Satellite remote sensing applications in Meteorology

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Satellite remote sensing applications in
Meteorology
2nd Ewiem Nimdie International Summer School
Weather and Climate Forecasting in Africa and its
Application to Agriculture Water Resource
Management. 19 July 31 July 2010 Kumasi, Ghana
Gizaw Mengistu, Dept. of Physics, Addis Ababa
University, Ethiopia
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Outline

• 1. Introduction
• Meteorological satellites
• Instrumentation
• 2. Retrieval of meteorological parameters
• Measurement of sea and land surface temperature
• Retrieval of vertical profiles of temperature and
  humidity
• 3. Measurement of rainfall
• Visible Infra-red (IR)

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Outline

• 4. Measurement of winds
• Cloud motion vectors (CMV)
• 5. Satellite image/signal
• Satellite image/signal interpretation

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1. Introduction

• Meteorological satellites
• Satellites instrumentation

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Classification of Satellites

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Satellite System
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Sensor System
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First application of satellite remote sensing

• Began with TIROS1, launched in April 1960
• Simple TV system on board to map clouds
• Satellites are now a vital an integral part of
  our weather forecasting system.

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Satellite remote sensing

• Now both polar orbiting and geostationary
  satellites are used
• Polar orbiters operate in a similar way to other
  remote sensing satellites (Landsat, SPOT etc.)
• Geostationary satellites continually view the
  same portion of the Earth.

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Satellite remote sensing

• Geostationary Satellites
• Orbit above the equator at 35,800 Km and complete
  one orbit every 24hrs.
• Remain over the same point on the surface of the
  Earth.
• Continually view the same portion of the Earth.
• A network provides coverage of the entire globe

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Satellite remote sensing

• Major Applications
• Solar radiation exposure
• Uses a model based on an advanced estimate of
  cloud cover
• Cloud and Water Vapour Motion vectors
• Tracks identifiable cloud features
• Entered into weather forecasting models

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Instrument Observing Characteristics
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Instrument Observing Characteristics

• Observations depend on
• telescope characteristics (resolving power,
  diffraction)
• detector characteristics (signal to noise)
• communications bandwidth (bit depth)
• spectral intervals (window, absorption band)
• time of day (daylight visible)
• atmospheric state (T, Q, clouds)
• earth surface (Ts, vegetation cover)

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2. Retrieval of meteorological parameters

• Measurement of sea and land surface
  temperature
• Retrieval of vertical profiles of temperature
  and humidity.

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Definition

• Radiance is the amount of energy/per unit
  time/per area of a detector/per spectral
  interval/per solid angle

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Surface Temperature and Emissivity Estimation
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Surface Temperature and Emissivity Estimation
The radiance at the sensor is given by LS j
ejLjBB(T)(1-ej)LjskytLjatm Where ej is
surface emissivity , LjBB is spectral radiance of
a blackbody at the surface at temperature
T, Ljsky is spectral radiance incident upon the
surface from the Atmosphere, calculated using
radiative transfer equation (e.g. MODTRAN
etc), Ljatm is spectral radiance emitted by the
atmosphere, again from Model, t is spectral
atmospheric transmission and LS j is spectral
radiance observed by the sensor.
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Surface Temperature and Emissivity Estimation
After getting all the necessary data from RT
model as stated on the previous slide, radiance
from the Surface, Lj, is
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Emission characteristics of different objects
Water is a good approximation of a black body
(grey body)
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Emission characteristics of different objects
Quartz is not a good approximation of a black
body (selective radiator)
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Snow, vegetation, rock spectra of mixed pixels
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Surface Temperature and Emissivity Estimation
Relative Emissivity (to the average of all
channels, say 5 channels in this example) is
given by
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Surface Temperature and Emissivity Estimation

• From laboratory measurement, relationship
  between minimum emissivity, max.-min. relative
  emissivity difference can be constructed i.e
  eminf(ßmax- ßmin). Therefore, the revised
  emissivity can be computed from
• ej ßj(emin/ ßmin)

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Split-window methodsatmospheric correction for
surface temperature measurement

