Satellites for Atmospheric Sciences 2 E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
How can atmospheric variables such as temperature, wind, rain and ozone be measured by satellites? How are these measurements taken and what has been learned since the first measurements in the 1970s? What data are currently available and what data are expected in the future? The second volume of this encyclopedic book presents each field of application - meteorology, atmospheric composition and climate - with its main aims as well as the specific areas which can be addressed through the use of satellite remote sensing. This book presents the satellite products used for operational purposes as well as those that allow for the advancement of scientific knowledge. The instruments that are at their origin are described, as well as the processing, delivery times and the knowledge they provide. This book is completed by a glossary and appendices with a list of supporting instruments already in use.
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Table of Contents
Cover
Table of Contents
Title Page
Copyright Page
Acknowledgments
List of Acronyms
Introduction
PART 1: Meteorology
Introduction to Part 1: Meteorology and the Contribution of Satellite Observations
I1.1. From image use…
I1.2. …to measurement use
1 Operational Sounding of Thermodynamic Variables in the Atmosphere
1.1. Introduction
1.2. Operational use of TIR and MW sounders
1.3. Acknowledgments
1.4. References
2 Wind Observations
2.1. Introduction
2.2. AMVs
2.3. 3D winds derived from hyperspectral sounders
2.4. Measuring wind from space using Doppler lidar
2.5. References
3 Surface Variables
3.1. Observation of the Earth’s surface from space
3.2. Energy balances at the surface and at the top of the atmosphere
3.3. Ocean surfaces
3.4. Continental surfaces
3.5. Snow-covered surfaces
3.6. Expected changes
3.7. References
4 The Assimilation of Satellite Data in Numerical Weather Prediction Systems
4.1. Introduction
4.2. Early meteorological satellites
4.3. Assimilation of satellite soundings 1970–2000
4.4. Relevant aspects of data assimilation theory
4.5. The modern era (2000 to present)
4.6. Summary and conclusion
4.7. References
5 Nowcasting
5.1. Introduction
5.2. Satellite data for nowcasting
5.3. Observed phenomena
5.4. Nowcasting of detected phenomena
5.5. Perspectives
5.6. References
6 Observation and Monitoring of Tropical Cyclones from Space
6.1. Introduction
6.2. Visible and infrared imagery
6.3. Microwave imaging
6.4. Microwave sounding
6.5. Surface wind measurements
6.6. Ocean parameters
6.7. Climatology of cyclones
6.8. Conclusion
6.9. References
PART 2: Atmospheric Composition
Introduction to Part 2: Air Composition and the Contribution from Satellite Observations
I2.1. Air composition
I2.2. The ozone hole
I2.3. Air quality
I2.4. The greenhouse effect
I2.5. Dust and its impact
I2.6. Stratosphere
I2.7. References
7 Reactive Tropospheric Chemistry
7.1. Introduction
7.2. Methane
7.3. Reactive organic species
7.4. Reactive inorganic species
7.5. Conclusion
7.6. Acknowledgment
7.7. References
8 Major Pollutants: Ozone and Fine Particulate Matter
8.1. Introduction
8.2. Tropospheric ozone
8.3. Pollution aerosols
8.4. References
9 Desert Dust
9.1. Introduction
9.2. Qualitative satellite detection of desert dust
9.3. Satellite observation of the optical depth of desert dust
9.4. Vertical profiles of desert dust by spaceborne lidar
9.5. 3D distribution of desert dust by infrared spectrometer
9.6. Conclusion
9.7. References
10 Species Emitted by Fires
10.1. Introduction
10.2. Biomass burning gases
10.3. Biomass burning aerosols
10.4. Fire detection systems from space
10.5. Conclusion
10.6. Acknowledgments
10.7. References
11 Stratospheric Chemistry
11.1. Introduction
11.2. Stratospheric ozone chemistry
11.3. Stratospheric chemistry of other species
11.4. Satellite measurements of trace species in the stratosphere
11.5. Conclusion
11.6. Acknowledgments
11.7. References
PART 3: Atmosphere and Climate
Introduction to Part 3: Atmosphere and Climate and the Contribution of Space
I3.1. References
12 Climate Monitoring
12.1. General concepts about the climate
12.2. From space-based measurements to climate products
12.3. Climate data records and uncertainty estimates
12.4. The usage of climate data records in science and services
12.5. Looking ahead
12.6. References
12.7. References of the data sources cited in Figure 12.1
13 Anthropogenic Greenhouse Gases: CO
2
and CH
4
13.1. Monitoring anthropogenic greenhouse gases
13.2. Contribution of spatial observation of greenhouse gases
13.3. Measurement techniques
13.4. From radiation measurement to gas flux at the surface
13.5. Challenges for the future
13.6. References
14 Clouds and Water Vapor
14.1. Atmospheric water cycle and climate
14.2. Observations of water vapor
14.3. Observation of cloud properties
14.4. References
15 Precipitation
15.1. Need for global precipitation measurements
15.2. Satellite observation of rainfall
15.3. Observation of solid precipitation
15.4. Precipitation and the Earth water cycle
15.5. References
Appendices
Appendix 1: Atmospheric Profiles
A1.1. AFGL profiles
A1.2. Other profile databases
A1.3. References
Appendix 2: Spectroscopic Parameters for Radiative Transfer
A2.1. Spectroscopic parameters and line profiles
A2.2. Spectroscopic databases
A2.3. Atmospheric radiative transfer codes
A2.4. References
Appendix 3: List of Sensors and Satellites Cited in the Book
Appendix 4: Microwave Sensors and Infrared Hyperspectral Sounders
A4.1. Non-exhaustive list of passive microwave instruments
A4.2. Non-exhaustive list of active microwave instruments
A4.3. Non-exhaustive list of hyperspectral sounders
Glossary
List of Authors
Index
Summary of Volume 1
End User License Agreement
List of Tables
Chapter 4
Table 4.1. Early satellite sounding instruments
Chapter 10
Table 10.1. Global emission (Tg.a
-1
) of selected biomass burning species
Chapter 11
Table 11.1. Limb viewing instruments that provided interesting climatologies f...
Chapter 12
Table 12.1. Over 50 Essential Climate Variables are necessary to describe the ...
Chapter 14
Table 14.1. Methods for measuring atmospheric water vapor (see also Kämpfer (2...
Appendix 1
Table A1.1. AFGL profiles
List of Illustrations
Introduction
Figure I.1. The different compartments of the Earth System, taken into account...
Introduction to Part 1
Figure I1.1. Example of nephanalysis produced by the Météo-France Space Meteorology...
Figure I1.2. Example of an image in multispectral colored composition (March 4, 2...
Chapter 1
Figure 1.1. Upper stratospheric temperature sounding by IASI-B (left) and IASI...
Figure 1.2. Temperature weight functions of SEVIRI channels (left). Those of I...
Figure 1.3. Simulated spectra illustrating the coverage and spectral resolutio...
