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Satellite remote sensing applications in Meteorology 2 nd Ewiem Nimdie International Summer School 2 nd Ewiem Nimdie International Summer School Gizaw.

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Presentation on theme: "Satellite remote sensing applications in Meteorology 2 nd Ewiem Nimdie International Summer School 2 nd Ewiem Nimdie International Summer School Gizaw."— Presentation transcript:

1 Satellite remote sensing applications in Meteorology 2 nd Ewiem Nimdie International Summer School 2 nd Ewiem Nimdie International Summer School Gizaw Mengistu, Dept. of Physics, Addis Ababa University, Ethiopia

2 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) Outline

3 4. Measurement of winds  Cloud motion vectors (CMV) 5. Satellite image/signal o Satellite image/signal interpretation Outline

4 1. Introduction  Meteorological satellites  Satellites instrumentation

5 Classification of Satellites

6 Satellite System

7 Sensor System

8 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.

9 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.

10 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 sensing

11 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 Satellite remote sensing

12 Instrument Observing Characteristics

13 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) Instrument Observing Characteristics

14

15 2. Retrieval of meteorological parameters  Measurement of sea and land surface temperature;  Retrieval of vertical profiles of temperature and humidity.

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

17 Surface Temperature and Emissivity Estimation

18 The radiance at the sensor is given by LS j =[ε j L j BB (T)+(1-ε j )L j sky ]*t+L j atm Where ε j is surface emissivity, L j BB is spectral radiance of a blackbody at the surface at temperature T, L j sky is spectral radiance incident upon the surface from the Atmosphere, calculated using radiative transfer equation (e.g. MODTRAN etc), L j atm 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.

19 After getting all the necessary data from RT model as stated on the previous slide, radiance from the Surface, L j, is: Surface Temperature and Emissivity Estimation

20 Emission characteristics of different objects Water is a good approximatio n of a black body (grey body) Water is a good approximatio n of a black body (grey body)

21 Quartz is not a good approximat ion of a black body (selective radiator) Emission characteristics of different objects

22 Snow, vegetation, rock: spectra of mixed pixels

23 Surface Temperature and Emissivity Estimation Relative Emissivity (to the average of all channels, say 5 channels in this example) is given by

24 From laboratory measurement, relationship between minimum emissivity, max.-min. relative emissivity difference can be constructed i.e ε min =f(β max - β min ). Therefore, the revised emissivity can be computed from ε j = β j (ε min / β min ) Surface Temperature and Emissivity Estimation

25 “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

26 Surface Temperature and Emissivity Estimation Spectral radiance (LS j ) 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: LS j =Gain*DN+Bias=DN*(L max -L min )/255+Bias

27 Picture elements of multispectral image

28 Image from different bands

29 Retrieval of vertical profiles of temperature and humidity

30 .

31

32 Weighting function (http://goes.gsfc.nasa.gov/)

33 Weighting function

34

35 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

36 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)

37 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)

38 Retrieval of vertical profiles of temperature :demonstration

39

40 Stratospheric chemistry & Dynamics Mengistu et. al., doi:10.1029/2004JD004856, 2004, doi:10.1029/2004JD005322, 2005

41

42 3. Measurement of rainfall  Visible & Infra-red (IR)

43 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

44 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. Introduction

45 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.

46 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. Spectral bands

47 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.

48 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 algorithms

49 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. VIS and/or IR algorithms

50 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. VIS and/or IR algorithms

51 TAMSAT algorithm over Africa o TAMSAT (Tropical Application of Meteorology using Satellite and other data) o 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.

52 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.