• Water-vapor absorption in 10-12 ?m window is
  greater than in 3-5 ?m window
• Greater difference between TB (3.8 ?m) and TB (11
  ?m) implies more water vapor
• Enables estimate of atmospheric contribution (and
  thereby correction)
• Best developed for sea-surface temperatures
• Known emissivity
• Close coupling between atmospheric and surface
  temperatures
• Liquid water is opaque in thermal IR, hence
  instruments cannot see through clouds

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Surface Temperature and Emissivity Estimation

• Spectral radiance (LSj) data are acquired as
  the 8 or more bit gray-scale imagery in Level 1b
  products for most surface observing satellite.
  So, 8 or more bits digital number (DN) should be
  converted to radiance in order to apply TES
  algorithm outlined earlier. The equation and
  constants for converting the 8 bits digital
  number of the image data into the spectral
  radiance is as follows
• LSjGainDNBiasDN(Lmax-Lmin)/255Bias

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Picture elements of multispectral image
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Image from different bands
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Retrieval of vertical profiles of temperature and
humidity
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Retrieval of vertical profiles of temperature and
humidity

.
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Retrieval of vertical profiles of temperature and
humidity
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Weighting function (http//goes.gsfc.nasa.gov/)
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Weighting function
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Weighting function
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Retrieval of vertical profiles of temperature
demonstration

• The preceding RT equation is commonly known as
  Fredholm integral equation of the first kind
  whose solution is difficult to find.
• Consider the following example

where the kernel is a simple exponential function
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Retrieval of vertical profiles of temperature
demonstration

• 1. Assume the function f(x) is given by
  f(x)x4x(x-1/2)2, we can compute g(k) for ki(0
  10) interval using
• 2. Write the integral equation in summation form
• Let , and compute g(k)

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Retrieval of vertical profiles of temperature
demonstration
and compare with step 1 3. Use direct linear
inversion method ((AT.A)-1AT.g) to recover
f(x) 4. If result in (3) is not good, use
constrained inversion ((AT.Agamma.H)-1AT.g)
where H is constraining matrix (smoothing
matrix)
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Retrieval of vertical profiles of temperature
demonstration
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Retrieval of vertical profiles of temperature
demonstration
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Stratospheric chemistry Dynamics
Mengistu et. al., doi10.1029/2004JD004856, 2004,
doi10.1029/2004JD005322, 2005
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Mengistu et. al., doi10.1029/2004JD004856, 2004,
doi10.1029/2004JD005322, 2005
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3. Measurement of rainfall

• Visible Infra-red (IR)

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Introduction

• Geostationary satellites (e.g. GOES, GMS,
  Meteosat) typically carry infrared (IR) and
  visible (VIS) imagers with surface resolutions
  ranging from 1-4 km. NOAA polar orbiters carry
  VIS/IR imagers with 1 km resolution

