Analysis and Modeling of a Long-Lasting Fog Event over Beijing in February 2007

Transcription

1 426 ACTA METEOROLOGICA SINICA VOL.24 Analysis and Modeling of a Long-Lasting Fog Event over Beijing in February 2007 ZHANG Chaolin 1,2 ( ), ZHANG Lina 1 ( ), WANG Bizheng 3 ( ), HU Ning 1 ( ), and FAN Shuiyong 1 ( ) 1 Institute of Urban Meteorology, China Meteorological Administration, Beijing Department of Earth Sciences, National Natural Science Foundation of China, Beijing Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing (Received March 23, 2009; revised May 20, 2010) ABSTRACT The evolution characteristics of a long-lasting fog event over Beijing during February 2007 are investigated using the 5-min automatic visibility data and conventional meteorological observations. Data analysis results reveal that there is a close relationship between the development/evolution of this fog event and the weather conditions such as high humidity, light wind, and low temperature in the lower troposphere. Furthermore, numerical simulations of this event are carried out by using the Advanced Research WRF (ARW) V2.2 Noah LSM land-atmosphere coupled model. The model is two-way and two-nested with improvement in the description of the interface between the urban underlying surface and the atmospheric boundary layer. The horizontal grid spacings are 9 and 3 km for the two domains. The ground-based GPS precipitable water vapor content and automatic and conventional meteorological observations at the initial time are assimilated. The simulation results indicate that the control experiment with local data assimilation has successfully captured the spatial-temporal evolution of this fog event, especially the synoptic patterns and characteristics of the weather conditions (high humidity, light wind, and lower temperature). The results are well consistent with observations. Comparison of two experiments (with and without local data assimilation) shows that the model initial conditions are considerably modified with the WRF 3DVAR. The assimilation of local observations leads to significant improvement in the prediction of this event with better representation of static stability, enhanced southeasterly winds, concomitant warmer moisture in the south of Beijing, intensified temperature inversion between the lower and upper troposphere, and generally decreasing environmental temperature. Key words: fog, atmospheric visibility, data assimilation, numerical simulation, haze Citation: Zhang Chaolin, Zhang Lina, Wang Bizheng, et al., 2010: Analysis and modeling of a long-lasting fog event over Beijing in February Acta Meteor. Sinica, 24(4), Introduction With the rapid development of national economy and urbanization, the negtive impact of fog events on the safety of civil traffic, economy, public health, and crop production is getting more and more critical. Fog events considerably reduce visibility of the atmosphere, and lead to more traffic accidents, casualties, and transportation disruptions (especially on expressways). These incidents often cause personal injuries, property damages, and economic losses (Zhang et al., 2007a). Fog events are also bad for public health because they are often accompanied with static stable weather conditions that inhibit pollutant diffusion. Long-lasting fog events also cause the lack of light for crops, which brings about crop diseases and insect pests, and eventually reduces crop production. Therefore, theoretical and practical investigations on fog events, especially the long-lasting ones, are widely demanded. Besides the relationship with liquid water content (LWC), concentration of airborne fine particles, and local weather conditions, fog events are also closely associated with geographic environment, synoptic Supported by the National Natural Science Foundation of China under Grant No and the Ministry of Science and Technology of China under Grant Nos. 2005DIB3J098 and 2008BAC37B03. Corresponding author: zhangcl@nsfc.gov.cn.

