1. Introduction
Limited-area models (LAMs) with high spatial and temporal resolution, mainly focusing on precise nowcasting, tend to have poor forecast skills. This is mostly due to energy imbalance and the lack of local information (Schwitalla and Wulfmeyer 2014). By specifying the thermodynamic structure and providing hydrometer information in a convective-permitting system, assimilating radar data into LAMs can improve short-range quantitative precipitation forecast (QPF) (Sheng et al. 2006; Xiao 2007; Zhao and Xue 2009). However, unconventionally observed parameters and mismatched resolutions between model and observation make radar data assimilation difficult to implement (Fabry and Kilambi 2011; Thompson et al. 2012).
Doppler radar can give only the radial component of the wind velocity at each point, but the spatial distribution characteristics of the radial wind can provide some information about the tangential wind component. For example, the velocity–azimuth display (VAD) method (Lhermitte and Atlas 1961; Browning and Wexler 1968; Lindskog et al. 2002; Benjamin et al. 2010) can be used to obtain the whole-domain-averaged wind within the radar scan range using the spatial distribution characteristics of radial wind. Therefore, besides the radial wind, assimilating the spatial distribution characteristics of the radial winds should help improve the model wind field. The traditional observation operator given in Eq. (1) however cannot incorporate the spatial distribution characteristics into a data assimilation system.
Rather than the VAD method that can provide only the whole-domain-averaged wind field, the integrating velocity–azimuth processing (IVAP) method proposed by Liang (2007) can provide locally averaged wind within a given area. In this study, a new observation operator based on the IVAP method is proposed to assimilate the radar radial wind and its spatial distribution characteristics.
In the meantime, the resolution mismatch of observation and model must be handled before radar data assimilation. Generally, a thinning method is used to match the two resolutions. The existing thinning methods (Purser et al. 2000; Alpert and Kumar 2007) used in radar data assimilation discard massive observations and induce interpolation errors. Such as that in GSI, the mean radial velocity in a box (e.g., 5 km in range, 5° in azimuth, and 0.25° in elevation) is used to represent the whole area, which is called super-observations. Meanwhile, the pointwise measurements used in the traditional observation operator require that the radial velocity is averaged in a box within the radar volume, which produces large errors when the system is far away from the radar center (Wood et al. 2009). Because the IVAP method uses a given area to obtain the information about local wind, the size of the area can be uniformly set in both radar and model coordinates; therefore, it has a thinning function automatically without having to discard observations and induce interpolation errors.
In this study, single analysis unit experiments and a case study of Typhoon Fitow (2015) are used to make detailed comparative analyses, to show the superiority of this improved operator. The paper is organized as follows. The formula of the improved observation operator is described in section 2. The single analysis unit experiments to demonstrate the improved observation operator are analyzed in section 3. Data processing procedures and elaboration on the outcome of the selected typhoon case are given in section 4, followed by a summary and a discussion in section 5.
