English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Tropical cyclones present a serious threat to many coastal communities aroundthe world. Many numerical weather prediction models provide deterministicforecasts with limited measures of their forecast uncertainty. Standardpostprocessing techniques may struggle with extreme events or use a 30-daytraining window that will not adequately characterize the uncertainty of atropical cyclone forecast. We propose a novel approach that leveragesinformation from past storm events, using a hierarchical model to quantifyuncertainty in the spatial correlation parameters of the forecast errors(modeled as Gaussian processes) for a numerical weather prediction model. Thisapproach addresses a massive data problem by implementing a drastic dimensionreduction through the assumption that the MLE and Hessian matrix represent alluseful information from each tropical cyclone. From this, simulated forecasterrors provide uncertainty quantification for future tropical cycloneforecasts. We apply this method to the North American Mesoscale model forecastsand use observations based on the Stage IV data product for 47 tropicalcyclones between 2004 and 2017. For an incoming storm, our hierarchicalframework combines the forecast from the North American Mesoscale model withthe information from previous storms to create 95\% and 99\% prediction maps ofrain. For six test storms from 2018 and 2019, these maps provide appropriateprobabilistic coverage of observations. We show evidence from the log scoringrule that the proposed hierarchical framework performs best among competingmethods.

A Bayesian Hierarchical Model Framework to Quantify Uncertainty of Tropical Cyclone Precipitation Forecasts