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  • View in gallery

    Time series of the minimum central pressures (PMIN; hPa) and the maximum surface winds (VMAX; m s−1) of Typhoon Megi (2010) from the best-track analysis.

  • View in gallery

    Model outer domain of the observed tracks of Typhoon Megi (black) and the WRF 3-day forecasts that are initialized at 0000 UTC 17 Oct (light blue), 0000 UTC 18 Oct (red), 1200 UTC 18 Oct (purple), and 0000 UTC 19 Oct 2010 (dark blue). The tracks near the Philippines are magnified at the top right corner.

  • View in gallery

    As in Fig. 1, but for the observed maximum surface wind (dashed) and the 3-day maximum 10-m wind forecast (solid) that are initialized at (a) 0000 UTC 17 Oct, (b) 0000 UTC 18 Oct, (c) 1200 UTC 18 Oct, and (d) 0000 UTC 19 Oct 2010.

  • View in gallery

    LETKF wind analysis increments (blue barbs) and observed wind increments (black barbs) valid at 1200 UTC 18 Oct 2010 for four 30-hPa layers centered at (a) 750-, (b) 300-, (c) 250-, and (d) 200-hPa levels.

  • View in gallery

    (a) The ensemble mean track forecast (crossed solid), CTL track forecast (circled solid), best track (starred dashed), and individual member tracks (thin) for Typhoon Megi initialized at 0000 UTC 18 Oct; (b) time series of the maximum 10-m wind for 21 ensemble members (thin solid), the ensemble mean (thick solid), and the observed maximum surface wind (dashed); and (c) as in (b), but for minimum sea level pressure.

  • View in gallery

    As in Fig. 5, but for the forecast cycle at 1200 UTC 18 Oct.

  • View in gallery

    Height–time diagram of the environmental steering flows area-averaged within the domain of 10°–25°N, 110°–125°E for the (left) CTL run and (right) assimilation of all CIMSS satellite wind. The dashed lines denote the interval in which CTL forecast starts to deviate from the observation.

  • View in gallery

    Geopotential height at 500-hPa level for the (left) CTL run and (right) assimilation of satellite wind (ASW) experiment valid at (a) 1200 UTC 19 Oct, (b) 1800 UTC 19 Oct, and (c) 0000 UTC 20 Oct 2010. Superimposed are the wind barbs at the corresponding level.

  • View in gallery

    Time series of the area-averaged (1000 km × 1000 km) storm-following vertical shear vectors between 200 and 850 hPa for the CTL experiment (solid) and FAA experiment (dashed).

  • View in gallery

    (left) The CIMSS AMV 800–300-hPa satellite wind (black vectors) assimilated in the LAA experiment, and (right) the 300–80-hPa satellite wind used in the UAA experiment. Superimposed are the background wind vectors (green) that are averaged within the same layer.

  • View in gallery

    Megi’s ensemble mean track (+), the best track (×), and the deterministic track (circles) for the experiments with assimilation of the (left) lower-level AMV winds and (right) upper-level winds that are initialized at 1200 UTC 18 Oct. Gray thin lines denote individual member tracks in each corresponding experiment.

  • View in gallery

    As in Fig. 8, but for the (left) LAA and (right) UAA experiments.

  • View in gallery

    As in Fig. 5, but for the experiments that assimilate the (left) low-level AMV winds and (right) upper-level wind.

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Sensitivity of the Track and Intensity Forecasts of Typhoon Megi (2010) to Satellite-Derived Atmospheric Motion Vectors with the Ensemble Kalman Filter

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  • 1 Laboratory for Weather and Climate Forecasting, Hanoi College of Science, Vietnam National University, Hanoi, Vietnam
  • | 2 Department of Meteorology, Hanoi College of Science, Vietnam National University, Hanoi, Vietnam
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Abstract

In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development.

Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills.

Corresponding author address: Dr. Chanh Kieu, I. M. Systems Group at NOAA/NWS/NCEP/EMC, Camp Springs, MD 20746. E-mail: chanh.kieu@noaa.gov

Abstract

In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) University of Wisconsin satellite atmospheric motion vector (AMV) dataset are examined. Assimilation of the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in the Weather Research and Forecasting model shows that the AMV data can significantly improve the track forecast of Typhoon Megi, especially the sharp turn from west-northwest to north after crossing the Philippines. By broadening the western Pacific subtropical high to the west, the AMV data can help reduce the eastward bias of the track, thus steering the storm away inimical shear environment and facilitating its subsequent development.

Further sensitivity experiments with separated assimilation of the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds reveal that, despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecasts tend to be more sensitive to the low-level than to the upper-level wind observations. This indicates that the far-field low-level observations can improve the large-scale environmental flow that storms are to move in, giving rise to a better representation of the steering flow and subsequent intensity change. While much of the recent effort in tropical cyclone research focuses on inner-core observations to improve the intensity forecast, the results in this study show that the peripheral observations outside the storm center could contribute considerably to the intensity and track forecasts and deserve attention for better typhoon forecast skills.

Corresponding author address: Dr. Chanh Kieu, I. M. Systems Group at NOAA/NWS/NCEP/EMC, Camp Springs, MD 20746. E-mail: chanh.kieu@noaa.gov

1. Introduction

It is well known that tropical cyclone (TC) motion is largely determined by environmental steering flows (see, e.g., Chan and Gray 1982; Holland 1984; Carr and Elsberry 1995; Berger et al. 2011). Numerous modeling studies with barotropic models showed that reasonably accurate TC tracks could be obtained without details of the inner-core dynamics (Aberson and DeMaria 1994). Despite such prevailing control of the large-scale slow manifold environment, forecasting TC tracks is still a challenging problem because of the multiple interactions of TCs with their surrounding environment.

