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  • View in gallery

    Locations of the JMA operational Doppler radars and tracks of the TCs used in this study. The circles show the observation ranges of all radars (~200 km) for CAPPI data at 2-km altitude. Thick lines indicate TCs with maximum sustained (10-min average) surface winds of greater than 17 m s−1 (i.e., tropical storms), thin lines indicate TCs with maximum sustained surface winds of 17 m s−1 or less (i.e., tropical depressions), and gray lines indicate extratropical cyclones. The data are the JMA best track data.

  • View in gallery

    The procedures used for the Doppler radar intensity estimation. Input and output data for each step are indicated in italics. Before (since) 2009, the estimate was performed at 10-min (5 min) intervals.

  • View in gallery

    Radial profiles of retrieved , interpolated , and SLP anomalies (with the sign reversed) for T1215. SLPs observed at the Nago and Naha weather stations, which were used for the MSLP estimation, are shown along with the estimated MSLPs. MSLPs from Naha were estimated by extrapolating the SLP gradient over the distance from Naha.

  • View in gallery

    Tracks of the six simulated TCs used in the preliminary experiment. Black circles indicate the ranges, ~200 km, of the Ishigakijima and Okinawa radars (red dots). Each color represents one simulated case.

  • View in gallery

    Radial distributions of (a) and (b) axisymmetric SLP anomalies of S1408B averaged over the estimation period. In (a) the black line is the true , the green line is the retrieved , and the red line is the difference between them. In (b) the black line represents the true SLP anomalies, the green line the deduced SLP anomalies, and the red line the difference between them.

  • View in gallery

    Scatter diagram comparing the estimated MSLPs with the best track MSLPs. The corresponding RMSE, bias, and correlation coefficient R are also shown.

  • View in gallery

    (a) Time evolutions of the DR MSLPs (color lines), best track MSLPs (black line), and Dvorak MSLPs (blue dashed line) for T1312. The vertical black line indicates the time of the radar observation shown in (b). The red line indicates MSLPs derived from SLPs at Miyakojima, the green line indicates MSLPs derived from SLPs at Ishigakijima, and the blue line indicates MSLPs derived from SLPs at Iriomotejima. (b) Doppler radial velocity of the 2-km CAPPI observed from the Ishigakijima radar (red dot) at 2225 UTC 20 Aug 2013. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1312 at 2225 UTC 20 Aug 2013.

  • View in gallery

    (a) As in Fig. 7a, but for T1007. The blue star is the SLP (965.1 hPa) observed at Nago, 13.2 km from the TC center. [The vertical black line indicates the time of the observation shown in (b).] The green line indicates MSLPs derived from SLPs at Naha, and the red line indicates MSLPs derived from SLPs at Nago. (b) Doppler radial velocity of the 2-km CAPPI observed from the Okinawa radar (black dot) at 0245 UTC 31 Aug 2010. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1007 at 0245 UTC 31 Aug 2010.

  • View in gallery

    (a) As in Fig. 7a, but for T1418. The red star is the SLP (962.1 hPa) observed at Shionomisaki, which was 6.2 km from the TC center. [The vertical black line indicates the time of the observation shown in (b).] (b) The Doppler radial velocity of 2-km CAPPI observed from the Tanegashima radar (black dot) at 0540 UTC 5 Oct 2014. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1418 at 0540 UTC 5 Oct 2014.

  • View in gallery

    As in Fig. 7a, but for (a) T1011, (b) T1324A, and (c) T1411.

  • View in gallery

    Scatter diagrams comparing the difference between the estimated MSLPs and the best track MSLPs (y axis) with (a) the distance between the radar location and the TC center, (b) the distance between the weather station and the TC center, (c) the RMW at 2-km altitude, (d) radar coverage, and (e) the GBVTD wind retrieval accuracy (x axis). The RMWs were determined from all the retrieved values from step 5 in Fig. 2. The radar coverage is defined as the radial average of the maximum azimuthal gap (rad) at each radius on the GBVTD-specified coordinate system. The wind retrieval accuracy is defined as the overall average of the RMSD between the resampled from the GBVTD-retrieved winds and the observed , identical to the RMSE shown in Fig. 3 of Zhao et al. (2012). The error bars show the bias and the RMSE within the range indicated by the double-headed arrows.

  • View in gallery

    Schematic diagram of retrieved and observed SLP gradients. The values were calculated when data from multiple SLP observations around the TCs were available. To obtain as many SLP gradients in the inner region of TCs as possible, when three or more SLP observations were available, only SLP gradients relative to the innermost observation point were calculated.

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Evaluation of the Accuracy and Utility of Tropical Cyclone Intensity Estimation Using Single Ground-Based Doppler Radar Observations

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  • 1 Meteorological Research Institute, Tsukuba, Ibaraki, Japan
  • | 2 University of the Ryukyus, Nishihara, Okinawa, Japan
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Abstract

Intensities (central pressures) of 28 cases of 22 tropical cyclones (TCs) that approached Japan were estimated by using single ground-based Doppler radar observations, and the accuracy and utility of the estimation method were evaluated. The method uses the ground-based velocity track display (GBVTD) technique, which retrieves tangential winds, and the gradient wind balance equation. Before application of the method to the 28 cases, a preliminary experiment was performed with pseudo-Doppler velocities obtained by numerical simulation to confirm that the method could reasonably estimate central pressures. Compared with best track data from the Regional Specialized Meteorological Center (RSMC) Tokyo, the estimated intensities of the 28 cases had a root-mean-square error of 8.37 hPa and showed a bias of 1.51 hPa. This level of accuracy is comparable to or better than the accuracies of Dvorak and satellite microwave-derived estimates. Two distance metrics are defined: 1) the distance between the TC center and the radar location and 2) the distance between the TC center and the weather station whose sea level pressure was used as an anchor for pressure measurement. In general, the accuracy of the Doppler radar estimates was higher when the distance metrics were shorter, as well as when wind retrieval accuracy was better and radar coverage was denser. For TCs with a radius of maximum wind of 20–70 km, the estimated central pressures had a root-mean-square error of 5.55 hPa. These results confirm that Doppler radar intensity estimates have sufficient accuracy and utility for operational use.

