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,