volcanic SIGMETS are not always timely and accurate! Nabro volcano in Eritrea erupted for the first time in recorded history on June 12, 2011 at ~20:30 UTC, injecting volcanic ash and dangerous concentrations of SO 2 high into the atmosphere The first VAA was issued at 04:00 UTC on June 13 (~7.5 hours after the start of the eruption!) Nabro The height estimate for this cloud was also severely underestimated

17 Automated alert would have been issued after 20:45 UTC image on June 12, 2011

18 Subscriptions to the experimental NOAA volcanic cloud alerting system will be more broadly available in 2014

19 Brenot et al., 2013

20 Example Procedure for Combining Measurements from Different Sensors on the Same Spacecraft 1). Perform volcanic cloud retrieval using hyperspectral sounder (e.g. CrIS, IASI, AIRS) EUMETSAT 2). Perform retrieval using high spatial resolution imager (e.g. VIIRS, AVHRR, MODIS) a.use hyperspectral retrieval as first guess b.fill in important spectral gaps (e.g. LW CO 2 ) needed for high quality retrieval by interpolating from sounder spatial resolution to imager spatial resolution The end result is a high spatial resolution product that is more accurate because hyperspectral information was incorporated into the retrieval!

21 Example Procedure for Combining Measurements from Different Sensors on Different Spacecraft (e.g. LEO/GEO) High quality retrieval results from LEO sensors can be used as a first guess into GEO sensor retrievals, which generally have less spectral information to work with. The end result is a high temporal resolution product with an accuracy similar to that achievable from LEO sensors (information from LEO is transferred to GEO). A relatively recent LEO overpass of the cloud is required though!

22 Integrating passive satellite remote sensing products with other tools

23 Eruptive source term inversion modeling Source term parameters that minimize the difference between the satellite derived mass loadings and Lagrangian model predicted mass loadings are chosen. Effective automation of this process requires highly accurate satellite products (low false alarms, high detection, consistently reasonable mass loading estimates Stohl et al., 2011

24 Using satellite retrievals to initialize forward trajectories (collaboration between BoM and NOAA) NOAA/ARL and NOAA/NESDIS are working to automate this process with HYSPLIT

25 MER can be estimated from the geometric properties of volcanic clouds this process can potentially be automated using advanced satellite algorithms Pouget et al., 2013

26 Ash probability determined from satellite observations alone

27 Ash probability determined from PUFF simulations alone (Bursik et al., 2012)

28 Ash probability determined from satellite + PUFF simulations alone

29 Getty Images Summary Photo: Photo/Jose Luis Pos Harmonization of volcanic ash products is a daunting task, as achieving consistency through time using a single sensor is, itself, a challenge. A multi sensor approach may be the best path to harmonization Harmonization of product display practices and user training are also important

30 Merapi ash plume/cloud The 24 hour animation of 1 hour MTSAT 1R on November 11, 2010.

Volcanic Ash Monitoring: Product Guide

Volcanic Ash Monitoring: Product Guide Doc.No. Issue : : EUM/TSS/MAN/15/802120 v1a EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 2 June 2015 http://www.eumetsat.int WBS/DBS : EUMETSAT

More information

Authors: Thierry Phulpin, CNES Lydie Lavanant, Meteo France Claude Camy-Peyret, LPMAA/CNRS. Date: 15 June 2005

Authors: Thierry Phulpin, CNES Lydie Lavanant, Meteo France Claude Camy-Peyret, LPMAA/CNRS. Date: 15 June 2005 Comments on the number of cloud free observations per day and location- LEO constellation vs. GEO - Annex in the final Technical Note on geostationary mission concepts Authors: Thierry Phulpin, CNES Lydie

More information

REMOTE SENSING OF CLOUD-AEROSOL RADIATIVE EFFECTS FROM SATELLITE DATA: A CASE STUDY OVER THE SOUTH OF PORTUGAL

REMOTE SENSING OF CLOUD-AEROSOL RADIATIVE EFFECTS FROM SATELLITE DATA: A CASE STUDY OVER THE SOUTH OF PORTUGAL REMOTE SENSING OF CLOUD-AEROSOL RADIATIVE EFFECTS FROM SATELLITE DATA: A CASE STUDY OVER THE SOUTH OF PORTUGAL D. Santos (1), M. J. Costa (1,2), D. Bortoli (1,3) and A. M. Silva (1,2) (1) Évora Geophysics

More information

Best practices for RGB compositing of multi-spectral imagery

Best practices for RGB compositing of multi-spectral imagery Best practices for RGB compositing of multi-spectral imagery User Service Division, EUMETSAT Introduction Until recently imagers on geostationary satellites were limited to 2-3 spectral channels, i.e.