2. Observation operators for radar radial velocity assimilation
The improved observation operator can be applied in GSI, as shown in Fig. 1. First, the observations of 
Flowchart of applying the improved observation operator in GSI.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Flowchart of applying the improved observation operator in GSI.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Flowchart of applying the improved observation operator in GSI.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Generally, a thinning method should be used because of the resolution differences between the observation and the model mentioned in the introduction. In Eqs. (6) and (7), the size and shape of 
In summary, compared with the traditional operator, which introduces radial velocity using point-to-point “super observations (super-obs),” the improved operator absorbs more observed information, including both radial wind and its spatial distribution characteristics. In addition, compared with single-point measurements, the improved observation operator, calculating the observation variables in 
3. Single analysis unit experiments
To compare the performances of the two observation operators in the GSI system, single analysis unit experiments were conducted. A domain with 725 × 625 horizontal grid points, 3-km resolution, and 51 vertical levels is used. The model top is at 10 hPa. The background wind is uniform, with 
The wind speed increments were computed between the observation and background fields [observation minus background (OMB)] and between the observation and analysis fields [observation minus analysis (OMA)] in each experiment (Table 1). The OMBs were mostly in the westerly direction with a slight southerly increment, with the values of 5 and −1 m s−1 in the x and y directions, respectively. By using the traditional observation operator, an innovation against the radar center can be found in Exp RW1 that reduced the radial velocity error from 1.34 m s−1 (OMB) to 1.13 m s−1 (OMA). With the analysis unit size increased from 1 km × 1 km to 48 km × 48 km, the number of super-obs points increased from 1 to 119, while the OMA decreased from 1.13 to 0.72 m s−1. However, the tangential velocity showed almost no improvement, which only changed from −4.92 to −4.43 m s−1. Figures 2a, 2c, 2e, and 2g show that the wind increments had little improvements in the east–west direction, and the directions of the innovation appeared mainly toward or against the radar center, since the traditional observation operator can ingest only radial velocity; in other words, it did not have information about tangential velocity. By using the improved observation operator, the observation parameters, calculated in the given area using Eqs. (6) and (7), ingested both radial velocity and its spatial distribution characteristics into the assimilation system, which contained the information about tangential velocity. Thus, the improvements appeared not only in radial velocity but also in tangential velocity. With the analysis unit size increased from 1 km × 1 km to 48 km × 48 km, the radial velocity error decreased from 1.34 m s−1 (OMB) to about −0.25 m s−1 (OMA) in Exp IVAP4, while the tangential velocity decreased from −9.98 m s−1 (OMB) to −4.92 m s−1 in Exp IVAP1, and to 0.65 m s−1 in Exp IVAP4. As mentioned in section 2, when the analysis unit was small, the improved observation operator was similar to the traditional one, which was demonstrated by the similar results of IVAP1 (Fig. 2b) and RW1 (Fig. 2a). Since there were fewer observations in a smaller analysis unit, less tangential information could be deduced and less advantage the improved observation operator had compared to the traditional one. When the analysis unit became bigger, the abundance of observed radial velocity provided more tangential information through the improved observation operator; the holonomic wind field made the analysis results closer to the observation (Figs. 2d,f,h). These analyses indicate the essential differences of these two observation operators and demonstrate the superiority of the improved observation operator. As shown by the results of the single analysis unit experiments, the traditional observation operator cannot improve the wind in the tangential direction by assimilating radar observation even in the case of a uniform wind field.
Wind increments between the observation (OBS) and background fields and between the observation and analysis fields.
Distributions of wind increment (m s−1) of the single analysis unit test using two different observation operators with various analysis unit sizes. (left) Experiments using the traditional observation operator: (a) RW1 (1 km × 1 km), (c) RW2 (12 km × 12 km), (e) RW3 (24 km × 24 km), and (g) RW4 (48 km × 48 km). (right) Experiments using the improved observation operator: (b) IVAP1 (1 km × 1 km), (d) IVAP2 (12 km × 12 km), (f) IVAP3 (24 km × 24 km), and (h) IVAP4 (48 km × 48 km).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Distributions of wind increment (m s−1) of the single analysis unit test using two different observation operators with various analysis unit sizes. (left) Experiments using the traditional observation operator: (a) RW1 (1 km × 1 km), (c) RW2 (12 km × 12 km), (e) RW3 (24 km × 24 km), and (g) RW4 (48 km × 48 km). (right) Experiments using the improved observation operator: (b) IVAP1 (1 km × 1 km), (d) IVAP2 (12 km × 12 km), (f) IVAP3 (24 km × 24 km), and (h) IVAP4 (48 km × 48 km).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Distributions of wind increment (m s−1) of the single analysis unit test using two different observation operators with various analysis unit sizes. (left) Experiments using the traditional observation operator: (a) RW1 (1 km × 1 km), (c) RW2 (12 km × 12 km), (e) RW3 (24 km × 24 km), and (g) RW4 (48 km × 48 km). (right) Experiments using the improved observation operator: (b) IVAP1 (1 km × 1 km), (d) IVAP2 (12 km × 12 km), (f) IVAP3 (24 km × 24 km), and (h) IVAP4 (48 km × 48 km).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
4. Case study
a. Data processing
Because nonmeteorological echoes can ruin the observation and analysis results, it is essential to process the observation data before assimilation. We processed the radar data using the following methods.
