Title: Overview of Oxford MIPAS Cloud Products
1Overview of Oxford MIPAS Cloud Products
- Jane Hurley, Anu Dudhia, Don Grainger
- University of Oxford
2- Aim to retrieve most obvious macrophysical cloud
properties - Cloud Top Height CTH (relative to instrument
pointing)? - Cloud Top Temperature CTT
- Cloud Extinction Coefficient kext
3Cloud Forward Model (CFM) Radiance in MIPAS FOV
- Assume that
- a cloud in the MIPAS FOV is horizontally
homogeneous that is, has a constant cloud top
height across the FOV and can be characterized by
a single extinction coefficient. - the temperature structure within the cloud can be
determined by the wet adiabatic lapse rate
estimated downwards from the cloud top
temperature.
4The radiance is considered in the clearest
microwindow of the MIPAS A band 960 cm-1 961
cm-1
... has been tried with other microwindows ...
O3 CO2
O3 CO2
O3
O3
O3
O3 NH3
O3
5Gas Correction and Validation with Simulations
Real MIPAS measurements Rm will include
significant gaseous radiation contributions Rg,
while the CFM calculates only the radiation
contribution by the cloud itself Rc. It is thus
necessary to deduce what portion of the measured
signal is due to the cloud. Assume that the
cloud has a continuum signal and that the gaseous
contribution has emission/absorption lines.
6Total radiance measured within two MIPAS FOVs
(the FOV containing the cloud top and the FOV
immediately below) calculated by the CFM for
varying cloud top heights and extinction
coefficients.
7Retrieval and A Priori Dependence
- Cloud modelling is a highly non-linear process,
even when considered on the vastly simplified
scale. - Given a pair of radiances from two adjacent
sweeps in a scan pattern, there can be two
possible clouds present a high thin cloud, or a
low thick cloud. - Depending upon which a priori is supplied to an
OER, equally valid different solutions will
result. - NEED TO ADD MORE INFORMATION, to better
characterize the a priori extinction. - Easiest way to get information is to use quantity
we already have - THE COLOUR INDEX CI
- which should already be highly correlated with
Cloud Effective Fraction EF, which is the
effective blocking power of the cloud in the
FOV.
8Optimal Estimations retrieval of form
with state
vector and using
Real Measurements 2 radiance measurements from
MIPAS spectrum the first sweep flagged as
cloudy and the one immediately below DIRECT
Pseudo-Measurements Tret temperature
corresponding to first flagged cloudy sweep EF
Cloud effective fraction, as estimated from
CI RELATE
9Application to MIPAS Spectra
- Hot spot of high cloud over Indonesian toga
core, mountainous regions such as the Southern
Andes and Rockies, Amazon Basin and the Congo - Increasing cloud top height towards the tropics
- Retrieved CTT is nearly fully correlated with
CTH - Retrieved log(kext) is more or less constant over
the globe.
10If there IS a cloud in the FOV at a certain
latitude, this shows the probability that it will
occur at a given altitude
11- If there IS a cloud in the FOV at a certain
latitude, this shows the probability that it will
occur at a given temperature - Basically anti-correlated with cloud top
height/altitude
12If there IS a cloud in the FOV at a certain
latitude, this shows the probability that it will
have a given extinction
13Future Work
- Check retrieval against other retrievals of
macroscopic properties McClouds etc - Run over larger MIPAS dataset to get a high
cloud climatology - Compare high cloud climatology with others
ISCCP etc