More information

Validation of SEVIRI cloud-top height retrievals from A-Train data

Validation of SEVIRI cloud-top height retrievals from A-Train data Validation of SEVIRI cloud-top height retrievals from A-Train data Chu-Yong Chung, Pete N Francis, and Roger Saunders Contents Introduction MO GeoCloud AVAC-S Long-term monitoring Comparison with OCA Summary

More information

WMO 7 International Workshop on Volcanic Ash, Anchorage, AK

WMO 7 International Workshop on Volcanic Ash, Anchorage, AK An algorithm for automated cloud pattern recognition and mass eruption rate estimation from umbrella cloud or downwind plume observed via satellite imagery 1 WMO 7 International Workshop on Volcanic Ash,

More information

Denis Botambekov 1, Andrew Heidinger 2, Andi Walther 1, and Nick Bearson 1

Denis Botambekov 1, Andrew Heidinger 2, Andi Walther 1, and Nick Bearson 1 Denis Botambekov 1, Andrew Heidinger 2, Andi Walther 1, and Nick Bearson 1 1 - CIMSS / SSEC / University of Wisconsin Madison, WI, USA 2 NOAA / NESDIS / STAR @ University of Wisconsin Madison, WI, USA

More information

P3.8 DETECTING VOLCANIC ASH AND BLOWING DUST USING GOES, MODIS, AND AIRS IMAGERY

P3.8 DETECTING VOLCANIC ASH AND BLOWING DUST USING GOES, MODIS, AND AIRS IMAGERY P3.8 DETECTING VOLCANIC ASH AND BLOWING DUST USING GOES, MODIS, AND AIRS IMAGERY Bernadette H. Connell Cooperative Institute for Research in the Atmosphere (CIRA) Colorado State University, Fort Collins,

More information

Evaluation of VIIRS cloud top property climate data records and their potential improvement with CrIS

Evaluation of VIIRS cloud top property climate data records and their potential improvement with CrIS Evaluation of VIIRS cloud top property climate data records and their potential improvement with CrIS Dr. Bryan A. Baum (PI) Space Science and Engineering Center University of Wisconsin-Madison Madison,

More information

GOES-R AWG Cloud Team: ABI Cloud Height

GOES-R AWG Cloud Team: ABI Cloud Height GOES-R AWG Cloud Team: ABI Cloud Height June 8, 2010 Presented By: Andrew Heidinger 1 1 NOAA/NESDIS/STAR 1 Outline Executive Summary Algorithm Description ADEB and IV&V Response Summary Requirements Specification

More information

Joint Polar Satellite System (JPSS)

Joint Polar Satellite System (JPSS) Joint Polar Satellite System (JPSS) John Furgerson, User Liaison Joint Polar Satellite System National Environmental Satellite, Data, and Information Service National Oceanic and Atmospheric Administration

More information

Night Microphysics RGB Nephanalysis in night time

Night Microphysics RGB Nephanalysis in night time Copyright, JMA Night Microphysics RGB Nephanalysis in night time Meteorological Satellite Center, JMA What s Night Microphysics RGB? R : B15(I2 12.3)-B13(IR 10.4) Range : -4 2 [K] Gamma : 1.0 G : B13(IR

More information

Evaluation of the Effect of Upper-Level Cirrus Clouds on Satellite Retrievals of Low-Level Cloud Droplet Effective Radius

Evaluation of the Effect of Upper-Level Cirrus Clouds on Satellite Retrievals of Low-Level Cloud Droplet Effective Radius Evaluation of the Effect of Upper-Level Cirrus Clouds on Satellite Retrievals of Low-Level Cloud Droplet Effective Radius F.-L. Chang and Z. Li Earth System Science Interdisciplinary Center University

More information

Cloud detection and clearing for the MOPITT instrument

Cloud detection and clearing for the MOPITT instrument Cloud detection and clearing for the MOPITT instrument Juying Warner, John Gille, David P. Edwards and Paul Bailey National Center for Atmospheric Research, Boulder, Colorado ABSTRACT The Measurement Of

More information

Remote Sensing of Clouds from Polarization

Remote Sensing of Clouds from Polarization Remote Sensing of Clouds from Polarization What polarization can tell us about clouds... and what not? J. Riedi Laboratoire d'optique Atmosphérique University of Science and Technology Lille / CNRS FRANCE

More information

GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Low Cloud and Fog

GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Low Cloud and Fog NOAA NESDIS CENTER for SATELLITE APPLICATIONS and RESEARCH GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Low Cloud and Fog Corey Calvert, UW/CIMSS Mike Pavolonis, NOAA/NESDIS/STAR

More information

A remote sensing instrument collects information about an object or phenomenon within the

A remote sensing instrument collects information about an object or phenomenon within the Satellite Remote Sensing GE 4150- Natural Hazards Some slides taken from Ann Maclean: Introduction to Digital Image Processing Remote Sensing the art, science, and technology of obtaining reliable information

More information

Passive Remote Sensing of Clouds from Airborne Platforms

Passive Remote Sensing of Clouds from Airborne Platforms Passive Remote Sensing of Clouds from Airborne Platforms Why airborne measurements? My instrument: the Solar Spectral Flux Radiometer (SSFR) Some spectrometry/radiometry basics How can we infer cloud properties

More information

Realization of a UV fisheye hyperspectral camera

Realization of a UV fisheye hyperspectral camera Realization of a UV fisheye hyperspectral camera Valentina Caricato, Andrea Egidi, Marco Pisani and Massimo Zucco, INRIM Outline Purpose of the instrument Required specs Hyperspectral technique Optical