1) Suppression of ground clutters
Ground clutters show nonzero data in areas with zero velocity in low elevation of the radial velocity field (Yu 2006). Therefore, we omit all velocities smaller than 0.5 m s−1.
2) Removal of isolated data
To ensure the consistency of the radar radial field, we removed all isolated observations, around which the number of no-data points was more than N (N = 4 in this study).
3) Exclusion of mass-averaged terminal velocity
4) Data thinning
The mismatch in resolutions between the observation and model coordinate systems imposes a burden for the assimilation system. It is desirable to maximize the compression of radar data while minimizing the degradation of the information content (Alpert and Kumar 2007).
A thinning method is used for the traditional observation operator. The GSI rarefied the high-density radar data to super-obs points, which are comparable to the model’s resolution, to reduce the amount of redundant data. The mean radial velocity and other properties (such as azimuth, range, longitude, latitude, and height) in a box (e.g., 5 km in range, 5° in azimuth, and 0.25° in elevation) were selected to represent all the data as one super-obs point. This thinning method usually introduces interpolation errors and discards a lot of data manually.
For the improved observation operator, the thinning process can be done during the process of calculating the observation parameters. A super-obs point was extended to an analysis unit, and the observation field was segmented into a series of squares (analysis units). All the data within the squares were used by the operator [Eq. (6)].
Figure 3a shows the raw radar radial velocity data at the elevation of 0.5° of the Wenzhou radar. Figures 3b and 3c show the results after different processing procedures; both results correctly represent the observation field. Figure 3b implies that the observed field was divided into many super-obs points, which discarded many observations. However, Fig. 3c shows that the observed radar radial velocity field was divided into many squares, which kept the spatial distribution information about the raw data. The shaded grids in one rectangle formed one analysis unit and were used to calculate the observation parameters, which implied a large amount of data was processed. In addition, the IVAP can filter out short waves (Liang 2007), and therefore it is unnecessary to smooth the pulsation in data processing. The analysis units at the 0.5° elevation of the five radars are shown in Fig. 3d.
Raw radar radial velocity observations and results from different thinning methods of the lowest elevation (0.5°) in Zhejiang province at 1200 UTC 06 Oct 2013. (a) Raw radar data from the Wenzhou radar. (b) Results of the traditional thinning method using the data from only the Wenzhou radar. (c) Results of the improved observation operator with an analysis unit of 12 km × 12 km using the data from only the Wenzhou radar. (d) As in (c), but using the data from all five radars. The location of each radar station (filled star) and the center of Typhoon Fitow (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Raw radar radial velocity observations and results from different thinning methods of the lowest elevation (0.5°) in Zhejiang province at 1200 UTC 06 Oct 2013. (a) Raw radar data from the Wenzhou radar. (b) Results of the traditional thinning method using the data from only the Wenzhou radar. (c) Results of the improved observation operator with an analysis unit of 12 km × 12 km using the data from only the Wenzhou radar. (d) As in (c), but using the data from all five radars. The location of each radar station (filled star) and the center of Typhoon Fitow (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Raw radar radial velocity observations and results from different thinning methods of the lowest elevation (0.5°) in Zhejiang province at 1200 UTC 06 Oct 2013. (a) Raw radar data from the Wenzhou radar. (b) Results of the traditional thinning method using the data from only the Wenzhou radar. (c) Results of the improved observation operator with an analysis unit of 12 km × 12 km using the data from only the Wenzhou radar. (d) As in (c), but using the data from all five radars. The location of each radar station (filled star) and the center of Typhoon Fitow (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
b. Numerical modeling system configuration
The WRF Model was used to conduct the numerical simulations; it is a state-of-the-art atmosphere modeling system designed for both meteorological research and numerical weather prediction (Skamarock et al. 2008; http://www2.mmm.ucar.edu/wrf/users). The prognostic variables in WRF include velocity components u and υ in the Cartesian coordinate, vertical velocity w, perturbation potential temperature, perturbation geopotential, perturbation surface pressure of dry air, and hydrometeors (water vapor mixing ratio, rain/snow mixing ratio, and cloud water/ice mixing ratio).