Figure 1.4. Simulation of an MTG-IRS observation in a window channel (cold clo...
Figure 1.5. On the left, conditional atmospheric instability highlighted by IA...
Figure 1.6. Vertical resolution of the three sounders IASI, IASI-NG and MTG-IR...
Figure 1.7. Cumulative degrees of freedom in temperature (left), humidity (cen...
Figure 1.8. Limitation of atmospheric sounding in the infrared in the presence...
Figure 1.9. At the top, coincidence of the in situ (radiosonde) and satellite ...
Figure 1.10. Evaluation of the quality of the operational products (IASI+AMSU+...
Figure 1.11. Schematic view of the dimensions and geometry of the footprints o...
Figure 1.12. Evaluation of operational sounding performance in the EPS program...
Figure 1.13. Coverage of the EARS-IASI regional service (left) and illustratio...
Chapter 2
Figure 2.1. All infrared winds extracted from geostationary and polar satellit...
Figure 2.2. Examples of wind vectors extracted from water vapor channels at 6....
Figure 2.3. Example of IASI 3D winds observed at 700, 500 and 100 hPa from the...
Figure 2.4. Principle of wind measurement by the Aeolus space mission
Figure 2.5. Geographical distribution of Aeolus wind data used at Météo-France...
Chapter 3
Figure 3.1. Schematic representation of the surface energy balance for estimat...
Figure 3.2. Example of a daily product (OSTIA analysis) of sea surface tempera...
Figure 3.3. Microwave emissivity of an ocean surface at 10.69 GHz without wind...
Figure 3.4. Representation of surface swaths (pink bands) of the ASCAT scatter...
Figure 3.5. Diurnal cycle of surface temperature before (curve without symbols...
Figure 3.6. Schematic dependency of the brightness temperature (a) and the bac...
Figure 3.7. Artist’s impression of the SMOS satellite launched in 2009 by ESA ...
Figure 3.8. Daily mean albedo produced by EUMETSAT’s LSA SAF for June 28, 2021...
Figure 3.9. Schematic representation of the spectral dependency of the surface...
Chapter 4
Figure 4.1. Increase in reforecast skill in the ERA-Interim and ERA5 reanalyse...
Figure 4.2. Satellite data coverage during FGGE: temperature retrievals from T...
Figure 4.3. Schematic of the assimilation of observations into an NWP model
Figure 4.4. Data coverage plot showing the AMVs assimilated into the Met Offic...
Figure 4.5. The geometry of a radio occultation measurement showing the refrac...
Figure 4.6. Impact of the various components of the global observing system in...
Chapter 5
Figure 5.1. Convective systems observed on the current MSG imagery (top), comp...
Figure 5.2. Main characteristics of a storm cell
Figure 5.3. RDT objects detected from SEVIRI channels in the western Mediterra...
Figure 5.4. Example of cloud types deduced from the SEVIRI imager onboard MSG,...
Figure 5.5. Observations (top line) and forecasts by machine learning (bottom ...
Chapter 6
Figure 6.1. Visible (a) and infrared (b) images of tropical cyclone (Hurricane...
Figure 6.2. Characteristic cloud structures for classifying tropical cyclones ...
Figure 6.3. Images of brightness temperatures in the 37 GHz (a) and 89 GHz (b)...
Figure 6.4. Vertical cross-section of the temperature anomaly associated with ...
Figure 6.5. Surface winds for the tropical cyclone (Super Typhoon) Noru on Aug...
Figure 6.6. Surface winds for tropical cyclone (typhoon) Hagibis on October 8,...
Figure 6.7. Trajectory and intensity of the tropical cyclone (Hurricane) Katri...
Chapter 7
Figure 7.1. The global reactive organic carbon (ROC) tropospheric budget
Figure 7.2. Means (on a 0.5 × 0.5 grid) of different VOC total columns retriev...
Figure 7.3. NO2 concentration differences (2020–2019) derived from TROPOMI ove...
Chapter 8
Figure 8.1. Average tropospheric ozone columns in Dobson units (DU) for the mo...
Figure 8.2. Annual tropospheric ozone column in Dobson units (DU) from OMI/MLS...
Figure 8.3. Observations of ozone in the very low troposphere (LMT: lowermost ...
Figure 8.4. Observations of the AOD at 550 nm retrieved from the measurements ...
Figure 8.5. Transects of the profiles of (a) attenuated backscattering of aero...
Chapter 9
Figure 9.1. Horizontal distribution of Saharan dust on June 18, 2011, observed...
Figure 9.2. Horizontal distribution of the optical depth of Saharan dust on Ju...
Figure 9.3. Transect of vertical profiles of desert dust extinction coefficien...
Figure 9.4. (Upper panel) IASI spectra simulated in the thermal infrared for f...
Figure 9.5. Distribution of dust over the Sahara on June 17, 2011, retrieved f...
Chapter 10
Figure 10.1. CO concentrations derived from IASI during California fires occur...
Figure 10.2. Vertical abundance (total column) of gas fire tracers over the Am...
Chapter 11
Figure 11.1. Total column ozone (TCO) in Dobson Units from the IASI instrument...
Figure 11.2. Total column ozone in Dobson units from the IASI instrument durin...
Figure 11.3. (a) Globally averaged CH4 dry air mole fractions and (b) instanta...
Figure 11.4. Vertical profiles for monthly mean CH4 for the period 2003–2006 a...
Introduction to Part 3
Figure I3.1. Infrared images (1978) illustrating the early days of operational ob...
Chapter 12
Figure 12.1. Variations in air temperature may be estimated from various data ...
Figure 12.2. Components of the Earth system relevant to climate timescales
Figure 12.3. Left: drifting buoy with high-precision calibrated platinum tempe...
Figure 12.4. Area extent of polar sea ice from 1979 to 2020. Thin blue lines s...
Figure 12.5. Timeline of observations, as assimilated in ERA5, from top to bot...
Figure 12.6. Time-series of (top) recalibrated top-of-atmosphere clear-sky ref...
Figure 12.7. Flowchart from satellite remote sensing to directly-usable climat...
Figure 12.8. Cover page of the WMO State of the Climate for the year 2020
Chapter 13
Figure 13.1. Example of weighting functions for CO2 in the case of nadir measu...
Figure 13.2. For the month of July 2019, CO2 field (top) and CH4 field (bottom...
Figure 13.3. Past, present and future missions observing CO2 (yellow), methane...
Figure 13.4. The three measurement principles aiming at the nadir for greenhou...
Figure 13.5. Monthly variation of integrated MT-CO2 measured by IASI from 2007...
Figure 13.6. Monthly variation of the integrated MT-CH4 columns measured by IA...
Figure 13.7. Monthly variation of XCO2 (left) and XCH4 (right) in three latitu...
Chapter 14
Figure 14.1. MSG images obtained on September 5, 2021 at 12:00 UTC. Left: 0.8 ...
Figure 14.2. Left: Spectra of emission (top) and absorption (bottom) by the at...
