Probabilistic Forecasting of the 500 hPa Geopotential Height over the Northern Hemisphere Using TIGGE Multi-model Ensemble Forecasts

Qixiang Luo,Luying Ji,Xiefei Zhi,Yan Ji

doi:10.3390/atmos12020253

Abstract

Bayesian model averaging (BMA) and ensemble model output statistics (EMOS) were used to improve the prediction skill of the 500 hPa geopotential height field over the northern hemisphere with lead times of 1–7 days based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and UK Met Office (UKMO) ensemble prediction systems. The performance of BMA and EMOS were compared with each other and with the raw ensembles and climatological forecasts from the perspective of both deterministic and probabilistic forecasting. The results show that the deterministic forecasts of the 500 hPa geopotential height distribution obtained from BMA and EMOS are more similar to the observed distribution than the raw ensembles, especially for the prediction of the western Pacific subtropical high. BMA and EMOS provide a better calibrated and sharper probability density function than the raw ensembles. They are also superior to the raw ensembles and climatological forecasts according to the Brier score and the Brier skill score. Comparisons between BMA and EMOS show that EMOS performs slightly better for lead times of 1–4 days, whereas BMA performs better for longer lead times. In general, BMA and EMOS both improve the prediction skill of the 500 hPa geopotential height field.

Highlights

Numerical weather prediction is an important technique in modern weather forecasting in which the basic equations of atmospheric motion are solved numerically to predict the future state of the atmosphere given the initial and boundary conditions [1,2]
We selected two extreme events to investigate the performance of the deterministic forecasts by raw ensembles and two statistical post-processing methods (BMA and ensemble model output statistics (EMOS))
The observed distribution of the 500 hPa geopotential height showed two relatively stable ridges of high pressure located to the west of the Ural Mountains and east of Lake Baikal, respectively (Figure 1)

Summary

Introduction

Numerical weather prediction is an important technique in modern weather forecasting in which the basic equations of atmospheric motion are solved numerically to predict the future state of the atmosphere given the initial and boundary conditions [1,2]. The prediction skill of a single deterministic forecast is limited by the uncertainties associated with the models and the initial conditions [5,6]. Probabilistic weather prediction describes the probability of the occurrence of future weather events as a percentage [7]. The forecast information (i.e., the uncertainty and probability) provided by probabilistic weather prediction is important in decision-making and can help to predict extreme weather events [8,9,10]. Probabilistic forecasting gradually became operational in many countries and regions from about 1960—for example, the Meteorological Development Laboratory of the US National Weather Service has tested and operationally run the model output statistics forecast equation as a tool for weather forecasting since the 1990s

Methods

Results

Conclusion