1. Introduction
Howard (Howard, 1833) made the first documented observation of a temperature difference between an urban area and its rural environment. Manley (Manley, 1958) termed this contrast the “urban heat island” (UHI). The UHI has now become a widely acknowledged, observed, and researched phenomenon because of its broad implications. It is estimated that by the year 2025, 60% of the world’s population will live in cities [the United Nations Population Fund (UNFP) 1999]. In the United States, the current urban growth rate, based on 1990 and 2000 census figures, is approximately 12.5%, with 80% currently living in urban areas. The U.S. population is not only growing but is tending to concentrate more in urban areas in coastal zones (Culliton et al., 1990). As cities continue to grow, urban sprawl creates unique problems related to land use, transportation, agriculture, housing, pollution, and development for policy makers. Urban expansion and its associated urban heat islands also have measurable impacts on weather and climate processes. The UHI has been documented in the literature to affect local and regional temperature distributions (Hafner and Kidder, 1999), wind patterns (Hjemfelt, 1982), and air quality (Quattrochi et al., 1998). The UHI may also impact the global water cycle through the development of clouds and precipitation in and around cities.
Several observational and climatological studies have theorized that the UHI can have a significant influence on mesoscale circulations and resulting convection. Early investigations (Changnon, 1968; Landsberg, 1970; Huff and Changnon, 1972) found evidence of warm seasonal rainfall increases of 9%–17% over and downwind of major cities. The Metropolitan Meteorological Experiment (METROMEX) was an extensive study that took place in the 1970s in the United States (Changnon et al., 1977; Huff, 1986) to further investigate the modification of mesoscale and convective rainfall by major cities. In general, results from METROMEX have shown that urban effects lead to increased precipitation during the summer months. Increased precipitation was typically observed within and 50–75 km downwind of the city reflecting increases of 5%–25% over background values (Huff and Vogel, 1978; Changnon, 1979; Changnon et al., 1981, 1991). More recent studies have continued to validate and extend the findings from pre-METROMEX and post-METROMEX investigations (Balling and Brazel, 1987; Jauregui and Romales, 1996; Bornstein and Lin, 2000; Kusaka et al., 2000; Thielen et al., 2000; Baik et al., 2001; Ohashi and Kida, 2002). However, a recent U.S. Weather Research Panel report (Dabberdt et al., 2000) indicated that more observational and modeling work is required because previous results were heavily based on a few specific cities and statistical inferences.
Shepherd et al. (Shepherd et al., 2002) recently established that space-based precipitation observing systems might be able to detect UHI-induced rainfall variability. This is particularly intriguing because understanding urban effects on rainfall is far from complete. First, previous research used ground observations to study one or a few selected cities. However, urban effects vary with the micro- to mesoscale features of individual cities. Global assessment of urban climate is necessary to generalize the most important characteristics of urban effects. Second, previous studies, via different approaches, reached conflicting understanding on urban rainfall relations. It is reported that urban areas reduce rainfall due to cloud microphysics, in contradiction with studies cited above. The mechanisms of urban effects on rainfall are complex. On the one hand, cloud microphysics in response to increased urban aerosols may reduce rainfall, as suggested by Rosenfield (Rosenfield, 1999) and Ramanathan et al. (Ramanathan et al., 2001). On the other hand, local dynamics and thermodynamics associated with an UHI-induced convergence zone and a destabilized boundary layer may enhance urban rainfall (Shepherd et al., 2002; Changnon and Westcott, 2002; Ohashi and Kida, 2002).
