Can Cities Create Their Own Snowfall? What Observations are required to find out?

By J. Marshall Shepherd and Thomas L. Mote , posted on September 6th, 2011 in Articles, Earth Observation, Urban Monitoring Theme, Weather

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Figure 1: Dodge City, Kansas area and downwind region of unusual snow event. Source: National Weather Service..

Figure 1: Dodge City, Kansas area and downwind region of unusual snow event. Source: National Weather Service.

By J. Marshall Shepherd and Thomas L. Mote, University of Georgia

Introduction

If you do not think human activities can create or alter snowfall patterns, then consider the events of Jan. 19, 2011 in Dodge City, Kansas. The National Weather Service documented an unusual weather pattern that caused a very narrow band of snow downwind of Dodge City (Fig. 1). The NWS website states that “the atmosphere was cold and moist with low clouds and fog preceding the formation of the snow. It appears that two slaughter plants and a power generating plant (upwind of the region) contributed to the snow as ice nuclei and copious amounts of water vapor were fed into the boundary layer. East/southeast winds carried the vapor and nuclei aloft into the lower clouds and then precipitated out as snow downwind of the source. Snowfall of as much as 0.7″ was reported in the snowfield with no snow observed at all outside of this area.” The band of snowfall is quite evident in the Doppler radar image at 11:24 local time (Fig. 2a) and in a subsequent satellite image of snow cover (Fig. 2b).

While “pork or beef flavored snowflakes” may seem unusual, inadvertent modification of precipitation systems is not uncommon. While the climate change discussion is dominated by greenhouse gas forcing and its effects, the IPCC [1] noted a growing interest in understanding what role urban land cover/land use and pollution have on climate change. Cities in themselves modify weather and climate at scales ranging from local to global [2].

The most comprehensively studied effect of the built environment on climate is the urban heat island(UHI). While the UHI is relatively well understood, urban effects on the precipitation and storms still require additional scientific interrogation. There is renewed and spirited debate on how the urban environment affects rainfall variability. Possible mechanisms for an urban impact on rainfall or convection include one or a combination of the following: (1) enhanced convergence due to increased surface roughness from buildings in the urban environment; (2) destabilization due to UHI-thermal perturbation of the boundary layer and resulting downstream translation of the UHI circulation or UHI-generated convection; (3) enhanced aerosols in the urban environment for cloud condensation nuclei sources; and (4) bifurcating or diverting of precipitating systems by the urban canopy or related processes.

Figure 2: Dodge City unusual snow event. (a.) radar image at 1124 LST and (b) satellite image after the event. Source: National Weather Service.

Figure 2: Dodge City unusual snow event. (a.) radar image at 1124 LST and (b) satellite image after the event. Source: National Weather Service.

Studies from 1920s to the present [3] suggest that urban landscape and/or pollution in cities alter rainfall budgets and distributions, particularly over and 25-75 km downwind of the city center. Fig. 3 reveals a warm season anomaly in rainfall east of Atlanta using a Doppler radar climatology [4]. The rainfall climatology in the figure clearly reveals the expected high rainfall regions associated with the mountains of North Georgia and the coastal plain regime. More interestingly, over (center yellow box) and climatologically- downwind of Atlanta (right-most yellow box) is a higher rainfall region even though the background meteorological-climate regime is the same in all three boxed regions. The figure also shows that a lightning anomaly exists in the same area [5]. It should be noted that some studies have argued that urbanization reduces rainfall [6] because of the indirect effect of pollution aerosols.

Even with the uncertainties about the “sign” of the change in precipitation due to urbanization, it is widely accepted that urban environments affect rainfall processes. A recent review article [3] states that “though several plausible hypotheses for urban effects on rainfall have emerged within the literature, there is no conclusive theory on what mechanisms, independently or synergistically, affect urban rainfall processes. A large body of literature has been devoted to pattern description and visualization. These methods are vital for generating and testing these hypotheses, but emphasis has now shifted to documenting mechanism and elucidating relative contributions [7].” While some questions remain about the “urban rainfall effect,” the topic has received considerable attention recently. However, the impact of the urban environment on snowfall processes is not well understood and has received relatively little attention.

Figure 3: Average daily rainfall (shaded and units of millimeters) from June-August (200-2006) composited for days with weak atmospheric forcing (lef). The 5 mm contour is shown in blue and the yellow boxes represent the mean upwind (left box), city (center box), and downwind (right box) regions. Lightning flash anomalies (May to September, 1995–2003) on days dominated by weak atmospheric forcing (right) in Atlanta, Georgia. Image sources: (4) and (5), respectively.

