Nat Hazards DOI 10.1007/s11069-013-0595-0 ORIGINAL PAPER
Impacts of topography and land cover change on thunderstorm over the Huangshan (Yellow Mountain) area of China Die Wang • Junfeng Miao • Zhemin Tan
Received: 28 October 2012 / Accepted: 7 February 2013 Ó Springer Science+Business Media Dordrecht 2013
Abstract In this study, the Weather Research and Forecasting (WRF) model (version 3.1.1) was used to simulate a thunderstorm event which occurred on August 18, 2010, over the Yellow Mountain area of China. This event was a typical thunderstorm embedded in high-pressure systems. First, the development processes of mountain–valley breeze and convective cells were studied; second, this study focused on revealing the influencing mechanism of complex topography and heterogeneous land cover on thunderstorm by removing the Yellow Mountain and changing the land use categories. On flat terrain, the simulated results displayed that the convection weakened persistently, cloud top decreased sharply, and intensity of precipitation reduced. Moreover, there was no up-slope valley breeze, convergence, and lifting of water vapor could be found on the mountaintop. Then, the role of land use was revealed by changing original land cover into grassland, mixed forests, and bare soil in the innermost area, respectively. When covered by grassland, there were less sensible heating and lower moisture, leading to the planet boundary layer height decreasing and vertical lifting weakening, which tended to cause more stable atmosphere and less rainfall on the mountaintop; when covered by mixing forests, only small differences presented in simulated meteorological fields, including wind fields, moisture, cloud water mixing ratio, precipitation, and other fields; when covered by bare soil, the latent heating was more important in influencing the process of thunderstorm. There were less latent heating and lower accumulated water vapor compared to other experiments, causing vertical lifting weakening, stability of atmosphere increasing, and precipitation decreasing. Keywords
The Yellow Mountain Thunderstorm Topography Land cover change
D. Wang J. Miao (&) Key Laboratory of Meteorological Disaster of Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China e-mail:
[email protected] Z. Tan Key Laboratory of Mesoscale Severe Weather of Ministry of Education, Nanjing University, Nanjing, China
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1 Introduction Orography plays a significant character in influencing weather processes by exchanging momentum and energy between large-scale and mesoscale weather systems. When mountains are encountered, many factors, including water vapor, wind shear, atmospheric stability, and direction of the leading air, will be combined to influence the local weather situations. Besides, the orographic wave and vortex which may generate torrential flood, debris flow, cold air damming, exceptional track of storm, and coastally trapped disturbance are also regarded as the important weather phenomena induced by orography (Lin 2007). The orographic precipitation is very complex as the primary factor to be considered in forecasting local mesoscale weather (Hohenegger et al. 2005). When the mountains are high enough, a large number of moist airs lift forcedly and decrease its temperature to dewpoint by expansion and adiabatic cooling over the slopes (Wallace and Hobbs 1977). The condensation of water vapor contained within the air initially forms the orographic clouds over the mountains. After that, the cloud droplets gradually grow into raindrops and fall to the ground when large sufficiently. Furthermore, heterogeneous terrain has great effects on duration, location, scope, quantity, and intensity of rainfall. During the past several tens of years, the trigger and enhancement mechanisms of orographic rainstorm have been summed up as thermal force, dynamic factors, and even topographic features (e.g., shapes and dimensions) (Smith 1979; Roe 2005). To huge mountains, greater precipitation is related to up-slope condensation, small disturbance, or wind convergence on the leeward slopes in stable atmospheric conditions (Mass 1981; Chu and Lin 2000); to monticules, the seeder–feeder mechanism which can lead to intense rainfall is particularly important, because it helps condensation nucleus grow into droplets (Bergeron 1949; Robichaud and Austin 1988). Previously, for understanding the fundamental features of local circulation and development processes of orographic precipitation, two-dimensional (2D) hydrostatic models (e.g., Gallus and Klemp 2000; Chu and Lin 2000) and diagnostic models (e.g., Chris and Joel 2004; Kunz and Kottmeier 2006) are accessed successfully to be used. Without considering the static stability of the atmosphere and the dimensions of topography, the flow dynamics and three-dimensional (3D) structures of wind fields cannot be described clearly over complex terrains. Therefore, it is crucial to use a 3D non-hydrostatic numerical model to reveal the 3D structure of convection cells, and the vertical circulation of the mountain–valley breeze by incorporating an actual terrain and land cover databases (e.g., Paul and Nikolai 2011). The Yellow Mountain which located near Huangshan City of Anhui Province in Eastern China in the subtropics has a typical monsoon humid climate. There are three major peaks among 72 distinguished peaks with various shapes, namely Lotus Flower Peak, Bright Summit Peak, and Celestial Capital Peak. Most studies focus on the sea of clouds and mesoscale rainstorm under the beneficial synoptic-scale circulation background, using models with simple dynamic frameworks and physical parameterizations in the Yellow Mountain region (e.g., Zhai et al. 1995; Wu et al. 2005; Chen and Zhao 2006). Not too many studies pay close attention to the circulation and thunderstorms triggered by inhomogeneous heating from the earth’s surface. Moreover, Land cover over the Yellow Mountain area has been modified significantly during the past decades. This region will be reshaped continuously by exploiting the natural scenic spots and expanding urban areas in coming future. Land cover changes have far-reaching influences on the characteristics of PBL and regional land–atmosphere
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interaction (Pielke 2001; Cui et al. 2006; Mahmood et al. 2006; Ter Maat et al. 2006; Pielke et al. 2007a, b; Raddatz 2007; Nair et al. 2011). Various numerical models have been used for studying the impacts of land cover changes on wind fields, temperature, soil moisture, atmospheric water vapor content, convective cloud, and convective precipitation (e.g., Chang and Wetzel 1991; Clark and Arritt 1995; Shen 1998; Crawford et al. 2001; Pielke 2001; Adegoke et al. 2003; Narisma and Pitman 2003; Gero and Pitman 2006; Sen Roy et al. 2007, 2011). The development and evolution of intense convective weather processes are influenced profoundly by increasing available energy over vegetated ground (Pielke et al. 2007a; Pielke 2001). Nevertheless, the most previous studies concentrate on orographic effects instead of land cover changes in the Yellow Mountain region. Consequently, it is necessary for us to reveal the impacts of different land use types on local circulation and severe convection deeply, using 3D mesoscale numerical model.