More information

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D Munn V. Shukla and P. K. Thapliyal Atmospheric Sciences Division Atmospheric and Oceanic Sciences Group Space Applications

More information

Satellite remote sensing using AVHRR, ATSR, MODIS, METEOSAT, MSG

Satellite remote sensing using AVHRR, ATSR, MODIS, METEOSAT, MSG Satellite remote sensing using AVHRR, ATSR, MODIS, METEOSAT, MSG Ralf Meerkötter, Luca Bugliaro, Knut Dammann, Gerhard Gesell, Christine König, Waldemar Krebs, Hermann Mannstein, Bernhard Mayer, presented

More information

Overview of the IR channels and their applications

Overview of the IR channels and their applications Ján Kaňák Slovak Hydrometeorological Institute Jan.kanak@shmu.sk Overview of the IR channels and their applications EUMeTrain, 14 June 2011 Ján Kaňák, SHMÚ 1 Basics in satellite Infrared image interpretation

More information

Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect

Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect Tuuli Perttula, FMI + Thanks to: Nadia Fourrié, Lydie Lavanant, Florence Rabier and Vincent Guidard, Météo

More information

The Sentinel-4/UVN instrument on-board MTG-S

The Sentinel-4/UVN instrument on-board MTG-S The Sentinel-4/UVN instrument on-board MTG-S Grégory Bazalgette Courrèges-Lacoste; Berit Ahlers; Benedikt Guldimann; Alex Short; Ben Veihelmann, Hendrik Stark ESA ESTEC European Space Technology & Research

More information

ECMWF Aerosol and Cloud Detection Software. User Guide. version 1.2 20/01/2015. Reima Eresmaa ECMWF

ECMWF Aerosol and Cloud Detection Software. User Guide. version 1.2 20/01/2015. Reima Eresmaa ECMWF ECMWF Aerosol and Cloud User Guide version 1.2 20/01/2015 Reima Eresmaa ECMWF This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction

More information

16 th IOCCG Committee annual meeting. Plymouth, UK 15 17 February 2011. mission: Present status and near future

16 th IOCCG Committee annual meeting. Plymouth, UK 15 17 February 2011. mission: Present status and near future 16 th IOCCG Committee annual meeting Plymouth, UK 15 17 February 2011 The Meteor 3M Mt satellite mission: Present status and near future plans MISSION AIMS Satellites of the series METEOR M M are purposed

More information

P1.70 NIGHTTIME RETRIEVAL OF CLOUD MICROPHYSICAL PROPERTIES FOR GOES-R

P1.70 NIGHTTIME RETRIEVAL OF CLOUD MICROPHYSICAL PROPERTIES FOR GOES-R P1.70 NIGHTTIME RETRIEVAL OF CLOUD MICROPHYSICAL PROPERTIES FOR GOES-R Patrick W. Heck * Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison Madison, Wisconsin P.

More information

Cloud Climatology for New Zealand and Implications for Radiation Fields

Cloud Climatology for New Zealand and Implications for Radiation Fields Cloud Climatology for New Zealand and Implications for Radiation Fields G. Pfister, R.L. McKenzie, J.B. Liley, A. Thomas National Institute of Water and Atmospheric Research, Lauder, New Zealand M.J. Uddstrom

More information

Sky Monitoring Techniques using Thermal Infrared Sensors. sabino piazzolla Optical Communications Group JPL

Sky Monitoring Techniques using Thermal Infrared Sensors. sabino piazzolla Optical Communications Group JPL Sky Monitoring Techniques using Thermal Infrared Sensors sabino piazzolla Optical Communications Group JPL Atmospheric Monitoring The atmospheric channel has a great impact on the channel capacity at optical

More information

VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA

VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA VALIDATION OF SAFNWC / MSG CLOUD PRODUCTS WITH ONE YEAR OF SEVIRI DATA M.Derrien 1, H.Le Gléau 1, Jean-François Daloze 2, Martial Haeffelin 2 1 Météo-France / DP / Centre de Météorologie Spatiale. BP 50747.

More information

Hyperspectral Satellite Imaging Planning a Mission

Hyperspectral Satellite Imaging Planning a Mission Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute of Aerospace, Langley, VA Outline Objective

More information

Cloud Masking and Cloud Products

Cloud Masking and Cloud Products Cloud Masking and Cloud Products MODIS Operational Algorithm MOD35 Paul Menzel, Steve Ackerman, Richard Frey, Kathy Strabala, Chris Moeller, Liam Gumley, Bryan Baum MODIS Cloud Masking Often done with

More information

SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations

SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations 22 September 2011 Hervé LE GLEAU, Marcel DERRIEN Centre de météorologie Spatiale. Lannion Météo-France 1 Fog or low level clouds?

