Improving Numerical Weather Prediction–Based Near-Cloud Aviation Turbulence Forecasts by Diagnosing Convective Gravity Wave Breaking

Soo Hyun Kim, Hye Yeong Chun, Dan Bi Lee, Jung Hoon Kim, Robert D. Sharman

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Based on a convective gravity wave drag parameterization scheme in a numerical weather prediction (NWP) model, previously proposed near-cloud turbulence (NCT) diagnostics for better detecting turbulence near convection are tested and evaluated by using global in situ flight data and outputs from the operational global NWP model of the Korea Meteorological Administration for one year (from December 2016 to November 2017). For comparison, 11 widely used clear air turbulence (CAT) diagnostics currently used in operational NWP-based aviation turbulence forecasting systems are separately computed. For selected cases, NCT diagnostics predict more accurately localized turbulence events over convective regions with better intensity, which is clearly distinguished from the turbulence areas diagnosed by conventional CAT diagnostics that they mostly failed to forecast with broad areas and low magnitudes. Although overall performance of NCT diagnostics for one whole year is lower than conventional CAT diagnostics due to the fact that NCT diagnostics ex-clusively focus on the isolated NCT events, adding the NCT diagnostics to CAT diagnostics improves the performance of aviation turbulence forecasting. Especially in the summertime, performance in terms of an area under the curve (AUC) based on probability of detection statistics is the best (AUC = 0.837 with a 4% increase, compared to conventional CAT forecasts) when the mean of all CAT and NCT diagnostics is used, while performance in terms of root-mean-square error is the best when the maximum among combined CAT and single NCT diagnostic is used. This implies that including NCT diagnostics to currently used NWP-based aviation turbulence forecasting systems should be beneficial for safety of air travel.

Original languageEnglish
Pages (from-to)1735-1757
Number of pages23
JournalWeather and Forecasting
Issue number5
Publication statusPublished - 2021 Oct

Bibliographical note

Funding Information:
This work was supported by the Korea Meteorological Administration (KMA) Research and Development Program under Grant KMI2020-01910 (SHK and JHK), KMI2018-07810 (SHK, HYC, and DBL). SHK was also supported by the 4th Brain Korea 21 Project (through the School of Earth and Environmental Sciences, Seoul National University) in 2020 and 2021. The authors are grateful to two anonymous reviewers for their constructive comments and suggestions. We thank KMA for providing the GDAPS data for research pur-poses and also thank NCAR for providing the USEDR data for evaluation of turbulence diagnostics.

Publisher Copyright:
© 2021 American Meteorological Society. Policy (

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


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