Application of FY-2C Cloud Drift Winds in a Mesoscale Numerical Model

Transcription

1 740 ACTA METEOROLOGICA SINICA VOL.24 Application of FY-2C Cloud Drift Winds in a Mesoscale Numerical Model LI Huahong 1,2 ( ), WANG Man 3 ( ), XUE Jishan 4 ( ), and QI Minghui 2 ( ) 1 Department of Atmospheric Sciencs,Yunnan University, Kunming Meteorological Observatory of Yunnan Province, Kunming Yunnan Institute of Meteorology, Kunming State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing (Received November 25, 2009) ABSTRACT Statistical tests and error analysis of cloud drift winds (CDWs) from the FY-2C satellite were made by using radiosonde observations. According to the error characteristics of the CDW, a bias correction using the thermal wind theory was applied in the data quality control. The CDW data were then assimilated into the GRAPES-meso model via the GRAPES-3DVar. A torrential rain event that occurred in northwestern China during 1 2 July 2005 was simulated. The results indicate that the CDW data were mainly distributed above 500 hpa and the largest amount of data were at 250 hpa. The CDW data below 500 hpa had errors in both the wind direction and wind speed, and the distribution of the errors was irregular, so these data were discarded. The CDW data above 500 hpa had smaller errors, which presented a Gaussian distribution, so these data were adopted. With the assimilation of the CDW data, the southwest airflow near the torrential rain area became stronger in the initial wind field, which intensified the moisture transport and water vapor flux convergence, and finally improved the accuracy of the 24-h forecast of the torrential rain in both rain intensity and rain areal coverage. Key words: cloud drift wind (CDW), quality control, variational data assimilation, torrential rain forecast Citation: Li Huahong, Wang Man, Xue Jishan, et al., 2010: Application of FY-2C cloud drift winds in a mesoscale numerical model. Acta Meteor. Sinica, 24(6), Introduction Since the quality of numerical weather prediction (NWP) depends heavily on the accuracy of the initial fields, data assimilation becomes the most crucial topic in the development of NWP. In China, a new generation mesoscale NWP system GRAPES-meso has been developed by the Numerical Prediction Research Center of Chinese Academy of Meteorological Sciences (Xue et al., 2003) and run operationally at the National Meteorological Center in Beijing, the Shanghai Regional Meteorological Center, the Guangzhou Regional Meteorological Center, etc. With the upgrade of model dynamics and physics and refinement of model resolution, the ability of the model in simulating mesoscale weather systems is enhanced greatly. However, treatment of model initial fields still needs much improvement. The most serious challenge encountered in the efforts to improve the initial fields is the shortage of conventional observation data. The usage of remote sensing data in NWP is the best way to solve this problem. The cloud drift wind (CDW) data, which are also called the atmospheric motion vectors, derived from geostationary or polar-orbiting satellite images, are one type of remote sensing data used most popularly in NWP. The CDW is calculated by tracking the displacement of cloud or water vapor features in consecutive satellite images. Huang and Wu (1987) pointed out that the CDW product can be an abundant data source for weather analysis and numerical prediction between 50 S and 50 N. Many studies have been conducted to investigate the use of CDW in NWP. Le Marshall et al. (1997) Supported by the Natural Science Foundation of Yunnan Province (U ), the Yunnan Province Science and Technology Program (2009CA023), and the Yunnan Province Key Science and Technology and High-tech Project (2006SG25). Corresponding author: lihuahong08@163.com. (Chinese version published in Vol. 66, No. 1, 50 58, 2008)