More information

José A. Morales-Collazo. Geology Department, University of Puerto Rico, Mayagüez Campus Call Box 9000 Mayagüez, Puerto Rico, 00681

José A. Morales-Collazo. Geology Department, University of Puerto Rico, Mayagüez Campus Call Box 9000 Mayagüez, Puerto Rico, 00681 Measuring Sulphur Dioxide (SO 2 ) Emissions in October, 2010 Catastrophic Eruption from Merapi Volcano in Java, Indonesia with Ozone Monitoring Instrument (OMI) José A. Morales-Collazo Geology Department,

More information

Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction

Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction Content Remote sensing data Spatial, spectral, radiometric and

More information

International coordination for continuity and interoperability: a CGMS perspective

International coordination for continuity and interoperability: a CGMS perspective International coordination for continuity and interoperability: a CGMS perspective Peng Zhang, CGMS WG-III Co-Chair NSMC/CMA In Cooperation with Suzanne Hilding, CGMS WG-III Co-Chair OPPA/NESDIS/NOAA 1

More information

Concept/prototype of a FAQ section on the MACC-III website

Concept/prototype of a FAQ section on the MACC-III website MACC-III Deliverable D65.2 Concept/prototype of a FAQ section on the MACC-III website Date: 04/2015 Lead Beneficiary: BENEFICIARY (#33) Nature: R Dissemination level: PP Grant agreement n 633080 Work-package

More information

Scientific Progress and Recommendations from the International Volcanic Ash Task Force

Scientific Progress and Recommendations from the International Volcanic Ash Task Force Scientific Progress and Recommendations from the International Volcanic Ash Task Force Marianne Guffanti, USGS Larry Mastin, USGS Andrew Tupper, Australian Bureau of Meteorology Raul Romero, ICAO Peter

More information

Eruption of Mt. Kilauea Impacted Cloud Droplet and Radiation Budget over North Pacific

Eruption of Mt. Kilauea Impacted Cloud Droplet and Radiation Budget over North Pacific Western Pacific Air-Sea Interaction Study, Eds. M. Uematsu, Y. Yokouchi, Y. W. Watanabe, S. Takeda, and Y. Yamanaka, pp. 83 87. by TERRAPUB 2014. doi:10.5047/w-pass.a01.009 Eruption of Mt. Kilauea Impacted

More information

EUMETSAT Satellite Programmes

EUMETSAT Satellite Programmes EUMETSAT Satellite Programmes Nowcasting Applications Developing Countries Marianne König marianne.koenig@eumetsat.int WSN-12 Rio de Janeiro 06-10 August 2012 27 Member States & 4 Cooperating States Member

More information

Clouds and the Energy Cycle

Clouds and the Energy Cycle August 1999 NF-207 The Earth Science Enterprise Series These articles discuss Earth's many dynamic processes and their interactions Clouds and the Energy Cycle he study of clouds, where they occur, and

More information

Cloud Mask Product: Product Guide

Cloud Mask Product: Product Guide Doc.No. Issue : : EUM/TSS/MAN/15/801027 v1a EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 21 August 2015 http://www.eumetsat.int WBS : EUMETSAT

More information

Electromagnetic Radiation (EMR) and Remote Sensing

Electromagnetic Radiation (EMR) and Remote Sensing Electromagnetic Radiation (EMR) and Remote Sensing 1 Atmosphere Anything missing in between? Electromagnetic Radiation (EMR) is radiated by atomic particles at the source (the Sun), propagates through

More information

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data Mentor: Dr. Malcolm LeCompte Elizabeth City State University

More information

Evaluations of the CALIPSO Cloud Optical Depth Algorithm Through Comparisons with a GOES Derived Cloud Analysis

Evaluations of the CALIPSO Cloud Optical Depth Algorithm Through Comparisons with a GOES Derived Cloud Analysis Generated using V3.0 of the official AMS LATEX template Evaluations of the CALIPSO Cloud Optical Depth Algorithm Through Comparisons with a GOES Derived Cloud Analysis Katie Carbonari, Heather Kiley, and

More information

Evaluating GCM clouds using instrument simulators

Evaluating GCM clouds using instrument simulators Evaluating GCM clouds using instrument simulators University of Washington September 24, 2009 Why do we care about evaluation of clouds in GCMs? General Circulation Models (GCMs) project future climate

More information

Remote Sensing of Cloud Properties from the Communication, Ocean and Meteorological Satellite (COMS) Imagery

Remote Sensing of Cloud Properties from the Communication, Ocean and Meteorological Satellite (COMS) Imagery Remote Sensing of Cloud Properties from the Communication, Ocean and Meteorological Satellite (COMS) Imagery Choi, Yong-Sang, 1 Chang-Hoi Ho, 1 Myoung-Hwan Ahn, and Young-Mi Kim 1 1 School of Earth and

More information

Summary Report on National and Regional Projects set-up in Russian Federation to integrate different Ground-based Observing Systems

Summary Report on National and Regional Projects set-up in Russian Federation to integrate different Ground-based Observing Systems WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR INSTRUMENT AND METHODS OF OBSERVATION OPAG-UPPER AIR EXPERT TEAM ON REMOTE SENSING UPPER-AIR TECHNOLOGY AND TECHNIQUES First Session Geneva, Switzerland,