Corresponding author address: Udai Shimada, Typhoon Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: ushimada@mri-jma.go.jp

Abstract

Intensities (central pressures) of 28 cases of 22 tropical cyclones (TCs) that approached Japan were estimated by using single ground-based Doppler radar observations, and the accuracy and utility of the estimation method were evaluated. The method uses the ground-based velocity track display (GBVTD) technique, which retrieves tangential winds, and the gradient wind balance equation. Before application of the method to the 28 cases, a preliminary experiment was performed with pseudo-Doppler velocities obtained by numerical simulation to confirm that the method could reasonably estimate central pressures. Compared with best track data from the Regional Specialized Meteorological Center (RSMC) Tokyo, the estimated intensities of the 28 cases had a root-mean-square error of 8.37 hPa and showed a bias of 1.51 hPa. This level of accuracy is comparable to or better than the accuracies of Dvorak and satellite microwave-derived estimates. Two distance metrics are defined: 1) the distance between the TC center and the radar location and 2) the distance between the TC center and the weather station whose sea level pressure was used as an anchor for pressure measurement. In general, the accuracy of the Doppler radar estimates was higher when the distance metrics were shorter, as well as when wind retrieval accuracy was better and radar coverage was denser. For TCs with a radius of maximum wind of 20–70 km, the estimated central pressures had a root-mean-square error of 5.55 hPa. These results confirm that Doppler radar intensity estimates have sufficient accuracy and utility for operational use.

Corresponding author address: Udai Shimada, Typhoon Research Department, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: ushimada@mri-jma.go.jp

1. Introduction

It is of great importance to analyze tropical cyclone (TC) intensity (i.e., central pressure and maximum sustained wind) with high accuracy, not only from the perspective of improving scientific understanding of various phenomena associated with TCs, but also for disaster prevention and mitigation. TC intensity estimates are generally made using infrared satellite data, for example, by the Dvorak technique (e.g., Dvorak 1975, 1984; Koba et al. 1990; Velden et al. 1998; Olander et al. 2004; Olander and Velden 2007), satellite microwave sounding data (Brueske and Velden 2003; Herndon and Velden 2004; Demuth et al. 2004; Oyama 2014), or satellite microwave imager data (Bankert and Tag 2002; Hoshino and Nakazawa 2007; Sakuragi et al. 2014). The Dvorak technique is an empirical method based on past reconnaissance observations and the subjective classification of cloud patterns, although the advanced Dvorak technique is a fully automated method (Olander and Velden 2007). Methods using satellite microwave sounding and imager data are entirely objective, but in the western North Pacific, satellite methods refer to the best track data as truth. Because these data are based mainly on the Dvorak technique, the uncertainty of the satellite methods includes the estimation error associated with the Dvorak technique. In addition, these conventional methods estimate TC intensity from relevant physical values such as cloud patterns and upper-level warm-core anomalies, rather than by using a straightforward physical equation that describes pressure distributions. Therefore, conventional estimates can have a large margin of error. Although it is not realistically possible at present to obtain dropsonde observations of minimum sea level pressures (MSLPs) of TCs in the western North Pacific Ocean, it is still necessary to estimate TC intensities with as much accuracy as possible, particularly for TCs that approach populated areas.

One method of addressing this problem is to estimate central pressures by using a physical equation, namely, the gradient wind equation. Lee et al. (2000) developed a method that uses data from a single ground-based Doppler radar (DR; hereafter the DR method). In this method, the ground-based velocity track display (GBVTD) technique (Lee et al. 1999) is used to retrieve axisymmetric tangential wind velocities, , of a TC from the Doppler radial velocities, , under the assumption that there is one primary circular vortex around the TC center and that the asymmetric radial wind is much smaller than the corresponding tangential wind. Then, an MSLP is estimated by using an axisymmetric pressure deficit deduced by applying the retrieved to the gradient wind equation and using a sea level pressure (SLP) observation around the TC as an anchor for pressure measurement. Because the DR method calculates MSLPs by using a physical equation related to the TC wind field, the estimated MSLPs are expected to have higher accuracy than those derived from cloud patterns and upper-level warm-core anomalies, although the DR method is applicable only to TCs that approach radar locations. To apply the DR method, it is essential to retrieve from with high accuracy. Because the GBVTD technique has been successfully used to retrieve for many TCs (Lee et al. 2000; Harasti et al. 2004; Lee and Bell 2007; Zhao et al. 2008; Zhao et al. 2012), the DR method is promising.

The DR method has already been implemented by the National Hurricane Center (NHC; Harasti et al. 2007), where it is called Vortex Objective Radar Tracking and Circulation (VORTRAC), but the accuracy with which the DR method estimates MSLPs and the operational utility of the DR method have not yet been clarified comprehensively, probably owing to the lack of sufficient TC examples. It is of great importance that the disaster prevention information have high reliability and consistency for the forecasts to be seen as credible and to avoid confusion. Thus, it is essential to examine the accuracy of the estimated MSLPs and to determine their suitability for operational use. In addition, analysts adopting the DR method for TC intensity analysis need to have a good understanding of the DR method’s accuracy and reliability, as well as of its strengths, weaknesses, and limitations.

Many strong TCs approach and make landfall in Japan. In particular, many TCs approach southwestern Japan, including the Okinawa Islands, where there is no topography effect on the TC structure. Therefore, TCs approaching this region are ideally suited for GBVTD retrievals. The Japan Meteorological Agency (JMA) upgraded all of the radars in its observation network to Doppler radars between 2006 and 2013, and the new Doppler radars have collected data on many TCs since their installation.

The purpose of this study was to estimate TC intensities by using the accumulated Doppler radar data and to evaluate the accuracy and utility of the DR method. Before the estimation of TC intensities, we performed a preliminary experiment with pseudo obtained by numerical simulation to confirm that the method could reasonably estimate central pressures.

This paper consists of six sections. We describe the data and TC cases used and the study methodology in section 2. We describe the preliminary experiment and present its results in section 3. In section 4, we present the DR estimation results for real TC cases and their statistical verification. In section 5, we discuss the results, including their utility, reliability, and limitations for operational use, and present additional statistical verifications. Section 6 includes a summary and conclusions.

2. Data, cases, and methodology

a. Data

We used data observed by JMA C-band operational Doppler radars. Figure 1 shows the radar locations used and the observational range of 2-km, constant altitude plan position indicator (CAPPI) data (~200 km) for each location. The Doppler observation parameters of the Okinawa radar, which is representative of the JMA radars, are listed in Table 1. We used both high and low dual-pulse repetition frequency (dual-PRF) data for the lowest-elevation plan position indicator (PPI) in order to see out to ~200-km range at 2-km altitude (see section 2c). Before 2009, the radars observed at 10-min intervals, and since 2009 the observation interval has been 5 min. In this study, dealiasing of by the Hybrid Multi-PRI (HMP) method (Yamauchi et al. 2006) and by a newly developed correction method (not shown) that assumes there is a wavenumber-1 pattern around a radar location influenced by TC circulation was performed automatically. Additionally, noise caused by dealiasing failure and sea clutter was removed from the data automatically by various quality control (QC) procedures. Although problems associated with the dealiasing correction of can greatly affect the accuracy of TC intensity estimates, describing how to cope with these problems is beyond the scope of this study. Therefore, we examine the accuracy of the estimated MSLPs based on the premise that the Doppler velocity data have been dealiased and that most noise has been removed.

