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2 nd E wiem Nimdie International Summer School. Satellite remote sensing applications in Meteorology. Weather and Climate Forecasting in Africa and its Application to Agriculture & Water Resource Management. 19 July – 31 July 2010 Kumasi , Ghana.

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satellite remote sensing applications in meteorology

2ndEwiem Nimdie International Summer School

Satellite remote sensing applications in Meteorology

Weather and Climate Forecasting in Africa and its Application to Agriculture & Water Resource Management.

19 July – 31 July 2010

Kumasi, Ghana

GizawMengistu, Dept. of Physics, Addis Ababa University, Ethiopia

outline
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)
outline3
Outline

4. Measurement of winds

  • Cloud motion vectors (CMV)

5. Satellite image/signal

  • Satellite image/signal interpretation
1 introduction
1. Introduction
  • Meteorological satellites
  • Satellites instrumentation
first application of satellite remote sensing
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.
satellite remote sensing
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.
satellite remote sensing10
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
satellite remote sensing11
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
instrument observing characteristics13
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)
2 retrieval of meteorological parameters
2. Retrieval of meteorological parameters
  • Measurement of sea and land surface temperature;
  • Retrieval of vertical profiles of temperature and humidity.
definition
Definition

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

surface temperature and emissivity estimation18
Surface Temperature and Emissivity Estimation

The radiance at the sensor is given by

LS j =[εjLjBB(T)+(1-εj)Ljsky]*t+Ljatm

Where εj 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.

surface temperature and emissivity estimation19
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:

emission characteristics of different objects
Emission characteristics of different objects

Water is a good approximation of a black body (grey body)

emission characteristics of different objects21
Emission characteristics of different objects

Quartz is not a good approximation of a black body (selective radiator)

surface temperature and emissivity estimation23
Surface Temperature and Emissivity Estimation

Relative Emissivity (to the average of all channels, say

5 channels in this example) is given by

surface temperature and emissivity estimation24
Surface Temperature and Emissivity Estimation

From laboratory measurement, relationship between minimum emissivity, max.-min. relative emissivity difference can be constructed i.e εmin=f(βmax- βmin). Therefore, the revised emissivity can be computed from

εj = βj(εmin/ βmin)

split window methods atmospheric correction for surface temperature measurement
“Split-window” methods—atmospheric 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
surface temperature and emissivity estimation26
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:

LSj=Gain*DN+Bias=DN*(Lmax-Lmin)/255+Bias

retrieval of vertical profiles of temperature demonstration
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

retrieval of vertical profiles of temperature demonstration36
Retrieval of vertical profiles of temperature :demonstration
  • 1. Assume the function f(x) is given by f(x)=x+4x(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)
retrieval of vertical profiles of temperature demonstration37
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.A+gamma.H)^-1AT.g) where H

is constraining matrix (smoothing matrix)

stratospheric chemistry dynamics
Stratospheric chemistry & Dynamics

Mengistu et. al., doi:10.1029/2004JD004856, 2004, doi:10.1029/2004JD005322, 2005

3 measurement of rainfall
3. Measurement of rainfall
  • Visible & Infra-red (IR)
introduction
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
slide44

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.

spectral bands
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.

spectral bands46
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.

vis and or ir algorithms
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.

vis and or ir algorithms48
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 235°K 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.

vis and or ir algorithms49
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 R10and R40 were originally specified as 8 and 2 mm/hr, respectively, but have been adjusted slightly in more recent applications.

vis and or ir algorithms50
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 apure 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.

tamsat algorithm over africa
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.
tamsat algorithm over africa52
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.

tamsat algorithm over africa53
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:
tamsat algorithm over ethiopia
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)
tamsat algorithm over ethiopia55
TAMSAT algorithm over Ethiopia

Comparison of actual and estimated rainfall at different rainfall ranges, July 1995 for the whole country

tamsat algorithm over ethiopia56
TAMSAT algorithm over Ethiopia

Comparison of observed and estimated over western Ethiopia for the period June to September 1994.

tamsat algorithm over ethiopia57
TAMSAT algorithm over Ethiopia

Comparison of observed and estimated over northeastern Ethiopia for the period June to September 1994.

meteosat channels bands
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)

meteosat channels bands59
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 radiation’s 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.

4 measurement of winds
4. Measurement of winds
  • Cloud motion vectors (CMV)
satellite derived motion fields
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
satellite derived motion fields goes visible ir wv channels
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
satellite derived motion fields basic methodology
Satellite Derived Motion Fields: BASIC METHODOLOGY
  • Image acquisition
  • Automated registration of imagery
  • Target selection process
  • Height assignment of targets
  • Target tracking
  • Quality control (Autoeditor)
satellite derived motion fields image acquisition
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
satellite derived motion fields auto registration of imagery
Satellite Derived Motion Fields: Auto-registration 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
satellite derived motion fields auto registration cont d
Satellite Derived Motion Fields: Auto-registration (Cont’d)
  • 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
satellite derived motion fields target selection process
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
satellite derived motion fields target selection process cont d
Satellite Derived Motion Fields: TARGET SELECTION PROCESS (Cont’d)
  • Locate maxima in brightness
  • Select target/feature associated with strongest gradient
  • Target density is controlled by size of target selector area
satellite derived motion fields height assignment of targets
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
satellite derived motion fields target height assignment cont d
Satellite Derived Motion Fields: Target Height Assignment (Cont’d)
  • 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
satellite derived motion fields target height assignment cont d71
Satellite Derived Motion Fields:Target Height Assignment (Cont’d)
  • 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
satellite derived motion fields target tracking algorithm
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 1&2; 1 for image 2&3
satellite derived motion fields quality control autoeditor
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
satellite derived motion fields quality control cont d
Satellite Derived Motion Fields: Quality Control (Cont’d)
  • Method (Cont’d)
    • 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
goes high density water vapor winds
GOES High Density Water Vapor Winds

100mb - 250mb

250mb - 400mb

400mb - 700mb

goes high density cloud drift winds
GOES High Density Cloud Drift Winds

100mb - 400mb

400mb - 700mb

Below 700mb

satellite derived motion fields sources of errors
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
5 satellite image signal
5. Satellite image/signal
  • Satellite image/signal interpretation
clouds in satellite image
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.
clouds in satellite image84
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.
clouds in satellite image85
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.

clouds in satellite image86
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.
clouds in satellite image87
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.
clouds in satellite image88
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.

clouds in satellite image89
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.

clouds in satellite image90
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.
clouds in satellite image91
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.
weather related satellite image
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.
weather related satellite image93
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°.

weather related satellite image94
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.

weather related satellite image95
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.
weather related satellite image96
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.
non weather related satellite image
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. California’s 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.
various cloud forms
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.