OCTOBER 2012 H S I A O E T A L . 1249 Application of WRF 3DVAR to Operational Typhoon Prediction in Taiwan: Impact of Outer Loop and Partial Cycling Approaches LING-FENG HSIAO Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, Taipei, Taiwan DER-SONG CHEN Central Weather Bureau, Taipei, Taiwan YING-HWA KUO AND YONG-RUN GUO National Center for Atmospheric Research, Boulder, Colorado TIEN-CHIANG YEH,JING-SHAN HONG, AND CHIN-TZU FONG Central Weather Bureau, Taipei, Taiwan CHENG-SHANG LEE Taiwan Typhoon and Flood Research Institute, National Applied Research Laboratories, and Department of Atmospheric Sciences, National Taiwan University, Taipei, Taiwan (Manuscript received 11 October 2011, in final form 15 May 2012) ABSTRACT In this paper, the impact of outer loop and partial cycling with the Weather Research and Forecasting Model’s (WRF) three-dimensional variational data assimilation system (3DVAR) is evaluated by analyzing 78 forecasts for three typhoons during 2008 for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings, including Sinlaku, Hagupit, and Jangmi. The use of both the outer loop and the partial cycling approaches in WRF 3DVAR are found to reduce typhoon track forecast errors by more than 30%, averaged over a 72-h period. The improvement due to the outer loop approach, which can be more than 42%, was particularly significant in the early phase of the forecast. The use of the outer loop allows more observations to be assimilated and produces more accurate analyses. The assimilation of additional non- linear GPS radio occultation (RO) observations over the western North Pacific Ocean, where traditional observational data are lacking, is particularly useful. With the lack of observations over the tropical and subtropical oceans, the error in the first-guess field (which is based on a 6-h forecast of the previous cycle) will continue to grow in a full-cycling limited-area data assimilation system. Even though the use of partial cycling only shows a slight improvement in typhoon track forecast after 12 h, it has the benefit of sup- pressing the growth of the systematic model error. A typhoon prediction model using the Advanced Re- search core of the WRF (WRF-ARW) and the WRF 3DVAR system with outer loop and partial cycling substantially improves the typhoon track forecast. This system, known as Typhoon WRF (TWRF), has been in use by CWB since 2010 for operational typhoon predictions. 1. Introduction island that is roughly 200 km wide and 400 km long centered at 23.58N, 1218E. On average, three to four Typhoons are among the most significant weather typhoons make landfall there each year. Although the systems in Taiwan (Wu and Kuo 1999). Taiwan is an heavy rainfall and gusty winds from typhoons routinely produce significant damage to the island, the rainfall is Corresponding author address: Ling-Feng Hsiao, Taiwan Ty- also one of the most important water resources in Taiwan. phoon and Flood Research Institute, 11F, No. 97, Sec. 1, Roosevelt Rd., Taipei 10093, Taiwan. Therefore, providing accurate typhoon forecasts is the E-mail: [email protected] highest priority of Taiwan’s Central Weather Bureau DOI: 10.1175/WAF-D-11-00131.1 Ó 2012 American Meteorological Society 1250 WEATHER AND FORECASTING VOLUME 27 (CWB). Operational typhoon forecasts are primarily et al. (2009) compared the forecast differences between based on the guidance of numerical weather prediction full and partial cycling from 17 December 2007 to 25 models. Typhoon prediction in the vicinity of Taiwan February 2008. The results show a significant improve- faces two major challenges: (i) the lack of traditional ment from partial cycling for height and wind fields at observational data over the western North Pacific and (ii) 48- and 72-h forecasts particularly in the 250–300-hPa the influence of Taiwan’s Central Mountain Range layer. Moreover, the largest improvement in tempera- (CMR) on the typhoon circulation patterns. Because of ture occurs in the middle troposphere between 700 and the influence of the CMR, the distribution of winds and 400 hPa. The partial cycling reduces error growth with rainfall for a typhoon approaching Taiwan is highly sen- time, which results from the accumulation of forecast sitive to the track and direction of the approaching storm. error in the full cycling run. As a result, partial cycling The accuracy of winds, rainfall, ensuing storm surge, has been implemented in NDAS operations since and severe weather forecasts for typhoons affecting December 2008. Taiwan critically depend on the performance of track In variational data assimilation, the observation op- forecasts. Research on tropical cyclone track and in- erators are linearized in the form of a first-order Taylor tensity forecasts has suggested this to be a multiscale series expansion and then the inner loop procedure problem, from the synoptic scale to the convective scale solves a minimization algorithm for a quadratic (line- (Holland and Merrill 1984; Molinari and Vollaro 1990; arized) problem (Courtier et al. 1998). The outer loop Liu et al. 