The model physics of the numerical modeling system include the new Thompson microphysics scheme (Thompson et al. 2008), the new Rapid Radiative Transfer Model for GCMs (RRTMG) shortwave and longwave radiation parameterizations (Iacono et al. 2008), the Yonsei University planetary boundary layer scheme (Hong et al. 2006), the revised Monin–Obukhov mixed-layer scheme (Jiménez et al. 2012), and the unified Noah land surface model (Chen and Dudhia 2001). With a horizontal resolution of 3 km, the cumulus parameterization is turned off (Schwitalla and Wulfmeyer 2014). The same domain as that used in the single analysis unit experiments was used (see Fig. 4). The background of initial conditions and the lateral boundary conditions (LBCs) are from 6-hourly ERA-Interim reanalysis (Dee et al. 2011) at 0.75° resolution.
Analysis and prediction domain at 3-km horizontal resolution, with the best track locations of Typhoon Fitow marked at 1-h intervals from 0600 UTC 6 Oct to 0000 UTC 7 Oct 2013. The locations of the radar stations (filled stars), the maximum Doppler range of the radar data (circle), and the terrain (m, gray shading) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Analysis and prediction domain at 3-km horizontal resolution, with the best track locations of Typhoon Fitow marked at 1-h intervals from 0600 UTC 6 Oct to 0000 UTC 7 Oct 2013. The locations of the radar stations (filled stars), the maximum Doppler range of the radar data (circle), and the terrain (m, gray shading) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Analysis and prediction domain at 3-km horizontal resolution, with the best track locations of Typhoon Fitow marked at 1-h intervals from 0600 UTC 6 Oct to 0000 UTC 7 Oct 2013. The locations of the radar stations (filled stars), the maximum Doppler range of the radar data (circle), and the terrain (m, gray shading) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
GSI, the same as the single analysis unit experiments, was used with the default background error covariance 
c. Experimental setup
To facilitate the comparison of the two operators, we designed three experiments: Exps CTL, RW, and IVAP (Fig. 5). Exp CTL was designed to give the reference results without any radar data, was started at 0600 UTC 6 October 2013 from the ERA-Interim data, and was followed by 18-h forecasts covering the landfall and postlandfall periods of Fitow (2013). Exp RW used the traditional observation operator in Eq. (2), and by cycling assimilated radar data from 0600 to 1200 UTC with an interval of 1 h, 12-h forecasts were then launched from 1200 UTC. Exp IVAP was the same as Exp RW, except it used the improved observation operator in Eqs. (6) and (7). The impacts on the horizontal wind, track, and intensity of Typhoon Fitow were compared and analyzed when using the two observation operators that assimilated radar radial velocity.
