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In this study, three typhoons—Maria (2018), Lekima (2019), and Mitag (2019)—are chosen to investigate the impacts of assimilating the FY-4A AGRI radiances on the typhoon initialization. The three typhoons were generated after the operational run of the FY-4A satellite and had destructive effects on the southeast coastal region of China. It is interesting to evaluate the impact of FY-4A AGRI radiance DA considering the variety of the three typhoons, since their landfall locations were rather different with distinct moving directions. The observed tracks of the typhoons are shown in Fig. 1. The best-track datasets used in this study are from the China Meteorological Administration (CMA) (Ying et al., 2014).
Figure 1. The tracks of Typhoon (a) Maria (2018), (b) Lekima (2019), and (c) Mitag (2019). The areas are the model domains and the filled colors are the terrain altitude (units: m).
Typhoon Maria (2018) was the eighth typhoon in 2018. After its formation, it moved northwest and intensified to a severe typhoon. At about 0000 UTC 11 July, Maria (2018) landed in Fujian, China, and was weakened sharply in the subsequent 12 h due to frictional consumption. According to official statistics, Maria (2018) caused two casualties and $628 million of economic losses (Yin et al., 2021). Typhoon Lekima (2019) was the ninth typhoon in 2019, which was the strongest typhoon to make landfall in China that year (Xu et al., 2022). It landed in Wenling, Zhejiang Province, at 1945 UTC 9 August, with a minimum sea level pressure (MSLP) of 930 hPa and brought large amounts of precipitation to eastern China. Typhoon Mitag (2019) was also a typhoon that occurred in 2019. It was the eighteenth typhoon of the year and initiated at 0000 UTC 28 September. Although it did not cause long-lasting landfall, heavy precipitation occurred in Zhejiang Province.
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AGRI has 14 spectral bands from visible (0.47 μm) to longwave infrared (13.5 μm) wavelength (Zhang et al., 2018), compared to its predecessor, the FY-2 series satellites, with five infrared bands. Channels 1–6 are visible and near-infrared bands, mainly detecting information on the reflection and scattering of the surface and atmosphere during daytime. Channels 7–8 are mid-infrared bands, which can detect information from the sun, the earth, and clouds. Channels 9–10 are the vapor-absorptive bands, which can reflect the moisture information in the middle and upper atmosphere, while channel 14 is the CO2 absorption band. Channels 11–13 are the atmospheric window channels with low atmospheric absorptivity. The differences in the radiance observations and the simulated radiance from the background with the RTTOV (the observed minus the simulated radiance) are applied to estimate the observation error statistitically for roughly one week every 6 h.
Figure 2 shows the Jacobian functions of the water vapor channels 9 and 10 using the clear-sky atmosphere in the case of Maria (2018). The Jacobian functions explain the sensitivity of the top-of-the-atmosphere radiance to changes in the atmosphere or surface. It can be found that the Jacbians of the water vapor for channel 9 and channel 10 are generally large at higher levels around 300 hPa and 400 hPa, respectively. In this study, the two water vapor absorption channels are assimilated since it has previously been shown that water vapor channels can provide accurate moisture information (Xu et al., 2021).
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In this study, version 4.3 of the WRF model (Skamarock et al., 2021) is used. The domains for each typhoon case are illustrated in Fig. 1 and their detailed domain settings are listed in Table 1. The initial values and boundary conditions are provided by 0.25° × 0.25° NCEP GFS analysis data. The physics parameterization schemes used in the experiments include the WDM6 microphysics scheme (Lim and Hong, 2010), the Grell–Freitas cumulus scheme (Grell and Freitas, 2014), the YSU boundary layer scheme (Hong et al., 2006), the Dudhia scheme (Dudhia, 1989), and the RRTM scheme (Mlawer et al., 1997) for shortwave and longwave radiation.
Typhoon Model center Horizontal grid (spacing) (lat×lon) Vertical levels (pressure top) Maria (2018) (25°N, 123°E) 501×401 (9 km) 57 (10 hPa) Lekima (2019) (30°N, 123°E) 381×311 (9 km) 57 (10 hPa) Mitag (2019) (30°N, 123°E) 559×469 (9 km) 57 (10 hPa) Table 1. Domain settings.