In general, there are several factors that govern the TC movement including environmental flows, the beta effect, vertical wind shear, or topography interaction. These factors are apparent in the western North Pacific (WPAC) basin where multiscale interactions of TCs with ambient flows and the topography nearby result in substantially larger track errors as compared to the track errors in the North Atlantic basin (see, e.g., Pike and Neumann 1987; Carr et al. 2001; Payne et al. 2007). Recent studies by Brown et al. (2010) and Kehoe et al. (2007) showed that the most significant sources of typhoon (TY) track errors in the WPAC are related to the large-scale interaction and/or direct vortex–vortex interaction, with the 3-day forecast errors as large as 500 km in some cases.

Because of multiple sources of uncertainties in TC models, a single deterministic forecast is in general not capable of capturing the most accurate track. Because of this, ensemble TC forecasts in which either an ensemble of realizations of initial/boundary conditions or a set of forecasting models are used have been recently considered as the most useful approach in providing a more reliable picture of the TC track forecasts and uncertainties. A vast number of studies have demonstrated that the use of the ensemble approach is beneficial to the TC track and intensity forecast (e.g., Neumann and Pelissier 1981; Zhang and Krishnamurti 1997; Krishnamurti et al. 1997, 1999; Goerss 2000, 2007; Elsberry and Carr 2000; Aberson 2001). Current advances in the ensemble data assimilation, especially the ensemble Kalman filter (EnKF), offer even more opportunities to look into the predictability of the TC forecasts. Specifically, the EnKF algorithm provides not only an optimum set of initial conditions but also the propagation of the forecast error covariance.

Although much of the current TC research is shifting more toward improving intensity forecasts, the current 3-day forecast track error is still roughly 270 km according to the official average track errors by the National Hurricane Center. The fact that the TC movement is strongly influenced by environmental flow suggests that continuous inclusion of various satellite datasets should have positive impacts on the TC track forecast skill. With a sparse observing network over the WPAC basin, the satellite data are an especially valuable source of information for improving the environmental flows that could help increase the accuracy of the TC track forecast and the related intensity forecast skill. A recent study on the impacts of the satellite-derived atmospheric motion vector (AMV) data on the TC tracks by Berger et al. (2011) showed that assimilation of the hourly AMVs could reduce the track errors significantly for lead times beyond 3 days in the WPAC basin. At the shorter time scale, their results are nonetheless inconclusive because of the lack of statistical significance. Given the potential benefit of the AMV data over the “targeted observations” as emphasized by Berger et al. (2011), it is of importance to examine further various influences of the AMV data on the TC track and intensity forecast skills.

In this study, the impacts of the AMV data that are postprocessed by the Cooperative Institute for Meteorological Satellite Studies University of Wisconsin (CIMSS-UW) on the track forecast of Typhoon Megi (2010) will be examined, using an efficient variant of the ensemble Kalman filtering, the so-called local ensemble transform Kalman filter (LETKF; Hunt et al. 2007). The case of Typhoon Megi (2010) is interesting, as it experienced a sharp turn to the north after making landfall over the Philippines. Numerical models tend to have some difficulty in forecasting its track (Peng et al. 2011), thus this case offers a good opportunity to validate the importance of the AMV data and efficiency of the LETKF algorithm. Previous studies of LETKF showed that this Kalman filtering scheme has potential to be a realistic choice for various global/regional implementations (Hunt et al. 2007; Miyoshi and Yamane 2007; Li et al. 2009). Thus, the LETKF algorithm is chosen in this study to examine the role of the CIMSS AMV data in the forecasts of TY Megi.

The rest of this paper is organized as follows. In the next section, an overview of TY Megi is provided. The model experiments are discussed in section 3. Sections 4 and 5 present results from deterministic and ensemble forecasts as well as sensitivity experiments with the AMV wind. Some concluding remarks are given in the final section.

2. Overview of Typhoon Megi

Typhoon Megi (2010) was one of the most intense tropical cyclones on record in the WPAC basin, reaching the minimum sea level pressure of 885 hPa and the 10-min sustained surface wind of 63 m s−1 (Fig. 1). It was the first typhoon of the 2010 season in the WPAC basin to achieve supertyphoon status. Megi emerged from an area of disturbed weather around 0000 UTC 12 October 2010, about 600 km to the east of the Philippine archipelago. The system developed quickly throughout the day, and the Joint Typhoon Warning Center (JTWC) classified the system as a tropical depression near 0900 UTC 13 October. Because of the strong influence by the western Pacific subtropical high (WPSH), the system moved slowly west-northwest toward the Philippines, and the depression subsequently intensified into a tropical storm around 1200 UTC 13 October. Late on 13 October and for the next 24 h, Megi became a quasi-stationary tropical storm with a central dense overcast that developed over the center of Megi, allowing for its further intensification. An eye appeared on satellite imagery near 0000 UTC 16 October, resulting in the JTWC upgrading Megi to typhoon status.

Fig. 1.
Fig. 1.

Time series of the minimum central pressures (PMIN; hPa) and the maximum surface winds (VMAX; m s−1) of Typhoon Megi (2010) from the best-track analysis.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

The storm moved generally west-northwestward along the southern periphery of the WPSH and underwent significant intensification along the track because of highly favorable conditions for development (warm sea surface temperatures >28°C along the entire track). Other favorable environmental conditions included low vertical wind shear, significant upper-level divergence, and poleward outflow. When it made landfall over the Philippines on 18 October, it became one of the strongest tropical cyclones recorded to make landfall. It weakened to a category 2 after traversing Luzon, but rapidly regained strength over SSTs of 30°C in the South China Sea, strengthening back to a category 4 on early 19 October.