Flowchart of Exp CTL and the Exps assimilating radar data via the traditional observation operator (RW) and the improved observation operator (IVAP). Each upward arrow indicates the time when the radar radial velocity is assimilated. A 12-h forecast is carried out, following the final analysis at 1200 UTC 6 Oct 2013 in each Exp.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Flowchart of Exp CTL and the Exps assimilating radar data via the traditional observation operator (RW) and the improved observation operator (IVAP). Each upward arrow indicates the time when the radar radial velocity is assimilated. A 12-h forecast is carried out, following the final analysis at 1200 UTC 6 Oct 2013 in each Exp.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Flowchart of Exp CTL and the Exps assimilating radar data via the traditional observation operator (RW) and the improved observation operator (IVAP). Each upward arrow indicates the time when the radar radial velocity is assimilated. A 12-h forecast is carried out, following the final analysis at 1200 UTC 6 Oct 2013 in each Exp.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
d. Results and discussion
1) Analysis increments
The impacts of radar data during the assimilation cycles were analyzed first. The minimum seal level pressure (MSLP) (Fig. 6a) and the maximum wind speed (MWS) (Fig. 6b) in the ERA-Interim background before the first analysis were about 37 hPa higher and 21 m s−1 weaker, respectively, than the observations from the official best track data from the China Meteorological Administration (CMA). The intensity increased in all three experiments, especially in Exps RW and IVAP, which frequently assimilated radar radial velocities. The MSLP (MWS) error decreased to 35 hPa (−8 m s−1) at the end of the cycles (1200 UTC) in Exp RW, while it decreased to 31.5 hPa (−8 m s−1) in Exp IVAP. It is worth noting that the analysis error in MWS decreased more significantly than that in MSLP. This is because the GSI is a univariate analysis system, and the wind field was updated by directly absorbing radial velocity observations through the data assimilation, while the change in surface pressure is the result of model adjustment to the analysis. In additional, there was larger uncertainty in estimating MSLP errors than in estimating MWS errors (Zhao et al. 2012).
The (a) MSLP and (b) MWS during the data assimilation period from Exps CTL, RW, and IVAP.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The (a) MSLP and (b) MWS during the data assimilation period from Exps CTL, RW, and IVAP.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The (a) MSLP and (b) MWS during the data assimilation period from Exps CTL, RW, and IVAP.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
To better understand the behavior of radar radial velocity assimilation, the horizontal wind component increments at 3-km height after the last analysis cycle were plotted in Fig. 7. For Exp RW, the analysis produced larger wind increments with a much better organized cyclonic structure (Fig. 7a) than that in CTL, which is consistent with the larger decrease in MWS error shown in Fig. 6b. We can see that there were two clear regions with increasing wind speed to the east and south of Wenzhou radar station that were associated with the large negative and positive radial velocities observed, respectively. This is because the wind field was directly updated by ingesting the observed large radial velocity via the traditional observation operator. Similar behavior can be seen in Exp IVAP, except that the magnitude was larger (Fig. 7b). Furthermore, obvious increments can be found in the region between the Wenzhou radar station and the center of the enhanced cyclone, which can be shown more clearly in the next subsection of the analysis of tangential velocity. It is worth noting that the wind direction was orthogonal to the radial direction in this region and that the observed radial velocity was nearly zero. Thus, larger wind increments could be found in Exp IVAP but not in Exp RW, since additional tangential wind information was ingested by the improved observation operator.
Horizontal wind increments at z = 3 km for the last analysis (1200 UTC) from (a) Exp RW and (b) Exp IVAP. The wind speed (shading), the location of the radar stations (filled stars), and the typhoon centers in the background (purple dot) and observation fields (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Horizontal wind increments at z = 3 km for the last analysis (1200 UTC) from (a) Exp RW and (b) Exp IVAP. The wind speed (shading), the location of the radar stations (filled stars), and the typhoon centers in the background (purple dot) and observation fields (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Horizontal wind increments at z = 3 km for the last analysis (1200 UTC) from (a) Exp RW and (b) Exp IVAP. The wind speed (shading), the location of the radar stations (filled stars), and the typhoon centers in the background (purple dot) and observation fields (green dot) are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
2) Radial and tangential velocities of the analysis wind field
Figure 8a shows the observed radial velocity at the lowest elevation (0.5°) of the Wenzhou radar (which was the nearest radar to the typhoon center), with one large negative velocity region to the east and one large positive velocity region to the south, indicating an intense cyclone system. However, two extreme velocity regions appeared to the northeast and southwest of the Wenzhou radar in CTL that were associated with the underestimation of intensity and the southwest position error of the typhoon (Fig. 8b). With the assimilation of radial velocity by the traditional observation operator, a more reasonable distribution of radial velocity was simulated in Exp RW; especially, the extreme velocity regions were reproduced as a result of directly absorbing the large radial velocity observations in these two regions (Fig. 8c). Further improvement of the distribution of radial velocity can be found in the IVAP Exps (Fig. 8d). Reasonable cyclone structure and position were produced as a result, namely, the extreme velocity regions matched the observation much better. These increments (Figs. 8c,d) illustrate that both operators can ingest radar radial data but that the new observation operator is better.