More information

USING THE GOES 3.9 µm SHORTWAVE INFRARED CHANNEL TO TRACK LOW-LEVEL CLOUD-DRIFT WINDS ABSTRACT

USING THE GOES 3.9 µm SHORTWAVE INFRARED CHANNEL TO TRACK LOW-LEVEL CLOUD-DRIFT WINDS ABSTRACT USING THE GOES 3.9 µm SHORTWAVE INFRARED CHANNEL TO TRACK LOW-LEVEL CLOUD-DRIFT WINDS Jason P. Dunion 1 and Christopher S. Velden 2 1 NOAA/AOML/Hurricane Research Division, 2 UW/CIMSS ABSTRACT Low-level

More information

On the use of Synthetic Satellite Imagery to Evaluate Numerically Simulated Clouds

On the use of Synthetic Satellite Imagery to Evaluate Numerically Simulated Clouds On the use of Synthetic Satellite Imagery to Evaluate Numerically Simulated Clouds Lewis D. Grasso (1) Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado Don Hillger NOAA/NESDIS/STAR/RAMMB,

More information

Obtaining and Processing MODIS Data

Obtaining and Processing MODIS Data Obtaining and Processing MODIS Data MODIS is an extensive program using sensors on two satellites that each provide complete daily coverage of the earth. The data have a variety of resolutions; spectral,

More information

COMPUTING CLOUD MOTION USING A CORRELATION RELAXATION ALGORITHM Improving Estimation by Exploiting Problem Knowledge Q. X. WU

COMPUTING CLOUD MOTION USING A CORRELATION RELAXATION ALGORITHM Improving Estimation by Exploiting Problem Knowledge Q. X. WU COMPUTING CLOUD MOTION USING A CORRELATION RELAXATION ALGORITHM Improving Estimation by Exploiting Problem Knowledge Q. X. WU Image Processing Group, Landcare Research New Zealand P.O. Box 38491, Wellington

More information

Cloud Remote Sensing during VOCALS- REx: Selected U.S. Efforts

Cloud Remote Sensing during VOCALS- REx: Selected U.S. Efforts Cloud Remote Sensing during VOCALS- REx: Selected U.S. Efforts Paquita Zuidema, U of Miami Qabs ~ 4xI{(m 2-1)/(m 2 +2)} Qscat ~ 8/3 x 4 (m 2-1)/(m 2 +2) 2 x=2 r/ VOCALS Educational Talk 10/31/08 1. Satellite

More information

Climatology of aerosol and cloud properties at the ARM sites:

Climatology of aerosol and cloud properties at the ARM sites: Climatology of aerosol and cloud properties at the ARM sites: MFRSR combined with other measurements Qilong Min ASRC, SUNY at Albany MFRSR: Spectral irradiances at 6 six wavelength passbands: 415, 500,

More information

Validating MOPITT Cloud Detection Techniques with MAS Images

Validating MOPITT Cloud Detection Techniques with MAS Images Validating MOPITT Cloud Detection Techniques with MAS Images Daniel Ziskin, Juying Warner, Paul Bailey, John Gille National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307 ABSTRACT The

More information

RESULTS FROM A SIMPLE INFRARED CLOUD DETECTOR

RESULTS FROM A SIMPLE INFRARED CLOUD DETECTOR RESULTS FROM A SIMPLE INFRARED CLOUD DETECTOR A. Maghrabi 1 and R. Clay 2 1 Institute of Astronomical and Geophysical Research, King Abdulaziz City For Science and Technology, P.O. Box 6086 Riyadh 11442,

More information

Focus Group Proposal Whitepaper. ASR Quantification of Uncertainty in Cloud Retrievals (QUICR) Focus Group

Focus Group Proposal Whitepaper. ASR Quantification of Uncertainty in Cloud Retrievals (QUICR) Focus Group Focus Group Proposal Whitepaper ASR Quantification of Uncertainty in Cloud Retrievals (QUICR) Focus Group Mission Statement The mission of the ASR Quantification of Uncertainty in Cloud Retrievals (QUICR)

More information

Corso di Fisica Te T cnica Ambientale Solar Radiation

Corso di Fisica Te T cnica Ambientale Solar Radiation Solar Radiation Solar radiation i The Sun The Sun is the primary natural energy source for our planet. It has a diameter D = 1.39x10 6 km and a mass M = 1.989x10 30 kg and it is constituted by 1/3 of He

More information

'Developments and benefits of hydrographic surveying using multispectral imagery in the coastal zone

'Developments and benefits of hydrographic surveying using multispectral imagery in the coastal zone Abstract With the recent launch of enhanced high-resolution commercial satellites, available imagery has improved from four-bands to eight-band multispectral. Simultaneously developments in remote sensing

More information

Data Processing Flow Chart

Data Processing Flow Chart Legend Start V1 V2 V3 Completed Version 2 Completion date Data Processing Flow Chart Data: Download a) AVHRR: 1981-1999 b) MODIS:2000-2010 c) SPOT : 1998-2002 No Progressing Started Did not start 03/12/12

More information

Remote sensing instrumentation for volcanic ash clouds

Remote sensing instrumentation for volcanic ash clouds Remote sensing instrumentation for volcanic ash clouds Dr. Adam J. Durant (adu@nilu.no) Norwegian Institute for Air Research Fred Prata1,2, Cirilo Bernardo1, and Ian Davies3 1. Norwegian Ins;tute for Air

