Observation System Experiments for Typhoon Nida (2004) Using the CNOP Method and DOTSTAR Data

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1 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2011, VOL. 4, NO. 2, Observation System Experiments for Typhoon Nida (2004) Using the CNOP Method and DOTSTAR Data CHEN Bo-Yu 1,2 1 State Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China 2 Graduate University of Chinese Academy of Sciences, Beijing , China Received 30 December 2010; revised 3 Feburary 2011; accepted 11 Feburary 2011; published 16 March 2011 Abstract This study investigated the influence of dropwindsonde observations on typhoon forecasts. The study also evaluated the feasibility of the conditional nonlinear optimal perturbation (CNOP) method as a basis for sensitivity analysis of such forecasts. This sensitivity analysis could furnish guidance in the selection of targeted observations. The study was performed by conducting observation system experiments (OSEs). This research used the fifth-generation Mesoscale Model (MM5), the Weather Research and Forecasting (WRF) model, and dropsonde observations of Typhoon Nida at 1200 UTC 17 May The dropsondes were collected under the operational Dropsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR) program. In this research, five kinds of experiments were designed and conducted: (1) no observations were assimilated; (2) all observations were assimilated; (3) observations in the sensitive area revealed by the CNOP method were assimilated; (4) the same as in (3), but for the region revealed by the first singular vector (FSV) method; and (5) observations within a randomly selected area were assimilated. The OSEs showed that (1) the DOTSTAR data had a positive impact on the forecast of Nida s track; (2) dropsondes in the sensitive areas identified by the MM5 CNOP and FSV remained effective for improving the track forecast for Nida on the WRF platform; and (3) the greatest improvement in the track forecast resulted from the CNOP-based (third) simulation, which indicated that the CNOP method would be useful in decision making about dropsonde deployments. Keywords: targeted observations, OSE, CNOP, sensitive area Citation: Chen, B.-Y., 2011: Observation system experiments for typhoon Nida (2004) using the CNOP method and DOTSTAR data, Atmos. Oceanic Sci. Lett., 4, Introduction Over the past few decades, considerable progress has been made in forecasting the tracks of tropical cyclones (TCs) using numerical models. However, inaccuracies in initial conditions and uncertainties in model formulations are still problems. The lack of observations over the ocean regions where TCs spend most of their lifetime seriously Corresponding author: CHEN Bo-Yu, chenboyu_1985@163.com degrades the accuracy of forecasts. It is worthwhile, in principle, to predict the location of sensitive areas using objective analysis methods and thereby to optimize the additional but limited observations in the sensitive areas to maximally improve the accuracy of forecasts. To improve TC prediction and develop targeted observing techniques, recent research efforts have involved two projects: The Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR; Wu et al., 2009) program and the Tropical Cyclone Structure Experiment (TCS-08) conducted by the US Navy. Weissmann et al. (2010) have shown that the dropsondes deployed from surveillance aircraft help to improve TC track forecasts. The degree of improvement was approximately 20% 40% for operational global models of the National Centers for Environmental Prediction (NCEP) and for the Weather Research and Forecasting (WRF) model. Some studies show that the singular vectors (SVs) method is likely to capture the sensitivity signals associated with the dynamic systems under study and would be useful for designing a sampling strategy for targeted observations (Yamaguchi et al., 2009; Reynolds et al., 2010). Recently, a series of studies on targeted observations for high-impact weather forecasts have been conducted based on a newly proposed method called conditional nonlinear optimal perturbation (CNOP; Mu et al., 2007, 2009; Qin, 2010). This method allows the generalization of SVs in term of optimal perturbations having the largest amplification rate in the fully nonlinear regime and has already been used in some research fields, such as ENSO predictability (Mu et al., 2003; Duan et al., 