Figure 3: Average daily rainfall (shaded and units of millimeters) from June-August (2002-2006) composited for days with weak atmospheric forcing (lef). The 5 mm contour is shown in blue and the yellow boxes represent the mean upwind (left box), city (center box), and downwind (right box) regions. Lightning flash anomalies (May to September, 1995–2003) on days dominated by weak atmospheric forcing (right) in Atlanta, Georgia. Image sources: (4) and (5), respectively.

Observing Shape and Form of Cities

At the University of Georgia, we are leveraging several observational and modeling methodologies to fill gaps in our knowledge of urban-snow effects. Phase I of our research characterizes urban shape/form and snowfall climatology around major North American urban areas with the intent of identifying possible relationships. Phase II will utilize advanced land-atmosphere modeling systems to investigate physical mechanisms. Phase III will attempt to address the role of pollution and aerosols. Herein, we highlight some of the observational aspects of Phase I.

[8] utilized radiance calibrated digital Defense Meteorological Satellite Program (DMSP) Operational Linescan System data to assess shape and form of cities in China. Using image processing and Geographic Information System software, the shape and volume of the cities were extracted. We, in collaboration with the UGA Center for Remote Sensing and Mapping Science, are employing a similar method to identify the shapes and sizes of North American cities (Fig. 4).

To observe the three-dimensionality of urban landscapes, detailed urban canopy parameters like roughness length, sky view factor, and mean building height are required. We employ data from the National Urban Database (NUD). These data will be available via the emerging NUD Access Portal. We are already working with lidar data provided by Steve Burian (University of Utah) from the National Building Statistics Database. Recently, [12] applied such airborne lidar-derived data to depict the urban canopy for Houston, Texas, in a coupled atmosphere-land surface modeling system (Fig. 5). The combined OLS-Lidar analysis is a fairly new way to assess urban shape and form, and the eventual synergy with coupled models is quite novel.

Figure 4: DMPS/OLS data showing nights at light in the United States. Source: NASA.

Figure 4: DMPS/OLS data showing lights at night in the United States. Source: NASA.

Observing Urban-Snow Relationships

Though extremely limited and not contemporary, early research on urban effects on snowfall suggested reduced snowfall and freezing rain downwind of major U.S. cities occurs due to higher temperatures [10,11]). [11] suggested that spatio-temporal climatologies of freezing rain events might be affected by urbanization because the UHI would reduce the number of below freezing days in the city as compared to the surrounding rural environment. [12] have suggested that snowfall anomalies in Fairbanks may be due to urban effects. However, the literature on urban effects on frozen precipitation is scarce and most of it focuses on aerosol influences rather than urban landscape effects.

A working hypothesis is that an urban area in the winter is analogous to a lake-enhanced snow environment (Fig. 6). Both a city and a lake share three elements that aid in snowfall enhancement: thermal instability, increased moisture, and wind convergence [13] Therefore, we hypothesize that urban land cover may have an effect similar to a warm lake in snowfall enhancement. We further hypothesize that this unique mechanism associated with winter precipitation acts in concert with the urban-enhancement mechanisms discussed in Section 1.

Doppler radar is a unique tool that can aid in addressing the questions of the mechanisms and magnitude of the urban effect on snowfall. Radar reflectivity data more accurately determines if the UHI is melting the falling snow (i.e., [10]), or if the urban environment is enhancing snowfall production. We are developing urban snowfall climatology for six cities in the Midwest and Eastern U.S.: Kansas City, MO, St. Louis, MO, Indianapolis, IN, Cincinnati, OH, Columbus, OH, and Baltimore, MD. The nearest WSR-88D radar sites for each city and surrounding surface meteorological (first-order and cooperative) observations are employed to identify snowfall events for winters (December–March) from 2005 through 2009 using Level-II and Level-III data from the National Climatic Data Center (NCDC) Hierarchical Data Storage System.

Figure 5: Urban canopy (Houston, Texas) representation in a coupled atmosphere-land surface model framework as derived from airborne lidar data. The data are coarsened to the ~1 kilometer resolution of the model grid. Source: (9).

Figure 5: Urban canopy (Houston, Texas) representation in a coupled atmosphere-land surface model framework as derived from airborne lidar data. The data are coarsened to the ~1 kilometer resolution of the model grid. Source: (9).

