Progress and Status on the Development of NASA's Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) model

Mathematics – Logic

Scientific paper

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7924 Forecasting (2722), 7938 Impacts On Humans, 7959 Models, 7974 Solar Effects, 7984 Space Radiation Environment

Scientific paper

The NASA Applied Sciences Program recently selected a project for funding through the Research Opportunities in Space and Earth Sciences (ROSES) solicitation. The project objective is to develop a nowcast prediction of air-crew radiation exposure from both background galactic cosmic rays (GCR) and solar energetic particle events (SEP) that may accompany solar storms. The new air-crew radiation exposure model is called the Nowcast of Atmospheric Ionizing Radiation for Aviation Safety (NAIRAS) model. NAIRAS will provide global, data-driven, real-time radiation dose predictions of biologically harmful radiation at commercial airline altitudes. Observations are utilized from the ground (neutron monitors), from the atmosphere (the NCEP reanalysis), and from space (NASA/ACE and NOAA/GOES). Atmospheric observations provide the overhead shielding information and the ground- and space-based observations provide boundary conditions on the incident GCR and SEP particle flux distributions for transport and dosimetry simulations. Dose rates are calculated using the parametric AIR (Atmospheric Ionizing Radiation) model and the physics-based HZETRN (High Charge and Energy Transport) code. In this paper we discuss the concept and design of the NAIRAS model, and present recent progress in the implementation and give examples of the model results. Specifically, we show predictions of representative annual background exposure levels and radiation exposure levels for selected SEP events during solar cycle 23, with emphasis on the high-latitude and polar region. We also characterize the suppression of the geomagnetic cutoff rigidity during these storm periods and their subsequent influence on atmospheric radiation exposure. We discuss the key uncertainties and areas that need improvement in both model and data, the timeline for project completion, and access to model results.

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