Probabilistic forecast of low-level wind shear over Jeju international airport using non-homogeneous regression model

Abstract

Ensemble verification of low-level wind shear (LLWS) is an important issue in airplane landing operation and management. However, there have been few studies on the probabilistic forecasts of LLWS obtained from ensemble prediction system. In this study, we analyzed a reliability analysis to verify LLWS ensemble member forecasts and observation based on the limited grid points around Jeju International Airport in Jeju. Homogeneous and non-homogeneous regression models were used to reduce the bias and dispersion existing ensemble prediction system and to provide probabilistic forecast. Prior to applying probabilistic forecast model, reliability analysis was conducted by using rank histogram to identify the statistical consistency of LLWS ensemble forecasts and corresponding observations. Based on the results of our study, we found that LLWS ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed for all seasons. To correct such biases, homogeneous regression and non-homogeneous regressions as EMOS (Ensemble Model Output Statistics) and EMOS exchangeable model by assuming exchangeable ensemble members were applied. The prediction skills of the methods were compared by the mean absolute error and continuous ranked probability score. We found that the prediction skills of probabilistic forecasts of EMOS exchangeable model were superior to the bias-corrected forecasts in terms of deterministic prediction.

Keywords

Ensemble model output statistics ensemble verification homogeneous and non-homogeneous regression models low-levelwind shear probabilistic forecast

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References

Zhou

McQueen

, et al. 2004. An introduction to NCEP SREF aviation project. In: 11th conference on aviation range and aerospace, Oct 4–8, Hyannis, MA, American Meteorological Society.

Zhou

McQueen

, et al. 2005. Ensemble forecast and verification of low level wind shear by the NCEP SREF system. In: 21st conference on weather analysis and forecasting, July 28–August 5, Washington D.C.: American Meteorological Society.

Lee

Y-G

Ryoo

S-B

Han

, et al. Inter-comparison of ensemble forecasts for low level wind shear against local analyses data over Jeju area. Atmosphere 2020; 11: 198–120. https://doi.org/10.3390/atmos11020198.

Raftery

Gneiting

Balabdaoui

, et al. Using Bayesian model averaging to calibrate forecast ensembles. Monthly Weather Rev 2005; 133: 1155–1174.

Gneiting

Raftery

Westveld

, et al. Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Rev 2005; 133: 1098–1118.

Lerch

Thorarinsdottir

TL.

Comparison of non-homogeneous regression models for probabilistic wind speed forecasting. Tellus 2013; 65: 21206. https://doi.org/10.3402/tellusa.v65i0.21206.

Baran

Lerch

Log-normal distribution based ensemble model output statistics models for probabilistic wind-speed forecasting. QJR Meteorol Soc 2015; 141: 2289–2299.

Junk

Delle Monache

Alessandrini

Analog-based ensemble model output statistics. Monthly Weather Rev 2015; 143: 2909–2917.

Wesonga

Nabugoomu

Ababneh

, et al. Simulation of time series wind speed at an international airport. Simulation 2019; 95: 171–184. DOI: 10.1177/0037549718788955.

10.

Recognition of partial scanning low-level wind shear based on support vector machine. Adv Mech Eng. 2018; 10: 1–7. DOI: 10.1177/1687814017754151.

11.

Karthick

Malathi

A performance evaluation of hybrid genetic algorithm approach for forecasting tropical cyclone categories. Int J Electr Eng Educ. Epub ahead of print 5 January 2020. DOI: 10.1177/0020720919891076.

12.

Han

Choi

Kim

2016. Comparison of prediction performance using statistical postprocessing methods. Asia-Pacific J Atmos Sci 2016; 52: 495–507.

13.

Thorarinsdottir

Gneiting

Probabilistic forecasts of wind speed: ensemble model output statistics by using heteroscedastic censored regression. J R Stat Soc 2010; 173: 371–388.

14.

Han

Choi

Kim

Comparison of statistical post-processing methods for probabilistic wind speed forecasting. Asia–Pacific J Atmos Sci 2018; 54: 91–101.

15.

Grimit

Mass

CF.

Initial results of a mesoscale short-range ensemble forecasting system over the Pacific northwest. Weath Forecast 2002; 17: 192–205.

16.

Sloughter

Gneiting

Raftery

AE.

Probabilistic wind speed forecasting using ensembles and Bayesian model averaging. J Am Stat Assoc 2010; 105: 25–35.

17.

Baran

Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components. Comput Stat Data Anal 2014; 75: 227–238.

18.

Fraley

Raftery

Gneiting

Calibrating multimodel forecast ensembles with exchangeable and missing members using Bayesian model averaging. Month Weather Rev 2010; 138: 190–202.

19.

Gneiting

Raftery

AE.

Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc 2007; 102: 359–378.

20.

Wilks

, 2011. Statistical methods in the atmospheric sciences. 3rd ed. Cambridge, MA: Elsevier Academic Press.

21.

Bertsekas

DP.

1999. Nonlinear programming. 2nd ed. Cambridge, MA: Athena Scientific, pp.14–158.

22.

Hamil

TM.

Interpretation of rank histograms for verifying ensemble forecasts. Monthly Weath Rev 2001; 129: 550–560.

23.

Delle Monache

Hacker

Zhou

, et al. Probabilistic aspects of meteorological and ozone regional ensemble forecasts. J Geophys Res; Atmospheres 2006; 111(D24307). DOI: 10.1029/2005JD006917.