2 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al. 427 weather patterns, and aerosol distributions. The spatial-temporal structure of each fog event usually varies much greater than expected, so it is hard to be well predicted at present (Wu et al., 2007; Zhang et al., 2008a, b). In the numerical simulation studies, earlier investigations focued on establishing models of local-scale fog processes so as to simulate fog formation, dissipation, and development. For instance, in the early 1960s, Fisher and Caplan (1963) established an one-dimensional fog model and well simulated the formation and development of a fog event. Zdunkowski and Nielsen (1969), Roach and Brown (1976), and Frokel et al. (1984) also made contributions to the improvement of fog models. In China, Zhou (1987), Yin and Xu (1993, 1994), Qian and Lei (1990), and Shi et al. (1996, 1997) simulated local fogs using one-, two-, and three-dimensional fog models, respectively. Because the above models were designed to simulate the local-scale fog process, they had difficulty in reflecting correctly the interaction between the large-scale background weather and the local fog. With the advance in cloud physical process studies and the increase of model resolution, many numerical experiments have been conducted to investigate fog events by using mesoscale models since the 1990s (Ballard et al., 1991; Fan et al., 2003, 2004; Shi et al., 2005, 2006; Dong et al., 2006; Fu et al., 2004). Recently, Li et al. (2007) and Liang et al. (2007) studied heavy fog events over Beijing by using the WRF model. They revealed the formation, dissipation, and development mechanism of advection fogs and assessed the impacts of assimilation of airplane weather reports on the heavy fog simulation. Zhang et al. (2008) designed a fine-resolution road weather information system in the Great Beijing Metropolitan areas of North China (Beijing-Tianjin-Hebei areas), and this system has been providing core technical support to the special meteorological service for Beijing. However, most of the studies have focused on process investigation of heavy fog events. Few investigations concentrated on the impacts of underlying urban surface on atmospheric boundary layer, the reciprocal transformation between haze and fog, the development of long-lasting fog events, and especially the effects of local observational data (such as ground-based GPS precipitable water measurements and automatic meteorological observations) assimilation on the fog prediction. During February 2007, a long-lasting fog event occurred in Beijing, Tianjin, south and central Hebei Province and south Liaoning Province, and seriously affected the regional economy. In the daytime of 21 February, the atmospheric visibility was below 100 m in most areas of Beijing and Tianjin and some areas of Liaoning. In some parts of Beijing, the atmospheric visibility was lower than 50 m (minimum was less than 30 m). Seven expressways had to be shut down. At least 250 flights from the Beijing Capital International Airport were delayed, and more than 30 flights were cancelled. The heavy fog affected about 2500 passengers. The community economy suffered a significant loss. Using the 5-min automatic visibility data and conventional meteorological observations, this longlasting fog event is analyzed with observations first in this paper. Furthermore, the 9/3-km two-way and two-nested Advanced Research WRF (ARW) V2.2 Noah LSM land-atmosphere coupled model, and the WRF 3DVAR system, are used to make two 48-h numerical experiments. The description of the interaction between the underlying urban surface and the atmospheric boundary layer is improved. The effect of local data assimilation on the fog evolution characteristics and its numerical prediction is finally assessed. 2. Observations In this section, based on meteorological observations of the disastrous long-lasting fog event over Beijing during February 2007, the evolution and spatial distribution of atmospheric features such as visibility, specific humidity, wind speed, synoptic patterns, and the vertical variation of temperature stratification are analyzed. Since a transformation between haze and fog (Wu, 2006) needs to be considered here, the definitions of fog and haze are given by the following two criteria according to atmospheric visibility and humidity:

3 428 ACTA METEOROLOGICA SINICA VOL.24 1) if atmospheric visibility is below 1 km, it is defined as fog; 2) if visibility is between 1 and 10 km, and relative humidity is above 90%, it is also defined as fog; but if relative humidity is below 78%, it is defined as haze; otherwise a mixture of haze and fog is defined. 2.1 Hourly evolution of visibility, humidity, temperature, and wind speed Using surface observations at the Beijing Observatory (39 48 N, E), the evolutions of visibility, humidity, temperature, and wind speed are presented in Fig. 1. The visibility data are also collected at the Wenyuhe Road Weather Station ( N, E), in the road section where heavy fogs frequently occur. The visibility data are obtained by using the Finland Vaisala ROSA instrument with 5- min intervals (Zhang et al., 2008a, b). It can be seen from Figs. 1a and 1b that the visibility values were between 1 and 10 km, and the humidity values were between 26% and 77% from 0200 BT 20 to 0200 BT 21 February. According to the two criteria given above, it was mild haze during this period. The visibility was below 1 km from 0530 to 1330 BT 21 February In the afternoon of 21 February, it was improved with values between 1 and 10 km, and then it decreased once again from 2205 BT 21 to 0240 BT 22 February. Since the visibility was below 1 km from the early morning to noon of 21 February 2007, let alone the relative humidity was above 90%, a heavy fog occurred. In the afternoon of 22 February 2007, heavy fog occurred only in some areas while mild fogs (with visibility between 1 and 10 km and relative humidity above 90%) occurred over other areas (figure omitted). It should be noted that atmospheric temperature had rapidly dropped from 7.7 to 1.5 C from 2000 BT 20 to 0200 BT 21 February (right before the heavy fog event). Figure 1c shows the change of surface wind speed (with north wind being positive and south wind being negative). It can be seen that the surface wind was very weak before and during the heavy fog event (below 2 m s 1 in most periods). From 1800 BT 20 to 0500 BT 21 February (before the heavy fog event) and from 1700 BT to 2200 BT 21 February (after the heavy fog event), south winds prevailed. In general, south winds are predominant before the heavy fog Fig. 1. Changes of (a) atmospheric visibility (logarithmic coordinates), (b) humidity and temperature, and (c) wind speed during the long-lasting fog event in Beijing.

4 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al. 429 event, while north winds prevail during the heavy fog event. From the above observations, we find that the formation and development of low visibility (fog) require high humidity, light wind, and low temperature. This agrees with our previous studies (e.g., Zhang et al., 2008a). Moreover, this low visibility event around Beijing is due to the combined impacts of both haze and fog. The haze first occurred on 20 February (with visibility above 10 km on 19 February), and then it went along with fog in the early morning of 21 February until the late afternoon of 22 February. 2.2 Spatial distributions of visibility, specific humidity, and wind speed Figure 2 gives distributions of visibility, temper- Fig. 2. Distributions of atmospheric visibility, surface temperature, wind barbs, and specific humidity at (a) 0500 BT, (b) 0800 BT, (c) 1100 BT 21 February 2007, and (d) 0200 BT 22 February The grey shaded area denotes atmospheric visibility ( 100 m), red dashed line denotes temperature ( C), and green line denotes specific humidity (g kg 1 ).