More information

Active Fire Monitoring: Product Guide

Active Fire Monitoring: Product Guide Active Fire Monitoring: Product Guide Doc.No. Issue : : EUM/TSS/MAN/15/801989 v1c EUMETSAT Eumetsat-Allee 1, D-64295 Darmstadt, Germany Tel: +49 6151 807-7 Fax: +49 6151 807 555 Date : 14 April 2015 http://www.eumetsat.int

More information

Synoptic assessment of AMV errors

Synoptic assessment of AMV errors NWP SAF Satellite Application Facility for Numerical Weather Prediction Visiting Scientist mission report Document NWPSAF-MO-VS-038 Version 1.0 4 June 2009 Synoptic assessment of AMV errors Renato Galante

More information

Data Processing Developments at DFD/DLR. Stefanie Holzwarth Martin Bachmann, Rudolf Richter, Martin Habermeyer, Derek Rogge

Data Processing Developments at DFD/DLR. Stefanie Holzwarth Martin Bachmann, Rudolf Richter, Martin Habermeyer, Derek Rogge Data Processing Developments at DFD/DLR Stefanie Holzwarth Martin Bachmann, Rudolf Richter, Martin Habermeyer, Derek Rogge EUFAR Joint Expert Working Group Meeting Edinburgh, April 14th 2011 Conclusions

More information

Outline of RGB Composite Imagery

Outline of RGB Composite Imagery Outline of RGB Composite Imagery Data Processing Division, Data Processing Department Meteorological Satellite Center (MSC) JMA Akihiro SHIMIZU 29 September, 2014 Updated 6 July, 2015 1 Contents What s

More information

Multiangle cloud remote sensing from

Multiangle cloud remote sensing from Multiangle cloud remote sensing from POLDER3/PARASOL Cloud phase, optical thickness and albedo F. Parol, J. Riedi, S. Zeng, C. Vanbauce, N. Ferlay, F. Thieuleux, L.C. Labonnote and C. Cornet Laboratoire

More information

A climatology of cirrus clouds from ground-based lidar measurements over Lille

A climatology of cirrus clouds from ground-based lidar measurements over Lille A climatology of cirrus clouds from ground-based lidar measurements over Lille Rita Nohra, Frédéric Parol, Philippe Dubuisson Laboratoire d Optique Atmosphérique université de Lille, CNRS UMR 8518 Objectives

More information

P1.24 USE OF ACTIVE REMOTE SENSORS TO IMPROVE THE ACCURACY OF CLOUD TOP HEIGHTS DERIVED FROM THERMAL SATELLITE OBSERVATIONS

P1.24 USE OF ACTIVE REMOTE SENSORS TO IMPROVE THE ACCURACY OF CLOUD TOP HEIGHTS DERIVED FROM THERMAL SATELLITE OBSERVATIONS P1.24 USE OF ACTIVE REMOTE SENSORS TO IMPROVE THE ACCURACY OF CLOUD TOP HEIGHTS DERIVED FROM THERMAL SATELLITE OBSERVATIONS Chris R. Yost* Patrick Minnis NASA Langley Research Center, Hampton, Virginia

More information

Review for Introduction to Remote Sensing: Science Concepts and Technology

Review for Introduction to Remote Sensing: Science Concepts and Technology Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director ann@baremt.com Funded by National Science Foundation Advanced Technological Education program [DUE

More information

Advances in Cloud Imager Remote Sensing

Advances in Cloud Imager Remote Sensing Advances in Cloud Imager Remote Sensing Andrew Heidinger NOAA/NESDIS/ORA Madison, Wisconsin With material from Mike Pavolonis, Robert Holz, Amato Evan and Fred Nagle STAR Science Symposium November 9,

More information

Quantifying Seasonal Variation in Cloud Cover with Predictive Models

Quantifying Seasonal Variation in Cloud Cover with Predictive Models Quantifying Seasonal Variation in Cloud Cover with Predictive Models Ashok N. Srivastava, Ph.D. ashok@email.arc.nasa.gov Deputy Area Lead, Discovery and Systems Health Group Leader, Intelligent Data Understanding

More information

Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies.

Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies. Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies. Sarah M. Thomas University of Wisconsin, Cooperative Institute for Meteorological Satellite Studies

More information

http://www.isac.cnr.it/~ipwg/

http://www.isac.cnr.it/~ipwg/ The CGMS International Precipitation Working Group: Experience and Perspectives Vincenzo Levizzani CNR-ISAC, Bologna, Italy and Arnold Gruber NOAA/NESDIS & Univ. Maryland, College Park, MD, USA http://www.isac.cnr.it/~ipwg/

More information

A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS

A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi:10.1029/2009jd013422, 2010 A review of cloud top height and optical depth histograms from MISR, ISCCP, and MODIS Roger Marchand, 1 Thomas Ackerman, 1 Mike

More information

International Civil Aviation Organization

International Civil Aviation Organization CNS/MET SG/9-IP/14 (MET) International Civil Aviation Organization NINTH MEETING OF THE COMMUNICATIONS/NAVIGATION/SURVEILLANCE AND METEOROLOGY SUB-GROUP OF APANPIRG (CNS/MET SG/9) Bangkok, Thailand, 11

