Climate Cloud Model Forecast Verification-an Engineering Perspective

Abstract

The processes relevant to the verification of cloud forecasts generated by climate models are discussed from an engineering perspective. These processes include an assessment of cloud product requirements to be evaluated, the creation of a verification test plan including procedures and data to be analyzed, the development of independent sources of validation or truth datasets, and the quantitative comparisons between the cloud forecast products and the truth data needed to establish model performance. The engineering perspective means minimal effort is focused on assessing the veracity of the physics contained in the cloud forecast model, rather emphasis is upon evaluating the results produced by it. It is postulated that these procedures are critical to improve the reliability of climate model predictions. The World Meteorological Organization has stated accuracy requirements for cloud products created from satellite observations, through the Global Climate Observing System (GSOC) program; however, no similar requirements have been defined for cloud forecast products. A statement of accuracy requirements is urgently needed. Meanwhile, it is assumed herein that cloud observation and cloud forecast requirements are identical. The assessment of model performance exploits high quality, manually-generated cloud truth products created from remotely-sensed satellite data which serve as truth data. Results show clouds under-specified in reanalysis cloud datasets created for use to initialize climate models but an over-specification of clouds by the cloud forecast model, in short-range predictions. This system level analysis demonstrates the need to improve the accuracy of cloud forecasts, especially lower-level water clouds which are responsible for most of the uncertainty in climate model predictions.