Wind information throughout the middle-atmosphere is crucial for the understanding of atmospheric dynamics but became available only recently, thanks to developments in remote sensing and modelling approaches. We present the first thorough assessment of the quality of the wind estimates by comparing co-located observations from lidar and microwave radiometry and opposing them to the major atmospheric models. Moreover we evaluated a new approach for measuring mesopause region wind by radiometry.

Tropical atmospheric variability is often described using proxy indices of the Quasi-Biennial Oscillation and the El Niño-Southern Oscillation. We introduce new proxies derived from GNSS radio occultation (RO) satellite measurements. Using the high vertical resolution of the RO temperature fields we obtain altitude-resolved indices which can improve the description of atmospheric variability patterns and can be used in climate studies where a detailed knowledge of these patterns is required.

Low-cost sensors promise neighborhood-scale air quality monitoring but have been plagued by inconsistent performance for precision, accuracy, and drift. CMU and SenSevere collaborated to develop the RAMP, which uses electrochemical sensors. We present a machine learning algorithm that overcomes previous performance issues and meets US EPA's data quality recommendations for personal exposure for NO₂ and tougher "supplemental monitoring" standards for CO & ozone across 19 RAMPs for several months.

Microwave radiometers have the capability of observing temperature and humidity profiles with a few minute time resolution. This study investigates the potential benefit of this instrument to improve weather forecasts thanks to a better initialization of the model. Our results show that a significant improvement can be expected in the model initialization in the first 3 km with potential impacts on weather forecasts.

Choices and assumptions made to represent the state of the atmosphere introduce an uncertainty of 42% to the air mass factor calculation in trace gas satellite retrievals in polluted regions. The AMF strongly depends on the choice of a priori trace gas profile, surface albedo data set and the correction method to account for clouds and aerosols. We call for well-designed validation exercises focusing on situations when AMF structural uncertainty has the highest impact on satellite retrievals.