Figure 14.3. Total water vapor content (TCWV in cm, expressed as the height of...
Figure 14.4. Average upper tropospheric relative humidity (UTH, in %), for the...
Figure 14.5. Net radiative effect of clouds at the top of the atmosphere from ...
Figure 14.6. Average value of liquid water cloud path (in g/m2) from 1988 to 2...
Figure 14.7. Latitude/altitude transects observed by active instruments. On th...
Figure 14.8. Lidar scattering ratio histogram as a function of altitude observ...
Figure 14.9. Radiative heating rate in K/day related to different cloud types ...
Figure 14.10. Global average cloud cover (%) from left to right: for all cloud...
Figure 14.11. Geographic distribution of annual cloud cover for high-altitude ...
Chapter 15
Figure 15.1. The Global Precipitation Measurement (GPM) mission Core Observato...
Figure 15.2. Artist’s impression of the Tropical Rainfall Measuring Mission (T...
Figure 15.3. One of the first storms observed by the NASA/JAXA GPM Core Observ...
Figure 15.4. Global IMERG precipitation products on March 12, 2021 11:30 UTC. ...
Figure 15.5. Maps of the annual climatology of precipitation indexes for the p...
Appendix 1
Figure A1.1. Temperature variation as a function of altitude for the six AFGL ...
Figure A1.2. Pressure variation as a function of altitude for the six AFGL pro...
Figure A1.3. Variation of the H2O mixing ratio as a function of altitude for t...
Figure A1.4. Variation of the CH4 mixing ratio as a function of altitude for t...
Figure A1.5. Variation of the NO2 mixing ratio as a function of altitude for t...
Guide
Cover Page
Table of Contents
Title Page
Copyright Page
Acknowledgments
List of Authors
Introduction
Begin Reading
Appendix 1 Atmospheric Profiles
Appendix 2 Spectroscopic Parameters for Radiative Transfer
Appendix 3 List of Sensors and Satellites Cited in the Book
Appendix 4 Microwave Sensors and Infrared Hyperspectral Sounders
Glossary
List of Authors
Index
Summary of Volume 1
WILEY END USER LICENSE AGREEMENT
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SCIENCES
Space Exploration and Technology,Field Director – Jean-Luc Lefebvre
Space and Earth, Subject Head – Jean-Luc Lefebvre
Satellites for Atmospheric Sciences 2
Meteorology, Climate and Atmospheric Composition
Coordinated by
Thierry Phulpin
Didier Renaut
Hervé Roquet
Claude Camy-Peyret
First published 2023 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.
Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:
ISTE Ltd27-37 St George’s RoadLondon SW19 4EUUK
www.iste.co.uk
John Wiley & Sons, Inc.111 River StreetHoboken, NJ 07030USA
www.wiley.com
© ISTE Ltd 2023The rights of Thierry Phulpin, Didier Renaut, Hervé Roquet and Claude Camy-Peyret to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s), contributor(s) or editor(s) and do not necessarily reflect the views of ISTE Group.
Library of Congress Control Number: 2023941805
British Library Cataloguing-in-Publication DataA CIP record for this book is available from the British LibraryISBN 978-1-78945-141-2
ERC code:
PE10 Earth System Science
PE10_1 Atmospheric chemistry, atmospheric composition, air pollution
PE10_2 Meteorology, atmospheric physics and dynamics
PE10_14 Earth observations from space/remote sensing
PE9 Universe Sciences
PE9_15 Space Sciences
PE9_17 Instrumentation - telescopes, detectors and techniques
Acknowledgments
The coordinator is very grateful to Didier Renaut, Hervé Roquet and Claude Camy-Peyret for their significant help in editing this book through their advice, proofreading, corrections to chapters and help with the organization. Their support has been invaluable in bringing together quality contributions in a book that will cover the different facets of the topic Space and Atmosphere in a comprehensive manner. Gratitude is also expressed to Jérome Lafeuille and Jean Pailleux for their proofreading and improvements to various chapters. Finally, special thanks to the journal La Météorologie for having authorized the use of the glossary established for the special issue of the journal, No. 97 in 2019, devoted to satellite missions for meteorology and climate.
List of Acronyms
(3D/4D)-Var
(3/4 Dimensional) – Variational
4A/OP
Automatized Atmospheric Absorption Atlas/OPerational
AC-VC
Atmospheric Composition Virtual Constellation
ACCP
Aerosols, Clouds, Convection and Precipitation
ACX
GeoXO Atmospheric Composition
ADM
Atmospheric Dynamics Mission
AERIS
Data and Services for the Atmosphere
AERONET
AErosol RObotic NETwork
AI
Artificial Intelligence
AMV
Atmospheric Motion Vector
AOD
Aerosol Optical Depth
APHRODITE
Asian Precipitation – Highly-Resolved Observational Data Integration Towards Evaluation
APT
Automatic Picture Transmission
ARTS
Atmospheric Radiative Transfer Simulator
ASI
Agenzia Spaziale Italiana (Italian Space Agency)
AVCS
Advanced Vidicon Camera System
BC
Black Carbon
BIPM
International Bureau of Weights and Measures
BRDF
Bidirectional Reflectance Distribution Function
C3IEL
Cluster for Cloud evolution, ClImatE and Lightning
C3S
Copernicus Climate Change Service
CAMS
Copernicus Atmosphere Monitoring Service
CAPE
Convective Available Potential Energy
CALIPSO
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation
CATS
Cloud-Aerosol Transport System
CCI
Climate Change Initiative
CCSDS
Consultative Committee on Space Data Systems
CDR
Climate Data Record
CDRD
Cloud Dynamics and Radiation Database
CEOS
Committee on Earth Observation Satellites
CFC
ChlorFluoroCarbon
CGMS
Coordination Group for Meteorological Satellites
CI
Convection Initiation
CIMSS
Cooperative Institute for Meteorological Satellite Studies
CLW
Cloud Liquid Water
CM
Cloud Mask
CM-SAF
Climate Monitoring – Satellite Application Facility
CMA
Chinese Meteorological Agency
CMEMS
Copernicus Marine Environment Monitoring Service
CMORPH
CPC MORPHing technique
CMV
Cloud Motion Vector
CNES
Centre national d’études spatiales France
CNRM
Centre national de la recherche météorologique France
CNRS
Centre national de la recherche scientifique France
CNSA
Chinese National Space Administration
COCCON
COllaborative Carbon Column Observing Network
CoMet
Carbon Dioxide and Methane field campaign
CONTRAIL
Comprehensive Observation Network for TRace gases by AIrLiner
COP
Conference Of the Parties
CORRA
COmbined Radar-Radiometer Algorithm
CPC
Climate Prediction Center
CRM
Cloud Resolving Model
CRTM
Community Radiative Transfer Model
CTTH
Cloud Top Temperature and Height
CWDP
Commercial Weather Data Pilot
CYGNSS
CYclone Global Navigation Satellite System
DAOD
Differential AOD
DBNet
Direct Broadcast NETwork WMO-OMM
DCS
Data Collection System
DDA
Discrete Dipole Approximation
DIAL
DIfferential Absorption Lidar
DISORT
DIScrete Ordinate Radiative Transfer
DLR
Deutsches Zentrum für Luft- und Raumfahrt Allemagne
DMSP
Defense Meteorological Satellite Program
DNB
Day and Night Band
DOFS
