Cloud Oxygen Pressure Algorithm for POLDER-2
|
|
|
- Emery Hudson
- 10 years ago
- Views:
Transcription
1 Cloud Oxygen ressure Algorithm for OLDER-2 1/7 Cloud Oxygen ressure Algorithm for OLDER-2 Aim of the : Determination of cloud gen pressure from arent pressure by removing the contribution. Date of the document: April 2003 (revised) Author: C. Vanbauce Laboratoire d Optique Atmosphérique UMR CNRS Université des Sciences et Technologies de Lille, Villeneuve d Ascq Cedex (France) [email protected] Content: 1. INTRODUCTION 2. ALGORITHM DESCRITION 3. OUTUT ARAMETERS 4. EXECTED RESULTS 5. REFERENCES Development of the OLDER Earth radiation budget, water vapor, and clouds s results from a joint effort of Laboratoire d Optique Atmosphérique (LOA), Laboratoire des Sciences du Climat et de l Environnement (LSCE) and Laboratoire de Météorologie dynamique (LMD). It has been supported by CNES (Centre National d Etudes Spatiales), CNRS (Centre National de la Recherche Scientifique) and Région Nord-as de Calais.
2 Cloud Oxygen ressure Algorithm for OLDER-2 2/7 1. INTRODUCTION The aim of this is to determine the cloud gen pressure from the arent pressure by removing the contribution. In a first step, the ground reflectivity effect is neglected. An arent pressure is inferred by assuming that the atmosphere behaves as a pure absorbing medium overlying a perfect cloud reflector located at pressure (Vanbauce et al., 1998). is determined from differential absorption between the radiances measured in the channels centered at 763 and 765 nm respectively (Buriez et al., 1997). More details can be found in the Apparent Oxygen ressure Algorithm for OLDER-2. Because of the effects of reflection and multiple scattering inside the cloud, the arent pressure is almost always higher than the cloud top pressure. It is even higher than the cloud base pressure when a lot of photons reach the before being reflected back to space, that is in the case of a thin cloud layer above a bright. Cloud Oxygen pressure is determined from arent pressure by removing the contribution when necessary. This correction is made using the cloud spherical albedo inferred from the Spectral Albedo and Cloud Optical Thickness Algorithm. 2. ALGORITHM DESCRITION The Cloud Oxygen ressure Algorithm is lied to each cloudy super-pixel. When the cloud spherical albedo S is not determined (over sea ice or snow covered land s for example) the cloud gen pressure is not calculated. A schematic description of the is reported in Fig.1. In the following, all operations are made viewing direction by viewing direction. In a first step, the arent pressures ( ), the absorption-corrected reflectances at 765 nm (R 765 ) and the cloud spherical albedoes (S) are averaged over the cloudy pixels for which the cloud spherical albedo is greater than a threshold value S o = Thick clouds In the case of thick clouds, is generally situated between the cloud top level and the cloud base one. For superpixels over ice-free ocean we consider that a correction is not necessary because the sea- albedo is weak and only cloudy pixels for which the cloud spherical albedo is larger than 0.3 have been selected. It is also the case for superpixels over snow-free continental s with great enough cloud spherical albedo (S> 0.75). In these two cases, the cloud gen pressure is simply equal to the arent pressure. For all other cases, further calculations are needed to obtain. The different cases are resumed in Table 1. ocean land S 0.3 < S < 0.75 S a b a Table 1: Description of the different cases treated in the Cloud Oxygen ressure Algorithm. a: =, b: = f(, r, )