2 NO.6 LI Huahong, WANG Man, XUE Jishan, et al. 741 assimilated hourly CDW data at the infrared channel by different methods and made a 24-h prediction. The results showed that, after the CDW data with high spatial and temporal resolutions were assimilated into the numerical model by the variational assimilation method, the prediction of tropical cyclone track was improved. Bhatia et al. (1999) efficiently predicted the tropical cyclone track in the Indian Ocean with the water vapor channel CDW data of METEOSAT- 5. In China, Zhang and Wang (1999) and Fang et al. (2000) also improved prediction of the tropical cyclone track over the South China Sea by assimilating the CDW data. Zhou et al. (2002) used the optimal interpolation method in MM5 to assimilate CDW and evaluated the impact of assimilation on the prediction of the rainstorm in the middle and lower reaches of the Yangtze River. Their results showed that the assimilation of CDW data significantly improved the quality of high level winds. Huang et al. (2003a, b) used GMS-5 CDW in a mesoscale numerical model with η coordinate (REM), and found that the CDW data helped to capture the small- and meso-scale features which were usually lost owing to the sparseness of conventional data. Zhuang and Xue (2004) assimilated GMS-5 CDW with the WRF-3DVar in a typhoon case and reported that the CDW improved the quality of wind and pressure in both analysis and prediction fields. Recently, Wang et al. (2005) used the GMS-5 CDW data in a 4DVar system to numerically predict tropical cyclone tracks in northwestern Pacific. The results indicated that the prediction accuracy for the tracks of intense tropical cyclones can be significantly improved through assimilation of the CDW data. In China, CDW data have not been commonly used in NWP operations because the data may have large errors, and few studies have been performed on CDW error features and associated quality control techniques. How to efficiently use the CDW data in NWP, especially the use of CDW data produced by Chinese satellite FY-2C, the first Chinese operational geostationary meteorological satellite, is worth a further investigation. Because the error features of CDW data are satellite dependent, studies should be done to learn the specific use of FY-2C CDW and the impact of assimilated FY-2C CDW on NWP. For this reason, statistical features of the errors of the FY-2C CDW data are investigated based on radiosonde observations, and the quality control methods are studied in this paper. The data that have passed the quality control are then fed into the variational assimilation package, and short-term prediction experiments with the GRAPES-meso model are conducted to evaluate the impact of CDW data from FY-2C on the simulation results. 2. Distribution and error characteristics of the CDW data 2.1 Data and method Although radiosonde data (RA for short hereafter) are erroneous due to inaccurate instrumentation and the ballon drift, they are the unique available and reliable in situ observations of aloft winds, and are commonly used to measure the errors of unconventional data. The FY-2C CDW data include two channels: the infrared and the water vapor channels. In this paper, we randomly select a 31-day data sample from 27 June to 31 July 2005 (excluding 7 10 July 2005) within the domain N, E. The CDW data are irregularly distributed in both horizontal and vertical directions. However, the RA data are on the mandatory levels over station spots. In order to compare these two kinds of data, each CDW datum in the sample is matched with the nearby radiosonde station within a distance less than 0.3 latitude/longitude and the closest mandatory level of radiosonde observation according to the pressure assigned to it. Table 1 shows how the RA mandatory pressure levels correspond to CDW pressure levels. The deviation of CDW from the corresponding RA (called error hereafter) is computed. The sample mean error, root-mean-square (RMS) error, standard deviation, Table 1. Correspondence of radiosonde mandatory pressure levels to CDW pressure levels (unit: hpa) Radiosonde CDW <60

