Cloud-Resolving Typhoon Rainfall Ensemble Forecasts for Taiwan with Large Domain and Extended Range Through Time-Lagged Approach
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FEBRUARY 2016 W A N G E T A L . 151 Cloud-Resolving Typhoon Rainfall Ensemble Forecasts for Taiwan with Large Domain and Extended Range through Time-Lagged Approach CHUNG-CHIEH WANG,SHIN-YI HUANG,SHIN-HAU CHEN, AND CHIH-SHENG CHANG Department of Earth Sciences, National Taiwan Normal University, Taipei, Taiwan KAZUHISA TSUBOKI Hydrospheric Atmospheric Research Center, Nagoya University, Nagoya, Japan (Manuscript received 2 April 2015, in final form 23 October 2015) ABSTRACT In this study, the performance of a new ensemble quantitative precipitation forecast (QPF) system for Taiwan, with a cloud-resolving grid spacing of 2.5 km, a large domain of 1860 km 3 1360 km, and an extended range of 8 days, is evaluated for six typhoons during 2012–13. Obtaining the probability (ensemble) in- formation through a time-lagged approach, this system combines the strengths of high resolution (for QPF) and longer lead time (for hazard preparation) in an innovative way. For the six typhoons, in addition to short ranges (#3 days), the system produced a decent QPF at a longest range up to days 8, 4, 6, 3, 6, and 7, providing greatly extended lead times, especially for slow-moving storms that pose higher threats. Moreover, since forecast uncertainty (reflected in the spread) is reduced with lead time, this system can provide a wide range of rainfall scenarios across Taiwan with longer lead times, each highly realistic for the associated track, allowing for advanced preparation for worst-case scenarios. Then, as the typhoon approaches and the predicted tracks converge, the government agencies can make adjustments toward the scenario of increasing likelihood. This strategy fits well with the conventional wisdom of ‘‘hoping for the best, but preparing for the worst’’ when facing natural hazards. Overall, the system presented herein compares favorably in usefulness to a typical 24-member ensemble (5-km grid size, 750 km 3 900 km, 3-day forecasts) currently in operation using similar computational resources. Requiring about 1500 cores to execute four 8-day runs per day, it is not only powerful but also affordable and feasible. 1. Introduction of its weather hazards (e.g., Cheung et al. 2008; Su et al. 2012; Chang et al. 2013). Over the past two decades, a. Background of research linked to the needs for probability information and to Quantitative precipitation forecasts (QPFs), espe- quantify forecast uncertainty (foremost in tracks), an cially those for heavy to extreme rainfalls due to their ensemble approach utilizing multiple members simul- hazardous nature, are among the most challenging tasks taneously, sometimes a large group of members, has in modern numerical weather prediction (NWP) and are been adopted as the primary method for producing under heavy demands around the world (e.g., Fritsch QPFs (e.g., Epstein 1969; Leith 1974; Toth and Kalnay et al. 1998; Golding 2000; Fritsch and Carbone 2004; Cuo 1993; Du et al. 1997; Molteni et al. 1996; Kalnay 2003). et al. 2011). They are particularly important in Taiwan, This is also the case in Taiwan, as both the Central where heavy rainfalls brought on by tropical cyclones Weather Bureau (CWB) and the Taiwan Typhoon and (TCs; mainly during July–October) and during the mei- Flood Research Institute (TTFRI) use multimember yu season (May–June) are responsible for the majority ensembles (e.g., Hsiao et al. 2013; Hong et al. 2015). As the computer technology advances and in- creasingly higher resolutions can be used for QPFs in NWP, convective clouds that produce the intense rain- Corresponding author address: Chih-Sheng Chang, Dept. of Earth Sciences, National Taiwan Normal University, No. 88, Sec. 4, fall (as opposed to stratiform clouds) can no longer be Ting-Chou Rd., Taipei 11677, Taiwan. treated as ‘‘subgrid scale’’ processes in most, if not all, E-mail: [email protected] regional models (e.g., Frank 1983; Molinari and Dudek DOI: 10.1175/WAF-D-15-0045.1 Ó 2016 American Meteorological Society Unauthenticated | Downloaded 10/09/21 09:33 AM UTC 152 WEATHER AND FORECASTING VOLUME 31 1992; Arakawa 2004). In other words, convection must be (7–8 August) very close to the observation (about ‘‘resolved’’ by the model grid and treated explicitly (e.g., 2200 mm), other models at the time struggled to reach Done et al. 2004; Kong et al. 2006). From a scientific point 1400 mm in their 3- or 4-day totals (e.g., Wu et al. 2010; of view, the immediate logical question is what is the grid Hendricks et al. 2011). Thus, the evidence points strongly size Dx necessary to adequately resolve the basic struc- toward model configuration and resolution for improved ture, such as updrafts and downdrafts, of deep convective QPFs, as attested to by W15. The study of W15 also clouds? Many earlier studies suggest the answer to be demonstrates the strong basic property of ‘‘the more rain, around 2 km (e.g., Rotunno et al. 1988; Roebber et al. the higher the score’’ in categorical statistics, and it is a 2002; Kong et al. 2006), as six to eight grid points are misperception that the models (CRMs in particular) have needed in each direction for typical deep cumuli about little ability to predict extreme rainfalls. 