Degrees of Freedom for Signal
DU
Dobson Unit
DVB
Digital Video Broadcast
DVB-S
Digital Video Broadcast by Satellite
EC
European Commission
ECMWF
European Centre for Medium-term Weather Forecast
ECOSTRESS
Ecostress Spectral Library Database
ECV
Essential Climate Variable
EDR
Environmental Data Record
EE
Earth Explorer
EEA
European Environment Agency
EF
Emission Factor
ELDO
European Launcher Development Organisation
ENSO
El Niño Southern Oscillation
EOS
Earth Observing System
EOSDIS
EOS Data Information System
EPS
EUMETSAT Polar System
ERA
ECMWF ReAnalysis
ERB
Earth Radiation Budget
ESA
European Space Agency
ESE
Earth Science Enterprise
ESRO
European Space Research Organisation
ESSA
Environmental Science Services Administration
ESSP
Earth System Science Pathfinder
ESTO
Earth Science Technology Office
EUMETSAT
EUropean organisation for the exploitation of METeorological SATellites
EW
Earth Watch
EXIM
EXtrapolated IMagery products
FASTEM
FAST Emissivity Model
FCDR
Fundamental Climate Data Record
FCI
Flexible Combined Imager
FCover
Fractional Cover (of vegetation)
FDR
Fundamental Data Record
FGGE
First GARP Global Experiment
FFT
Fast Fourier Transform
FGGE
First Global GARP Experiment WMO-OMM
FIR
Far IR
FOR
Field Of Regard
FOV
Field Of View
FSOI
Forecast Sensitivity Observation Impact
FSR
Forecast Sensitivity to R (the observation error covariance matrix)
FT
Fourier Transform
FTS
Fourier Transform Spectrometer
FY
Feng Yun
GADS
Global Aerosol Data Set
GARP
Global Atmospheric Research Programme WMO-OMM
GAS
GMES Atmospheric Service
GASP
GOES Aerosol/Smoke Product
GAW
Global Atmospheric Watch
GCM
Global Climate Model
GCOS
Global Climate Observing System
GDP
Gross Domestic Product
GEO
GEostationary Orbit
GEO
Group on Earth Observation
GEOS-5
Goddard Earth Observing System model – version 5
GEOSS
Global Earth Observation System of Systems
GeoXO
GEOstationary eXtended Observations
GEWEX
Global Energy and Water Cycle Experiment
GHCN
Global Historical Climatology Network
GHG
GreenHouse Gas
GLM
Geostationary Lightning Mapper
GMES
Global Monitoring for Environment and Security
GMF
Geophysical Model Function
GNP
Gross National Product
GNSS
Global Navigation Satellite System
GOCCP
GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP)
GPCC
Global Precipitation Climatology Center
GPCP
Global Precipitation Climatology Project
GPI
GOES Precipitation Index
GPM-CO
GPM-Core Observatory
GPROF
Goddard PROFiling algorithm
GPS
Global Positioning System
GRUAN
Global Reference Upper-Air Network GCOS
GSFC
Goddard Space Flight Center
GSICS
Global Space-based Inter-Calibration System
GSIP
GOES Surface and Insolation Project
GSMaP
Global Satellite Mapping of Precipitation
GSOD
Global Summary Of the Day
GV
Ground Validation
H-SAF
Hydrology – Satellite Application Facility
HCFC
HydroChloroFluoroCarbon
HEO
High Eccentricty Orbit
HEPAD
High Energy Proton and Alpha Detector
HFC
HydroFluoroCarbon
HLOS
Horizontal Line Of Sight
HOAPS
Hamburg Ocean Atmosphere Parameters and fluxes from Satellite data
HRIT
High Rate Image Transmission
HRPT
High Resolution Picture Transmission
HSRL
High Spectral Resolution Lidar
IAGOS
In-service Aircraft for a Global Observing System
IBTrACS
International Best Track Archive for Climate Stewardship
ICOADS
International Comprehensive Ocean-Atmosphere Data Set
ICOS
Integrated Carbon Observation System
IEC
International Electrotechnical Commission
IF
Intermediate Frequency
IFS
Integrated Forecasting System
IIP
Instrument Incubator Program
IJSP
International Joint Polar System
ILS
Instrument Line Shape
IMD
Indian Meteorological Department
IMERG
Integrated Multi-satellitE Retrievals for GPM
IMS
Ice Mapping System
IMS
Interactive Multisensor Snow
INDOEX
INDian Ocean EXperiment
INPE
Instituto Nacional de Pesquisas Espaciais Brésil
InVEST
In-space Validation of Earth Science Technology
IOC
Intergovernmental Oceanographic Commission UNESCO
IODC
Indian Ocean Data Coverage
IPCC
Intergovernmental Panel on Climate Change
IPDA
Integrated Path Differential Absorption
IPSL
Institut Pierre-Simon Laplace France
IPWG
International Precipitation Working Group CGMS
IR
Infra-Red
IROWG
International Radio Occultation Working Group CGMS
ISA
Israel Space Agency
ISCCP
International Satellite Cloud Climatology Project
ISO
International Standardization Organization
ISRF
Instantaneous Spectral Response Function
ISRO
Indian Space Research Organization
ISS
International Space Station
ITOS
Improved TIROS Operational System
ITWG
International TOVS Working Group CGMS
IUPAC
International Union of Pure and Applied Chemistry
IWP
Ice Water Path
IWWG
International Winds Working Group CGMS
JAXA
Japan Aerospace eXploration Agency
JCSDA
Joint Center for Satellite Data Assimilation
JMA
Japan Meteorological Agency
JPL
Jet Propulsion Laboratory
JPSS
Joint Polar Satellite System
JRA
Japanese ReAnalysis
KMA
Korean Meteorological Agency
LAI
Leaf Area Index
LAMP
Laboratoire de météorologie physique France
LATMOS
Laboratoire atmosphères, milieux, observations spatiales France
LEO
Low Earth Orbit
LI
Lifted Index
LIDORT
LInearized Discrete Ordinate Radiative Transfer
LMD
Laboratoire de météorologie dynamique France
LMT
Local Mean solar Time
LMT
LowerMost Troposphere
LOA
Laboratoire d’optique atmosphérique France
LOS
Line Of Sight
LRIT
Low Rate Information Transmission
LRPT
Low Resolution Picture Transmission
LRR
Laser Retro-Reflector
LSE
Land Surface Emissivity
LSI
Low Stream Approximation
LST
Land Surface Temperature
LTE
Local Thermodynamic Equilibrium
LUT
Look-Up Table
MAG
Mission Advisory Group
MAGIC
Monitoring Atmospheric composition and Greenhouse gases through multi-Instrument Campaigns
MEO
Middle Earth Orbit
MEPED
Medium Energy Proton and Electron Detector
MERRA
Modern-Era Retrospective analysis for Research and Applications
MOP
Meteosat Operational Programme
MOZAIC
Measurement of OZone and water vapour by Airbus In-service airCraft)
MSWEP
Multi-Source Weighted-Ensemble Precipitation
MTP
Meteosat Transition Programme
MW
MicroWave
MWR
MW Radiometer
NASA
National Aeronautics and Space Administration
NCEP
National Center for Environmental Prediction
NDACC
Network for the Detection of Atmospheric Composition Change
NDVI
Normalized Difference Vegetation Index
NEDT
Noise Equivalent Differential Temperature
NESDIS
National Environmental Satellite, Data, and Information Service (NOAA)
NIR
Near InfraRed (~ 1.0 to 0.7 µm)
NMVOC
Non-Methane Volatile Organic Compounds
NOAA
National Oceanic and Atmospheric Administration
NOAA/NESDIS
National Oceanic and Atmospheric Administration Satellite and Information Service
NPP
NPOESS Preparatory Program
NRT
Near Real-Time
NSOAS
National Satellite Ocean Application Service
NSOSA
NOAA Satellite Observing System Architecture study
NSSDC
NASA Space Science Data Center
NUCAPS
NOAA Unique Combined Atmospheric Processing System
NWP
Numerical Weather Prediction
NWP-SAF
