ESA Earth Home Missions Data Products Resources Applications
    21-Apr-2014
EO Data Access
How to Apply
How to Access
Reference documents
MERIS Product Handbook
MERIS Product Handbook
MERIS Product Handbook
Index
MERIS Credits
MERIS Data Formats Products
MERIS Glossary and reference documents
Glossary
Optics Glossary
Vegetation Glossary
Water Vapour Glossary
Ocean Colour Glossary
Neural Network Glossary
Meteorology Glossary
Cloud Glossary
Atmosphere Glossary
Product Glossary
Geometry Glossary
Acronyms and Abbreviations
MERIS Instrument
Instrument Characteristics and Performance
MERIS Quality Status
Instrument characteristic
Characterisation and Calibration
Onboard Calibration Hardware
Calibration Modes
Instrument Description
Instrument model philosophy
Instrument Concept
Digital processing Unit
Video Electronic Unit
Detection Focal Plane
Instrument optics
The MERIS instrument
MERIS Products and Algorithms
Auxiliary Files
Common Auxiliary Datasets
Auxiliary Datasets for Level 2 Processing
Auxiliary Datasets for Level 1b Processing
Summary of Auxiliary Datasets
Water Vapour Parameters Data File
Atmosphere Parameters Data File
Level 2 Control Parameters Data File
Aerosol Climatology Data File
Coastline/Land/Ocean Data File
Digital Roughness Model Data File
Radiometric Calibration Data File
MERIS Level 1b Control Parameters Data File
Digital Elevation Model
ECMWF Data Files
ENVISAT Orbit Data Files
Surface Confidence Map File
Land Vegetation Index Parameters Data File
Cloud Measurement Parameters Data File
Ocean II Parameters Data File
Ocean I Parameters Data File
Land Aerosols Parameters Data File
Ocean Aerosols Parameters Data File
MERIS Instrument Data File
MERIS-Specific Topics
Level 2 Products and Algorithms
Level 2 Products
Level 2 Geophysical Products
Annotation data set
Flags
Water Vapour products
Land products
Meris Terrestrial Chlorophyll Index
Meris Global Vegetation Index
Aerosol Angström Coefficient
Aerosol optical thickness
Reflectance
Cloud products
Cloud reflectance
Cloud Type
Cloud top pressure
Cloud albedo
Cloud optical thickness
Ocean products
The MERIS Aerosol Angström Coefficient
Aerosol optical thickness
Photosynthetically Active Radiation (PAR)
Yellow substance
Suspended matter
Algal Pigment Index II
Algal Pigment Index I
Normalized water leaving radiance / reflectance
Product description
Level 2 High-Level Organisation of Products
Full Resolution Geophysical Product
Extracted Vegetation Indices
Extracted Cloud Thickness and Water Vapour for Meteo Users
Extracted Cloud Thickness and Water Vapour
Reduced Resolution Geophysical Product
Level 2 Algorithms
Level 2 Accuracies
Level 2 Algorithm Description
MERIS Level 2 Product Formatting Algorithm
Measurement Data Sets
Annotation Data Set "Tie Points Location and corresponding Auxiliary Data"
Global Annotation Data Set - Scaling Factors
Annotation Data Set "Summary Product Quality"
Specific Product Header
Main Product Header
MERIS Land Pixels Processing
MERIS Bottom Of Atmosphere Vegetation Index (BOAVI) (step 2.8)
Atmospheric correction over land (step 2.6.23)
MERIS Top Of Atmosphere Vegetation Index (TOAVI) (step 2.2)
Water Processing
MERIS Ocean Colour Processing (step 2.9)
Clear water atmospheric corrections (step 2.6.9)
Turbid water screening and corrections (steps 2.6.8, 2.6.10)
Water Confidence Checks (step 2.6.5)
Cloud Processing
Cloud type processing (step 2.4.8)
Cloud Optical Thickness processing (step 2.4.3)
Cloud Albedo processing (step 2.4.1)
Total Water Vapour Retrieval
Water vapour polynomial (function)
Range checks (steps 2.3.0, 2.3.6)
Water vapour retrieval over clouds (step 2.3.3)
Water vapour retrieval over water surfaces (steps 2.3.2, 2.3.5)
Water vapour retrieval over land surfaces (step 2.3.1)
MERIS Pixel Identification
Land Identification (step 2.6.26) and Smile Effect Correction (step 2.1.6)
Gaseous absorption corrections (step 2.6.12)
Stratospheric Aerosol Correction (step 2.1.9)
Cloud screening (steps 2.1.2, 2.1.7, 2.1.8)
MERIS Pressure Processing
Atmospheric pressure confidence tests (steps 2.1.2)
Atmospheric pressure estimate (steps 2.1.5, 2.1.12)
MERIS Pre-processing
Pre processing step
Level 1b product check
Level 2 Physical Justification
Level 1b Products and Algorithms
Level 1b product definition
Browse Products
Level 1b Essential Product Confidence Data
Level 1b Engineering Quantities
Level 1b Accuracies
Level 1b High-Level Organisation of Products
Measurement Data Sets
Annotation Data Set "Product Quality"
Annotation Data Set "Tie Points Location and corresponding Auxiliary Data"
Global Annotation Data Set
Specific Product Header
Main Product Header
Full Resolution Geolocated and Calibration TOA Radiance
Reduced Resolution Geolocated and Calibration TOA Radiance
Level 1b Algorithms
Formatting
External Data Assimilation
Pixel Classification
Geolocation
Stray Light Correction
Radiometric Processing
Saturated Pixels
Source Data Packet Extraction
Level 0 Products
Product Evolution History
Definitions and Conventions
Notations and Conventions
Product Grid
Units
Organisation of Products
MERIS product data structure
File naming convention
Acquisition identification scheme
Product identification scheme
Latency, Throughput and Data Volume
Introduction
MERIS products overview
MERIS product types
Full and reduced resolutions
MERIS product processing levels
MERIS User Guide
Image gallery
How to Use MERIS Data
Software Tools
BEAM
EnviView
General Tools
How to Choose MERIS Data
Summary of Applications vs. Products
Land
Atmosphere
Oceans
Introduction
Special Features of MERIS
Geographical Coverage
Principles of Measurement
Scientific Background
Mission Objectives
MERIS Level 3 products
Heritage
Geophysical Measurements
MERIS Product Handbook
Services
Site Map
Frequently asked questions
Glossary
Credits
Terms of use
Contact us
Search


