Planetary surfaces

Abstract:

Abstract The impact of anthropogenic aerosols on the mean, spatial, and temporal distribution of precipitation remains a persistent source of uncertainty in climate research. In particular, absorbing aerosols are known to influence cloud formation and precipitation in ways that are not yet fully understood. On average, warming induced by absorbing aerosols is balanced by reduced latent heating from precipitation, so the atmospheric energy budget constrains mean precipitation. This constraint does not apply to spatial or temporal patterns, making the impact of absorbing aerosols on these aspects more uncertain. A recent idealized study suggests that absorbing aerosols can trigger a transition to episodic precipitation, where rainfall occurs in intense, short‐lived events followed by extended dry periods. This transition resembles a previously reported shift under hothouse climate conditions. Specifically, lower tropospheric radiative heating from absorbing aerosols decouples the lower and upper troposphere, suppressing convection for multiple days. During these dry periods, instability builds up until a strong rain event occurs. In this paper, we build on this previous work to further investigate the effects of absorbing aerosols on precipitation characteristics and extremes. We conduct a hierarchy of model simulations that incorporate online aerosol–radiation coupling, the diurnal cycle of solar radiation, convective aggregation in a large‐domain, and large‐scale tropical circulation in a mock Walker simulation. Our results show that the transition to episodic precipitation events under absorbing aerosol perturbation is robust and occurs across all model configurations. We also examine the role of diurnal solar radiation variations and large‐scale circulation in shaping this transition.

Abstract:

Plain Language Summary: We present laboratory tests of a new instrument, the High‐resolution Volatiles and Minerals Moon Mapper ( H V M 3 ${\mathrm{H}\mathrm{V}\mathrm{M}}^{3}$ ). H V M 3 ${\mathrm{H}\mathrm{V}\mathrm{M}}^{3}$ will launch onboard the National Aeronautics and Space Administration's Lunar Trailblazer mission. It is an imaging spectrometer that will measure the spectrum of reflected sunlight in visible and infrared wavelengths for every pixel of an image. Scientists will interpret these spectra with the goal of measuring how water and ice are distributed across the lunar surface. Our laboratory tests indicate that the instrument is sensitive enough to measure small changes in the content of water on the lunar surface. We find the instrument capable of measuring areas of the Moon that are in permanent shadow using light reflected from neighboring surfaces like crater walls.

Abstract:

The Lunar Trailblazer mission launched in February of 2025 with the goal of characterizing lunar surface water through a targeted campaign. One instrument on the mission, the Lunar Thermal Mapper (LTM), was tasked with measuring the surface temperature to compare with maps of the form and abundance of water on the lunar surface. LTM's secondary science goals were to identify regolith composition and thermophysical properties as exhibited by mid‐infrared spectral features. Here we show the utility of LTM in distinguishing lunar regolith composition with its 11 narrow bands. Five spectral parameter products were developed to aid in early identification of regions of interest for follow‐on spectral analyses. These products include the Christiansen feature (CF) value, weighted absorption center (WAC) value, WAC band depth, Transparency Roll‐off, and a Diviner CF value equivalent. These products would be used mainly to flag these regions for more detailed follow‐up study with the entire spectral capabilities of the mission instrumentation. The Lunar Thermal Mapper (LTM) is one of two instruments on the Lunar Trailblazer mission launched in February 2025. LTM's primary goal is to provide surface temperature measurements for the lunar surface, in particular for identifying and mapping water on the Moon. LTM is also capable of identifying the compositional and physical properties of different rocks on the surface. Here, we test those capabilities and determine five methods for quickly distinguishing bulk properties of the lunar rocks that can be used by the community to identify regions of interest for further investigation. Mid‐infrared compositional parameters were created and tested for the Lunar Trailblazer mission Spectral parameters can distinguish bulk silicate mineralogy, and identify regions of compositional interest The Christiansen feature roll‐off parameter can provide an initial identification of areas with distinct thermophysical properties Mid‐infrared compositional parameters were created and tested for the Lunar Trailblazer mission Spectral parameters can distinguish bulk silicate mineralogy, and identify regions of compositional interest The Christiansen feature roll‐off parameter can provide an initial identification of areas with distinct thermophysical properties

Abstract:

The Lunar Trailblazer mission aimed to assess the presence of water on the lunar surface using imaging spectroscopy in visible shortwave infrared (VSWIR) coupled with high‐resolution multispectral imaging in thermal midwave‐infrared (MWIR), captured simultaneously over the same target from orbit around the Moon with two different instruments. Uncertainties in clock timing, instrument models, and instrument pointing knowledge manifest as geospatial offsets between the two data sets that must be corrected in post‐processing to enable co‐registration, tying the acquired images to their precise latitudes and longitudes on the Moon. This work describes an algorithmic approach to co‐registering and geolocalizing images after acquisition without high precision instrument and spacecraft pointing models, the Iterative Matching Pipeline for Post‐Acquisition Image Localization (IMPPAIL), utilizing previously acquired data for development. We use Lunar Orbiter Laser Altimetry (LOLA) and Kaguya data to make shaded relief maps as the basemap on which to project data. To test our processing pipeline prior to Lunar Trailblazer data collection, we use Moon Mineralogy Mapper (M3) data for VSWIR images and simulated MWIR images. When demonstrated on these data sets, IMPPAIL produces a 98% success rate registering VSWIR data to LOLA/Kaguya shaded relief maps and successfully co‐registered MWIR and VSWIR in all four simulation cases. We include a code package with software tools allowing this algorithm to be used for a variety of data sets across many other missions. The Lunar Trailblazer spacecraft was designed to map surface temperature and water content on the Moon via satellite imagery. Onboard are two imagers that measure thermal and visible light bands, which must be aligned to be used in conjunction with each other as well as located precisely relative to pre‐existing topography data. To do this, we develop an algorithm which iteratively matches key features between the images, allowing for the co‐registration of data points. We test the algorithm on topography and visible imagery data from a past satellite mission and simulated data for thermal images. We show 98% accuracy and success in both imagery wavelength bands. While this was originally created for use on the Lunar Trailblazer mission, the process is applicable to future missions requiring similar functionality, and we make the code available for other users. We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions We present a process for post‐acquisition, image‐based data localization for satellite missions with use for the Lunar Trailblazer mission We demonstrate image matching success across short‐ and mid‐wave infrared image wavelength bands and topographic hillshade data sets This IMPPAIL procedure is applicable to future missions that require higher pointing accuracy than is possible with hardware solutions