Fig. 1.
Fig. 1.

Locations of the JMA operational Doppler radars and tracks of the TCs used in this study. The circles show the observation ranges of all radars (~200 km) for CAPPI data at 2-km altitude. Thick lines indicate TCs with maximum sustained (10-min average) surface winds of greater than 17 m s−1 (i.e., tropical storms), thin lines indicate TCs with maximum sustained surface winds of 17 m s−1 or less (i.e., tropical depressions), and gray lines indicate extratropical cyclones. The data are the JMA best track data.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

Table 1.

Doppler observation parameters of the Okinawa operational C-band radar.

Table 1.

In addition to the data, we used SLP data observed at weather stations at 10-min intervals. To verify our results, we used best track and Dvorak MSLPs from the Regional Specialized Meteorological Center (RSMC) Tokyo. The best track data from the RSMC Tokyo are based on both Dvorak MSLPs and observations. These Dvorak MSLPs are obtained by using the Dvorak technique to obtain current intensity (CI) numbers, which are then converted into MSLPs by referring to the conversion table in Koba et al. (1990). When the radar data were available at 5-min intervals, all other data were interpolated linearly to 5-min intervals.

b. Cases

We used two criteria to select TC cases for estimation: the TC center had to be located within the 2-km altitude range (~200 km) of a Doppler radar and we had to be able to subjectively identify the TC center from the pattern (an indication that, in general, radar coverage was sufficient). The circulation of a TC is generally depicted by a dipole pattern of positive (away from the radar) and negative (toward the radar) across the TC center, with a wavenumber-1 velocity pattern surrounding the radar location. This method was chosen under the supposition that analysts subjectively pick out a TC and estimate its duration by examining fields and radar reflectivity imagery. We selected 22 cases including TCs that struck southern Japan and strong TCs that passed over the Okinawa Islands (Fig. 1, Table 2). If the same TC was observed by two or more radars, then the observations of each radar were treated as a case, and the cases were distinguished by appending the letters A, B, or C to the TC name. Therefore, in total we estimated the intensity of 28 cases. The estimated duration of each case was about 9 h on average. Although this number of TC cases may not be a large enough sample size for statistical verification, it enabled us to test the TC intensity estimation method on a variety of TCs.

Table 2.

Cases used for the intensity estimation.

Table 2.

c. Methodology

The primary procedures used for the DR intensity estimates are based on the work of Lee et al. (1999, 2000), Lee and Marks (2000), and Bell and Lee (2012). There are six steps in the procedures (Fig. 2). After the dealiasing procedure and the removal of sea clutter and other noise (step 1), PPI data less than the elevation angle of 10.0° were interpolated into CAPPI data (step 2). After this step, the 2-km CAPPI data were used because at that altitude radar coverage for is the most extensive over the longest period, and the TC wind fields are generally approximately in gradient wind balance (Bell and Montgomery 2008; Willoughby 1990). Note that for radar ranges greater than ~130 km, from the lowest-elevation PPI scan whose height is between 2 and 4 km is just projected onto the 2-km CAPPI. Because vertical profiles of wind speed for TCs show a general increase from 3 km down to below 1 km (e.g., Franklin et al. 2003), there is a possibility that this CAPPI method contributes to positively biased MSLP estimates when the TC center is far from the radar. Then, by using the GBVTD-simplex center-finding algorithm (Lee and Marks 2000; Bell and Lee 2012), where a radius of maximum wind (RMW) and its possible range are subjectively given, TC center positions at 5-min intervals were detected from the first-guess center positions interpolated linearly to 5-min intervals from center positions subjectively determined at 1-h intervals while viewing radar reflectivity imagery (step 3). Then, using the detected centers, the 2-km CAPPI data were interpolated to TC cylindrical coordinates with an azimuthal resolution of ~0.7° and a radial resolution of 500 m (step 4). After the retrieval of by the GBVTD technique (step 5), the data were substituted into the gradient wind equation:
e1
where r is the radius from the TC center, f is the Coriolis parameter corresponding to the latitude of each radar location, is the axisymmetric SLP, and is the environmental air density at sea level. The value of was assumed to be 1.15 kg m−3, which corresponds to the density in a tropical environment with a virtual temperature of 30.0°C at 1000 hPa. At the second decimal place, this value may be a little higher than that in the TC inner environment, which would make the radial SLP gradient calculated from the gradient wind balance with Eq. (1) slightly steeper than the actual gradient. When one or more SLP observations were available within radii of the deduced SLP gradient, the axisymmetric SLP deficits of the TC were obtained by integrating the radial SLP gradient [the right-hand side of Eq. (1)] inward from each observation point toward the center of the TC (Lee et al. 2000). Finally, MSLPs were estimated by adding each SLP deficit to the corresponding SLP observation (step 6). When an SLP observation point was located within 15 km outside the GBVTD’s outer-most ring, MSLPs were estimated by extrapolating the SLP gradient over the distance from the observation point. As many MSLPs were estimated as there were observations. Note that the DR method assumes that asymmetric components of the pressure distribution have little impact on the estimation results.
Fig. 2.
Fig. 2.

The procedures used for the Doppler radar intensity estimation. Input and output data for each step are indicated in italics. Before (since) 2009, the estimate was performed at 10-min (5 min) intervals.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