5 430 ACTA METEOROLOGICA SINICA VOL.24 ature, wind speed, and specific humidity. It can be seen that the atmospheric visibility over Beijing at 0500 BT 21 February decreased from 1 km and above (north regions) to 200 m and below (south regions). In the meantime, specific humidity increased from 2 (north regions) to 3.5 g kg 1 (south regions). Southeast/north winds prevailed over the southern/northern area with a low speed of 2 mm s 1, while temperature increased from 4 to 2 C (along the north-south section). From 0800 BT 21 February onwards and during the heavy fog event, the low visibility areas expanded obviously, and specific humidity over the southern area increased to 4.5 g kg 1, but the temperature variation was smaller than before. At that time, there was predominant north wind over Beijing. By 1100 BT, the fog region further expanded, but the heavy fog region (with visibility below 200 m) had become narrower. When the heavy fog started to weaken at 0200 BT 22 February, the visibility rapidly increased. The visibility in the southern area of Beijing was below 1 km, while it was above 1 km over other areas. The specific humidity decreased in the same time (with the maximum dropping to 3 g kg 1 ). Moreover, the northwest wind speed increased to 4 m s 1, and temperature also rose to some extent (by about 2 4 C). In general, the atmospheric visibility around Beijing decreased from north to south during the whole fog event, and low visibility was corresponding to high humidity, light wind, and low temperature. 2.3 Synoptic patterns From the 500-hPa weather map (figure omitted), it can be seen that Beijing was dominated by westerly airflows from 2000 BT 20 February (before the heavy fog event) to 0800 BT 21 February (during the heavy fog event). By 0800 BT 22 February, Beijing was controlled by northwest cold advection behind an upper-level trough. Then the fog started to dissipate with an increasing wind speed. In the corresponding surface weather maps, there was a strong depression center located at 50 N, 105 E at 2000 BT 20 February, and it moved eastward and controlled Beijing at 0200 BT 22 February. At 0800 BT 22 February, Beijing was under the rear of the low depression with strong cold air. 2.4 Vertical variations of temperature stratification and wind field Temperature inversion in the atmospheric boundary layer can restrict exchange of water vapor between the lower layers and upper ones. Through analysis we find that the intensity and duration of heavy fogs coincide with those of the atmospheric temperature inversion layer. By analyzing the vertical variation of temperature and wind speed in each period (figure omitted), we find that the temperature inversion layer existed before and during the heavy fog event with different intensities. When the fog began to dissipate, the inversion layer disappeared completely, and the wind direction and the advection changed in the lower atmosphere. At 20 h prior to the heavy fog event, there were north wind and cold advection, but at 6 h before the heavy fog event, they evoluted into south wind and warm advection. Thereafter, the wind direction changed into north, but warm advection still existed in the lower layer. Moreover, before and during the heavy fog event, the wind speed was very low ( 2 m s 1 ), which favored the persistence of high relative humidity over these regions. In brief, Beijing was wrapped in haze on 20 February 2007 even when there was not enough water vapor due to the atmospheric temperature inversion and the accumulation of large number of suspended dust, soot, and salt particles in the lower boundary layer. When south wind in the lower layer brought in warm and moist air, atmospheric humidity increased rapidly. In the early morning of 21 February, fog appeared instead of haze. Because of the persistent inversion, high humidity, light wind, and low temperature, the fog persisted until around 0500 BT 22 February. Thereafter, a trough crossed Beijing and brought cold air mass, so the stable weather conditions were destroyed, the wind speed increased, and the humidity decreased. Finally, the heavy fog dissipated because there no longer existed favorable conditions for its maintenance.