The radial velocity (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity (no vector), (b) Exp CTL, (c) Exp RW, and (d) Exp IVAP. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The radial velocity (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity (no vector), (b) Exp CTL, (c) Exp RW, and (d) Exp IVAP. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The radial velocity (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity (no vector), (b) Exp CTL, (c) Exp RW, and (d) Exp IVAP. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Correspondingly, the tangential wind field (relative to the radar) and its increments are retrieved and plotted in Figs. 9b–d. Figure 9b shows the calculated background tangential velocity as the baseline, and indicates large tangential velocities appeared to the northwest and south of the Wenzhou radar. It is not surprising that small tangential velocity increments in Exp RW can be found (Fig. 9c). On the other hand, larger increments were found to the southeast of the Wenzhou radar in Exp IVAP (Fig. 9d), where the radial velocity was nearly zero (Fig. 9a). The reason is that the tangential velocity information was ingested through spatial distribution characteristics of the radial velocity within the analysis unit, as implied by Eqs. (6) and (7). The increments of tangential velocity were related to the northeast adjustment of the typhoon position and the enhanced intensity in IVAP (Fig. 7b).
The radial and tangential velocities (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity and the background wind field from Exp CTL, (b) the tangential velocity and its direction from Exp CTL, (c) the increment of tangential velocity between Exps RW and CTL, and (d) the increment of tangential velocity between Exps IVAP and CTL. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The radial and tangential velocities (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity and the background wind field from Exp CTL, (b) the tangential velocity and its direction from Exp CTL, (c) the increment of tangential velocity between Exps RW and CTL, and (d) the increment of tangential velocity between Exps IVAP and CTL. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The radial and tangential velocities (shading) and wind field (vector) at the lowest elevation (0.5°) of the Wenzhou radar station at 1200 UTC 6 Oct 2013: (a) observed radar radial velocity and the background wind field from Exp CTL, (b) the tangential velocity and its direction from Exp CTL, (c) the increment of tangential velocity between Exps RW and CTL, and (d) the increment of tangential velocity between Exps IVAP and CTL. The location of the radar station (filled star) and the approximate center of the observed typhoon (green dot), near the lower-right corner of each panel, are indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
3) Typhoon structures
Figure 10 shows the sea level pressure and surface wind speed from CTL after a 6-h integration, and from Exps RW and IVAP after the last analysis cycle (at 1200 UTC 6 October 2013). The intensity was too weak in CTL, with an MSLP bias of 37 hPa and an MWS bias of −21 m s−1 compared to the CMA best track data. Fitow was significantly stronger in Exps RW and IVAP, with the MWS error reduced to about −8 m s−1 in both experiments. The vortex in Exp IVAP was the strongest with larger wind speed north of the typhoon center and with the MSLP error reduced to about 31.5 hPa (Fig. 10).