Daily observations from cooperative stations of temperature, precipitation, snowfall, and snow depth have been assembled into a database, quality controlled based on recommendations by [14], and interpolated to 0.25o latitude x 0.25o longitude (roughly 25km) grids from 1950 through 2003 [15,16]. Additional station data have been quality controlled through December 2010. Data pertaining to lower-tropospheric conditions above the region will be composed of radiosonde measurements and modeled data.

Wind measurements at 1200 UTC at 850 hPa above the nearest upper-air observing station have been acquired from the University Center for Atmospheric Research. These data included geopotential height, air temperature, and dewpoint temperature at various atmospheric levels. The North American Regional Reanalysis (NARR) is a long-term, consistent, high-resolution climate data set for the North American domain from 1979 to present at 3h intervals and 32km grid spacing [17]. Among numerous other meteorological variables, NARR includes geopotential height, temperature, humidity, and wind information at 25hPa intervals from 1000hPa (near the surface) to 700hPa (roughly 3km), including the 850hPa level. Both datasets assist in defining upwind and downwind regions of each urban area by month.

Multidecadal data from a robust number of stations can facilitate the use of statistical analyses to detect and explain spatial anomalies in precipitation. Following the methods of [18], we are attempting to (a) assess spatiotemporal variations in monthly snowfall during consecutive epochs, (b) test for significant changes in monthly snowfall between consecutive epochs, (c) test for differences in atmospheric conditions between consecutive epochs, and (d) ascertain whether the changes in monthly snowfall may include increases that may have been caused by urbanization. To assess changes of a phenomenon over time, the study period will be divided into 20-year consecutive epochs: 1950–1970, 1971-1990, 1991-2010. The analysis will be conducted using the individual station data and the 0.25º grid points.

Figure 5: Urban canopy (Houston, Texas) representation in a coupled atmosphere-land surface model framework as derived from airborne lidar data. The data are coarsened to the ~1 kilometer resolution of the model grid. Source: (9).

Figure 6: Lake effect snow from a satellite perspective. Source: NASA.

Broader Impacts and Significance

Recently, the National Academy of Science (NAS) convened a summer study committee and workshop (July 2011) to explicitly scope current and future science and stakeholder observational needs in urban meteorology. The project was co-sponsored by NSF, NASA, and NOAA.

Our emerging study of urban effects on snowfall is a potentially transformational study that approaches the topic of urban precipitation effects from a completely different perspective, using traditional geographic methods with cutting-edge models and remotely sensed datasets. Furthermore it may allow us to mitigate a weather hazard that can paralyze cities and result in enormous expenditures. This research is necessary for the development of accurate forecasting techniques that will ensure both greater public safety as well as more economical and sustainable urban planning.

Urban snowfall has significant human and economic impacts [19] and also is an important mechanism for runoff of contaminants into rivers and lakes (Fig.7). Snowfall has a substantial human and economic impact in transportation, building failure, and business closings. Snowfall damages property and requires costly removal efforts. Heavy snow creates hazardous driving surfaces, which can lead to accidents, injuries, and fatalities. Urban areas also see much more rapid snowmelt than surrounding rural areas, which can overwhelm urban storm drainage systems and transport large amounts of pollutants. Therefore, we see both scientific and societal merit for our ongoing studies. Finally, forthcoming NASA missions like the Global Precipitation Measurement (GPM) mission, with a capacity for spaceborne snowfall measurement, will enable new research and knowledge in this area for cities outside of the United States.

Dr. J. Marshall Shepherd is a full professor of geography/atmospheric sciences at the University of Georgia. He conducts research, advising, and teaching in atmospheric sciences, climatology, water cycle processes and urban climate systems. Dr. Shepherd is the Director of the UGA Atmospheric Sciences Program. Prior to joining the UGA faculty, Dr. Shepherd spent 12 years as a research meteorologist at NASA. Dr. Shepherd also was Deputy Project Scientist for the Global Precipitation Measurement (GPM) mission. For his work on urban climate, Dr. Shepherd was honored in 2004 at the White House with the Presidential Early Career Award (PECASE) for pioneering scientific research. Dr. Shepherd is a fellow of the American Meteorological Society and has more than 60 publications. He is an editor for the Journal of Applied Meteorology and Climatology and co-section editor (climatology) for the journal Geography Compass. Dr. Shepherd also is the author of the forthcoming textbook, “The Urban Climate System.” Dr. Shepherd received his B.S., M.S. and PhD in physical meteorology from Florida State University.