There is increasing evidence that large coastal cities, like Tokyo, Japan, and Houston, Texas, can influence weather through complex urban land use–weather–climate feedbacks. A review of Tokyo’s impact on urban rainfall can be found in Kusaka et al. (Kusaka et al., 2000) and Ohashi and Kida (Ohashi and Kida, 2002). An engineering study by Bouvette et al. (Bouvette et al., 1982) presented statistical evidence from four Houston area rainfall-recording stations that the 24-h 100-yr storm depth had increased by 15% in suburban areas when compared to the 24-h 100-yr storm depth published in 1961 by the National Weather Service. They speculated that the change was linked to heavy urban development in Houston, which covers an area of 937 km2. Orville et al. (Orville et al., 2001) analyzed 12 yr (1989–2000) of ground-based lightning data for the Houston area. They found that the highest annual and summer flash densities were over and downwind (e.g., northeast–east) of the Houston area (Figure 1a). Using mesoscale model simulations (Figure 1b), they hypothesized that the lightning distribution was caused by either a combination of UHI-induced convergence or enhanced lightning efficiency by increased urban aerosols. Since lightning is a signature of convection in the atmosphere, it would seem reasonable that urbanized Houston would also impact the distribution of rainfall. This paper presents results using data from the world’s first satellite-based precipitation radar (PR) aboard the Tropical Rainfall Measuring Mission (TRMM) and ground-based rain gauges to quantify rainfall anomalies that we hypothesized to be linked to extensive urbanization in the Houston area. It is one of the first rigorous efforts to quantify an urban-induced rainfall anomaly near a major U.S. coastal city and one of the first applications of space-borne radar data to the problem.
2. Hypothesis and Houston climatological background
The primary hypothesis is that the central Houston urban zone and the seasonally variant downwind regions (e.g., generally northeast for Houston, but northwest–northeast during the summer) exhibit enhanced rainfall relative to regions upwind of the city. Section 3 will discuss the methodology for defining the upwind control regions and the downwind “urban-impacted regions.” Possible mechanisms for the urban-induced rainfall include one or a combination of the following: 1) an enhanced convergence zone created by Houston UHI–sea breeze–Galveston Bay coastline interaction in a subtropical environment, 2) an enhanced convergence due to increased surface roughness in the urban environment, 3) destabilization due to UHI-thermal perturbation of the boundary layer and resulting downstream translation of the UHI circulation or UHI-generated convective clouds, or 4) enhanced aerosols in the Houston environment for cloud condensation nuclei sources. As previously cited, recent studies suggest that item 4 might actually reduce rainfall. Movie 1 conceptually illustrates possible mechanisms for urban-generated rainfall. The mechanisms for the Houston urban rainfall anomaly are further examined in future work. Here, the primary objective is to utilize a unique satellite-based rainfall dataset to support the guiding hypothesis and provide quantification of this phenomenon. Furthermore, we seek to corroborate very recent findings related to the Houston lightning anomaly (Orville et al., 2001).
Houston sits on the 5,000 km2 Gulf Coastal Plain with an elevation of 27 m above sea level. The entire eastern third of the state of Texas including the Houston area (upwind and downwind) is considered a subtropical humid climate. Southeast Texas receives, on average, more than 140 cm of rain annually (Lyons, 1990). Since Houston is located near Galveston Bay and the Gulf of Mexico, its weather is significantly influenced by sea-breeze circulations, particularly during the warm season months. An analysis of annual surface wind direction by the National Weather Service spanning the years 1984–92 indicates that the prevailing surface flow is from the southeast. This would indicate the dominance of the sea-breeze circulation. As indicated later in section 3, the steering level flow (e.g., 700 hPa) and midlevel flow are typically more southwesterly. This fact has important implications for how the upwind and downwind regions are defined in this study. Sea–bay-breeze circulations (Pielke and Segal, 1986) and coastline irregularities (McPherson, 1970) are well-known forcing agents for convection; however, we present results that support the hypothesis that the urbanization in the Houston area is a primary factor causing the observed anomalies. Figure 2 is an image from the Geostationary Operational Environmental Satellite (GOES) that shows the extent of Houston’s UHI. The 3.9-µm infrared channel on GOES is used to depict relatively cold and warm surfaces. Cooler regions like rivers or lakes are indicated as whiter shades while warmer areas are indicated by darker shades. The thermal perturbation of the Houston UHI is clearly evident. The magnitude (e.g., mean urban temperature minus mean rural temperature) of the Houston UHI, like previously studied cities, is typically proportional to the city size (Oke, 1981) and most apparent after sunset (Oke, 1987). However, the UHI circulation is more clearly observed during the daytime than nighttime because of the urban–rural pressure gradient and vertical mixing during daytime hours. As hypothesized in the following text, these dynamic factors will play a major role in the observed rainfall anomalies (Shreffler, 1978; Fujibe and Asai, 1980).
