Read Interpretation of Satellite Imageries text version

Interpretation of Satellite Imageries

Interpretation of Satellite Imageries

Satellite images can provide indirect evidence of the occurrence or magnitude of various meteorological parameters besides clouds. Can be used to describe the weather and in forecasting the weather changes.

Interpretation of Satellite Imageries

UV radiation

Interpretation of Satellite Imageries

UV radiation

Interpretation of Satellite Imageries

·Absorption, Scattering and Reflection.

Interpretation of Satellite Imageries

Albedo of a surface expresses the fraction of visible that is reflected away from the surface.

Interpretation of Satellite Imageries

Polar Orbiting Satellites

Polar orbiting satellites are nearly from pole to pole, with an inclination of 98°.

Interpretation of Satellite Imageries

Geostationary Satellites

Geostationary satellites orbit the Earth at an altitude of about 35,000 km above the equator.

Interpretation of Satellite Imageries

Different Satellite Channels

Interpretation of Satellite Imageries

Different Satellite Channels

VIS imagery · Sees scattered and reflected energy · Daytime only · Sees clouds and earth surface · Senses haze, smoke, and dust early and late in day · Sensitive to soil, water, and cloud type · Sees fog in daytime · Shadows useful for estimating heights of clouds

Interpretation of Satellite Imageries

Different Satellite Channels

Water Vapour

· Very sensitive to moisture · Shows variations in upper tropospheric moisture · Typically senses upper half of troposphere; higher in moist and lower in dry regions · Atmospheric wave structures are very apparent; short waves readily seen · Tracking features in sequences of water vapor images useful for inferring atmospheric motion · Demonstrates limb darkening

Interpretation of Satellite Imageries

Different Satellite Channels

3.7µm channel

· Detects both reflected solar and earth-emitted thermal radiation · Little atmospheric attenuation · Sees earth surface and clouds · Less sensitive to subpixel clouds than longwave infrared window channel, conversely it is more sensitive to subpixel fires · Detects fog and water clouds at night when used with longwave infrared window channel · Discriminates water versus ice cloud during daytime based on reflectivity · Helps with detection of snow and ice during daytime

Interpretation of Satellite Imageries

Different Satellite Channels

IR Window Channel (10.7 µm) · Little atmospheric attenuation · Reveals surface and cloud temperatures · Used in combination with 12 µm window for estimating low-level moisture and temperatures, and for volcanic ash sea-surface detection · Used in combination with shortwave window for cloud phase estimation and fog detection · Used for storm intensity and rainfall estimation · Used for tracking cloud features in time to estimate atmospheric motion

Interpretation of Satellite Imageries

Different Satellite Channels

IR Window Channel (12 µm) · Moderately sensitive to moisture low in the atmosphere · Usually has cooler brightness temperatures than the longwave IR window · Enables surface temperature estimate when used as split window · Used to estimate total precipitable water vapor and atmospheric instability · Helps in detection of volcanic ash plumes when combined with 10.7 µm channel

Interpretation of Satellite Imageries

Enhancement of Satellite Imageries

Interpretation of Satellite Imageries

Enhancement of Satellite Imageries

ZA enhancement curve for thermal IR imagery combines aspects of stretching and changing the brightness of the image to enhance cold cloud tops

Interpretation of Satellite Imageries

ZA enhancement

.

Interpretation of Satellite Imageries

Enhancement of Satellite Imageries

MB enhancement curve for thermal IR imagery artificially defines brightness values for temperatures of less than -35 º C.

Interpretation of Satellite Imageries

MB Enhancement

.

Interpretation of Satellite Imageries

Enhancement of Satellite Imageries

Dvorak (1984) developed a technique for measuring the intensity of tropical cyclones. The curve is usually displayed to measure the intensity of tropical cyclones that contain eyes.

Interpretation of Satellite Imageries

BD Enhancement

.

Interpretation of Satellite Imageries

Using GOES Channel

Interpretation of Satellite Imageries

The channel characteristics of GOES satellite

Characteristics of GOES Channels

Channel Wavelength /Resolution 1

0.55 ­ 0.75 um; 1 km. 3.8 ­ 4.0 um; 4 km. 6.5 ­ 7.0 um; 8 km. 10.2 ­ 11.2 um; 4 km. 11.5 ­ 12.5 um; 4 km.

Typical Applications

(visible) Day-time cloud, surface mapping (intermediate IR) Night-time fog/mist, heat source (water vapour) Mid to upper level moisture

Comparison with GMS-5

VIS, 0.55 ­ 0.90 um; 1.25 km. Nil.

2 3

WV, 6.5 ­ 7.0 um; 5 km.