Figure 3 illustrates the experimental flow chart of the Maria (2018) case. Firstly, a 6-h forecast driven by 0.25° GFS data at 1800 UTC 9 July is used as the first guess for the first analysis cycle. In the following 6 h, six assimilation experiments are conducted with hourly intervals. The six experiments differ in their observations assimilated and cloud detection methods employed, as shown in Table 2. The distributions of global telecommunications system (GTS) data used for the Maria (2018) case in the first and the final assimilation are shown in Fig. 4 as an example. A 30-h forecast is carried out from the analysis at 0600 UTC 10 July for each experiment. In order to avoid correlations between adjacent pixels, a thinning mesh with six times the horizontal distance is applied. Similar to the Maria (2018) case, the differences of the Lekima (2019) and Mitag (2019) cases in their experimental setup is their start times and forecast durations. The analysis starts at 1200 UTC 8 August 2019 and a 30-h forecast is performed for Lekima (2019), while the Mitag (2019) case begins the analysis at 0600 UTC 30 September 2019 followed by a 48-h forecast after the DA cycles.
Expriment abbreviation Data employed Cloud detection CNTL GTS None PF GTS and AGRI radiances Particle filter MMR GTS and AGRI radiances Multivariate and minimum residual CLM0 GTS and AGRI radiances Reject pixels with value = 0 (cloudy) in cloud mask file CLM01 GTS and AGRI radiances Reject pixels with value ≤ 1 (cloudy and probably cloudy) in cloud mask file CLM012 GTS and AGRI radiances Reject pixels with value ≤ 2 (cloudy, probably cloudy, and probably clear) in cloud mask file Table 2. The experiments.
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In this section, the track and MSLP forecast of Maria (2018) is first presented, followed by detailed analyses and diagnoses of Maria (2018). Lastly, the track and intensity forecasts of Lekima (2019) and Mitag (2019) are shown.
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Figure 5 shows the forecast results of all experiments. For the track forecast (Fig. 5a), the differences of all experiments are not evident for the first 12 h. Owing to the weakened northeast pressure gradient, the track of the PF experiment moves more northward, leading to reduced track errors for later hours. This can be shown by the quantitative track error in Fig. 5b. In the first 12 h, the track error of the MMR experiment is the smallest, while the errors of other experiments are comparable. The error of the MMR experiment undergoes a rapid increase in the final 12 h, while the track error of the PF experiment decreases in the final 18 h. For the intensity forecast (Fig. 5c), the PF experiment is superior in the first 12 h, while the intensity errors of all experiments are similar in the remaining time.
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Figure 6a shows the cloud mask information from the FY-4A product, while Figs. 6b–e display the reserved pixels with different cloud detection schemes. The numbers of reserved pixels are marked in the top-right corner in each figure panel. In Fig. 6a, the pixels over the spiral cloud system of Typhoon Maria (2018) and tropical convective clouds are recognized as cloudy. At the edges of these regions, pixels are mainly viewed as probably cloudy or probably clear, while pixels away from these regions are mainly determined as clear. By comparing the PF scheme and the MMR scheme with the same channel (Fig. 6b versus Fig. 6d or Fig. 6c versus Fig. 6e), it can be found that the number of reserved pixels in the PF scheme is overall larger than that in the MMR scheme (2155 versus 1239 for channel 10), especially in the northeast of the typhoon. This is consistent with the cloud height retetrievals from the PF and MMR methods in Xu et al. (2016). In addition, as expected, for the same cloud detection scheme, the number of pixels of channel 9 is larger than that of channel 10, consistent with the higher peaking Jacobian of channel 9 in Fig. 1 with a higher possibility of being free from the contamination of clouds. Overall, the PF scheme reserves the most pixels, while the scheme using cloud masks from the FY-4A product keeps the least pixels.
Figure 6. The (a) cloud mask product, (b, c) used pixels of (b) channel 9 and (c) channel 10 with the PF cloud detection scheme, and (d, e) used pixels of (d) channel 9 and (e) channel 10 with the MMR cloud detection scheme, at 0000 UTC 10 July 2018. The number of reserved pixels is marked in the top-right corner of each panel.
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Figure 7 shows the brightness temperature scatterplots of channel 9 and channel 10 with the PF scheme at the first analysis time. Before bias correction (Figs. 7a and d), the brightness temperature simulated by the background field is higher than the observation for most cases. After bias correction (Figs. 7b and e), the relatively higher brightness temperature simulated by the background field is partially corrected. After the analysis (Figs. 7c and f), dots converge more to the diagonal with a smaller dispersion. Evidently, the mean, standard deviation, and root-mean-square values are decreased dramatically. Generally, the brightness temperature simulated by the analysis field matches well with the observations.
