Larissa Gunst ( ) Supervisors: Reinder Ronda & Natalie Theeuwes. MAQ Meteorology and Air Quality Wageningen University.

Transcription

1 MSc Thesis: Urban Induced Precipitation over Houston, Texas and How It Is Controlled by Urban Heat, Surface Roughness, Building Height and Aerosol Concentration Larissa Gunst ( ) Supervisors: Reinder Ronda & Natalie Theeuwes MAQ Meteorology and Air Quality Wageningen University March 23, 2016

2 Abstract A growing urban population on global scales has led to research assessing the impact of urban areas on (convective) precipitation. The urban heat island, building barrier effect, surface roughness and increased aerosol concentrations in urban areas have all been identified as potential urban influencers of precipitation. In this research we have pursued to distinguish the individual contributions of these urban characteristics. Therefore, we have set up a case study over Houston, Texas for an event with strong convective precipitation. In order to perform the sensitivity analysis the Weather Research and Forecast model (WRF) is used. The results indicate a clear pattern of decreased urban precipitation over the city for an increased building height. This is due to the building barrier effect, which generally restricts the advection of convective precipitation over urban areas. In addition, the results indicate that enhanced anthropogenic heat emissions in urban areas, lead to an increased precipitation rate in the city and downwind of the urban area. The effect of increased aerosol concentration appears to be limited. The results contribute to understanding the mechanisms behind precipitation patterns over urban areas, local weather predictions and urban planning. Keywords: Urban Precipitation, WRF, Houston, Urban Heat, Surface Roughness, Building Barrier, Aerosols 1 Introduction All over the world, cities are becoming more densely populated or are extending at the cost of the surrounding rural areas. In 2014 more than half of the worlds total population was living in urban areas, and in the next decades urbanization is expected to continue (WHO,2015). The difference in land use between cities and rural areas has consequences on water storage and run-off after a precipitation event. The high percentage of impervious surface and the reduction of vegetation cover in urban areas limit the infiltration and evapotranspiration of water into the subsoil. Moreover, climate change scenarios indicated more extreme precipitation events ((IPCC), 2013). Extreme precipitation in combination with the limited infiltration make cities more vulnerable to floods than the rural surroundings. Since of the growing population and flood risk relevance, research is done to determine the interaction between the urban surface and precipitation. Almost a century ago, Horton (1921) found that thunderstorms appear more frequently over large cities than in rural areas. Thereafter, enhanced urban or downwind precipitation have been found in studies in Northern America (e.g. Changnon et al., 1971; Huff and Changnon, 1973; Bornstein and Lin, 2000; Thielen et al., 2000; Baik et al., 2001; Burian and Shepherd, 2005; Han and Baik, 2008; Shepherd et al., 2010; Carrió et al., 2010; Ganeshan and Murtugudde, 2015) and Asia (e.g. Miao et al., 2011; Hu, 2015). In these studies urban surface characteristics are linked as drivers for the precipitation patterns in cities, which are; i) anthropogenic heat sources (e.g. Changnon et al., 1971; Huff and Changnon, 1973; Thielen et al., 2000; Baik et al., 2001; Han and Baik, 2008; Shepherd et al., 2010; Miao et al., 2011), ii) an increased surface roughness by buildings (e.g. Thielen et al., 2000; Shepherd et al., 2010), and iii) enhanced aerosol concentration due to industry and transport (e.g. Changnon et al., 1971; Huff and Changnon, 1973; Carrió et al., 2010). However, these studies show contradicting results and therefore the processes and their contribution to urban surface precipitation is not yet understood. Most studies link the difference between urban and rural precipitation pattern to the urban heat island (UHI) effect; higher surface temperatures in the city compared to its rural surrounding. This effect especially occurs during the evening and the night. The UHI has multiple causes; i) heating by anthropogenic heat sources by buildings and traffic, ii) heating through reduced vegetation cover decrease the evapotranspiration and therefore reduce latent heat and increase sensible heat, iii) trapping of radiation due to the canyon geometry and a reduced sky 1

