Significance: The deep clouds that make up tropical disturbances, the precursors to more intense tropical cyclones (TCs) (including hurricanes and typhoons), effectively trap infrared radiation emitted by Earth’s surface and lower atmosphere. Our results demonstrate that the local atmospheric warming caused by this “cloud greenhouse effect” is a key trigger for promoting and accelerating the evolution of such precursor storms into intense TCs. The forecasting of TC formation remains extremely challenging, while the representation of cloud processes and their feedback with radiation is a large source of uncertainty in the numerical models that forecasts rely upon. Our results suggest that focusing future research on constraining these processes in models holds promise for key progress in the prediction of these devastating storms.

The National Aeronautics and Space Administration (NASA) Short-term Prediction Research and Transition Center (SPoRT) has been part of a collaborative effort within the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) Proving Ground and Risk Reduction (PGRR) Program to develop gridded satellite sounding retrievals for the operational weather forecasting community. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) retrieves vertical profiles of temperature, water vapor, trace gases, and cloud properties derived from infrared and microwave sounder measurements. A new, optimized method for deriving NUCAPS level 2 horizontally and vertically gridded products is described here. This work represents the development of approaches to better synthesize remote sensing observations that ultimately increase the availability and usability of NUCAPS observations. This approach, known as “Gridded NUCAPS”, was developed to more effectively visualize NUCAPS observations to aid in the quick identification of thermodynamic spatial gradients. Gridded NUCAPS development was based on operations-to-research feedback and is now part of the operational National Weather Service display system. In this paper, we discuss how Gridded NUCAPS was designed, how relevant atmospheric fields are derived, its operational application in pre-convective weather forecasting, and several emerging applications that expand the utility of NUCAPS for monitoring phenomena such as fire weather, the Saharan Air Layer, and stratospheric air intrusions.

Hyperspectral infrared satellite sounding retrievals are used to examine thermodynamic changes in the tropical cyclone (TC) environment associated with the diurnal cycle of radiation. Vertical profiles of temperature and moisture are retrieved from the Suomi National Polar–orbiting Partnership (S–NPP) satellite system, National Oceanic and Atmospheric Administration (NOAA)–20, and the Meteorological Operational (MetOp) A/B satellite system, leveraging both infrared and microwave sounding technologies. Vertical profiles are binned radially based on distance from the storm center and composited at 4–hr intervals to reveal the evolution of the diurnal cycle. For the three cases examined – Hurricane Dorian (2019), Hurricane Florence (2018) and Hurricane Irma (2017) – a marked diurnal signal is evident that extends through a deep layer of the troposphere. Statistically significant differences at the 95% level are observed in temperature, moisture, and lapse rate profiles, indicating a moistening and destabilization of the mid to upper troposphere that is more pronounced near the inner core of the TC at night. Observations support a favorable environment for the formation of deep convection caused by diurnal differences in radiative heating tendencies, which could partially explain why new diurnal pulses tend to form around sunset. These findings demonstrate that the diurnal cycle of radiation affects TC thermodynamics through a deep layer of the troposphere, and suggest that hyperspectral infrared satellite sounding retrievals are valuable assets in detecting thermodynamic variations in TCs.

The deep convective clouds of developing tropical cyclones (TCs) are highly effective at trapping the infrared (or longwave) radiation welling up from the surface. This “cloud greenhouse effect” locally warms the lower–mid-troposphere relative to the TC’s surroundings – an effect that manifests in all stages of the TC lifecycle. While idealized studies suggest the importance of this feedback for TC formation, this issue has remained unexplored for TCs in nature, where non-zero background flow, wind shear, and synoptic-scale variability are known to greatly constrain TC development.To address this gap, we examine the potential role of this cloud–infrared (or longwave) radiation feedback in the context of two archetypal storms: Super Typhoon Haiyan (2013) and Hurricane Maria (2017). We conduct a set of numerical model experiments for both storms with a convection-resolving model (WRF-ARW) from the very early stages of TC development. We examine sensitivity experiments wherein this cloud–radiation feedback is removed at various lead-times prior to TC genesis and the onset of rapid intensification (RI). In both storms, removing this cloud–radiation feedback at a lead-time of ~1 day or less leads to delayed and/or weaker intensification than in the control case. When this feedback is removed with a lead-time of two days or longer, however, the storms altogether fail to development and intensify. This local cloud greenhouse effect strengthens the thermally direct transverse circulation of the incipient storm, in turn both promoting saturation within its core and accelerating the spin-up of its surface tangential circulation via angular momentum convergence. These findings indicate that the cloud greenhouse effect plays a critical role in accelerating and promoting TC development in nature. Progress in the prediction of TC formation and intensification has been very limited in recent decades. Cloud–radiation feedback represents a large source of uncertainty in models, which hence manifests as uncertainty in the prediction of TC development. Our findings highlight the pressing need to better constrain this feedback in models. Doing so holds promise for advancing our ability to forecast TCs.