Megi slowed in forward speed because of the arrival of a trough over central China that extended over the South China Sea and caused the existing subtropical ridge to a break. Because of the strong influence of the trough and the subtropical high over the South China Sea, Megi experienced a sharp turn around 0000 UTC 19 October and subsequently tracked north-northeast. It weakened to category 3 on 20 October as vertical wind shear increased. Because of colder SSTs, Megi weakened to category 1 on 22 October and lost its structure before making landfall in the Fujian Province, China. It later weakened to a tropical storm on 23 October and by early 24 October it had further weakened into a tropical depression before dissipating completely several hours later. The evolutions of the maximum 10-m wind and minimum sea level pressure during the entire life cycle of Megi are provided in Fig. 1.

3. Experiment descriptions

a. Model

To capture the storm-scale dynamics within our computational capability, forecasts of TY Megi (2010) are configured with a two-way interactive, movable, double-nested (36/12 km) grid version of the nonhydrostatic Weather Research and Forecasting (WRF) model (V3.2) in this study. Because of the high demand of computational and storage resources for ensemble experiments, the nested-grid domains have 31σ levels in the vertical, and the (x, y) dimensions of 155 × 155 and 151 × 151 grid points for the 36- and 12-km domains, respectively. The outer model domain covers an area of ~5600 km × 5600 km, which is centered in the South China Sea, to the east of Vietnam (Fig. 2). Although the model configuration with the finest resolution of 12 km is not optimal as compared to the current operational hurricane forecast setups, the main aim of this study is to examine the sensitivity of Megi’s forecast to the CIMSS AMV dataset and the above double-nested configuration is expected to be sufficient for the sensitivity investigation.

Fig. 2.
Fig. 2.

Model outer domain of the observed tracks of Typhoon Megi (black) and the WRF 3-day forecasts that are initialized at 0000 UTC 17 Oct (light blue), 0000 UTC 18 Oct (red), 1200 UTC 18 Oct (purple), and 0000 UTC 19 Oct 2010 (dark blue). The tracks near the Philippines are magnified at the top right corner.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

The model microphysics schemes used in the deterministic track forecasts include (i) a modified version of the Betts–Miller–Janjic (BMJ) scheme cumulus parameterization scheme for the 36- and 12-km-resolution domains in which deep convection and a broad range of shallow convection are both parameterized, (ii) the Yonsei University planetary boundary layer parameterization with the Monin–Obukhov surface layer scheme, and (iii) the Rapid Radiative Transfer Model scheme for both longwave and shortwave radiations with six molecular species. For the ensemble experiments, the entire spectra of the microphysics, radiative, and boundary layer parameterization schemes are used as a way to take into account the model internal errors associated with the inadequate representation of physical processes in the WRF model.

b. Data

The model initial and lateral boundary conditions for the ensemble and deterministic forecasts are taken from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) operational forecast with a resolution of 1° × 1°. The forecasted period is from 0000 UTC 17 October to 0000 UTC 21 October 2010 during which Megi was the most active with a near-90° direction change from west-northwest to north around 1200 UTC 20 October near the Philippines.

The boundary conditions are updated every 6 h with no bogus vortex. Despite its low resolution, the GFS forecast appears to capture marginally a mesoscale cyclonic flow as early as 1200 UTC 16 October 2010 as compared to satellite images (not shown). However, the subsequent GFS forecasts could not follow the development of Megi until 0300 UTC 19 October when Megi reached its near-peaked intensity. Thus, higher-resolution forecasts with the WRF model are needed to better resolve Megi’s track and intensity changes.

For the observational data used in the LETKF experiments, the AMV data postprocessed by CIMSS-UW during the same period are chosen. A number of studies with the CIMSS AMV data showed that this dataset could help improve the forecast quality of various mesoscale systems (see, e.g., Velden et al. 2005; Cherubini et al. 2006; Bedka and Mecikalski 2005; Berger et al. 2011). The main advantage of the CIMSS AMV data is that the observational errors have been highly quality controlled and calibrated using the recursive filter algorithm. Each data point is checked for the overall consistency with the surrounding data using the quality-indicator technique. If the wind data at any point have a low-quality-indicator analysis score (<65), this data point is eliminated during the quality control process. For those data points whose quality-indicator score satisfies the selection criteria, expected errors available in the dataset are assigned properly. Details of the quality-indicator technique can be found in Velden et al. (2005) and Berger et al. (2007). The whole CIMSS AMV dataset is categorized into different regions and is currently supported in several data formats including ASCII and/or BUFR (more details of the CIMSS AMV dataset can be found at http://tropic.ssec.wisc.edu). In this study, only the northwestern Pacific dataset is used in the ensemble experiment, as the coverage of this dataset is sufficiently broad to take into account the environmental influence on Megi’s track.

It should be noted that while most of the satellite sources are supposed to be included at each of the GFS analysis cycles before the cutoff limit, we notice that the initial characteristics of the environmental flows and associated mesoscale structures depend sensitively on how effective an assimilating scheme is as well as the scale of the circulations (Nguyen and Chen 2011; Zhang and Krishnamurti 1999). Because the GFS employs a global variational assimilation scheme, it is anticipated that reassimilation of the CIMSS AMV dataset with the LETKF algorithm can help enhance some detailed mesoscale features of Megi that are not seen from the GFS products. Note also that the postprocessing component of the WRF model that interpolates the GFS coarse-resolution (1° × 1°) fields to the higher-resolution domain (12 km) may discard some initial important information during the interpolation steps. As a result, the assimilation of the AMV wind data is expected to restore the loss of information during the interpolation as well.

c. Ensemble Kalman filter

For this study, the LETKF scheme has been implemented in WRF V3.2 (referred to as the WRF-LETKF system) to examine the sensitivities of the track and intensity forecasts of TY Megi to the AMV wind. Recent studies with LETKF have demonstrated that this ensemble filtering scheme is capable of handling a wide range of scales and observation types (see, e.g., Hunt et al. 2007; Szunyogh et al. 2008; Li et al. 2009). The main advantage of LETKF is that it allows the analysis to be computed locally in the space spanned by the forecast ensemble members at each model grid point, which greatly reduces the computational cost and facilitates the parallel computation effectively.

