The synoptic environment around tropical cyclones plays a significant role in vortex evolution. To capture the environment, the operational and research communities calculate diagnostic quantities. To aid with applications and research, the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED) combines disparate data sources. A key part of TC PRIMED is the environmental context. Often, environmental diagnostics come from multiple sources. However, TC PRIMED uses the European Centre for Medium-Range Weather Forecasts fifth-generation reanalysis (ERA5) product to provide a more complete representation of the storm environment from a single source. But, reanalysis products poorly resolve tropical cyclones and their surrounding environment. To understand the uncertainty of large-scale diagnostics, ERA5 is compared to the Statistical Hurricane Intensity Prediction Scheme developmental data set and the National Oceanic and Atmospheric Administration Gulfstream IV-SP dropwindsondes. This analysis highlights biases in the ERA5 environmental diagnostic quantities. Thermodynamic fields show the largest biases. The boundary layer exhibits a cold temperature bias that limits the amount of convective instability. And, the upper troposphere contains temperature biases and shows a high relative humidity bias. Yet, the upper-troposphere large-scale kinematic fields and derived metrics are low biased. In the lower troposphere, temperature gradient and advection calculated from the thermal wind suggests that the low-level wind field is not representative of the observed distribution. These diagnostics comparisons provide uncertainty so that users of TC PRIMED can assess implications to specific research and operational applications.

This study assesses where tropical cyclone (TC) surface winds can be measured as a function of footprint sizes and wavelengths (Ka- Ku- and C-band). During TCs, most high-resolution surface observations are impeded by considerable ‘rain contamination.’ Under these conditions, high-resolution surface observations typically come from operational aircraft. Other techniques that provide high-resolution surface observations through rain are also hindered somewhat by rain contamination and are very sparse in space and time. The impacts of rain are functions of the remotely sensed wavelength and rain–drop size. Therefore, relative long wavelengths have been used to observe the surface, but at the cost of a larger footprint. We examine how smaller footprint sizes could be used to observe through gaps between moderate to heavy rainbands that circulate around the main low-pressure center of a TC. Aircraft data from the National Oceanic and Atmospheric Administration’s (NOAA’s) WP-3D turboprop aircraft will be used to create realistic maps of rain. Our results provide information on the satellite instrument characteristics needed to see the surface through these gaps. This information is expected to aid in developing hurricane-related applications of new higher-resolution satellites.

Advancing the understanding of how variations in the climate over the ocean influences the weather over the United States can be aided by developing marine climatic indices. Herein, wind component indices are developed using nearly 125 years of wind observations from ships. A new technique using probability density functions for the values of meridional and zonal wind components is developed to create indices for a user-selected region and accumulation interval (e.g., annual or seasonal) over a climatological period. The index is a measure of the shift in the likelihood of values above or below a threshold for a given season or year as compared to the long-term (e.g., 125 year) probability distribution. The new index method is demonstrated using ship-based wind observations for select regions of the Atlantic Ocean. Ship observations are extracted from release 3.0.0 of the International Comprehensive Ocean-Atmosphere Data Set. Prior to index creation, an assessment of wind data quality is completed, and suspect observations are removed. The method to create a probabilistic wind component index is described along with a metric of the uncertainty in the calculated index. Two wind component indices, for regions in the north Atlantic and eastern Gulf of Mexico, are presented to demonstrate the technique. Using the Gulf of Mexico index as a case study, we compare the wind component indices to precipitation measured over the Gulf coastal states and identify several relationships between multi-year changes in winds in the eastern Gulf of Mexico and precipitation on a seasonal basis. Exploring the spatiotemporal patterns of the onshore/offshore component wind indices derived from seasonal wind forecasts could provide a metric for future prediction of seasonal or annual precipitation to support the agricultural sector. The index method demonstrated can be applied to other spatiotemporal regions for different parameters and using other source datasets.