Analyzed SLP (thick solid contour, hPa), surface wind speed (shading, m s−1), and wind barbs for Typhoon Fitow at 1200 UTC 6 Oct 2013 from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP. The approximate center of the observed typhoon (black dot), near the domain center in each panel, is indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Analyzed SLP (thick solid contour, hPa), surface wind speed (shading, m s−1), and wind barbs for Typhoon Fitow at 1200 UTC 6 Oct 2013 from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP. The approximate center of the observed typhoon (black dot), near the domain center in each panel, is indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Analyzed SLP (thick solid contour, hPa), surface wind speed (shading, m s−1), and wind barbs for Typhoon Fitow at 1200 UTC 6 Oct 2013 from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP. The approximate center of the observed typhoon (black dot), near the domain center in each panel, is indicated.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The azimuthally averaged vertical structure of the typhoon is presented in Fig. 11. In CTL, the vortex was weak and broad (Fig. 11a), characterized by a large radius of maximum tangential wind (RMTW, relative to the typhoon center) of ~170 km (contours in Fig. 11a) and by a weak warm core at 350 hPa (contours in Fig. 11d). These deficiencies were improved in Exps RW and IVAP, with the RMTW reduced to 130 and 100 km, respectively. Although the RMTW was still too large [this value was only about 28 km in the Joint Typhoon Warning Center (JTWC) best track data; http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/wpindex.php], the improvements can be found when ingesting radial velocity in both Exps RW and IVAP. With the improvement of vortex strength, the stronger absolute vorticity can be found near the typhoon eye, especially in Exp IVAP. Thus, larger reflectivity was produced that was associated with stronger vertical convection. These results illustrate that assimilating radial velocity can improve typhoon circulation when using the tradition observation operator, which is consistent with the findings of existing studies (e.g., Zhao and Jin 2008; Zhao et al. 2012), and that the improvements are more significant when using the improved observation operator.
Azimuthally averaged tangential wind (contours with 2.5 m s−1 intervals) and reflectivity (shading, dBZ) from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP at 1200 UTC 6 Oct 2013; (d)–(f) as in (a)–(c), but for horizontal temperature deviation (contours with 1.0°C intervals) and absolute vorticity (shading, 10−5 s−1).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Azimuthally averaged tangential wind (contours with 2.5 m s−1 intervals) and reflectivity (shading, dBZ) from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP at 1200 UTC 6 Oct 2013; (d)–(f) as in (a)–(c), but for horizontal temperature deviation (contours with 1.0°C intervals) and absolute vorticity (shading, 10−5 s−1).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Azimuthally averaged tangential wind (contours with 2.5 m s−1 intervals) and reflectivity (shading, dBZ) from (a) Exp CTL, (b) Exp RW, and (c) Exp IVAP at 1200 UTC 6 Oct 2013; (d)–(f) as in (a)–(c), but for horizontal temperature deviation (contours with 1.0°C intervals) and absolute vorticity (shading, 10−5 s−1).
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
4) Track and intensity predictions
The predicted typhoon track, MSW, and MSLP from Exps CTL, RW, and IVAP are compared with the best track data in Fig. 12 for the 12-h forecast period from 1200 UTC 6 October to 0000 UTC 7 October 2013. As shown in Fig. 12a, the predicted typhoon in CTL moved southward along the coastline, made landfall at about 120 km southwest of the observed landfall site, and then moved westward and disappeared 5 h later. The 12-h mean track error in CTL was about 75.9 km, and the track error increased obviously after 1600 UTC (2 h before the landfall; Fig. 12b). After assimilating the radial velocity in Exp RW using the traditional observation operator, the 12-h mean track error decreased to about 59.7 km, with clearly shown track improvement in Fig. 12a. By using the improved observation operator, Exp IVAP gave the best results, with the predicted typhoon track being the closest to the best track data (Fig. 12a) and the smallest 12-h mean track error of 46.1 km. Note that the error forecasted by the CMA’s operational NWP system (T639 model, whose resolution is about 50 km) was 82.1 km. These results indicate that the assimilation of radar radial velocity had a positive impact on the track forecast and demonstrate the superiority of the improved observation operator.