3 view factor, and iv) urban surface properties with the ability of more heat storage (e.g. asphalt) (Oke, 1982). The relative warm air rises (convergence) faster than the cool air from the surroundings. As the air reaches higher altitudes, it cools and water vapour can condense to cloud droplets in moist conditions. If the boundary layer of the city or its surroundings is unstable, heavy precipitation or even thunderstorms can be formed in these conditions. As found by e.g. Bornstein and Lin (2000); Shepherd et al. (2010); Ganeshan and Murtugudde (2015), the urban heat island (UHI) effect attracts the storm towards the urban area and creates a convergence zone over the Northern American cities Atlanta, Houston and Minneapolis respectively. Enhancement of the UHI results in an intensification of total precipitation due to an increased planetary boundary layer (PBL) height (Miao et al., 2011). Results from Baik et al. (2001); Dou et al. (2014) show that rainfall starts closer to the heating centre in intensified UHI conditions. The examples above suggest urban heat has an effect on urban precipitation patterns, but the results are dependent on multiple urban characteristics and atmospheric conditions. Next to the UHI effect, the urban surface roughness enhances convergence over the city as well due to increased frictional drag in the rougher terrain (e.g. Thielen et al., 2000; Bornstein and Lin, 2000; Rozoff et al., 2003; Shepherd et al., 2010; Ganeshan and Murtugudde, 2015). This effect, which is caused by the building height and location, changes the turbulence and circulation pattern (Coceal et al., 2006). In Shepherd et al. (2010), convergence was present at the edge of Houston city (Texas) and did not occur for non-urban simulations. Bornstein and Lin (2000) found convergence at the border between the city and downwind of Atlanta (Georgia), but had a maximum divergence downwind of the city. As a result, precipitation was redistributed to the downwind area Bornstein and Lin (2000); Thielen et al. (2000); Shepherd et al. (2010). In e.g. Ganeshan and Murtugudde (2015) both the urban roughness and UHI effect enhanced the convergence zone, but they are both initiated by different processes. By removing the city in models it does not become clear which mechanism dominates in creating the convergence zone. Moreover, although rougher terrain enhances a convergence zone in the city, increased precipitation was detected in the urban area (Ganeshan and Murtugudde, 2015) but in other studies also downwind of the city (e.g. Thielen et al., 2000; Bornstein and Lin, 2000). In addition to the modified turbulence effect for increased surface roughness, the building height influences the urban precipitation in different way. In case of strong regional flows, the buildings block airflow penetration from the upwind area towards the city and bifurcated air masses are thus moved around the urban area (Bornstein and LeRoy, 1990; Tumanov et al., 1999; Bornstein and Lin, 2000; Rozoff et al., 2003; Miao et al., 2011). This mechanism is known as the building barrier effect and results in reduced precipitation in the urban area (Rozoff et al., 2003; Cao and Lin, 2014) while it increases precipitation aside (Bornstein and Lin, 2000) or downwind of the urban area (e.g. Shepherd and Burian, 2003; Shepherd et al., 2010; Dou et al., 2014). The building barrier effect is usually studied in combination with the surface roughness effect. However, the influence on precipitation pattern differ; while an enhanced surface roughness increases the precipitation in the city and downwind (e.g. Thielen et al., 2000; Bornstein and Lin, 2000; Rozoff et al., 2003; Shepherd et al., 2010; Ganeshan and Murtugudde, 2015), the building barrier effect reduces the urban precipitation and is enhanced aside or downwind (Bornstein and LeRoy, 1990; Tumanov et al., 1999; Bornstein and Lin, 2000; Rozoff et al., 2003; Miao et al., 2011). For this reason we suggest to take different approaches to determine their individual contribution. The role of aerosols on precipitation patterns around urban areas is, despite a large amount of previous research, not well understood yet (e.g. Rosenfeld, 2000; Lynn et al., 2007; Li et al., 2008; Lacke et al., 2009; Ntelekos et al., 2009). Aerosols function as cloud condensation nuclei (CCN) and thus support the formation of cloud droplets. Generally, due to industry and traffic cities contain higher aerosol concentrations than rural areas (e.g. Levy et al., 2013), therefore increasing the cloud droplet concentration and enhance precipitation in the city centre (Lacke 2

4 et al., 2009). However, other studies found a tipping point in the effect of CCN on urban precipitation; once the concentration was increased and reached a certain level, the drop size decreased and prevent rain from falling (Rosenfeld, 2000; Li et al., 2008; Carrió et al., 2010; Carrió and Cotton, 2011). Ntelekos et al. (2009) found in their study that suppression of precipitation by high CCN concentrations only occurred under slightly unstable atmospheric conditions (low convective available potential energy (CAPE)), low wind shear or low relative humidity. In Lynn et al. (2007) however, the reduced precipitation in the city appeared to be replaced by enhanced precipitation downwind in lower CCN concentrations. These examples made clear that many contradictions within the effect of aerosols on precipitation-urban interaction have been found and further research is required. As of contradicting results from previous research, the contribution of each individual mechanism on urban induced precipitation is still unclear. At the moment, especially the effect of increased surface roughness (rainfall in cities or downwind) and higher aerosol concentrations (increasing or decreasing urban precipitation and the effect of size distributions) have to be researched by systematic modelling studies. Most studies however only focus on the difference between a city and no-city experiments or only investigate one urban surface characteristic. As a consequence, the exact processes and their contribution are not yet understood and difficult to compare. A solution for this literature gap is a study in which all mechanisms that contribute to precipitation patterns over urban areas are analysed separately to determine their role and contribution. The objective of this study is to determine the processes that take place during a precipitation event over an urban area and how these mechanisms enhance, interact or perhaps counteract each other. In order to determine each contribution separately, four mechanisms are studied individually, which are: 1. Building barrier 2. Surface roughness 3. Aerosol concentrations 4. Urban heat The second objective is to verify the total precipitation intensification or reduction for each mechanism and find which of the four mechanisms influences precipitation patterns over urban areas most. In this report the study area, case selection, model set-up and sensitivity analysis are discussed in section two (Method & materials). The third section (Results & discussion) elaborates on the results of the modelled current situation, the removal of the city and the four mechanisms separately. In section four conclusions are given. Finally, some recommendations for further research are given in section five. 2 Methods & materials 2.1 Study area We chose a convective rainfall event over the large North American coastal agglomeration of Houston (Texas) to be the study area for our research. The city is located in the mid southern part of the United States of America with its centre coordinates around (29.79N,95.26W), see Appendix Figure 20. In south-easterly direction, the city is located next to the Trinity and Galveston Bay, which discharges into the Gulf of Mexico. In other directions, Houston is surrounded by dry crop land. Houston is the fourth city of the United States of America and was one of the fastest growing cities the past three decades. It has 2.2 million inhabitants and this 3