1997; Ross and Kurihara 1995; Marks and Shay procedure solves the nonlinear aspects of the assimila- 1998). Therefore, having an accurate initial state for a tion problem with the first guesses updated for each regional model with realistic representation of multiple- minimization process (inner loop). Because of the outer scale processes is a key element in the system’s success. loop procedure and the Weather Research and Fore- The performance of a data assimilation system that casting Model (WRF) three-dimensional variational combines observations with short-term model forecasts, data assimilation system’s (3DVAR) incremental for- analysis update cycles with a well-developed spun-up mulation, the approximate solution, which is derived vortex, and/or a proper method for vortex implantation from a minimization algorithm, can be improved by can significantly impact the skill of a tropical cyclone solving Taylor series expansion recursively. With the prediction model (Kurihara et al. 1995; Hsiao et al. 2009). outer loop procedure, the analysis should be as accurate The data assimilation update cycle is an important as possible through the linearized Taylor series terms in component of a typhoon prediction model. In addition the calculation of the observation innovation; mean- to improving the model’s initial conditions, a cycling while, more observations would be used during the data assimilation can mitigate model imbalance re- subsequent outer loops. For observations such as global sulting from interpolation of global model forecasts. positioning system (GPS) radio occultation (RO) re- Moreover, the data assimilation update-cycling pro- fractivity, which are nonlinearly related to the analysis cedure allows higher-resolution forecasts to be used as control variables, more information can be extracted the background (Hodur 1987; Xiao et al. 2005; Zhao from outer loops (Rizvi et al. 2008). In WRF 3DVAR, et al. 2006). A data assimilation system relies on spec- the background check quality control (BgQC) pro- ified error statistics to obtain an analysis with an opti- cedure, as described by Andersson and Ja¨rvinen (1999), mal combination of observations and background was implemented. The background, or first guess, is (which is derived from a short-range forecast). obtained from the analysis of the previous outer loop. Over the tropical and subtropical oceans, there are Because the first guess is closer to the observations, a limited number of traditional observations. Under some observations that were rejected by the previous such conditions, the errors associated with the back- outer loop can now pass the BgQC in the current outer ground field can continue to grow, as there are insuf- loop and influence the analysis. Consequently, the ficient observations to constrain the model error number of observations assimilated is increased with growth. As a result, a data assimilation system with more outer loops. Guo et al. (2006) applied the outer continuous (full) cycling can produce inferior analyses. loop strategy for the analysis of Typhoon Haitang Recently, the National Centers for Environmental (2005) and obtained a positive impact on track forecasts. Prediction (NCEP) adopted a partial cycling strategy for In addition, Massart et al. (2010) demonstrated the the North American Mesoscale (NAM) model’s Data benefits of the outer loop in the 3DVAR approach with Assimilation System (NDAS) with a goal of improving the first guess at appropriate time (3D-FGAT) assimi- its forecast skills. NDAS partial cycling consists of a cold lation scheme for atmospheric transport. When the as- start from 12 h prior to the analysis time and four sub- similation operators are nonlinear, the outer loop is sequent data assimilation cycles at 3-h intervals. Rogers able to update the linearization around a better state OCTOBER 2012 H S I A O E T A L . 1251 and thus improve the quality of the analysis. Rosmond CWB operational configuration used here is based on and Xu (2006) showed that the outer loop strategy pro- version 3.1.1 with a 221 3 127 grid and 45-km grid duced better assimilation of surface wind speeds and spacing. There are 45 vertical levels with higher reso- precipitable water in the Naval Research Laboratory lution in the planetary boundary layer.1 The model Atmospheric Variational Data Assimilation System physics options include the Goddard microphysics (NAVDAS). Gridpoint statistical interpolation (GSI) scheme (Tao et al. 2003), the Kain–Fritsch cumulus in the National Centers for Environmental Prediction’s parameterization scheme (Kain and Fritsch 1990), (NCEP) Global Data Assimilation System (GDAS), the Yonsei University (YSU) planetary boundary which has been in operation since 2007 (Kleist et al. layer scheme (Hong et al. 2006), the Noah land sur- 2009), included two outer loops. With the lack of ob- face model (Chen and Dudhia 2001), and the Rapid servations over the tropical and subtropical oceans, an Radiative Transfer Model (RRTM) longwave effective assimilation of limited observations is crucial (Mlawer et al.