2 Model description and initialization The 3D non-hydrostatic atmospheric model WRF (Skamarock et al. 2005) is a mesoscale numerical model designed for a wide range of operational forecasting and academic research needs. In recent years, it generally gains popularity in simulating the severe convective weathers. Therefore, in this study, the ARW-WRF version 3.1.1 model with four nested domains (D1, D2, D3, and D4) and two-way nesting schemes is adopted for use. Domains are centered on the mountaintop (30.15°N, 118.15°E), and the horizontal grid resolutions are 27, 9, 3, and 1 km, with 150 9 150, 196 9 196, 184 9 184, and 124 9 124 grid points for D1, D2, D3, and D4, respectively (Fig. 1). D1 covers central and Eastern China, which is responsible for simulating the large-scale circulation and synoptic-scale
Fig. 1 Map showing the model domains (D1, D2, D3, and D4) and topography (color). Bold solid lines (rectangle) denote the geographic locations of the nested grids. The central point is located on the mountaintop (30.15°N, 118.15°E)
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Fig. 2 Terrain of D4 (color and with contours every 200 m) in the a CNTL and b TOPO runs, solid line (AB) indicates the location of the vertical cross-section used in this study (along 118.15°E). Location of ten AWSs marked by circle signs: Xiuning (XN), Zhaixi (ZX), Huangshan (HS), Jiuhuashan (JHS), Jingde (JD), Qimen (QM), Yixian (YX), Shexian (SX), Tunxi (TX), and Huangshanqu (HSQ) indicated for verification of model outputs
weather systems. The D2 and D3 are setted up to capture mesoscale and local weather situation, and the D4 is the area of especial concern. The locations of the three inner grids are far enough away from the boundaries of the outer grids that they would not be greatly affected by the values at the outer boundaries during a 48-h period. The vertical grids for all domains consist of 35 levels ranging from the ground to 100 hPa, and the lowest layer is approximately at 48 m AGL. The topography (Fig. 2a) and land use (Fig. 3) are obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) databases in 2001 at 10, 5, 2 arc-min, and 30 arc-sec resolutions for D1, D2, D3, and D4, separately. The physical parameterizations used in this study are given in Table 1, and the cumulus scheme is not used in D3 and D4 (Miao et al. 2008, 2009). The final reanalysis (FNL) data from National Center for Environmental Prediction (NCEP) updated every 6 h with horizontal resolution of 1° 9 1° was used in this study. Interpolated FNL data in horizontal and vertical directions were considered as the initial fields and lateral boundary conditions.
3 Numerical experiments With the purpose of capturing the significant influence of topography and land use changes on mountain–valley breeze and thunderstorm, a series of sensitivity tests were performed. The run which used the real topography and land use types was denoted as the control (CNTL) experiment. In contrast, the remaining three other runs which were identical to the CNTL run except for land use types were designed to run with uniform grassland (GRASS), mixed forests (FOREST), and bare soil (DESERT) in D4, respectively (Table 2). Furthermore, in order to show the dynamic and thermodynamic effects of
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Fig. 3 Land use categories of D4 with 30 arc-second resolution data from the MODIS (2001) datasets Table 1 WRF physical parameterizations Parameterization
Reference
Microphysics scheme: Lin et al.
Lin et al. (1983)
Longwave radiation: rapid radiative transfer model
Mlawer et al. (1997)
Shortwave radiation: Dudhia
Dudhia (1989)
Land surface scheme: Noah land surface model Surface scheme: Monin–Obukhov (Janjic´)
Chen and Dudhia (2001) Janjic´ (2002)
Planetary boundary layer scheme: Yonsei University (YSU)
Hong et al. (2006)
Cumulus scheme: Kain-Fritsch scheme (only for D1 and D2)
Kain (2004)
terrain, a TOPO experiment was conducted, with the region (29.80–30.35°N, 117.80–118.45°E) being cut down in elevation (to 200 m) (see Fig. 2b). The integral time period was 48 h (from 0000 UTC 17 August to 0000 UTC August 19, 2010). The first 16 h of this period was discarded as a part of the model spin-up, and the remaining 32 h formed the time period of interest for this study.
4 Case description The period from 0900 to 1400 LST on August 18, 2010, witnessed a thunderstorm over the Yellow Mountain area. By observation, the relatively heavy precipitation cells were
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Nat Hazards Table 2 Summary of the numerical experiments Experiment
Land use
Topography
CNTL
Heterogeneous land use (MODIS data)
Realistic terrain
TOPO
Heterogeneous land use (MODIS data)
Flat terrain (200 m) (29.80–30.35°N, 117.80–118.45°E)
GRASS
Homogeneous land use (grassland)
Realistic terrain
FOREST
Homogeneous land use (mixed forests)
Realistic terrain
DESERT
Homogeneous land use (bare soil)
Realistic terrain
situated at Jiuhuashan (JHS), Huangshanqu (HSQ), and Huangshan (HS) automatic weather stations (AWSs), with the 12-hour-accumulated amount of precipitation reaching 71.7, 68.1, and 58.3 mm, respectively (Fig. 6a). As can be seen clearly from the 500 hPa height field (Fig. 4a), the southeast of China was subject to the West Pacific subtropical high (WPSH), and the Yellow Mountain (signed by the red point) located to the top and back portion of the WPSH. The upper southerly air current would carry the warm and moist air into the Yellow Mountain area, which could create large-scale background field for thunderstorm. At 850 hPa (Fig. 4b), the counterpart region was covered by warmer (above 20 °C) and drier air and prevailed southwest winds without any shear lines. The large temperature differences between surface and upper air were beneficial to accumulate convective instability energy and produce thunderstorm. Hence, the selected thunderstorm mainly developed under weak synoptic pressure gradients. The differences of thermal properties of non-homogeneous underlying surface layer and the local circulation played a leading role in triggering this severe convection.
