Creating Meteorological Products from Satellite Data
A Webcast by Dr. Marianne König
Print Version
Produced by the COMET® Program in collaboration with EUMETSAT
Welcome
Dr.
Marianne König
I am Marianne König, EUMETSAT's scientific coordinator for the meteorological products extracted
from the Meteosat Second Generation satellites, known as MSG. My background is in atmospheric physics. I have worked
in satellite meteorology for 28 years - first on the European Meteosat programme at the European Space Agency and
then, since 1995, on the Meteosat Second Generation series and its products at EUMESAT. My personal scientific interest
is in nowcasting applications using meteorological satellite data. As part of my current responsibilities, I support
EUMETSAT training activities, which helps me understand the needs and concerns of forecasters and other users.
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Section 1: Introduction
1.1 Overview
Today's meteorological satellite data provide vast amounts of information that, when
properly extracted, are used to quantify physical properties. We refer to this information as "derived meteorological
products" or, more simply, "products."
Products provide detailed descriptions of various atmospheric, ocean, and land features.
Examples include volcanic ash and trace gases, thunderstorms, land and sea surface temperature, dust, and wildfire
and smoke detection.
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1.2 Advantages
Products offer many benefits:
-
Each product focuses on a single parameter of interest to a particular user community,
such as fire detection for fire managers.
-
Relatively "simple" products highlight specific properties and are intended
for visual interpretation; this RGB Ash Product shows conditions several hours after the eruption of the Jebel
al Tair volcano in the Red Sea on 30 September 2007.
-
More advanced products quantify physical properties and produce parameters with
quantified values. This corresponding SO2 product was derived for the morning after the volcano erupted, when the
SO2 cloud had already drifted westward. The SO2 content is displayed in Dobson Units-a unit often used to describe
the contents of a minor atmospheric gas.
-
"Cutting edge" products, such as this 3-dimensional depiction of temperature
retrievals from the IASI (Infrared Atmospheric Sounding Interferometer) instrument on the Metop polar-orbiting
satellite, take advantage of the vast array of data from hyperspectral satellite-based sounding instruments with
their thousands of channels. Imagine inspecting and interpreting the imagery from each channel individually!
-
Product generation can be an automated and objective process that does not depend
on the knowledge or skill of a particular user. This flowchart depicts a general process for product generation.
-
Products often have associated quality indices, error flags, or error estimates,
which are helpful for forecasting in complex situations and critical for assimilating data into numerical weather
prediction models.
In this satellite-derived wind product, vector quality is indicated by colour, with yellow vectors being of high
quality and those in red of low quality. If you compare the red vectors to their neighbours, you'll notice large
differences in wind speed and/or direction.
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1.3 Limitations
Products have limitations as well. For example, automated product retrievals are based
on underlying assumptions, which might not apply to every situation.
For example, we typically assume that dark spots on visible images, such as the one
over the Sahara, are related to surface characteristics, such as vegetation or flooding. But both of these are very
unlikely in the Sahara Desert!
(View Animation)
We're actually seeing the shadow of the moon during the solar eclipse of 03 October
2005. The animation shows how the shadow moved from west to east.
Products are constantly improving as developers incorporate more advanced mathematical
techniques and computing methods, and as scientists gain a deeper understanding of the physical processes involved.
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1.4 About the Webcast
This Webcast presents an overview of how satellite data can be turned into products,
including:
-
Simple image products that use channel differencing, RGB compositing, and other
techniques to highlight specific properties
-
Quantitative products that use a variety of inputs and tools to produce single
parameters with set values
-
More advanced products that use the thousands of channels on hyperspectral instruments
to derive a variety of parameters
We will discuss how simple products are produced and then focus on the retrieval of
derived or quantitative products using the example of the Meteosat cloud mask. Cloud masks indicate whether each pixel
in a satellite image is cloud free or cloudy.
This is essential for being able to determine a pixel's properties, for example, its
cloud type if it is cloudy or its surface type if it is cloud free.
These sea surface temperature (or SST) products show the effects of proper cloud screening.
This first product has proper screening, hence the realistic looking values and clouds indicated in shades of grey.
The second product was derived without a pre-computed cloud mask. We see SSTs below about 20°C over southern portions
of the tropical Atlantic, which probably do not represent actual SSTs, and SSTs below -2°C, which are simply not
possible!
We will also examine sources of error in the final product caused by inaccuracies in
the satellite data or retrieval process. Some of these errors are specific to a particular product, while others are
shared by all product retrieval strategies.