3 Cloud Oxygen ressure Algorithm for OLDER-2 3/ Thin clouds In the case of a thin cloud layer, a lot of photons can reach the before being reflected back to space. In this case, the arent pressure can be outside the cloud layer limits (arent pressure larger than cloud base pressure). To remove this contribution a method has already been proposed by Buriez et al. (1997). Let the corrected cloud pressure be defined as the arent pressure that would be observed if the reflectivity was equal to zero. Because the gen A-band correspond to strong absorption lines, the gen transmission T O2 can be treated in first roximation by means of a random band model (Goody, 1964): T O exp( C m ) (1) 2 where m is the air-mass factor and C a constant depending on spectroscopic data. Schematically, this transmission can be decomposed in a term corresponding to the light directly reflected by the cloud and a term corresponding to the light reflected after reaching the (Fig. 2): exp ( C m ) r.exp ( C m ) (1 r).exp ( C m M [ ] ) (2) where r is the fraction of photons directly reflected by the cloud, M is the effective air-mass factor corresponding to the mean photon path between the cloud and the and the pressure. 0 Figure 2: Schematic representation of radiation transfer through a cloudy atmosphere (Buriez et al., 1997). Because is not directly deducible from Eq. (2), we use a simplified method. Assuming that the effective air-mass factor M is equal to m, Eq. (2) can be rewrite in exp ( C m ) exp ( C m ). 1 r. exp ( C m( )) 1 (3)
4 Cloud Oxygen ressure Algorithm for OLDER-2 4/7 Considering that the transmission between the cloud and the is a small corrective term Eq. (3) can be roximated as: exp ( C m ) exp ( C m ). 1 r C m ( ) (4) using again this roximation, we find which is equal to exp ( C m ) exp ( C m ). exp ( r C m( ) ) (5) exp ( C m ) exp ( C m r ( ) ) (6) which can finally be rewritten as (7) [ ( r 1) ]/ r. The fraction of photons directly reflected by the cloud, r, is calculated using r = R o 765 / R 765 where R 765 is the reflectance measured by OLDER at 765 nm after correction for gaseous absorption and R o 765 is the reflectance that would be measured if in addition the was black. is obtained from the ECMWF (European Center for Medium range Weather Forecasts) analysis. In practice, R o 765 is computed by using the cloud spherical albedo determined from OLDER measurements at 670 nm and look-up tables (LUTs) of calculated reflectances at 765 nm Look up tables The LUTs used to compute R o 765 are similar to those used in the Spectral Albedo and Cloud Optical Thickness Algorithm: The top-of-atmosphere bidirectional reflectances are calculated by using the plane-parallel radiative transfer model developed by de Haan et al. (1987). These tables are built at 765 nm, with an atmosphere model overlaying a black and for: - 2 cloud types (ice and liquid water) - 20 values of cloud spherical albedo (which is a one-to-one function of the cloud optical thickness) - 33 values of cosine of solar zenith angle - 28 values of viewing zenith angle - 37 values of relative azimuth angle The atmosphere model and the cloud types are those used over land s in the Spectral Albedo and Cloud Optical Thickness Algorithm : the atmosphere is only composed of molecules (no aerosol) ; liquid water clouds are composed of droplets with effective radius of 9 m and ice clouds are composed of inhomogeneous hexagonal monocrystals (IHM) as described in C.-Labonnote et al. (2000) Averaging over the directions The last step of the is the averaging over the viewing directions i. The averaging of all the calculated values (i) is weighted by the percentage of photons directly reflected by the cloud r(i) and by the cloud cover CC(i). The associated angular standard deviation is also calculated.
