For more than a decade, the remote sensing community has called for longwave spectral measurements of the Earth system to facilitate closing the radiation budget. In an effort to address this large gap in Earth-observing capability, NASA has supported the development and implementation of the Polar Radiant Energy in the Far-Infrared (FIR) Experiment (PREFIRE), which realizes two miniaturized thermal IR spectrometers (TIRSs), one on each of two polar orbiting small satellites with different ascending node crossing times. The spectral radiances measured by the TIRS instruments are utilized to extract cloud presence, atmospheric state, surface properties, and the associated top-of-atmosphere (TOA) spectral fluxes at both poles. This article describes the major system and algorithm components and introduces the geophysical products. PREFIRE data will be used to inform and improve ice-sheet and coupled-Earth system models to better predict the future state of the Earth’s poles.

Surface emissivity (ϵ) plays a critical role in Earth’s radiation budget and climate. The Polar Radiant Energy in the Far-InfraRed Experiment (PREFIRE) and the Far-Infrared Outgoing Radiation Understanding and Monitoring (FORUM) satellite missions aim to estimate surface spectral emissivities in mid-IR and far-IR regions. This study presents a neural network (NN)-based surface spectral emissivity retrieval algorithm under clear-sky that offers comparable performance to optimal-estimation (OE)-based methods but reduces computation time by a factor of 105. The NN-based method has achieved a mean relative retrieval error (|∆ϵ|) of 0.0028 with a standard deviation of 0.0013. Shapley values are employed to interpret the algorithm’s results and assess the relative importance of input features. The results derived from the Shapley values analysis are in good agreement with the physical understanding. The study highlights the effectiveness of the proposed neural network approach for surface emissivity estimation in forthcoming satellite missions.

In February 2018, NASA selected PREFIRE (The Polar Radiant Energy in the Far Infrared Experiment) to perform first-of-a-kind infrared and far-infrared measurements of Earth’s atmosphere from space. This compelling new science mission will attempt to answer the question "Why is the Arctic warming faster than the rest of the planet?" by filling an important gap in our knowledge of energy flows in Polar Regions. PREFIRE will fly a pair of small CubeSat satellites to probe a little-studied portion of the radiant energy emitted by Earth for clues about Arctic warming, sea ice loss and ice-sheet melting. Two miniaturized Thermal Infrared Spectrometers (TIRS) on two CubeSat satellites, are based on technology previously flown on the Mars Climate Sounder, an instrument on NASA's Mars Reconnaissance Orbiter. The CubeSats will orbit Earth's poles to measure far-infrared emissions and how they change throughout the day and over seasons. By building, characterizing, and operating, TIRS payloads in two different polar orbits, the PREFIRE mission has potential impact on NASA in these focus areas through (1) Quantification of the influence of variable surface conditions on Arctic and Antarctic thermal emission, (2) Estimation of the impact of atmospheric constituents on the polar greenhouse effect, and, (3) Determination of the impacts of variations in far-infrared emissivity and greenhouse effect on ice sheet melt and sea level rise. The observations will allow scientists to assess how changes in thermal infrared emissions at the top of Earth's atmosphere are related to changes in cloud cover and surface conditions below. Incorporation of PREFIRE measurements into current ice sheet process models and global climate models is expected to improve confidence in predictions of global climate changes that impact environmental and national security.

CloudSat has provided the first spaceborne snowfall observations in polar regions. Nevertheless, CloudSat retrievals may be affected by ground clutter even if the snowfall rate at the surface is estimated from the reflectivity measured at about 1200 m above land/ice surface. In this study, the impact of ground clutter contamination on CloudSat snowfall retrievals over the Antarctic and Greenland ice sheets is investigated. Our results suggest that ground clutter affects CloudSat snowfall observations over some areas, particularly over complex terrain such as mountain ranges and fjords. Over these areas, the snowfall rates deduced from CloudSat observations can be, therefore, significantly overestimated. This has implications when developing snowfall climatologies from CloudSat products.

Snowfall provides a significant source of freshwater for ecological and human needs at higher latitudes and represents a significant component of the energy and water cycles in northern climate systems. Furthermore, seasonal runoff from snowmelt, along with over-ocean snowfall, contributes to freshening in the Arctic and high-latitude North Atlantic oceans that can have a significant impact on global ocean circulations. Despite this importance, much of the region in which snowfall occurs is poorly monitored by surface observations. As a result, satellite measurements of falling snow are essential for providing large-scale estimates of this important component of the hydrologic cycle. This presentation will outline the latest techniques for identifying and inferring the intensity of falling snow using spaceborne radar. The dominant sources of uncertainty in these techniques will be highlighted and the potential for reducing these uncertainties using passive microwave constraints will be explored.