It is assumed that users understand the fundamental interactions between radiation and
matter that enable satellites to detect various elements and their properties within the earth-atmosphere system. This
background information is essential for understanding the spectral signatures that occur throughout the spectrum, how
they influence channel selection, and how channel selection ultimately relates to the retrieval of specific meteorological
products.
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Section 2: Simple Products
2.1 Introduction
Many of the “simple” products that forecasters work with, such as this
airmass product, are straightforward and relatively easy to produce. They are derived directly from the original image
data using basic image manipulation techniques, such as channel differencing and RGB colour compositing. These can
be accomplished with popular image or photography processing software. “Simple” products highlight specific
properties and are intended for visual interpretation.
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2.2 Differencing: Vegetation Product
Let's look at one such example. In this visible 0.8 micrometre image over South Africa,
it is easy to visually distinguish between land, sea, and clouds.
If we want to investigate the land surface in more detail, though, this single picture
provides only limited information. It is better to combine it with another visible image taken at a different wavelength,
such as 0.6 micrometres.
If we subtract the grey values representing the amounts of reflected sunlight in the
two images, we get a simple vegetation product.
Land areas with green vegetation stand out as white or light grey.+
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2.3 Differencing: Cloud Phase Product
Although the difference image shows many land surface features, it does not provide
much more information about clouds than we get by looking at the individual images. That's because of the small differences
in cloud reflectances between the two visible channels.
Clouds are relatively bright reflective features in the visible images (first image)
and darker greyish objects in the difference image (second image).
For a better qualitative cloud analysis, we'll introduce the near-infrared channel at
1.6 micrometres and take the difference between it and the 0.6 micrometre visible image.
The resulting simple cloud phase product shows differences in cloud top reflectance
related to cloud phase. We can distinguish between cloud tops in the ice phase (dark grey to black) and in the water
phase (light grey).
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2.4 RGB Composites
If we composite all three channels (two visible and one near-infrared) into an RGB image,
we produce a simple combined “vegetation cloud phase” product. By assigning each channel to a respective
red, green, and blue colour, we achieve an almost realistic looking product, where green indicates vegetation, brown/reds
indicate other types of land surface, the sea surface is dark, and clouds are white. Only the ice clouds stand out
(unrealistically) in cyan.
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2.5 Other Product Examples
If we expand the channels used for making basic products to include the longwave infrared,
which is sensitive to different thermal properties, we are able to detect features such as:
-
Low clouds at night; while they are not apparent in this single longwave infrared window channel,
they appear as purple in the RGB composite of three infrared channels (second image), and as darker grey structures
in the infrared difference product (third image).
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Section 3: Quantitative Products
3.1 Need for Quantitative Products
In the dust RGB product on the previous page, we can visually distinguish between areas
of thicker and thinner dust layers. But it is impossible to infer the exact dust load in terms of kg/m3.
For that, we need a derived product, which quantifies a physical property and produces a parameter with set values.
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3.2 Generating Quantitative Products
Derived products require more advanced retrieval methods and techniques to handle a
variety of data inputs. These often come from sources other than just meteorological satellites, such as numerical
weather prediction models, physical models such as radiative transfer models, in situ observations, and climatological
data sets.
The inputs require more advanced retrieval methods and tools to handle them, such as
thresholding techniques, decision trees, statistical algorithms, and physical models.
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3.3 Cloud Mask Example
In the next section, we will examine an example of a quantitative product, namely the
operational implementation of the Meteosat cloud mask product, which is similar to cloud masks for other imaging instruments.
By stepping through the retrieval process, you will learn about some of the general
strategies that can be used for product extraction. You will also learn about the importance of the underlying image
data and other ancillary inputs. Note, though, that every product has its own derivation process complete with its
own set of inputs, tools, and underlying assumptions. The detailed description of the cloud mask product merely serves
as a guide to illustrate general principles and considerations used in the generation of derived products from satellite
observations.
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Section 4: Meteosat Cloud Mask Product
4.1 What is a Cloud Mask Product?
One of the first steps in retrieving many satellite products is to apply a cloud mask
product, which indicates whether each pixel contains clouds or is cloud free. Good cloud detection is extremely important
since clouds obscure the surface view in all solar and thermal spectral channels.
We see that in this first example. Clouds largely obscure the Alps, making it difficult
to distinguish between snow cover and cloud cover.