5 Cloud Oxygen ressure Algorithm for OLDER-2 5/7 Cloud detection CC 0 Cloud optical thickness S Apparent gen pressure For every viewing direction i CC(i) 0 Averaging over the cloudy pixels with S > S o R 765 Gaseous absorption correction Surface conditions Further calculations needed? no yes Geometry conditions = LUTs R o 765 hase Cloud thermodynamic phase fraction of photons directly reflected : R r R o ( r 1) r Averaging over the viewing directions Figure 1: Scheme of the Cloud Oxygen ressure Algorithm 3. OUTUT ARAMETERS Concerning the gen pressure, only non-directional parameters are delivered in the ERB, WV & clouds products: - the mean cloud gen pressure - the Angular standard Deviation AD( )
6 Cloud Oxygen ressure Algorithm for OLDER-2 6/7 4. EXECTED RESULTS From comparisons between OLDER-1 cloud gen pressure and ARM/MMCR (Clothiaux et al., 2000) cloud boundaries pressures, ears to indicate the cloud middle pressure rather than the cloud top pressure as shown in Fig. 3 (Vanbauce et al., 2003). Figure 3: Location of ADEOS1-OLDER corrected gen pressure in relation with ARM/MMCR cloud boundaries pressures, as a function of cloud geometric thickness for the 37 selected cases. In this figure, the y-axis pressures have been linearly rescaled as follows: cloud tops are fixed to 0 and cloud bases are fixed to 1. Circles represent mono-layered clouds and triangles multi-layered clouds. White symbols are for ice clouds while liquid clouds ear in black. OLDER data are means and standard deviations over 3*3 pixels around the SG Central Facility and over all viewing directions. 5. REFERENCES Buriez, J. C., Vanbauce, C., arol, F., Goloub,., Herman, M., Bonnel, B., Fouquart, Y., Couvert,. and Sèze, G., 1997: Cloud detection and derivation of cloud properties from OLDER, Int. J. Remote Sensing, 18, C.-Labonnote, L., Brogniez, G., Gayet, J. F., Doutriaux-Boucher, M. and Buriez, J. C., 2000: Modeling of light scattering in cirrus clouds with inhomogeneous hexagonal monocrystals. Comparison with in-situ and ADEOS-OLDER measurements, Geophys. Res. Lett., 27, Clothiaux, E. E., Ackerman, T.., Mace, G. G., Moran, K.., Marchand, R. T., Miller, M. A. and Martner, B. E., 2000: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites, J. Appl. Meteor., 39, Goody, R. M., 1964: Atmospheric Radiation I. Theoretical Basis, Oxford: Clarendon ress.
7 Cloud Oxygen ressure Algorithm for OLDER-2 7/7 de Haan, J. F., Bosma. B. and Hovenier J. W., 1987: The adding method for multiple scattering computations of polarized light, Astron. Astrophys., 183, Vanbauce, C., Buriez, J. C., arol, F., Bonnel, B., Sèze, G. and Couvert,., 1998: Apparent pressure derived from ADEOS-OLDER observations in the gen A-band over ocean, Geophys. Res. Lett., 25, Vanbauce, C., Cadet, B. and Marchand, R.T., Comparison of OLDER arent and corrected gen pressure to ARM/MMCR cloud boundary pressures, Geophys. Res. Lett., 30(5), 1212, doi: /2002GL016449, 2003.
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
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
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
Assessing Cloud Spatial and Vertical Distribution with Infrared Cloud Analyzer
Assessing Cloud Spatial and Vertical Distribution with Infrared Cloud Analyzer I. Genkova and C. N. Long Pacific Northwest National Laboratory Richland, Washington T. Besnard ATMOS SARL Le Mans, France
Measurement of the effect of biomass burning aerosol on inhibition of cloud formation over the Amazon
Supporting Online Material for Koren et al. Measurement of the effect of biomass burning aerosol on inhibition of cloud formation over the Amazon 1. MODIS new cloud detection algorithm The operational
Let s consider a homogeneous medium characterized by the extinction coefficient β ext, single scattering albedo ω 0 and phase function P(µ, µ').
Lecture 22. Methods for solving the radiative transfer equation with multiple scattering. Part 4: Monte Carlo method. Radiative transfer methods for inhomogeneous ouds. Objectives: 1. Monte Carlo method.
Total radiative heating/cooling rates.