Numerical Weather Prediction – Satellite Application Facility
OceanRAIN
Ocean Rain And Ice-phase precipitation measurement Network
ODS
Ozone Depleting Substances
OI
Optimal Interpolation
OLR
Outgoing Longwave Radiation
OLS
Operational Linescan System
OPAC
Optical Properties of Aerosols and Clouds
OPD
Optical Path Difference
OSCAR
Observing Systems Capability Analysis and Review tool
OSE
Observing System Experiments
OSSA
Office of Space Science Applications
OSSE
Observing System Simulation Experiments
OSTIA
Operational Sea surface Temperature and Ice Analysis
PACRAIN
Pacific Rainfall Database
PAN
PeroxyAcetyl Nitrate
PAOB
PAid OBservation
PAR
Photosynthetically Active Radiation
PATMOS-x
Pathfinder ATMOSpheres – eXtended
PERSIANN
Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks
PERSIANN-CCS
PERSIANN-Cloud Classification System
PM
Particulate Matter
PM2.5
Particulate Matter (diameter smaller than 2.5 µm)
PMW
Passive MicroWave
PNPR
Passive microwave Neural network Precipitation Retrieval
POA
Primary Organic Aerosol
POEM
Polar-Orbit Earth-Observation Mission
PPS
Precipitation Processing System
PRPS
Precipitation Retrieval and Profiling Scheme
PSC
Polar Stratospheric Cloud
QA4EO
Quality Assurance Framework for Earth Observation
QI
Quality Index
QRNN
Quantile Residual Neural Network
R&T
Research & Technology
RDT
Rapidly Developing Thunderstorm
RF
RadioFrequency
RO
Radio Occultation
ROC
Reactive Organic Carbon
ROSCOSMOS
Russian Federal Space Agency
RR
Rain Rate
RSS
Rapid Scanning Service
RT
Radiative Transfer
RTE
Radiative Transfer Equation
RTTOV
Rapid Transmission for TOVs
RTTOV-SCAT
RTTOV with SCATtering
SAF
Satellite Application Facility
SAG
Science Advisory Group
SAGE
Stratospheric Aerosol and Gas Experiment
SAM-II
Stratospheric Aerosol Measurement II
SC
Snow Cover
SCaMPR
Self-Calibrating Multivariate Precipitation Retrieval
SEM
Space Environment Monitor
SFCG
Space Frequency Coordination Group
SI
International System
SIC
Sea Ice Concentration
SNPP
Suomi National Polar-orbiting Partnership
SNR
Signal to Noise Ratio
SOA
Secondary Organic Aerosol
SORCE
SOlar Radiation and Climate Experiment
SOS
Sum Of Squares
SPARC
Stratosphere-troposphere Processes And their Role in Climate
SR
Scattering Ratio
SRSOR
Super Rapid Scan Operations for GOES-R
SSA
Single Scattering Albedo
SSEC
Space Science and Engineering Center
SST
Sea Surface Temperature
STAR
(Center for) Satellite Applications and Research
SWE
Snow Water Equivalent
SWIR
ShortWave InfraRed (~ 2.5 to 1.0 µm)
SZA
Solar Zenithal Angle
Tb
Brightness temperature
TCCON
Total Carbon Column Observing Network
TCHP
Tropical Cyclone Heat Potential
TCWV
Total Column Water Vapor
TED
Total Energy Detector
TEMPEST-D
Temporal Experiment for Storms and Tropical Systems – Demonstration
TEMPO
Tropospheric Emissions: Monitoring of Pollution
TIR
Thermal InfraRed (~ 15 to 5 µm)
TLS
Temperature of the Lower Stratosphere
TLT
Temperature of the Lower Troposphere
TMPA
TRMM Multisatellite Precipitation Analysis
TMS
Temperature of the Mid-Stratosphere
TMT
Temperature of the Mid-Troposphere
TOAR
Tropospheric Ozone Assessment Report
TOR
Tropospheric Ozone Residual
TPW
Total Precipitable Water
TTS
Temperature of the Top Stratosphere
TUS
Temperature of the Upper Stratosphere
TUT
Temperature of the Upper Troposphere
TV
TeleVision
UMORA
Unified Microwave Ocean Retrieval Algorithm
UN
United Nations
UNESCO
United Nations Educational, Scientific and Cultural Organization
UNFCCC
United Nations Framework Convention on Climate Change
UT/LS
Upper Troposphere/Lower Stratosphere
UTH
Upper Tropospheric Humidity
UV
Ultraviolet (~ 400 to 200 nm)
UV-B
Ultraviolet-B (~ 315 to 280 nm)
UVAI
Ultraviolet Aerosol Index
UVN
Ultraviolet, Visible and Near-infrared
VAAC
Volcanic Ash Advisory Centre
VarBC
Variational Bias Correction
VHI
Vegetation Health Index
VIS
VISible
VLIDORT
V-LInearized Discrete Ordinate Radiative Transfer
VOC
Volatile Organic Compound
WCRP
World Climate Research Program
WEFAX
Weather Facsimile
WF
Weighting Function
WMO
World Meteorological Organization
WWW
World Weather Watch
Introduction
Thierry PHULPIN
Retired from CNES and Météo-France, Toulouse, France
Observation of the Earth System from space
Satellite images are now in common use during television weather forecasts and are now also accessible on smartphones. Nowadays, everyone is introduced to this vision of the Earth from space at school. Thanks to satellites and their imagers, planet Earth appears to everyone as a familiar environment and is seen as a system in which all environments are interconnected. When a storm breaks out, its intensity is marked by the depth of the gyre formed by the depression and translates into very powerful winds whose impact on the ocean is very noticeable in data from oceanographic satellites. A volcanic eruption or an earthquake leaves traces in the atmosphere (ash clouds, ionospheric signals, etc.), which can be perceived and monitored. Heavy precipitation visualized using microwave instruments and droughts measured with sounders leave surface footprints that can be detected with satellites intended for monitoring continental resources. The relationship between the nature of the surface (oceans, desert, soil, bare ground, urban environment, crops, forests, etc.) and its state at a given moment (roughness, water state), in connection with atmospheric conditions, is decisive for meteorology via the mechanisms of thermal emission, or evaporation. All of these relationships can be characterized, inferred or measured by satellites, some intended for oceanography and others for monitoring land surfaces, or even for meteorology. These links between the various compartments of the Earth System began to be clearly highlighted as soon as the human or animal populations inhabiting the Earth began to move between the continents, thanks to discoveries by explorers. These discoveries, by allowing the development of a trade economy, led to the establishment of an interdependent system and had a major impact on a planetary scale, particularly on the various ecosystems. We could therefore already speak of an Earth System as early as the 16th century. However, it is the links between the observed natural phenomena that have made it possible to construct the notion of the climate system. Intuitively, we have perceived over the centuries that many phenomena responsible for the evolution of measurable variables were linked. These concepts can be visualized in diagrams, for example, Figure I.1. However, thanks to satellites, it has been possible to advance in the understanding of the processes (in addition to field studies), quantify certain phenomena at scales not possible from ground measurements alone, establish parametric links between certain variables and ultimately improve the physics of the models and then calibrate or validate these models.