5 number is expected to increase (WPR,2015). Because of the large population number in Houston and other cities it is important to understand how weather phenomena and precipitation patterns differ from rural areas and under which circumstances differences between the city and rural areas become apparent. Next to as being one of the largest American cities, two other reasons contribute in choosing Houston, i.e.; i) good quality and quantity of meteorological measurement data (as temperature, precipitation, wind speed and direction) in the centre and surrounding area of the city is provided and ii) the city has been used to research urban induced precipitation before (e.g. Shepherd and Burian, 2003; Burian and Shepherd, 2005; Lacke et al., 2009; Shepherd et al., 2010; Carrió et al., 2010; Carrió and Cotton, 2011), making it able to test the results with other studies. In these studies, enhanced cloud-to-ground lightning have been found over and downwind of Houston during both summer and winter months (Orville et al., 2001). According to Shepherd and Burian (2003) Houston rainfall anomalies were caused by urban influences, probably in combination with the sea breeze circulation. Lacke et al. (2009), Carrió et al. (2010) and Carrió and Cotton (2011) found evidence for increased precipitation under higher concentrations of aerosols. However, a reduction in rainfall took place when CCN reached the highest modelled concentrations. In this report, results from these studies will be compared to ours. 2.2 Case selection We gathered sub-hourly rainfall data from the National Oceanic and Atmospheric Administration (NOAA) to select for convective rainfall events over Houston and surroundings. In addition we used the meteorological measurement data from a weather station in the centre (Dunn Helistop) and in the northern rural area (Houston International Airport). According to Burian and Shepherd (2005) the best period to find convective precipitation over Houston is during the meteorological summer (June, July and August) due to sub-tropical convection. We set up two criteria for the convective precipitation event, i.e.; 1. A strong UHI max effect, which is the diurnal maximum temperature difference between the city and rural area at the surface. According to Zhou et al. (1980) (as cited in (Wang and Hu, 2006)) a strong UHI is defined as > 2 C. 2. A minimum precipitation of 15 mm measured at Houston International Airport during the total event to filter for big events only. In addition, we used NOAA radar images and weather maps to confirm the rainfall events were initiated by convective moisture. For this reason, events including clear fronts on the weather map and large homogeneous precipitation over the area on the radar images were eliminated. The selected date for this case study is the 20 th of June 2012, which has convective rainfall in the late morning till afternoon in Houston. That day, the UHI became maximum at UTC (8.00 LT) (2.8 C). The event past over from UTC (9.00 LT) till UTC (next day) (19.00 LT) in west-northwesterly direction and resulted in a total precipitation of 19.8 mm. Multiple short but heavy showers passed the area. At 16 UTC, Figure 1 shows bifurcation between the southwestern part and the northern edge. Most precipitation occurred in the builtup area in the mid-south and centre, while the area around the city centre remains relatively dry. We prefer the case to be a convective event over a frontal event since frontal systems usually give a large layer of wide-spread precipitation. Convective precipitation is more locally distributed and therefore responses more on land use characteristics. The NOAA weather map shows no frontal systems that could have triggered the precipitation around Houston (Figure 2). 4

6 Figure 1 Radar image of Houston convective rainfall and east-southeastern wind on June 20 th 2012 at 16.00h UTC. From NationalClimaticDataCenter,2015. Figure 2 Surface weather map of the U.S.A. for June 20 th 2012 (7.00 A.M. EST). The purple circle (mid-bottom) indicates the agglomeration of Houston. From ( NOAADailyWeatherMap,2002). 5

7 2.3 Model description The simulation was performed by using Weather Research and Forecasting (WRF) model version This non-hydrostatic and compressible model (Skamarock et al., 2001) is specialised in mesoscale processes like a convective precipitation event. The model is optimal to study the effect of adjusting land use change in urban areas (Shem and Shepherd, 2009; Theeuwes et al., 2013). The National Center for Atmospheric Research (NCAR) extended the WRF model with an urban modelling system. As a result, the mesoscale weather model is coupled to detailed urban surface parametrization (Chen et al., 2004, 2011) and allows us to investigate on the UHI effect and urban boundaries. More over, for 44 cities in the U.S.A., including Houston, the National Urban Data and Access Portal Tool (NUDAPT44) is provided. The dataset includes urban parameters as building height, roughness length, sky view factor and height to width ratio at a spatial scale of 250 m (Burian et al., 2007; Ching et al., 2009). The remaining urban parameters (e.g. heat capacity, albedo and emissivity) will be set to their default values (Chen et al., 2011). We implemented the 6-hourly data from the National Centers of Environmental Predictions (NCEP) to provide WRF for its meteorological forecast data. After careful testing we distinguish two nested grids in the model, which were centred around Houston (29.79N,95.26W). The outer domain (1) simulates the mesoscale weather phenomena and covers a large part of the mid-southern part of the U.S.A., see Figure 3. With an area of 1200 x 1200 km (see Table 1), the first domain is slightly larger than the one Carrió et al. (2010) used in their research on aerosol effects on convection over Houston. The inner domain (2) has four times as many grids as the first domain. In the inner domain the NUDAPT data is upscaled to two km resolution. Since this domain has a resolution higher than five km, the convection scheme is only implemented in the domain 1 and not in the inner domain. To prevent model instability that result in crashes we set 51 ETA vertical levels (maximum height at 1000 Pa) and used an adaptive time step. # grids Resolution [km] Domain x Domain x Table 1 Domain properties of study area 6