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Introduction
The choice of polar-orbiting vs geostationary
platforms for precipitation estimation entails
several tradeoffs with regard to temporal and
spatial sampling and geographical coverage a
geostationary satellite positioned over the
equator can provide high frequency (hourly or
better) images of a portion of the tropics and
middle latitudes, while a polar orbiter provides
roughly twice-daily coverage of the entire globe.
Polar orbiters also fly in a low Earth orbit
which is more suitable for the deployment of
microwave imagers on account of the latter's
coarse angular resolution.
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Spectral bands
The choice of spectral band for observing
precipitation also involves tradeoffs.
Historically, infrared (IR) and visible (VIS)
imagery have been widely available for the
longest period of time, with high quality
microwave (MW) imagery becoming widely available
only after the launch of the SSM/I in
1987. Advantages of the VIS and IR bands include
high spatial resolution as well as the
possibility of frequent temporal sampling from
geostationary platforms. A major disadvantage is
the indirectness of the relationship between
cloud top albedo or temperature and surface
precipitation rate.
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Spectral bands
The evidence to date suggests that VIS/IR methods
produce highly smoothed depictions of
instantaneous rainfall fields which become useful
only when averaged over larger space and/or time
scales, and then only when carefully calibrated
for the region and season in question.
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VIS and/or IR algorithms
Almost all IR techniques are based on variations
of the premise that precipitation is most likely
to be associated with deep clouds and thus with
cold cloud tops, as observed by an infrared
imager. Visible cloud albedos are generally used,
if at all, as supplemental information to
discriminate cold clouds which are optically thin
and presumably non-precipitating from those which
are optically thick and therefore possibly
precipitating. Of course, visible imagery is only
usable during the time that the sun is high above
the horizon. IR-only methods are often
preferred for the simple reason that their
performance is less likely to be a strong
function of the time of day and therefore less
likely to introduce spurious day-night biases in
estimated precipitation.
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VIS and/or IR algorithms
Because rainfall usually occupies only a small
fraction of the cold or bright cloud area visible
from space, VIS/IR algorithms tend to
overestimate significantly actual rain area. To
avoid systematic overestimates in temporal or
spatial averages, this tendency is usually
accounted for by assigning very low rain rates
(empirically derived) to the area identified as
precipitating in the instantaneous images. The
GOES Precipitation Index (GPI) is one of the
simplest and most widely used IR indices of
precipitation in the tropics and subtropics. The
GPI is computed by simply taking the fraction of
pixels within a region whose IR brightness
temperatures are less than some threshold, T0,
and multiplying that fraction by a constant rain
rate, RQ. Most commonly, T0 is taken to be 235K
and RQ is taken to be 3.0 mm/hr. However, other
values for these parameters may be more
appropriate in some cases, depending on location,
season, spatial averaging scale and other
factors.
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VIS and/or IR algorithms
The Negri-Adler-Wetzel Technique (NAWT)
technique NAWT assigns rain rates to "cloudy
pixels" based on a threshold brightness
temperature, T0, which was originally taken to be
253 K but has been modified in more recent
versions of the algorithm. Of the area defined as
cloud, the coldest 10 is assigned a rain rate
R10 and the next coldest 40 is assigned a lower
rain rate R40. Values for R10 and R40 were
originally specified as 8 and 2 mm/hr,
respectively, but have been adjusted slightly in
more recent applications.
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VIS and/or IR algorithms
RAINSAT is a supervised classification algorithm
which is trained to identify areas of
precipitation from a combination of VIS and IR
imagery. At night, visible imagery is unavailable
and RAINSAT reverts to a pure IR technique.
RAINSAT and its relatives are among the few
VIS/IR algorithms that are used operationally in
middle latitudes. Recently, a similar approach
by using a multivariate classification scheme and
raingauge data to estimate daily mean areal
precipitation is proposed.
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TAMSAT algorithm over Africa

• TAMSAT (Tropical Application of Meteorology using
  Satellite and other data)
• A regular series of thermal infrared (TIR)
  images of an area is received, pixels with
  apparent temperatures lower than some
  predetermined threshold are classified as cold
  cloud and their charastristics accumulated over
  some period.
• The procedures adopted and the form of the
  algorithms are regarded as a statistical model,
  which is calibrated through comparisons between
  observed cold cloud characteristics and sets of
  conventional raingauge data.

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TAMSAT algorithm over Africa

• The factors to be considered in comparing methods
  include the following
• - the type of regression model employed (linear,
  non-linear? multivariate)
• - the inteval between images (slots) the time
  averaging period
• - the space averaging scale the threshold
  temperature adopted
• - data treatment (e.g. linear or temperature
  weighted accumulation) additional data
  incorporated (e.g. water vapour Channel, visible
  Channel or contemporary surface
• raingauge measurements)
• - localization of calibration (time or space
  varying TIR features, variation with geographic
  location, time of year, character of season,
  topography and local storm climatology.

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TAMSAT algorithm over Africa

• The technique is simple. Local seasonally varying
  temperature thresholds which best discriminate
  between precipitating and non-precipitating
  clouds of convective origin are determined.
• The CCD is defined to be the duration of a cloud,
  with top temperature below a predetermined
  threshold, over a given area. Therefore, the
  relation between CCD and rainfall (RR) is given
  as

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TAMSAT algorithm over Ethiopia

• It is noted that instead of relating rainfall to
  CCD, the regression is performed between
  midpoints of CCD classes and the median of the
  rainfall in the CCD class in order to overcome
  the skewness of the rainfall frequency
  distribution.
• In Ethiopia, the original TAMSAT model is
  modified to account for spatial inhomogeneity due
  to complex topography since 1993
• Homogeneous zones are delineated
• Selection of a best temperature threshold which
  reasonably discriminates between rain giving and
  inactive (non-rain giving) clouds (archieved CCD
  at TAMSAT-40, -50, -60 0C are used)

