Abstract
A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.
Highlights
The accuracies of cloud model predictions play critical roles in many real-time meteorological applications including air quality [1] and solar energy management [2] as well as a host of military and civilian aerodrome operations [3]
It became necessary to evaluate the cloud spin-up characteristics of the Weather Research and Forecasting (WRF) model to ensure the reliability of CCfWRF forecasts, which is highlighted in Section 2 along with an overview of the study domain and data used in the WRF simulations
Cloudy and cloud-free pixels were identified in a binary, manually-generated cloud/no cloud (MGCNC) mask product [14,15], which is derived from the moderate resolution Visible Infrared Imager Radiometry Suite (VIIRS) imagery
Summary
Iisager 2 , Sudhakar Dipu 3 , Xiaoyan Jiang 2 , Johannes Quaas 3 and Randy Markwardt 2. Received: 7 August 2019; Accepted: 3 September 2019; Published: 5 September 2019
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