8 Figure 3 Domains of study area Houston and surroundings. 2.4 Model configuration The WRF model provides many parametrizations to simulate radiation, the boundary and surface layer and microphysics. For the radiation, surface layer and land surface parametrization schemes we used the common settings, see Table 2. Further more we used the single-layer urban canopy model. We tested microphysics, planetary boundary layer and cumulus schemes since we expected them to have most important influence on the results. The justification of the CCN concentration ([CCN]) is in most studies done by the use of the WRF-Chem model. However, Li et al. (2008) and Lim and Hong (2009) performed studies in which two-moment bulk microphysics schemes were able to adjust [CCN]. We performed a parametrization with four microphysics schemes, see Table 2. In the WDM 6-class and Morrison parametrization the [CCN] background conditions (ccn0) can be adjusted. However, in both parametrization schemes the [CCN] decreased rapidly before the event started and did not respond to cloud formation or precipitation, making these parametrizations unsuitable for the sensitivity analysis. The NSSL (short for National Severe Storm Laboratory) parametrization scheme with steady [CCN] remained at one constant concentration, which simplifies the analysis. However, this steady concentration is less realistic since the rain-out effect of CCN in the 7

9 Table 2 Parameterization in the WRF model Process Scheme or model Microphysics WRF Double-Moment (WDM) 6-class (Lim and Hong, 2009) Morrison (Morrison and Gettelman, 2008) NSSL (Steady [CCN]) (Gilmore et al., 2004) NSSL (interacting [CCN]) Longwave radiation Rapid radiative transfer model (RRTM) (Mlawer et al., 1997) Shortwave radiation Dudhia (Dudhia, 1996) Surface layer Monin-Obukhov (Monin and Obukhov, 1954) Land surface Unified Noah land surface (Chen and Dudhia, 2001) Boundary layer (PBL) Yonsei University (YSU) (Hong et al., 2006) Mellor-Yamada-Janjic (MYJ) (Janjić, 2001) Cumulus scheme Kain-Fritsch (new Eta) (Kain and Fritsch, 1990) Betts-Miller-Janjic (Janjić, 1994) Grell-Freitas ensemble (Grell and Freitas, 2014) Urban scheme Single-layer urban canopy model (SLUCM) (Kusaka et al., 2001) downwind region after a shower is excluded. The NSSL parametrization with interacting [CCN] on clouds and precipitation is the only parametrization scheme that remains at a constant concentration until cloud formation takes places and recovers an hour later (see Appendix Figure 22). For this reason the NSSL interacting parametrization scheme was chosen as to simulate the microphysics. The influence of the planetary boundary layer (PBL) parametrization scheme on the precipitation pattern was conducted with the two commonly used non-local closure (Yonsie University (YSU)) and local closure (Mellor-Yamada-Janjic (MYJ)) parametrization schemes. The two parametrizations showed only minor differences, mostly in timing in the YSU parametrization corresponded better to the NOAA radar images (Appendix Figure 23). (Holtslag and Boville, 1993) found that the local closure model transported moisture slower than the non-local model. Therefore, the non-local closure formed clouds in a higher, more realistic, level in the atmosphere and is for this reason more suitable for convective situations. The cumulus parametrizations Kain-Fritsch, Betts-Miller-Janjic and Grell-Freitas, which are only implemented in domain 1, showed a major difference between precipitation amount during the event (see Appendix Figure 23). Compared to observations, the Betts-Miller-Janjic scheme was the only one not underestimating the precipitation amount in and around the Houston agglomeration. Next to the schemes, the model spin-up time influences the results (Kleczek et al., 2014). We tested spin-up times of eight (short), fourteen (moderate) and 86 (long) hours. The moderate spin-up time severely improved the precipitation pattern compared to the short one. The long spin-up time was chosen to simulate the urban air temperatures (during day and night) better and create a reliable UHI effect. However, the fourteen and 86 hours barely showed differences in precipitation pattern and air temperature was for the moderate run more corresponding to the observations than the long run, see Figure 4. For this reason we used the spin-up time of fourteen hours for further analysis. Including the passage of the event for ten hours the total run time lasts for 24 hours. 8

10 Figure 4 Temperature of the observations (blue line) compared to the long run (grey dotted line) and moderate run (black dashed line) shows that the moderate spin-up time simulates the night time (0-12 UTC) better than the long spin-up run. The transition to morning (12-14 UTC) corresponds for both runs very well to the observations. During the event the temperature is two degrees higher in the model and the moderate spin-up time follows the observed trend well. 2.5 Sensitivity analysis Approach Sensitivity analysis We performed a sensitivity analysis for the effect of urban land use and the four mechanisms (building barrier, surface roughness, CCN concentration and urban heat) individually (Table 3). First we performed a run in which we eliminated the urban landscape and replaced it with the most dominant land use category: dry crop land (see Appendix Figure 21). In this run urban features as the UHI effect and buildings are removed and the roughness has a constant value of 0.15m. To determine the effect of roughness we included a second run in which all dry crop land features exists and the roughness length is set to an average of urban settings (1.00m). To research the effect of the building barrier effect on induced urban precipitation we multiplied the NUDAPT building height with two lower (0.6x and 0.7x) and higher (1.5x and 2.0x) factors than the control run. In Houston skyscrapers are located at the southern edge, meaning that at the border between the upwind area and the city the effect of the building barrier is expected to be detected. The urban roughness however depends on the building height, meaning that once we adapt the building height the roughness changes too. In our study we want to research both mechanisms separately. Therefore we did an analysis in which we changed the urban roughness and kept the building height constant to the reference run. The surface roughness depends on building height, the ratio road width/roof width and some constants. WRF distinguishes between the roughness of the urban canyon (Z 0 Canyon), which indicates the deepness of the canyons, and the roughness of roofs (Z 0 Roof), which increases when the roof top height varies on the grid size. The WRF model handles slightly different formulas for Z 0 Canyon and Z 0 Roof concerning the frontal area index (λ f ) in the latter part of the equation. While the Z 0 Roof contains the standard deviation of building height (stdh urb ), the street width (SW) and building with (BW), see Equation 1, the frontal area index in Z 0 Canyon is set to a constant (Equation 2) (Grimmond and Oke, 1999; Loridan et al., 2010). 9