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TAMSAT algorithm over Ethiopia
Comparison of actual and estimated rainfall at
different rainfall ranges, July 1995 for the
whole country
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TAMSAT algorithm over Ethiopia
Comparison of observed and estimated over
western Ethiopia for the period June to September
1994.
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TAMSAT algorithm over Ethiopia
Comparison of observed and estimated over
northeastern Ethiopia for the period June to
September 1994.
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METEOSAT Channels (bands)
The IR information is separated into three
classes, based on temperature thresholds, for
improving quantitative rainfall estimation for
cold convective clouds, middle layer clouds and
warm coastal clouds. The METEOSAT spin scan
radiometer operates in three spectral bands
0.5 - 0.9 µm (visible band - VIS) 5.7 - 7.1 µm
(infra-red water vapour absorption band - WV)
10.5 - 12.5 µm (thermal infra-red band - IR)
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METEOSAT Channels (bands)
The amount of radiation absorbed by water vapour
is dependent on the amount of moisture in the
radiation's path and the wavelength of the
radiation. Increased amounts of moisture, or
water content, in the radiations path lead to
more absorption of the radiation emitted from
lower layers. Therefore, if the air temperature
decreases with height, higher moisture content
result in colder brightness temperature. On a
6.7µm image the coldest temperatures correspond
to high cloud tops, whilst the warmest are
observed over lower altitude areas when the air
is very dry through a deep layer in the
atmosphere. For the 6.7µm water vapour channel,
the radiation values may also be converted to
brightness temperatures. A difference exists
between WV (6.7µm) brightness temperature and
that of the standard IR (11µm) channel. This is
attributed to the absorption and re-radiation by
water vapour above the earth's surface or clouds.
It is this difference that allows a distinction
to be drawn between cirrus and moist updraft
regions.
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4. Measurement of winds

• Cloud motion vectors (CMV)

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Satellite Derived Motion Fields

• Clouds are passive tracers of winds at a single
  level
• use infrared and visible radiances
• Water vapor features (ie., moisture gradients are
  passive tracers of winds)
• both in clear air and cloudy conditions
• use water vapor infrared radiances
• We can properly assign height of tracer

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Satellite Derived Motion Fields GOES Visible,
IR, WV Channels

• Imager
• Water vapor channel (6.7µm) Band 3
• Longwave IR window chan. (10.7µm) Band 4
• Visible Channel (0.65µm) Band 1
• Sounder
• Water vapor channel (7.3µm) Band 10
• Water vapor channel (7.0µm) Band 11

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Satellite Derived Motion Fields BASIC
METHODOLOGY

• Image acquisition
• Automated registration of imagery
• Target selection process
• Height assignment of targets
• Target tracking
• Quality control (Autoeditor)

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Satellite Derived Motion Fields Image
Acquisition

• Select 3 consecutive images in time
• Which channels are selected is a function of
  which wind product (cloud-drift, water vapor,
  visible) is to be generated

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Satellite Derived Motion Fields Auto-registratio
n of Imagery

• Registration is a measure of consistency of
  navigation between successive images
• Landmark features (ie., coastlines) must remain
  stationary from image to image
• Satellite-derived winds are much more sensitive
  to changes in registration than to errors in
  navigation

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Satellite Derived Motion Fields Auto-registratio
n (Contd)

• Manual registration corrections applied
  operationally to imagery 5 of the time
• New automated registration
• hundreds of landmarks used
• each landmark is sought in all images
• middle image in loop is assumed to have perfect
  navigation
• mean line and element correction is computed and
  possibly applied for the 1st and 3rd image

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Satellite Derived Motion Fields TARGET
SELECTION PROCESS

• Consider small sub-areas (target area) of an
  image in succession
• Perform a spatial coherence analysis of all
  targets. Filter out targets where
• multi-deck cloud signatures are evident

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Satellite Derived Motion Fields TARGET
SELECTION PROCESS (Contd)

• Locate maxima in brightness
• Select target/feature associated with strongest
  gradient
• Target density is controlled by size of target
  selector area

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Satellite Derived Motion Fields Height
Assignment of Targets