To stably estimate MSLPs for operational purposes, we modified the original DR method in four primary ways. First, the GBVTD-simplex center-finding method was modified by restricting the center-finding area to within a 5-km radius of the first-guess point and confining the first-guess RMW to that associated with the primary eyewall (hereafter P-RMW). These modifications enabled a reasonable center to be identified around the guess point at all times; if there is no limitation placed on the simplex, it is difficult to detect a reasonable center for a dissipating TC or a TC with poor radar coverage, as described by Murillo et al. (2011). In addition, the latter modification enabled more accurate to be retrieved around the P-RMW. This was important because the SLP gradient calculated from Eq. (1) is sensitive to the magnitude of around the P-RMW. Second, we filled in missing values of by using a spline function with the assumption that = 0 m s−1 at the TC center. However, when the spline function produced a negative or an artificial local maximum in the eye region, we applied a quadratic function instead of the spline function. In this way, the radial distribution of within the eye region was deduced (Fig. 3). Third, we used the TC translational speed as a proxy for in the GBVTD technique. One weakness of the GBVTD technique is that, in principle, it cannot retrieve the component of the mean environmental wind perpendicular to a line connecting the TC center and the radar location (hereafter, the cross-beam component of the mean environmental wind, ). Instead, is aliased into , which leads to a decrease in the retrieval accuracy of at outer TC radii (Lee et al. 1999; Harasti et al. 2004; Chen et al. 2013). To resolve this aliasing problem, must be obtained from an independent source. The hurricane volume velocity processing (HVVP) technique, which provides by using a single-Doppler radar (Harasti 2014; Chen et al. 2013), is one possibility, but the results of our preliminary examination showed that from an operational perspective the use of the HVVP technique is not suitable for the stable provision of , owing to occasional poor radar coverage (not shown). Hence, we decided to use the cross-beam component of the TC translational speed as a proxy for to resolve the aliasing problem. The effect of using this proxy is described in sections 3b and 4a. Fourth, we introduced two QC procedures into the GBVTD retrieval:

  1. Retrieved values were rejected when associated asymmetric tangential winds with an amplitude of more than 25 m s−1 were obtained, because such a large asymmetry is, in general, unlikely for TCs and likely to be a retrieval failure because of, for example, noise contamination.
  2. To assess the accuracy of the GBVTD-retrieved winds, we examined the root-mean-square difference (RMSD) between the resampled from the GBVTD-retrieved winds and the observed at each radius, following Zhao et al. (2012). If the RMSD was more than 8 m s−1 inside the P-RMW and the TC had a maximum absolute of more than 60 m s−1 with a single, closed eyewall, the wind retrieval accuracy was extremely poor, and the corresponding values were rejected.
Fig. 3.
Fig. 3.

Radial profiles of retrieved , interpolated , and SLP anomalies (with the sign reversed) for T1215. SLPs observed at the Nago and Naha weather stations, which were used for the MSLP estimation, are shown along with the estimated MSLPs. MSLPs from Naha were estimated by extrapolating the SLP gradient over the distance from Naha.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

There are other limitations inherent in the GBVTD technique. First, the GBVTD technique has a weakness in that the wavenumber-1 tangential wind can bias via the nonlinear azimuthal coordinate system employed by the technique, particularly when the radar is around the radius of the GBVTD analysis ring [see Fig. 13a in Lee et al. (1999)]. This can lead to poor MSLP estimations especially when the distance between the TC center and the weather station whose sea level pressure is used as an anchor of the pressure measurement is close to that from the radar to the TC center. Second, the GBVTD-simplex center-finding algorithm cannot detect centers when the radar is inside the P-RMW. In this case, we used the first-guess center positions as centers although in most of the cases the GBVTD technique also failed to retrieve because of there being few scatterers in the eye region. Fortunately, in this study, this case applied to only T0813 and T1217. Third, the GBVTD technique assumes that radial winds are much weaker than tangential winds. When this assumption is violated, the retrieval accuracy of decreases.

3. Simulation experiment

To assess the accuracy with which the DR method could estimate MSLPs of TCs of known intensity, we used data obtained by numerical simulation. In particular, we verified two points of the procedures. First, we confirmed that the use of 2-km and surface air density in the gradient wind equation would yield reasonable MSLPs. Second, we confirmed that we could improve retrieval accuracy by using TC motion as a proxy for the mean wind to correct an inherent bias in GBVTD-retrieved . Here, we describe the settings of the simulation and present the estimation verification results.

a. Simulation settings

We performed simulations of Typhoon Bolaven (2012), Typhoon Neoguri (2014), and Typhoon Vongfong (2014) with the JMA Nonhydrostatic Model (JMA-NHM; Saito et al. 2006, 2007). The physical parameterizations were mostly the same as those used by JMA’s operational mesoscale model [see Japan Meteorological Agency (2015) for details]. The horizontal grid spacing of the simulation was 2.5 km, and results were output at 5-min intervals.

For simplicity, we constructed pseudo- data at 2-km altitude corresponding to each radar location from the simulated winds at 2-km altitude without considering the effects of radar beamwidth and zenith angle. We regarded only grid points with a nonzero mixing ratio of rain, snow, and graupel at 1-km altitude as the grid points where at 2-km altitudes were observed, because a check for the presence of nonzero mixing ratios at 2-km altitude did not provide a sufficient distribution. As in the real TCs, there were no pseudo- values around the TC center. Although the grid of the simulation was coarser than the radar resolution (Table 1), this was unlikely to be a serious problem for the results because the resolution of the radar data was reduced through the averaging and interpolation procedures. The tracks of the simulated TCs deviated from the observed tracks, so we adjusted the simulated tracks by displacing them parallel to the observed tracks. In addition, to increase the number of estimated cases, we prepared two virtual cases by parallel displacement of the simulated data. Because the simulated TCs were over the ocean, where there is no topography effect, this displacement presents no disadvantage for the experiment. In total, we prepared six simulated TCs in this experiment (Fig. 4, Table 3).

Fig. 4.
Fig. 4.

Tracks of the six simulated TCs used in the preliminary experiment. Black circles indicate the ranges, ~200 km, of the Ishigakijima and Okinawa radars (red dots). Each color represents one simulated case.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

Table 3.

The six TCs used in the simulation experiment.

Table 3.

Simulated Typhoon Bolaven (2012) had an RMW of ~46 km, about 3 times the observed RMW, and a typical TC structure. The simulated track was almost the same as the observed track, but the TC location was approximately 2 h ahead of the observation. The intensity of the simulated Bolaven was estimated for two cases: along the actual track of the simulated TC (S1215A) and along a second track displaced southwestward from S1215A (S1215B, blue line in Fig. 4). Simulated Typhoon Neoguri (2014) had an RMW of ~60 km, which was larger than the observed RMW of 35–40 km. Because the simulated track of Neoguri deviated southwestward from the observed track, the location of the simulated Neoguri was displaced northeastward (0.5379°N and 0.4415°E). The intensity of the simulated Neoguri was estimated for both T1408A (observed from the Ishigakijima radar, S1408A) and T1408B (observed from the Okinawa radar, S1408B). In the S1408A case, the coverage of the Ishigakijima radar was limited to the western part of Neoguri, and in the S1408B case, the coverage of the Okinawa radar was limited to its eastern part. In addition, we prepared a third track for the simulated Neoguri displaced northeastward (0.5379°N and 1.0415°E) from the simulated track; this S1408C case (yellow line in Fig. 4) had better coverage from the Okinawa radar than that of the S1408B case. Simulated Typhoon Vongfong (2014) (S1419) had a disappearing eyewall. Because such TCs frequently approach Japan, it was important to verify how accurately the DR method could estimate MSLPs of such TCs. In this experiment, we were not concerned with how well the numerical simulation could reproduce the observed TC structures, including their vertical structures.