11 Table 3 Approach sensitivity analysis Non-urban Crop land Crop land roughness 1.00m Building barrier building height x0.6 building height x0.7 building height x1.5 building height x2.0 Roughness (Z 0 ) Canyon and Roof Roof Canyon CR 0.15m Canyon 0.15m CR 0.5x Roof 0.5x Canyon 0.5x CR 1.2x Roof 1.15x Canyon 1.15x CR 1.35x Roof 1.3x Canyon 1.3x CR 1.5x Roof 1.6x Canyon 1.5x CCN concentration [CCN] x0.4 [CCN] x0.7 [CCN] x1.3 [CCN] x1.7 Anthropogenic heat 20 W/m 2 50 W/m W/m 2 λ f = stdh urb SW + BW (1) λ f = 0.19 (2) Since Z 0 Canyon and Z 0 Roof will not give the same results, assuming λ f significantly contributes to the roughness length, we did a sensitivity analysis for i) Z 0 Canyon, ii) Z 0 Roof, and iii) both Z 0 Canyon and Z 0 Roof (CR), see Table 3. We both increased and decreased the urban roughness and added two runs of a roughness equal to 0.15m. This latter value simulates the roughness of the dry crop land surrounding Houston. To investigate the role of [CCN] we force two lower (0.4x and 0.7x) and two higher (1.3x and 1.7x) concentrations compared to the control run. In the NSSL microphysics scheme we adjusted the entire domain to a constant background value. We could not use typical urban concentrations (Levy et al. (2013) measured concentrations around 2x10 9 m -3 for southeasterly winds) since then we would model urban CCN concentrations in the total inner domain. Consequently, we did a parametrization to match the precipitation pattern during the event from the radar images compared to the model under multiple CCN concentrations. Here we found a [CCN] of 4x10 8 m -3 as the best reference run. Li et al. (2008) found enhanced total precipitation over Houston under increased modelled [CCN] within the range or our reference run and sensitivity analysis. Finally, we performed a sensitivity analysis for the urban heat effect. According to Oke (1982) anthropogenic heat enhances the UHI effect. NUDAPT does not take this into account and therefore runs with 20, 50 and 200 W/m 2 were added. The numbers were not constant, but followed a diurnal cycle (Quah and Roth, 2012). Anthropogenic heat of 20 and 50 W/m 2 are averaged values for large cities while in Houston 200 W/m 2 could be reached or even exceeded (Shepherd et al., 2010), e.g. in the commercial zone during traffic rush hour Data analysis During the sensitivity analysis we compared: 10

12 1. Spatial distribution of precipitation 2. Wind speed and direction on ETA height level two (approximately 250m, see Appendix Figures 24 and 25. The second ETA level was chosen since the first ETA level mostly illustrated the land-sea transition.) 3. Divergence (and convergence) on ETA height level two 4. Convective available potential energy (CAPE) on 0.5, 1 and 1.5 km height 5. Lifting condensation level (LCL) on 0.5, 1 and 1.5 km height To distinguish rainfall patterns inside and around the city, part of the second domain is divided over eight boxes representing e.g. down- and upwind areas (See Figure 5 and Table 4). The boxes are not equal in size since the downwind areas are extending in space by the mean flow. We corrected for this effect in the total precipitation calculations. The maximum downwind area (from the northwestern edge of box 1 to the northwerstern part of box 4) is approximately 144 km. The most important boxes are number 1, 3, 4, 5, 6 and 7. Boxes number 2 and 8 are downwind areas of the Galveston bay and are expected to be least impacted on urban characteristics as the other. Table 4 Grid coordinates of the eight boxes Box number Box name X-grids Y-grids 1 City North Direct downwind (DD) Further downwind (FD) W(estern) most West Upwind East

13 Figure 5 Locations of the eight boxes in domain 2. 12

14 3 Results & Discussion In this section we will first discuss the reference run and compare the modelled precipitation pattern with the observations. Next the urban landscape is replaced by dry crop land, which illustrates the impact of precipitation of city vs. no-city. Thereafter each mechanism (building barrier, roughness, CCN concentration and urban heat) was adapted and discussed separately to determine their individual effect. 3.1 Reference run The best performing reference run contains; i) the YSU boundary layer scheme, ii) NSSL microphysics model, iii) Betts-Miller-Janjic s cumulus scheme, and iv) a [CCN] of 4x10 8 m -3 (see the previous section). During the event, showers pass the Houston agglomeration multiple times. Figure 6 shows that the model (b and d) succeeds in simulating the radar observations (a and c) in the city, upwind, downwind and on the western side of Houston in both the timing of the event and the total precipitation. On the eastern border of the city however, we detect a high radar reflectivity in the model (b and d) in the Galveston Bay (17 UTC) and over the Gulf of Mexico (20 UTC), which is not observed (a and c). Similar results were also found for other [CCN] and different cumulus schemes. The radar has a good coverage of the bay, so the model should not have detected precipitation over this area and is overestimating the total amount of precipitation in Figure 6b. However, radar accuracy is limited in the Gulf of Mexico and can therefore not prove the rainfall occurrence in d). The spatial boxes of Figure 5 do not contain precipitation from the bay and Gulf of Mexico (only in box 2 and partly box 8). Consequently, we consider the overestimation of precipitation in this area not to have a major influence on the further analysis. Apart from the overestimation over the bay and Gulf of Mexico, the WRF model is able to represent the convective precipitation event over Houston during the ten hours. The model performs best on the western border and downwind of the city, since at the east and upwind we experience effects from the bay and Gulf of Mexico. For the same reason, the model is best during the first two hours of the event and after 17 UTC. 13