• Infrared window technique
• oldest method of assigning heights to
  cloud-motion winds
• not suitable for assigning heights of
  semi-transparent cloud (ie., thin cirrus)
• still provides a suitable fallback to other
  methods

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Satellite Derived Motion Fields Target Height
Assignment (Contd)

• CO2 Slicing Technique
• most accurate means of assigning heights to
  semi-transparent tracers
• utilizes IR window and CO2 (13µm) absorption
  channels viewing the same FOV

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Satellite Derived Motion Fields Target Height
Assignment (Contd)

• H2O Intercept Method
• Utilizes Water Vapour channel (6.7µm) Band 3 and
  longwave IR window chan. (10.7µm) Band 4
• Algorithm these two sets of radiances from a
  single-level cloud deck vary linearly with cloud
  amount
• Adequate replacement of CO2 slicing method

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Satellite Derived Motion Fields TARGET
TRACKING ALGORITHM

• Define tracking area centered over each target
• Search area in second image which best matches
  radiances in tracking area
• Confine search to search area centered around
  guess displacement of target
• Two vectors per target 1 for image 12 1 for
  image 23

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Satellite Derived Motion Fields Quality Control
(Autoeditor)

• Functions
• Target height reassignment
• Wind quality estimation flag
• Method (4 Steps)
• 1) 3-dimensional objective analysis of model
  forecast wind field on 1st pass
• 2) Incorporate sat winds into analysis on
  2nd pass. Remove those differing
  significantly from analysis

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Satellite Derived Motion Fields Quality Control
(Contd)

• Method (Contd)
• 3) Target heights readjusted by minimizing a
  penalty function which seeks the optimum
  fit of the vector to the analysis
• 4) Perform another 3-dimensional objective
  analysis (at reassigned pressures) and assign
  quality flag

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GOES High Density Water Vapor Winds
100mb - 250mb 250mb - 400mb 400mb - 700mb
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GOES High Density Cloud Drift Winds
100mb - 400mb 400mb - 700mb Below 700mb
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GOES High Density Winds(Cloud Drift, Imager H2O,
Sounder H2O)
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GOES High Density Visible Winds
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Satellite Derived Motion Fields Sources of
Errors

• Assumption that clouds and water vapor features
  are passive tracers of the wind field
• Image registration errors
• Target identification and tracking errors
• Inaccurate height assignment of target

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5. Satellite image/signal

• Satellite image/signal interpretation

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Interpretation Contamination
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Interpretation Attenuation
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Clouds in Satellite Image

• High Clouds - composed of small ice crystals.
• a) Cirrus - thin hooks, strands, and filaments or
  dense tufts and sproutings.
• Visible imagery - thin cirrus is difficult to
  detect due to visual contamination. Dense cirrus
  shows as patches, streaks, and bands, casting
  shadows on lower clouds or terrain.
• (1) Brightness - normally a darker or translucent
  appearance, often obscuring definitions of lower
  features. A light gray compared to thicker
  clouds.
• (2) Texture - fibrous with banding perpendicular
  to winds.
• ii) IR imagery
• (1) Brightness - usually dense patches are very
  bright but thin cirrus is subject to considerable
  contamination and appears much warmer (darker
  gray) than the actual temperature.

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Clouds in Satellite Image

• (2) Texture - subject to variation due to
  contamination.
• b) Cirrostratus - High/thin to dense continuous
  veil of stable ice crystals covering an extensive
  area. Commonly found on equatorial side of jet
  streaks.
• Visible imagery - generally appears white, thick,
  smooth, and organized when associated with
  cyclones. Casts shadows on surfaces below.
• ii) IR imagery - appears as uniformly cold
  (white), often the coldest, cloud layer (except
  when cumulonimbus clouds are present) with small
  variations in gray shades. Thin
• cirrostratus has considerable contamination
  problems.
• c) Anvil Cirrus (detached from cumulonimbus
  clouds) - dense remains of thunderstorms, usually
  irregularly shaped, aligned parallel to the upper
  level winds. Vary in shape and
• especially in size from 5 to 500 km. Tends to
  become thin and dissipate rapidly.