The same mountains appear relatively cloud free in the second image, although it is
still hard to determine whether the nearby bright white areas are cloud or snow cover. These examples highlight the
need for a cloud mask in order to estimate alpine snow coverage. Although the cloud mask product itself is not used
much in operational forecasting, it is the basis for many other products.
Surface-related products, such as sea and land surface temperature, vegetation cover,
snow cover, and wildfire detection, can only be inferred for pixels where the surface is not obscured by clouds.
(View Animation)
You can see the benefits of applying a cloud mask in this product, which shows land
surface temperatures for cloud-free surfaces over the course of a day.
Conversely, cloud products can only be inferred for pixels identified as cloudy. In
this example, a cloud mask identified cloudy pixels, and subsequent processing generated a cloud top height product.
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4.2 Introducing the Retrieval Process
Some cloud masks, such as the Meteosat product, are derived from conventional visible
and infrared imagery and use a so-called thresholding technique that identifies pixels as cloudy or cloud-free.
This technique is similar to the one that meteorologists use when visually inspecting
single channel images for cloud cover. They look for relatively cold features (groups of pixels) in infrared images
and bright features in visible images.
Likewise, the thresholding technique tries to detect features that are colder than expected
for clear sky conditions in infrared imagery and then detects features that are brighter than expected for a given
region and clear sky conditions using visible imagery.
The actual choice of threshold (cut-off value for a cloudy vs. cloud-free scene) comes
from years of experience, testing, and comparisons with image data. Thresholding techniques are usually highly tuned
algorithms where a certain set of thresholds may only be applicable to a particular instrument, region, or set of conditions.
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4.3 IR & VIS Thresholding
In this close-up infrared image, we have a clear sky IR brightness temperature of 300
K and a cloud top temperature of 213 K (an 87 K difference). Since this difference exceeds the threshold (for example,
5 K), the pixel is identified as cloud.
About brightness temperature (BT):
Satellites observe energy emitted by the earth-atmosphere system as digital counts.
They are typically used for generating imagery for qualitative analyses, such as infrared and visible imagery. To generate
quantitative products or compare satellite observations to other observations and model output, etc., we need a calibration
process that converts the counts to a physical quantity, such as a radiance or brightness temperature.
In this visible example, the clear sky visible reflectance was determined to be 24%,
the cloud top reflectance 83%. Given such a large difference, the thresholding technique determined that the 83% reflectance
was cloud and labelled those pixels as cloudy.
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4.4 Determining IR Clear Sky Values
Infrared clear sky brightness temperatures are generated by a radiative transfer model
(or RTM), which simulates the brightness temperature that the satellite would observe for cloud-free conditions. The
automated thresholding technique compares satellite measurements to RTM values. If the measurement is much colder than
expected, it is identified as cloud.
Note that for the infrared thresholding technique, an infrared window channel is used,
such as the 10.8 micrometre channel onboard Meteosat. An infrared window is a region of the spectrum where absorption
by atmospheric gases is minimal, meaning that views of the cloud-free surface and clouds at various levels are not
significantly affected by atmospheric gases such as water vapour.
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4.5 More about Radiative Transfer Models
Radiative transfer models are an important tool for many product retrieval strategies.
Such models simulate the radiative transfer processes of the atmosphere at a given wavelength or spectral region for
a given set of surface and atmospheric conditions. The radiative transfer model is thus used as a tool to compute an
expected brightness temperature for cloud-free conditions.
To get a close match between the RTM result and measured infrared brightness temperatures,
we need as realistic an estimate of the surface temperature as possible. For the Meteosat cloud masking process, this
estimate is derived from numerical model output.
Land surfaces undergo large diurnal cycles in their skin temperature, which numerical
weather prediction models often do not accurately resolve. This can create a fairly large mismatch between the observed
and modelled brightness temperatures, which can, in turn, lead to either a missed detection (if the modelled brightness
temperature is too cold) or false cloud detection (if the modelled brightness temperature is too warm).
Since SSTs are far less variable, we can expect a better performance of the infrared
thresholding technique over open water.
The animations show IR 10.8 micrometre brightness temperatures over specific (cloud-free)
locations. Notice the large diurnal cycle over land but not sea. We will look at additional examples on the following
pages.
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4.6 IR-Based Cloud Mask Over Ocean
Here is an infrared window image over part of the South Atlantic Ocean. Through visual
inspection, we see high cold clouds in the south and broken stratocumulus in the rest of the image.
The cloud mask appears to reproduce the cloud patterns fairly well. It was produced
by applying a carefully crafted threshold temperature of 2 K.