For simplicity, we do not address the problem of the accuracy of the center-finding algorithm here. So, this experiment started from step 4 in Fig. 2. TC centers used by the GBVTD technique were centroid centers calculated by applying a 1–2–1 smoothing filter 500 times (Nolan et al. 2009). Note that in this experiment, the true MSLP was not the SLP at the centroid center but the lowest SLP within the eye region in the simulated TCs. We extracted pseudo-SLP observations at points corresponding to surface weather stations from each simulation.

b. Results and verification

We performed MSLP estimates using pseudo at 5-min intervals. Table 4 shows the estimates in the preliminary experiment compared with the true values in the simulated TCs. The RMSE of the estimated MSLPs relative to the true MSLPs was 2.84 hPa, and the bias was −0.77 hPa, although individual results could have a large effect because of the small number of TCs included. The incorporation of the cross-beam component of TC motion as a proxy for helped to improve the biases of the TCs. For example, the bias of S1408B was improved by ~5 hPa, from −8.54 to −3.55 hPa, though the negative bias was not eliminated. Figure 5a shows the axisymmetric radial distributions of the retrieved and true in S1408B averaged over the estimation period. The difference between the retrieved and true values was quite small on average, except near the TC center, where the retrieved were interpolated by a spline function, resulting in slightly weak estimates, but the difference had little effect on the SLP field (not shown). Figure 5b shows the axisymmetric radial distributions of SLPs in S1408B averaged over the estimation period. The differences between the true and deduced SLPs show that at outer radii of more than 50 km, the pressure gradient was slightly steeper than the true gradient. In contrast, the pressure gradient was slightly shallower than the true gradient within the RMW. On the whole, a negative bias was evident. In S1419, the effect of the spline interpolation within the eye region led to a positive MSLP bias (not shown). The trends of the pressure distributions were similar in the other cases. Thus, the results of the preliminary experiment confirmed that the DR method using data at 2-km altitude and the cross-beam component of TC motion as a proxy for could reasonably reproduce MSLPs, as reported by Harasti et al. (2004).

Table 4.

Estimation accuracy of the six TCs in the simulation experiment. The P-RMW was determined from the retrieved values after step 5 in Fig. 2 within a possible range of the subjectively determined P-RMW. The P-RMW values and central pressures were averaged over the estimation periods shown in Table 3.

Table 4.
Fig. 5.
Fig. 5.

Radial distributions of (a) and (b) axisymmetric SLP anomalies of S1408B averaged over the estimation period. In (a) the black line is the true , the green line is the retrieved , and the red line is the difference between them. In (b) the black line represents the true SLP anomalies, the green line the deduced SLP anomalies, and the red line the difference between them.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

4. Estimation results

In this section, we present the estimation results for the real TC cases. We first describe the overall statistical verification results; then, we classify the TC cases into four groups according to the estimation results and describe the characteristics of the individual cases in each of the four groups in detail.

a. Overall statistical verification

A scatter diagram of the estimated and best track MSLPs in the 28 cases shows that, despite large errors in some estimated MSLPs, most data points are concentrated in the vicinity of the 1:1 line (Fig. 6). The RMSE, bias, and correlation coefficient between the estimated and best track MSLPs were 8.37 hPa, 1.51 hPa, and 0.87, respectively. The MSLP estimate error was within ±5 (±10) hPa in 60.3% (84.1%) of all estimates. In contrast, for the best track MSLP of 960 hPa, the estimated MSLPs were distributed from 975 to 908 hPa. Most of these large differences were attributed to T1007, which is discussed in the next subsection.

Fig. 6.
Fig. 6.

Scatter diagram comparing the estimated MSLPs with the best track MSLPs. The corresponding RMSE, bias, and correlation coefficient R are also shown.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

The use of the cross-beam component of translational speed as a proxy for in the GBVTD technique was remarkably effective. The MSLPs estimated using the proxy had RMSEs smaller than 94% and positive biases smaller than 55% of those of the MSLPs estimated without using the proxy (Table 5). Although for most of the TCs, biases were positive (not shown), incorporation of in the GBVTD technique generally reduced this positive bias. This result is consistent with the results of the preliminary simulation experiment (section 3).

Table 5.

Overall accuracy of the DR intensity estimates for the 28 TC cases. The results are shown both with and without incorporation of . The overall accuracy excluding T1007 is given in parentheses.

Table 5.

Therefore, MSLPs can be reasonably estimated from at 2-km altitude by using TC motion as a proxy for . Differences in the accuracy of the estimates under several specific conditions are discussed in section 5b.

b. Classification of the individual cases

We grouped the MSLP estimates of the 28 cases into the following four groups: excellent MSLP estimates, large differences between the DR and best track MSLPs, a consistent positive bias relative to the best track, and erratic fluctuations.

We defined an estimate as excellent if the RMSE was less than 4 hPa and the bias was within ±4 hPa. Ten cases met these criteria: T0709, T0813, T1115, T1215, T1307, T1312, T1323, T1408A, T1408B, and T1411B. We show the characteristics of T1312 in Fig. 7 as an example of an excellent estimate. The estimated MSLPs and their trend correlated well with those of the best track (Fig. 7a). Figures 7b and 7c show the Doppler velocity field and radial distribution of the retrieved axisymmetric tangential wind and axisymmetric SLP deficit at 2225 UTC 20 August 2013. Although radar coverage was not very dense, the MSLPs estimated from the SLPs at both Miyakojima (49.3 km from the TC center) and Ishigakijima (107.5 km from the TC center) were almost the same as the best track MSLP at that time. The accuracies of all 10 cases are listed in Table 6. One of our most important findings here is that the DR method could provide almost the same MSLPs as the best track in real time, whereas the accuracy of the estimates made by the Dvorak method was not necessarily good.

Fig. 7.
Fig. 7.

(a) Time evolutions of the DR MSLPs (color lines), best track MSLPs (black line), and Dvorak MSLPs (blue dashed line) for T1312. The vertical black line indicates the time of the radar observation shown in (b). The red line indicates MSLPs derived from SLPs at Miyakojima, the green line indicates MSLPs derived from SLPs at Ishigakijima, and the blue line indicates MSLPs derived from SLPs at Iriomotejima. (b) Doppler radial velocity of the 2-km CAPPI observed from the Ishigakijima radar (red dot) at 2225 UTC 20 Aug 2013. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1312 at 2225 UTC 20 Aug 2013.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

Table 6.