4 5

(thermal IR) IR1, 10.5 ­ 11.5 um; Cloud mapping, SST 5 km. (thermal IR) Cloud mapping, volcanic ash IR2, 11.5 ­ 12.5 um; 5 km.

Interpretation of Satellite Imageries

Using Visible Imagery to identify thunderstorms

Strong thunderstorms form on low-level boundaries such as cold fronts, drylines, outflows, etc. These boundaries are frequently marked by organized lines of cumulus cloudiness. By monitoring pre-existing cumulus lines with satellite imagery, forecasters can focus their attention on specific, preferred regions of potential storm formation.

Interpretation of Satellite Imageries

Developing thunderstorms as seen on GOES

Interpretation of Satellite Imageries

Well developed thunderstorms seen on GOES.

Interpretation of Satellite Imageries

High Altitude turbulence as seen on GOES visible image.

Interpretation of Satellite Imageries

Using Channel 2 (3.9 µm) Imagery at Night to Identify Fog & Stratus The 3.9 µm channel responds to both emitted terrestrial radiation, and reflected solar radiation. Since the emissivity of water droplets at 3.9 µm is less than that for longer wavelengths, it is often easier to identify fog and stratiform cloudiness in the channel 2 imagery, and to discriminate between water and ice clouds.

Interpretation of Satellite Imageries

Using Channel 3 (6.7 µm) Imagery The 6.7 µm channel responds to mid- and upper-level water vapor and clouds. The water vapor data can often be used to locate and define synoptic features such as shortwave troughs, ridges, jet streams, etc. Mesoscale regions of moistening/drying at the 300-500 hPa level (such as subsidence associated with thunderstorms' anvils) are also seen using this channel's imagery.

Interpretation of Satellite Imageries

Using Channel 3 (6.7 µm) Imagery Jet axis lies close to the moist/dry boundary and CAT near dark area.

Interpretation of Satellite Imageries

Using Channel 3 (6.7 µm) Imagery Convection and subsidence seen on WV image.

Interpretation of Satellite Imageries

Using Channel 3 (6.7 µm) Imagery Upper troughs/Jet Stream confluence as seen on WV image.

Interpretation of Satellite Imageries

Using Channel 4 (10.7 µm) Imagery Channel 4 imagery has a wide variety of uses, including

determination of cloud top heights, identification of cloud top features, tracking synoptic and mesoscale features at night, etc.

Channel 4 imagery are often used in the tropical regions, where other data is sparse or not available, to make assessments of vertical wind shear, intensity and intensity change in tropical cyclones

Interpretation of Satellite Imageries

Using Channel 4 (10.7 µm) Imagery A tropical cyclone seen on GOES channel 4 image.

Interpretation of Satellite Imageries

Using Channel 5 (12.0 µm) Imagery The 12.0 µm Channel 5 imagery is similar to that from Channel 4 (10.7 µm), except that this wavelength has a unique sensitivity to low-level water vapor. Used alone, it often appears identical to the Channel 4 imagery.

Interpretation of Satellite Imageries

Using Channel 1 and Channel 2 Imagery to Help Discriminate Snow & Cloud Cover Visible imagery alone is often insufficient in defining the boundaries of snow swaths, because both clouds and snow are highly reflective at visible wavelengths. Because liquid water clouds are reflective at 3.9 µm, and snow fields are not, imagery from the 3.9 µm channel, in conjunction with the visible imagery, can reveal both snow fields and the low-level clouds over those snow fields. In the 3.9 µm image below, snow appears as a dark gray and liquid water clouds are white

Interpretation of Satellite Imageries

Using Channel 1 and Channel 2 Imagery to Help Discriminate Snow & Cloud Cover In the 3.9 µm image (left), snow appears as a dark gray and liquid water clouds are white

Interpretation of Satellite Imageries

Using Channel 2 (3.9 µm) and Channel 4 (10.7 µm) Imagery to Identify Night-time Fog By subtracting the brightness temperatures of Channel 2 from those of Channel 4, we can produce an image that distinguishes fog and low-lying stratus clouds from other clouds and snow cover.

Interpretation of Satellite Imageries

Using Channel 2 (3.9 µm) and Channel 4 (10.7 µm) Imagery to Identify Night-time Fog

Original Channel 2 GOES Image

Channel 4 ­ Channel 2

Interpretation of Satellite Imageries

Using Channel 2 (3.9 µm) and Channel 4 (10.7 µm) Imagery to Identify Night-time Fog

Comparing different channel and their combination

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery Besides using visible image, multi-channel imaging techniques using the "split window" brightness temperature difference (10.7 µm - 12.0 µm) and the 3.9 µm - 10.7 µm radiance difference have also been used. The imagery that follows demonstrates the ability of certain GOES Imager channels, alone and in combination, to locate and monitor plumes of volcanic ash during both daylight and night-time hours.