More information

Treasure Hunt. Lecture 2 How does Light Interact with the Environment? EMR Principles and Properties. EMR and Remote Sensing

Treasure Hunt. Lecture 2 How does Light Interact with the Environment? EMR Principles and Properties. EMR and Remote Sensing Lecture 2 How does Light Interact with the Environment? Treasure Hunt Find and scan all 11 QR codes Choose one to watch / read in detail Post the key points as a reaction to http://www.scoop.it/t/env202-502-w2

More information

McIDAS-V - A powerful data analysis and visualization tool for multi and hyperspectral environmental satellite data

McIDAS-V - A powerful data analysis and visualization tool for multi and hyperspectral environmental satellite data McIDAS-V - A powerful data analysis and visualization tool for multi and hyperspectral environmental satellite data Thomas Achtor, Thomas Rink, Thomas Whittaker, David Parker and David Santek Space Science

More information

Improved diagnosis of low-level cloud from MSG SEVIRI data for assimilation into Met Office limited area models

Improved diagnosis of low-level cloud from MSG SEVIRI data for assimilation into Met Office limited area models Improved diagnosis of low-level cloud from MSG SEVIRI data for assimilation into Met Office limited area models Peter N. Francis, James A. Hocking & Roger W. Saunders Met Office, Exeter, U.K. Abstract

More information

CLOUD CLASSIFICATION EXTRACTED FROM AVHRR AND GOES IMAGERY. M.Derrien, H.Le Gléau

CLOUD CLASSIFICATION EXTRACTED FROM AVHRR AND GOES IMAGERY. M.Derrien, H.Le Gléau CLOUD CLASSIFICATION EXTRACTED FROM AVHRR AND GOES IMAGERY M.Derrien, H.Le Gléau Météo-France / SCEM / Centre de Météorologie Spatiale BP 147 22302 Lannion. France ABSTRACT We developed an automated pixel-scale

More information

RPG MWR PRO TN03 2012 09 Page 1 / 12 www.radiometer physics.de Radiometer Physics GmbH +49 2225 99981 0

RPG MWR PRO TN03 2012 09 Page 1 / 12 www.radiometer physics.de Radiometer Physics GmbH +49 2225 99981 0 Applications Tropospheric profiling of temperature, humidity and liquid water High resolution boundary layer temperature profiles, better resolution than balloons Input for weather and climate models (data

More information

Multisensor Atmospheric data Mapping System: a Web-based Graphic Tool for multisensor observations of atmospheric data and NWP model forecasts.

Multisensor Atmospheric data Mapping System: a Web-based Graphic Tool for multisensor observations of atmospheric data and NWP model forecasts. Multisensor Atmospheric data Mapping System: a Web-based Graphic Tool for multisensor observations of atmospheric data and NWP model forecasts. Marco Petracca, Daniele Casella, Stefano Dietrich, Giulia

More information

Lectures Remote Sensing

Lectures Remote Sensing Lectures Remote Sensing OPTICAL REMOTE SENSING dr.ir. Jan Clevers Centre of Geo-Information Environmental Sciences Wageningen UR EM Spectrum and Windows reflection emission 0.3 0.6 1.0 5.0 10 50 100 200

More information

Amy K. Huff Battelle Memorial Institute huffa@battelle.org BUSINESS SENSITIVE 1

Amy K. Huff Battelle Memorial Institute huffa@battelle.org BUSINESS SENSITIVE 1 Using NASA Satellite Aerosol Optical Depth Data to Create Representative PM 2.5 Fields for Use in Human Health and Epidemiology Studies in Support of State and National Environmental Public Health Tracking

More information

3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview

3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview Page 1 of 6 3.3 Normalised Difference Vegetation Index (NDVI) 3.3.1 NDVI: A non-technical overview The Normalised Difference Vegetation Index (NDVI) gives a measure of the vegetative cover on the land

More information

Thoughts on Richter et al. presentation. David Parrish - NOAA ESRL

Thoughts on Richter et al. presentation. David Parrish - NOAA ESRL Thoughts on Richter et al. presentation David Parrish - NOAA ESRL Analysis of satellite data moving from pretty pictures to quantitative results. Richter et al. represents one of at least 5 groups pursuing

More information

GLOBAL FORUM London, October 24 & 25, 2012

GLOBAL FORUM London, October 24 & 25, 2012 GLOBAL FORUM London, October 24 & 25, 2012-1 - Global Observations of Gas Flares Improving Global Observations of Gas Flares With Data From the Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS)

More information

Remote sensing is the collection of data without directly measuring the object it relies on the

Remote sensing is the collection of data without directly measuring the object it relies on the Chapter 8 Remote Sensing Chapter Overview Remote sensing is the collection of data without directly measuring the object it relies on the reflectance of natural or emitted electromagnetic radiation (EMR).

More information

Lecture 1. The nature of electromagnetic radiation.