Fig. 4 The synoptic weather pattern at 1000 LST on August 18, 2010, at a 500 hPa and b 850 hPa, showing geopotential height (dam, blue lines), temperature (°C, red lines), and relative humidity (color). The red point shows the position of the mountaintop of the Yellow Mountain
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5 Results and discussion 5.1 Comparison with observations There are ten AWSs located within D4, namely Jiuhuashan (JHS), Huangshanqu (HSQ), Jingde (JD), Huangshan (HS), Qimen (QM), Yixian (YX), Shexian (SX), Tunxi (TX), Xiuning (XN), and Zhaixi (ZX) (Fig. 2a). The more detailed information of these AWSs was given in Table 3. In order to evaluate the model performance, four of them (SX, HSQ, JD, and HS) with different positions, elevations, dominant land use types, and soil moistures were chosen to perform model-observation comparisons. The analysis of the outputs in outer domains (D1, D2, and D3) (not shown) indicated that the CNTL run not only well reproduced the pattern, intensity, position, and evolution of the synoptic-scale systems, but also showed the similar distribution and variation of meteorological variables (e.g., temperature, wind, and humidity). Moreover, in D4, the model outputs (e.g., temperatures and wind speeds) were used for comparing with the observation fact at the closest grid points to AWSs. The hourly observed wind speeds and temperatures are recorded at 10 and 2 m above ground level (AGL), respectively. Figure 5 showed the diurnal variations of simulated and observed 10-m wind speeds and 2-m temperatures at SX, HSQ, JD, and HS AWSs, respectively. For 10-m wind speeds, the WRF model did fairly well in reproducing the variation trends at both SX and HSQ AWSs (Fig. 5a, c). However, at JD AWS (Fig. 5e), the 10-m wind speed was greatly overestimated between 0000 and 1600 LST, with the differences reaching about 3 m/s at 0700 LST. At HS AWS (Fig. 5g), although the simulated variation trend was found to be in good agreement with observations, the maximums (6 and 8 m/s at 0600 and 2100 LST, respectively) and minimums (0.2 and 1.2 m/s at 1000 and 1400 LST, respectively) were not successfully simulated. Therefore, it might be inferred that the relatively larger differences occurred in more high-elevation regions (e.g., at JD and HS AWSs), where the stronger turbulent mixing would exist. Simultaneously, the complex terrains, to a great degree, also caused difficulty on wind forecasts. As for the 2-m temperatures (Fig. 5b, d, f), they reached their maximums (36, 34, and 35.2 °C) at around 1200 LST in simulation, while peaking at 35, 33, and 33.2 °C at 1600 LST in observation at SX, HSQ, and JD AWSs, respectively. The counterparts were overestimated all day at HS AWS (Fig. 5h). The discrepancies of land cover types and terrains between simulated and real values might Table 3 Name, location, and elevation for AWSs used in this study (Lat.: latitude, Lon.: longitude, Elev.: elevation), as well as dominant land use (LU) and soil moisture (SM) represented in D4 closest to the AWSs Name
Lat. (°N)
Lon. (°E)
Xiuning (XN)
29.77
118.17
Zhaixi (ZX)
30.06
118.17
Jiuhuashan (JHS)
30.48
Jingde (JD)
Elev. (m)
LU
SM
173.4
Grasslands
0.15
601.0
Open shrub-lands
0.15
117.78
643.2
Mixed forests
0.30
30.30
118.53
221.7
Permanent wetlands
0.42
Qimen (QM)
29.85
117.72
138.9
Closed shrub-lands
0.10
Yixian (YX)
29.92
117.92
222.8
Croplands
0.30
Shexian (SX)
29.87
118.42
170.1
Closed shrub-lands
0.10
Tunxi (TX)
29.72
118.27
141.7
Croplands
0.25
Huangshan (HS)
30.13
118.15
1,835.0
Huangshanqu (HSQ)
30.30
118.13
192.8
Deciduous needle leaf forest
0.30
Croplands
0.30
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Fig. 5 Diurnal variations of simulated and observed 10-m wind speed and 2-m temperature at SX, HSQ, JD, and HS AWSs on August 18, 2010
be attributed to the differences of 2-m temperature. Hence, these analyses confirmed that the WRF simulation might reasonably capture the dominant regional-scale features and supplement the observations by providing a broader context in this study. 5.2 Evolution of local circulation 5.2.1 Horizontal structure First, the diurnal circulation of horizontal 10-m wind fields over the Yellow Mountain area on August 18, 2010, was analyzed. In Fig. 6, it made clearly that the mountain–valley breeze was well established, transformed, and developed in this region. There was a diminishing down-slope wind on the mountaintop and north slope (30.12–30.2°N) between 0000 and 0700 LST, with the wind speed reducing to 2 m/s. After sunrise (0900 LST), the mountain’s surface heated air quicker than the valley bottom could, causing up-slope flow. Simultaneously, the convergence zone of wind directions and convective cloud (see Fig. 11a) appeared on the mountaintop (30.14°N). It might be inferred that the valley wind played an important role in producing thunderstorm. The valley breeze (up-slope wind) prevailed from 0900 to 1800 LST when there was an opposite conversion of wind direction occurred. After that time, the convergence zone moved to the leeward slopes, and there was a down-slope wind of cooler air along the Yellow Mountain slopes, with the maximum
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Fig. 6 N–S and time cross-sections of simulated 10-m wind fields (U and V-components, vector), topography (contours), and 10-m wind speed (color) along line AB on August 18, 2010