15 Figure 6 Radar observations (a and c) and control run reflectivity [dbz] (b and d) for 17 UTC and 20 UTC respectively. The red contour represents Houston. 3.2 Urban and non-urban The total influence a city has on the precipitation pattern is represented in the difference of the simulation including and excluding Houston. In the model we replaced the urban area by dry crop land, but we will discuss the results in opposite direction: what would happen to the precipitation pattern if out of a dry crop land we would built the city of Houston? Figure 7 shows the average precipitation over the eight spatial boxes for each simulation summed over the event. The figure illustrates that if we replace a crop land area (green) by the city of Houston (reference (Control), blue) precipitation reduces in the city and further downwind, but is enhanced in the direct downwind area. The map of Figure 8 gives the difference in total precipitation if the crop land area is replaced by the city of Houston and clearly shows a reduction in the city and further downwind while precipitation in the direct downwind area is enhanced. In addition to rainfall, Orville et al. (2001) detected more lightning over the downwind area of Houston compared to the city and other rural surroundings. In our results the CAPE showed a pattern of lower CAPE in Houston, see Figure 9 (right), with clear maxima around the city. 14

16 At the edge of the city and the direct downwind area the stability decreases, explaining thus the enhanced rainfall in box 3 (direct downwind) and more lightning downwind in the study of Orville et al. (2001). The lower CAPE values in the city were detected during most part of the event and might be due to the reduced wind speed in the city and therefore reduce mixing. Figure 7 Average precipitation in the spatial boxes for crop land (non-urban (green)) and the control run (blue) show that rainfall is reduced in the city and further downwind while it was increased direct downwind. Crop land with adjusted urban roughness (green dashed) gained city precipitation but precipitation was reduced in the downwind areas (box 3 and 4). The modelled crop land has a roughness length of only 0.15m, while the Houston city can exceed a roughness length of a meter. In rural areas as in forests, the canopy roughness can reach these urban roughness lengths, therefore a high surface roughness is not an urban characteristic per sé. To study the effect of roughness we added a crop land run with a roughness length approximately equal to the average Houston area (1.00m). Figure 7 shows that for both the low roughness crop land and the adjusted crop land run precipitation is higher than for the urban area (box 1) and lower direct downwind (box 3). The pattern differs for the further downwind area (box 4), in which the adjusted crop land landscape has less precipitation than the urban (control) run, where as in the low roughness crop land precipitation increases. Since the two crop land runs have more in common than the control run has, the precipitation pattern in urban areas is influenced by more than only the urban roughness length. At last, we compared the total precipitation of the three (Figure 10). The total precipitation during the ten hour lasting event summed over all eight boxes is highest for the low roughness crop land and least for the adjusted (1.00m) crop land run. However, the difference between total precipitation is small. In agreement with our results, Ganeshan and Murtugudde (2015) did not find an intensification (we even found a minor reduction) of precipitation over an urban area compared to non-urban runs, only spatial differences. Breaking up the urban area in individual mechanisms might explain the spatial differences or can reveal contradicting effects on precipitation. In the remainder of this section, we will discuss the results of the building barrier effect, the urban surface roughness, CCN concentration and urban heat in succession. 15

17 Figure 8 Total precipitation difference between the control run (Houston city) and crop land show increased rainfall over the direct downwind area and less precipitation in the city area and further downwind. 16

18 Figure 9 CAPE [J/kg] over the non-urban crop land simulation (left) and urban area (right) at 14 UTC is much lower (less unstable air) over Houston area. Figure 10 The model shows a minimum increased total precipitation over the crop land covered landscape compared to the urban (control) run. The small difference is therefore considered as no reduction or intensification. Crop land with urban roughness is reduced in precipitation from the control run due to the reduction in rainfall downwind. 17

19 3.3 Building barrier The buildings of a city function as a barrier for precipitation, meaning that we would expect reduced precipitation in the city and increased rainfall side- and downwind. Plots of precipitation difference from the control run per box reveal a decreasing rainfall trend in the city (box 1) and upwind (7) for higher building heights up to -58% (see Figure 11) whereas the side-ward area in the west (6) of the city rainfall increases to a maximum of +45%. Figure 11 Precipitation surplus compared to reference run of the four adjusted building height runs. Increasing the height lowers precipitation in the city whereas the side-wind area (direct left) gains it. Figure 12 shows that for both lower (left) and higher (right) Houston building height streamlines will flow around the urban area. If we apply a building height of 0.6 times the building height, the transition between the rural surrounding and built up area remains large, since the dry crop land barely contains high plants or trees. The streams vary on the western border of the city: due to the reduced building barrier effect (left) streamlines flow along the city and conjoin direct downwind. We can compare these results to a run in which we increase the building height of the control run by a factor two. The twice as high buildings (right) pushes air flow away from the city (west from the city) and merging takes place further downwind. For this reason we obtain enhanced precipitation in the further downwind area (Figure 11 box 4) for the largest building heights. Disruption or bifurcation of systems due to the building barrier effect was found by Bornstein and LeRoy (1990); Tumanov et al. (1999); Bornstein and Lin (2000) too in New York, Bucharest (Romania) and Atlanta respectively. In other studies increased precipitation was found aside (Bornstein and Lin, 2000) or downwind (e.g. Shepherd and Burian, 2003; Shepherd et al., 2010; Dou et al., 2014). Although both lower and higher building heights include the building barrier effect, precipitation still responds to larger barrier effect: higher building barrier effects result in fewer precipitation in the city. Figure 12 shows that lower building height convergence is found at the border between the upwind area and the city, resulting in precipitation in box 1. If the buildings are higher (right), convergence takes place at the western border of the city, increasing precipitation in box 6. Divergence in the city retains cloud formation. Bornstein and LeRoy (1990) found a likewise divergence conditions over New York City during a moving thunderstorm. 18