Accuracy of the 10 cases with excellent estimation results. The P-RMW was determined from the retrieved values after step 5 in Fig. 2 within its possible range used in the GBVTD-simplex center-finding algorithm. The P-RMW values and central pressures were averaged over the estimation periods shown in Table 2.

Table 6.

For T1007, large differences were found between the DR and best track MSLPs (Fig. 8). In the first half of the estimation period, T1007 had a negative minima of about −70 m s−1 and a positive maximum of about 55 m s−1 near the 12-km radius at 2-km altitude (Fig. 8b). The DR method retrieved greater than 60 m s−1, and the estimated MSLPs were around 930 hPa on average (Figs. 8a,c), although they fluctuated somewhat erratically. As the TC approached Okinawa Island, weakened rapidly. At the time of the TC’s nearest approach to Nago on Okinawa Island, when the SLP was 965.1 hPa (Fig. 8a, blue star), Nago was located just within the RMW (~13 km from the TC center) and was 47 m s−1. The DR method estimated MSLPs that were more than 10 hPa below the SLP at Nago. By contrast, the best track MSLP was consistently 960 hPa (black line in Fig. 8a), and the Dvorak MSLP was consistently 947 hPa (dashed blue line in Fig. 8a) during the estimation period. Considering the fact that the at around the RMW (~12 km) changed from ~63 to ~47 m s−1 during the estimation period and the observed SLP was 965.1 hPa around the RMW during the second half of the period, the MSLPs obtained by the DR method seem more plausible than the best track values. This suggests that the DR method can capture some TC intensity changes on a short time scale (less than 6 h) that the conventional methods fail to capture. If T1007 is removed from the overall statistical verification described in section 4a, the RMSE improves to 7.13 hPa (Table 5). Other cases in this group, with similarly large differences between the DR and best track MSLPs, were T1204 and T1419 (not shown).

Fig. 8.
Fig. 8.

(a) As in Fig. 7a, but for T1007. The blue star is the SLP (965.1 hPa) observed at Nago, 13.2 km from the TC center. [The vertical black line indicates the time of the observation shown in (b).] The green line indicates MSLPs derived from SLPs at Naha, and the red line indicates MSLPs derived from SLPs at Nago. (b) Doppler radial velocity of the 2-km CAPPI observed from the Okinawa radar (black dot) at 0245 UTC 31 Aug 2010. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1007 at 0245 UTC 31 Aug 2010.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

The estimates of five TC cases showed consistent positive biases: T1109, T1217, T1318, T1418A, and T1418B. Figure 9a shows the time evolution of the MSLPs of Typhoon Phanfone (T1418A, T1418B, and T1418C). T1418A showed a significant positive bias during the last 2 h of the estimation period (around 0300 UTC 5 October 2014) compared with the prior and subsequent estimates. This bias was caused by poor radar coverage (not shown). Except for that period, however, the DR estimates of Typhoon Phanfone seem reasonable because the retrieved structure corresponds well to the distribution and because values were retrieved in the eye region (Figs. 9b,c). One possible reason for the consistent positive biases of the DR MSLPs, compared with the best track, is that if the wavenumber-1 asymmetry is caused by a vortex Rossby wave and then it may be stationary near the eye region (e.g., Lamb 1932), it can potentially cause a constant bias in the GBVTD-retrieved (see section 2c), resulting in consistent MSLP biases. However, because this possibility can lead to both positive and negative biases and because there was no case with a consistent negative bias, it may not be the reason for the consistent positive biases. Another possible reason is that the radial SLP structure inside the P-RMW assumed in the best track analysis was different from that deduced from the DR method. Table 7 shows the accuracies and the P-RMWs of the TCs in this group. Except for T1318, the P-RMW was more than 70 km. When the P-RMW is large, the effect of the difference between the SLP structures inside the P-RMW assumed in the best track analysis and deduced from the DR method on MSLP estimates may be large. Interestingly, the DR MSLPs were better correlated with the Dvorak MSLPs than with the best track MSLPs in this group (Fig. 9a, Table 7). Considering these findings, the estimated MSLPs of these cases characterized as having consistent positive biases may not be wrong, although we do not have true MSLPs with which to compare them.

Fig. 9.
Fig. 9.

(a) As in Fig. 7a, but for T1418. The red star is the SLP (962.1 hPa) observed at Shionomisaki, which was 6.2 km from the TC center. [The vertical black line indicates the time of the observation shown in (b).] (b) The Doppler radial velocity of 2-km CAPPI observed from the Tanegashima radar (black dot) at 0540 UTC 5 Oct 2014. The circles show the radii at 10, 30, 60, 90, 120, and 150 km from the TC center. (c) As in Fig. 3, but for T1418 at 0540 UTC 5 Oct 2014.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

Table 7.

As in Table 6, but for the five cases with consistent positive biases.

Table 7.

The estimated MSLPs fluctuated erratically in some cases, as they also did in some results from the simulation experiment (not shown). According to Harasti et al. (2004), unreasonable TC center finding by the simplex method is one reason for such erratic fluctuations, and we identified four additional reasons for erratic fluctuations. First, noise contamination caused by failure of the dealiasing correction sometimes resulted in violent fluctuations with amplitudes of 5–15 hPa, for example, in T1011 (Fig. 10a), T1216, and T1324B (not shown). In those cases, noisy data in small areas were not fully removed by the automatic processes. Second, in some cases the GBVTD assumption that radial winds are much weaker than tangential winds was apparently not valid inside the eyewall. If there are active mesovortices inside the P-RMW, there is no assurance that asymmetric radial winds are negligible, and the wind retrieval accuracy would not be good. The estimated MSLPs of T1007 in the first half of the estimation period (Fig. 8a) and of T1324A (Fig. 10b) fluctuated with an amplitude of 5–15 hPa for this reason, because in these two cases, RMSDs between the resampled from the GBVTD-retrieved winds and the observed were sometimes quite large inside the P-RMW. Although the QC procedures described in section 2c remove retrieved winds with large RMSDs, the results can be problematic. When too many retrieved winds were rejected by the QC procedure, the estimated MSLPs tended to be too high. With insufficient rejection, the retrieved tended to be too strong in the eye region, and the MSLPs were too low. As a result, the estimated MSLPs showed erratic fluctuations. The third reason is illustrated by T1411A (Fig. 10c), for which the estimated MSLPs fluctuated violently between two extremes. When the radar coverage was sufficient for wind retrieval around the disappearing eyewall, the estimated MSLPs were about 950 hPa, which is consistent with the best track. By contrast, poor radar coverage around the disappearing eyewall led to MSLPs of around 965 hPa. Fourth, the aliasing of wavenumber-2 radial winds into in the GBVTD retrieval formula (see details in Lee et al. 1999; Murillo et al. 2011) probably caused wavy fluctuations with a period of a couple of hours, as seen in T1204, T1215, T1411B (Fig. 10c), and T1411C (Fig. 10c). If wavenumber-2 radial winds are dominant within the eye region and their distribution moves around the eyewall cyclonically together with mesovortices (e.g., Kossin and Schubert 2004; Braun et al. 2006) or vortex Rossby waves (e.g., Montgomery and Kallenbach 1997; Wang 2002), the radial winds could bias the retrieved with a period of a few hours.