Figure I.1.The different compartments of the Earth System, taken into account in a climate model.
It is therefore logical to address the various subsystems of the Earth in a work devoted to space science, and to consider them in a coherent approach.
The atmosphere
Volume 1 is devoted to satellite observation of the atmosphere and the sciences it has developed: meteorology, atmospheric composition, as well as knowledge of the climate and its evolution.
According to Blum (2019)1, it is the mastery of meteorology that would have allowed the WWII allies to land, demonstrating the major strategic importance of investing in an observation network. Space science and technology started early on, having been boosted by know-how of German engineers who developed the V2. The first photos of the Earth were taken in 1960, which highlighted how important the images were for providing an overview of the spatial distribution of cloud cover and its movement. The US then created a program of meteorological satellites, preceding the Soviets, followed by Europe and Japan. The history of these developments will be discussed in Chapter 1 of Volume 1. For meteorology, it is useful for the satellite measurements to be taken at the same solar time, to better follow the evolution of the thermodynamic parameters by comparing them with the readings of the previous day at the same time, hence the use of Sun-synchronous polar orbits. On the other hand, the ability to observe the Earth continuously from geostationary orbit is also very attractive for deducing information about the wind from the apparent movements of clouds and observing the development of storms or the movement of cyclones. The orbits used for these missions and their advantages are described in Chapter 8 of Volume 1. It quickly became clear that the long-term continuity of observations under identical conditions is necessary to integrate these observations into the international network of meteorological measurements and to facilitate their use in forecasting models. We then saw a dichotomy of missions appear; on the one hand, a series of identical operational satellites for a program spanning 20 or 30 years and, on the other hand, satellites equipped with innovative instruments for research. In the US, this dichotomy is reflected in the division of responsibilities between NOAA and NASA. These agencies pioneered meteorology from space. The history of their programs to date is presented in the first chapters of Part 1 of Volume 1. This part shows how the programs are developed to meet the needs of users, using the most advanced technologies whose developments are supported by the agencies that lead in this field. Following the American agencies, Chapters 4–6 of Volume 1 are devoted to the European programs of ESA, EUMETSAT and CNES. We see in Chapter 7 of Volume 1 the role played by the World Meteorological Organization in coordinating operational projects from the US, Europe or Asia. The chapter also discusses the issue of data acquisition anywhere on the planet for real-time use.
Part 2 of Volume 1 is devoted to the physical basis of satellite observations for atmospheric sciences. Chapter 8 of Volume 1 presents the principles of orbital mechanics to determine how to meet the need for observations by meteorologists or orbits more suited to different issues such as monitoring precipitation in intertropical zones or air quality in the most polluted regions. The first operational satellites only offered observation in a visible “channel”, similar to a photograph from the sky, and another in an infrared channel giving an idea of the temperature of the objects targeted. These passive observations have allowed advances in cloud mapping and sea temperature but have proven insufficient to access other meteorological information. The range of observations was quickly extended with observations in multiple optical channels, and the observation domain was extended to the microwave domain, allowing for, due to the digitization of measurements, the transition to the quantitative use of observations. Measurement physics, developed to propose passive or active measurement techniques and better use the resulting data, is presented in Chapter 9 of Volume 1. In Chapter 10 of Volume 1, we present the problem of finding atmospheric variables of interest from the observations and the main methods used for this purpose.
Part 1 of this book is more specifically devoted to meteorology. With regard to the three-dimensional distribution of temperature, humidity and wind, the main data useful for meteorology has long been provided by radiosondes. However, these observations are rare over the oceans and are therefore supplemented with satellite measurements of vertical distributions of temperature and humidity. The technique used is based on the selective absorption of carbon dioxide or oxygen, which is scarce but evenly distributed in the atmosphere (at least to the first order), and of water vapor and other greenhouse gases, which strongly absorb infrared or microwave radiation. This method, first tested by the Americans with the Infrared Interferometer Spectrometer (IRIS*) on the Nimbus IV satellite, then gave rise to the TIROS Operational Vertical Sounder (TOVS*) radiometric sounders, before technically evolving towards much more precise spectrometric measurements with the Atmospheric Infrared Sounder (AIRS*) and Infrared Atmospheric Sounding Interferometer (IASI*). Chapters 1 to 3 present the restitution of the main variables characterizing the state of the atmosphere, or that of the surface variables directly useful or necessary for the inversion of the atmospheric characteristics. Chapters 4 to 6 focus on the major applications of these observations: numerical weather forecasting, short-term forecasting and cyclone tracking.
Atmospheric sounding missions using spectrometry are successful by analyzing minority atmospheric components that are important for the planet or for human health, such as stratospheric ozone, gases and particles that are involved in air quality or the greenhouse effect.
The use of space observations and the benefits derived from them are presented in Part 2 of this book: detection of polluting species dangerous to health (Chapters 7 and 8), observation of clouds of desert dust and elements emitted by bush or forests. Chapter 11 is devoted to stratospheric chemistry, better known and understood thanks to the many satellite observations devoted to monitoring the hole in the ozone layer.