10–15 km in diameter (e.g., Bluestein 1992, section 1.1.2). As shown above, the 2.5-km CReSS can provide high While obviously smaller Dx values are required to resolve quality QPFs for large, hazardous events not only on days finer details of convection (e.g., Klemp and Wilhelmson 1 and 2, but also often on day 3 (48–72 h), at the longest 1978; Bryan et al. 2003; Petch 2006; Wang and Huang range in those forecasts (e.g., Figs. 2, 3, 7, and 9 in W15). 2009), models with Dx # 3 km can be termed cloud re- As a rule of thumb in all forecasts, one tries to make a solving and those with Dx ’ 4–5 km are usually referred decent forecast as early as possible. Thus, we seek to find to as convection permitting instead (e.g., Weisman et al. out whether a high quality QPF is attainable at ranges 1997; Walser et al. 2004; Roberts and Lean 2008; Clark longer than 3 days, and if so, how frequently can it be et al. 2009; Zhang et al. 2010). Simulation works on some achieved? These questions provide a basic incentive for heavy-rainfall events in Taiwan have shown that cloud- us to carry out the present study. Nevertheless, our mo- resolving models (CRMs) can properly capture their tivation and the study objectives within a larger context of evolution and magnitude (e.g., Wang et al. 2005, 2009, forecast strategy for hazard preparation and reduction 2011, 2013a, 2014; Yang et al. 2011), including the dev- will be elaborated upon further below. astating case of Typhoon (TY) Morakot in 2009 (e.g., Tao b. Motivation and objectives of study et al. 2011; Wang et al. 2012, 2013b; Huang et al. 2014). When used in forecasting, CRMs also often show Produced by a single model, the CReSS forecasts promising results, such as in Adlerman and Droegemeier discussed above are deterministic forecasts that can (2002), Xue et al. (2003), Done et al. (2004), Liu et al. better resolve convection and topography and produce (2006), Kong et al. (2006), Weisman et al. (2008), Clark more realistic intensity and evolution of high-impact et al. (2009),andSchwartz (2014). These efforts, however, weather such as TCs, but are generally regarded as are mostly limited to individual events or fixed campaign having no helpful information in terms of probability. periods. Since 2008, the Cloud-Resolving Storm Simula- Ensemble forecasts, on the other hand, with the com- tor (CReSS; Tsuboki and Sakakibara 2002, 2007) has been putational resources divided among their members, can used for typhoon forecasts in Taiwan, and routine oper- measure confidence in forecasts and quantify un- ations (3-day forecasts every 6 h) throughout the entire certainty, better cover possible event scenarios through year, with a Dx of 2.5 km, have been achieved since 2010. spread, but typically underpredict heavy rainfall as a Recently, Wang (2015, hereafter referred to as W15) result of their lower resolution. Although the two ap- evaluated 24-h QPFs by this model for all 15 TCs during proaches are complementary to each other (Roebber 2010–12 and demonstrated its superior performance. For et al. 2004), in reality, however, compromises are often the periods with the most rainfall and highest hazard po- made with a fixed (and often limited) amount of com- tential (roughly the top 5% of the sample), the 0–24-h putational resources (e.g., Clark et al. 2009), and the (day 1) QPFs generated by CReSS have mean threat ensemble approach has been adopted as mentioned. scores (TSs; to be detailed in section 2c) of 0.67, 0.58, 0.51, Based on and modified from Nakazawa (2010), Table 1 and 0.32 at thresholds of 50, 130, 200, and 350 mm; the 24– (top) lists five major items for typhoons provided by 48-h (day 2) QPFs yield mean TSs of 0.73, 0.57, 0.42, and regional low-resolution ensemble forecasts versus high- 0.17; and the 48–72-h (day 3) QPFs yield mean TSs of 0.57, resolution (cloud resolving) deterministic forecasts and 0.37, 0.33, and 0.22, respectively. For the biggest event in their general quality: rainfall, track, intensity, striking Morakot, the scores (from real-time forecasts) are even probability, and lead time. At this time, our discussion higher, and those of day-2 QPFs reach 0.87, 0.69, 0.50, and focuses only on the first two columns in Table 1.Asa 0.38 at heavy to extreme thresholds of 200, 350, 500, benchmark, the low-resolution ensemble approach is and 1000 mm, respectively [see also Wang (2014)].