visible optical depth,  Optically thicker clouds correlate with colder tops Ship tracks Note, retrievals done on cloudy pixels which are spatially uniform in 2x2 array effective particle radius, r e Larger particles correlate with optically thicker clouds Ship tracks Reliance of Retrieval on Measurements 0 = 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, r e, and optical depths, , are rarely made (ie. not done by ISCCP or NOAA) Most retrievals using imager at night fix r e to some set value or to be a function of cloud-top temperaure, T c which limits utility of data for cloud studies. This study shows r e estimation is possible for many clouds. Diurnal variation of r e 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, 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 T 11 and smaller values of T 11 - T 12 ). 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 Heidinger NOAA/NESDIS Office of Research and Applications 5200 Auth Road Rm 712 Camp Springs, MD ph x191 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 r e to be a function of T c 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 Physical Basis of Retrievals The goal is to retrieve , r e and T c with as little need of constraint as possible Contours of T 4 -T 11 and T 11 -T 12 reveal variation of sensitivity with  and r e Optically thick region, only sensitive to r e, needs  constrained but no constraint on r e Moderate optical thickness, quasi-orthogonal relationship reduces need for constraints T 4 - T 11 T 11 - T 12 Contours of T c are not shown but retrieval has large sensitivity to it through T 11 Data Source - AVHRR GAC (4 km) 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 , r e and T c 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 A31B-05 Forward Model optical depth,  effective radius, r e Cloud top temperature, T c 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  m taken from CERES IGBP data-set Forward model estimates brightness temperatures: T 4, T 11 and T 12 Retrieval estimates , r e and T c liquid water path is then derived AVHRR Constraints used in this approach  = 16, 200% uncertainty r e = 10  m or f(T c ) (ice cloud), 100% uncertainty T c = T 11 with 20 K effective radius, r e Retrieval of r e relies solely on measurements for thinner stratus but slightly affected by constraints for thicker clouds Slight dependence on a priori constraint No dependence on a priori constraint Significant reliance on constraint Ship tracks