Because they offer precise and well-targeted observations with global spatial coverage, satellites have contributed significantly to better understanding the processes at play in the Earth’s climate. In addition, the continuity of long-term observations (more than 30 years of data!) makes it possible to monitor the evolution of the climate and to attribute certain phenomena to this evolution. Part 3 of this book concerns the use of satellite observations for the analysis of climatic processes and the monitoring of climatic variables. In its introduction, Chapter 12 recalls the importance of well-calibrated data, the need to reprocess the data acquired over time in a homogeneous manner and to repeat the reprocessing of the products as and when more efficient algorithms and processes become available. Chapter 13 is devoted to the restitution of the greenhouse gases CO2 and CH4 and to the location of the sinks and sources of these gases, which play a major role in the warming of the atmosphere. Clouds and water vapor also play an essential role. However, these variables have been monitored from the very first satellites. Analytical techniques have been refined and new data produced with more precise instruments have arrived, making it possible to more precisely estimate the spatio-temporal distribution of atmospheric water (Chapter 14). Clouds and water vapor are also involved in precipitation: the distribution and intensity of which are regularly monitored (Chapter 15).
In addition to an index, this book also includes a glossary to clarify the definition of certain concepts, appendices including a compilation of the names of instruments and satellites in question (to be updated as and when new missions occur), as well as a list of acronyms.
Notes
1
. Blum, A. (2019).
The Weather Machine. How We see in the Future
. The Bodley Head, London.
*
See
Glossary
.
PART 1Meteorology
Introduction to Part 1Meteorology and the Contribution of Satellite Observations
Hervé ROQUET
Direction de l’Enseignement Supérieur et de la Recherche de Météo-France, Saint-Mandé, France
I1.1. From image use…
Observation is an essential activity in meteorology. Observations are essential for real-time knowledge and monitoring of the main relevant atmospheric variables (pressure, temperature, humidity, wind, cloud variables and precipitation), as well as for the detection and monitoring of hazardous weather phenomena affecting human activities. They are also used for the preparation and monitoring of weather predictions. In particular, they make it possible to determine the initial state of the atmosphere from which numerical weather prediction models can predict its evolution. Finally, observations allow for continuous progress in the knowledge of meteorological phenomena and the physical processes that govern them.
In the history of meteorological observations, the advent of satellite imagery was a major milestone, giving meteorologists real-time access for the first time to a spatialized view of cloud systems and the meteorological phenomena they represent, such as fronts, mid-latitude weather disturbances, thunderstorms and tropical cyclones. The direct interpretation of an image by forecasters1 is still an important part of the use of satellite imagery in meteorology today, whether to analyze a situation or to assess predictions derived from numerical weather predicting models.
Figure I1.1.Example of nephanalysis produced by the Météo-France Space Meteorology Center on March 3, 1966 from images taken by the Vidicon camera of the American satellite ESSA-2
(source: Météo-France, Space Meteorology Center)
The instrument for observing on the first meteorological satellites was only a camera, which provided analog black and white photographs of the Earth and clouds of the regions they flew over. Their meteorological use was greatly limited not only by the image quality, but also by the fact that the identification of cloud types from a single black and white image in the visible domain is difficult even for an experienced user. An important step was the advent of the first radiometers with simultaneously a channel in the visible and in the thermal infrared, providing not only a usable observation at night, but also a better ability to distinguish the different types of clouds, the infrared channel providing information on the temperature and therefore on the altitude of their summit. For this reason, meteorologists have become accustomed to simultaneously exploiting images in the visible range in black and white with a natural dynamic (clouds appearing in white, because they are brighter than the surface), and images in the infrared range with an inverted dynamic (clouds also appearing in white, because they are cooler than the surface).
At the beginning of the operational use of satellite imagery in meteorology, the means of reception, processing and dissemination being essentially analogous, an operational production of nephanalyses was set up in some meteorological services (see Chapter 1). These maps, developed manually by experts in the meteorological interpretation of images (referred to as “nephanalysts”), made it possible to delineate the main cloud zones and the associated cloud cover, as well as the main types of clouds identified, and were distributed by facsimile to various weather prediction centers. This activity disappeared when computerized means of receiving, processing and displaying data became widespread, which made it possible not only to automate processing operations, such as the application of different dynamics to improve image display and the change of geographical projection, but also to transmit images via computer networks and direct display on forecasters’ workstations.
The advent of geostationary meteorological satellites was a crucial advance in providing access to visible and thermal infrared images with high temporal repetitiveness, enabling meteorologists to animate them to visualize the movements and evolution of cloud systems, and thus gain access to information on the dynamics of the atmosphere. In addition, the presence of an infrared channel in the water vapor absorption band (referred to by meteorologists as the “water vapor” channel) provided additional information on humidity in the mid-troposphere. The structure and evolution of the water vapor field is an important element in anticipating or monitoring the evolution of meteorological phenomena such as weather disturbances and convective systems, and indirectly provides information on the vertical movements of the atmosphere on a large scale; drying zones are associated with subsidence movements2 and vice versa.
Figure I1.2.Example of an image in multispectral colored composition (March 4, 2004, 10 am UT), obtained by combining the channels of the SEVIRI imager of the Meteosat Second Generation satellite. This image processing makes it possible to visually distinguish low clouds (ochre shades) from high clouds (white shades), as well as in the absence of clouds to detect the presence of snow on the ground (shiny white)
(source: Météo-France, Space Meteorology Center).
With the advent of recent generation meteorological satellites, carrying imaging radiometers with a large number of channels (e.g. the SEVIRI imager** of Meteosat Second Generation satellites, with 12 channels whose central wavelengths are distributed between 0.6 and 13.4 microns), the direct use by forecasters of all the individual images corresponding to each channel has become difficult, if not impossible, in an operational context. However, all of these channels and their combination can provide important additional information, for example, on the microphysical properties of clouds, on the presence of volcanic dust or ash or on the type of air mass. For this reason, false-color imaging products have been developed (often called multispectral colored compositions or “RGB” images), each red, green and blue color plane using the following as input: the data of a channel or a combination of channels, with the choice of channel depending on the type of information that it is desired to highlight through the color variations. In addition to basic images of a limited number of channels, RGB products dedicated to the general recognition of clouds, the identification of the air mass, the detection of low clouds and fog, and the detection of aerosols are nowadays often used operationally by forecasters.
I1.2. …to measurement use
But meteorologists do not just use satellite imagery. As will be shown in this section, they also use data from the various satellite instruments to access measurements of a large number of geophysical variables crucial for operational meteorology: vertical profiles of temperature and atmospheric humidity, physical and microphysical properties of clouds, wind, surface variables, etc. For this reason, satellite observations nowadays largely dominate in terms of data quantity of the other sources, and are particularly used intensively for Nowcasting and numerical weather prediction. However, despite their ever-increasing quality and diversity, they do not cover all the needs for meteorological observations, since measurements by remote sensing from space have limitations intrinsically linked to the physics of measurement, or to the current state of technology. Thus, at present, certain meteorological variables (such as atmospheric pressure) cannot be measured with sufficient accuracy, or certain regions (such as the atmospheric boundary layer) cannot be sampled with sufficient spatial or temporal resolution by satellite.