Fig. 10.
Fig. 10.

As in Fig. 7a, but for (a) T1011, (b) T1324A, and (c) T1411.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

Given these characteristics of our estimation results, we recommend using a running mean of DR MSLPs of a few hours. Additionally, by paying attention to the characteristics of each group, analysts should be able to utilize those MSLPs with higher accuracy and reliability than is suggested by the overall statistical verification results described in section 4a.

5. Discussion

We discuss the utility and feasibility of the operational use of DR intensity estimates compared with conventional estimation methods and we examine additional statistical verification results.

a. Comparison with conventional methods

Koba et al. (1990) have reported that the error range of Dvorak MSLPs in the western North Pacific is 7–19 hPa, values that are almost equivalent to the RMSE; Martin and Gray (1993) have reported that the standard deviation of Dvorak MSLPs in the western North Pacific is 9 hPa; and Velden et al. (2007) have reported that the RMSE of Dvorak MSLPs is 11.7 hPa. According to Oyama (2014), Advanced Microwave Sounding Unit (AMSU) MSLPs in the western North Pacific have an RMSE of 10.1 hPa relative to the JMA best track, and according to Velden et al. (2007), the RMSE of the AMSU method using the Cooperative Institute for Meteorological Satellite Studies (CIMSS) algorithm is 7.5 hPa and that using the Cooperative Institute for Research in the Atmosphere (CIRA) algorithm is 10.3 hPa, relative to aircraft reconnaissance observations. These error statistics indicate that the accuracy of the MSLPs obtained by our DR method is comparable to or better than the accuracies of the Dvorak and AMSU methods. We stress that the DR intensity estimates are totally independent of the past best track. Therefore, we can now obtain plausible MSLPs in real time by a method other than aircraft reconnaissance observations that is different from that used to obtain the best track. The use of DR estimates would be a new paradigm of TC intensity analysis in the western North Pacific, where aircraft observations are not available.

b. Additional statistical verification

Aiming for the effective operational use of the DR intensity estimates, we further examined the estimation accuracy of the MSLPs with respect to several specific conditions (Fig. 11). Note, however, that because we used only 22 TCs for the estimations, the statistical results for some specific conditions may be dependent on extreme cases. The RMSEs were, in general, smaller when the distance between the TC center and the radar location was shorter, and the estimates exhibited a positive bias when the distance was large. The positive bias may result from the CAPPI method, as described in section 2c. Similarly, RMSEs were smaller when the distance between the TC center and the weather station whose sea level pressure was used as an anchor for pressure measurement was shorter. One reason for this similarity is likely the fact that the best track analysis uses the same weather station observations, and weather stations are near the radar locations.

Fig. 11.
Fig. 11.

Scatter diagrams comparing the difference between the estimated MSLPs and the best track MSLPs (y axis) with (a) the distance between the radar location and the TC center, (b) the distance between the weather station and the TC center, (c) the RMW at 2-km altitude, (d) radar coverage, and (e) the GBVTD wind retrieval accuracy (x axis). The RMWs were determined from all the retrieved values from step 5 in Fig. 2. The radar coverage is defined as the radial average of the maximum azimuthal gap (rad) at each radius on the GBVTD-specified coordinate system. The wind retrieval accuracy is defined as the overall average of the RMSD between the resampled from the GBVTD-retrieved winds and the observed , identical to the RMSE shown in Fig. 3 of Zhao et al. (2012). The error bars show the bias and the RMSE within the range indicated by the double-headed arrows.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

The RMW was determined from all of the retrieved values after step 5 in Fig. 2. For TCs with an RMW of 20–70 km, the estimated central pressures had an RMSE of 5.55 hPa and showed a bias of 0.69 hPa. In contrast, for TCs with an RMW of 75–120 km, the estimates had a large positive bias of 5.23 hPa, as indicated in section 4b. Large RMWs greater than 120 km may contain false RMWs. Bell and Lee (2012) stated that missing data outside the radar range can cause a false RMW at a large radius. Thus, large errors accompanying large RMWs may be due in part to the false RMWs and, more specifically, the lack of radar data.

Because the above results suggest that the pressure gradients retrieved by the DR method have a systematic error, we examined the relationship between the observational SLP gradient when there were multiple SLP observations around a TC and the corresponding retrieved SLP gradient. Figure 12 shows the result schematically: the retrieved SLP gradient was on average 0.55 hPa steeper than the observational SLP gradient over a 44.3-km interval. This result is apparently inconsistent with the positive biases of the MSLPs described above. However, given that most of the weather stations were outside of the RMW, these findings suggest that the SLP gradient retrieved from outside the RMW is steeper than the true gradient, and that the SLP gradient inside the RMW, where some of the were interpolated by using a spline function, is shallower than the gradient inferred from the best track. These features are consistent with those of the average SLP gradient in the preliminary experiment (Fig. 5b). The radial SLP gradient error is attributed to 1) retrieval errors associated with the use of the TC motion as a proxy for and several limitations inherent in the GBVTD technique, 2) the improper use of a value for surface air density that is a little too high and at 2-km altitude in the gradient wind equation (1), and 3) an interpolation error by the spline function. The development of a method for interpolating within the TC eye in a plausible way is a topic for future work.

Fig. 12.
Fig. 12.

Schematic diagram of retrieved and observed SLP gradients. The values were calculated when data from multiple SLP observations around the TCs were available. To obtain as many SLP gradients in the inner region of TCs as possible, when three or more SLP observations were available, only SLP gradients relative to the innermost observation point were calculated.

