Microwave Remote Sensing of Atmospheric Trace Gases

Remote Sensing I

Lecture 6

Summer 2006

Rotational Energy Levels

J F(J)

)0()1(~10 JFJFJJ

)0()1(~01 FF

]cm[202~ 101

BB

]cm[426~ 112

BBB

Rotational Transitions

121~1 JBJJJBJJ

JJJJB 22 23

12 JB

Rotational Transitions

12~1 JBJJ

allowed transitions:

1J

Microwave Spectrum of HCl

Microwave Spectrum of ClO

Degeneracies of Rotations

posibleorientations

12 J

J=1

Degeneracies of Rotations

J=2 J=3

Intensities of Rotational Lines

•Probability for transition between level l and level udepends on the number of molecules in level l•In thermal equilibrium given by Boltzmann distribution:

TkEN

NBJ

l

u exp

TkJBhcJ B1exp

(tends to decrease with increasing J)

Intensities of Rotational Lines

•Depends also on degenaracies of the levels:

12 Jg J(tends to increase with increasing J)

Overall proportional to:

TkJBhcJJ B1exp12

Intensities of Rotational Lines

May be used to derivetemperature from observedspectrum

Microwave Spectrum of N2O

The N2O Molecule

NN O

N2O is a linear molecule

Microwave Spectrum of H2O

The Water Molecule

O

H H

0.09578 nm

104.48°

Microwave Spectrum of Ozone

Microwave Limb Sounding:

MLS / UARS

(Source: MLS Website)

Part 1: Airborne Microwave Remote Sensing of Atmospheric Trace Gases.

Airborne Submillimeter Radiometer (ASUR)

ASUR frequency range and primary species

ASUR onboard the NASA DC-8

Part 2: Ground-based Microwave Remote Sensing of Atmospheric Trace Gases.

Observations in Spitsbergen (79°N)

Observations in Spitsbergen (79°N)

Radiometer for Atmospheric Measurements (RAM)

Schematic Overview of the RAM

Measured Microwave Spectrum by the RAM

Pressure Broadening of Spectral Lines

50km / 0.5 hPa

20km / 50 hPa

10km / 200 hPa

Weighting Functions for Ozone Retrieval

Retrieval techniques / Inverse Modelling

xy F

xKy

Assume that the measured spectrum y is a known function of the atmospheric profile x plus some noise ε.

Linearize F (also known as the forward model):

However,

can not be directly inverted (ill-posed problem)

Optimal Estimation

xKy

aTa

Taa KxySKKSKSxx

1ˆ

A-priori profile

A-priori profile covariance matrix

Measurement error covariance matrix

Best guess profile

Best estimate given by Optimal Estimation solution:

Example Ozone Profile: RAM vs. Ozonesonde

Optimal Estimation: Averaging Kernels

aTa

Taa KxySKKSKSxx

1ˆ

1 SKKSKSD T

aT

a

aa xxKDxx̂

DxxAxx aaˆ

DxAIAx a

Optimal estimation solution:

Define:

Then:

Define Averaging Kernel Matrix A = DK:

Averaging Kernel Functions