3 742 ACTA METEOROLOGICA SINICA VOL.24 and error probability distribution are calculated, respectively. 2.2 Distribution of the CDW data We use IR1 and IR3 to denote the infrared channel and water vapor channel. Figure 1 shows that IR1 data distribute dispersedly from 100 to 925 hpa with a peak of about 7.4% at 400 hpa. IR3 data concentrate between 150 and 500 hpa with the maximum of 20.2% at 250 hpa. Because there are more IR3 data than IR1 data, 79.4% CDW data are located between 150 and 500 hpa, 12.6% CDW data are below 500 hpa, and 8.0% CDW data are above 150 hpa. These numbers indicate that the CDW data concentrate in the mid-upper troposphere. 2.3 Error characteristics of the CDW data To evaluate the quality of the FY-2C CDW data and analyze corresponding error features, the mean error, RMS error, standard deviation, and wind direction deviation of the CDW data relative to the RA are calculated. Table 2 shows that the CDW data have large errors at low levels. The results at 50 and 1000 hpa are omitted due to lack of data. With the height increasing, the errors gradually decrease, reach its minimum near 200 hpa, and then gradually increase again (Fig. 2). From the statistical results of wind direction error, we see that large errors appear below 500 hpa. The error at 70 hpa is also big, but only a small amount of CDW data are available at that level. Fig. 1. Vertical distribution of the CDW data amount in percentage. To further understand the error features at each level, we analyze the probability distribution of CDW errors. Figure 3 shows that the probability distribution of the CDW data error at 700 hpa is non- Gaussian, but at 300 hpa, it presents a Gaussian distribution with the peak occurrence frequency appearing around the error of zero. It is known that the kurtosis coefficient and the skewness coefficient are commonly used to describe the curve shape of a probability density function. We perform kurtosis and skewness tests for the CDW error distributions. The results show that the error probability distributions between 400 and 150 hpa are all Gaussian, and centered around zero with the confidence level of 95%. This indicates that the CDW errors at high levels are systematic Table 2. Statistical errors of the CDW data Level Mean error (m s 1 ) RMS error (m s 1 ) Standard deviation (m s 1 ) Probability of opposite signs (%) (hpa) u v u v u v u v

4 NO.6 LI Huahong, WANG Man, XUE Jishan, et al. 743 CDW data from FY-2C at high levels are expected to be usable and helpful in the NWP. 3. Data quality control Fig. 2. Vertical distribution of mean errors of the CDW data. Fig. 3. Occurrence probability of CDW errors at (a) 700 and (b) 300 hpa. errors. In summary, large errors of CDW data from FY- 2C in both wind speed and wind direction appear mainly below 500 hpa, and their probability distributions are non-gaussian at low levels. Use of low-level CDW data without special treatment of the data and proper assimilation algorithms is not meaningful. On the other hand, the errors above 500 hpa in both the wind speed and wind direction are much smaller and the error of the wind speed obeys a normal distribution. After quality control is properly conducted, the To take full advantage of the CDW data in NWP, it is necessary to have the data first go through a proper quality control procedure. The systematic bias can be removed from the CDW data by deviation correction. Since the atmospheric winds change continuously with height, the thermal wind theory, i.e., the relationship between vertical wind shear and background temperature, is used to correct the CDW data at low levels. The unreliable data will be rejected. The steps are as follows. (1) Deviation correction. The mean error at a certain level is subtracted from each CDW data value at the same level. (2) Thermal wind theory control. Based on the thermal wind theory, the wind speed and wind direction shear values between two levels are calculated with radiosonde data. If the shear between the CDW data and the radiosonde data does not satisfy the thermal wind theory, the CDW data are rejected. To test the effect of quality control and the influence of CDW data assimilation on torrential rain prediction, we select a torrential rain event that occurred in northwestern China during 1 2 July Analysis of the quality control results at each level reveals that the CDW data with too large wind speed deviations and wrong wind directions are efficiently rejected. It can be seen from Fig. 4 that the CDW data over southeast of Taiwan and north of the Yellow River are rejected because of abnormal wind speed values. Since the east coast of China around 27 N was controlled by the subtropical high, the wind speed was very small at the center of the subtropical high, and southerlies appeared over southwest of the high. However, the CDW data in the same region give north or east wind, which are different from the real observation. These data are rejected by quality control. Comparing Fig. 4c with Fig. 4d discloses that some of the CDW data corrected by the deviation have been rejected because they do not satisfy the thermal wind theory. The CDW data quality has been significantly improved by rejecting