2008) and thermohaline circulation (Sun et al., 2005). Qin (2010) has evaluated the effect of the CNOP sensitive areas by designing observing system simulation experiments (OSSEs). However, the CNOP method has not been examined in the context of observing system experiments (OSEs) using real targeted observations such as the DOTSTAR data. Hence, as a followup paper to these previous studies, this study aimed to evaluate the following three issues: (1) impact of the DOTSTAR data on the track forecast of Nida at 1200 UTC 17 May 2004; (2) effectiveness of the fifth-generation Mesoscale Model (MM5) CNOP and first singular vector (FSV) products for targeting observations for use in forecasts of Nida based on the WRF platform; and (3) feasibility of the CNOP method as guidance for targeted observations in

2 NO. 2 CHEN: A STUDY ON CNOP AS A TARGETED OBSERVATION GUIDANCE FOR TYPHOONS 119 support of TC forecasts. The structure of this paper is as follows: section 2 provides an introduction to the data, the case to be studied and the relevant details of experimental design; the results are presented in section 3; and section 4 contains a summary. 2 Brief overview of data, case, and experiment design 2.1 The DOTSTAR data for typhoon Nida Since 2003, the DOTSTAR research program has been conducted to obtain and collect targeted airborne dropsonde observations for typhoons that may affect the Taiwan area. This program aims to achieve significant progress in scientific research and in typhoon forecasting in the western North Pacific (Wu et al., 2005). In support of this program s studies of typhoon Nida, fifteen dropwindsondes were released around Nida between 1000 and 1400 UTC 17 May The squares in Fig. 1a show the location of the released dropsondes. Most dropsondes were deployed every km in a circular pattern with its center at Nida s central position. The observations include data on wind speed, wind direction, height, temperature, dewpoint temperature, and relative humidity below 196 hpa. 2.2 Description of typhoon Nida The best track of Nida analyzed by the Joint Typhoon Warning Center (JTWC) is shown in Fig. 1a. Nida developed into a supertyphoon in the Northwest Pacific near the east coast of Luzon Island at 1200 UTC 16 May Subsequently, it moved northward and abated into a typhoon at 1200 UTC 17 May At 1200 UTC 18 May 2004, Nida moved away from the east coast of Luzon Island and kept moving northeastward until its extinction at 0600 UTC 21 May 2004 near the east coast of Japan. To describe the synoptic environment, the analysis fields of geopotential height at 500 hpa and deep-layer- mean (DLM) wind ( hpa) are shown in Fig. 1b. The center of Nida was located on the western edge of the subtropical high (indicated by 5880 gpm), and a breakage of the subtropical high and a westerly jet appeared on the north side of Nida. These characteristics were the main features of the synoptic environment around Nida when the DOTSTAR observations were conducted. 2.3 Details of the experimental design Methodology Suppose we have the following model: Χ F( Χ ) 0 t, (1) Χ Χ t 0 0 where X is the state vector of the model with initial condition X 0, and F is a nonlinear partial differential operator. * For the chosen norm C, an initial perturbation Χ 0 is called the CNOP if and only if * J ( Χ 0 ) max J ( Χ 0 ; 0), (2) Χ C Χ 0 where J ( X 0 ) [ PM ( X 0 X 0 ) PM ( X 0 )]; C[ PM ( X 0 X 0 ) PM ( X 0 )], (3) and the angle bracket represent a Euclidean inner product. Here, P is a local projection operator and takes value 1 (0) within (without) the verification area, and M is a nonlinear propagator. The first guess of the initial perturbation X 0 should be adjusted to satisfy the constraint condition X 0; C X 0. However, assume that for a sufficiently small initial perturbation X 0 and moderate integration time interval, the evolution of X 0 at time t can be approximated by the following equation: Χ t L X 0, (4) where L represents the tangent linear propagator. FSV is obtained by solving the following optimization Figure 1 (a) DOTSTAR observations at 1200 UTC 17 May 2004 (square points) and best track of Nida from 1200 UTC 17 to 0000 UTC 20 May 2004 (solid line), (b) 500-hPa height (contour; gpm) and DLM wind (streamline). The numbers in (a) from 1 to 15 indicate the sequence of dropsonde observations. The black dot in (b) indicates the position of Nida s center at 1200 UTC 17 May 2004.

