Tropical cloud clusters (TCCs) are traditionally defined as synoptic-scale areas of deep convection and associated cirrus outflow. They play a critical role in the energy balance of the tropics, releasing large amounts of latent heat high in the troposphere. If conditions are favorable, TCCs can develop into tropical cyclones (TCs), which put coastal populations at risk. Previous work, usually connected with large field campaigns, has investigated TCC characteristics over small areas and time periods. Recently, developments in satellite reanalysis and global best track assimilation have allowed for the creation of a much more extensive database of TCC activity. The authors use the TCC database to produce an extensive global analysis of TCCs, focusing on TCC climatology, variability, and genesis productivity (GP) over a 28-yr period (1982–2009). While global TCC frequency was fairly consistent over the time period, with relatively small interannual variability and no noticeable trend, regional analyses show a high degree of interannual variability with clear trends in some regions. Approximately 1600 TCCs develop around the globe each year; about 6.4% of those develop into TCs. The eastern North Pacific Ocean (EPAC) basin produces the highest number of TCCs (per unit area) in a given year, but the western North Pacific Ocean (WPAC) basin has the highest GP (~12%). Annual TCC frequency in some basins exhibits a strong correlation to sea surface temperatures (SSTs), particularly in the EPAC, North Atlantic Ocean, and WPAC. However, GP is not as sensitive to SST, supporting the hypothesis that the tropical cyclogenesis process is most sensitive to atmospheric dynamical considerations such as vertical wind shear and large-scale vorticity.

This article describes results from a new four‐year (2009–2012) radar‐based precipitation climatology for the southeastern United States (SE USA). The climatology shows that a size‐based classification between mesoscale precipitation features (MPF) and isolated precipitation reveals distinct seasonal and diurnal variability of precipitation. On average, from 70 to 90% of precipitation is associated with MPF, generally less in the summertime and in southern coastal regions. MPF precipitation has a relatively small seasonal cycle except in Florida and the warm offshore waters of the Gulf Stream. In contrast, isolated precipitation has a dramatic seasonal cycle that outlines the SE USA coastline whereas the MPF precipitation does not, consistent with a thermodynamic mechanism for onshore isolated storms in coastal regions. In summer, the isolated precipitation preferentially forms offshore at night, and dramatically ‘flips’ inland by early afternoon. In contrast, MPF precipitation has no clear diurnal variations except in the southern coastal region in the summer, likely associated with sea breeze convection organized on the mesoscale. These results suggest that the MPF versus isolated precipitation system framework provides a useful basis for future studies of large‐scale and local controls on precipitation and resulting implications for long‐range predictability of precipitation.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for the reduction of atmospheric contributions in unmanned aerial systems (UAS) imagery and for the production of scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions were used to develop and test an ELM radiometric calibration framework designed for the reduction of atmospheric contributions from UAS imagery in studies with limited site accessibility or data acquisition time constraints. The ELM radiometric calibration framework was developed specifically for water-based operations and the efficacy of using generalized study area calibration equations averaged across variable illumination and atmospheric conditions was assessed. The framework was effective in reducing atmospheric and other external contributions in UAS imagery. Unique to the proposed radiometric calibration framework is the radiance-to-reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equation development. While image-by-image calibrations are still preferred for high accuracy applications, this paper provides an ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in UAS imagery in situations with limited site accessibility or data acquisition constraints.

The current study sets out to develop an empirical line method (ELM) radiometric calibration framework for reducing atmospheric contributions in UAS imagery and for producing scaled remote sensing reflectance imagery. Using a MicaSense RedEdge camera flown on a custom-built octocopter, the research reported herein finds that atmospheric contributions have an important impact on UAS imagery. Data collected over the Lower Pearl River Estuary in Mississippi during five week-long missions covering a wide range of environmental conditions was used to develop and test a simplified ELM radiometric calibration framework designed specifically for the reduction of atmospheric contributions to UAS imagery in studies with limited site accessibility or data acquisition time constraints. The framework was effective in reducing atmospheric and other external contributions to UAS imagery. Unique to the proposed radiometric calibration framework is the radiance to reflectance conversion conducted externally from the calibration equations which allows for the normalization of illumination independent from the time of UAS image acquisition and from the time of calibration equations development. This paper presents the simplified ELM radiometric calibration framework that can be used as a time-effective calibration technique to reduce errors in the UAS imagery.

Numerical weather prediction (NWP) models are limited with respect to initial and boundary condition data and possess an incomplete description of underlying physical processes. To account for this, modelers have adopted the method of ensemble prediction to quantify the uncertainty within a model framework; however, the generation of ensemble members requires considerably more computational time and/or resources than a single deterministic simulation, especially at convection-allowing horizontal grid spacings. One approach to solving this issue is the development of both a large and small horizontal grid spacing model framework over the same domain for ensemble and deterministic simulations, respectively. This approach assumes that model grid spacing has no influence on model uncertainty; therefore, the objective of this paper is to quantify the influence of horizontal grid spacing on the statistical spread of NWP model ensembles over a regional domain. A series of 24-h simulations using the Weather Research and Forecast (WRF) Model are generated over a static domain with horizontal grid spacings of 35, 25, 15, and 9 km, using both a stochastic kinetic energy backscatter scheme and a multiphysics ensemble approach. Results indicate that horizontal grid spacing does influence the magnitude of uncertainty within an ensemble, although the exact magnitude and type of statistical relationship (direct versus inverse) varies by case. As such, at shorter lead times (<12 h) the dominant atmospheric process associated with each event and the type of ensemble being used outweigh the individual impacts of horizontal grid spacing on ensemble spread.