About

Professor Andrew W. Ellis is associated with the Center for Geospatial Information Technology (CGIT) at Virginia Tech, which collaborates across research, education, and outreach with a transdisciplinary approach to address complex problems using geospatial science. His work involves applying geospatial science to improve quality of life, environment, and community through smart decision making. The center utilizes extensive knowledge in Geographic Information Systems to develop powerful geospatial tools with user-friendly interfaces, transforming spatial data into secure, intuitive decision-making tools that empower agencies, researchers, and communities across the Commonwealth. His research focuses on creating decision-making tools that fuse geospatial science, software engineering, and user experience design to develop applications that translate complex datasets into practical insights. These tools support various sectors such as transportation, environment, and security by mapping risk, tracking infrastructure, forecasting change, and enhancing safety and strategic planning. Key contributions include redesigning the DMV Geocoding Tool for address accuracy and safety analytics, developing the Virginia State Police Crash Analysis Platform to visualize crash data, and creating dashboards that connect data across sectors for smarter policy and community development. His work emphasizes intelligent mapping, data visualization, secure GIS and automation tools, AI and analytics for predictive modeling, and decision-support systems, all aimed at advancing data-driven decision-making for a more informed, resilient, and secure future.

Research topics

• Environmental science
• Geography
• Geology
• Climatology
• Computer Science
• Mathematics
• Meteorology
• Statistics
• Oceanography
• Atmospheric sciences

Selected publications

• Comparative assessment of POWER and CMAP satellite-derived rainfall products for hydroclimate monitoring in Nigeria

  Theoretical and Applied Climatology · 2026-03-19

  articleOpen access1st authorCorresponding

  Rainfall is a critical element of the climate across the west African nation of Nigeria. The spatial inhomogeneity of tropical rainfall makes high-resolution monitoring essential, but this is difficult to achieve with a sparse network of in-situ gauges. Supplementing gauge data with satellite-derived rainfall products is appealing but requires validation to determine usefulness. In this study, two widely available satellite-derived rainfall datasets are aligned with daily rainfall data from 20 stations across Nigeria for a comparative statistical analysis for the period 1982–2011. Both the United States Climate Prediction Center’s (CPC) Merged Analysis of Precipitation (CMAP) and the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (POWER) data replicate the spatial variability in mean rainfall across Nigeria as portrayed by the gauge network. However, each dataset considerably overestimates rainfall frequency, while rainfall amount exhibits weak interannual co-variability with gauge data and temporal trends that are in the opposite direction of gauge data. A coarser temporal resolution, such as that of the pentad-level CMAP data, produces better alignment with gauge data. The results indicate that the POWER and CMAP rainfall data are inadequate for hydroclimate monitoring across Nigeria.

  PublisherOA PDFDOI

• Distinguishing Precipitation by Process as a Prerequisite for Understanding Hydroclimate Change: An Example from the Southeastern Lake-Effect Region of the Great Lakes Basin

  Climatic Change · 2025-12-01

  articleOpen access1st authorCorresponding

  Understanding precipitation change is made difficult by the complexity of precipitation processes, which vary by region but also within region. Improved understanding may require refined analysis based upon stratification of precipitation by cause. As an example, the cool season (November – April) of the North American Great Lakes region involves precipitation resulting from two broadly different mechanisms – synoptic-scale dynamical forcing and mesoscale lake-effects. This paper shows that station data from within a distinct lake-effect region indicate that precipitation from the contrasting causes trended in opposite directions over a recent 47-year period. These changes are masked within total precipitation series, for which trends are not statistically evident. An increasing amount of synoptically forced precipitation was countered by a decreasing amount and frequency of lake-effect precipitation. Increases in the frequencies of light, heavy, and very heavy synoptic precipitation days are not evident for lake-effect precipitation, such that when aggregating to total precipitation, a frequency increase is only evident for very heavy precipitation days. Increasing volatility in daily synoptic precipitation amount is opposite decreasing volatility for lake-effect precipitation. The binary stratification of precipitation by basic cause explains a misalignment with precipitation change for the broader region of the northeastern United States. The changes in synoptically forced precipitation match those of total precipitation for neighboring areas outside the lake-effect region. This highlights the value of distinguishing precipitation by process prior to analysis of hydroclimatic change.

  PublisherOA PDFDOI

• Cool season near-surface wind speed trends across the central Appalachian Mountains region of the eastern United States, 1995-2022

  Physical Geography · 2023-10-27

  article1st authorCorresponding

  Prompted by recent findings of a common trend toward lesser near-surface winds (NSW), or stilling, time series of cool season NSW variables for 20 stations in the central Appalachian Mountains of the eastern United States were examined for trends during the period 1995–2022. While not universal across the station array, results generally indicate an increase in the frequency of calm conditions, a decrease in mean wind velocity, and a decrease in the frequency of high-wind days. Contrasting this was a general increase in high-wind gust days. The evidence for stilling was supported by an increase in the frequency of days with a 12-hour pressure change of zero and an apparent strengthening and northwestward expansion of the subtropical ridge over the region. Supporting the increase in high-wind gust days was an increase in the frequency of days with a large 3-hour pressure change, and lesser geopotential heights in the lower atmosphere northwest of the region, which contrasts the expanding subtropical ridge and together presents the potential for periodic placement of stronger dynamics across the region.