Figure 7. Brightness temperature scatterplots of the background versus observations before bias correction for (a) channel 9 and (d) channel 10; the background versus observations after bias correction for (b) channel 9 and (e) channel 10; and the analysis versus observations after bias correction for (c) channel 9 and (f) channel 10, valid at 0000 UTC 10 July 2018.
Figure 8 shows the time series of the hourly brightness temperature deviation of observation minus background (OMB) and observation minus analysis (OMA) with the PF scheme from 0000 UTC 10 July to 0600 UTC 10 July. It can be seen that the number of reserved pixels of channel 9 is larger than that of channel 10. In addition, the number of pixels for both channels reduces after 5 cycles. Before bias correction, channel 9 has greater cold biases than channel 10. After bias correction, the biases are systematically reduced with an overall same magnitude for both channels. After bias correction, both the mean and standard deviation of OMB are systematically reduced. After the analysis, the mean of OMA approaches 0 and the standard deviation decreases sharply.
Figure 8. The reserved number of pixels for (a) channel 9 and (b) channel 10; the mean value of OMB for (c) channel 9 and (d) channel 10; and the standard deviation value for (e) channel 9 and (f) channel 10 in different analysis cycles. The blue line is the OMA, the red line is the OMB with bias correction, and the purple line is the OMB without bias correction.
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Figure 9 shows the 500-hPa geopotential height increments of all experiments at 0000 UTC 10 July 2018, describing the effects of DA on the upper trough. In the CNTL experiment (Fig. 9a), there is a positive increment to the northeast of the typhoon and a negative increment to the southwest of the typhoon. Under the influence of the pressure gradient, the track of the typhoon in the CNTL experiment tends to be southward. By comparing the two experiments with different channel-sensitive cloud detection schemes (Figs. 9b and c), the pressure gradient near the typhoon in the PF experiment is weakened, while that in the MMR experiment is strengthened. This likely explains the more northward track in the PF experiment than in the MMR experiment in the above forecast. Besides, it is found that the more data are employed, the more obvious the observed geopotential height increment is to the northeast of the typhoon via applying the cloud mask information to different extents (Figs. 9d–f). However, their increment magnitudes are consistently larger than in the PF experiment.
Figure 9. The 500-hPa geopotential height field (units: dagpm) and geopotential height increment (units: gpm) of the (a) CNTL, (b) PF, (c) MMR, (d) CLM0, (e) CLM01, and (f) CLM012 experiments at 0000 UTC 10 July 2018. The blue contours are the geopotential height fields, the green rectangle highlights the differences in geopotential increments, the filled colors are the increment fields, and the black dot represents the location of the typhoon center.
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Figure 10 shows the 850-hPa temperature increments of all experiments at 0000 UTC 10 July 2018. In the CNTL experiment, no obvious increment is found in the typhoon center. By assimilating the AGRI radiance data, the warm-core structure becomes more apparent in most experiments, with a notable positive temperature increment in the vortex. In comparison, the positive temperature increment in the PF experiment is more obvious both near the typhoon core and to the southwest of the typhoon. Besides, the area of the cold center to the west of the typhoon is slightly reduced, with disconnected contours from −0.1 to 0.
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Figure 11 shows the analysis increments of the water vapor path of all experiments at 0000 UTC 10 July 2018. In the CNTL experiment, a noticeable water vapor increment can be found in the northeast of the typhoon, accompanied by a negative increment in the west of the typhoon. In the PF experiment, the area with a positive water vapor path increment is extended to the outer side of the typhoon core. In the remaining four experiments, the magnitudes of the water vapor path increments are relatively small.
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Figure 12 shows the sea-level fields of all experiments at the final analysis time. It can be seen that the 1008 and 1012 isobars marked by red rectangles in the PF experiment are contracted towards the center of the typhoon, which can strengthen the pressure gradient force in the outer side of the typhoon. According to geostrophic theory, the outside wind of the typhoon is also augmented and the area of wind speed varying from 15 to 20 m s−1 is slightly enlarged in the PF experiment. By contrast, the outside isobars are sparse in other experiments, especially in the MMR experiment. With the PF scheme, assimilating AGRI radiances yields a slightly more intensified vortex.
Figure 12. The sea-level fields of the (a) CNTL, (b) PF, (c) MMR, (d) CLM0, (e) CLM01, and (f) CLM012 experiments at 0600 UTC 10 July 2018. The black contours are the sea level pressure (units: hPa), the filled colors and the arrows are the wind speed (units: m s−1), the blue line is the cross-section line used in the next section, and the red rectangles are the highlighted areas.