Lecture 1. The nature of electromagnetic radiation. Lecture 1. The nature of electromagnetic radiation. 1. Basic introduction to the electromagnetic field: Dual nature of electromagnetic radiation Electromagnetic spectrum. Basic radiometric quantities:

More information

IRS Level 2 Processing Concept Status

IRS Level 2 Processing Concept Status IRS Level 2 Processing Concept Status Stephen Tjemkes, Jochen Grandell and Xavier Calbet 6th MTG Mission Team Meeting 17 18 June 2008, Estec, Noordwijk Page 1 Content Introduction Level 2 Processing Concept

More information

VIIRS-CrIS mapping. NWP SAF AAPP VIIRS-CrIS Mapping

VIIRS-CrIS mapping. NWP SAF AAPP VIIRS-CrIS Mapping NWP SAF AAPP VIIRS-CrIS Mapping This documentation was developed within the context of the EUMETSAT Satellite Application Facility on Numerical Weather Prediction (NWP SAF), under the Cooperation Agreement

More information

IMPACTS OF IN SITU AND ADDITIONAL SATELLITE DATA ON THE ACCURACY OF A SEA-SURFACE TEMPERATURE ANALYSIS FOR CLIMATE

IMPACTS OF IN SITU AND ADDITIONAL SATELLITE DATA ON THE ACCURACY OF A SEA-SURFACE TEMPERATURE ANALYSIS FOR CLIMATE INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 25: 857 864 (25) Published online in Wiley InterScience (www.interscience.wiley.com). DOI:.2/joc.68 IMPACTS OF IN SITU AND ADDITIONAL SATELLITE DATA

More information

Introduction to teledection

Introduction to teledection Introduction to teledection Formation Sébastien Clerc, ACRI-ST sebastien.clerc@acri-st.fr ACRI-ST Earth Observation Actors and Markets 2 Earth Observation economic importance Earth Observation is one of

More information

Input from ESA colleagues on Sentinel-3, -4, and -5 gratefully acknowledged) ICAP AEROSOL OBSERVABILITY MEETING 5/11/2013 8/11/2013, TSUKUBA, JAPAN

Input from ESA colleagues on Sentinel-3, -4, and -5 gratefully acknowledged) ICAP AEROSOL OBSERVABILITY MEETING 5/11/2013 8/11/2013, TSUKUBA, JAPAN Aerosol Measurements from Current and Future EUMETSAT Satellites R. Munro, R. Lang, M. Grzegorski, G. Poli, A. Holdak, A. Kokhanovsky & C. Retscher (Atmospheric Composition Team) T. Marbach (EPS-SG Science

More information

Update on EUMETSAT ocean colour services. Ewa J. Kwiatkowska

Update on EUMETSAT ocean colour services. Ewa J. Kwiatkowska Update on EUMETSAT ocean colour services Ewa J. Kwiatkowska 1 st International Ocean Colour Science meeting, 6 8 May, 2013 EUMETSAT space data provider for operational oceanography Operational data provider

More information

Suomi / NPP Mission Applications Workshop Meeting Summary

Suomi / NPP Mission Applications Workshop Meeting Summary Suomi / NPP Mission Applications Workshop Meeting Summary Westin City Center, Washington, DC June 21-22, 2012 Draft Report (updated March 12, 2013) I. Background The Suomi National Polar- orbiting Partnership

More information

Comparison between current and future environmental satellite imagers on cloud classification using MODIS

Comparison between current and future environmental satellite imagers on cloud classification using MODIS Remote Sensing of Environment 108 (2007) 311 326 www.elsevier.com/locate/rse Comparison between current and future environmental satellite imagers on cloud classification using MODIS Zhenglong Li a,, Jun

More information

A Microwave Retrieval Algorithm of Above-Cloud Electric Fields

A Microwave Retrieval Algorithm of Above-Cloud Electric Fields A Microwave Retrieval Algorithm of Above-Cloud Electric Fields Michael J. Peterson The University of Utah Chuntao Liu Texas A & M University Corpus Christi Douglas Mach Global Hydrology and Climate Center

More information

The Surface Energy Budget

The Surface Energy Budget The Surface Energy Budget The radiation (R) budget Shortwave (solar) Radiation Longwave Radiation R SW R SW α α = surface albedo R LW εσt 4 ε = emissivity σ = Stefan-Boltzman constant T = temperature Subsurface

More information

Satellite Imagery Interpretation

Satellite Imagery Interpretation Satellite Types 1. Polar Orbiter (POES) 2. Geosynchronous Orbiters Geostationary Operational Env. Satellite (GOES) GMS EUMETSAT Image Types Garp 1. Visible: radiation reflected by objects 4km/0.65 2. Infrared:

More information

P1.2 NUMERICAL SIMULATION OF LONG DISTANCE TRANSPORTATION OF VOLCANO ASH FROM PINATUBO

P1.2 NUMERICAL SIMULATION OF LONG DISTANCE TRANSPORTATION OF VOLCANO ASH FROM PINATUBO P1.2 NUMERICAL SIMULATION OF LONG DISTANCE TRANSPORTATION OF VOLCANO ASH FROM PINATUBO Tan Jiqing Xu Juan (Institution of Meteorological Information and Prediction of Disaster Events, Zhejiang University,

More information