wind velocity reaching 6 m/s after 2000 LST. Note that the frequency of slight breeze (low to 0.5 m/s) was higher during the period of valley wind. Specifically, it could be exhibited clearly in the horizontal cross-sections which involved the evolution of complex wind fields in D4 (Fig. 7). At 0200 LST (Fig. 7a), an intense mountain breeze which flowed from the mountaintop to the valley floor was organized along two sides of the Yellow Mountain, especially on the south slope. However, with the generally enhanced solar radiation, wind pattern changed dramatically at 0900 (Fig. 7b). The transition from down- to up-slope wind occurred on higher elevations first, and then, the up-slope flow generally covered the whole valley. A convergence line in accordance with the orientation of the mountain ridge ranged from northeast to southwest and displayed a northwest development tendency apparently. Until sunset, being absented from solar radiation, the mountain breeze prevailed, and wind speed increased rapidly (Fig. 7d). Thus, through the above analysis, there was obvious and important flow pattern (mountain–valley breeze) in the Yellow Mountain region on August 18, 2010. 5.2.2 Vertical structure The vertical cross-sections of wind fields, vertical velocity (W 9 10), and meridional wind speeds along line AB (see in Fig. 2a) were clearly exhibited in Fig. 8. Although the thunderstorm produced, the background wind speeds in the Yellow Mountain region were low under high-pressure systems. The meridional wind speeds less than 4 m/s during the whole day above 2.5 m. Before accessing to the solar heating (Fig. 8a), cold air slided down the north slope, and only a slight disturbance of wind cloud be seen at
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Fig. 7 Simulated differences between 10-m wind fields and the innermost domain average of 10-m wind fields (U and V-components, vector) and V-component (color) at a 0200, b 0900, c 1600, and d 2000 LST on August 18, 2010, and topography (m, contours)
0200 LST, with the vertical velocity reaching 0.35 m/s. Then, the convective layer started to develop due to enhanced solar radiation, and an up-slope flow flushed out from the valleys, before converging on the mountaintop. At 0900 LST, the velocity of updraft increased (up to 1.5 m/s) at 30.15°N in Fig. 8b, which indicated a well-developed valley circulation. At this time, the initial convection appeared on the mountaintop and developed sharply in the following hours (Figs. 11a, b). However, the airflow crossed the mountaintop and formed the down-slope wind on the north slope (30.17–30.3°N) at 1600 LST, because the temperature gradient reversed the background wind rose generally
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Fig. 8 Vertical cross-sections of simulated wind fields (V and W 9 10-components, vector), vertical velocity (color), and V-component (ms-1, contours) along line AB at a 0200, b 0900, c 1600, and d 2000 LST on August 18, 2010
(Fig. 8c). The later period experienced that the down-slope wind increased and pooled down below. At 2000 LST, a downward motion (up to 1.2 m/s) accompanying with warming might produce to motivate a reversed ascending air current near 30.2°N in Fig. 8d. Summing up the above, the vertical structure of mountain–valley breeze displayed clearly, especially the valley wind in the daytime, providing favorable explanation for the formation of thunderstorm.
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5.3 Evolution of cloud and precipitation In order to evaluate the model performance in simulating the development process of orographic cloud and precipitation evens, a comparison between observational and simulated 24-h accumulated rainfall was given. Because of the dispersed AWSs in positions, the precipitation of every AWS in observation tended to be marked by figures rather than contours. Figures. 9a, b showed that the WRF model could duplicate the precipitation process reasonably and integrally, whether in scope or strength of rainfall. More specifically, the maximum accumulated precipitation centers were highly concentrated on the mountaintop area in the CNTL run, reaching 50 and 35 mm which was slightly less than the observation (58.3 and 71.7 mm) at HS and JHS AWSs, respectively. It might be attributed to the convergence of wind fields and lifting movement in this area (Fig. 10a). But another maximum precipitation center which located at HSQ AWS (30.3°N, 118.13°E) was not successfully reproduced. This was likely contributed to terrain height errors between real (up to 1,864.8 m) and modeled (up to 1,200 m) regions, as well as the system errors of numerical model. To sum up, it suggested that this should be considered as a relatively reasonable and effective simulation of this thunderstorm event. In Fig. 11, the three stages of thunderstorm, including the developing, mature, and dissipation stages, were well captured by the WRF model on August 18, 2010. Individual orographic cloud appeared first on the mountaintop (about 30.15°N) at approximately 0900 LST (Figs. 11a, 12a), which was supposed to be the cumulus stage. Weak as it was, the disturbances in up-flow (1.4 m/s), cloud water mixing ratio (0.7 g/kg), and water vapor (below 3 km) was discernible. Then, under the influences of topography and mountain– valley winds, the airflow converged and lifted forcedly on the summit of the Yellow Mountain, companying with a great amount of moisture perturbation. Being isolation from the surface heating, the rising air and moisture quickly cooled into water drops, which could appear as cumulus cloud. Besides, the lower temperature aloft further contributed to condensate vapor into raindrops, which could lead to latent release and atmosphere warming. Until 1000 LST (Figs. 11b, 12b), the maximum vertical velocity was 6 m/s, and the cloud and rain water mixing ratio peaked at 1.1 and 1.9 g/kg, respectively. During this period, the initial convection barely moved, with the wet tongue deepening and potential temperature falling sharply. Moreover, as crucial indicators of atmospheric instability, the convective available potential energy (CAPE) and convective inhibition (CIN) which could indicate the vertical development of strong convection were considered. The cloud top height grew rapidly with the increasing CAPE, elevating to 6 km. The CIN decreased by 11 J/kg in the valley, which created atmospheric instability. With the up-flow, a shallow convection appeared between 29.9 and 30°N, growing sharply in the following time. Until 1100 LST (Figs. 11c, 12c), The CIN further dropped (to 9 J/kg), while the vapor disturbance, vertical velocity, and CAPE rose in the counterpart area. Nevertheless, on the mountaintop, the potential temperature perturbations only appeared in lower layer (within 2 km), and the velocity of up-flow decreased to 1.2 m/s (Fig. 12c). Then, background wind increased, causing two convection cells on the south slope merging with each other and moving to the peak (Figs. 11d, 12d). The stronger downdraft (12 m/s at 3–3.5 m) near 30.2°N caused by the dissipated cells halted the flow traveling north. The local up-flow transported moist air into the thunderstorm cell which gave rise to a deep moist tongue. An hour later (at 1300 LST), the convective cells developed after being provided together the dynamic lifting (e.g., the vertical velocity reached 3.5 m/s) and abundant water vapor between 30.15 and 30.2°N (see Figs. 11e, 12e). As inferred from Figs. 11f and 12f, the mountain air was cooled by the less solar radiation after sunset. The momentum