53 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:

54 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: 1. Homogeneous zones are delineated 2. 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 0 C are used)

55 TAMSAT algorithm over Ethiopia Comparison of actual and estimated rainfall at different rainfall ranges, July 1995 for the whole country

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

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

58 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)

59 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. METEOSAT Channels (bands)

60 4. Measurement of winds  Cloud motion vectors (CMV)

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

62 Satellite Derived Motion Fields: GOES Visible, IR, WV Channels  Imager Water vapor channel (6.7µm) Band 3 Water vapor channel (6.7µm) Band 3 Longwave IR window chan. (10.7µm) Band 4 Longwave IR window chan. (10.7µm) Band 4 Visible Channel (0.65µm) Band 1 Visible Channel (0.65µm) Band 1  Sounder Water vapor channel (7.3µm) Band 10 Water vapor channel (7.3µm) Band 10 Water vapor channel (7.0µm) Band 11 Water vapor channel (7.0µm) Band 11

63 Satellite Derived Motion Fields: BASIC METHODOLOGY  Image acquisition  Automated registration of imagery  Target selection process  Height assignment of targets  Target tracking  Quality control (Autoeditor)

64 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

65 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

66 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 hundreds of landmarks used each landmark is sought in all images each landmark is sought in all images middle image in loop is assumed to have “perfect” navigation 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 mean line and element correction is computed and possibly applied for the 1st and 3rd image

67 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 multi-deck cloud signatures are evident

68 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

69 Satellite Derived Motion Fields: Height Assignment of Targets  Infrared window technique oldest method of assigning heights to cloud-motion winds oldest method of assigning heights to cloud-motion winds not suitable for assigning heights of semi-transparent cloud (ie., thin cirrus) not suitable for assigning heights of semi-transparent cloud (ie., thin cirrus) still provides a suitable fallback to other methods still provides a suitable fallback to other methods

70 Satellite Derived Motion Fields: Target Height Assignment (Cont’d)  CO 2 Slicing Technique most accurate means of assigning heights to semi-transparent tracers most accurate means of assigning heights to semi-transparent tracers utilizes IR window and CO 2 (13µm) absorption channels viewing the same FOV utilizes IR window and CO 2 (13µm) absorption channels viewing the same FOV

71 Satellite Derived Motion Fields: Target Height Assignment (Cont’d)  H 2 O Intercept Method Utilizes Water Vapour channel (6.7µm) Band 3 and longwave IR window chan. (10.7µm) Band 4 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 Algorithm: these two sets of radiances from a single- level cloud deck vary linearly with cloud amount Adequate replacement of CO 2 slicing method Adequate replacement of CO 2 slicing method

72 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

73 Satellite Derived Motion Fields: Quality Control (Autoeditor)  Functions Target height reassignment Target height reassignment Wind quality estimation flag Wind quality estimation flag  Method (4 Steps) 1)3-dimensional objective analysis of model forecast wind field on 1st pass 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 2)Incorporate sat winds into analysis on 2nd pass. Remove those differing significantly from analysis

74 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 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 4)Perform another 3-dimensional objective analysis (at reassigned pressures) and assign quality flag

75 GOES High Density Water Vapor Winds 100mb - 250mb 250mb - 400mb 400mb - 700mb

76 GOES High Density Cloud Drift Winds 100mb - 400mb 400mb - 700mb Below 700mb

77 GOES High Density Winds (Cloud Drift, Imager H2O, Sounder H2O)

78 GOES High Density Visible Winds

79 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

80 5. Satellite image/signal  Satellite image/signal interpretation

81 Interpretation: Contamination

82 Interpretation: Attenuation

83 Clouds in Satellite Image 1)High Clouds − composed of small ice crystals. a) Cirrus − thin hooks, strands, and filaments or dense tufts and sproutings. i)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.

84 (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. i)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 Image

85 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 Image

86 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. i)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 Image

87 (1)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 Image

88 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 Image

89 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 Image

90 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. i)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 Image

91 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. i)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. Clouds in Satellite Image

92 a)Snow and Ice i)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 Image

93 (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 Image

94 (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 Image

95 b) Haze and Smog − suspended fine droplets and particulates in still, stable conditions. i)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 Image

96 c) Dust or Sand Plumes and Storms − suspended surface particles carried aloft by strong surface winds and carried downwind long distances. i)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. Weather Related Satellite Image

97 a)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. Non-Weather Related Satellite Image

98 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. Non-Weather Related Satellite Image

99 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. i)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. Non-Weather Related Satellite Image

100 Various cloud forms 1) Open Cell Cumulus − cumulus clusters whose edges are clearly visible. a)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.

101 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. Various cloud forms


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