3. Methodology
The TRMM satellite was launched in November 1997 as a joint U.S.–Japanese mission to advance understanding of the global energy and water cycle by providing distributions of rainfall and latent heating over the global Tropics. The TRMM PR operates at a frequency of 13.8 GHz and can achieve quantitative rainfall estimation over land as well as ocean. The horizontal resolution of 4.3 km at nadir and about 5 km at the scan edge allows the TRMM PR to observe small convective cells as well as larger systems. TRMM is in a precessing, low-inclination (35°), low-altitude orbit, and because of the non-sun-synchronous orbit strategy, the equatorial crossing time gradually shifts. For this reason, it is unlikely that results reflect any biases from diurnal forcing.
The study employs the “control coordinate system” approach of Shepherd et al. (Shepherd et al., 2002), which was based heavily on an approach by Huff and Changnon (Huff and Changnon, 1972). The study identified the most frequent lower-tropospheric wind flow for Houston, annually and by season, and defined the hypothesized “downwind urban-impacted region” and upwind control regions. Figure 3 is an example of the theoretical coordinate system. The black vector indicates that the mean annual 700-hPa wind direction over the Houston area is from 230° (∼southwesterly). It serves as the horizontal reference axis that determines the orientation of the control coordinate system. The 700-hPa level was chosen as a representative level for the mean steering flow for convective storms and is supported by previous work in the literature (Hagemeyer, 1991). Wind direction data covering the years 1979–98 from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis dataset (Kalnay et al., 1996) were used to determine the mean annual and seasonal “prevailing” flow at 700 hPa for Houston. For each season, the horizontal reference axis is oriented according to the mean prevailing wind direction. The 125° sector in the downwind urban-impacted region (DUIR) accounts for the mean direction and the spread of values that encompass the mean direction (e.g., the deviation). The use of monthly means in the study prevents an analysis of event-based wind directions. For example, it is quite likely that many events during a summer period will have a mean steering level wind vector that is different from the mean value. However, since mean monthly and seasonal rainfall data are analyzed, there is a high degree of confidence in this approach. Future efforts with more appropriate datasets will examine more event-specific meteorological and rainfall linkages.
Space–time-averaged PR data are utilized to investigate rainfall modification due to urban effects. The analysis was conducted on mean monthly “conditional” rainfall rates (mm h–1) at a height of 2.0 km. Conditional rainfall rates (hereafter referred to as rainfall rates) account for events when rainfall was detected in the grid box. At this point, it is useful to note that the TRMM PR rainfall rates are not the ideal dataset for detecting specific urban-induced events. Yet, there is a critical reason to employ the PR-derived rainfall rates rather than passive microwave or infrared datasets. Over land, the infrared or passive microwave–based rainfall estimates rely heavily on scattering signatures from ice in the upper portion of convective clouds. Often these signatures are not collocated with the actual surface rainfall location. The active PR is a more physically based measurement of rainfall and provides more certainty in the actual location of rainfall cells. Accurate cell location is important for this particular study since one objective is to understand the relative influence of the urban area on the location of rainfall anomalies.
Rainfall rates were calculated as a part of the standard reflectivity–rainfall rate algorithm described in the National Space Development Agency of Japan (NASDA) and the National Aeronautics and Space Administration (NASA) (NASDA and NASA, 2000) study. The PR algorithm calculates rain statistics only when rain is judged to be certain in a 0.5° cell (as opposed to clutter, noise, etc.). For more detailed analysis, the mean rainfall-rate value at each grid cell was calculated over the period (January 1998–May 2002, excluding August 2001). The month of August 2001 was excluded because the TRMM satellite underwent an orbit adjustment during the period and PR data were not available. For a given grid box, the 52 mean monthly rainfall rates were averaged. For the seasonal analysis, only the months corresponding to the season are included. Because conditional rainfall rates are used, the denominator used to calculate the 1998–2002 means at each grid box will vary. To ensure that the upwind and downwind gridbox values contained “sufficient” rainy samples, an analysis of warm season and annual rainy pixels was conducted. This analysis revealed that the mean number of summer rainy pixels per grid box over the study period was 95.3 (downwind) and 98.6 (upwind). For the annual season, these numbers were 59.1 (downwind) and 93 (upwind), respectively. Because means are used, there appear to be ample “rainy samples” in the mean calculations. However, the analysis did reveal that samples during the winter months are relatively low in both regions.
