A Unified Cloud-Defined Weather State Dataset for Process-Resolving Data Analysis and Model Evaluation
Principal Investigator (PI): George Tselioudis, NASA's Goddard Space Flight Center
Cloud-defined Weather States (WS) have been derived in the past decade through the application of clustering techniques on International Satellite Cloud Climatology Project (ISCCP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) cloud property distributions. Since their inception, they have been used in numerous studies, which showed that the Weather States are an efficient method to separate regimes with distinct radiative, thermodynamic, and dynamic properties, and to examine the evolution of physical processes and evaluate the output of climate models. The ongoing reprocessing of the ISCCP dataset, which will cover at higher spatial resolution the period from 1982 to the present, along with the continuous extension of the MODIS and CloudSat/CALIPSO datasets, provides the opportunity to leverage the existing A-Train datasets and create an enhanced Weather State dataset that covers the entire satellite era. We plan, therefore, to analyze the combined ISCCP, MODIS, and CloudSat/CALIPSO data records, with the objective to produce an enhanced Unified Weather State dataset that, using the ISCCP Weather States as the anchor, will span in a coherent manner the 35-year period of satellite observations.
This Unified WS dataset will provide to the community a process-resolving coordinate system on which other satellite datasets, such as those coming from additional A-train instrument retrievals, can be mapped and analyzed. The result of such a mapping will be the creation of datasets that will resolve synoptic and mesoscale variability, and will be directly relatable to physical atmospheric states and thus suitable for process understanding explorations, and for physically meaningful model evaluation studies.
Last Updated: Aug 29, 2019 at 11:08 AM EDT