Cloud Forecasts from NWP and Climate Models

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Meteorology".

Deadline for manuscript submissions: closed (29 February 2020) | Viewed by 3368

Special Issue Editor


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Guest Editor
Center for Space Research, The University of Texas at Austin, Austin, USA
Interests: satellite remote sensing of clouds fields; cloud simulations; cloud model forecast accuracy

Special Issue Information

Dear Colleagues,

Clouds play critical roles in a host of meteorological and climate applications. The accurate analyses and forecasts of clouds by NWP models are important to air quality managers, in the economic use of power generated by solar energy resources, and in aviation safety. In addition, the accurate prediction of cloud fields is critical to climate change studies. Differences in the treatments of clouds between climate models have been identified as the largest source of uncertainty in the prediction of the future mean temperature of the Earth. However, the accuracy of cloud cover model parameters, including the elemental parameter of cloud cover fraction, is seldom addressed in the literature. Therefore, the purpose of this Special Issue is to solicit and document the state of advancements in the analysis of clouds in reanalyses datasets used in cloud simulations, the treatment of clouds in NWP and climate models, the sources of truth data for cloud cover fraction and cloud microphysical properties, the accuracy of cloud analyses in simulation datasets, and the accuracy of forecast products based upon these datasets, as well as to provide an assessment of the technological shortfalls that must be overcome in order to achieve future gains in the prediction of cloud fields with NWP and climate models.

Dr. Keith D. Hutchison
Guest Editor

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Keywords

  • clouds
  • analyses
  • model predictions
  • NWP models
  • climate models
  • cloud optical parameters
  • cloud cover fraction

Published Papers (1 paper)

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Research

15 pages, 3113 KiB  
Article
A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study
by Keith D. Hutchison, Barbara D. Iisager, Sudhakar Dipu, Xiaoyan Jiang, Johannes Quaas and Randy Markwardt
Atmosphere 2019, 10(9), 521; https://doi.org/10.3390/atmos10090521 - 05 Sep 2019
Cited by 2 | Viewed by 2958
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Cloud Forecasts from NWP and Climate Models)
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