As stated in Shepherd et al. (Shepherd et al., 2002), the maximum number of samples is found in the northern tier of latitudes (28°–35°N) due to the sampling strategy and orbit of TRMM. Therefore, over a nearly 5-yr period, an adequate number of samples can accumulate for long-term averaging. It has been acknowledged in Shepherd et al. (Shepherd et al., 2002) and herein that the TRMM satellite’s orbit, sampling strategy, and narrow radar swath are not optimal for specific event studies. TRMM data are best suited for monthly analysis and climate studies, but as the data record lengthens, some real mesoscale signal in the data (e.g., urban anomalies, sea-breeze anomalies) become evident. For this reason, we state that the results of this study are more robust than our results (Shepherd et al., 2002). In terms of accuracy, Kummerow et al. (2000) reported that comparisons of PR-measured radar reflectivities with those measured by ground-based radar at NASA’s Florida ground validation site show good agreement (differences within about 1 dB).
4. Results
Analysis of the mean annual rainfall rates for Houston and surrounding areas supports the research hypothesis and is consistent with lightning results reported by Orville et al. (Orville et al., 2001). In Figure 3, the orange oval [urban zone (UZ)] covers 0.5° grid boxes centered on (29.75°N, 95.75°W) and (29.75°N, 95.25°W), respectively. The black vector represents the mean annual 700-hPa steering wind direction used to define the upwind control region [(UCR) rectangular box] and the DUIR (pentagon). The DUIR has a pentagon shape to create an approximately 125° sector in the downwind region to account for variability in the mean steering direction. The sides that are parallel to the wind vector are ∼150 km in length. The orthogonal side is ∼300 km in length. In the UCR, the rectangular box is roughly 150 km × 300 km. This approach was utilized successfully in Shepherd et al. (Shepherd et al., 2002) and is based on an earlier approach in St. Louis, Missouri, by Huff and Changnon (Huff and Changnon, 1972).
The largest annual rainfall rates are located in the eastern UZ (orange oval) and DUIR, particularly northeast of the city. As Table 1 indicates, the mean rainfall rate in the DUIR is 2.97 mm h–1. The maximum rate in the DUIR is greater than 3.7 mm h–1. In the urban zone, the mean rainfall rate (albeit only two grid boxes) is 2.66 mm h–1. In the UCR, the mean rainfall rate is 2.06 mm h–1. Table 1 indicates that the mean rainfall rate in the DUIR (UZ) is 44% (29%) larger than UCR. Referring back to Figure 1, Orville et al. (Orville et al., 2001) also found evidence of elevated lightning flash rates in the locations of the rainfall anomalies. It is clearly evident that the northwestern to southeastern sectors of the study area do not contain as many occurrences of higher rainfall rates compared to the climatalogically indicated downwind regions to the northeast and east of the city.
4.1 Seasonal stratification
The overwhelming consensus from the METROMEX studies of St. Louis is that urban effects on precipitation are most pronounced during the warm-season months (Huff and Changnon, 1972; Changnon et al., 1991; Jauregui and Romales, 1996). Therefore, the results in this study were stratified by season. The seasons were designated as summer (June–August), fall (September–November), winter (December–February), and spring (March–May).
Figure 4 shows the results of the seasonal stratification. The most dramatic shift in the coordinate system orientation is observed in the summer season when the prevailing flow at the steering levels shifts from southwesterly in June to southeasterly in August, resulting in a mean summer vector of 178°. Analysis of the results reveals consistencies with the historical work of the 1970s and more recent work by Orville et al. (Orville et al., 2001). The largest mean rainfall rates during the summer season are found over the urban zone and in the DUIR, north to northeast of the urban area. Table 1 indicates that the mean rainfall rate in the summer DUIR (UZ) is 3.79 mm h–1 (4.66 mm h–1). The mean rainfall rate in the summer UCR is 2.97. There is a 27.6% (56.9%) increase in the mean rainfall rate in the DUIR (UZ) over the UCR. It is particularly interesting to note the large increase in the summer UZ relative to the UCR. This fact provides strong evidence that urban forcing is further enhanced during the summer months.