  PublisherDOI

• A Hybrid Dataset of Historical Cool-Season Lake Effects From the Eastern Great Lakes of North America

  Frontiers in Water · 2022-02-21 · 4 citations

  articleOpen access1st authorCorresponding

  The moistening of cold air passing over the Great Lakes of North America has a profound impact on the cool season climate of regions downwind, from relatively benign air mass modification to highly-impactful snowfall events. The importance of lake effects has led to the development of varying techniques for systematically identifying lake-effect days. The results of two such methods are merged here to yield a more thorough record of lake-effect days for the eastern Great Lakes. Comparative analysis of the data sets illustrates the different objectives of the two methodologies, where one identifies days with a synoptic setup conducive to lake-effect snowfall, and the other identifies days with lake-effect modification of the overlying air mass. A smaller population of “absolute” lake-effect days are those identified by both methods, while a larger population of “hybrid” lake-effect days are absolute days plus those identified by one method but not the other. For a 51-year study period ending with the 2014–15 cool season, the absolute data set yields a mean of about 15 lake-effect days per year, or 8% of the November through April season, while the hybrid data set yields a mean of 56 lake-effect days per year, or 31% of the season. The frequencies of absolute, air mass modification-defined, and hybrid lake-effect days decreased through the study period, with days within the hybrid data set declining at a statistically significant rate of 2.8 days per decade, although most obviously from the late 1970s through the early 2000s. The result is a general drying of the cool-season lake-effect hydroclimate. The merged data set offers a more thorough historical record of days available for atmospheric and hydroclimatic study of the lake-effect phenomenon within the eastern Great Lakes region.

  PublisherOA PDFDOI

• Analysis of Model Thermal Profile Forecasts Associated with Winter Mixed Precipitation within the United States Mid-Atlantic Region

  Journal of Operational Meteorology · 2022-03-04 · 1 citations

  articleOpen access1st authorCorresponding

  Winter mixed-precipitation events across the mid-Atlantic region of the United States from 2013–2014 through 2018–2019 were used to analyze common short-term model forecasts of vertical atmospheric thermal structure. Using saturated forecast soundings of the North American Mesoscale (NAM), higher-resolution nested NAM (NAMnest), and the Rapid Refresh models—corresponding with observed warm-nose precipitation events (WNPEs)—several thermal metrics formed the basis of the analysis of observed and forecast soundings, including Bourgouin positive and negative areas. While the three models accurately forecast the general thermal structure well during WNPEs, a warm bias is evident within each. Well forecast are maximum and minimum temperatures within the warm nose and surface-based cold layer, respectively, but the cold layer is commonly too thin for each of the models, and the warm nose is regularly too thick, particularly within NAM and NAMnest forecasts. Forecasts of a cold layer that is too shallow tend to coincide with observations of stronger synoptic-scale upward motion, a deeper cold surface-based layer, and a higher isentropic surface. Forecasts of a warm nose that is too thick tend to coincide with observations of weaker upward motion, a shallower cold surface-based layer, and a lower isentropic surface across the region. Two-thirds of precipitation-type estimates from model soundings agreed with those derived from observed soundings, with the remaining third predominantly representing a warm bias in precipitation type.

  PublisherOA PDFDOI

• Analysis of urban Heat Island intensity through air mass persistence: A case study of four United States cities

  Urban Climate · 2022-11-18 · 6 citations

  articleSenior author
  PublisherDOI

• Perceptions and realities of hydroclimatic change affecting Guyanese rice farming

  Climate Risk Management · 2021-01-01 · 2 citations

  articleOpen accessSenior author

  This study explores small farmers' perceptions of changes in climate across Guyana’s rice-producing regions. Qualitative, primary data were collected from a random sample of 189 small farmers, supplemented with 28 key informants, from across Guyana’s five main rice-producing regions. The most prevalent perception related to precipitation among farmers is an increase in rainfall year-round (56%), while for informants, it is an increase in rainfall intensity (81%). When considering the atmospheric conditions of temperature and humidity, farmers (88%) and informants (96%) overwhelmingly perceive warmer conditions. Considering weather and climate volatility, farmers (72%) and informants (82%) most prevalently perceive an increase in excess rainfall/flooding, but secondly, farmers (58%) and informants (71%) communicated a perceived increase in drought. Secondary quantitative hydroclimate data support the perception of a wetter climate, and to some degree, increased hydroclimatic volatility. Precipitation is critical to rice cultivation, and the data sets, combined, signal a wetter Guyanese climate, which has major economic implications for small farmers, the broader rice industry, and the economy of Guyana. However, granularity in farmers’ perceptions suggests a need for more detailed hydroclimate monitoring across Guyana. Thus, strengthening the Guyanese Hydrometeorological Service to support improved spatial and temporal monitoring and collection of primary weather data would be a wise investment in short- and long-term climate mitigation efforts.