6 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al Numerical experiments and the WRF 3DV- AR system Using the 9/3-km two-way and two-nested ARW V2.2 Noah LSM land-atmosphere coupled model and the WRF 3DVAR system, two 48-h numerical experiments were performed with and without assimilation of local meteorological data. In the model vertical direction, mass coordinate is used. As the fog event occurred mainly on 20 and 21 February, the simulation covers a period of 48 h from 0800 BT 20 to 0800 BT 22 February The physical schemes of the model include Thompson microphysics scheme, Kain-Fritsch cumulus parameterization, Yonsei-University planetary boundary layer (PBL) scheme, Monin-Obukhov similarity theory for surface layer, Noah Land Surface Model, and Dudhia cloud radiation scheme that considers the interaction between long/short wave radiation and explicit cloud, which is important because of the role of radiation in the formation of fogs. The model domain is centered at Beijing (40 N, 116 E). The model grid system has mother grids and nest grids. The top-of-atmosphere pressure is 50 hpa. Both background fields and lateral boundary conditions at the initial time are obtained from the NCEP GFS (Global Forecast System) global gridded analysis dataset (with a 1 1 resolution). The lateral boundary conditions are updated every 6 h. Considering the potential effects of local topography on numerical simulations (Zhang et al., 2005), the model topography of the 9-km resolution domain is generated from USGS (US Geological Survey) data smoothed with a grid-filtering method for 2 resolution (approximately 3.7-km grid spacing), while the model topography of the 3-km resolution domain with 30 resolution (approximately 1-km grid spacing). Meanwhile, using the land use data around Beijing as of 2000 with a resolution of 500 m, the land use information is updated in order to reveal more realistically the underlying surface classifications around Beijing, especially the expansion of the urban area. The deciduous broad-leaved forest in mid latitudes of Asia is inappropriately classified as tropical (or subtropical) savanna by USGS data. This is revised here. The new land use information contributes to a better reflection of the effects of urban underlying surface on the physical process of PBL, cloud and precipitation (Zhang et al., 2007b, 2009; Jiang et al., 2007). Four kinds of observational data (radiosonde, ground-based GPS precipitable water vapor content, surface conventional, and automatic weather station observations) were assimilated using the WRF 3DVAR system. For each automatic weather station, meteorological elements (p s, u, v, T, and q) were assimilated after considering the topographic difference between surface and the model (Ruggiero et al., 1996). Using the 24- and 12-h forecast outputs valid at the same time every day, we estimated the climatic background error covariance matrix via the NMC-method (Parrish and Derber, 1992). The initial lateral boundary conditions for the WRF model were further updated by the 3DVAR system. According to the climatic characteristics of Beijing, the horizontal length scale of background errors were reasonably adjusted (Zhang et al., 2006; Fan et al., 2006). Figure 3 shows distributions of the individual components of the atmospheric observation network in the Beijing-Tianjin-Hebei areas. It can be seen from Fig. 3a that there are relatively fewer conventional radiosonde stations (with a station spacing of km), but the radiosonde stations have a uniform spatial distribution, capable of monitoring largeand meso-scale weather systems in the mid and upper troposphere surrounding Beijing. From Figs. 3b and 3c, it can be seen that the automatic and conventional surface weather observations are about km apart and distributed over land with a high density (especially within the Beijing area). This is useful for monitoring the genesis, development, and dissipation of microscale weather systems. The synchronous ground-based GPS-MET experimental network (Fig. 3d) consisting of 6 ground-based stations (3 in the Beijing area) is helpful in capturing the evolution of IPW (integrated precipitable water vapor) over the Beijing area. To evaluate the effects of data assimilation of local observations on the initial fields and forecast results, two numerical experiments (Exps. 1 and 2) were carried out. For Exp. 1, the background fields are directly from the NCEP GFS global gridded analysis data. By using the WRF 3DVAR system observational data (including 171 conventional weather stations,

7 432 ACTA METEOROLOGICA SINICA VOL.24 Fig. 3. Distributions of (a) conventional radiosonde stations, (b) conventional surface observation stations, (c) automatic weather stations, and (d) ground-base GPS-MET data points in the atmospheric observational network over the Beijing- Tianjin-Hebei areas. 14 sounding stations, 49 automatic weather stations in the Beijing area, and 6 ground-based GPS stations in Beijing-Tianjin-Hebei areas) are assimilated to provide initial and boundary conditions for the numerical simulations. For Exp. 2, the initial analysis field is directly introduced from NCEP GFS analysis data without data assimilation. By comparing the initial analysis fields and forecast results of Exps. 1 and 2, the effects of assimilation of local observations on the simulation of this fog event are investigated. 4. Numerical simulations 4.1 Comparison of Exp. 1 and observations Spatial evolution of fog area, specific humidity, temperature, and wind speed In routine meteorological observations, fog is seen as lots of visible aggregates of water droplets or ice crystals. Recently, some studies (e.g., Zou et al., 1982; Cotton and Anthes, 1993) have indicated that the

8 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al. 433 visibility of radiation fog is less than 200 m above the ground, temperature is between 15 and 0 C, and LWC is between 0.05 and 0.5 g kg 1. However, the LWC ranges of fog events are different due to local fog characteristics and regional fog formation conditions. Here we use LWC to describe the formation and dissipation processes in the fog simulations, and define 0.05 g kg 1 as a critical value. The empirical formula describing the relationship between visibility and LWC (Kunkel, 1984; Bergot and Guedalia, 1994) is adopted to evaluate simulated visibility. The formula is: V is = ln(0.02), β = 144.7(ρ 0 q 1 ) 0.88, (1) β where ρ 0 is the density of water (g cm 3 ), q 1 is LWC (g kg 1 ), and β is a physical parameter obtained from experiments. According to this formula, LWC values of 0.48, 0.24, 0.12, and 0.02 g kg 1 correspond to visibility values of 16, 30, 60, and 300 m, respectively. Results of Exp. 1 (Fig. 4) indicate that fog Fig. 4. Simulated distributions of LWC, temperature, wind barbs, and specific humidity at 1000 hpa in Exp. 1 at (a) 0500 BT, (b) 0800 BT, (c) 1100 BT 21 February 2007, and (d) 0200 BT 22 February Color shadings denote LWC (g kg 1 ); red dashed line stands for temperature ( C); green line stands for specific humidity (g kg 1 ).