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Fig. 9 Simulated 24-h accumulated precipitation (color), topography (m, contours) on August 18, 2010, for a OBS, b CNTL, c TOPO, d GRASS, e FOREST, and f DESERT. The measured precipitation was marked on every AWS with red numbers
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Fig. 10 Simulated moisture flux divergence (color), wind fields (V and W 9 10-components, vector), and topography (m, contours) on 1.8 km at 1000 LST on August 18, 2010, for the a CNTL, b TOPO, c GRASS, d FOREST, and e DESERT experiments
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Fig. 11 Vertical cross-sections of simulated cloud water mixing ratio (color), rain water mixing ratio (dashed, 10-1g/kg), wind fields (V and W 9 10-components, vector), and water vapor mixing ratio (solid, g/kg) along line AB at a 0900, b 1000, c 1100, d 1200, e 1300, and f 1400 LST on August 18, 2010
descending and down-slope wind got together to bring high near-surface wind, before suppressing the development of moisture and potential temperature disturbance. The CIN increased from 5 to 20 J/kg, which indicated a weakened convection. As the cloud and rain water mixing ratio dropped to 0.3 and 0.4 g/kg, the thunderstorm had been started to dissipate. Consequently, the trigger mechanisms of thunderstorm were likely attributed to the mountain–valley breeze and topographic feature, including dynamic and thermal factors. The former could create the up-flow and convergence centers on the mountaintop, and the latter warmed and humidified the mountain air over the Yellow Mountain area. 5.4 Effect of topography In Fig. 9c, after the Yellow Mountain was removed, the rainfall sharply reduced to around 5 mm, and the main precipitation cores disappeared in D4. Without orographic forcing, a sustaining southwesterly prevailed, causing no vertical motion and convergence lines (convergence centers of wind and moisture) near the mountains (Fig. 10b). It implied that the thunderstorm was triggered by the climbing airflow on the windward slopes and the valley winds during the daytime, as the dynamic and thermodynamic effects of the mountainous area.
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Fig. 12 Vertical cross-sections of simulated CAPE (color), CIN (blue contours, J/kg), vertical velocity (dashed, 10-1m/s), and potential temperature (solid, K) along line AB at a 0900, b 1000, c 1100, d 1200, e 1300, and f 1400 LST on August 18, 2010
In addition, Fig. 13a, b showed the vertical cross-sections of the wind fields (U- and W 9 10-components), CIN, CAPE, cloud water mixing ratio, water vapor mixing ratio, vertical velocity, and potential temperature along line AB at 1000 LST in the TOPO experiment. It was found that the maximum vertical velocity was only 0.06 m/s, and the cloud water mixing ratio nearly disappeared. The atmosphere developed into a stable one, with evenly distributed potential temperature, CIN, CAPE, and water vapor mixing ratio in north–south direction. This was disadvantage to the vertical transport of moisture and release of convective instable energy. Hence, the terrain was the main factor of creating the mountain–valley circulation and convection. Under complex topography, the daytime period was experienced a strong enough thermally induced up-slope flow, contributing to transported the warm and moisture into the upper air. Along with the condensation of water vapor and latent heat release, the potential instable energy accumulated and released, before causing this thunderstorm event. 5.5 Effect of land cover In the 1950s, the proportion of land use and land cover in the Yellow Mountain Scenic Area was 75 %, whereas the percentage rapidly declined to 73.6 % in 1971, by reason of
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Fig. 13 Vertical cross-sections of simulated cloud water mixing ratio (color), rain water mixing ratio (dashed, 10-1g/kg), water vapor mixing ratio (solid, g/kg), and wind fields (V and W 9 10-components, vector) on the left column; CAPE (color), CIN (blue contours, J/kg), vertical velocity (dashed, 10-1m/s), and potential temperature (solid, K) on the right column along line AB at 1000 LST on August 18, 2010, for the TOPO, GRASS, FOREST, and DESERT experiments
engineering construction, water and soil erosion, forest fire, and trampling. From then on, effective management and careful protection were implemented to increase land cover, the figure reaching 93 % in 2009. From the above, we designed three sensitivity experiments to study the impacts of land use changes on the development processes of mountain–valley breeze and thunderstorm. Reducing plant height was considered first, in other words, inhomogeneous land cover was replaced by uniform grassland (GARSS) to show the
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Fig. 13 continued
effects of vegetation deterioration. Second, a FOREST experiment with homogeneous mixed forests was designed to reveal the influencing mechanism of natural expansion of forests in recent years. Finally, the extent of influence which the natural distribution of vegetation could lead to over the Yellow Mountain area was worthy of consideration. Hence, it was necessary to give an extreme experiment (DESERT), running without any vegetation.
