Title: Temporal Experiment for Storms and Tropical Systems (TEMPEST) CubeSat Constellation: Demonstration and Risk Reduction
Presenting Author: Steven Reising
Organization: Colorado State University

Abstract:
The current suite of Earth-observing satellites, including CloudSat/CALIPSO and GPM, measures comprehensive cloud and precipitation parameters using radar and radiometric observations. Even when considered together, these low Earth-orbiting satellites provide only a snapshot in time of each storm's development, due to their repeat-pass times of 3 hours or more. Geostationary (GEO) observations can provide high temporal resolution but are presently limited to the visible and infrared that observe only the tops of clouds. However, processes that control the development of cloud systems and the transition to precipitation occur primarily on shorter than 30-minute time scales. The proposed TEMPEST constellation directly observes the time evolution of clouds to study the conditions that control the transition of clouds to precipitation using high-temporal resolution observations. The TEMPEST millimeter-wave radiometers penetrate into the cloud to observe key changes as the cloud begins to precipitate or ice accumulates inside the storm. The evolution of ice formation in clouds is important for climate prediction since it largely drives Earth's radiation budget. TEMPEST improves understanding of cloud processes and helps to constrain one of the largest sources of uncertainty in climate models. TEMPEST provides observations at five millimeter-wave frequencies from 90 to 183 GHz using a single compact instrument that is well suited for a 6U CubeSat architecture and fits well within the capabilities of NASA's CubeSat Launch Initiative (CSLI). Five identical CubeSats deployed in the same orbital plane with 5-10 minute spacing at ~400 km altitude and 50-65 degree inclination capture 3 million observations of precipitation, including 100,000 deep convective events in a one-year mission. TEMPEST provides critical information on the time evolution of cloud and precipitation microphysics, yielding a first-order understanding of the behavior of assumptions in current cloud-model parameterizations in diverse climate regimes.