  PublisherDOI

• Regional‐scale variability and change in daily precipitation across the contiguous United States, 1949–2018

  International Journal of Climatology · 2021 · 13 citations

  Senior authorCorresponding
  • Climatology
  • Environmental science
  • Atmospheric sciences

  Abstract Recent research suggests that the characteristics of precipitation are changing with a warming global climate. This study uses two traditional measures of precipitation, amount and frequency, in addition to two metrics that have not been widely used in hydroclimatological research, the Gini coefficient (GC) and the Lorenz asymmetry coefficient (LAC), to analyse change and variability in precipitation characteristics across the United States from 1949 through 2018. The GC quantifies the inequity of an accumulating variable across individual contributors. For this study, the GC was used to quantify equity in the accumulating distribution of daily precipitation amounts through an annual timeframe. The LAC quantifies the relative magnitude of individual contributors which were primarily responsible for inequity in an accumulating distribution. For this analysis, the LAC assesses the relative magnitude of precipitation events (light, heavy) primarily responsible for occurrences of inequity in the temporal distribution of precipitation. Time series analysis of regionally averaged values of precipitation amount, precipitation frequency, GC, and LAC values suggest change in the hydroclimate for many regions across the United States, including a trend towards greater inequity in the temporal distribution of precipitation. The more recent trends and variability of the four precipitation characteristics were statistically examined for relationships with time series of two key atmospheric features – total column water vapour (moisture availability) and the 850 hPa–500 hPa lapse rate (static stability). Results of this study show that change and variability in the atmospheric characteristics likely help explain the observed trends and variability of several precipitation characteristics across the United States.

  PublisherDOI

• Changes in the Frequency of Cool Season Lake Effects within the North American Great Lakes Region

  Annals of the American Association of Geographers · 2020-07-30 · 6 citations

  article1st authorCorresponding

  The North American Great Lakes influence surface weather downwind, distinctly in winter when southward migrating cold air passes over relatively warm lakes. Study of the synoptic atmospheric patterns favorable for lake effects has focused on lake-effect snowfall, the most impactful effect of the lakes. Although the patterns are conducive to lake effects, they might not actually yield discernible modification of downwind surface weather. This study uses historical daily data (1964–1965 through 2017–2018) of weather types to detect cool season (November–April) modification of cold, dry air upwind of the Great Lakes to cool, moist air downwind of the eastern (Erie, Ontario) and western (Michigan, Superior) lakes. A spatial arrangement of weather types across the region is shown to identify individual days characterized by a lake effect. The frequency of lake effects increased through the first one third of the record, but it has since decreased, most profoundly since a change point in the late 1990s and more prominently for the eastern lakes. At stations immediately downwind of the lakes, the result is a changed cool season hydroclimate, with fifty-four-year declines in lake-effect precipitation amount and frequency and in the percentages of seasonal precipitation amount and frequency attributed to lake effects.

  PublisherDOI

• Delineating Precipitation Regions of the Contiguous United States from Cluster Analyzed Gridded Data

  Annals of the American Association of Geographers · 2020 · 8 citations

  Senior authorCorresponding
  • Computer Science
  • Climatology
  • Environmental science

  Spatially homogenous precipitation regions were delineated for the contiguous United States using a gridded data set of daily precipitation. Seasonal means (1981–2010) of four variables, together characterizing seasonal precipitation, were computed and subjected to a principal component analysis (PCA). PCA reduced the original 30,665 grid cells by sixteen precipitation variables (four variables, four seasons) in the data set. The standardized scores of the three retained principal components, which together retain 78.4 percent of the original data set’s variance, were then subjected to three agglomerative hierarchical clustering techniques. Using an objective method, several cluster solutions were examined, and the average linkage thirteen-cluster solution was deemed optimal. The average linkage solution was then subjected to a k-means partitioning technique under the premise that objects are not considered for reassignment during agglomerative hierarchical cluster procedures. The result is fifteen precipitation regions across the contiguous United States. Results indicate that the regions successfully minimize intraregion variability and maximize interregion variability when compared to the nine climate regions defined by the United States National Centers for Environmental Information. It is therefore suggested that the regions defined by this work will better serve research aimed at an improved understanding of long-term hydroclimate change and variability at regional to synoptic scales across the United States.

  PublisherDOI

Frequent coauthors

• Stephen J. Keighton

  NOAA National Weather Service

  8 shared

• K. W. Murphy

  Arizona State University

  7 shared

• Paul W. Miller

  7 shared

• Daniel J. Leathers

  6 shared

• Michael L. Marston

  6 shared

• Timothy W. Hawkins

  Shippensburg University

  6 shared

• Robert C. Balling

  4 shared

• Gregory B. Goodrich

  Western Kentucky University

  3 shared

Labs

• Center for Geospatial Information TechnologyPI

  Not provided

Awards & honors

• Decision Center for a Desert City II: urban climate adaptati…
• Spatial and temporal analysis of climatological variations i…
• Utilizing the hydroclimatic index in drought forecasting and…
• Instituting multi-scale hydroclimatic indices in drought mon…
• Arizona drought monitoring sensitivity and verification anal…