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5.5.1 GRASS experiment In both GRASS and CNTL (Fig. 9b, d), the precipitation centers had the same locations at (30.2°N, 118.11°E), (30.19°N, 118.2°E), (30.1°N, 118.4°E), and (30.52°N, 117.81°E), respectively. When covered by grassland, the precipitations reduced by 20 and 25 mm at the first two positions, while increased by about 10 and 15 mm at the last two centers. The convective core on the summit of the Yellow Mountain (30.2°N, 118.11°E) was considered in more detail. Figure 10c showed that the rainfall decreased according as a slight decrease in moisture and momentum gathered in the most region of D4, especially on the mountaintop in the GRASS experiment. In Fig. 13c, d, it showed that the vertical velocity dropped to 2.5 m/s at 1000 LST, lead to less condensation of water vapor in the main body of orographic cumuli. For this reason, the cloud top height decreased (to around 5 km), and there was an outflow from the cloud base at 30.16°N, which implied that the deep convection experienced a shorter period and had been in dissipative stage. The cloud and rain water mixing ratio were 0.88 and 1.1 g/kg, respectively. Moreover, the lesser CAPE and higher CIN were the part factors of weakening of the convection (Fig. 14a). From Fig. 14b, the perturbation potential temperature reduced (by 1.2 K) on the mountaintop, causing an increased lapse rate, and higher static stability. Based on this, relatively less moisture and heat released upward to maintain the development of thunderstorm. Covered by grassland, the innermost domain average surface sensible heat flux had reduced (Fig. 15f), lead to relatively stable atmospheric. Besides, it could be found that the surface roughness declined (0.12 m), causing the horizontal wind speeds increasing sharply (Figs. 10c, 15e), and the valley breeze well developing. Therefore, the decrease in soil moisture could not bring higher evapotranspiration and latent heating (Fig. 15h). Combining the above, it could not provide advantages in thermal conditions to maintain the development of thunderstorm. Note that the differences in 2-m temperature, skin surface temperature, net radiation, PBL height, and latent heat flux were almost negligible (Fig. 15c, d, g, h). 5.5.2 FOREST experiment In the FOREST experiment (Figs. 9e, 10d), the differences in simulated wind fields and the development of convective rainfall were small due to the fact that the most of D4 in the CNTL run was covered by mix forests. But the rain belt which located near Jiuhua Mountain expanded and the rain center (30.19°N, 118.2°E) shifted to its southwest. Figure 13e, f indicated that the vertical velocity dropped to 2.4 m/s, which provided disadvantage in vertical transporting moisture and energy followed by a decreased cloud top height (around 4 km) and rain water mixing ratio (0.4 g/kg). Furthermore, it could be indicated from Fig. 14c, d that the differences between the FOREST and CNTL runs were small in CAPE, CIN, and perturbation potential temperature. In spite of this, the perturbation potential temperature increased in the lower atmosphere, while had opposite trend in the higher counterpart. So, the convective cloud could not develop higher and stronger. Figure 15 revealed that the changes had slight influence on the simulated results of surface variables. In other words, the diurnal variations were nearly the same and difficult to distinguish. Due to vegetation height and surface roughness increasing (around 0.5 m), there was a slight decline in wind speed (Fig. 15e), companying with a weaker convergence and vertical motion (2.4 m/s in Fig. 13f). Along with lower soil moisture, it was hardly for warm and moisture air near the ground to be delivered to form the deeper
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Fig. 14 Vertical cross-sections of simulated differences of CAPE (color) and CIN (contours, J/kg) between the a GRASS and CNTL, c FOREST and CNTL, as well as e DESERT and CNTL experiments on the left column; simulated differences of perturbation potential temperature (color, contours, K) between the b GRASS and CNTL, d FOREST and CNTL, as well as f DESERT and CNTL experiments on the right column along line AB at 1000 LST on August 18, 2010
cumulus convection core (2.8 m in Fig. 13e). Therefore, heterogeneous underlying surface was propitious to accumulate the water vapor and CAPE, which was likely to continue and reinforce the thunderstorm. 5.5.3 DESERT experiment In Fig. 9f, there was almost no center of precipitation in mountainous area without any vegetation. With the valley wind and orographic forcing, the moisture only gathered on the
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Fig. 14 continued
top of the Yellow Mountain (Fig. 10e). However, compared to the CNTL run (Fig. 13g, h), the up-flow aroused by the valley wind and orographic forcing was very weaker at 1000 LST, with lower velocity (0.55 m/s), less range, and lower extended height (3.3 km) on the mountaintop. The vertical velocity decreased with height rapidly. In Figs. 14e, f, Both the CAPE and perturbation potential temperature fallen, while the CIN rose. The differences of three variables between DESERT and CNTL were up to 1,000 J/kg, 0.8 K, and 20 J/kg, respectively. Therefore, it could be inferred that the bare soil restrained the transportation of water upward and development of cumulus (below 3 km). In the CNTL run (Fig. 15b), net radiation increased drastically after 0500 LST with the results that transpiring water went up, and turbulence of near-surface air enhanced greatly. In Fig. 15a, before turning into a strong convection at 1000 LST, the turbulent mixing and ground surface evaporation kept the same level of intensity, causing water vapor mixing ratio increased slowly. The period between 1600 and 1800 LST witnessed weaker cumulus convection and decreased turbulent motion, with the near-surface moisture accumulating quickly and reaching its maximum at 20 g/kg. However, the water vapor mixing ratio remained steady below 18.4 g/kg for a day in the DESERT experiment. Additionally, the water vapor content was related to many factors, including convergence and divergence of airflow, ground surface evaporation, and water–vapor exchange vertically. Partly, due to the reduction in surface roughness (0.01 m), maximum differences of surface wind speeds between CNTL and DESERT were up to 2 m/s (Fig. 15e). For lack for vapor source, it was difficult to transfer the vapor upward and release latent heat flux, despite the wind speed increasing sharply. The maximum latent heat flux was only 130 W/m2 at 1300 LST (Fig. 15h). Furthermore, the solar radiation could easily pass through the shallow clouds and heated the surface of the mountaintop. Hence, as compared to their CNTL counterparts, the differences between skin and near-surface temperatures rose because of higher albedo (0.38), bringing larger sensible heat flux after 1200 LST compared to CNTL
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Fig. 15 Diurnal variations of the innermost domain average modeled a water vapor mixing ratio, b net radiation, c surface skin temperature, d 2-m temperature, e 10-m wind speed, f sensible heat flux, g PBL height, and h latent heat flux for the CNTL, GRASS, FOREST, and DESERT experiments on August 18, 2010
(Fig. 15c, d, f). The sensible heat flux rose smoothly and reached its peak at 1300 LST. From Fig. 15f and g, it was inferred that the sensible heat flux was supposed to be the primary contributing factors for the diurnal variation of PBL height, which was similar to the main findings by Zhang et al. (2009). Figure 15g showed that the maximum PBL height was 1,200 and 1,600 m in CNTL and DESERT, respectively. Therefore, after desertization, it was adverse for accumulating vapor and heat, which cloud restrain strong convection within the lower PBL.