3 120 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 4 problem: * J ( X 0 ) J ( X 0), (5) Χ 0 ;max C Χ 0 where J ( X 0 ) PL X 0 ; CPL X 0. (6) Thus, both the CNOP and FSV can be obtained using the spectral projected gradient 2 (SPG2) optimization algorithm (Birgin et al., 2001) Calculation of CNOP and FSV The MM5 model, which includes the nonlinear MM5, tangent linear model (TLM), and corresponding adjoint model (Zou et al., 1997), and NCEP Global Forecasting System (GFS) global reanalysis (1 1 ) were utilized in our numerical approach. The domain for the nonlinear and adjoint models was a 60-km, horizontal grid, and the vertical range was divided into 23 sigma levels, with the top pressure at 100 hpa. The forecast and optimization time interval was selected as 24 h from 1200 UTC 17 to 1200 UTC 18 May We chose both the initial and final constraints as a symmetric positive definite matrix C in calculating CNOP and FSV (see the calculations of CNOP and FSV in the appendix). The matrix C is defined as the metric of dry energy, and its continuous expression is written as X 0; C X 0 u v 3. 72T D D p d dd, (7) where X 0 is the initial state vector, D is the horizontal research region, and is the vertical coordinate. Here, u, v, T, and p, which are components of the state vector, are the perturbed zonal and meridional wind components, temperature, and surface pressure, respectively Description of OSEs Two kinds of sensitive regions needed to be defined based on the distributions of vertically-integrated energy (VIE) of MM5 CNOP and FSV (Buizza et al., 2007). The OSEs marked as OSE-WRF were performed in the context of the WRF model with its 3-DVar data assimilation system. Specifically, in OSE-WRF, five sets of initial conditions were created: the first used no dropsonde data (NODROP); the second used all dropsonde data (ALLDROP); the third used dropsonde observations in or near the CNOP sensitive area (CNOPDROP); the fourth used observations in or near the FSV sensitive area (FSVDROP); and the fifth used observations in a randomly selected area (RANDROP). 3 Results 3.1 CNOP and FSV sensitive areas Figure 2 presents the distributions of VIE and the wind component of CNOP and FSV at the level = 0.7. Note that the VIE and the high-magnitude wind component of CNOP are largely concentrated at the north side of the initial typhoon center, located at approximately N E and. Hence, we chose the 2nd, 3rd, 4th, and 5th dropsondes (pentangle points in Fig. 2a) located near the maximal VIE area of CNOP as the targeted observations in CNOPDROP. Similarly, no dropsondes were located exactly in the maximal VIE area of FSV. Thus, we used the 10th 13th dropsondes (triangle points in Fig. 2b) in FSVDROP. These locations were adjacent to a maximal VIE area on the west side of the initial storm center. Additionally, the 8th 11th dropsondes at the south side of the initial storm center were used in RANDROP (see Fig. 1a). 3.2 Results of OSE-WRF Forecast improvements The results of OSE-WRF are shown in Table 1. CNOPDROP showed the greatest improvement (16.2%) measured by the dry energy reduction of forecast error in the verification area (dashed box in Fig. 2). The second greatest improvement was shown by ALLDROP (5.4%). However, the dropsondes in the FSV sensitive area and in the randomly selected area had negative effects on the verification forecast. The values of dry energy reductions for FSVDROP and RANDROP were 7.9% and 14.6%, respectively. Furthermore, ALLDROP, CNOPDROP, and Figure 2 VIE (shaded; J kg 1 ) and wind (vector; m s 1 ) component at the level = 0.7 of (a) CNOP and (b) FSV. The box defined by dashed lines indicates the verification area. Pentangle points indicate the dropsonde observations used in (a) CNOPDROP and (b) FSVDROP. The symbol indicates the position of Nida s center at 1200 UTC 17 May 2004.

4 NO. 2 CHEN: A STUDY ON CNOP AS A TARGETED OBSERVATION GUIDANCE FOR TYPHOONS 121 FSVDROP showed tendencies to improve the forecast of Nida s track. Specifically, for both the 24-h and 36-h track forecasts, the dropsondes in the CNOP sensitive area had the greatest influence on the forecasts, and the values of the track error reductions were 63.6% and 35.0%, respectively. ALLDROP showed considerable improvements (values of 39.4% and 28.5%), whereas FSVDROP showed slight improvements (values varied by approximately 7% 8%). In contrast, the dropsondes in RANDROP had a negative influence on the forecast of Nida s track. The degree of impact on the 24-h (36-h) track forecast was 23.8% ( 3%). These results indicate that the dropsondes in the sensitive areas were more effective than those in the randomly selected area for improving the forecast of Nida s track forecast. Furthermore, the dropsondes in the CNOP sensitive area demonstrated the most positive effects. Figure 3 shows the sea level pressure (SLP) fields of 36-h simulated typhoons in the four experiments in which dropsondes were considered. Note the southeastward migration of all the simulated typhoon centers from the center of the simulated typhoon in NODROP. The simulated Table 1 Verification dry energy norms (J kg 1 ) and 24-h and 36-h forecast track errors (km) of NONDROP, ALLDROP, CNOPDROP, FSVDROP, and RANDROP as well as corresponding error reductions of ALLDROP, CNOPDROP, FSVDROP, and RANDROP. NODROP ALLDROP CNOPDROP FSVDROP RANDROP Dry energy (Verification area) Reduction (24-h forecast error) 5.4% 16.2% 7.9% 14.6% Track error (24-h forecast) Reduction (24-h track error) 39.4% 63.6% 8.1% 23.8% Track error (36-h forecast) Reduction (36-h track error) 28.5% 35.0% 7.5% 3.0% Figure 3 SLP fields of 36-h forecasts in (a) ALLDROP, (b) CNOPDROP, (c) FSVDROP, and (d) RANDROP. The symbol indicates the observed position of Nida s center at 0000 UTC 19 May The point at which the two dashed lines cross indicates the simulated typhoon center in NODROP at 0000 UTC 19 May 2004.