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery 10.7 µm image showing the ash plume (outlined in yellow) from an October 21, 1997 eruption of the Soufriere Hills Volcano on Montserrat.

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery 10.7 µm - 12.0 µm brightness temperature difference, in which the main portion of the volcanic ash plume is distinguished by large negative differences

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery

3.9 µm image, the plume is hard to detect.

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery

3.9 µm - 10.7 µm difference, in which the plume is readily distinguished

Interpretation of Satellite Imageries

Volcanic Ash Detection Using Multi-channel Imagery In order to remove as much background information as possible and show only the ash plume, a 3-channel product using the 3.9 µm, 10.7 µm and 12.0 µm imagery has been created (an experimental product)

Interpretation of Satellite Imageries

Comparing different channel to see high and low altitude mountain waves

High altitude mountain waves

Interpretation of Satellite Imageries

Comparing different channel to see high and low altitude mountain waves

Low altitude mountain waves

Interpretation of Satellite Imageries

Using POES Satellite

Interpretation of Satellite Imageries

Characteristics of POES

Channel 1 2 3A Wavelength (um) 0.58 ­ 0.68 (visible/yellow) 0.725 ­ 1.0 (near IR) 1.58 ­ 1.64 (short wave IR) 3B 4 5 3.55 ­ 3.93 (intermediate IR) 10.3 ­ 11.3 (thermal IR) 11.5 ­ 12.5 (thermal IR) ·Five channels (channel 1, 2, 3A or 3B, 4 and 5) available for each pass. ·Channel 3A is only available during day passes of NOAA-16. ·No water vapour channel. ·Resolution for all channels at sub-satellite point is 1.1 km, decrease to around 5 k m at the rims of the swath. Cloud mapping, vo lcanic ash Night-time fo g/mist, heat source, SST Cloud mapping, SST Hybrid reflected solar energy/radiative energy Radiative energy (GMS IR1, 10.5 ­ 11.5, 5 km) Radiative energy (GMS IR2, 11.5 ­ 12.5, 5 km) Typical Applications Day-time cloud, surface mapp ing Day-time cloud, vegetation, land-water boundaries Snow and ice detectio n Remarks Reflected solar energy (GMS VIS, 0.55 ­ 0.90, 1.25 km) Reflected solar energy Reflected solar energy

Interpretation of Satellite Imageries

False-colour Images

Use of Composite (false-colour) Images · Red, green and blue colours can be assigned to different channels. · Advantage of viewing an image for 3 different channels at the same time. · Illustrations

Interpretation of Satellite Imageries

Observing mist/fog in the vicinity of Hong Kong

Mist/fog in our vicinity

1921UTC on 29 Nov 2001

Interpretation of Satellite Imageries

Detection of mist/fog at night

Suggested com posite image used ´ R (ch3 B), G (ch4/5), B (ch4/5 ) Mist/fo g ap pears as red and sm ooth in texture.

17 53UTC on 21 Oct 2 001

Interpretation of Satellite Imageries

Detection of mist/fog day time

Appearance of mist/fog after day break

Suggested composite image used ­ R (ch1), G (ch2), B (ch4) Mist/fog appears as white and smooth in texture.

2223UTC on 22 Oct 2001

0021UTC on 23 Oct 2001

Interpretation of Satellite Imageries

Detection of snow

C omposite Images

R (Ch 1 ), G (Ch 2 ), B (Ch 4 )

R (Ch 1), G (Ch 3 A), B (Ch 4)

Snow, appears as purple, is much more easily distinguis hable from clouds by using Ch 3A

Interpretation of Satellite Imageries

Detection of ice-bearing cloud

R (ch1), G (ch2), B (ch4) R (ch1), G (ch3A), B (ch4)

Ice-bearing clouds appear purple in colour

Interpretation of Satellite Imageries

Some example use of POES

Time Applications/ Combination Major Features

Red ­ ch1 Green ­ ch2 Blue ­ ch4 Red ­ ch1 Green ­ ch1 Blue ­ ch2 Red ­ ch1 Green ­ ch3A Blue ­ ch4 Red ­ ch1 Green ­ ch3A Blue ­ ch4 Red ­ ch3B Green ­ ch4 Blue ­ ch5 Red ­ ch4 Green ­ ch3B Blue ­ ch3B Developed area: red to yellow Vegetation: green High ground: blue Haze: pale yellow

Appearance of Clouds

Clouds (in general): white Low clouds/fog: pale yellow High clouds: light blue Clouds (in general): white

