Typhoons, as one of the most destructive weather systems, have a significant impact on human life, property, and socioeconomic development. Meteorologists and modeling experts have steadily improved typhoon track forecasts over the last few decades to mitigate these challenges. However, forecasting typhoon intensity has made only marginal progress, particularly for short forecast lead times.
The initial conditions, which must accurately represent a typhoon’s intensity, structure, and the large-scale environment, are an important component of numerical weather prediction or model forecasting for typhoons. By combining information from observations and short-term model forecasts, data assimilation seeks the storm’s optimal initial conditions.
Prof. Zhe-Min Tan at Nanjing Universtiy, alongside a group of modeling researchers, conducted a study focused on improving typhoon intensity forecasts through ensemble data assimilation. They used the Weather Research and Forecasting model (WRF) with an 80-member ensemble Kalman filter to conduct the regional ensemble cycling assimilations and forecasts for the western North Pacific typhoons that occurred during the 2016 tropical cyclone season. Advances in Atmospheric Sciences just published their full research report.
study focused on improving typhoon intensity forecasts through ensemble data assimilation. They used the Weather Research and Forecasting model (WRF) with an 80-member ensemble Kalman filter to conduct the regional ensemble cycling assimilations and forecasts for the western North Pacific typhoons that occurred during the 2016 tropical cyclone season.
Prof. Zhe-Min Tan
Several advanced data assimilation methods have shown significant promise in terms of improving typhoon forecasts. The ensemble Kalman filter, a Monte Carlo method, integrates observations into the model based on flow-dependent error statistics and also provides ensemble analyses. Ensemble forecasts, which are derived from these analyses, can provide probabilistic distributions that paint a more accurate picture of potential typhoon tracks and intensities. As a result, the ensemble Kalman filter could be especially useful for typhoon track and intensity analyses and forecasts.
That said, short-term (six-hourly) ensemble forecasts from these cycling processes have an appropriate amount of variance for typhoon tracks but still produce insufficient variance for typhoon intensity. The 6-hourly ensemble forecasts tend to overestimate the intensity for weak storms and underestimate the intensity for strong storms. This indicates that a 6-km horizontal grid spacing is still unable to resolve the large gradients of wind and mass fields within typhoon’s core-region.
To continue their investigation, the team compared the regional forecasts using WRF model with the cycling ensemble Kalman filter to global forecasts from the European Center for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) models. They found that the 5-day deterministic regional forecasts have less errors and biases for typhoon intensity than the ECMWF and NCEP global forecasts.
However, due to an inferior representation of the large-scale environment surrounding typhoons, the 5-day deterministic regional forecasts produce greater track error than ECMWF and NCEP global forecasts.
Overall, the 5-day ensemble forecasts from the cycling ensemble assimilation improved ensemble forecasts for typhoon track and intensity, particularly for short forecast lead times. In terms of predicting typhoon intensity, these regional typhoon ensemble forecasts with higher spatial resolution outperform global ensemble forecasts.
In the future, the research team will create more advanced data assimilation algorithms that can better capture multiscale features of typhoons with dynamic constrained and balanced vortex initial conditions.