5 744 ACTA METEOROLOGICA SINICA VOL.24 Fig. 4. Vector wind fields (m s 1 ) at 0000 UTC 1 July (a) Real-time analysis at 500 hpa, (b) the eliminated CDW data at 500 hpa, (c) the CDW before quality control, and (d) the CDW after quality control. error data but preserving relatively reasonable information. Furthermore, although the CDW data amount has been reduced to some extent by the quality control procedure, the spatial resolution of the corrected CDW data is still better than routine radiosonde observations. We find from quality control results that systematic errors in the CDW data are reduced, and the abnormal CDW data are rejected. 4. Assimilation experiments 4.1 Experimental design The GRAPES-meso (Version 2.5) developed by the Numerical Prediction Center of Chinese Academy of Meteorological Sciences is adopted in this study. The three-dimensional variational data assimilation system of the GRAPES-meso (GRAPES-3DVar) has the capability of assimilating CDW data. The observation errors in the variational system are replaced with the errors obtained by our method. The GRAPES- 3DVar processes the corrected CDW data from FY- 2C in the standard format. In the assimilation experiments, 12-h forecast fields of the medium-range operational prediction model T213 developed by the National Meteorological Center of China are used as the assimilation background fields and the boundary

6 NO.6 LI Huahong, WANG Man, XUE Jishan, et al. 745 conditions. The domain covers the area N, E. The model horizontal resolution is 30 km. To test the influence of CDW data assimilation on the initial field and the precipitation forecast, three groups of assimilation experiments are conducted. Scheme 1 represents a control experiment, which directly uses the background field as the initial field without assimilating the CDW data. Scheme 2 assimilates the radiosonde data into the initial field. Scheme 3 assimilates the FY-2C CDW data into the initial field. 4.2 Analysis of simulation results Figure 5a shows the observed 24-h accumulated rainfall between 0000 UTC 1 and 0000 UTC 2 July The torrential rain occurred mainly from north of Chendu to southwest of Taiyuan. The rainbelt strength reached 50 mm from southwest to northeast. There were four centers along the rainbelt. Three of them were located in southeastern Gansu, in which the most severe rainfall (133.8 mm) appeared at Xifeng town. Another center, with 24-h accumulated precipitation reaching mm, was located at Beichuan station in Sichuan. Figures 5b, 5c, and 5d present the predicted 24-h rainfalls from schemes 1, 2, and 3, respectively. Figure 5b shows that the control experiment cannot forecast the strong rainbelt, and only gives a 50-mm rainfall center around 35 N, 106 E. The rainbelt above 25 mm has been successfully predicted by scheme 3 in Fig. 5d. The predicted rainbelt is located from north of Sichuan to southeast of Gansu and west of Shanxi (Fig. 5d). The rainfall center above 50 mm between southeastern Gansu and northern Sichuan has also been captured (Fig. 5d). Althou- Fig h accumulated rainfall (mm) between 0000 UTC 1 and 0000 UTC 2 July 2005 from (a) observation, (b) Scheme 1 forecast, (c) Scheme 2 forecast, and (d) Scheme 3 forecast.