This overlay shows the RTM simulated infrared brightness temperatures for
this case, which are used in the infrared thresholding. Certain sections of the image are much colder (brighter) due
to the presence of clouds.
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4.7 IR-Based Cloud Mask Over Land
The principle of infrared-based cloud detection over land is very similar to that over
ocean. Only the threshold value needs to be adjusted since land surface skin temperatures vary more spatially and temporally,
and may not be as accurately depicted or forecasted by NWP models. This additional complexity means that thresholds
over land may be set larger than those over ocean, resulting in a missed detection of some cloud cover.
In this scene over central Africa taken near midday, very moist tropical air in the
south contrasts with the dryer Sahara region in the northern part of the image. Visually, it is very difficult to differentiate
the low clouds and thin cirrus from the cloud-free surroundings because of the infrared channel's sensitivity to abundant
low-level moisture.
The low-level moisture absorbs some of the surface radiation at 10.8 micrometres and
reemits the radiation at a cooler temperature, giving the appearance of a cooler surface than what we would expect.
Accurate representations of the moist air mass by forecast models are especially invaluable in this type of situation
since they provide the radiative transfer model with the atmospheric conditions needed for an accurate simulation of
the brightness temperatures that the satellite would observe.
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4.8 Augmenting IR with VIS
We have seen that cloud identification is a fairly straightforward matter in the infrared
as long as the cloud features appear colder than the expected temperature of a cloud-free land or sea surface.
With the addition of visible data, we can identify clouds as features that are brighter
than they would appear in a cloud-free image, which provides us with a technique for complementing the infrared method
in problem areas.
The challenge for discriminating cloud from ground in visible data is to quantify the
appropriate visible brightness for a cloud-free scene. Actual surface brightness or reflectance depends upon the surface
type and actual solar illumination, which changes by time of day and season.
The expected reflectance can be easily inferred for water surfaces, where the reflectance
is generally very low except under sun glint conditions and is easily predictable for any sun-satellite viewing geometry.
(View Animation)
The situation is more complicated over land due to variable reflectances, including
sun glint over rivers and lakes, and land surfaces, such as desert sand, salt, and certain types of rock that can appear
almost as bright as cloud cover.
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4.9 Using Climate Data for Clear Sky VIS
Using background data from climatological datasets, such as land surface type maps,
makes it possible to compensate for these problems. Average visible reflectances can be assigned according to land
surface type.
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4.10 Using Satellite Data for Clear Sky VIS
Another option for generating a cloud-free visible background is to use satellite data
as a starting point. Careful filtering of the data collected over a specific location over a period of time will result
in a cloud-free visible image, which can then be used as the background surface information.
Shown here is the Meteosat Visible 0.8 micrometre channel surface reflectance for 1200
UTC as a filtered value over the month of May in 2006. Each pixel represents an average of all cloud-free observations
identified during the period.
Quite noticeable are the non-homogeneous features in the southern portion of the image.
The patchy white cloud-like patterns are the result of areas that were pretty much cloud covered throughout the entire
month and, even more importantly, experienced very little solar illumination typical during that time of year.
With its higher spatial resolution, the Meteosat Second Generation (MSG) visible channel
also helps to improve the detection of clouds, particularly smaller cumuliform clouds.
Notice how many more clouds are detected in the infrared and visible-based cloud mask
(first image) than in the infrared-only mask (second image).
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4.11 Ocean Example
Now let's look at an example over the ocean. The low clouds in this infrared image appear
similar in temperature to the underlying sea surface, meaning that the thresholding technique may not detect all of
the cloud cover.
Looking at the corresponding visible image, notice how the clouds stand out from the
darker ocean background.
When we compare the infrared- and infrared/visible-based cloud masks, we see how the
addition of visible information has led to significant improvement in the detection of cloud cover.
Based on the two previous examples, we know that the impact of using visible information
is greater over ocean than land. During daytime over land, the warm surface normally contrasts better with the cooler
cloud top temperatures, which enables the infrared cloud mask to capture most cloud features.
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4.12 Using Spatial and Temporal Analyses
In addition to thresholding, other information and techniques can be used to help with
cloud detection, such as spatial analyses. Visible reflectances or infrared brightness temperatures that show large
differences over small scales may indicate scenes with scattered cloud cover.
A temporal analysis can also aid in cloud detection for geostationary satellites given
their high repeat cycles. Bright or cold pixels that are relatively stationary over time are more likely to be snow-covered
surfaces than clouds.
In looking at this single image of the Alps, can you determine if the highlighted area
is small cumuli or stationary snow fields?