The idea of the LETKF algorithm is to use a background ensemble matrix as a transformation operator from a model space spanned by the grid points within a local patch to an ensemble space spanned by the ensemble members, and to perform the analysis in this ensemble space at each grid point. If the background ensemble and a set of observations yo are given, the LETKF update step can be recapitulated as follows (see Hunt et al. 2007):
e1
where is the background ensemble mean, is the ensemble perturbation matrix that serves as a transformation matrix from the model space to the ensemble space, and and are the local ensemble mean analysis vector and analysis error covariance in the local space. By minimizing the local cost function and assuming the square root filter, and can be obtained locally as follows:
e2
e3
where is the background ensemble matrix valid at the observed locations, is the observational error covariance matrix, and is the identity matrix. Detailed derivations as well as different treatments for handling more general nonlinear and synchronous observations in the LETKF algorithm can be found in Hunt et al. (2007).

The spurious cross correlations in the LETKF algorithm are reduced by employing a homogeneous covariance localization with a horizontal scale of 800 km such that far-field observations outside the TC main circulation will have minimum impacts on the TC inner region. The local volume consists of (9 × 9) grid points in the (x, y) direction and has a vertical extent of 0.2 (in σ coordinate). To take into account the model errors, multiple physical parameterizations and a multiplicative inflation factor of 1.2 are employed. Previous studies by Fujita et al. (2007) and Meng and Zhang (2007) showed that such use of the multiple physics ensemble could improve the performance of the EnKF significantly for mesoscale systems. So, this multiple physics approach is adopted in this study along with the covariance inflation method.

Because of computational limitations, experiments with a fixed number of 21 ensemble members are used in all ensemble experiments in this study. The cold-start background ensemble members are initialized by first adding a random noise with standard deviations of 3 m s−1 for wind, 3 K for temperature, and 3 × 10−3 kg kg−1 for specific humidity into the GFS data 12 h earlier. These randomly added backgrounds are next integrated for 12 h. The outputs from these 12-h integrations are then used as the backgrounds for the ensemble assimilation of the AMV winds valid at the end of these 12-h integrations.

As a step to orient the WRF-LETKF system to be more consistent with the WRF data assimilation (WRFDA) system, all of the observations are subject to further quality control by the WRF three-dimensional variational data assimilation (3DVAR) component before being assimilated. For the lateral boundary conditions, a utility provided in the WRFDA system is used to update the boundary condition separately for each ensemble member once the analysis update for that member is obtained. By doing this, each ensemble member possesses its own boundary that is dynamically consistent with its own analysis.

d. Experiments

For preliminary assessment of the deterministic track and intensity forecasts of TY Megi with the WRF model, four 3-day forecasts that are initialized at 0000 UTC 17 October, 0000 UTC 18 October, 1200 UTC 18 October, and 0000 UTC 19 October 2010 are first attempted. This set of experiments is used to probe the general predictability of Megi’s movement during its most destructive periods over the Philippines as well as the capability of the WRF configuration in capturing the sharp turn of Megi to the north near 0000 UTC 20 October.

Next, the cycles that have 3-day track forecast error greater than 400 km (i.e., the cycles of 0000 and 1200 UTC 18 October, as will be seen in the next section) are chosen as control (CTL) experiments for subsequent comparisons with the ensemble forecasts. Upon obtaining the CTL runs, three ensemble experiments are then conducted for the same cycles as in the CTL experiments. In the first ensemble experiment (FAA), the entire AMV dataset is assimilated to examine how beneficial the AMV data are as compared to the CTL experiments. In the other two ensemble experiments, the AMV dataset is separated into an upper (>300 hPa) and a lower (1000–300 hPa) subset; the upper (UAA) and lower (LAA) ensemble experiments assimilate the AMV wind in each corresponding layer, respectively. These experiments study how the track forecasts depend on the representation of the steering layers versus the upper-level control of the environment. Previous studies have demonstrated the importance of the low- to midlevel steering flow in determining the TC track, but the relative importance of the upper-level wind to TCs, particularly to strong TCs, is still elusive (Chan and Gray 1982; Carr and Elsberry 1995; Wu and Cheng 1999). The studies of Wu and Emanuel (1995) and Wu and Cheng (1999) suggested that the upper levels could indeed be important in contributing to the TC movement. So, it is of interest to see the relative importance of the satellite-enhanced upper- and low-level flows to the forecast of Megi.

4. Results

a. Deterministic experiments

Figure 2 shows the 72-h deterministic track forecasts of Megi that are initialized at 0000 UTC 17 October, 0000 UTC 18 October, 1200 UTC 18 October, and 0000 UTC 19 October. One notices first that except for the initialization at 0000 UTC 17 October, in which the track forecast shows a good consistency with the best-track analysis, these cycles display a fairly poor performance beyond the 2-day lead time. Even though the forecasts initialized at 0000 and 1200 UTC 18 October can both capture the sharp turn of Megi around 0000 UTC 20 October, the forecasted tracks have a substantially large bias to the east of the best track with an averaged 72-h error of roughly 410 km for the 0000 UTC 18 October cycle and 405 km for the 1200 UTC 18 October cycle. Such a strong eastward bias has in fact been observed in several global forecasting models initialized at those cycles (see Peng et al. 2011), which could be attributed partly to the abnormally strong northwesterly flows associated with the trough over mainland China. A recent study by Brown et al. (2010) showed that most of the cases with large track errors in the WPAC basin are related to either strong interaction with the subtropical trough or direct vortex–vortex interaction. As there was no nearby vortex during the forecasted period, the large eastward bias as seen in the experiments initialized after 0000 UTC 18 October is therefore connected somehow to the larger-scale environmental flows.