5 122 ATMOSPHERIC AND OCEANIC SCIENCE LETTERS VOL. 4 typhoon center in CNOPDROP is the one nearest to the observed typhoon center Characteristics of initial fields and sensitivity analysis Figure 4 shows the differences in initial wind and temperature fields between NODROP and ALLDROP, CNOPDROP, FSVDROP, and RANDROP, respectively. As shown in Figs. 4a and 4b, the differences in wind fields between NODROP and ALLDROP and between NODROP and CNOPDROP exhibited an enhancement of east-northeastward wind that occurred at the north side of the typhoon center, where an environmental westerly jet also appeared (Fig. 1b). The differences between NODROP and ALLDROP and between NODROP and FSVDROP both exhibit the following features. In response to the dropsonde data assimilated, a southeastward wind increment and a cold center appeared at the southwest side of the typhoon center. The difference between NODROP and RANDROP involved an eastward wind increment and a cold center that appeared on the south side of the typhoon center. In contrast, the difference between NODROP and CNOPDROP involved a cold center that appeared at the north side of the typhoon center. These features of the difference in the initial fields between NODROP and CNOPDROP would be a possible explanation for the slower movement of the simulated typhoon in CNOPDROP than that observed in the other experiments. It is worth noting the equality of the final values of the cost function J B for the initial conditions of CNOPDROP, FSVDROP, and RANDROP. The cost fuction J B is employed in 3-DVar data assimilation system to measure the difference between the analysis and the background field (Skamarock et al., 2005). For ALLDROP, the final value of J B was three times that of CNOPDROP. Moreover, we investigate the verification dry energy norms of the differences in 24-h forecasts between NODROP and the experiments in which dropsondes were considered. The 24-h forecast difference between NODROP and ALLDROP was the greatest ( J kg 1 ), followed by CNOPDROP ( J kg 1 ), FSVDROP ( J kg 1 ), and RANDROP ( J kg 1 ). These results imply that for a given constraint condition, those differences with their major parts located in the CNOP sensitive area are more likely to have great impacts on the final verification forecast than those with their major parts located in the FSV sensitive area or randomly selected areas. 4 Summary The main topics of the investigations in this study were the impacts of the DOTSTAR data on the track forecast of typhoon Nida and the feasibility of the CNOP method as a source of guidance for targeted observations to be used in Figure 4 Differences in the initial fields between NODROP and (a) ALLDROP, (b) CNOPDROP, (c) FSVDROP, and (d) RANDROP showing the wind (vector, m s 1 ) and temperature (shaded, K) at 850 hpa. The symbol indicates the position of Nida s center at 1200 UTC 17 May 2004.

