Ka-band (32 GHz) communications links utilized by the National Aeronautics and Space Administration (NASA) flight missions for science downlink are susceptible to degradation due to weather. In this study, a customized real-time forecast system has been developed to predict zenith atmospheric noise temperature (Tatm) at the Deep Space Network (DSN) tracking sites using machine learning (ML). A random forest model is trained with the Global Forecast System (GFS) forecast and analysis datasets in addition to the Tatm measurements derived from on-site advanced water vapor radiometers (AWVR). The real-time forecast uncertainty is quantified for different error regimes using the Self-Organizing Map method. The results show that the Root Mean Square Error (RMSE) of the 24-hour Tatm prediction at Goldstone, CA increases with the increase of Tatm. Ninety percent of the forecasts have RMSE (bias) of less than 3.50 K (0.22 K) for fair-weather conditions with Tatm < 17 K. In comparison to the current approach in designing Ka-band communications links, application of weather forecasts can increase data return to the downlink for 80% of the time. A downlink gain of up to 1.61 dB (45% more data) can be realized at 20 elevation angle when Tatm = 9 K.

Following the 15 January 2022 Hunga Tonga-Hunga Ha’apai eruption, several trace gases measured by the Aura Microwave Limb Sounder displayed anomalous stratospheric values. Trajectories and radiance simulations confirm that the H2O, SO2, and HCl enhancements were injected by the eruption. In comparison with those from previous eruptions, the SO2 and HCl injections were unexceptional, although they reached higher altitudes. In contrast, the H2O injection was unprecedented in both magnitude (far exceeding any previous values in the 17-year MLS record) and altitude (penetrating into the mesosphere). We estimate the mass of H2O injected into the stratosphere to be 146+-5 Tg - ~10% of the stratospheric burden. It may take several years for the H2O plume to dissipate. This eruption could impact climate not through surface cooling due to sulfate aerosols, but rather through surface warming due to the radiative forcing from the excess stratospheric H2O.

Lack of high-resolution observations at the inner-core region of tropical cyclones introduces uncertainty into the structure’s true initial state. More accurate measurements at the inner-core are essential for accurate tropical cyclone forecasts. This study seeks to improve the estimates of the inner-core structure by utilizing background information from prior assimilated conventional observations. We provide a scheme for targeted high-resolution observations for platforms such as the Coyote sUAS. In an effort to identify potential locations of high uncertainty, an exploratory investigation of the background information of the state variables pressure, temperature, wind speed, and a combined representation of the state variables given by their linear weighted average is presented. A sampling-based path planning algorithm that considers the Coyote’s energy usage then locates the regions of high uncertainties along a Coyote’s flight, allowing us to maximize the removal of uncertainties. The results of a data assimilation analysis of a typical Coyote flight mission using the proposed deployment scheme shows significant improvements in estimates of the tropical cyclone structure after the resolution of uncertainties at targeted locations.

Changes in aerosol optical depth, both positive and negative, are observed across the globe during the 21rst Century. However, attribution of these changes to specific sources is largely uncertain as there are multiple contributing natural and anthropogenic sources that produce aerosols either directly or through secondary chemical reactions. Here we show that satellite-based changes in small-mode AOD between 2002 and 2019 observed in data from MISR can primarily be explained by changes, either directly or indirectly, in combustion emissions. We quantify combustion emissions using MOPITT total column CO observations and the adjoint of the GEOS-Chem global chemistry and transport model. The a priori fire emissions are taken from the Global Fire Emission Data base with small fires (GFED4s) but with fixed a priori for non-fire emissions. Aerosol precursor and direct emissions are updated by re-scaling them with the monthly ratio of the CO posterior to prior emissions. The correlation between modeled and observed AOD improves from a mean of 0.15 to 0.81 for the four industrial regions considered and from 0.52 to 0.75 for the four wildfire-dominant regions considered. Using these updated emissions in the GEOS-Chem global chemistry transport model, our results indicate that surface PM2.5 have declined across many regions of the globe during the 21rst century. For example, PM2.5 over China has declined by ~30% with smaller fractional declines in E. USA and Europe (from fossil emissions) and in S. America (from fires). These results highlight the importance of forest management and cleaner combustion sources in improving air-quality.