20 Figure 12 Streamlines [m/s] and divergence (positive) [s 1 ] in and around the city of Houston (green contour) for a building height of 0.6x (left) and 2.0x (right). Both show air circulation around the city, but merging takes place further downwind for a higher building height. Figure 13a shows that, although city precipitation reduces for increased building heights, the total precipitation does not show a decreasing (or increasing) pattern. The run with the lowest building height (factor 0.6 lower than the control run) produces most precipitation, which occurs in the city and upwind. The building barrier effect in Houston does not trigger or suppress rainfall, but effects the precipitation pattern locally. Increased building height might be a cause for the reduced precipitation in the urban run compared to the crop land run. 19

21 Figure 13 Total precipitation over all eight boxes do not show a clear intensified or reduced pattern for the building barrier effect (a), the canyon surface roughness (b), the CCN concentration (c) and anthropogenic heat (d). 3.4 Surface roughness The urban surface roughness length is altered for the roof top (Roof), canyon depth (Canyon) and a combination of both (CR). A sensitivity test for the combination of roof and canyon for both lower surface roughness (0.15m and 0.5x control run) and higher (1.2x, 1.35x and 1.5x) barely shows influence on precipitation (see Figure 14). In all eight spatial boxes no precipitation pattern has been found and the difference between the control run and sensitivity analysis runs are minimal since most results do not exceed (minus) 40%. We divided the surface roughness runs over individual canyon depth and roof top effect to determine their individual effects on urban precipitation. The canyon surface roughness shows, in contrast to the combined roughness sensitivity test, most influence on precipitation pattern in the city (box 1), see Figure 15. The results however are not as straightforward as the building barrier effect and do not show a pattern from lower to higher roughness lengths. In the city, rainfall is doubled compared to the reference for an increased roughness length of 1.3x, but is also enhanced for lower than reference roughness runs (0.15m and 0.5x). In addition we detect increased precipitation upwind (box 7) and decreased direct downwind and over the west (box 4 and 6 respectively). This suggests that precipitation is attracted to the city, which is in contrast to other studies. Shepherd and Burian (2003); Shepherd et al. (2010) concluded that a higher surface roughness leads to more precipitation downwind. In our results we found a shift towards the city and further downwind areas. Note however that the runs close to the control (e.g. 1.15x and 1.2x) barely follow the same pattern as higher factors (e.g. 1.5x and 1.6x). Impact of roughness length on precipitation pattern is triggered after a threshold value around 1.35x the control run. An explanation for this is that the roughness of a city influence e.g. wind flows at a small (street level) scale. Small changes in the roughness length will influence local climates (heat and wind) but may not be dominant over a convective precipitation event. This might explain the trigger points and small difference to create noise. This does not mean however that surface roughness lengths have a minor role in impacting precipitation in urban areas. The total precipitation summed over the eight boxes show a small decreasing trend with 20

22 Figure 14 Precipitation difference compared to reference run of the five adjusted combined canyon and roof roughness runs. increasing surface roughness, see Figure 13b. However, the differences are minimal and the roughest run is an outlier and no clear in- or decreasing pattern is shown. Enhanced precipitation in the city with a higher roughness length can be explained as follows: increased surface roughness length decreases the wind speed over the urban area (see Figure 16). Higher roughness creates calmer atmospheric conditions in which convective activity initiates in a convergence UHI zone around the city centre as in Bornstein and LeRoy (1990). This suggests that increased urban roughness strengthens the urban heat effect on precipitation. Figure 17 shows the convergence zone at the border between the upwind and city area and causes precipitation in the urban area. Research from Shepherd et al. (2010) found the convergence zone at the fringe of Houston and did not occur for non-urban simulations. In a study in Atlanta (Bornstein and Lin, 2000), the maximum divergence and precipitation was found downwind. In our study the divergence sets in behind the strong convergence zone at the upwind-city edge. We also performed a sensitivity test with adjusted roof top surface roughness lengths. The results, see Appendix Figure 26, show no clear trend for any of the eight boxes. This suggests that not the variance in roof top heights induces precipitation but the depth of the urban canyon. The calculation of the urban canyon roughness does not include the a varying frontal area index depending on the standard deviation of the building height, street width and building width. Further research is needed to determine the reason why this index dominates the precipitation pattern for different roughness lengths. 21

23 Figure 15 Precipitation difference compared to reference run of the five adjusted canyon roughness runs. 22

24 Figure 16 Wind speed difference between surface roughness canyon 1.5x compared to the control run illustrate lower wind speeds over Houston at 17 UTC. 23

25 Figure 17 Convergence zone at the border between upwind area and city for increased surface canyon roughness at 16 UTC results in precipitation in the city. 24