Day-time

Snow/ice: purple

Clouds (in general): white

Low clouds: pale yello w Rain-bearing clouds: High clouds: violet pink to purple Fog/mist/low stratus: red (smooth in texture) Hill fire: red to dark spots Clouds (in general): white High clouds: light blue Clouds (in general): white Low clouds: light blue High clouds: red

Nighttime

Interpretation of Satellite Imageries

Using MODIS Satellite

M OD IS Specifications

Band 1-19,26 (Reflective bands ), Band 20-25, 27-36 (Emissive Band)

Interpretation of Satellite Imageries

Detection of high level cloud

Applications of false color image

True colour (RGB: 1/4/3)

False colour (RGB: 26/2/1)

Red ­ channel 26 (1.360 ­ 1.390 um) short wave IR Green ­ channel 2 (0.841 ­ 0. 876 um), near I R Blue ­ channel 1 (0.620 ­ 0.670 um), visible (red) High reflectance of high cloud (cirrus) in channel 26 is used to distinguish bet ween high and low cloud

Interpretation of Satellite Imageries

Detection of fog

Fog detection

True colour (RGB: 1/4/3)

False colour (RGB: 3/6/7)

Red ­ channel 3 (0.459 ­ 0.479 um), visible (blue) Green ­ channel 6 (1.628 ­ 1.652 um), short wave IR Bl ue ­ channel 7 (2.105 ­ 2.155 um), short wave IR Strong absorption for snow and ice in channel 7 (SWIR) make them appear vivid red. Small ice crystals in high-level clouds will appear reddish-orange or peach

Interpretation of Satellite Imageries

Haze detection using MODIS

Haze detection

True colour

False colour Ch 26,2,1 0550Z 19 August 2004

Haze (blue)

Interpretation of Satellite Imageries

Snow detection using MODIS

Snow detection

True colour (RGB: 1/4/3)

False colour (RGB: 3/6/7)

Red ­ channel 3 (0.459 ­ 0.479 um), visible (blue) Green ­ channel 6 (1.628 ­ 1.652 um), short wave IR Blue ­ channel 7 (2.105 ­ 2.155 um), short wave IR

Interpretation of Satellite Imageries

Detection of volcanic ash using false colour

Volcanic ash detection

True colour

Red ­ channel 7 (2.105 ­ 2.155 um), short wave IR Green ­ channel 2 (0.841 ­ 0.876 um), near IR Blue ­ channel 1 (0.620 ­ 0.670 um), vi sible

False colour (RGB: 7/2/1)

Interpretation of Satellite Imageries

Detection of Cleveland Eruption (2001) using MODIS

Interpretation of Satellite Imageries

Suggested use of MODIS false colour

Time Daytime Combination Red ­ channel 1 Green ­ channel 4 Blue ­ channel 3 Red ­ channel 26 Green ­ channel 2 Blue ­ channel 1 Red ­ channel 3 Green ­ channel 6 Blue ­ channel 7 Red ­ channel 7 Green ­ channel 2 Blue ­ channel 1 Red ­ channel 20 Green ­ channel 31 Blue ­ channel 32 Application True colour image Appearance of clouds Cloud (in general): white

Cirrus

Snow

Smoke/volcanic ash

High clouds : red Low clouds: light blue white Haze: pale blue Snow: Red Low clouds/Fog: white Ice-bearing clouds: reddishorange or peach Smoke: pale blue volcanic ash: pale red

Night time

Fog/mist/low status: Cloud (in general): white red (fog: smooth in Low clouds: red texture) High clouds: light blue

Interpretation of Satellite Imageries

REFERENCES

M.J.Bader et al. (1995) Images in weather forecasting. A practical guide for interpreting satellite and radar imagery. E.D.Conway (1997) An introduction to satellite image interpretation. Ellrod (2003) Satellite ­ Combined Channel Products ww.wmo.ch/web/aom/amprog/Documents/ TORONTO%20Seminar/Presentations/WMO_toronto_ellrod2.ppt Satellite Meteorology Module Library http://euromet.meteo.fr/courses/english/navig/begins.htm Basics of Remote Sensing from Satellite http://www.orbit.nesdis.noaa.gov/smcd/opdb/tutorial/intro.html GOES Imager Tutorial http://www.cira.colostate.edu/ramm/newgoes/gimgrtoc.htm

Interpretation of Satellite Imageries

Thank you

Information

Interpretation of Satellite Imageries

67 pages

Report File (DMCA)

Our content is added by our users. We aim to remove reported files within 1 working day. Please use this link to notify us:

Report this file as copyright or inappropriate

206938