1081 Cloud Boundary Estimates from Operational Satellite Data: Recent Progress and Applications

Wednesday, 10 January 2018
Exhibit Hall 3 (ACC) (Austin, Texas)
William L. Smith Jr., NASA, Hampton, VA; and S. Sun-Mack, P. Minnis, G. Hong, D. Spangenburg, and Y. Chen

Knowledge of the horizontal and vertical location of cloud hydrometeors in the atmosphere is important for weather and climate applications. Passive satellite imager data are well suited to identify the geographic location of clouds and cloud top information at high spatial and temporal resolution but obtaining accurate estimates of cloud layering, geometric thickness and cloud ceiling have proven difficult. During the daytime, satellite solar reflectance measurements provide a vertical dimension, since they are related to a wide range of cloud optical thicknesses. Nighttime is problematic since only infrared data are available, and theoretically, optical thickness sensitivity is limited to thinner clouds. The challenge is in developing methods that best exploit the information contained in the satellite radiances to improve cloud vertical structure characterizations over the entire diurnal cycle. This paper describes new techniques, including an artificial neural network approach, to estimate cloud geometric thickness from satellite imager data. Accurate observations on cloud layering obtained from a combination of CloudSat and CALIPSO (CC) data are used to train the algorithms using data matched with MODIS radiances and cloud properties. The techniques are validated with independent CC data and compared to other known published methods. The techniques are also used to derive cloud thickness and base height from GOES data which are compared to cloud ceiling estimates from the plethora of ceilometer data available from surface stations across the U.S. The accuracies are summarized to illustrate the utility of satellite imager data for diagnosing vertical cloud profiles over a wide range of cloud conditions. This work has the potential to improve satellite characterizations of clouds for short term weather forecasting, improved knowledge of the global distribution of ice and liquid water content, and improved diagnoses of hazardous aviation weather (e.g. icing conditions and low cloud ceilings). Progress in these areas will be briefly reported.
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