Blending of Global and Regional Analyses with a Spatial Filter: Application to Typhoon Prediction over the Western North Pacific Ocean

Abstract

A blending method to merge the NCEP global analysis with the regional analysis from the WRF variational data assimilation system is implemented using a spatial filter for the purpose of initializing the Typhoon WRF (TWRF) Model, which has been in operation at Taiwan's Central Weather Bureau (CWB) since 2010. The blended analysis is weighted toward the NCEP global analysis for scales greater than the cutoff length of 1200 km, and is weighted toward the WRF regional analysis for length below that. TWRF forecast experiments on 19 typhoons from July to October 2013 over the western North Pacific Ocean show that the largescale analysis from NCEP GFS is superior to that of the regional analysis, which significantly improves the typhoon track forecasts. On the other hand, the regional WRF analysis provides a well-developed typhoon structure and more accurately captures the influence of the Taiwan topography on the typhoon circulation. As a result, the blended analysis takes advantage of the large-scale analysis from the NCEP global analysis and the detailed mesoscale analysis from the regional WRF analysis. In additional to the improved track forecast, the blended analysis also provides more accurate rainfall forecasts for typhoons affecting Taiwan. Because of the improved performance, the blending method has been implemented in the CWB operational TWRF typhoon prediction system.

Constraining the Large‐Scale Analysis of a Regional Rapid‐Update‐Cycle System for Short‐Term Convective Precipitation Forecasting

“…It is noted that neglecting the correlations could introduce some error in the analysis. However, previous studies (Hsiao et al, ; Vendrasco et al, ) have shown that the benefit of these bogus data may far exceed the error caused by neglecting the correlation error.…”

“…The model states from both the LAM forecast and the global model forecast were each separated into two different scales, and the large‐scale part of the global model was then combined with the small‐scale part of the LAM. The blending method was shown to improve precipitation forecast (Hsiao et al, ; Wang et al, ). However, the rather brutal merging of fields from different models could introduce shock in the transition zone near the cutoff wavelength.…”

Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices

“…At present, climate predictions are usually produced and evaluated using physical and/or statistical models. The former are based on a set of differential partial equations and physical parameterizations representing different physical-dynamic processes in the atmosphere [47][48][49]. During recent years, such models have progressed in some aspects of precipitation predictions.…”

Improving initial condition perturbations in a convection‐permitting ensemble prediction system

“…synoptic) flow than global models due to their typically small domains, LBC formulation limitations, less effective LAM data assimilation (a fraction of the available observations are assimilated; see Gustafsson et al, ), etc. This problem can partially be addressed by applying a blending technique (Brožkovà et al, ; Yang, ; Wang et al, ; Hsiao et al, ). Different authors have developed various approaches to blending problem.…”