visible optical depth , t

1. 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 aerosolstudies). 3 4 Retrieval Results Example Application of Retrieval Reliance of Retrieval on Measurements 0 = pure constraint, 1 = pure measurement • 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 (t > 20) would offer an easier retrieval scenario. visible optical depth, t visible optical depth, t Optically thicker clouds correlate with colder tops Objectives Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform in 2x2 array • 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 Retrieval of t relies solely on measurements (unconstrained) No dependence on a priori constraint effective radius, re effective particle radius, re Slight dependence on a priori constraint Larger particles correlate with optically thicker clouds Data Source - AVHRR GAC (4 km) Significant reliance on constraint Retrieval of re relies solely on measurements for thinner stratus but slightly affected by constraints for thicker clouds Ship tracks In Night-time, AVHRR has 3 useable channels (4, 11 & 12 mm) Ship tracks 2 Conclusions 5 Forward Model Retrieval Methodology Physical Basis of Retrievals AVHRR • 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 in • NWP requires knowledge of error covariance matrices which are computed automatically by this technique • 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 • 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 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 • multiple scattering code used to compute • cloud emissivities and transmittances • clouds are imbedded in a non-scattering • atmosphere and assumed to be plane • parallel and single-layer. • Pressure thickness of cloud varies with • cloud type, lapse rate used to modify • cloud emission • Atmospheric profiles taken from • NCEP/AVN model analyses/forecasts • Surface emissivity at 4,11,12 mm • taken from CERES IGBP data-set Cloud top temperature, Tc optical depth,t T4 - T11 T11 - T12 effective radius, re Forward model estimates brightness temperatures: T4 , T11and T12 Retrieval estimates t , re and Tc liquid water path is then derived Optically thick region, only sensitive to re, needs t constrained but no constraint on re Moderate optical thickness, quasi-orthogonal relationship reduces need for constraints Constraints used in this approach t = 16 , 200% uncertainty re = 10 mm or f(Tc) (ice cloud), 100% uncertainty Tc = T11 with 20 K Contours of Tc are not shown but retrieval has large sensitivity to it through T11