Lecture. Total radiative heating/cooling rates. Objectives:. Solar heating rates.. Total radiative heating/cooling rates in a cloudy atmosphere.. Total radiative heating/cooling rates in different aerosol-laden
Retrieval of cloud spherical albedo from top-of-atmosphere reflectance measurements performed at a single observation angle
Atmos. Chem. Phys., 7, 3633 3637, 2007 Author(s) 2007. This work is licensed under a Creative Commons License. Atmospheric Chemistry and Physics Retrieval of cloud from top-of-atmosphere reflectance measurements
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
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
An Introduction to Twomey Effect
An Introduction to Twomey Effect Guillaume Mauger Aihua Zhu Mauna Loa, Hawaii on a clear day Mauna Loa, Hawaii on a dusty day Rayleigh scattering Mie scattering Non-selective scattering. The impact of
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
Overview of the IR channels and their applications
Ján Kaňák Slovak Hydrometeorological Institute [email protected] Overview of the IR channels and their applications EUMeTrain, 14 June 2011 Ján Kaňák, SHMÚ 1 Basics in satellite Infrared image interpretation
The Next Generation Flux Analysis: Adding Clear-Sky LW and LW Cloud Effects, Cloud Optical Depths, and Improved Sky Cover Estimates
The Next Generation Flux Analysis: Adding Clear-Sky LW and LW Cloud Effects, Cloud Optical Depths, and Improved Sky Cover Estimates C. N. Long Pacific Northwest National Laboratory Richland, Washington
Cloud Radiation and the Law of Attraction
Convec,on, cloud and radia,on Convection redistributes the thermal energy yielding (globally-averaged), a mean lapse rate of ~ -6.5 o C/km. Radiative processes tend to produce a more negative temperature
Data processing (3) Cloud and Aerosol Imager (CAI)
Data processing (3) Cloud and Aerosol Imager (CAI) 1) Nobuyuki Kikuchi, 2) Haruma Ishida, 2) Takashi Nakajima, 3) Satoru Fukuda, 3) Nick Schutgens, 3) Teruyuki Nakajima 1) National Institute for Environmental
Technical note on MISR Cloud-Top-Height Optical-depth (CTH-OD) joint histogram product
Technical note on MISR Cloud-Top-Height Optical-depth (CTH-OD) joint histogram product 1. Intend of this document and POC 1.a) General purpose The MISR CTH-OD product contains 2D histograms (joint distributions)
Radiative effects of clouds, ice sheet and sea ice in the Antarctic
Snow and fee Covers: Interactions with the Atmosphere and Ecosystems (Proceedings of Yokohama Symposia J2 and J5, July 1993). IAHS Publ. no. 223, 1994. 29 Radiative effects of clouds, ice sheet and sea
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
Clear Sky Radiance (CSR) Product from MTSAT-1R. UESAWA Daisaku* Abstract
Clear Sky Radiance (CSR) Product from MTSAT-1R UESAWA Daisaku* Abstract The Meteorological Satellite Center (MSC) has developed a Clear Sky Radiance (CSR) product from MTSAT-1R and has been disseminating
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
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
Radiation models for the evaluation of the UV radiation at the ground
Radiation models for the evaluation of the UV radiation at the ground Peter Koepke UV-Group Meteorological Institute Munich Ludwig-Maximilians-University [email protected] www. jostjahn. de Natural UV
CALIPSO, CloudSat, CERES, and MODIS Merged Data Product
CALIPSO, CloudSat, CERES, and MODIS Merged Data Product Seiji Kato 1, Sunny Sun-Mack 2, Walter F. Miller 2, Fred G. Rose 2, and Victor E. Sothcott 2 1 NASA Langley Research Center 2 Science and Systems