Dr. Thomas L. Mote serves as professor and head for the Department of Geography at the University of Georgia. He also is a member of UGA’s Faculty of Water Resources and co-director for the Southern High Resolution Modeling Consortium, a collaboration with the USDA Forest Service. He is an associate editor for the Journal of Applied Meteorology and Climatology. Dr. Mote has studied abroad, most recently on Fulbright exchanges to Egypt and Brazil. Dr. Mote received his doctoral degree from the University of Nebraska-Lincoln, where he also received his masters degree, in geography (meteorology/climatology).

References

[1] Trenberth K.E., et al., “Observations: Surface and atmospheric climate change. In: Climate Change 2007: The Physical Science Basis” Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.

[2] Seto, K., J.M. Shepherd, 2009, “Global urban land-use trends and climate impacts.” Current Opinion in Environmental Sustainability, 2009, doi:10.1016/j.cosust.2009.07.012.

[3] Shepherd, J.M., J.A. Stallins, M. Jin, and T.L. Mote, “Urbanization: Impacts on clouds, precipitation, and lightning.” Monograph on Urban Ecological Ecosystems. Eds. Jacqueline Peterson and Astrid Volder. American Society of Agronomy-Crop Science Society of America- Soil Science Society of America, 2010, 354 pp.

[4] Mote, T.L., M.C. Lacke, and J. M.Shepherd, “Radar signatures of the urban effect on precipitation distribution: a case study for Atlanta, Georgia.” Geophys. Res. Lett., 34, 2007, L20710, doi:10.1029/2007GL031903.

[5] Stallins, J. A., and L. S. Rose, “Urban lightning: Current research, methods, and the geographical perspective.” Geogr. Compass, 2, 2008, 620–639.

[6] Rosenfeld D, Lohmann U, Raga GB, O’Dowd CD, Kulmala M et al., “Flood or drought: How do aerosols affect precipitation?” Science, 321, 2008, 1309-1313.

[7] Teller A, Levin Z, “Factorial method as a tool for estimating the relative contribution to precipitation of cloud microphysical processes and environmental conditions: Method and application.” Journal Of Geophysical Research-Atmospheres 113(D2), 2008, D02202, doi:10.1029/2007JD008960.

[8] Lo, C. P., “Urban Indicators of China from Radiance-Calibrated Digital DMSP-
OLS Nighttime Images,” Annals of The Association Of American Geographers,
92(2), 2002, 225-240.

[9] Carter, W.M., J.M. Shepherd, S. Burian, and I. Jeyachandran, “Integration of lidar data into a coupled mesoscale-land surface model: A theoretical assessment of sensitivity of urban-coastal mesoscale circulations to urban canopy.” Journal of Atmospheric and Oceanic Technology (In Press), 2011.

[10] Landsberg, H. E., “The Urban Climate.” Academic Press, 1981, 275 pp.

[11] Changnon, S. A., “Urban Modification of Freezing-Rain Events.” J. Appl. Meteor., 42, 2003, 863–870.

[12] Mölders, Nicole, Mark A. Olson, “Impact of Urban Effects on Precipitation in High Latitudes.” J. Hydrometeor, 5, 2004, 409–429.

[13] Perryman, N., and P.G. Dixon, “Understanding the urban heat island effect of Minneapolis-St. Paul: a radar analysis of snowfall modification.” Annual Meeting of the Association of American Geographers, Washington DC., 2010.

[14] Robinson, D. A., “Construction of a United States historical snow data base.” Proc. Eastern Snow Conf., 45, 1989, 50–59.

[15] Dyer, J.L., and T.L. Mote, “Spatial variability and patterns of snow depth over North America.” Geophys. Rese. Lett., 33, 2006, L16503, doi:10.1029/2006GL027258.

[17] Dyer, J.L., and T.L. Mote, “Trends in snow ablation over North America.” Intl. J. Climatology, 27, 2007, 739-748, doi:10.1002/joc.1426.

[18] Mesinger F and 18 others, “North American regional reanalysis.” Bull. Amer. Meteor. Soc., 87, 2006, 343–360.

[19] Diem, J.E., and T.L. Mote, “Interepochal changes in summer precipitation in the Southeastern United States: Evidence of possible urban effects near Atlanta, Georgia.” J. Appl. Meteor. 44, 2005, 717–730.
[20] Changnon, S.A. and D. Changnon, “Snowstorm catastrophes in the United States.” Environ. Hazards, 6, 2005, 158-166.

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