Search

Cloud and rainfall distributions in urban spaces have implications for planning, hydrological response, reservoir management, renewable energy generation, transportation, and agricultural productivity. Studies have confirmed that large urban areas can initiate or modify precipitation, but there are still questions about the role of city size and atmospheric interactions. The majority of case study approaches have focused on large cities or urban clusters and have largely ignored small to moderate sized cities. Herein, an analysis of the Augusta, Georgia metropolitan statistical area is conducted. Using a gridded, daily multi-sensor precipitation dataset and satellite-based cloud cover climatology, the warm seasons (June, July, and August) covering the period from 2002 to 2019 were analyzed using spatial comparisons within an upwind-downwind framework and z-score statistics. Such methodologies have been published for larger urban areas. We confirmed that a moderate-sized city like Augusta, Georgia and neighboring Aiken, South Carolina is associated with spatial patterns consistent with the “urban rainfall effect” (URE) and possibly an “urban cloud effect” (UCE). Contextual analysis of other local mesoscale signatures related to nearby water bodies are also provided as a sanity check on process identification.

Abstract Within the Charlotte, North Carolina, to Atlanta, Georgia, megaregion (Charlanta), the Atlanta metropolitan area has been shown to augment proximal cloud‐to‐ground (CG) lightning occurrence. Although numerous studies have documented this “urban lightning effect” (ULE) with regard to CG lightning, relatively few have investigated urban effects on distributions of total lightning (TL). Moreover, there has yet to be a study of the ULE using TL observations from the Geostationary Lightning Mapper (GLM). In an effort to fill this gap, we investigated spatial distributions of TL around the cities of Atlanta, GA, Greenville, SC, and Charlotte, NC, using GLM data collected during the warm seasons of 2018–2021. Analyses reveal augmentation of TL intensity and frequency over the major cities of Atlanta and Charlotte, with a diminished urban signal over the smaller city of Greenville. This work also demonstrated the potential efficacy of the emerging satellite‐based TL climatology in ULE studies.

Observational and numerical modeling studies continue to affirm the existence of the “urban rainfall effect” (URE), or a discernible anomaly in warm season precipitation due to urbanization. However, the literature has been lacking a progression towards the predictability of the URE. Atlanta, Georgia has consistently appeared in the literature because of its well-studied urban rainfall anomalies. Using the Multi-sensor Precipitation Estimates (MPE) dataset and the ERA-Interim reanalysis dataset, an 18-year period (2002–2019) is examined. Three similar but distinct methods are used to define urban rainfall days (URDs), or periods when the precipitation in the urbanized areas of Atlanta are greater than the surrounding rural areas. A combination of compositing, wind rose, and k-means cluster analyses are employed to extract the synoptic framework supportive of the URE in Atlanta, Georgia. The synoptic-scale compositing analysis reveals that there are a consistent set of meteorological ingredients that are needed to produce an URD, including weaker-than-average southwesterly-to-northwesterly flow at 700 hPa, copious amounts of moisture throughout the tropospheric column, and a background low-level convergent flow. Composite atmospheric soundings reveal that there is enhanced moisture throughout the tropospheric column on URDs, leading to marginal instability that favors localized convection across the Atlanta metropolitan area. The study also provides clarity on how often the URE is present (roughly 8% of the time) during warm season days across the Atlanta metropolitan area. Taken together, this synoptic framework will aid in the forecasting of the URE in Atlanta and can be easily applied to other cities.

Tropical Cyclone Maintenance and Intensification (TCMI) is a generalized definition of tropical cyclones that strengthen or maintain intensity inland while maintaining tropical characteristics. Herein, a novel methodology, using a machine learning method was created to examine the tropical cyclone record to improve climatological representation of such cases. Using the International Best Track Archive for Climate Stewardship (IBTrACS) dataset, individual times of inland tropical cyclones were classified into TCMI and non-TCMI (weakening) events. The MERRA-2 dataset was applied to develop a prototypical machine-learning model to help diagnose future TCMI events. A list of possible TCMI storms for case studies in future analyses is provided. Two of these storms were examined for attributes characteristic of the Brown Ocean Effect, a hypothesized mechanism for TCMI centered on warm, moist soils. It was revealed that variables that were important at the time of storm arrival were important the prior day, which indicates that a TCMI event is a reaction to the environment. Moreover, the variables that were finally selected show a heavy emphasis on land-surface processes. This supports the idea that the accurate representation of the land surface state is critical to the accurate diagnosis of TCMI.

Water, weather, and climate affect everyone. However, their impacts on various communities can be very different based on who has access to essential services and environmental knowledge. Structural discrimination, including racism and other forms of privileging and exclusion, affects people's lives and health, with ripples across all sectors of society. In the United States, the need to equitably provide weather, water, and climate services is uplifted by the Justice40 Initiative (Executive Order 14008), which mandates 40% of the benefits of certain federal climate and clean energy investments flow to disadvantaged communities. To effectively provide such services while centering equity, systemic reform is required. Reform is imperative given increasing weather-related disasters, public health impacts of climate change, and disparities in infrastructure, vulnerabilities, and outcomes. It is imperative that those with positional authority and resources manifest responsibility through (1) recognition, inclusion, and prioritization of community expertise; (2) the development of a stronger and more representative and equitable workforce; (3) communication about climate risk in equitable, relevant, timely, and culturally responsive ways; and (4) the development and implementation of new models of relationships between communities and the academic sector., Competing Interests: No competing financial interests exist., (© Aradhna Tripati et al., 2023; Published by Mary Ann Liebert, Inc.)