Lagrangian approach for deriving cloud characteristics from satellite observations and its implications to cloud parameterization

A Lagrangian view is adopted for establishing the spatiotemporal cloud statistics and the scale dependent radiative properties using satellite data. Individual clouds are identified using a newly developed scheme. We sort all clouds by cloud type, cloud area, and number of clouds in each area bin, as well as their radiative properties. For seven different cloud types our analyses provide radiative properties, such as albedo and cloud top temperature, as a function of the cloud spatial scale. All clouds are marked by local time, and large clouds are tracked over time. These analyses provide diurnal variability, lifetimes, and evolution of cloud systems as a function of their spatial scales. These scale dependent cloud properties can be objectively used in guiding the development and evaluation of cloud parameterization in global climate models (GCMs). Particularly, we show how our Lagrangian approach can be used to establish the relative importance of resolvable and fully parameterized clouds to the total cloudy area and to the total amount of reflected visible irradiance. Focus in this 1 month satellite study is on the convective-stratiform cloud systems over the western and central tropical Pacific Ocean, including the so-called warm pool. We adopt the hourly Japanese geostationary satellite (GMS) window channel radiances in the visible and IR window region for cloud classification and characterization. To study the radiative contributions of different clouds in the area, we computed the bidirectional model (BDM) for the Visible and Infrared Spin Scan Radiometer instrument aboard GMS, which we show to agree well with the BDM of the Earth Radiation Budget instrument aboard the Nimbus 7 satellite. An iterative two-stage cloud detection scheme was developed to identify individual clouds. Furthermore, a tracking algorithm was developed to study the time evolution of mesoscale convective systems (MCS). It operates on area and orientationally equivalent ellipsoidal representations of these MCS. We show that the temporal statistics of these convective anvil clouds show good agreement with those reported in the literature. Our data indicate that for the convective-stratiform systems in the tropical Pacific, 95% of the radiatively important clouds (containing a core with an effective brightness temperature <219 K) are of scales resolvable by a GCM of about 50 km × 50 km. On the other hand, a GCM of 250 km × 250 km will only be able to resolve 50% of the radiatively important clouds. This, however, does not mean that the processes responsible for the formation and maintenance of these systems are also resolvable. The low clouds that are unattached to convective-stratiform systems are mostly unresolvable by available GCMs.

[1]  Ross M. McConnell,et al.  An ice-motion tracking system at the Alaska SAR facility , 1990 .

[2]  Fan Xianjin.  Spectral and anisotropic corrections for GMS satellite data , 1992 .

[3]  Hilding Sundqvist,et al.  A parameterization scheme for non-convective condensation including prediction of cloud water content , 1978 .

[4]  Sonia C. Gallegos,et al.  A new automated method of cloud masking for advanced very high resolution radiometer full-resolution data over the ocean , 1993 .

[5]  Mark Williams,et al.  Satellite-Observed Characteristics of Winter Monsoon Cloud Clusters , 1987 .

[6]  Robert A. Houze,et al.  Cloud clusters and superclusters over the oceanic warm pool , 1993 .

[7]  M. Desbois,et al.  Diurnal Variations and Modulation by Easterly Waves of the Size Distribution of Convective Cloud Clusters over West Africa and the Atlantic Ocean , 1993 .

[8]  M. Desbois,et al.  Structural Characteristics of Deep Convective Systems over Tropical Africa and the Atlantic Ocean , 1992 .

[9]  Comparison of Cloud Size Distribution from a Regional Atmospheric Model with Satellite Observations for GCM Cloud Parameterization , 1998 .

[10]  Raúl E. López,et al.  The Lognormal Distribution and Cumulus Cloud Populations , 1977 .

[11]  Bruce A. Wielicki,et al.  On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution , 1992 .

[12]  Brian E. Mapes,et al.  Multiscale variability of deep convection in relation to large-scale circulation in TOGA COARE , 1996 .

[13]  R. Houze,et al.  The Structure and Evolution of Convection in a Tropical Cloud Cluster , 1979 .

[14]  F. Bretherton,et al.  Cloud cover from high-resolution scanner data - Detecting and allowing for partially filled fields of view , 1982 .

[15]  Tetsuo Nakazawa,et al.  Tropical Super Clusters within Intraseasonal Variations over the Western Pacific , 1988 .

[16]  James J. Simpson,et al.  Automated cloud screening of AVHRR imagery using split-and-merge clustering☆ , 1991 .

[17]  P. Minnis,et al.  Cloud bidirectional reflectance functions: a comparison of experimental and theoretical results. , 1985, Applied optics.

[18]  Ronald M. Welch,et al.  Clustering, randomness, and regularity in cloud fields: 4. Stratocumulus cloud fields , 1992 .

[19]  W. M. Gray,et al.  Diurnal Variation of Deep Cumulus Convection , 1977 .

[20]  R. Cess,et al.  Improved angular directional models for clear sky ocean derived from Earth Radiation Budget Satellite shortwave radiances , 1993 .

[21]  Brian E. Mapes,et al.  Gregarious Tropical Convection , 1993 .

[22]  J. Michael Fritsch,et al.  Mesoscale Convective Complexes over the Indian Monsoon Region , 1993 .

[23]  J. Coakley,et al.  Retrieval of properties for semitransparent clouds from multispectral infrared imagery data , 1993 .

[24]  Judith A. Curry,et al.  Classification of clouds over the western equatorial Pacific Ocean using combined infrared and microwave satellite data , 1995 .