Unified ensemble mean forecasting of tropical cyclones based on the feature-oriented mean method

Abstract

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged.We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.

Improving Subseasonal‐To‐Seasonal Prediction of Summer Extreme Precipitation Over Southern China Based on a Deep Learning Method

“…From this perspective, the enhanced precipitation forecasts are obtained via reproducing the dynamical relationships of precipitation and associated dominant predictors (J. Zhang et al, 2021). Thus, the combination of numerical weather prediction models and deep learning frameworks is demonstrated promising in S2S precipitation forecasts and can also be applied to the routine forecast of other atmospheric and ocean phenomena in the future.…”

“…This could be attributed to the generally limited forecast skills of precipitation at longer lead times and the greater forecast performances on several atmospheric variables (Tian et al., 2017; S. Zhu et al., 2023a). From this perspective, the enhanced precipitation forecasts are obtained via reproducing the dynamical relationships of precipitation and associated dominant predictors (J. Zhang et al., 2021). Thus, the combination of numerical weather prediction models and deep learning frameworks is demonstrated promising in S2S precipitation forecasts and can also be applied to the routine forecast of other atmospheric and ocean phenomena in the future.…”

Performance of High-Resolution WRF Modeling System in Simulation of Severe Tropical Cyclones over the Bay of Bengal Using IMDAA Regional Reanalysis Dataset

“…In the last two decades, the forecast accuracy of TCs has increased by using high-resolution regional and global numerical weather prediction models [12][13][14][15][16] and improved and proper representations of physical parameterization schemes [17][18][19][20][21][22]. Additionally, TC forecast accuracy has improved by using advanced data assimilation techniques such as 3D/4D variational techniques, hybrid, and ensemble methods [5,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. Previous studies have indicated that track forecasts improved, but intensity forecasts are still limited.…”

Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

“…In order to solve the above simulation bias, this study attempts to construct a multiphysical ensemble transform Kalman filter (ETKF), which considers not only the uncertainty of the initial values [21][22][23][24][25][26][27] but also the effect of different microphysical parameterization schemes on simulations. The ensemble transform Kalman filter (ETKF) is a widely used initial perturbation method at present, which was first proposed by Bishop et al [28] in adaptive observation and can directly estimate the prediction error covariance matrix associated with a particular deployment of observational resources.…”