Regarding the intensity forecast, it is seen in Fig. 3 that the model forecasts show some underestimation of the storm intensity in all experiments, especially for the first 12 h during which the spinup of the incipient vortex occurs. Such large intensity forecast errors during the initial time are common among all real-time cycles but most apparent for the first two cycles, for which the maximum surface winds (VMAX) differences between the GFS forecasts and observations are as large as 25 m s−1 (Figs. 3a and 3b). Unless a bogussed vortex is implanted, the weak vortex representation is an inherent characteristic in the GFS forecasts due to its coarse resolution. Therefore, it takes some time for the model vortex to adjust to the surrounding environment before it can develop its consistent dynamics. Of course, the low intensity forecast skill in the deterministic forecasts is also due to the coarse resolution (12 km) of the innermost domain, which does not allow for detailed TC mesoscale processes to be captured properly. However, that the large intensity errors take place mostly during the first day into integration indicates that the poorly initialized vortex is the dominant factor. It is of interest to note that although the intensity is not well forecasted, the intensity tendency shows a better fit with observations. Apart from the incipient swift spinup, all cycles capture to some extent the intensifying as well as the weakening phases of Megi despite their large track errors.

Fig. 3.
Fig. 3.

As in Fig. 1, but for the observed maximum surface wind (dashed) and the 3-day maximum 10-m wind forecast (solid) that are initialized at (a) 0000 UTC 17 Oct, (b) 0000 UTC 18 Oct, (c) 1200 UTC 18 Oct, and (d) 0000 UTC 19 Oct 2010.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Because the cycles of 0000 and 1200 UTC 18 October have the largest 3-day track errors, these two initializations are hereinafter selected as the CTL experiments for which ensemble forecasts with assimilation of the CIMSS AMV dataset are further conducted to examine the sensitivities of Megi’s forecasts to the AMV data.

b. Ensemble experiments

To investigate next the large-scale mismatch between the initial GFS data and satellite observations, Fig. 4 shows observational increments of the wind vectors, which are differences between the observed AMV wind vectors and the GFS background wind vectors. One can notice that more than 80% of the observation points are nested at the upper levels (above z = 10 km), while the remaining are distributed sparsely within a thick low to middle layer. Of significance is that most of the data points, especially at the low levels, scatter in the periphery and to the south of Megi along the tropical belt. Such irregular data distribution is because the retrieval of the AMV dataset is based on the satellite top cloud motion, which is often hard to calculate near the TC center because of the dense coverage of the TC-related cloud overcast. The wind increments in Fig. 4 show that the GFS initial data apparently tend to underestimate the cyclonic circulation induced by Megi (which is illustrated in Fig. 4 as a prevailing distribution of the cyclonic wind increments over the entire domain). This is consistent with the fact that the vortex representation of Megi interpolated directly from the global GFS input is not able to capture the realistic strength of Megi initially, which later affects Megi’s track and intensity forecasts. While such underrepresentation of Megi’s cyclonic circulation is observed at all four initializations (not shown), it is most severe when Megi became sufficiently strong after 0000 UTC 18 October.

Fig. 4.
Fig. 4.

LETKF wind analysis increments (blue barbs) and observed wind increments (black barbs) valid at 1200 UTC 18 Oct 2010 for four 30-hPa layers centered at (a) 750-, (b) 300-, (c) 250-, and (d) 200-hPa levels.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Despite the relatively small number of members in the ensemble experiment, the analysis wind increments obtained from the WRF-LETKF system compare well with observations in both magnitude and direction (Fig. 4). For both cycles, the analysis captures consistently the cyclonic enhancement at all levels. Note here that because of the irregular distribution of the observation data with much denser data density at the upper levels, the covariance localization scale cannot be set too large (~800 km in all ensemble experiments) to prevent the influence of observation from smearing out too far. Also, the cross correlation between the wind vectors and other state variables contains a significant portion of artificial noise due to the small number of model realizations. As a result, the analysis increments are essentially restricted within the neighborhood of each observation point.

Figures 5 and 6 show the ensemble track forecasts initialized at 0000 and 1200 UTC 18 October with the entire AMV dataset assimilated at the beginning of the cycles. Overall, there is some considerable improvement in the track forecast with both the sharp turn at 1200 UTC 20 October and the translational speed of Megi well captured; the 3-day track forecast error reduces from 410 km in the deterministic forecast (Fig. 2b) to ~350 km in the ensemble forecast (Fig. 5) for the 0000 UTC 18 October cycle and from 405 km (Fig. 2c) to ~160 km for the 1200 UTC 18 October cycle (Fig. 6). Note that while the track error is reduced for the 0000 UTC 18 October cycle, the observed track is outside the spread of the ensemble members after 48 h. This signifies that the ensemble is drifting away from the true state and therefore could no longer encompass the model states properly. The situation is much improved for the 1200 UTC 18 October cycle for which the best track is well within the ensemble spread at the later times. Such difference forecast skill between two consecutive cycles is common because of differences in environmental flows and in the coverage and quality of observational data at different times, especially around the point where the sharp change in the storm track occurs.

Fig. 5.
Fig. 5.

(a) The ensemble mean track forecast (crossed solid), CTL track forecast (circled solid), best track (starred dashed), and individual member tracks (thin) for Typhoon Megi initialized at 0000 UTC 18 Oct; (b) time series of the maximum 10-m wind for 21 ensemble members (thin solid), the ensemble mean (thick solid), and the observed maximum surface wind (dashed); and (c) as in (b), but for minimum sea level pressure.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Fig. 6.
Fig. 6.

As in Fig. 5, but for the forecast cycle at 1200 UTC 18 Oct.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Although these track forecast improvements could be case-dependent, the track improvement at the later cycle clearly highlights some noticeable changes of the ambient environmental flows that the AMV dataset has helped improve. From the physical point of view, the track improvements are quite intriguing since the AMV winds are mostly at the upper levels rather than within the low- to midlevel (Fig. 4), which is often considered to be the main steering layer for the storm movement.1 The relative importance of the lower- and upper-level AMV wind will be examined in the next section. In this section, we focus instead on the physical roles of the AMV observations in improving Megi’s forecasts.

