Meteorological Satellite Image Interpretations, Part III Acknowledgement: Dr. S. Kidder at Colorado State Univ.
Dates EAS417 Topics Jan 30 Introduction & Matlab tutorial Feb 1 Satellite orbits & navigation I Feb 6 Satellite orbits & navigation II Feb 8 Satellite orbits & navigation III Feb 13 No class (Lincoln s Birthday) Feb 15 Satellite orbits & navigation IV Feb 20 No class (President s Day) Feb 22 In-class Lab 1: calculate and plot satellite orbits Feb 27 Atmospheric radiation I: BB radiation, & gaseous absorption Mar 1 Test 1 Mar 6 Atmospheric radiation II: Gaseous absorption & dielectric materials Mar 8 Atmospheric radiation III: Particle scattering Mar 13 Atmospheric RS I: absorption-scattering Mar 15 In-class Lab 2: access and analyze satellite data I (MODIS) Mar 20 Atmospheric RS II: absorption-emission Mar 22 Atmospheric RS III: radar & lidar Mar 27 In-class Lab 3: access and analyze satellites data, II (CloudSat) Mar 29 Test 2 Apr 3 Atmospheric RS IV: radar & lidar Apr 5 Meteorological satellites & instrumentation I Apr 10 No class (Spring Break) Apr 12 No class (Spring Break) Apr 17 No class (Spring Break) Apr 19 Meteorological Satellites & instrumentation II Apr 20 In-class Lab 4: cloud image analysis Apr 24 Satellite image interpretation I Apr 26 Satellite image Interpretation II May 1 Satellite image interpretation III May 3 Satellite data in weather forecasting & climate prediction May 8 Preparation for presentation May 10 Student presentation I May 15 Student presentation II May 17 Student presentation III
www.weather.gov 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. Nov 17 2014
Visible & IR for cloud detection and classification IR Warm Cold Thin cirrus Clear Deep convection Low clouds Dark Bright visible
High cloud (make the image colder) Schematic for Water vapor Wet condition (cold) Dry condition (warm) Low cloud ( invisible ) Surface is not visible in the water vapor channel Temperature
0.5 µm, visible (solar only): both clouds and snow are bright. 3.7 µm, NIR 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). 11 µm, IR (terrestrial only): similar emission temperatures for low clouds and snow
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 water vapor Infrared (window) 1.38 3.7
MODIS 1.38-µm channel for detecting thin cirrus Cirrus cloud obvious in new 1.38 micron channel, previously undetected Probably from airplane contrail 0.64 1.64 1.38 11.01 MAS image by Paul Menzel, CIMMS
1.38 micron is a both scattering and absorption channel High clouds Water vapor absorbs at 1.38 micron Low clouds
Outline 1. Traditional satellite images: visible, IR and water vapor 2. Microwave images for retrieving precipitation
The Microwave Spectrum Precipitation is measured in atmospheric windows (so that gaseous absorption won t get mixed into the problem) SSM/I, AMSR-E
A Few Facts Scattering coefficient Absorption coefficient 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
Two Conclusions 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.
Absorption-based scheme (ignore scattering for now) T s ε Tr T c (1-Tr) Low frequency T c (1-Tr)(1-ε)Tr c.f. IR radiative transfer. ε is 1 so there is no reflected portion. Absorb or transmit Absorb or reflect T c Tr T s ε Tr: cloud transmittance; (1-Tr) emissivity ε: surface emissivity; (1- ε): surface reflectance
T sat = T s εtr + T c (1 Tr) + T c (1 Tr)(1 ε)tr Low frequency = T c [1+ ε( T s T c 1)Tr (1 ε)tr 2 ] T c [1 (1 ε)tr 2 ] Important diff. b/w land & ocean Ocean: ε ~ 0.5 Land: ε ~ 1 T s T c = 300 K No rain (Tr=1) T sat Lots of rain (Tr=0) Ocean 150 K 300 K Land 300 K 300 K Ocean presents a cold background; rain appears warm
This half is IR image Low frequency Ocean: cold Rain: warm T sat T c [1 (1 ε)tr 2 ] Tr is related to rainfall
High frequency scattering Ice Large ice particles will decrease the brightness temperature (depression). Absorb or transmit Water T c Tr This is a major difference from IR radiative transfer, where scattering is negligible. Absorb or reflect Tr: cloud emissivity; (1-Tr) transmittance T s ε ε: surface emissivity; (1- ε): surface reflectance
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 Polarization Corrected Temperature (PCT): PCT = at v -bt H, where a-b=1 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 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
Red is ambiguous High frequency Hurricane Bonnie (1998) PCT
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 Putting MW images together 89 GHz PCT, sensitive to ice (upper level); appears cold
https://www.nrlmry.navy.mil/tc.html
https://www.nrlmry.navy.mil/tc-bin/tc_home2.cgi
For visible/ir/water images, https://www.ssec.wisc.edu/datacenter/archive.html