The 12-h predicted (a) tracks, (b) track errors, (c) MSLP (hPa), and (d) MWS (m s−1) of Typhoon Fitow from 1200 UTC 6 Oct to 0000 UTC 7 Oct 2013.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The 12-h predicted (a) tracks, (b) track errors, (c) MSLP (hPa), and (d) MWS (m s−1) of Typhoon Fitow from 1200 UTC 6 Oct to 0000 UTC 7 Oct 2013.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
The 12-h predicted (a) tracks, (b) track errors, (c) MSLP (hPa), and (d) MWS (m s−1) of Typhoon Fitow from 1200 UTC 6 Oct to 0000 UTC 7 Oct 2013.
Citation: Monthly Weather Review 145, 10; 10.1175/MWR-D-17-0002.1
Figure 12c compares MSLPs in Exps CTL, RW, and IVAP. Although there were still large overestimations of MSLP, Exp IVAP reproduced an MSLP of about 980 hPa, which was better than both Exps CTL and RW; specifically, Exp IVAP reduced the error by about 11 and 5.3 hPa compared to Exps CTL and RW, respectively. Greater improvement can be found in MWS (Fig. 12d). The MWS in CTL reached only about 35 m s−1, while the MWS in Exps RW and IVAP increased to about 38 and 43 m s−1, respectively; the observation was 42 m s−1. These improvements can be attributed to the improved vortex intensity and structure in the radar radial wind assimilation experiments, especially when using the improved observation operator.
5. Summary and perspectives
In this study we examined an improved observation operator for Doppler radar radial velocity assimilation using the GSI system and compared it with the traditional observation operator. Unlike the traditional observation operator, which simply ingests radial information about super-obs points, the new observation operator has the following advantages. It assimilates observation parameters calculated from radial velocity and its spatial distribution characteristics, and it can then provide information about tangential wind, which cannot be observed by the radar directly. In addition, the improved observation operator uses a uniform area in both radar and model coordinates; therefore, it has a thinning function without inducing interpolation errors and retains the spatial distribution information about the raw data.
The performance of the improved observation operator was evaluated through single analysis unit experiments using different analysis units. The distribution of the analysis wind increment of the improved operator became closer to the observation as the analysis unit became wider, as a result of ingesting more wind information into the assimilation system. Note that the traditional observation operator can be treated as a specific form of the improved observation operator when the analysis unit becomes small.
Three experiments, based on the WRF Model, were carried out in a case study of Typhoon Fitow (2013), to examine the performances of different operators in assimilating Doppler radial velocity. The increments of the results indicated that both operators had the capability to ingest radial velocity information and produced a more reasonable typhoon structure, which was weak in Exp CTL. The radial and tangential velocities were further improved by using a new observation operator in Exp IVAP. Because of the improved vortex intensity and structure, greater improvements were realized in the track and intensity predictions of the 12-h forecast.
All of these analyses indicated that the improved observation operator had good performance in Doppler radar radial velocity assimilation. However, there are still problems that need further research. The vertical wind component should be considered and its effects should be examined in case studies. From the results of the single analysis unit experiments, we noticed that different analysis unit sizes could affect the analysis results. In this study, the analysis unit size was chosen to be 4 times the model resolution. However, if the analysis unit size is too small relative to the model grid size, then limited tangential information can be obtained. When the analysis unit size becomes too big, a lot of detailed information about mesoscale or convective-scale weather events will be abandoned. The impact of the size of the analysis unit on a real case should be investigated in future research.
Acknowledgments
This work is supported by the National Basic Research Program of China (Grant 2013CB430101), the National Natural Science Foundation of China (Grant 41375056), the National Key Technology R&D Program of China (Grant 2012BAC22B02), and the National Innovation Project for Meteorological Science and Technology of China: Quality Control, Fusion, and Reanalysis of Meteorological Observations. We gratefully acknowledge Prof. Fang Zhao and Prof. Chunxiao Ji for providing the radar data, Dr. Meiyin Dong for assistance in data analysis, and Dr. Zuojun Yu for English editing. The initial and lateral boundary conditions used in the model are from the ECMWF (http://apps.ecmwf.int/datasets/data/interim-full-daily), and the raw radar data are obtained from the Zhejiang Meteorological Bureau.
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