meteorological satellite image interpretations, part iiluo/eas417/class_notes/eas4… · ·...
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Meteorological Satellite Image Interpretations, Part III
Acknowledgement: Dr. S. Kidder at Colorado State Univ.
Dates EAS417TopicsJan30 Introduction&MatlabtutorialFeb1 Satellite orbits & navigation IFeb6 Satellite orbits & navigation IIFeb8 Satellite orbits & navigation IIIFeb13 Noclass(Lincoln’sBirthday)Feb15 Satellite orbits & navigation IVFeb20 Noclass(President’sDay)Feb22 In-classLab 1: calculate and plot satellite orbitsFeb27 AtmosphericradiationI:BBradiation,&gaseousabsorptionMar1 Test1Mar6 AtmosphericradiationII:Gaseousabsorption&dielectric
materialsMar8 AtmosphericradiationIII:ParticlescatteringMar13 AtmosphericRSI:absorption-scatteringMar15 In-classLab2:accessandanalyzesatellitedataI(MODIS)Mar20 AtmosphericRSII:absorption-emissionMar22 AtmosphericRSIII:radar&lidarMar27 In-classLab3:accessandanalyzesatellitesdata,II(CloudSat)Mar29 Test2Apr3 AtmosphericRSIV:radar&lidarApr5 Meteorologicalsatellites&instrumentationIApr10 Noclass(SpringBreak)Apr12 Noclass(SpringBreak)Apr17 Noclass(SpringBreak)Apr19 MeteorologicalSatellites&instrumentationIIApr20 In-classLab4:cloudimageanalysisApr24 SatelliteimageinterpretationIApr26 SatelliteimageInterpretationIIMay1 SatelliteimageinterpretationIIIMay3 Satellitedatainweatherforecasting&climatepredictionMay8 PreparationforpresentationMay10 StudentpresentationIMay15 StudentpresentationIIMay17 StudentpresentationIII
The three most common channels on meteorological satellites are: 1) visible (~ 0.6 µm) 2) infrared (~ 11 µm) 3) water vapor (~ 6.7 µm) channels.
www.weather.gov
Nov 17 2014
Thin cirrus Deep convection
Clear Low clouds
visible
Dark Bright
War
m
Col
d
IR
Visible & IR for cloud detection and classification
Temperature
Schematic for Water vapor
Dry condition (warm)
Wet condition (cold)
High cloud (make the image colder)
Low cloud (“invisible”)
Surface is not “visible” in the water vapor channel
0.5 µm, visible (solar only): both clouds and snow are bright.
3.7 µm, NIR
11 µm, IR (terrestrial only): similar emission temperatures for low clouds and snow
More example on multi-spectral analysis: low clouds over bright background
Snow strongly absorbs NIR (from the Sun) while clouds reflect NIR.
Darker colors mean higher energy received by satellite (higher brightness temperature).
The three most common channels on meteorological satellites are:
1) visible (~ 0.6 µm) 2) infrared (~ 11 µm) 3) water vapor (~ 6.7 µm)
visible Infrared (window)
water vapor
3.7
1.38
1.38
0.64 1.64
11.01 Cirrus cloud obvious in new 1.38 micron channel, previously undetected
MAS image by Paul Menzel, CIMMS
MODIS 1.38-µm channel for detecting thin cirrus
Probably from airplane contrail
Water vapor absorbs at 1.38 micron
Low clouds
High clouds
1.38 micron is a both scattering and absorption channel
Outline
1. Traditional satellite images: visible, IR and water vapor
2. Microwave images for retrieving precipitation
Precipitation is measured in atmospheric windows (so that gaseous absorption won’t get mixed into the problem)
The Microwave Spectrum
SSM/I, AMSR-E
A Few Facts Sc
atte
ring
coef
ficie
nt
Abs
orpt
ion
coef
ficie
nt
Ice essentially does not absorb MW radiation; it only scatters Liquid drops both absorb and scatter, but absorption dominates Scattering and absorption both increase with frequency and with rain rate. Scattering by ice increases much more rapidly.
19, 37, 85 GHz
Below 22 GHz, absorption is the primary mechanism affecting MW radiative transfer in precipitation
Above 60 GHz, scattering dominates absorption;
At different frequencies, MW radiometers observe different parts of the rain structure:
Below 22 GHz, it responds directly to the rain layer;
Above 60 GHz, it senses the ice aloft.
Two Conclusions
Ts ε
Tc Tr
Ts ε Tr
Tc (1-Tr)
Tc (1-Tr)(1-ε)Tr
Tr: cloud transmittance; (1-Tr) emissivity ε: surface emissivity; (1- ε): surface reflectance
Absorb or transmit
Absorb or reflect
Absorption-based scheme (ignore scattering for now)
c.f. IR radiative transfer. ε is 1 so there is no reflected portion.
Low frequency
€
Tsat = TsεTr + Tc (1−Tr) + Tc (1−Tr)(1−ε)Tr
= Tc[1+ ε(TsTc−1)Tr − (1−ε)Tr2]
≈ Tc[1− (1−ε)Tr2]
Important diff. b/w land & ocean
Ocean: ε ~ 0.5 Land: ε ~ 1
No rain (Tr=1)
Lots of rain (Tr=0)
Ocean 150 K 300 K
Land 300 K 300 K
Ts≈Tc= 300 K Ocean presents a cold background; rain appears warm
Tsat
Low frequency
Ocean: cold
Rain: warm
This half is IR image
€
Tsat ≈ Tc[1− (1−ε)Tr2]
Tr is related to rainfall
Low frequency
Ts ε
Tc Tr
Tr: cloud emissivity; (1-Tr) transmittance ε: surface emissivity; (1- ε): surface reflectance
Absorb or transmit
Absorb or reflect
Water
Ice
Large ice particles will decrease the brightness temperature (depression). This is a major difference from IR radiative transfer, where scattering is negligible.
scattering
High frequency
Cold due to ice scattering (related to rainfall, albeit indirectly). Large amount of ice is indicative of deep convection.
High frequency
Low emissivity of the ocean and scattering by ice cloud can both cause cold temperature. How to solve the ambiguity?
Ocean
Ice scattering
High frequency
Emissivity of surface water body (e.g. ocean or lake) is a function of polarization (hor. & ver. components of the electric field).
Scattering by ice has very little polarization effect
Polarization Corrected Temperature (PCT):
PCT = aTv-bTH, where a-b=1
Spencer et al. 1989
High frequency
Emissivity of surface water body (e.g. ocean or lake) is a function of polarization (hor. & ver. components of the electric field).
Scattering by ice has very little polarization effect
High frequency
Red is ambiguous
PCT
Hurricane Bonnie (1998)
High frequency
37 GHz, sensitive to rain (lower level); appears warm compared to cold ocean
89 GHz, sensitive to ice (upper level); appears cold; ambiguous with cold ocean
89 GHz PCT, sensitive to ice (upper level); appears cold
Putting MW images together
https://www.nrlmry.navy.mil/TC.html
https://www.nrlmry.navy.mil/tc-bin/tc_home2.cgi
https://www.ssec.wisc.edu/datacenter/archive.html
For visible/IR/water images,