The Far-Infrared Enhanced Survey Spectrometer (FIRESS) for PRIMA: Approach and Estimated Performance
Authors:
C. M.,
Bradford,
Alan J. Kogut,
Dale Fixsen,
Klaus Pontoppidan,
C. Darren Dowell,
Jason Glenn,
Thomas Pagano,
Joseph Green,
Marc Foote,
James McGuire,
Michael Rodger,
Robert Calvet,
Hien Nguyen,
Steve Hailey-Dunsheath,
Logan Foote,
Elijah Kane,
Reinier M. J. Janssen,
Margaret Meixner,
Alexandra Pope,
Alberto Bolatto,
JD Smith
Abstract:
We present the architectural concept for the Far-Infrared Enhanced Survey Spectrometer (FIRESS) for the Probe Mission for far-IR Astrophysics (PRIMA). FIRESS spans the 24--235 micron range with four R ~ 100 slit-fed grating modules, each coupling to a 24 (spatial) by 84 (spectral) pixel array of kinetic inductance detectors (KIDs). All four arrays are read out simultaneously, and a point source of…
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We present the architectural concept for the Far-Infrared Enhanced Survey Spectrometer (FIRESS) for the Probe Mission for far-IR Astrophysics (PRIMA). FIRESS spans the 24--235 micron range with four R ~ 100 slit-fed grating modules, each coupling to a 24 (spatial) by 84 (spectral) pixel array of kinetic inductance detectors (KIDs). All four arrays are read out simultaneously, and a point source of interest can be coupled to two of the four bands at a time. A Fourier transform module can be engaged over a portion of the FIRESS slits to create a high-resolution mode in which the light is intercepted, processed by the interferometer then reinserted into the path to the grating modules for detection. We provide a simulation and description of the technique that will be used to obtain high-resolution spectra. We identify the most important system requirements imposed by the detector system, finding that they are met with the existing design. Finally, we present our performance modeling, including both direct estimates given our current design status, as well as durable guidelines for developing general-observer programs.
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Submitted 2 September, 2025;
originally announced September 2025.
The Jive Verification System and its Transformative Impact on Weather Forecasting Operations
Authors:
Nicholas Loveday,
Deryn Griffiths,
Tennessee Leeuwenburg,
Robert Taggart,
Thomas C. Pagano,
George Cheng,
Kevin Plastow,
Elizabeth Ebert,
Cassandra Templeton,
Maree Carroll,
Mohammadreza Khanarmuei,
Isha Nagpal
Abstract:
Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verifica…
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Forecast verification is critical for continuous improvement in meteorological organizations. The Jive verification system was originally developed to assess the accuracy of public weather forecasts issued by the Australian Bureau of Meteorology. It started as a research project in 2015 and gradually evolved to be a Bureau operational verification system in 2022. The system includes daily verification dashboards for forecasters to visualize recent forecast performance and "Evidence Targeted Automation" dashboards for exploring the performance of competing forecast systems. Additionally, Jive includes a Jupyter Notebook server with the Jive Python library which supports research experiments, case studies, and the development of new verification metrics and tools. This paper describes the Jive verification system and how it helped bring verification to the forefront at the Bureau of Meteorology, leading to more accurate, streamlined forecasts. Jive has provided evidence to support forecast automation decisions and has helped to understand the evolving role of meteorologists in the forecast process. It has given operational meteorologists tools for evaluating forecast processes, including identifying when and how manual interventions lead to superior predictions. Work on Jive led to new verification science, including novel metrics that are decision-focused, including diagnostics for extreme conditions. Jive also provided the Bureau with an enterprise-wide data analysis environment and has prompted a clarification of forecast definitions. These collective impacts have resulted in more accurate forecasts, ultimately benefiting society, and building trust with forecast users. These positive outcomes highlight the importance of meteorological organizations investing in verification science and technology.
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Submitted 15 August, 2024; v1 submitted 29 April, 2024;
originally announced April 2024.