To better see the difference in the large-scale flows between the CTL and FAA experiments near 0000 UTC 20 October when Megi experienced a sharp direction change, Fig. 7 compares the height–time cross sections of the environmental flow averaged within a domain of (10°–25°N, 110°–125°E) that covers Megi’s entire track initialized at 1200 UTC 18 October. It is apparent that the period of the most critical control for the track of Megi is between 1800 UTC 18 October and 0000 UTC 20 October. During this period, the steering flow in the CTL experiment is dominantly westerly from 700 up to 400 hPa whereas it is more southwesterly in the FAA experiment, consistent with the reduction in the track errors seen in Figs. 5 and 6.

Fig. 7.
Fig. 7.

Height–time diagram of the environmental steering flows area-averaged within the domain of 10°–25°N, 110°–125°E for the (left) CTL run and (right) assimilation of all CIMSS satellite wind. The dashed lines denote the interval in which CTL forecast starts to deviate from the observation.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Because the TC steering flows in the WPAC are determined by the competition between the midlatitude trough over central China to the east of the Tibetan Plateau and the WPSH, it is anticipated that the main physical mechanism for the change in the steering flows seen in Fig. 7 should be connected in some way to the spatial distribution and the strength of these two dominant systems. In this regard, Fig. 8 shows the horizontal cross section of the geopotential height at 500 hPa for the CTL and FAA experiments during the period that is of most influence to the track of Megi [i.e., from 1800 UTC 18 October to 0000 UTC 20 October (cf. Fig. 7)]. It is seen in Fig. 8 that the most noticeable change in the large-scale pattern is associated with the farther westward extension of the WPSH in the FAA experiment. For example, the edge of the 5875-gpm contour in the FAA experiment could reach as far as 130°E whereas it can only reach marginally the longitude of 128°E at 1800 UTC 19 October in the CTL experiment. In addition, the area of geopotential height >5880 gpm in the FAA experiment is larger than that in the CTL experiment, indicating the overall greater strength of the WPSH in the FAA experiment. Such broadening and subsequent strengthening of the WPSH are seen during the entire period from 1800 UTC 18 October to 0000 UTC 20 October and are responsible for the enhancement of the southeasterly flow on the southern rim of the WPSH. This could offset the strong westerly flow associated with the midlatitude trough over central China and lead to a weaker westerly steering flow in the FAA experiment. As a result, Megi is not strongly pushed to the east and has a better track forecast as seen in Figs. 5 and 6.

Fig. 8.
Fig. 8.

Geopotential height at 500-hPa level for the (left) CTL run and (right) assimilation of satellite wind (ASW) experiment valid at (a) 1200 UTC 19 Oct, (b) 1800 UTC 19 Oct, and (c) 0000 UTC 20 Oct 2010. Superimposed are the wind barbs at the corresponding level.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

In terms of the intensity forecast, Figs. 5b and 6b show that the ensemble mean intensity is slightly stronger than that in the deterministic forecast for both the 0000 and 1200 UTC 18 October cycle. A noticeable feature of the ensemble intensity forecast is the bifurcation starting at about 0000 UTC 19 October, from which half of the ensemble members show a stronger intensity while the other half show a weaker intensity. The higher-intensity members have one common feature: they all share the Kain–Fritsch (KF) cumulus parameterization scheme (but with different combinations of the shortwave radiation or microphysical schemes). The other half of the ensemble members with lower intensity have the same BMJ cumulus parameterization scheme. That the KF cumulus scheme produces stronger TC intensity while BMJ members have weaker TC intensity in all of the ensemble experiments appears to be consistent with a number of previous studies of TC intensity sensitivities and heavy rainfall forecasts (see, e.g., Davis and Bosart 2002; Ratnam and Kumar 2005). As discussed in Davis and Bosart (2002), such overestimation of the TC intensity with the KF scheme could be related to the enhancement of the anticyclonic outflow aloft due to the increase of subgrid-scale overturning. Apparently, the offset between the higher-intensity members associated with the KF scheme and the weaker-intensity members results in a better intensity forecast that the single deterministic forecast could not achieve.

Of further notice is that the KF cumulus members tend to have less eastward bias as compared to the BMJ members because the stronger storms produced by the KF schemes experience more westward influence of the upper-level easterly flow (cf. Fig. 7). In general, the steering layer between 800 and 300 hPa is the dominant layer that guides the movement of TCs. However, for sufficiently strong storms that could extend vertically to a sufficiently high altitude, the upper level could influence the track as well. As the upper-level large-scale flow in the present case is easterly (cf. Fig. 7), the stronger-intensity members in the FAA experiment become more resilient to the impacts of the low-level westerly flow associated with the trough over the Tibetan Plateau, thus leading to less eastward bias to the east as compared to the weaker-intensity members. A further look into the environmental vertical shear reveals that the weaker-intensity members with large eastward bias are subject to larger vertical shear, thus preventing the storm from developing too strongly. One can see here the mutual correlation between the intensity and track forecast; the overall stronger intensity of Megi in the FAA experiment explains its resilience to the strong eastward steering of the low-level flows while its less eastward bias track could in turn help veer the storm into a less-shear environment for its subsequent further development.