6 NO. 2 CHEN: A STUDY ON CNOP AS A TARGETED OBSERVATION GUIDANCE FOR TYPHOONS 123 TC forecasts. The FSV method, like the CNOP method, is employed as a reference. Calculations from the CNOP and the FSV are used in the MM5 platform to determine two kinds of sensitive areas. Accordingly, a set of data denial experiments, designed based on the CNOP-target, FSV-target, and randomly selected areas, were included in the OSEs. These experiments were performed in the context of the WRF model with its 3-DVar assimilation system. The results of the study reveal that the DOTSTAR data had a positive impact on the forecasts of typhoon Nida. Assimilation of the dropsondes in the sensitive area identified by the MM5 CNOP improved both the verification and the track forecasts significantly on the WRF platform. The dropsondes in the FSV sensitive areas also increased the accuracies of the 24-h and 36-h track forecasts, but the impact was comparatively small. However, the dropsondes in the randomly selected region had negative effects on both the verification and track forecasts, especially on the 24-h track forecast. These results indicate that the MM5 CNOP and FSV products provided an effective means of targeting the observations to be used for forecasts of Nida in the OSE-WRF. The common features of the environmental flow fields of typhoon Nida are mainly associated with the northward flow along the west edge of the subtropical high and with the westerly jet at the north side of the typhoon center. These features strongly affected Nida s subsequent movement. The structure of the difference in initial wind fields between NODROP and CNOPDROP revealed characteristics similar to the above features. In addition, comparison of CNOPDROP, FSVDROP, and RANDROP indicated that CNOPDROP had the greatest impact on the 24-h verification forecast of NODROP. This finding indicates that the CNOP method would be a useful source of guidance for maximizing the possible improvement of the TC forecast by using additional but limited observations. These conclusions are based only on one particular typhoon case. Therefore, more cases need to be investigated to understand the influence of DOTSTAR data on typhoon forecasts and to evaluate the potential value of the CNOP method as a source of guidance for targeted observations from the perspective of statistical analysis. Acknowledgements. This work was jointly sponsored by the National Natural Science Foundation of China (Grant No ), and the China Meteorological Administration (Grant No. GYHY ). Special thanks are given to Prof. Wu Chun-Chieh for providing the DOTSTAR dropsonde data. Detailed comments by two anonymous reviewers helped improved parts of the paper. References Birgin, E. G., J. E. Martinez, and R. Marcos, 2001: Algorithm 813: SPG Software for convex-constrained optimization, ACM Trans. Math. Softw., 27, Buizza, R., C. Cardinali, G. Kelly, et al., 2007: The value of observations. II: The value of observations located in singular-vectorbased target areas, Quart. J. Roy. Meteor. Soc., 133, Duan, W. S., H. Xu, and M. Mu, 2008: Decisive role of nonlinear temperature advection in El Niño and La Niña amplitude asymmetry, J. Geophys. Res., 113, C01014, doi: /2006jc Mu, M., W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applications, Nonlinear Processes Geophys., 10, Mu, M., H. L. Wang, and F. F. Zhou, 2007: A preliminary application of conditional nonlinear optimal perturbation to adaptive observations, Chinese J. Atmos. Sci. (in Chinese), 31, Mu, M., F. F. Zhou, and H. L. Wang, 2009: A method for identifying the sensitive areas in targeted observations for tropical cyclone prediction: Conditional nonlinear optimal perturbation, Mon. Wea. Rev., 137, Qin, X.-H., 2010: The sensitive regions identified by the CNOPs of three typhoon events, Atmos. Oceanic. Sci. Lett., 3, Reynolds, C. A., J. D. Doyle, R. M. Hodur, et al., 2010: Naval Research Laboratory multiscale targeting guidance for T-PARC and TCS-08, Wea. Forecasting, 25, Skamarock, W. C., J. B. Klemp, J. Duhia, et al., 2005: A Description of the Advanced Research WRF Version 2, NCAR/TN468+STR, National Center for Atmospheric Research, Boulder, Colorado, 88pp. Sun, L., M. Mu, D. J. Sun, et al., 2005: Passive mechanism of decadal variation of the thermohaline circulation, J. Geophys. Res., 110, C07025, doi: /2005JC Weissmann, M., F. Harnisch, C.-C. Wu, et al., 2010: The influence of assimilating dropsonde data on typhoon track and mid-latitude forecasts, Mon. Wea. Rev., 139, Wu, C.-C., J.-H. Chen, S. J. Majumdar, et al., 2009: Intercomparison of targeted observation guidance for tropical cyclones in the Northwest Pacific, Mon. Wea. Rev., 137, Wu, C.-C., P.-H. Lin, S. D. Aberson, et al., 2005: Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR): An overview, Bull. Amer. Meteor. Soc., 86, Yamaguchi, M., T. Iriguchi, T. Nakazawa, et al., 2009: An observing system experiment for Typhoon Conson (2004) using a singular vector method and DOTSTAR data, Mon. Wea. Rev., 137, Zou, X., F. Vandenberghe, M. Pondeca, et al., 1997: Introduction to Adjoint Techniques and the MM5 Adjoint Modeling System, NCAR Tech. Note, NCAR/TN-435 STR, 111pp.