A31B-05
Night-time Estimation of Cloud Properties from NOAA Imager Infrared Data
Andrew Heidinger
Dr Andrew Heidinger
NOAA/NESDIS Office of Research and Applications
5200 Auth Road Rm 712
Camp Springs, MD 20746-4304
ph. 301-763-8053 x191
email: aheidinger@nesdis.noaa.gov
NOAA/NESDIS, Office of Research and Application, Washington, DC
1
Motivation
• Night-time estimates of cloud-top effective particle size,
re, and optical depths, t, are rarely made (ie. not done by
ISCCP or NOAA)
•Most retrievals using imager at night fix re to some set
value or to be a function of cloud-top temperaure, Tc
which limits utility of data for cloud studies. This study
shows re estimation is possible for many clouds.
•Diurnal variation of re may give insight into cloud
formation and dissipation mechanisms
•the NOAA imager data record provides a 25 year record
of continuous data for climate studies
•Cloud properties are useful for other applications
(i.e. precipitation screening and aerosol studies).
Objectives
3
Example Application of Retrieval
•Following set of figures show a night-time pass of NOAA-14
AVHRR over the western pacific near California on
June 25, 1999. This period was part of the
Monterery Drizzle Entrainment Experiment
visible optical depth, t
visible optical depth, t
•Stratus cloud field shows two regimes one optically thin
and one of moderate optical thickness
(optical thicker clouds seen by colder values
of T11 and smaller values of T11 - T12).
•Retrievals behave differently in two regimes and have
different reliance on a priori constraints
Ship tracks
Note, retrievals done on cloudy pixels which are
spatially uniform in 2x2 array
effective particle radius, re
•Validate night-time infrared retrievals of
cloud top properties
•Apply retrieval to global data-set of AVHRR (Advanced
Very High Resolution Radiometer) data
Data Source -
Reliance of Retrieval on Measurements
0 = pure constraint, 1 = pure measurement
Optically thicker
clouds correlate with
colder tops
•this cloud field is relatively optically thin, an optically thick
cloud field (t > 20) would offer an easier retrieval scenario.
•Develop a night-time imager-based retrieval of
cloud properties.
4
Retrieval Results
Retrieval of t relies solely on measurements (unconstrained)
No dependence
on a priori
constraint
effective radius, re
Larger particles correlate
with optically thicker clouds
AVHRR GAC (4 km)
Slight dependence
on a priori constraint
Significant
reliance on
constraint
Ship tracks
In Night-time, AVHRR has 3 useable channels (4, 11 & 12 mm)
2
Ship tracks
Retrieval Methodology Forward Model
Employ Traditional Optimal Estimation Approach
because it can…
Physical Basis of Retrievals
AVHRR
•multiple scattering code used to compute
cloud emissivities and transmittances
• properly account for variable sensitivity across
parameter space
Cloud top temperature,
optical depth,
t
The goal is to retrieve t, re and Tc with as little need of constraint as possible
Contours of T4-T11 and T11-T12 reveal variation of sensitivity with t and re
Tc
T4 - T11
•clouds are imbedded in a non-scattering
atmosphere and assumed to be plane
Since it relies on forward model to compute sensitivities, parallel and single-layer.
it allows the retrieval to rely on different measurements
for different retrieval scenarios
•Pressure thickness of cloud varies with
effective radius, re
cloud type, lapse rate used to modify
•Allow constraints to be applied and used only when cloud emission
Forward model estimates brightness
needed
•Atmospheric profiles taken from
temperatures: T4 , T11 and T12
For example, constraining re to be a function of Tc for
NCEP/AVN model analyses/forecasts Retrieval estimates t , re and Tc
cirrus is only needed for thin cirrus, thick ice clouds
have no need of a constraint
liquid water path is then derived
•Surface emissivity at 4,11,12 mm
•Estimate metrics of performance and reliance on
constraints
Use of cloud properties to initialize or for assimilation in
NWP requires knowledge of error covariance matrices
which are computed automatically by this technique
taken from CERES IGBP data-set
Constraints
Retrieval of re relies solely on measurements for thinner
stratus but slightly affected by constraints for thicker clouds
used in this approach
t = 16 , 200% uncertainty
re = 10 mm or f(Tc) (ice cloud), 100% uncertainty
Tc = T11 with 20 K
T11 - T12
5
Conclusions
• an optimal estimation retrieval method was developed
which can be applied to NOAA night-time imager data
•The method is able to retrieve independent estimates of
t, re and Tc under many conditions and is able to use
constraints when necessary
•this retrieval is consistent with a previously validated
day-time algorithm
Moderate optical thickness,
quasi-orthogonal relationship
reduces need for constraints
Optically thick region,
only sensitive to re,
needs t constrained
but no constraint on re
Contours of Tc are not shown but retrieval has large sensitivity to it through T11
•this algorithm is part of routine global experimental
cloud processing system within NOAA/NESDIS/ORA
which uses mapped AVHRR data at 110 km resolution
http://orbit-net.nesdis.noaa.gov/crad/sat/atm/cloud/clavrx