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Figure 13 shows the cross section of the potential temperature anomaly and moisture field at the final analysis time. The cross-section line in Fig. 12b is used in all experiments to investigate the vertical structure across the center of the typhoon. It can be found that the lowest height of the 6-K contour is roughly at 750 hPa in the PF experiment, while that in other experiments is between 700 hPa and 750 hPa. The area of temperature anomalies with values of 15 K is expanded slightly as well in the PF experiment. Moreover, the moisture condition of the PF experiment is also favorable, with larger relative humidity observed in the southwest and northeast of the typhoon. The height of values from 80% to 90% in the southwest of the typhoon is elevated to 250 hPa (left-hand rectangle in Fig. 13b) and the originally disconnected values from 80% to 90% in the northeast of the typhoon become continuous (right-hand rectangle in Fig. 13b).
Figure 13. Cross sections of the (a) CNTL, (b) PF, (c) MMR, (d) CLM0, (e) CLM01, and (f) CLM012 experiments at 0600 UTC 10 July 2018. The black contours are the potential temperature anomaly (units: K), the filled colors are the relative humidity (%), the red dotted line is the lowest height of the 6-K contour, and the red rectangles are the moisture-rich areas.
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Figure 14 shows the forecast verification of u-wind, v-wind, temperature, and specific humidity against the conventional observations of soundings, with its distribution shown in Fig. 4. Overall, the PF experiment yields the smallest BIAS, especially for the last few cycles. Similarly, for the RMSE of u-wind, v-wind, and specific humidity, the PF experiment also brings some improvements, especially for the winds and humidity. It is also found that the RMSE of temperature in the MMR experiment is relatively lower than in the other three experiments that apply pixel-based cloud detection schemes.
Figure 14. The (a) BIAS of U (u-wind), (b) RMSE of U, (c) BIAS of V (v-wind), (d) RMSE of V, (e) BIAS of T (temperature), (f) RMSE of T, (g) BIAS of Q (specific humidity), and (h) RMSE of Q forecast verification against the sounding observations.
For better comparison among different experiments, the averaged BIASs and RMSEs are listed in Table 3 and Table 4, respectively. It can be found that the BIAS values of the PF experiment are the closest to 0, which means no deviation, except for the Q (specific humidity) variable being slightly larger than that of the MMR experiment. As for the averaged RMSEs, apart from the T (temperature) variable, the values of the PF experiment are the smallest. To sum up, the forecast error of the model variables of the PF experiment is the smallest overall.
Bias U V T Q (10−5) CNTL 0.26 0.19 −0.23 10.88 PF 0.19 0.15 −0.22 4.57 MMR 0.24 0.19 −0.28 4.55 CLM0 0.27 0.2 −0.26 10.81 CLM01 0.29 0.23 −0.25 12.55 CLM012 0.27 0.23 −0.23 11.03 Table 3. Averaged BIASs of all experiments.
RMSE U V T Q (10−4) CNTL 3.32 3.25 1.3 11.2 PF 3.29 3.31 1.32 11.02 MMR 3.41 3.38 1.32 11.57 CLM0 3.33 3.32 1.33 11.35 CLM01 3.32 3.35 1.31 11.36 CLM012 3.3 3.3 1.29 11.08 Table 4. Averaged RMSEs of all experiments.
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Figure 15 shows the Skew-T plots of the observation and the simulations of all experiments for the 30-h forecast valid at 1200 UTC 11 July 2018. The sounding station is in Shaowu, Fujian Province (27.33°N, 117.47°E). The simulations of the Skew-T plots are acquired by interpolation to this station from the model space. The black line is the air temperature line, and the blue line is the dewpoint temperature line. The air temperature and dewpoint temperature in the sounding observation (Fig. 15a) are almost completely overlapping below 450 hPa, indicating that the moisture condition is favorable for the occurrence of precipitation in the lower and middle layers. In all experiments, although obvious deviations can be seen compared to the observation, the air temperature and dewpoint temperature in the PF experiment are closer than in other experiments, indicating an improved water vapor condition. From the pressure of the lifting condensation level, the temperature at the lifting condensation level, the Showalter Index for stability, and the precipitable water, it seems that most of the indexes in the PF experiment match best with the observation, which are the contributors to the enhanced rainfall forecasting skill.
