Assessing the Impacts of Augmented Observations on the Forecast of Typhoon Wutip’s (2013) Formation Using the Ensemble Kalman Filter

Abstract

<jats:title>Abstract</jats:title> <jats:p>In this study, the impacts of different augmented observations on forecasts of Typhoon Wutip’s (2013) formation are examined. Using the local ensemble transformed Kalman filter (LETKF) implemented for the Weather Research and Forecasting (WRF) Model, it is found that the prediction of Wutip’s formation location and timing is strongly governed by the strength of a monsoon trough that extends from the Bay of Bengal to the Philippine Sea. By properly capturing the strength of the monsoon trough after assimilating augmented observations available during Wutip’s early stage, the WRF Model could provide better forecasts of Wutip’s formation location and timing as compared to the forecasts initialized directly from global model analyses. Among different types of augmented observations, the satellite atmospheric motion wind vector (AMV) maintained by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) appears to be the most critical in enhancing the large-scale representation of the monsoon trough. The benefit of augmented observations in Wutip’s formation forecast is most apparent at about 36 h prior to the observed formation time. At the shorter 24-h lead time, there is, however, no clear benefit of augmented observations in predicting the timing and the location of Wutip’s formation due to better global analyses. The results obtained in this study demonstrate the vital role of the CIMSS-AMV data in improving the large-scale environment required for TC formation that one should take into account for real-time TC forecasts.</jats:p>

Application of Kalman Filter and Breeding Ensemble Technique to Forecast the Tropical Cyclone Activity

“…Wutip's formation is strongly rooted in the monsoon trough, as most of the tropical cyclones in the BIEN DONG form within this pattern per year. The performance of WRF-LETKF with augmented observations in this case study has [35].…”

“…Ensemble mean distance errors between the forecasted and observed location of Wutip's formation reported at 1200 UTC 25 Sep for three cycles of 48, 36, and 24 h obtained from the WRF-LETKF forecast (black), NDA forecast (stripe), and NAMV forecasts (light shaded) [35].…”

“…Distribution of the location of the Wutip's formation centers as forecast by the WRF-LETKF (triangle), the assimilation without the CIMSS-AMV data ensemble (circle), and no data assimilation ensemble (cross) for (a) 48-, (b) 36-, and (c) 24-h cycles. Color symbols denote the ensemble means of corresponding forecasts[35].…”

Examining the Predictability of Tropical Cyclogenesis over the East Sea of Vietnam through the Ensemble-Based Data Assimilation System

“…The initial forecast field using 3DVAR scheme aims at improving the background field for the subsequent assimilation cycles through the stability of the variational assimilation scheme. More details of the assimilation algorithm was described in Tien et al (hereafter, TIEN20) [26].…”

“…Miyoshi and Kunii [21] found that LETKF is particularly useful for handling highly heterogeneous data, such as satellite observations. Multi-physics construction of this scheme within the EPS-DA [26] has demonstrated its effectiveness in forecasting TC Wutip (2013)'s genesis. Motivated from their case study, this study further examines the efficiency of EPS assimilating augmented observations on the forecasts of 45 different TCs from 2012 to 2019.…”