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Visualization, Evaluation, and Improvement of Nwp-based Cloud Analyses and Forecasts


From its synoptic-scale origins, Numerical Weather Prediction (NWP) expanded to address both larger (coupled global climate system) and finer scale (nowcasting) forecasting. In particular, great strides have been made in the use of radar and other observations for the initialization of convective systems in their precipitating phase. This led to some initial successes in Warn-on-Forecasting (WOF) [Stensrud et al., 2013] of severe weather events and other nowcasting applications (WOF [Bannister, 2007]). While there is plenty of room for the extension of the predictability of severe events via the enhanced use of radar and other observations in the analysis of precipitating systems, this article explores another potentially significant, yet mostly untapped potential: the NWP analysis of clouds and aerosols. In this approach, rather than enhancing predictability of severe events by extending the span of useful forecasts for existing precipitating events, we extend it by stepping back in time and analyzing and forecasting cloud and aerosol conditions incipient to the emergence of the severe weather events themselves. So far, the numerical analysis of clouds has remained as elusive as some clouds themselves. Progress in the areas of observations (e.g., visual imagery from new generation geostationary satellites and ground-based and airborne cameras), influence models (e.g., fast 3D visual radiative transfer models), data assimilation (e.g., 4dvar [Bannister, 2007]), and numerical modeling (e.g., advanced air chemistry and microphysics schemes) however, prime this area for rapid advances in the coming years. With careful design, the right tools, and proper funding, the next 5–10 years may see revolutionary advances in the realism of NWP-based aerosol and cloud analysis

Article / Publication Data
Available Metadata
Fiscal Year
Publication Name
JCSDA Newsletter, No. 61
Published On
October 01, 2018
Publisher Name
Joint Center For Satellite Data Assimilation
Print Volume
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