Except for the incapability of capturing the weakening phase during the initial vortex spinup in the ensemble experiments, it is encouraging to see that the overall ensemble mean does show a closer match with the observation as compared to the deterministic forecast with both the magnitude of VMAX and its quasi-stationary phase being well comparable to observations. In spite of the bad vortex initialization, the FAA experiment appears to illustrate that a better track forecast seems to play some role in controlling Megi’s intensity. While there are several factors that could determine TY intensity including SST, moisture supply, the vertical wind shear, or topography interaction, the most noticeable difference in the large-scale environment between the CTL and FAA experiments obtained so far is the decline of the vertical shear after 1200 UTC 19 October as Megi’s track has less eastward bias. As seen in Fig. 9, the vertical shear reduces from about 7.3 m s−1 in the CTL to ~6 m s−1 from 1200 UTC 19 October to 0000 UTC 20 October, during which Megi appears to attain the stationary phase of development. Such reduction of the vertical shear is small, but its prolonged reduction during the course of 1 day seems to help Megi develop during that period. It should be cautioned that both the track and intensity forecast improvements observed so far in the FAA experiment result from (i) the assimilation of the AMV data and (ii) the use of the multiple physics ensemble members. It is not completely conclusive from the FAA experiment alone which factor is more important. In the next section, the different role of the multiple physics ensemble versus the role of assimilation will be further examined.

Fig. 9.
Fig. 9.

Time series of the area-averaged (1000 km × 1000 km) storm-following vertical shear vectors between 200 and 850 hPa for the CTL experiment (solid) and FAA experiment (dashed).

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

5. Sensitivity of satellite wind

As seen in the FAA experiment, the WPSH exhibited a significant westward expansion after the AMV winds were assimilated. At first glance this is intriguing, as the AMV wind vectors are distributed mostly at the upper levels (>300 hPa) rather than at low- to midlevels where the WPSH is supposed to be most affected. For a quick illustration, Fig. 10 shows the AMV wind distribution for the low- to midlayer that is gathered from the 800- to 300-hPa level and similarly for the upper layer from the 300- to 150-hPa level. One can notice that the lower-level winds are mostly cyclonic and located far from Megi’s center, whereas the upper AMV winds concentrate somewhat more within the Megi’s main circulation with dominant anticyclonic flows. These AMV wind distributions bring up two issues. On the one hand, the well-known critical role of the steering flow from the low–midlevels appears to suggest that the low-level AMV wind would have minimal contributions to the track of Megi because of the sparse data points. On the other hand, with more than 80% of the observed data points at the upper levels as seen in Fig. 10, one may expect to see more influence of the upper-level wind on the track and intensity of Megi. To examine the above speculation of the relative importance of the upper-level versus low-level AMV winds to the forecasts of Megi, two sensitivity experiments in which the upper- and lower-level AMV winds are assimilated separately are conducted in this section. Since the most substantial improvement of Megi’s track forecast is for the 1200 UTC 18 October cycle, the focus in this section will be entirely on the forecast initialized at 1200 UTC 18 October so that the relative roles of the upper- and lower-level AMV wind can be investigated most clearly.

Fig. 10.
Fig. 10.

(left) The CIMSS AMV 800–300-hPa satellite wind (black vectors) assimilated in the LAA experiment, and (right) the 300–80-hPa satellite wind used in the UAA experiment. Superimposed are the background wind vectors (green) that are averaged within the same layer.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Figure 11 shows the ensemble track forecasts for the LAA and UAA experiments superimposed with the best track. Despite many less observed data points, it is of particular interest to see that the low-level AMV winds could indeed help improve the track of Megi very favorably as compared to the much denser distribution of the upper-level AMV winds; the 3-day ensemble mean track error is 175 and 188 km in the LAA and UAA experiments, respectively (note that even though the ensemble mean track forecast in the LAA experiment may look closer to the best track than that in the FAA experiment, its along-track errors are actually larger because of the slower translational speed as compared to the FAA experiment). Also, the ensemble track spread in the UAA experiment is substantially smaller than that in the LAA experiment, indicating a more systematical eastward bias of the ensemble members in the UAA experiment. Examination of the WPSH pattern reveals that it is the low-level AMV data that are most effective in strengthening and broadening the WPSH westward. As seen in Fig. 12, the distribution of the WPSH in the LAA experiment is very close to that observed in the FAA experiment with the 5875-gpm contour reaching as far as 130°E in both experiments, whereas it barely crosses 130°E longitude in the UAA experiment. This is significant, as it indicates that a small number of low-level observations in the far field2 could help improve the TC steering environment to a larger extent.

Fig. 11.
Fig. 11.

Megi’s ensemble mean track (+), the best track (×), and the deterministic track (circles) for the experiments with assimilation of the (left) lower-level AMV winds and (right) upper-level winds that are initialized at 1200 UTC 18 Oct. Gray thin lines denote individual member tracks in each corresponding experiment.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Fig. 12.
Fig. 12.

As in Fig. 8, but for the (left) LAA and (right) UAA experiments.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Comparison of the UAA and CTL experiments shows that the track forecast in the UAA does show some improvement with respect to the CTL track even though both experiments possess a weak WPSH system that does not extend far enough westward. While this track improvement in the UAA experiment indicates that the enhanced upper-level flows in the UAA experiment may have some contribution to the track forecast skill, the overall better ensemble mean track forecast in the UAA experiment must be attributed mostly to the use of the ensemble assimilation. A possible reason for such different forecast skills between the UAA and LAA experiments could be related to the fact that the denser upper-level winds might have been more represented in the GFS initial condition than the lower-level winds. Therefore, observational information from the upper-level winds assimilated in the GFS analysis is likely to be retained even after interpolated to the higher-resolution grid as compared to the sparse low-level winds. As a result, reassimilation into a higher-resolution grid of the low-level winds appears to bring more information into the model initial condition, increasing the forecast accuracy.

One can see in Figs. 11 and 13 that the strong-intensity members associated with the KF scheme are observed again in the UAA experiment. These strong-intensity members could experience a greater impact of the upper-level flows than the weak-intensity members, thus possessing less eastward bias as explained in section 4. This is another reason why the ensemble mean track in the UAA experiment has less error than the deterministic track in the CTL experiment. That the enhanced easterly flows at the upper levels in the UAA experiment could not help reduce entirely the eastward bias implies that the improvement of the steering flow from low- to midlevel is necessary to obtain the good track forecast. Despite many less data points, the LAA experiment demonstrates the importance of the low-level winds in correcting the large-scale WPSH system that modulates the environmental steering flow efficiently. In contrast, the UAA experiment shows clearly the roles of ensemble forecasts in capturing the spectrum of the intensity and track uncertainties.