7 746 ACTA METEOROLOGICA SINICA VOL.24 gh the precipitation strength is slightly smaller than the observation, the distribution of the rainbelt and rainfall centers are consistent with the observations (Fig. 5d vs. Fig. 5a). Figure 5c shows that in general, assimilating the radiosonde data into the initial fields cannot obviously improve the prediction of the strong precipitation. The rainbelt and the strong rainstorm centers cannot be reproduced unless the CDW data are assimilated. To find the reasons why the precipitation prediction is improved with assimilation of the CDW data, the model initial fields are analyzed. The incremental wind vector fields at 700 and 500 hpa are given in Figs. 6a and 6b. Figure 6a shows that the incremental field presents a cyclonic circulation north of the Bay of Bengal and an anticyclonic circulation south of Anhui Province. There is a greater southwesterly increment between the east of the cyclone and the west of the anticyclone, resulting in a southwesterly increment belt extending from the Bay of Bengal to the northwest of China. The same pattern is more significant at 500 hpa. Figure 6b shows that the maximum center of the southwesterly increment belt runs approximately parallel to the distribution of the observed rainbelt. This suggests that the improvement of the rainbelt prediction is closely related to the strengthening of the southwest flow in the initial field. Because of the strengthening of the southwest flow, water vapor can be continually transported from the Bay of Bengal into the rainstorm area. The extreme wind increment center nearby the rainstorm area is also beneficial to the moisture convergence and the intensification of the wind shear in the meridional direction. Figure 7 presents the wind data at 200 hpa that have been fed into the assimilation system. Figures 7a and 7b show the radiosonde data and the CDW data, respectively. By comparing Fig. 7a with Fig. 7b, it can be seen that the distribution of radiosonde data is homogeneous, but that of CDW data is much more inhomogeneous because of the inhomogeneity of cloud distribution. Although the homogeneity of the CDW data is worse than that of the sounding data, the spatial resolution of CDW data is higher than that of the radiosonde data in the local area. For example, the distribution of the radiosonde data is sparse while the distribution of CDW data is relatively dense in the rainstorm region. The CDW data accurately reflect the information nearby the rainstorm region, while the radiosonde data miss out the local information. The quality of the initial wind field nearby the rainstorm region is more significantly improved by assimilating CDW data than by radiosonde data. This is the main reason why the 24-h precipitation prediction with the CDW data assimilation is significantly better than Fig. 6. Incremental wind vector fields after the assimilation of CDW data at 0000 UTC 1 July 2005 at (a) 700 hpa and (b) 500 hpa. Shadings denote areas with large incremental wind speed (m s 1 ).

8 NO.6 LI Huahong, WANG Man, XUE Jishan, et al. 747 Fig. 7. Assimilated wind data (m s 1 ) at 200 hpa at 0000 UTC 1 July 2005 from (a) radiosonde and (b) CDW. that with the radiosonde data assimilation. To sum up, for the heavy rainfall case in southwest to northwest of China during 1 2 July 2005, the effect of assimilating radiosonde data into the initial wind field is not obvious, but assimilating CDW data into the initial wind field can strengthen the southwesterly flow at lower levels. 5. Conclusions and discussion According to the error characteristics of the FY- 2C CDW data, two quality control steps, i.e., deviation correction and thermal wind theory control, are performed. The CDW data that have passed the quality control are assimilated into the GRAPES-meso model by the GRAPES-3DVar. The numerical simulations of the rainstorm from 0000 UTC 1 to 0000 UTC 2 July 2005 in northwest of China are carried out. The results are obtained as follows. (1) The majority of CDW data distributes above 500 hpa with the maximum data amount appearing at around 250 hpa. (2) The availability of CDW data below 500 hpa is poor, because large errors, which distribute irregularly in the vertical, occur in both the wind direction and wind speed. The errors of the CDW data above 500 hpa are small, showing a normal distribution with symmetric characteristics. Thus, the CDW data above 500 hpa have good usability. (3) The CDW data with wind direction errors and large wind speed deviations are rejected by the deviation correction and thermal wind theory control. Thus, the quality of the CDW data is improved. (4) The rainfall strength and the rainfall area in the 24-h precipitation prediction can be improved by assimilating CDW data into the initial wind field. The strengthening of southwest flow is beneficial to the water vapor transport to the rainstorm area, the enhancement of flow shear in the meridional direction, and the moisture convergence, which improves the prediction accuracy of the rainfall area and strength. This work studies only one real case about the influence of CDW data assimilation on precipitation prediction. More assimilation cases such as precipitation of typhoon and Meiyu front need to be investigated to test the influence of assimilating the FY-2C CDW data on numerical precipitation prediction. In order to take full advantage of the high spatial and temporal resolutions of CDW data, experiments with multiple-time continuous assimilation cycles will be performed in our future work. The quality control methods may be further optimized. Some CDW data are rejected by wrong determination of the CDW data height. According to the background field and the radiosonde data, the rejected

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