To further corroborate the summer rainfall anomaly over the city, an analysis of annual and warm season totals from 13 yr (1984–97) of high-density rain gauges was conducted. Rain gauge data were obtained from the National Climatic Data Center (NCDC) and the City of Houston Office of Emergency Management (OEM). Rainfall records from the NCDC were extracted from CD-ROMs distributed by Hydrosphere, Inc., and the Houston OEM data were downloaded from their Web site. The rainfall records were screened to remove gauges outside of the area of study and those with insufficient data coverage or quality. After initial screening, 230 NCDC daily, 86 NCDC hourly, 32 NCDC 15-min gauges, and 121 Houston OEM 15-min gauges remained for use in the study. The analysis in Figure 5 reveals elevated rainfall amounts during the warm season over the city in the 14-yr record. In the nearly 5-yr TRMM satellite–based record, a rainfall anomaly is also evident over the city in comparison to the annual distribution. Although it is encouraging to find a consistent signature in the coarser satellite dataset and the higher-resolution rain gauge dataset over the city, the gauge data does not reveal the TRMM-indicated anomaly north of the city. There are a couple of possible reasons for this difference. First, TRMM data depicts conditional rainfall rates, and the gauge data depicts rain totals. Therefore, the TRMM data are likely revealing that the identified urban anomalies are highly convective and are revealed in the conditional rainfall rates more so than total amounts. Second, because of the spatial resolution differences, the TRMM data cannot represent the changes across the city in as much detail as the rain gauges. A complementary study to be reported elsewhere employs a more detailed gauge analysis.
It is particularly interesting to note how the general magnitude of the rainfall rates increases (relative to the fall and winter) within the UZ and DUIR in the summer months. This is indicative of the more convective nature of the precipitation during this time period. The most likely reason for pronounced urban effects on rainfall during the warm season is smaller large-scale forcing (e.g., frontal systems or baroclinicity). Advection associated with strong large-scale forcing tends to eliminate thermal differentiation between urban and surrounding areas. Also, during the warm season, the UHI-induced mesoscale convergence and circulation is more dominant and can significantly alter the boundary layer and interact with the sea-breeze circulation. Examining Figure 4, it is less evident during the spring, fall, and winter that a dominant urban area and downwind anomaly exist. One could argue that there is slight enhancement in the winter DUIR. Changnon et al. (Changnon et al., 1991) found some evidence that St. Louis could alter winter, fall, and spring precipitation. The figure also indicates a more diffuse rainfall pattern during the fall and spring seasons. The climatological likelihood of large-scale precipitation forcing during these transitional seasons explains such patterns and the lack of an urban sea-breeze mesoscale signature.
4.2 Texas coastal analysis
As stated earlier, Houston lies within a coastal zone and is greatly impacted by the sea breeze–Galveston Bay breeze circulation and a complex coastline. McPherson (McPherson, 1970), Negri et al. (Negri et al., 1994), and Baker et al. (Baker et al., 2001) showed that convex coastline curvature could enhance convective development by creating convergence zones for sea-breeze circulations. It might be suggested that the sea–bay-breeze–coastline interactions should explain the Houston lightning and precipitation anomalies presented in this study. To investigate this possibility, the entire Texas coast was divided into seven zones that extend 100 km inland. The rationale is that there are at least four to five major inlets or bays along the Texas coast. The working hypothesis is that if sea-breeze coastline curvature is considered a primary convective forcing mechanism for the observed anomalies, then enhanced regions should be found in several locations along the coast. Conversely, if the urban heat island and its interaction with mesoscale circulations were of primary significance, then an anomaly in precipitation would be expected in the urbanized regions near Houston. In Figure 6, a plot of the TRMM-derived mean annual rainfall rates for 52 months are plotted for the coastal zones. It is very evident that an anomaly in precipitation rate (mean rates > 3.0 mm h–1) is located in coastal zones 6 and 7. These zones (in and downwind of Houston) represent coastal regions where the sea–bay-breeze circulations can interact with the urban circulation. Yoshikado (Yoshikado, 1994), Kusaka et al. (Kusaka et al., 2000), and Ohashi and Kida (Ohashi and Kida, 2002) published modeling studies illustrating the potential convective forcing that can result from sea breeze and UHI interactions. They all present strong evidence that vertical motion and convergence fields are significantly stronger over the urban land surface and can impact convective processes. Coastal zones 1–5 (some of which include complex coastline curvature but no major urban–industrial area) do not exhibit significant differences in rainfall rate. A similar plot for the summer months is extremely consistent with this finding, and results are plotted in the zone-rainfall-rate bar graph of Figure 6. It is acknowledged that a general climatological east–west precipitation gradient in Texas might explain some aspects of the figure. The UIR and DUIR of the study primarily lie in the same climate–precipitation regime, and the observed anomalies (red values) in Figure 6 are quite consistent with the locations of elevated rainfall rates in the UCR–DUIR analyses.