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Clouds in Satellite Image
i) Visible imagery - bright white but diffuse.
Thick anvils may cast shadows on lower surfaces
whereas thin anvils are often translucent to
lower features. ii) IR imagery - bright white
patches, usually coldest (whitest) cloud, except
when active thunderstorms are present. d)
Cirrocumulus - cumuliform ice crystal clouds
formed by upward vertical motions in the upper
troposphere. May precede rapidly developing
cyclone. i) Visible imagery - thin patches of
clouds, gray to white, usually in advance of
a cyclone. Individual elements often below the
resolution of geostationary sensors. ii) IR
imagery - similar to cirrostratus, white to gray
clouds subject to contamination.
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Clouds in Satellite Image

• 2) Middle Clouds - composed of supercooled water
  droplets and graupel (soft hail).
• a) Altocumulus - indicates vertical motion and
  moisture in the mid-troposphere. Usually
  accompanies large, organized synoptic scale
  cyclones, minor upper tropospheric waves, and
  tropical waves. For well-developed systems,
  sometimes masked by extensive cirrus.
• Visible imagery - Bright white, textured, or
  lumpy, and very difficult to distinguish from
  stratocumulus.
• (1) Wave clouds appear as parallel bands.
• (2) Altocumulus castellanus (ACCAS) appear as a
  diffuse, ragged band of small blobs. In summer
  ACCAS may be found near air mass boundaries
  preceding thunderstorm development.
• ii) IR imagery - Colder (lighter gray) than
  stratocumulus but warmer (darker gray) than high
  clouds. Must be compared to other clouds in the
  area.

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Clouds in Satellite Image

• Wave clouds frequently appear warmer and lower
  (darker gray) than actual due to contamination.
  Individual waves may be below resolution of
  geostationary sensors.
• (2) ACCAS often appear with frontal systems.
  Rather large temperature variations may be
  observed.
• b) Altostratus/Nimbostratus - stratiform cloud in
  mid levels. Normally found in extensive sheets
  with cyclones.
• i) Visible imagery - Bright white, extensive
  sheet. May be difficult to distinguish from low
  or high stratiform clouds. Often textured, unlike
  cirrostratus, but uniform. May cast shadows,
  unlike stratus.
• ii) IR imagery - nearly uniform gray shade
  indicating the middle temperature ranges. Usually
  distinguishable by comparison with other cloud
  layers, warmer (grayer) than cirrus, colder
  (brighter) than stratus.

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Clouds in Satellite Image
3) Low Clouds - composed of water droplets.
Wintertime conditions and vertical growth may
allow glaciation. a) Cumulus - similar to
detached cauliflower-like clouds with sharp
outlines. Often, a region of unorganized cumulus
(popcorn) forms over landmasses during fair
weather. Cumulus clusters whose edges are clearly
visible are referred to as open cell
cumuli. i) Visible imagery - scattered
individual elements are often below the
resolution of geostationary sensors and appear as
gray areas due to contamination. Large individual
elements and groups of broken cumulus appear as
bright white blobs of clouds. ii) IR imagery -
only large areas show due to contamination,
appearing as dark gray blobs. b) Towering
Cumulus - cumulus of moderate or strong vertical
extent. i) Visible imagery - similar to cumulus
but elements are larger, so are more likely to be
distinguishable as bright white blobs. ii) IR
imagery - similar to cumulus, but appearing as
lighter gray blobs.
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Clouds in Satellite Image
c) Cumulonimbus - cumulus of strong vertical
development with or without cirrus anvils. Vary
greatly in size and shape depending on storm
intensity and environment. If upper level winds
are weak, mature thunderstorms are circular
cirrus clouds often with cirrus plumes
(filaments) streaming out nearly symmetrically in
all directions with occasionally lumpy,
penetrating tops (indicated by shadows on visible
imagery). Stronger winds aloft blow the cirrus
anvil downstream and create a diffuse downwind
boundary with a sharp, smooth upwind boundary. In
region of vigorous thunderstorms, cirrus anvils
may merge into cirrus canopies. The active cells
are indicated on visible imagery by their lumpy
penetrating tops. Much of the cirrus in the ITCZ
is actually decaying cirrus anvils. i) Visible
imagery - bright white cellular shape covered
with diffuse thin cirrus and often a lumpy
penetrating top. ii) IR imagery - bright white,
smooth cellular shape. Enhancement techniques
help identify the maximum cloud tops by relating
cloud top temperatures to height.
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Clouds in Satellite Image

