The NASA Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is hosted on a geostationary commercial communications satellite. TEMPO is an imaging spectrometer with primary mission to measure trace-gas concentrations from the observed spectra of reflected sunlight over the Continental United States and parts of Canada, Mexico, and the Caribbean. TEMPO produces an ultraviolet (UV, 293 nm - 494 nm) and a visible (538 nm - 741 nm) spectrum for each spatial pixel. TEMPO saw first light in August 2023. At night, TEMPO can observe city lights, gas flaring, maritime lights from fishing and offshore oil platforms, clouds and snow in the moonlight, lightning, aurorae, and nightglow without interfering with its primary daytime air quality/chemistry mission. This paper describes the capabilities of TEMPO to make nighttime observations and surveys of some early results. Repetitive coverage of North America enables production of clearest-sky composites that are similar to VIIRS Day-Night Band (DNB) ”Black Marble” products. Spectra of urban areas contain spectral signatures of artificial lighting of various types that allow the radiance from each class of lighting to be estimated. Moonlight imaging of clouds provides a useful capability for discriminating clouds and fog. Lightning illuminating cloud tops from below is seen with distinct spectral features. Gas flares, associated with oil production, are observed and flare temperatures can be estimated from their spectra. Known auroral and nightglow spectral lines of atomic oxygen and molecular nitrogen are seen in the UV and visible spectra. The sodium d-layer is also observed.

Vortex streets formed in the stratocumulus-capped wake of mountainous islands are the atmospheric analogues of the classic Kármán vortex street observed in laboratory flows past bluff bodies. The quantitative analysis of these mesoscale unsteady atmospheric flows has been hampered by the lack of satellite wind retrievals of sufficiently high spatial and temporal resolution. Taking advantage of the cutting-edge Advanced Baseline Imager, we derived km-scale cloud-motion winds at 5-minute frequency for a vortex street in the lee of Guadalupe Island imaged by Geostationary Operational Environmental Satellite-16. Combined with Moderate Resolution Imaging Spectroradiometer data, the geostationary imagery also provided accurate stereo cloud-top heights. The time series of geostationary winds, supplemented with snapshots of ocean surface winds from the Advanced Scatterometer, allowed us to capture the wake oscillations and measure vortex shedding dynamics. The retrievals revealed a markedly asymmetric vortex decay, with cyclonic eddies having larger peak vorticities than anticyclonic eddies at the same downstream location. Drawing on the vast knowledge accumulated about laboratory bluff body flows, we argue that the asymmetric island wake arises due to the combined effects of Earth’s rotation and Guadalupe’s non-axisymmetric shape resembling an inclined flat plate at low angle of attack. The asymmetric vortex decay implies a three-dimensional wake structure, where centrifugal or elliptical instabilities selectively destabilize anticyclonic eddies by introducing edge-mode or core-mode vertical perturbations to the clockwise-rotating vortex tubes.

Precipitation flag (precipitating or not; stratiform or convective) is a key parameter for us to make betterretrieval of precipitation characteristics as well as to understand the cloud-precipitation physicalprocesses. The Global Precipitation Measurement (GPM) Core Observatory’s Microwave Imager (GMI)and Dual-Frequency Precipitation Radar (DPR) together provide ample information on globalprecipitation characteristics. As an active sensor in particular, DPR provides an accurate precipitationflag assignment, while passive sensors like GMI were traditionally believed not to be able to tell apartprecipitation types. Using collocated precipitation flag assignment from DPR as the “truth”, this project employs machinelearning models to train and test the predictability and accuracy of using passive GMI-only observationstogether with ancillary atmosphere information from reanalysis. Precipitation types are classified intothe following classes: convective, stratiform, convective-stratiform mixed, no precipitation, and otherprecipitation. Sub-sampling with different probabilities is employed to construct a balanced trainingdataset. A variety of classification algorithms are tested, including Support Vector Machines, NaiveBayes, Random Forests, Gradient Boosting, and Neural Networks (Multilayer Perceptron Network), andtheir results are evaluated and compared. The trained model has ~ 85% of prediction accuracy for everytype of precipitation. High-frequency channels (166 GHz and 183 GHz channels) and 166 GHzpolarization difference are found among the most important factors that contribute to the modelperformance, which shed light on future instrument channel selection.

Stereo methods using GOES-17 and Himawari-8 applied to the Hunga Tonga-Hunga Ha’apai volcanic plume on 15 January 2022 show overshooting tops reaching 50-55 km altitude, a record in the satellite era. Plume height is important to understand dispersal and transport in the stratosphere and climate impacts. Stereo methods, using geostationary satellite pairs, offer the ability to accurately capture the evolution of plume top morphology quasi-continuously over long periods. Manual photogrammetry estimates plume height during the most dynamic early phase of the eruption and a fully automated algorithm retrieves both plume height and advection every 10 minutes during a more frequently sampled and stable phase beginning three hours after the eruption. Stereo heights are confirmed with Global Navigation Satellite System Radio Occultation (GNSS-RO) bending angles, showing that most of the plume was lofted 30–40 km into the atmosphere. Cold bubbles are observed in the stratosphere with brightness temperature of ~173K.

This work uses the Specified Dynamics-Whole Atmosphere Community Climate Model with Ionosphere/Thermosphere eXtension (SD-WACCM-X) to determine and explain the seasonality of the migrating semidiurnal tide (SW2) components of tropical upper mesosphere and lower thermosphere (UMLT) temperature, zonal-wind and meridional-wind. This work also quantifies aliasing due to SW2 in satellite-based tidal estimates. Results show that during equinox seasons, the vertical profile of tropical UMLT temperature SW2 and zonal wind SW2’s amplitudes have a double peak structure while they, along with meridional wind SW2, have a single peak structure in their amplitudes in June solstice. Hough mode reconstruction reveals that a linear combination of 5 SW2 Hough modes cannot fully reproduced these features. Tendency analysis reveals that for temperature, the adiabatic term, non-linear advection term and linear advection term are important. For the winds, the classical terms, non-linear advection term, linear advection term and gravity wave drag are important. Results of our alias analysis then indicate that SW2 can induce an ~60% alias in zonal-mean and DW1 components calculated from sampling like that of the Thermosphere Ionosphere Mesosphere Energetics and Dynamics satellite and the Aura satellite. This work concludes that in-situ generation by wave-wave interaction and/or by gravity waves play significant roles in the seasonality of tropical UMLT temperature SW2, zonal wind SW2 and meridional wind SW2. The alias analysis further adds that one cannot simply assume SW2 in the tropical UMLT is negligible.