4.3 Urban rainfall ratio analysis
5. Conclusions, future work, and implications
This statistical and quantitative analysis of “first-of-its-kind” space-borne rainfall radar data has identified Houston rainfall anomalies that are hypothesized to be caused by an urban land use interactions with atmospheric processes. The results found elevated rates over and downwind of Houston in the annual and warm-season datasets, as hypothesized. Results are remarkably similar to the lightning anomalies of Orville et al. (Orville et al., 2001), and they confirm speculative assertions introduced by Bouvette et al. (Bouvette et al., 1982). The study also presents evidence that the HRAs are linked to the urbanized region and not exclusively sea- or bay-breeze circulations. It is likely that an interaction between a UHI convergence-circulation pattern and the sea-breeze circulation may explain the anomalies. At this time, the role of microphysical (e.g., aerosol) forcing is unclear and more research is required.
Future work will integrate the TRMM PR analysis with an extensive high-density rain gauge analysis using a “downscaling” process. Additionally, numerical modeling of the Houston UHI–sea breeze–Galveston Bay breeze will be conducted to assess what forcing mechanisms (roughness, destabilized boundary layer, mesoscale interactions, or microphysics) may result in the HRAs. A climate change study will leverage a high-density, long-term rain gauge dataset against land use and population density data to detect the temporal evolution of the HRA relative to urban–industrial development around Houston. Finally, an engineering study will be conducted to update rainfall frequency analyses used in an urban drainage design, a transportation design, agriculture, and other practical applications. Additionally, the Houston Environment Aerosol Thunderstorm (HEAT) experiment (Orville et al., 2002) will provide a unique dataset in the 2004–05 time frame to further investigate these findings.
Acknowledgments
The authors acknowledge the support of NASA’s New Investigator Program, managed by Dr. Ming-Ying Wei and the NASA Faculty Fellowship Program. We also acknowledge Dr. Menglin Jin of the University of Maryland and the instructive comments of the anonymous reviewers.
References
Baik, J-J., Y. H. Kim, and H. Y. Chun. 2001. Dry and moist convection forced by an urban heat island. J. Appl. Meteorol 40:1462–1475.
Baker, R. D., B. Lynn, A. Boone, W. K. Tao, and J. Simpson. 2001. The influence of soil moisture, coastline curvature, and land-breeze circulations on sea-breeze-initiated precipitation. J. Hydrometeorol 2:193–211.
Balling, R. and S. Brazel. 1987. Recent changes in Phoenix summertime diurnal precipitation patterns. Theor. Appl. Climatol 38:50–54.
Bell, T. and N. Reid. 1993. Detecting the diurnal cycle of rainfall using satellite observations. J. Appl. Meteorol 32:311–322.
Bouvette, T., J. L. Lambert, and P. B. Bedient. 1982. Revised rainfall frequency analysis for Houston. Proc. Am. Soc. Civ. Eng 108:515–528.
Changnon, S. A. 1968. The LaPorte weather anomaly—Fact or fiction? Bull. Am. Meteorol. Soc 49:4–11.
Changnon, S. A. 1979. Rainfall changes in summer caused by St. Louis. Science 205:402–404.
Changnon, S. A. and N. E. Westcott. 2002. Heavy rainstorms in Chicago: Increasing frequency, altered impacts, and future implications. J. Am. Water Res. Assoc 38 5:1467–1475.
Changnon, S. A., F. A. Huff, P. T. Schickedanz, and J. L. Vogel. 1977. Summary of METROMEX, Vol. I: Weather Anomalies and Impacts. Illinois State Water Survey, Bull. 62, Champaign, IL, 260 pp.
Changnon, S. A., R. G. Semonin, A. H. Auer, R. R. Braham, and J. Hales. 1981. METROMEX: A Review and Summary. Meteor. Monogr., No. 40, Amer. Meteor. Soc., 81 pp.
Changnon, S. A., R. T. Shealy, and R. W. Scott. 1991. Precipitation changes in fall, winter, and spring caused by St. Louis. J. Appl. Meteorol 30:126–134.