CALCULATION OF CLOUD MOTION WIND WITH GMS-5 IMAGES IN CHINA. Satellite Meteorological Center Beijing 100081, China ABSTRACT
CALCULATION OF CLOUD MOTION WIND WITH GMS-5 IMAGES IN CHINA Xu Jianmin Zhang Qisong Satellite Meteorological Center Beijing 100081, China ABSTRACT With GMS-5 images, cloud motion wind was calculated. For
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
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
IMPACT OF DRIZZLE AND 3D CLOUD STRUCTURE ON REMOTE SENSING OF CLOUD EFFECTIVE RADIUS
IMPACT OF DRIZZLE AND 3D CLOUD STRUCTURE ON REMOTE SENSING OF CLOUD EFFECTIVE RADIUS Tobias Zinner 1, Gala Wind 2, Steven Platnick 2, Andy Ackerman 3 1 Deutsches Zentrum für Luft- und Raumfahrt (DLR) Oberpfaffenhofen,
The Effect of Droplet Size Distribution on the Determination of Cloud Droplet Effective Radius
Eleventh ARM Science Team Meeting Proceedings, Atlanta, Georgia, March 9-, The Effect of Droplet Size Distribution on the Determination of Cloud Droplet Effective Radius F.-L. Chang and Z. Li ESSIC/Department
ARM SWS to study cloud drop size within the clear-cloud transition zone
ARM SWS to study cloud drop size within the clear-cloud transition zone (GSFC) Yuri Knyazikhin Boston University Christine Chiu University of Reading Warren Wiscombe GSFC Thanks to Peter Pilewskie (UC)
An Airborne A-Band Spectrometer for Remote Sensing Of Aerosol and Cloud Optical Properties
An Airborne A-Band Spectrometer for Remote Sensing Of Aerosol and Cloud Optical Properties Michael Pitts, Chris Hostetler, Lamont Poole, Carl Holden, and Didier Rault NASA Langley Research Center, MS 435,
FRESCO. Product Specification Document FRESCO. Authors : P. Wang, R.J. van der A (KNMI) REF : TEM/PSD2/003 ISSUE : 3.0 DATE : 30.05.
PAGE : 1/11 TITLE: Product Specification Authors : P. Wang, R.J. van der A (KNMI) PAGE : 2/11 DOCUMENT STATUS SHEET Issue Date Modified Items / Reason for Change 0.9 19.01.06 First Version 1.0 22.01.06
Surface-Based Remote Sensing of the Aerosol Indirect Effect at Southern Great Plains
Surface-Based Remote Sensing of the Aerosol Indirect Effect at Southern Great Plains G. Feingold and W. L. Eberhard National Oceanic and Atmospheric Administration Environmental Technology Laboratory Boulder,
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,
How To Find Out How Much Cloud Fraction Is Underestimated
2248 J O U R N A L O F T H E A T M O S P H E R I C S C I E N C E S VOLUME 62 Parameterizing the Difference in Cloud Fraction Defined by Area and by Volume as Observed with Radar and Lidar MALCOLM E. BROOKS,*
Cloud retrieval algorithm for GOME-2: FRESCO+
PAGE : 1/23 Cloud retrieval algorithm for GOME-2: FRESCO+ P. Wang, O. Tuinder, P. Stammes (KNMI) (12 February 21, version 1.3) Eumetsat contract EUM/CO/9/46655/RM PAGE : 2/23 TABLE OF CONTENTS 1. INTRODUCTION...3
T.A. Tarasova, and C.A.Nobre
SEASONAL VARIATIONS OF SURFACE SOLAR IRRADIANCES UNDER CLEAR-SKIES AND CLOUD COVER OBTAINED FROM LONG-TERM SOLAR RADIATION MEASUREMENTS IN THE RONDONIA REGION OF BRAZIL T.A. Tarasova, and C.A.Nobre Centro
How To Find Out How Much Cloud Fraction Is Underestimated
Parameterizing the difference in cloud fraction defined by area and by volume as observed with radar and lidar MALCOLM E. BROOKS 1 2, ROBIN J. HOGAN, AND ANTHONY J. ILLINGWORTH Department of Meteorology,
How To Understand Cloud Properties From Satellite Imagery
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.