Citation: Monthly Weather Review 144, 5; 10.1175/MWR-D-15-0254.1

In addition, the estimated MSLPs tended to be more consistent with the best track MSLPs when radar coverage was denser and wind retrieval accuracy was better (Figs. 11d,e). TC analysts should consider these features when the DR method is used operationally.

c. Utility of the DR intensity estimation method

Estimation of MSLPs by the DR method has four advantages. First, the DR method can provide MSLPs a few hours before a TC approaches a populated area. Thus, with reliable nowcasting, a meteorological agency will be able to issue effective warnings to mitigate the TC’s impact. Second, the DR method can provide MSLPs at 5-min intervals with an accuracy comparable to or better than that of conventional methods. As a result, analysts will be able to nowcast a TC at shorter intervals than is possible with conventional methods, which allow nowcasts at 6-h intervals or several times per day. Third, the DR method can, in principle, estimate the intensity of any TC, as long as radar coverage is sufficient. By contrast, the Dvorak technique cannot handle rapid intensity changes, and the AMSU techniques, because of the coarse resolution of the instrument, are not suitable for estimating the MSLPs of a TC with a small eye (Oyama 2014). Fourth, in recent years the DR method could have been applied operationally to four to five TCs each year according to Table 2. In particular, TCs approaching the vicinity of the Okinawa Islands, where there is no topographical effect on the TC structure, are ideally suited for GBVTD retrievals. Therefore, given this TC frequency, it will be beneficial in Japan to utilize the DR method operationally to improve the accuracy of TC intensity analyses in real time.

However, DR intensity estimates also have some limitations. Compared with the lifetime of a TC, the estimation period is extremely limited—only 9 h on average. The DR method also uses some parameters, such as initial-guess centers, the P-RMW, and its possible range, that must be subjectively determined beforehand. In addition, the method cannot estimate MSLPs unless can be successfully dealiased. Furthermore, the GBVTD technique has several inherent limitations, as described previously. However, it is possible to make use of bias information between the Dvorak estimates and the DR estimates even for the period before and after the DR estimation period to correct the Dvorak estimates. The dealiasing problem should be addressed by improving dealiasing and noise-removal methods. Because some other techniques for relaxing the limitations of the GBVTD technique have been proposed (e.g., Jou et al. 2008; Wang et al. 2012; Chen et al. 2013), it is possible to further improve the DR method, which should be addressed in future studies.

6. Summary and conclusions

It is of great importance to analyze TC intensity with high accuracy, particularly for TCs approaching populated areas. However, conventional methods of TC intensity estimation, such as the Dvorak technique, which estimates TC MSLPs and maximum sustained wind speeds from cloud patterns, and the AMSU technique, which estimates MSLPs from the magnitude of the upper-level warm-core anomaly retrieved from satellite microwave sounding data, have limitations with regard to analysis accuracy. Furthermore, observing MSLPs in a straightforward way, such as by using dropsondes, is, for now, not a practical solution in the western North Pacific. However, as a result of the JMA upgrading all of its operational radars to Doppler radars from 2006 to 2013, Doppler velocity observations are available for the many TCs that have passed near these radars. If axisymmetric tangential winds retrieved from the Doppler velocities and the gradient wind equation can reasonably be used to infer a TC’s pressure field, then MSLPs can be estimated from the Doppler velocities with high accuracy. A set of procedures developed by Lee et al. (1999, 2000), Lee and Marks (2000), and Bell and Lee (2012) for estimating TC intensity by using the GBVTD technique to retrieve axisymmetric tangential winds from Doppler velocities has shown promising results. In this study, we evaluated the accuracy of this method by estimating the MSLPs of 28 TC cases; identified some limitations, strengths, and weaknesses of the method; and evaluated its utility for operational use.

Before estimating the intensity of real TCs, we performed a preliminary experiment using pseudo-Doppler velocities obtained from numerical simulations to confirm that the set of intensity estimation procedures could provide reasonable estimates of TC central pressures. In particular, we assessed the effect of using the cross-beam (normal to the line connecting the radar with the TC center) component of the mean environmental wind,, assumed from the TC translational speed, to correct for the aliasing effect of the GBVTD algorithm. The results showed that when Doppler velocities at 2-km altitude and the assumed were used, the estimated MSLPs had an RMSE of 2.84 hPa and a bias of −0.77 hPa. In addition, the incorporation of assumed from the TC motion in the GBVTD algorithm decreased the bias and improved the accuracy of the estimate very well. We also confirmed that the DR method could reasonably estimate MSLPs by using Doppler velocities at 2-km altitude.

On the basis of these preliminary results, we used Doppler velocity data at 2-km altitude and the cross-beam component of the TC translational speed as to obtain DR intensity estimates for 28 TC cases. Compared with the best track MSLPs of RSMC Tokyo, the RMSE, bias, and correlation were 8.37 hPa, 1.51 hPa, and 0.87, respectively. The MSLP estimation error was within ±5 (±10) hPa for 60.3% (84.1%) of the total number of estimates. This estimation accuracy is comparable to or better than the accuracies of the Dvorak and AMSU methods. Additionally, we confirmed that the accuracy was poor if the was not incorporated into the GBVTD algorithm. Two distance metrics are defined: 1) the distance between the TC center and the radar location and 2) the distance between the TC center and the surface weather station whose sea level pressure was used as an anchor for pressure measurement. The MSLPs estimated here tended to be more consistent with the best track MSLPs when the distance metrics were shorter, when the wind retrieval accuracy was better, and when the radar coverage was denser. For TCs with an RMW of 20–70 km, the estimated MSLPs had an RMSE of 5.55 hPa and showed a bias of 0.69 hPa relative to the best track. Examination of cases with large differences between the best track and DR method MSLPs suggested that the DR method can capture TC intensity changes over a shorter time scale (less than 6 h) than is possible with the conventional methods. This result suggests that the DR method can improve not only real-time intensity analyses, but also best track analyses.

Our results confirm the feasibility of using DR intensity estimates operationally. TCs for which the DR method is applicable have approached Japan four to five times per year recently. Therefore, we believe that adopting the DR method, in addition to the Dvorak technique and methods using satellite microwave sounder and imager data for operational use, would enable more frequent and possibly more accurate intensity analyses of TCs approaching Japan.

Acknowledgments

Some data analyses were performed with the “DRAFT” Doppler radar analysis tool of the Meteorological Research Institute (MRI). US is deeply grateful to Mr. H. Yamauchi and Mr. E. Sato for helpful advice. Gratitude is also extended to colleagues at the RSMC Tokyo and MRI. The authors thank three anonymous reviewers for valuable comments that have greatly improved the manuscript. This work was partially supported by Ministry of Education, Culture, Sports, Science and Technology (MEXT) KAKENHI Grant 25400468. The opinions in this paper are those of the authors and should not be regarded as official RSMC Tokyo views.

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