Culliton, T., M. Warren, T. Goodspeed, D. Remer, C. Blackwell, and J. McDonough. 1990. 50 years of population change along the nation’s coasts 1960–2010: The second report of a coastal trends series. Strategic Assessment Branch, Oceans Assessments Division, Office of Oceanography and Marine Assessment, National Ocean Service, NOAA, Rockville, MD, 41 pp.
Dabberdt, W. F. et al. 2000. Forecast issues in the urban zone: Report of the 10th prospectus development team of the U.S. Weather Research Program. Bull. Am. Meteorol. Soc 81:2047–2064.
Fujibe, F. and T. Asai. 1980. Some features of the surface wind system associated with the Tokyo heat island. J. Meteorol. Soc. Japan 58:149–152.
Hafner, J. and S. Kidder. 1999. Urban heat island modeling in conjunction with satellite-derived surface/soil parameters. J. Appl. Meteorol 38:448–465.
Hagemeyer, B. C. 1991. A lower-tropospheric thermodynamic climatology for March through September: Some implications for thunderstorm forecasting. Weather Forecasting 6:254–270.
Hjemfelt, M. R. 1982. Numerical simulation of the effects of St. Louis on mesoscale boundary-layer airflow and vertical motion: Simulations of urban vs. non-urban effects. J. Appl. Meteorol 21:1239–1257.
Howard, L. 1833. Climate of London Deduced from Meteorological Observations. 3d ed., Vol. 1, Harvey and Darton, London, 348 pp.
Huff, F. A. 1986. Urban hydrological review. Bull. Am. Meteorol. Soc 67:703–712.
Huff, F. A. and S. A. Changnon. 1972. Climatological assessment of urban effects on precipitation at St. Louis. J. Appl. Meteorol 11:823–842.
Huff, F. A. and J. L. Vogel. 1978. Urban, topographic and diurnal effects on rainfall in the St. Louis region. J. Appl. Meteorol 17:565–577.
Jauregui, E. and E. Romales. 1996. Urban effects on convective precipitation in Mexico City. Atmos. Environ 30:3383–3389.
Kalnay, E. et al. 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bull. Am. Meteorol. Soc 77:437–471.
Kummerow, C. et al. 2000. The status of the Tropical Rainfall Measuring Mission (TRMM) after two years in orbit. J. Appl. Meteorol 39:1965–1982.
Kusaka, H., F. Kimura, H. Hirakuchi, and M. Mizutori. 2000. The effects of land-use alteration on the sea breeze and daytime heat island in the Tokyo metropolitan area. J. Meteorol. Soc. Japan 78:405–420.
Landsberg, H. E. 1970. Man-made climate changes. Science 170:1265–1274.
Lyons, S. 1990. Spatial and temporal variability of monthly precipitation in Texas. Mon. Weather Rev 118:2634–2648.
Manley, G. 1958. On the frequency of snowfall in metropolitan England. Q. J. R. Meteorol. Soc 84:70–72.
McPherson, R. D. 1970. A numerical study of the effect of a coastal irregularity on the sea breeze. J. Appl. Meteorol 9:767–777.
NASDA and NASA 2000. Tropical Rainfall Measuring Mission Precipitation Radar Algorithm: Instruction Manual Version 2.0. 115 pp.
Negri, A. J., R. F. Adler, E. J. Nelkin, and G. J. Huffman. 1994. Regional rainfall climatologies derived from Special Sensor Microwave Imager (SSM/I) data. Bull. Am. Meteorol. Soc 75:1165–1182.
Ohashi, Y. and H. Kida. 2002. Local circulations developed in the vicinity of both coastal and inland urban areas: Numerical study with a mesoscale atmospheric model. J. Appl. Meteorol 41:30–45.
Oke, T. R. 1981. Canyon geometry and nocturnal urban heat island: Comparison of scale model and field observations. J. Climatol 1:237–254.
Oke, T. R. 1987. Boundary Layer Climates. 2d ed., Methuen Co., London/New York, 435 pp.
Orville, R. E., G. Huffines, J. N. Gammon, R. Zheng, B. Ely, S. Steiger, S. Phillips, S. Allen, and W. Read. 2001. Enhancement of cloud-to-ground lightning over Houston Texas. Geophys. Res. Lett 28:2597–2600.