9 434 ACTA METEOROLOGICA SINICA VOL.24 appeared at 0500 BT 21 February. By 0800 BT 21 February, the maximal LWC value rose to higher than 0.6 g kg 1 and fog area also extended. At 1100 BT, the fog area expanded to the maximal extent, but the LWC decreased to the minimal value (below 0.54 g kg 1 in the whole fog region). This may be due to the fact that the ground surface receives solar radiation after sunrise; as the temperature increases, the heavy fog is thinning out with fog drops evaporating. Thereafter, the fog region gradually became narrow and the fog turned into a light fog (figure omitted). From 2200 BT 21 February, the LWC began to increase slowly once again, the fog appeared denser (but relatively less than before) over a considerably smaller area. After a few hours, the fog dissipated completely by 0800 BT 22 February, and LWC was below 0.05 g kg 1 then. Compared with the observations, Exp. 1 successfully simulated the spatial evolution of this fog event except for some small biases in the area distribution. The LWC area nearly coincides with that of heavy fog (with visibility below 50 m), though it is smaller. Spatial evolutions of specific humidity and temperature of Exp. 1 are in good agreement with the observations (Fig. 2). Meanwhile, the north wind during the period of the fog dissipation is also well simulated. In a word, the formation/dissipation time of the fog and its intensity are simulated in Exp. 1, and these results are well consistent with the observations. Since there are no observations of LWC, we use the simulated LWC to depict the formation/dissipation of the fog. During this fog event, the visibility variation is proportional to the LWC value Temporal evolution of LWC, temperature, and wind speed Figure 5 presents the area-averaged ( N, E) comparison between the observations and the results of Exp. 1. In Fig. 5a, the simulated formation/dissipation time of fog matches with observations very well, especially the transformation between haze and fog. When haze showed up on 20 February, the LWC was 0 g kg 1. When the fog appeared at 0500 BT 21 February, the LWC increased up to 0.05 g kg 1. During BT, the LWC reached the maximum of 0.35 g kg 1, which closely corresponded to the lowest visibility period. Thereafter, the LWC continued to decrease. At 2200 BT 21 February, the LWC started to increase again. At 0200 BT 22 February it rose above 0.05 g kg 1, and then the heavy fog appeared again. Finally, the LWC gradually dropped back to 0 g kg 1. Figure 5b shows that the temperature is also Fig. 5. Comparison of the area-averaged ( N, E) (a) liquid water vapor content, (b) surface temperature, and (c) surface wind speed between observations and the results of Exp. 1.