• d) Stratocumulus - formed by the spreading of
  cumulus or convective development of stratus.
• Large regions are found over cold ocean
  currents such as the California current off the
  West Coast (convective development of coastal fog
  and stratus) and in the lee of cold fronts
  (spreading of cumulus). Stratocumulus clouds form
  along the low level flow. Widely scattered and
  smaller patches of stratocumulus (trade wind
  cumulus) are found throughout the tropics. These
  scattered patches look like polygonal plates and
  range in diameter from 100-500 km and have
  limited vertical development.
• Visible imagery - light gray to white, appearing
  in cloud lines or sheets composed of parallel
  rolls. Textures are noticeable.
• ii) IR imagery - Dark gray, often difficult to
  distinguish from the surface due to
  contamination. Cellular or textured nature often
  not observed.

91
Clouds in Satellite Image

• e) Stratus and Fog - caused by various means.
• Large areas of stratus are found over cold ocean
  currents, as warm subsiding air underneath
  anticyclones meets the cold water below.
• Visible imagery - white to gray, uniform, smooth
  sheet, except when terrain features penetrate
  above the stratus tops. Coastal and valley
  stratus often outlines the surrounding terrain.
• ii) IR imagery - nearly invisible due to lack of
  contrast between the surface and cloud top
  temperatures. Occasionally, stratus forming
  beneath a radiation inversion will appear warmer
  (darker) than the surface, and is called black
  stratus.

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Weather Related Satellite Image

• Snow and Ice
• Visible imagery - the ability to discern snow
  cover on visible imagery depends on the type of
  terrain, the vegetation cover, the snow depth and
  age, sun angle, the amount of cloud cover, and
  wind.
• (1) Terrain - mountains permit easy snow cover
  identification, appearing as a white, dendritic
  pattern against a darker background. Rivers and
  lakes may be snow covered and may help to
  distinguish snow from a stratus or stratiform
  deck. Snow covered plains tend to have a smooth
  appearance, whereas clouds normally have texture.
  Snow swaths caused by passing lows tend to be
  long and narrow
• with smooth texture and sharp edges.

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Weather Related Satellite Image
(2) Vegetation - snow-covered forest regions
appear gray and mottled, rather than white.
Snow-covered short grass regions appear white and
smooth. The brightness of snow covered grass
decreases with increasing height of the grass and
decreasing snow depth. (3) Snow Depth and Age -
generally, the deeper and newer the snow, the
whiter it appears. Rain on snow makes it appear
grayer. (4) Sun Angle - brightness of snow
decreases rapidly when the sun angle drops below
45.
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Weather Related Satellite Image
(5) Cloud cover - since snow does not move,
looping imagery may assist in distinguishing snow
cover from clouds. (6) Fracture Lines - ice can
sometimes be distinguished by its location (water
bodies) and the presence of dark fracture lines.
Ice may also look chunky. (7) Wind may blow snow
around, and the peaks and depressions may add
texture to an otherwise smooth sheet, helping to
distinguish snow from fog and snow from ice.
ii) IR imagery - detection of ice, snow cover,
and low clouds is very difficult without
simultaneous visible imagery. Snow may appear as
a patch colder (lighter) than bare ground.
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Weather Related Satellite Image

• b) Haze and Smog - suspended fine droplets and
  particulates in still, stable conditions.
• Visible imagery - dull, filmy blob. Smog may be
  related to locations of major urban centers.
  Varying gray shades due to differential light
  scattering and absorption effects from the
  various constituents of the haze or smog.
• ii) IR imagery - contamination makes detection of
  haze or smog very difficult without simultaneous
  visible imagery, although smog may be inferred
  over urban centers.

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Weather Related Satellite Image

• c) Dust or Sand Plumes and Storms - suspended
  surface particles carried aloft by strong surface
  winds and carried downwind long distances.
• Visible imagery - dull, filmy plume often
  striated with a defined shape. Varying gray
  shades due to differential light scattering and
  absorption effects from the various constituents
  of the dust. Downwind of major deserts (e.g.
  Sahara, Outback) and dried-up river basins are
  likely locations for dust/sand plumes.
• ii) IR imagery - contamination makes detection of
  dust or sand very difficult without simultaneous
  visible imagery.

