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 . 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.

While deterministic forecasts provide a single realization of potential inundation, the inherent uncertainty associated with forecasts also needs to be conveyed for improved decision support. The objective of this study was to develop an ensemble framework for the quantification and visualization of uncertainty associated with flood inundation forecast maps. An 11‐member ensemble streamflow forecast at lead times from 0 to 48 hr was used to force two hydraulic models to produce a multimodel ensemble. The hydraulic models used are (1) the International River Interface Cooperative along with Flow and Sediment Transport with Morphological Evolution of Channels solver and (2) the two‐dimensional Hydrologic Engineering Center‐River Analysis System. Uncertainty was quantified and augmented onto flood inundation maps by calculating statistical spread among the ensemble members. For visualization, a series of probability flood maps conveying the uncertainty in forecasted water extent, water depth, and flow velocity was disseminated through a web‐based decision support tool. The results from this study offer a framework for quantifying and visualizing model uncertainty in forecasted flood inundation maps.

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.