(See animation)
The time series makes it obvious that we are looking at snow fields.
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Section 5: Sources of Error in the Final Product
5.1 Accuracy of Satellite Data
The cloud mask product is based on the combination of satellite observations, simulated
infrared brightness temperatures for a cloud-free surface, and visible reflectance of a cloud-free background.
While this strategy generally yields good results, there are some areas of concern that
can lead to error. In this section, we will examine various sources of error. Some are specific to the retrieval of
the cloud mask product while others impact all products.
Product errors can result from inaccuracies in the satellite data caused by, for example:
-
Calibration errors, which impact the accuracy of infrared brightness temperature values. The
top image is a cloud mask based on correctly calibrated infrared brightness temperatures, the second based on brightness
temperatures that are 1 K too cold. Although the effect is not very large, the apparent cloud coverage in the cloud
mask product does increase for cases where brightness temperatures are too cold. When brightness temperatures are
too warm, we capture less than expected cloud coverage, since we expect most clouds to appear colder than the underlying
surface. If the error is not corrected during the calibration process, the product can still be optimised by tuning
the infrared cloud threshold itself.
-
Problems with image navigation. Correct navigation means that the geographic location
of a given pixel (its geolocation) is correctly assigned. All product retrieval processes that depend on surface
characterisation (surface type or land vs. ocean, etc.) can suffer from even small errors in pixel navigation.
This example shows a correct cloud mask over the eastern Mediterranean.
This example has a navigation error of about 12 km. Notice the unrealistic
thin line of cloud cover that follows many of the coastlines.
(View Animation)
This example shows the ocean off the African coast as almost completely
cloud covered in the product when, in fact, the highlighted area was affected by afternoon sunglint.
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5.2 Product Retrieval Process
Product errors can also result from problems and inherent deficiencies in the retrieval
process, such as inaccuracies and uncertainties in meteorological analyses, forecast data, and other ancillary data
sets.
As we've seen, land and sea surface temperatures from NWP models are vital inputs to
the derivation of a cloud mask. A misrepresentation of the diurnal cycle has direct impacts on product quality. This
example shows desert regions just before sunrise when the land surface is coldest.
Cloud masks obtained from geostationary satellites often show a false cloud pattern
during night-time and early morning because of a warm bias in the model surface temperature. The product could be improved
by using a more realistic daily cycle of land surface temperatures in the radiative transfer calculations.
Another source of error involves low clouds that can appear warmer than the underlying
surface. This can occur with low-level temperature inversions, during night-time, and during winter in middle- and
high-latitude regions.
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Section 6: Other Products Using Threshold Techniques
6.1 Wildfire Detection
The channel thresholding technique shown for the cloud mask product is also used for
other types of scene identification.
The detection of active wildfires works by using a combination of observed brightness
temperatures in the infrared window region around 11 micrometres and in the shortwave infrared window region around
3.8 micrometres, which is very sensitive to hot spots within a pixel.
Hot spots and fires are isolated first by differencing the two channels and then highlighting
pixels with difference values beyond a prescribed threshold where the likelihood of fire is high.
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6.2 Detection of Volcanic Ash Plumes
The detection of volcanic ash plumes also uses a channel thresholding approach.
The spectral signature of volcanic ash within the infrared window region at 10 to 12
micrometres is different from the spectral signature of both water and ice clouds, so the volcanic ash plume can be
readily detected.
Thresholding can be used to help highlight and separate the brightness temperature
differences typically seen with volcanic ash plumes from the differences normally seen with water and ice clouds and
features on the ground.
This example shows the result of the channel differencing in terms of a visual product.
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Section 7: Benefits of Additional Spectral Information
7.1 Hyperspectral Data
A number of modern satellite instruments have moved away from traditional imagers and
sounders to so-called hyperspectral instruments that have thousands of channels. A more traditional imaging instrument
may average the incoming radiance over a fairly wide spectral range or band, while hyperspectral data have much finer
spectral resolution.
The most prominent hyperspectral instruments are the AIRS sounder on board the EOS Aqua
satellite, which samples the infrared spectrum in a total of 2378 channels, and the IASI sounder onboard Metop, which
samples a total of 8461 channels.
Compare a typical atmospheric spectrum as measured by IASI with that of a
much broader filter on an imaging instrument, such as MSG SEVIRI (Spinning Enhanced Visible and Infrared Imager). The
graphic shows how much detailed spectral information is averaged into one measurement on SEVIRI. (Compare the broad
shaded bands to the detailed information in the red lines).