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Non-Weather Related Satellite Image

• Smoke and Ash - suspended fine carbon and mineral
  particles from fires, industry, ships, and
  volcanic activity.
• i) Visible imagery - depends on level of
  activity, atmospheric stability, and windiness,
  ranging from a dull, filmy area to a bright,
  well-defined plume streaming downwind from a
  point. Varying gray shades due to differential
  light scattering and absorption effects from the
  various constituents and intensity of fires
  (etc.). Ships may leave long trails resembling
  aircraft contrails but thicker and grayer.
• ii) IR imagery - red-hot fires and explosive
  volcanic eruptions appear as black dots in IR and
  bright dots in visible. Lava flows may appear as
  small, narrow, winding black bands. Thin plumes
  are subject to contamination but high, thick
  plumes may be cold(bright) enough to be clearly
  discernable.

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Non-Weather Related Satellite Image
b) Surface Variation i) Visible imagery -
differences in reflectivity among land cover
types (e.g. grass and forest) may be apparent as
variations in shades of gray. Some highly
reflective sandy areas (e.g. White Sands, NM) may
be seen as white to gray unmoving blobs that
could be confused for snow. Shadowing in
mountainous areas may give the landscape
texture. ii) IR imagery - differences in land
cover may produce differences in surface heating
that may be apparent as variations in shades of
gray. Typically, these are large areas (e.g.
Californias Central Valley, the Great Salt Lake)
that are significantly warmer or colder than
their surroundings.
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Non-Weather Related Satellite Image

• c) Sun Glint - can be seen when the sun is
  directly above the viewing scene and sunlight is
  reflected off a highly reflective surface such as
  water or sand. Sun glint patterns appear
  regularly in visible imagery of both polar
  orbiting and geostationary satellites. The
  patterns vary in shape, size, and brightness
  depending on the solar sub point, sea state, and
  low-level distribution of aerosols and moisture.
  Simultaneous comparison to IR can help
  distinguish sun glint from a dust plume or
  similar filmy area.
• Geostationary imagery - appears as a large,
  diffuse, circular bright region located between
  the satellite sub-point and the solar subpoint,
  and thus would be found in the tropics near the
  equator. If the water surface is very smooth, the
  sun glint area is small and intensely brilliant.
• ii) Polar orbiting imagery - a large, diffuse,
  semi-bright area that typically stretches from
  the bottom to the top of a picture. Very dark
  areas cutting through diffuse sun glint indicate
  the presence of calm seas, and may signify the
  presence of surface ridges.

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Various cloud forms

• 1) Open Cell Cumulus - cumulus clusters whose
  edges are clearly visible.
• Cloud Street - orography or heating contrasts due
  to topography or vegetation may cause alignment
  of open cell cumuli in lines parallel to the
  low-level flow or the low to midlevel wind shear.
  Cloud streets can provide an excellent
  representation of the low level flow around
  anticyclones.
• b) Typically appear brighter than closed cell
  stratocumulus in IR imagery.
• 2) Closed Cell Stratocumulus - individual cloud
  elements or clusters of elements without clearly
  defined edges, instead forming smooth to slightly
  lumpy lines of merged elements.
• a) Cloud Sheet - may form from semi-merged lines
  of closed cell stratocumulus and will have a
  lumpy striated texture.
• b) Typically appear grayer than open cell cumulus
  in IR imagery.

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Various cloud forms
3) Enhanced Cumulus - area of cumulus congestus,
towering cumulus, or cumulonimbus clouds.
Associated with fronts, PVA, or orography, and
appear as very bright dots in a field of
otherwise uniform open cell cumulus. 4) Sea
Breeze/Land Breeze - a nearly continuous band of
cumulus clouds that tend to parallel the
coastline. Sea breezes are most often found
inland during the late afternoon, and land
breezes offshore during the early morning. A
cloud-free region along and off the coastline
indicates the subsidence portion of a sea breeze
cell. 5) Ship Tracks - Long, narrow,
stratocumulus cloud plumes that form in the wake
of ships when the winds are light, and there is a
subsidence inversion capping the rising air.