Chapter 1 presents the atmospheric temperature and humidity profiles that are produced operationally from measurements of infrared and microwave sounders, and their main uses in meteorology.
Wind at altitude is a very important meteorological variable, for which direct observations are provided either by radio soundings, the spatial coverage of which is very inhomogeneous and limited essentially to continental areas, or by airliners, the spatial coverage of which is limited to the flight path and level of the aircraft. Chapter 2 shows the long-standing contribution of satellite imagery for estimating winds at altitude, which is obtained from the temporal tracking of targets such as clouds or water vapor field structures, as well as that of measurements by Doppler lidar, the operational demonstration of which has just been made thanks to the ADM-Aeolus satellite launched in 2018 by the European Space Agency.
Weather prediction requires not only knowledge of the state of the atmosphere at a given moment, but also knowledge of certain properties of the surface, on land or at sea, which will influence the exchanges of energy, matter and quantity of movement between the two environments. Chapter 3 provides an overview of the various surface variables that are important for weather forecasting, and to which different types of satellite measurements give access.
Chapter 4 shows the essential contribution of satellite observations for data assimilation into numerical weather prediction models. Data assimilation is a set of techniques which, using available observations, are intended to determine the initial state of numerical models, from which the evolution of the atmosphere is calculated to provide weather predictions. Much progress has been made in recent decades in this area, making it possible to take full advantage of satellite observations. These techniques also make it possible to produce re-analyses of past and present climate, thanks to the series of satellite observations which have now been available for more than 40 years.
In meteorology, Nowcasting refers to all methods aimed at predicting weather conditions in the next few hours, usually for alerting purposes with a few hours of anticipation. It requires the production of predictions as quickly as possible, and updated as frequently as possible to take into account the latest observations available, which represents a strong constraint with regard to numerical weather prediction systems. For this reason, the direct use of observations, often combined with temporal extrapolation techniques, also holds an important place. Chapter 5 presents the state of the art with regard to satellite observations and the methods used for Nowcasting.
Tropical cyclones are one of the most destructive meteorological phenomena, whose direct observation remains very difficult because of the extreme conditions prevailing there. Since the advent of the first geostationary meteorological satellites, images provided have enabled forecasters to identify and estimate the characteristics of cyclones, as well as to track their trajectory. As shown in Chapter 6, although geostationary imagery remains the main source of information today for the operational monitoring of tropical cyclones, other satellite measurement systems also provide valuable information on certain meteorological variables within them.
Notes
1
. Persons in charge within the meteorological services of the final development of expert forecasts.
**
See
Appendix 3
.
2
. In meteorology, subsidence is the vertical downward movement of air, which then compresses, and therefore warms and dries.
1Operational Sounding of Thermodynamic Variables in the Atmosphere
Thomas AUGUST
European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
1.1. Introduction
Global knowledge of the vertical structure in temperature and humidity of the atmosphere is essential for weather forecasting and climate monitoring. These thermodynamic variables determine, for example, regional and large-scale air-mass circulation, as discussed in detail in Chapter 2. More locally, vertical thermal and humidity gradients are key ingredients of atmospheric stability. Knowledge of these gradients is critical to be able to alert populations and civil security components of the development of devastating weather events (massive hail, lightning, squall, flash floods, even tornadoes) or to monitor thermal inversions conducive to air quality degradation. It is also quite significant that these variables condition the storage capacity of water vapor (which is a powerful greenhouse gas) in the atmosphere as well as possible changes in the phase of the water, therefore microphysical processes associated with the formation of clouds (which are important in the global albedo of the Earth), fog layers, etc. More generally, these thermodynamic variables control the transfer of radiative energy between ground and atmosphere. They are therefore necessary to model radiative transfer, for example, for numerical weather prediction (Chapter 4), for modeling Earth’s radiation budget (Chapter 14) or finally to be able to detect and quantify other atmospheric constituents such as aerosols, greenhouse gases and molecules involved in air quality, etc.
This information was traditionally provided mainly by ground-based measurements or sounding balloon radiosondes, with consequently very limited spatio-temporal sampling and concentrated coverage on continental surfaces of the northern hemisphere. The development of satellite observation with TOVS instruments since the 1970s has made it possible to provide initial thermodynamic information on a synoptic scale thanks to radiometers operating in infrared (TIR) and microwaves (MW). But it was with the advent of hyperspectral sounders in polar orbit in the 2000s that a decisive step was taken to access temperature and humidity profiles with real vertical resolution.
In this chapter, we first present operational strategies at NOAA and EUMETSAT to operate passive vertical sounders and meet the needs of different users. They put into practice the theoretical principles of remote sensing explained in the two previous chapters with products of temperature, humidity, clouds and also ozone which is a tracer of the dynamics in the upper atmosphere. The sounders operating at nadir have a footprint ranging from about ten to a few tens of kilometers. They have maximum sensitivity and resolution in the free troposphere and also provide information in the stratosphere (Figure 1.1) and in a more limited manner in the planetary boundary layer. Atmospheric sampling of temperature and humidity using limb radio occultation (RO) signals from geo-positioning satellite systems (GNSS) has been described in Chapter 2. The RO technique makes it possible in particular to complete the knowledge of vertical structure in the upper layers of the atmosphere. It also offers a finer vertical resolution than the passive IR and MW nadir sounders, but on generally larger horizontal scales.
Figure 1.1.Upper stratospheric temperature sounding by IASI-B (left) and IASI-C (middle) after the explosion of the Hunga Tonga-Hunga Ha?apai volcano on January 14, 2022. On the right: temperature anomaly noted by IASI-B and IASI-C compared to previous days. Concentric circles reveal the propagation of gravity waves generated by the explosion to an altitude of 40 km
1.2. Operational use of TIR and MW sounders
1.2.1. Satisfying ever-more demanding users
The constant progress made in numerical weather prediction requires thermodynamic profiles that are increasingly resolved vertically. The same applies to very short-range prediction or nowcasting, as well as monitoring of climate, atmospheric composition and air quality.
As we have seen in Chapters 9 and 10 of Volume 1, the greater the spectral resolution of the measurements made by satellite sounders, the finer the vertical information restored. Deployed since the 1980s in polar orbit (Appendix 3), MW passive sounders operated in conjunction with broadband imagers and radiometers provided necessary vertical information until the late 1990s. Due to limited information content, however, the restitution of atmospheric profiles usually required the use of a priori information from numerical models.
The advent of hyperspectral sounders in TIR in the early 2000s made it possible to reach a milestone with vertical information content that allowed for the restitution of atmospheric profiles in temperature and humidity in a model-independent manner. They are operated in synergy with MW sounders. The respective information content of TIR and MW sounders have been discussed in Chapter 9 of Volume 1. The first hyperspectral instruments were launched in 2002 by the US with AIRS (Chapter 3 of Volume 1) on board Aqua (Aumann 2003
