visible optical depth,

Optically thickerclouds correlate withcolder tops

Ship tracksNote, retrievals done on cloudy pixels which are spatially uniform in 2x2 array

effective particle radius, re

Larger particles correlatewith optically thicker clouds

Ship tracks

Reliance of Retrieval on Measurements0 = pure constraint, 1 = pure measurement

visible optical depth,

Retrieval of relies solely on measurements (unconstrained)

Objectives•Develop a night-time imager-based retrieval of cloud properties.

•Validate night-time infrared retrievals of cloud top properties

•Apply retrieval to global data-set of AVHRR (Advanced Very High Resolution Radiometer) data

In Night-time, AVHRR has 3 useable channels (4, 11 & 12 m)

Motivation• Night-time estimates of cloud-top effective particle size, re, and optical depths, , 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).

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

•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

•this cloud field is relatively optically thin, an optically thick cloud field ( > 20) would offer an easier retrieval scenario.

Dr Andrew HeidingerNOAA/NESDIS Office of Research and Applications5200 Auth Road Rm 712Camp Springs, MD 20746-4304ph. 301-763-8053 x191email: aheidinger@nesdis.noaa.gov

Retrieval Results

Night-time Estimation of Cloud Properties from NOAA Imager Infrared Data Andrew Heidinger

NOAA/NESDIS, Office of Research and Application, Washington, DC

Retrieval Methodology Employ Traditional Optimal Estimation Approach because it can…

• properly account for variable sensitivity across parameter space

Since it relies on forward model to compute sensitivities, it allows the retrieval to rely on different measurements for different retrieval scenarios

•Allow constraints to be applied and used only when needed

For example, constraining re to be a function of Tc for cirrus is only needed for thin cirrus, thick ice clouds have no need of a constraint

•Estimate metrics of performance and reliance on constraints

Use of cloud properties to initialize or for assimilation inNWP requires knowledge of error covariance matrices which are computed automatically by this technique

Physical Basis of RetrievalsThe goal is to retrieve , re and Tc with as little need of constraint as possibleContours of T4-T11 and T11-T12 reveal variation of sensitivity with and re

Optically thick region, only sensitive to re, needs constrained but no constraint on re

Moderate optical thickness,quasi-orthogonal relationshipreduces need for constraints

T4 - T11

T11 - T12

Contours of Tc are not shown but retrieval has large sensitivity to it through T11

Data Source - AVHRR GAC (4 km)

Conclusions

1

2

3 4

5• 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 , re and Tc under many conditions and is able to use constraints when necessary

•this retrieval is consistent with a previously validatedday-time algorithm

•this algorithm is part of routine global experimental cloud processing system within NOAA/NESDIS/ORAwhich uses mapped AVHRR data at 110 km resolutionhttp://orbit-net.nesdis.noaa.gov/crad/sat/atm/cloud/clavrx

A31B-05

Forward Model

optical depth,

effective radius,

re

Cloud top temperature, Tc•multiple scattering code used to computecloud emissivities and transmittances

•clouds are imbedded in a non-scatteringatmosphere and assumed to be planeparallel and single-layer.

•Pressure thickness of cloud varies withcloud type, lapse rate used to modify cloud emission

•Atmospheric profiles taken fromNCEP/AVN model analyses/forecasts

•Surface emissivity at 4,11,12 mtaken from CERES IGBP data-set

Forward model estimates brightnesstemperatures: T4 , T11 and T12

Retrieval estimates , re and Tc

liquid water path is then derived

AVHRR

Constraints used in this approach = 16 , 200% uncertainty re = 10 m or f(Tc) (ice cloud), 100% uncertaintyTc = T11 with 20 K

effective radius, re

Retrieval of re relies solely on measurements for thinner stratus but slightly affected by constraints for thicker clouds

Slight dependence on a priori constraint

No dependenceon a priori constraint

Significantreliance onconstraint

Ship tracks