6 Summary and conclusions Using a mesoscale numerical model WRF version 3.1.1, the impacts of topography and land use changes on thunderstorm over the Yellow Mountain area of China were investigated. The case we chosen was occurred on August 18, 2010, under weak synoptic pressure gradients. Detailed analyses and comparative studies lead to primary conclusions on the research questions posed at the beginning of the study. First, assessment using observational data of AWSs (e.g., temperatures, wind speeds, and precipitations) confirmed that WRF could describe the meteorological fields accurately
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in spite of missing a precipitation cell in the north valley; second, the mesoscale local circulation and the development process of thunderstorm were analyzed. Simulation results indicated that the valley breeze prevailed after sunrise and converged on the mountaintop, which might provide uplift airflow and water vapor conditions for the formation of initial convection; third, a TOPO experiment (see Table 2) revealed that the complex topography (the Yellow Mountain) played a significant role in determining the amount and locations of the precipitation. On flat terrain, the main effects were local disturbance weakening without strong topographic convergence and lifting of wind and water vapor associated with the valley wind. In contrast, the CNTL showed stronger vertical mixing, raising the moisture, increasing potential temperature disturbance, and accumulating convective instability energy, which was conducive to the growth of convective clouds; finally, other three sensitivity tests with uniform grassland (GRASS), mixed forests (FOREST), and bare soil (DESERT) were conducted. Among the impacts of land use changes, both the thermal and momentum transport were significant for the localized thunderstorm. When covered by grassland, there were less sensible heating and lower moisture, leading to the PBL height decreasing and vertical lifting weakening, which tended to cause more stable atmosphere and less rainfall on the mountaintop. When covered by mixing forests, only small differences presented in simulated meteorological fields (e.g., wind fields, moisture, cloud water mixing ratio, precipitation, and other fields). In DESERT experiment, the latent heating was more important in influencing the process of thunderstorm. There were less latent heating and lower accumulated water vapor compared to other experiments, causing vertical lifting weakening, stability of atmosphere increasing, and precipitation reducing. Although the WRF model exhibited reasonable performance and revealed preliminary results about the impacts of land use changes on thunderstorm, shortcomings should be pointed out when understanding these conclusions. Because the characteristics of thunderstorms cannot give complete investigations in a case, particularly the changes in the microphysical features of the convection cells which were viewed as an important part of the simulated thunderstorm. Further investigations in revealing the formation mechanism of thunderstorm should be worth conducting using advanced numerical models. Acknowledgments This research was jointly supported by the Special Fund of Scientific Research for Public Welfare Industry (Meteorology) of the Ministry of Science and Technology of China (Grant No. GYHY201006004), the National Natural Science Foundation of China (Grant No. 41030962), and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2013BAK05B03).
References Adegoke JO, Pielke RA, Eastman J, Mahmood R, Hubbard KG (2003) Impact of irrigation of midsummer surface fluxes and temperature under dry synoptic conditions: a regional atmospheric model study of the U.S. High Plains. Mon Wea Rev 131:556–564 Bergeron T (1949) Problem of artificial control of rainfall on the globe. Tellus 1:32–43 Chang JT, Wetzel PJ (1991) Effects of spatial variations of soil moisture and vegetation on the evolution of a pre-storm environment: a numerical case study. Mon Wea Rev 119:1368–1390 Chen F, Dudhia J (2001) Coupling an advanced land surface–hydrology model with the penn state–NCAR MM5 modeling system. Part II: preliminary model validation. Mon Wea Rev 129:587–604 Chen Q, Zhao M (2006) A numerical experiment on the effect of terrain on the precipitation. Scientia Meteorol Sinica 26:484–493 (in Chinese) Chris F, Joel M (2004) A simplified diagnostic model of orographic rainfall for enhancing satellite-based rainfall estimates in data-poor regions. J Appl Meteorol 43:1366–1378
123