10 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al. 435 simulated successfully, and its tendency and intensity of variation are well consistent with the observations. Though the simulated temperature is lower than the observations as a whole, the maximal error is only 0.5 C. Furthermore, the simulated temperature is perfectly consistent with the observation, especially in the initial period of the fog event. The variations of both wind speed and direction (Fig. 5c) are well consistent with the observations except for biases at some individual times. On 20 February, north wind prevailed, and then it changed into south wind in the early morning of 21 February just before the heavy fog event. During the fog event, the simulated variation of wind direction coincides with the observations as a whole Synoptic patterns The simulated 500-hPa synoptic patterns in Exp. 1 indicate that both the temperature and pressure fields are simulated well during the period from 20 to 22 February 2007 (figure omitted). The westerly flow before and during the fog event and the synoptic patterns in which Beijing is behind the high-level trough after the fog event are also simulated successfully, and the temperature field is close to the observations. As to the wind field, the westerly flow at 500 hpa is simulated well, so do the surface synoptic patterns, such as the low pressure in the late period of the fog event. The surface wind field is simulated better than that at 500 hpa. In general, major weather systems during the long-lasting fog event are simulated successfully in Exp Effects of data assimilation on simulation results In order to analyze the effects of assimilation of local observations on the numerical simulation of the fog event, Exp. 2 is conducted with the NCEP GFS analysis data as initial fields (Fig. 6) and no local data assimilation is done. Compared with Exp. 1 (Fig. 4), it is obvious that the local data assimilation by the 3DVAR system leads to great improvement in the simulation of both the fog area and intensity. For example, during the fog event, dense fog mostly appeared at 0800 BT 21 February when the simulated LWC of Exp. 2 is only 0.5 g kg 1, while that of Exp. 1 is 0.6 g kg 1. That is to say, the simulation without the local data assimilation gives a lower LWC and a smaller fog area for this time period. For another instance, while the fog area extended and the LWC decreased at 1100 BT 21 February, the simulated LWC of Exp. 2 is greater than that of Exp. 1, i.e., the simulated fog is much denser with a larger area in Exp. 2 than in Fig. 6. Simulated distributions of LWC, temperature, wind barbs, and specific humidity at 1000 hpa at (a) 0800 BT 21 and (b) 1100 BT February 2007 for Exp. 2. Color shadings denote LWC (g kg 1 ); red dashed line stands for temperature ( C); green line stands for specific humidity (g kg 1 ).

11 436 ACTA METEOROLOGICA SINICA VOL.24 Fig. 7. Simulated vertical distributions of relative humidity for (a) Exp. 2 and (b) Exp. 1. Exp. 1 at this stage. Assimilation of local observational data also has great effects on the simulation of humidity field in the lower layer. Figure 7 shows simulated vertical distributions of relative humidity. In the upper layers, there is no significant humidity difference between Exps. 1 and 2, but in the lower layers, the results of Exp. 1 are much closer to the observations than those of Exp. 2. Before the fog event, the relative humidity ofexp. 2 is lower than that of Exp. 1. At 0800 BT 20 February, the relative humidity simulated by Exp. 1 is 70%, while it only reaches 50% in Exp. 2. During the fog event, the relative humidity fields of both experiments are up to 70% at 0400 BT 21 February, and reach 90% by 0800 BT. However, the simulated duration of high humidity of Exp. 1 is far better than that of Exp. 2. In Exp. 1, the relative humidity is above 80% from 0700 BT 21 to 0700 BT 22 February, while in Exp. 2 the relative humidity decreases rapidly to 40% from 0700 BT to 1400 BT 21 February, and then the fog begins to dissipate. Generally speaking, the simulated fog area in Exp. 1 is more reasonable than that in Exp. 2. The LWC of Exp. 1 is higher than that of Exp. 2. The simulation results with the local data assimilation are closer to observations. Furthermore, the relative humidity variation in Exp. 1 is more consistent with the observation. Therefore, the assimilation of local observational data contributes significantly to the improvement in the simulation of water vapor evolution in the lower troposphere. 4.3 Effects of data assimilation on the initial analysis fields Figure 8 shows the 1000-hPa meteorological field differences between Exps. 1 and 2 at 0000 BT 20 February Figures 8a and 8b describe the improvement in the initial water vapor field due to the local meteorological data assimilation at the initial time. It can be seen that there are large-scale positive differences in the humidity field at the initial time. The maximum is 20% for relative humidity (Fig. 8a) and 0.4 g kg 1 for specific humidity (Fig. 8b), respectively, and the maximal difference is located just within the simulated densest fog area (Fig. 8b). Hence, the initial local data assimilation is helpful to form the favorable moisture distribution for the heavy fog in the lower layers. Figure 8c presents the wind speed difference. To better reveal the wind difference between Exp. 1 and Exp. 2, Fig. 9a further shows the ratio of Exp. 1 Exp. 2 to Exp. 2. From both figures, it clearly shows that the wind speed in Exp. 1 could be 2.4 times larger than that in Exp. 2 at the initial time despite the fact that the maximal surface wind speed difference is only 1.8 m s 1. The southeast flow is stronger in Exp. 1,