Fig. 13.
Fig. 13.

As in Fig. 5, but for the experiments that assimilate the (left) low-level AMV winds and (right) upper-level wind.

Citation: Journal of Atmospheric and Oceanic Technology 29, 12; 10.1175/JTECH-D-12-00020.1

Regarding the intensity forecast skill, similar results to the FAA experiment are obtained for both the UAA and LAA experiments, which show a minimal improvement at all lead times (Fig. 13). The bifurcation with half of the ensemble members that possess stronger intensity and half with the BMJ scheme that produces lower intensity is also replicated in both the UAA and LAA experiments. Together with the FAA experiment, this appears to indicate that the slightly better intensity forecast in the ensemble experiments is related more to the use of the ensemble of members with multiple physics than to the assimilation of the AMV wind. As long as the KF scheme is employed, the ensemble members with the KF option tend to develop more strongly and thus experience less eastward bias as seen in the UAA experiment. Although the assimilation of the AMV wind will help reduce the bias further, the fact that the stronger-intensity members in the UAA experiment could intensify rapidly as in the FAA experiment implies that it is the physical parameterizations that play a larger role in the intensity forecast of Megi. Without the track correction, the combination of different physical parameterization schemes alone seems to capture the range of intensity evolution that the single deterministic forecast is not able to produce.

The better improvement of the track forecast in the LAA experiment is worth some attention, as it demonstrates that a relatively small number of additional observations at lower levels, which is relatively easier to retrieve from satellites as compared to those within the TC central region, could contribute significantly to the overall forecast skill. This is especially suitable for the TY track forecast problem for which observations directly related to TYs are often difficult to obtain. As the satellite coverage has been gradually increased with time, it is expected that the peripheral observations of wind vectors could be of increasing importance in enhancing the environmental flows over void areas where the traditional observation networks are not always available.

6. Conclusions

In this study, sensitivities of the track and intensity forecasts of Typhoon Megi (2010) to the Cooperative Institute for Meteorological Satellite Studies (CIMSS) satellite atmospheric motion vector (AMV) dataset have been examined. By assimilating the CIMSS AMV dataset using the local ensemble transform Kalman filter implemented in WRF, it was demonstrated that the AMV data could help improve significantly the forecast of Typhoon Megi. Specifically, the initial representation of the western Pacific subtropical high (WPSH), which is one of the key large-scale systems governing the typhoon steering flows in the WPAC basin, was enhanced after the AMV wind was assimilated, leading to its expansion to the west and strengthening after 1 day into integration. The westward broadening of the WPSH helps offset the strong low-level westerly flow associated with the subtropical trough over mainland China, thus correcting the steering flows and resulting in better track forecasts.

In addition to the enhanced large-scale environmental flows after assimilating the AMV dataset, it was found that the use of the multiple physics LETKF ensemble has some benefit to the intensity forecast skill. Of most significance is that the ensemble members with the Kain–Fritsch cumulus parameterization schemes produce stronger storms, while the members with the Betts–Miller–Janjic schemes tend to capture weaker storms. Since the stronger storms have a deeper layer of storm-related flows, their tracks are affected also by the upper-level easterly flows, which reduce the influences of the low-level westerly flows. Therefore, the strong-intensity members possess less eastward bias as compared to the weaker storms. In addition, the strong storms experience less vertical wind shear as their tracks bend more to the west, allowing them to intensify further. As a result, the stronger intensity associated with the KF scheme compensates for the low intensity bias that the members with the Betts–Miller–Janjic scheme tend to produce. The dual effects of the ensemble forecast can be seen here. On the one hand, it helps enhance the representation of the initial vortex and the surrounding environment that a storm is embedded in. On the other hand, the ensemble of initial realizations and physical schemes can take into account the internal uncertainties related to inadequate representations of physical processes and/or initializations of the forecasting model. These two factors play an essential role in the improvement of the forecast of Megi as seen in this study.

To isolate the roles of the upper- and lower-level AMV winds, two additional sensitivity experiments in which the low- to midlevel (800–300 hPa) and upper-level (300–100 hPa) AMV winds were assimilated separately were conducted. Results showed that despite the sparse distribution of the low-level AMV winds with most of the data points located in the periphery of Megi’s main circulation, the track forecast of Megi is more sensitive to the low-level than upper-level AMV winds. This is significant, as it indicates that the far-field low-level winds can indeed improve efficiently the large-scale environmental flow, giving rise to a better representation of the steering flow and the subsequent intensity change. This result is highlighted, as much of the recent effort in tropical cyclone research focuses more on near-core observations to improve the forecast skill. Such inner-core observations are currently acquired mostly on reconnaissance aircrafts and they are thus more costly to obtain as compared to satellite retrieval in the periphery of TCs. Therefore, the far-field observations should contribute significantly to the typhoon forecast skill and deserve attention for better TC forecast skill.

Acknowledgments

This research was supported by the Vietnam Ministry of Science and Technology Foundation (DT.NCCB-DHUD.2011-G/10). Numerical experiments and analyses were conducted using the HPC Linux cluster at the Department of Meteorology, Vietnam National University, and the National Foundation for Science and Technology Development (NAFOSTED). We thank three anonymous reviewers for their helpful comments and suggestions. We wish to extend our thanks to Dave Stettner at the Cooperative Institute for Meteorological Satellite Studies University of Wisconsin—Madison for his explanation of the CIMSS quality indicator.

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1

The steering layer is typically chosen between 800 and 300 hPa (see, e.g., Chan and Gray 1982).

2

The far-field observations in this study are defined as observations outside the TC main circulation (>800 km).

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