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7.2 Atmospheric Profiling
The profiling of atmospheric temperature and gases, such as water vapour, represents
the most common operational implementation of hyperspectral data collection from meteorological satellites today.
Compared to traditional sounders, hyperspectral sounders can better resolve the vertical
structure of temperature and humidity by providing a greater number of channels at higher spectral resolution. This
enables them to sense more atmospheric layers at discrete levels, that is, layers with less overlap.
Here we see the weighting functions of the AMSU microwave sounding instrument, which
are relatively broad and overlap with one another. Recall that a weighting function defines the relative contributions
to the outgoing radiance from various levels of the atmosphere and therefore determines the layer of the atmosphere
sensed for a given spectral channel.
With the thousands of channels on hyperspectral sounders, we now get a multitude of
weighting functions. This means that the atmosphere is probed over a larger number of thinner layers, which, in turn,
allows for greater vertical resolution.
Even more sophisticated data processing strategies and more detailed radiative transfer
models are needed to convert the additional spectral information into useful products. Examples include trace gases,
the characterisation of aerosols,
cloud detection and characterisation (phase and particle size distribution),
and higher resolution temperature and moisture profiles.
Note that the more detailed information from hyperspectral instruments enables us to
derive more information based on direct satellite observation. It also limits our dependence on ancillary data, such
as model approximations, whose errors contribute to uncertainty in the derived product.
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Section 8: Summary
8.1 Summary
Satellite products provide detailed descriptions of various atmospheric, ocean, and
land features.
Benefits of satellite products:
-
Focus on a single parameter of interest to a particular user community
-
Relatively “simple” products highlight specific properties and are intended for
visual interpretation
-
More advanced products quantify physical properties and produce parameters with quantified
values
-
“Cutting edge” products take advantage of the vast array of data from hyperspectral
satellite-based sounding instruments with their thousands of channels
-
Product generation can be an automated and objective process that does not depend on the knowledge
or skill of a particular user
-
Products often have associated quality indices, error flags, or error estimates, which are
helpful for forecasting in complex situations and critical for assimilating data into numerical weather prediction
models
Relatively “simple” products:
-
Straightforward and relatively easy to produce
-
Derived directly from the original image data using basic image manipulation techniques, such
as channel differencing and RGB colour compositing, which can be accomplished with popular image or photography
processing software
-
Highlight specific properties
-
Intended for visual interpretation
More advanced and ‘cutting edge’ products:
-
Require more advanced retrieval methods and techniques to handle a variety of data inputs,
which often come from sources other than just meteorological satellites, such as NWP models, physical models
such as radiative transfer models, in situ observations, and climatological data sets, etc.
-
Inputs require more advanced retrieval methods and tools, such as thresholding techniques,
decision trees, statistical algorithms, and physical models
Meteosat cloud mask product:
Thresholding technique:
-
Detects features (groups of pixels) that are colder than expected for clear sky conditions
in IR imagery and brighter than expected for a given region and clear sky conditions in VIS imagery
-
Choice of threshold (cut-off value for cloudy vs. cloud-free scene) comes from experience,
testing, and comparisons with image data
-
May only apply to a particular instrument, region, or set of conditions
-
Used for generating cloud masks and detecting active wildfires and volcanic ash plumes, etc.
Radiative transfer models:
-
Are an important tool for many product retrieval strategies
-
Simulate the radiative transfer processes of the atmosphere at a given wavelength or spectral
region for a given set of surface and atmospheric conditions
-
Used to compute an expected brightness temperature for cloud-free conditions
-
The RT model result depends on the surface temperature computed by NWP models, which often
do not correctly resolve the large diurnal cycles of land surface temperature
-
Can be augmented by using background data from climatological datasets or the satellite data
itself
Sources of product error:
Hyperspectral instruments:
-
Have thousands of channels at higher spectral resolution
-
Can sense more atmospheric layers in increasingly discrete levels (layers with less overlap)
-
Are used operationally to profile atmospheric temperature and gases, such as water vapour
-
Require more sophisticated data processing strategies and more detailed radiative transfer
models
-
Enable us to derive more information based on direct satellite observation and limit our dependence
on ancillary data whose errors contribute to uncertainty in the derived product
General process for generating meteorological products
Process for generating a cloud mask product
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Note that an Online Quiz and User Survey are available for this module. Access the module
homepage at http://meted.ucar.edu/EUMETSAT/products/ and click
on 'Quiz' or 'User Survey'.