12 NO.4 ZHANG Chaolin, ZHANG Lina, WANG Bizheng, et al. 437 Fig. 8. Distributions of the 1000-hPa differences of (a) relative humidity (%), (b) specific humidity (g kg 1 ), (c) wind field (m s 1 ), and (d) temperature ( C) between Exp. 1 and Exp. 2 at 0000 BT 20 February The model topography is higher than the 1000-hPa level in blank areas. which means that the local data assimilation helps to enhance the dynamic conditions for fog evolution in the lower layers. The difference in the initial temperature field is dominated by negative areas at both 1000 (Fig. 8d) and 950 hpa (Fig. 9b). This indicates that the local data assimilation helps to capture the initial lower temperature at the lower level. Meanwhile, comparison of Fig. 8d with Fig. 9b shows that the negativedifferences at 1000 hpa (maximum 2.7 C; minimum 1.5 C) were larger than those at 950 hpa (maximum 2.2 C; minimum 1.4 C). It means that the local data assimilation favors the genesis of the inversion structure with temperature at 1000 hpa lower than that at 950 hpa. Both the atmospheric temperature decrease and inversion enhancement are favorable boundary conditions for the long-lasting fog event, so the simulation results are improved with the local data

13 438 ACTA METEOROLOGICA SINICA VOL.24 Fig. 9. (a) The 1000-hPa wind speed difference ratio of Exp. 1 Exp. 2 to Exp. 2 and (b) the 950-hPa temperature difference (Exp. 1 Exp. 2) at 0000 BT 20 February The model topography is above the 1000-hPa level in blank areas. assimilation. In general, the local dataperature advection and boundary layer temperature inversion. assimilation reinforces the water vapor transport of the southeast flow at the lower levels over Beijing areas at the initial time, leading to much improvement in the initial moisture distribution. It also helps to introduce more favorable weather information for the heavy fog development, such as the cold temperature advection and boundary layer temperature inversion. 5. Conclusions With the rapid development of urbanization and national economy, meteorological services for livable city and traffic safety associated with long-lasting fog events have become increasingly demanded. Using the 5-min automatic visibility data, conventional meteorological observations, the ARW V2.2 Noah LSM landatmosphere coupled model, and the WRF 3DVAR system, the long-lasting fog event during February 2007 in Beijing is investigated. The evolution characteristics of atmospheric wind, humidity, and water vapor fields, as well as the formation, development and dissipation of this fog event are discussed. Meanwhile, we investigate the effects of local data assimilation on the initial fields and final results of the numerical simulations. The main conclusions are listed as follows: (1) The spatial-temporal evolution of this fog event is closely associated with the meteorological conditions, such as high humidity, light wind, static stability, and low temperature. Because of the existence of a boundary layer temperature inversion, haze firstly occurred in Beijing on 20 February under the limited water vapor condition. Then south wind brought warm and moist air into the lower layers, and humidity increased rapidly. The haze evolved into fog in the early morning of 21 February, and the synoptic patterns remained stable (low wind speed and abundant water vapor). Because of the persistent temperature inversion, high humidity, light wind, and low temperature, the fog lasted a long time. Near 0800 BT 22 Feburary, a trough moved into Beijing and brought cold and dry air with strong winds, which disfavored the heavy fog, and consequently the fog dissipated. (2) Based on the simulation results, LWC is used as an index to represent the formation and dissipation of the fog. The local data assimilation helps to better simulate the water vapor transport and distribution of southeast flow in the lower troposphere over southern

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