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
Microwave observations in the presence of cloud and precipitation
Microwave observations in the presence of cloud and precipitation Alan Geer Thanks to: Bill Bell, Peter Bauer, Fabrizio Baordo, Niels Bormann Slide 1 ECMWF/EUMETSAT satellite course 2015: Microwave 2 Slide
Sensitivity of Surface Cloud Radiative Forcing to Arctic Cloud Properties
Sensitivity of Surface Cloud Radiative Forcing to Arctic Cloud Properties J. M. Intrieri National Oceanic and Atmospheric Administration Environmental Technology Laboratory Boulder, Colorado M. D. Shupe
Observed Cloud Cover Trends and Global Climate Change. Joel Norris Scripps Institution of Oceanography
Observed Cloud Cover Trends and Global Climate Change Joel Norris Scripps Institution of Oceanography Increasing Global Temperature from www.giss.nasa.gov Increasing Greenhouse Gases from ess.geology.ufl.edu
Profiles of Low-Level Stratus Cloud Microphysics Deduced from Ground-Based Measurements
42 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 20 Profiles of Low-Level Stratus Cloud Microphysics Deduced from Ground-Based Measurements XIQUAN DONG* AND GERALD G. MACE Meteorology Department,
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
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
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,
Lectures Remote Sensing
Lectures Remote Sensing ATMOSPHERIC CORRECTION dr.ir. Jan Clevers Centre of Geo-Information Environmental Sciences Wageningen UR Atmospheric Correction of Optical RS Data Background When needed? Model
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,
Cloud Thickness Estimation from GOES-8 Satellite Data Over the ARM-SGP Site
Cloud Thickness Estimation from GOES-8 Satellite Data Over the ARM-SGP Site V. Chakrapani, D. R. Doelling, and A. D. Rapp Analytical Services and Materials, Inc. Hampton, Virginia P. Minnis National Aeronautics
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?
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
Chapter 2. The global energy balance. 2.1 Planetary emission temperature
Chapter 2 The global energy balance We consider now the general problem of the radiative equilibrium temperature of the Earth. The Earth is bathed in solar radiation and absorbs much of that incident upon
Climate Models: Uncertainties due to Clouds. Joel Norris Assistant Professor of Climate and Atmospheric Sciences Scripps Institution of Oceanography
Climate Models: Uncertainties due to Clouds Joel Norris Assistant Professor of Climate and Atmospheric Sciences Scripps Institution of Oceanography Global mean radiative forcing of the climate system for
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
MSG-SEVIRI cloud physical properties for model evaluations
Rob Roebeling Weather Research Thanks to: Hartwig Deneke, Bastiaan Jonkheid, Wouter Greuell, Jan Fokke Meirink and Erwin Wolters (KNMI) MSG-SEVIRI cloud physical properties for model evaluations Cloud
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
Cloud vegetation interaction: use of Normalized Difference Cloud Index for estimation of cloud optical thickness
Cloud vegetation interaction: use of Normalized Difference Cloud Index for estimation of cloud optical thickness A. Marshak, Y. Knyazikhin, A. Davis, W. Wiscombe, and P. Pilewskie NASA GSFC, Code 913,
Solar Energy. Outline. Solar radiation. What is light?-- Electromagnetic Radiation. Light - Electromagnetic wave spectrum. Electromagnetic Radiation
Outline MAE 493R/593V- Renewable Energy Devices Solar Energy Electromagnetic wave Solar spectrum Solar global radiation Solar thermal energy Solar thermal collectors Solar thermal power plants Photovoltaics
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.
Collection 005 Change Summary for MODIS Aerosol (04_L2) Algorithms
Collection 005 Change Summary for MODIS Aerosol (04_L2) Algorithms Lorraine Remer, Yoram Kaufman, Didier Tanré Shana Mattoo, Rong-Rong Li, J.Vanderlei Martins, Robert Levy, D. Allen Chu, Richard Kleidman,
Chapter Overview. Seasons. Earth s Seasons. Distribution of Solar Energy. Solar Energy on Earth. CHAPTER 6 Air-Sea Interaction
Chapter Overview CHAPTER 6 Air-Sea Interaction The atmosphere and the ocean are one independent system. Earth has seasons because of the tilt on its axis. There are three major wind belts in each hemisphere.