 
 
 


2.7.1.2.2.1 Atmospheric pressure estimate (steps 2.1.5, 2.1.12)

The pressure is estimated for each pixel from the MERIS bands 10: 753.75 nm and 11: 760.625 nm, following two methods in parallel :

· "cloud top pressure" that uses a Neural Network algorithm, and

· "surface pressure" that uses a polynomial algorithm.

Spatial registration of the instrument has to be taken into account. A spectral shift index is computed from annotations of the L1B product and look-up-tables.

To retrieve the Cloud Top Pressure, Ptop,a neural net (NN) approach is used. The MERIS signals in channel 10, 11, the surface albedo asurf and the geometry (sun zenith angle, viewing zenith angle and azimuth angle) are used as input of the Neural Network. The net produces the cloud Top pressure Ptop. Depending on the surface albedo two different neural nets are used (one for surface albedo equal to zero, one for non-zero surface albedo). Neural Nets are selected according to spectral shift index.

The Neural Nets apply generic Neural Net functions to specific auxiliary parameters and inputs, to obtain the required outputs..

Each pressure estimate produces Product Confidence Data (PCD).


Keywords: ESA European Space Agency - Agence spatiale europeenne, observation de la terre, earth observation, satellite remote sensing, teledetection, geophysique, altimetrie, radar, chimique atmospherique, geophysics, altimetry, radar, atmospheric chemistry