Figure 15. Skew-T plots of the (a) observation and (b) CNTL, (c) PF, (d) MMR, (e) CLM0, (f) CLM01, and (g) CLM012 experiments in Shaowu (27.33°N, 117.47°E) at 1200 UTC 11 July 2018. Plcl: pressure of the lifting condensation level; Tlcl: temperature at the lifting condensation level; Shox: Showalter Index for stability; Pwat: precipitable water.
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Figure 16 shows the 6-h accumulated precipitation from 0000 UTC to 0600 UTC on 11 July 2018 after the typhoon made landfall. The precipitation observation is from the CLDAS (CMA Land Data Assimilation System) V2.0, which is a grid fusion analysis product evaluated by more than 2400 in-situ national automatic stations of the CMA with a resolution of 0.0625° × 0.0625° (Sun et al., 2020). The observation shows some rainfall centers with values greater than 50 mm along the coast of Zhejiang Province and Fujian Province. In particular, the rainfall center located in Fujian Province has the largest coverage. For all the experiments, the predicted rainfall distributions with values below 50 mm are rather consistent with the observation, while for the rainfall exceeding 50 mm, the rainfall center in the north of Zhejiang Province is largely underestimated. Moreover, the precipitation centers over 50 mm in Fujian Province are situated further north in the CNTL, PF, and CLM012 experiments than in the other three experiments. That is, the MMR, CLM0, and CLM01 experiments mislocated the maximum in Fujian Province. Generally, precipitation exceeding 100 mm is slightly overestimated in all experiments.
Figure 16. The 6-h accumulated precipitation (units: mm) of the (a) observation and (b) CNTL, (c) PF, (d) MMR, (e) CLM0, (f) CLM01, and (g) CLM012 experiments from 0000 UTC to 0600 UTC on 11 July 2018.
To obtain quantitative verifications of the precipitation forecast, the equitable threat score (ETS) is calculated for comparison. The value of ETS ranges from −1/3 to 1. The higher the score, the better the simulation. Specifically, a score equal to 1 indicates that the simulation totally matches the observation, while a score less than or equal to 0 indicates no forecasting skill (Mesinger and Black, 1992). The ETSs of rainfall forecasts with different thresholds are presented in Fig. 17. The scores for the thresholds of 10 mm and 25 mm are higher than for the other thresholds. Generally, the PF experiment yields the highest ETSs for most of the thresholds (0.1 mm, 10 mm, and 25 mm). For the threshold of 50 mm, the advantage of the PF experiment is negligible since the scores from PF, CNTL, and CL012 are basically comparable.
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Figure 18 shows the overall verification results for the track forecasts of other cases. For Typhoon Lekima (2019) (Fig. 18a), the tracks of the three experiments are similar when the typhoon is over the ocean. However, due to friction near to where it made landfall, the moving speed of the typhoon in the model decelerates. The PF experiment has a faster vortex moving speed than the other experiments and produces the smallest track error after landfall. Consistently, the track error growth of the PF experiment is slowest, as shown in Fig. 18b. Originally, the gap between the PF and other experiments is 5 km, while the final gap increases to more than 25 km. For Typhoon Mitag (2019) (Fig. 18d), all experiments can simulate the twists in the track of the typhoon. In comparison, the track of the PF experiment matches best with the best track for most of the time. Rapid increases in error can be witnessed in the other experiments (Fig. 18e) after 18 h of model integration, whereas the error of the PF experiment remains almost stable, which results in the maximum error of the PF experiment being obviously smaller than in the other experiments. It should be mentioned that the improvement is rather slight in terms of the intensity forecasts for both Lekima (2019) (Fig. 18c) and Mitag (2019) (Fig. 18f) from the PF experiment. From the temperature, moisture, and dynamical conditions in the analyses and forecasts for Typhoon Lekima (2019) and Mitag (2019) (not shown), it is found that the large improvement in track forecasts can be attributed to the improvement in its surrounding geopotential height environment. It is also noticed that the improvement in the typhoon intensity forecasts is relatively slight when the change in the internal structure of the typhoon in terms of the moisture, temperature, and dynamical condition is limited.
Typhoon | Model center | Horizontal grid (spacing) (lat×lon) | Vertical levels (pressure top) |
Maria (2018) | (25°N, 123°E) | 501×401 (9 km) | 57 (10 hPa) |
Lekima (2019) | (30°N, 123°E) | 381×311 (9 km) | 57 (10 hPa) |
Mitag (2019) | (30°N, 123°E) | 559×469 (9 km) | 57 (10 hPa) |