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
The study of cloud and aerosol properties during CalNex using newly developed spectral methods
The study of cloud and aerosol properties during CalNex using newly developed spectral methods Patrick J. McBride, Samuel LeBlanc, K. Sebastian Schmidt, Peter Pilewskie University of Colorado, ATOC/LASP
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
3.4 Cryosphere-related Algorithms
3.4 Cryosphere-related Algorithms GLI Algorithm Description 3.4.-1 3.4.1 CTSK1 A. Algorithm Outline (1) Algorithm Code: CTSK1 (2) Product Code: CLFLG_p (3) PI Name: Dr. Knut Stamnes (4) Overview of Algorithm
Labs in Bologna & Potenza Menzel. Lab 3 Interrogating AIRS Data and Exploring Spectral Properties of Clouds and Moisture
Labs in Bologna & Potenza Menzel Lab 3 Interrogating AIRS Data and Exploring Spectral Properties of Clouds and Moisture Figure 1: High resolution atmospheric absorption spectrum and comparative blackbody
MOD09 (Surface Reflectance) User s Guide
MOD09 (Surface ) User s Guide MODIS Land Surface Science Computing Facility Principal Investigator: Dr. Eric F. Vermote Web site: http://modis-sr.ltdri.org Correspondence e-mail address: [email protected]
DISCRIMINATING CLEAR-SKY FROM CLOUD WITH MODIS ALGORITHM THEORETICAL BASIS DOCUMENT (MOD35) MODIS Cloud Mask Team
DISCRIMINATING CLEAR-SKY FROM CLOUD WITH MODIS ALGORITHM THEORETICAL BASIS DOCUMENT (MOD35) MODIS Cloud Mask Team Steve Ackerman, Richard Frey, Kathleen Strabala, Yinghui Liu, Liam Gumley, Bryan Baum,
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
CLOUD TOP PROPERTIES AND CLOUD PHASE ALGORITHM THEORETICAL BASIS DOCUMENT
CLOUD TOP PROPERTIES AND CLOUD PHASE ALGORITHM THEORETICAL BASIS DOCUMENT W. Paul Menzel Cooperative Institute for Meteorological Satellite Studies University of Wisconsin Madison Richard A. Frey Cooperative
OMI Algorithm Theoretical Basis Document. Volume III
OMI Algorithm Theoretical Basis Document Volume III Clouds, Aerosols, and Surface UV Irradiance P. Stammes (Editor) R. Noordhoek (Layout) ATBD-OMI-03, Version 2.0, August 2002 2 ATBD-OMI-03 ATBD-OMI-03
Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase
Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase MICHAEL D. KING 1 AND SI-CHEE TSAY 2 NASA Goddard Space Flight Center Greenbelt, Maryland 20771
CERES Edition 2 & Edition 3 Cloud Cover, Cloud Altitude and Temperature
CERES Edition 2 & Edition 3 Cloud Cover, Cloud Altitude and Temperature S. Sun-Mack 1, P. Minnis 2, Y. Chen 1, R. Smith 1, Q. Z. Trepte 1, F. -L. Chang, D. Winker 2 (1) SSAI, Hampton, VA (2) NASA Langley
Saharan Dust Aerosols Detection Over the Region of Puerto Rico
1 Saharan Dust Aerosols Detection Over the Region of Puerto Rico ARLENYS RAMÍREZ University of Puerto Rico at Mayagüez, P.R., 00683. Email:[email protected] ABSTRACT. Every year during the months
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,
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,
2 Absorbing Solar Energy
2 Absorbing Solar Energy 2.1 Air Mass and the Solar Spectrum Now that we have introduced the solar cell, it is time to introduce the source of the energy the sun. The sun has many properties that could
Physical properties of mesoscale high-level cloud systems in relation to their atmospheric environment deduced from Sounders
Physical properties of mesoscale high-level cloud systems in relation to their atmospheric environment deduced from Sounders Claudia Stubenrauch, Sofia Protopapadaki, Artem Feofilov, Theodore Nicolas &
What the Heck are Low-Cloud Feedbacks? Takanobu Yamaguchi Rachel R. McCrary Anna B. Harper
What the Heck are Low-Cloud Feedbacks? Takanobu Yamaguchi Rachel R. McCrary Anna B. Harper IPCC Cloud feedbacks remain the largest source of uncertainty. Roadmap 1. Low cloud primer 2. Radiation and low