26 3.5 Aerosol concentration The four adjusted aerosol concentrations runs do not show clear spatial patterns in precipitation differences compared to the reference run, see Appendix Figure 27. This is because no urban source could be implemented in the model. Since we changed the total concentration in the domain we can expect intensification or reduction over the summed eight boxes. Carrió et al. (2010) detected enhanced precipitation over more polluted areas in Houston. On the other hand, in studies from Rosenfeld (2000); Li et al. (2008) precipitation increases for higher [CCN], but after reaching a certain concentration the precipitation was reduced with enhanced concentrations. Our results show lowest total precipitation for the reference run and both the lower as higher [CCN] include increased total precipitation (Figure 13c). Therefore no increasing nor a decreasing precipitation trend is detected in our model. According to Heever and Cotton (2007) the aerosol effect on urban precipitation reduces if the background concentrations increase. In the WRF model the background values and city [CCN] had to be equal, which limits the aerosol difference effect. However, the effect of aerosols on precipitation patterns has not been understood since not all process have been explained in previous studies. Consequently, contradicting results have been found and understanding the process would help in obtain better results. 3.6 Anthropogenic heat The increased anthropogenic heat shows a clear decreasing pattern of rainfall in the city, although all values are higher than the reference Figure 18. The UHI effect attracts the storm towards the urban area and results in a convergence zone with increased precipitation in the city (e.g. Bornstein and Lin, 2000; Shepherd et al., 2010; Ganeshan and Murtugudde, 2015). Higher anthropogenic heat shows a decreasing trend in the city and can be explained by the lifting condensation level (LCL). Figure 19 illustrates how the LCL varies for the control run and the extreme anthropogenic heat run: increased height for higher anthropogenic heat delays the saturation process and retains rainfall from falling in the city. As a consequence, precipitation increases in the further downwind area (box 4). As in Miao et al. (2011), we find intensification of total urban precipitation for increased anthropogenic heat (see Figure 13d). This result contrasts with the urban vs non-urban results, in which the urban run contained less total precipitation. The reference run does not include anthropogenic heat and might underestimate the UHI effect. Therefore, the building barrier effect and urban heat have counteracting contribution to urban precipitation patterns and it will depend on the cities structure (e.g. start of blocking effect and average anthropogenic heat) which mechanism dominates. 25

27 Figure 18 Precipitation difference compared to reference run of the four adjusted anthropogenic heat runs. Figure 19 Lifting condensation level (LCL) for the control run (left) and extreme anthropogenic heat (right) at 19 UTC. Higher LCL for the extreme anthropogenic heat shoves precipitation to further downwind areas. 26

28 4 Conclusions In this report four mechanisms that influence precipitation in urban areas are studied separately by the WRF model to conduct their individual impact for the case of Houston, Texas. The four mechanisms all had different interaction with the convective rainfall event. The building barrier has the clearest pattern of all and shows decreasing precipitation over the city with increased building height, while the areas aside and downwind gain rainfall. The canyon surface roughness showed enhanced precipitation in the city due to the lower wind speed and convergence zone upwind. Roughness showed better results after a threshold value of around 1.35x the roughness of the reference run. The roof surface roughness however misses a precipitation pattern. No intensification or reduction of total precipitation has been found under different [CCN] since of the equal city and background aerosol concentrations. The anthropogenic heat revealed opposite effect on urban precipitation in the city compared to the building barrier effect. Extreme heat moves precipitation towards the downwind areas. In Houston, the building barrier effect contributed most to the urban precipitation pattern by creating a wall around the city. The results of this study can be used for urban planning and local weather predictions. Furthermore, these results can be compared to other cities to determine their most dominant mechanism. 5 Recommendations The influence of four urban mechanisms on precipitation has been studied for a case over Houston. To have a better understanding in the contribution of each mechanism more research is needed to compare cases and the effect of different cities. In our results, the building barrier effect was most dominant due to high built-up areas at the border between the upwind area and the city. Since other cities have different built-up areas the building barrier effect may shift in position or vanish. The results show that only the canyon depth induces urban precipitation, but no parameter has been found to explain this. It is recommended to do further research in the contribution of the canyon surface roughness and roof top surface roughness separately. The aerosol concentrations did not reveal precipitation trends, which might have been determined if the a [CCN] source could be implemented in the model. If this setting is available in WRF-Chem and still can include all the other runs, it would be advised to use this model to include better results for this mechanism. Detailed information about air quality over Houston have to be found in advance. At last, the reference run included enhanced precipitation over the bay and Gulf of Mexico, which was not observed by radar. We will determine whether the model considers the case as a sea breeze and is so, how important the land-sea transition in our influences precipitation initiation. Acknowledgements First of all Jisk Attema from the escience center for helping us out with implementing urban parameters from a longer term run to a shorter one. Furthermore, we would like to thank two staff members from the Wageningen University Meteorology and Air Quality department for their contribution: Maarten Krol for giving some information on aerosol concentration and their life time and Kees van den Dries for the technical support. 27

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33 Appendices Figure 20 Location of Houston and surrounded landscape. 32

34 Figure 21 Land use in domain 2. Category one indicates urban area (red) and number two Dry cropland and pasture (yellowish) as the most dominant land use surrounding Houston. 33

35 Figure 22 The [CCN] over domain 1 at 13 UTC remains constant unless clouds are formed due to microphysics scheme NSSL. 34

36 Figure 23 Parametrization for two PBL (YSU above and MYJ below) and three cumulus (from left to right: Kain-Fritsch, Betts-Miller-Janjic and Grell Freitas) schemes at 16 UTC indicate a small timing difference between the two PBL schemes. The cumulus schemes however variate in precipitation amounts (in mm/2h), in which Betts-Miller-Janjic agrees most to observed precipitation quantities. 35

37 Figure 24 Height [m] of the second ETA level equals approximately 200m in Houston and over 300m downwind due to the elevation. 36

38 Figure 25 As Figure 24, but with more detailed elevation difference around the Houston agglomeration. 37