Orville, R. E., R. Zhang, J. N. Gammon, D. Collins, B. Ely, and S. Steiger. 2002. Houston Environmental Aerosol Project: Scientific Overview and Operational Plan for HEAT-2004/2005. Department of Atmospheric Sciences, Texas A&M University, College Station, Texas, 58 pp.
Pielke, R. A. and M. Segal. 1986. Mesoscale circulations forced by differential terrain heating, in Mesoscale Meteorology and Forecasting,. edited by Peter S. Ray, Amer. Meteor. Soc., Boston, 793 pp.
Quattrochi, D., J. Luvall, M. Estes, C. Lo, S. Kidder, J. Hafner, H. Taha, R. Bornstein, R. Gillies, and K. Gallo. 1998. Project Atlanta (Atlanta Land Use Analysis: Temperature and Air Quality)—A study of how urban landscape affects meteorology and air quality through time, paper presented at the Second Urban Environment Symposium,. Amer. Meteor. Soc., Albuquerque, NM, 2–6 November 1998.
Ramanathan, V., P. J. Crutzen, J. T. Kiehl, and D. Rosenfeld. 2001. Aerosols, climate, and the hydrological cycle. Science 294:2119–2124.
Rosenfeld, D. 1999. TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophys. Res. Lett 26:3105–3108.
Shepherd, J. M., H. F. Pierce, and A. J. Negri. 2002. Rainfall modification by major urban areas: Observations from spaceborne rain radar on the TRMM satellite. J. Appl. Meteorol 41:689–701.
Shreffler, J. H. 1978. Detection of centripetal heat-island circulations from tower data in St. Louis. Bound.-Layer Meteorol 15:229–242.
Thielen, J., W. Wobrock, A. Gadian, P. G. Mestayer, and J-D. Creutin. 2000. The possible influence of urban surfaces on rainfall development: A sensitivity study in 2D in the meso-gamma scale. Atmos. Res 54:15–39.
UNFP 1999. The State of World Population 1999. United Nations Population Fund, United Nations Publications, New York, 76 pp. [Available online at http://www.unfpa.org/swp/1999/index.htm.].
Yoshikado, H. 1994. Interaction of the sea breeze with urban heat islands of different sizes and locations. J. Meteorol. Soc. Japan 72:139–142.
(a) Mean annual flash densities (km2 day–1): Highest flash densities are over and just downwind of the Houston urban area. (b) MM5 simulation of the low-level convergence field illustrating the interaction between the sea-breeze circulation and the UHI circulation (from Orville et al., 2001)
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
GOES 3.9-µm channel indicated thermal signatures of the Houston UHI
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
The “theoretical study coordinate system” with mean annual distribution of TRMM-derived “conditional” rainfall rates from Jan 1998 to May 2002 (excluding Aug 2001). The orange oval is the approximate Houston UZ and covers (29.75°N, 95.75°W) and (29.75°N, 95.25°W). The black vector represents the mean annual 700-hPa steering direction. The pentagon-shaped box is the DUIR and the rectangular box is the UCR
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Seasonal stratification of mean TRMM-derived conditional rainfall rates (mm h–1) over the 52-month study period
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Analysis of rain gauge totals from quality-controlled gauges in a dense urban network (e.g., within 250 km of Houston: 121 Houston flood alert, 230 NCDC daily, 86 NCDC hourly, and 32 NCDC 15 min). A greater urban influence is seen in the warm-season spatial rainfall distribution compared to the annual rainfall distribution over the 13-yr period
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Analysis of mean annual and summer TRMM-derived conditional rainfall rates (mm h–1) in the seven Texas coastal zones
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Plot of URR for the (left) annual and (right) summer datasets. The grid points are randomly selected from the upwind, urban, and downwind regions of the study coordinate system
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Movie 1. A Quick Time movie illustrating, conceptually, how a UHI might impact boundary layer stability and low-level convergence to produce precipitating clouds
Citation: Earth Interactions 7, 4; 10.1175/1087-3562(2003)007<0001:DOUIRA>2.0.CO;2
Mean “conditional” rainfall rates and percent rainfall-rate differences in the UCR, DUIR, and UZ
* Corresponding author address: Dr. J. Marshall Shepherd, NASA Goddard Space Flight Center, Earth Science Directorate, Code 912, Greenbelt, MD 20771. Marshall.Shepherd@nasa.gov