Nat Hazards Chu CM, Lin YL (2000) Effects of orography on the generation and propagation of mesoscale convective systems in a two-dimensional conditionally unstable flow. J Atmos Sci 57:3817–3837 Clark CA, Arritt RW (1995) Numerical simulations of the effect of soil moisture and vegetation cover on the development of deep convection. J Appl Meteorol 34:2029–2045 Crawford TM, Stensrud DJ, Mora F, Merchant JW, Wetzel PJ (2001) Value of incorporating satellitederived land cover data in MM5/PLACE for simulating surface temperatures. J Hydrometeorol 2:453–468 Cui X, Graf HF, Langmann B, Chen W, Huang R (2006) Climate impacts of anthropogenic land use changes on the Tibetan Plateau. Glob Planet Change 54:33–56 Dudhia J (1989) Numerical study of convection observed during the Winter Monsoon experiment using a mesoscale two-dimensional model. J Atmos Sci 46:3077–3107 Gallus WA, Klemp JB (2000) On the behavior of flow over step orography. Mon Wea Rev 128:1153–1164 Gero AF, Pitman AJ (2006) The impact of land cover change on a simulated storm event in the Sydney basin. J Appl Meteorol Clim 45:283–300 Hohenegger C, Walser A, Wolfgang L, Schar C (2005) Cloud resolving ensemble simulations of the August 2005 Alpine flood. QJR Meteorol Soc 134:889–904 Hong SY, Noh Y, Dudhia J (2006) A new vertical diffusion package with an explicit treatment of entrainment processes. Mon Wea Rev 134:2318–2341 Janjic´ ZI (2002) Nonsingular implementation of the Mellor-Yamada level NCEP mesomodel. NCEP Office Note 437:61 Kain JS (2004) The Kain–Fritsch convective parameterization: an update. J Appl Meteorol 43:170–181 Kunz M, Kottmeier C (2006) Orographic enhancement of precipitation over low mountain ranges. Part I: model formulation and idealized simulations. J Appl Meteorol Clim 45:1025–1040 Lin YL (2007) Mesoscale dynamics. Cambridge University Press, Cambridge, MA Lin YL, Farley RD, Orville HD (1983) Bulk parameterization of the snow field in a cloud model. Clim Appl Meteorol 22:1065–1092 Mahmood R, Foster SA, Keeling T, Hubbard KG, Carlson C, Leeper R (2006) Impacts of irrigation on 20th century temperature in the northern Great Plains. Glob Planet Change 54:1–18 Mass C (1981) Topographically forced convergence in western Washington State. Mon Wea Rev 109: 1335–1347 Miao JF, Chen D, Wyser K, Borne K, Lindgren J, Strandevall MKS, Thorsson S, Achberger C, Almkvist E (2008) Evaluation of MM5 mesoscale model at local scale for air quality applications over the Swedish west coast: influence of PBL and LST parameterizations. Meteorol Atmos Phys 99:77–103 Miao JF, Wyser K, Chen D, Ritchie H (2009) Impacts of boundary layer turbulence and land surface process parameterizations on simulated sea breeze characteristics. Ann Geophys 27(6):2303–2320 Mlawer EJ, Taubman SJ, Brown PD, Iacono MJ, Clough SA (1997) Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave. J Geophys Res 102: 16663–16682 Nair US, Wu Y, Kala J, Lyons TJ, Pielke RA, Hacker JM (2011) The role of land use change on the development and evolution of the west coast trough, convective clouds, and precipitation in southwest Australia. J Geophys Res 116:D07103. doi:10.1029/2010JD014950 Narisma GT, Pitman AJ (2003) The impact of 200 years of land cover change on the Australian near-surface climate. J Hydrometeorol 4:424–436 Paul MM, Nikolai D (2011) A numerical study of the effects of orography on supercells. Atmos Res 100:457–478 Pielke RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convection rainfall. Rev Geophys 39:151–177 Pielke RA, Adegoke J, Beltran-Przekurat A, Hiemstra CA, Lin J, Nair US, Niyogi D, Nobis TE (2007a) An overview of regional land use and land cover impacts on rainfall. Tellus 59B:587–601 Pielke RA, Adegoke J, Chase TN, Marshall CH, Matsui T, Niyogi D (2007b) A new paradigm for assessing the role of agriculture in the climate system and in climate change. Agric For Meteorol 142:234–254 Raddatz RL (2007) Evidence for the influence of agriculture on weather and climate through the transformation and management of vegetation: illustrated by examples from the Canadian Prairies. Agric For Meteorol 142:186–202 Robichaud AJ, Austin GL (1988) On the modeling of warm orographic rain by seeder-feeder mechanism. QJR Meteorol Soc 114:967–988 Roe GH (2005) Orographic precipitation. Annu Rev Earth Planet Sci 33:645–671 Sen Roy S, Mahmood R, Quintanar AI, Gonzalez A (2011) Impacts of irrigation on dry season precipitation in India. Theor Appl Climatol 104:193–207
123
Nat Hazards Sen Roy S, Mahmood R, Niyogi DDS, Lei M, Foster SA, Hubbard KG, Douglas E, Pielke RA (2007) Impacts of the agricultural Green Revolution-induced land use changes on air temperatures in India. J Geophys Res 112:D21108 Shen J (1998) Numerical modeling of the effects of vegetation and environmental conditions on the lake breeze. Boundary-Layer Meteorol 87:481–498 Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Wang W, Powers JG (2005) A description of the advanced research WRF version 2, NCAR Tech Note 468 + STR 88 pp Smith RB (1979) The influence of mountains on the atmosphere. Adv Geophys 21:87–230 Ter Maat HW, Hutjes RWA, Ohba R, Ueda H, Bisselink B, Bauer T (2006) Meteorological impact assessment of possible large scale irrigation in Southwest Saudi Arabia. Glob Planet Change 54:183–201 Wallace JM, Hobbs PV (1977) Atmospheric science: an introductory survey. Academic Press, New York Wu YX, Yang BG, Wang KQ, Hua RG, Xu W (2005) Climatic analysis of cloud deck in Huangshan. Scientia Meteorol Sinica 25:97–104 (in Chinese) Zhai GQ, Gao K, Yu ZX, Tu CH (1995) Numerical simulation of the effects of mesoscale topography in a heavy rain process. Scientia Atmospherica Sinica 19:475–480 (in Chinese) Zhang CL, Chen F, Miao SG (2009) Impacts of urban expansion and future green planting on summer precipitation in the Beijing metropolitan area. J Geophys Res 114(2):1–26
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