Integrating air quality information from models, satellites, and in-situ monitors allows for both better estimation of air quality and better quantification of uncertainties in this estimation. Uncertainty quantification is important to appropriately convey confidence in these estimates and forecasts to users who will base decisions on these. Uncertainty quantification also allows tracing the value of information provided by different data sources. This can identify gaps in the monitoring network where additional data could further reduce uncertainties. This paper presents a framework for data fusion with uncertainty quantification, applicable to multiple air-quality-relevant pollutants. Testing of this framework in the context of nitrogen dioxide forecasting at sub-city scales shows promising results, with confidence intervals typically encompassing the expected number of actual measurements during cross-validation. The framework is now being implemented into an online tool to support local air quality management decision-making. Future work will also include the incorporation of low-cost air sensor data and the quantification of uncertainty at hyper-local scales.

The final version of this paper has been published and is available (open-access) from the Earth & Space Science JournalWhile multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is therefore necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this paper, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the EPA network in the continental United States (US), the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

MERRA-2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder (M2-SCREAM) is a new reanalysis of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) between 2004 and the present (with a latency of several months). The assimilated fields are provided at a 50-km horizontal resolution and at a three-hourly frequency. M2-SCREAM assimilates version 4.2 Microwave Limb Sounder (MLS) profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. Dynamics and tropospheric water vapor are constrained by the MERRA-2 reanalysis. The assimilated species are in excellent agreement with the MLS observations, except for HNO3 in polar night, where data are not assimilated. Comparisons against independent observations show that the reanalysis realistically captures the spatial and temporal variability of all the assimilated constituents. In particular, the standard deviations of the differences between M2-SCREAM and constituent mixing ratio data from The Atmospheric Chemistry Experiment Fourier Transform Spectrometer are much smaller than the standard deviations of the measured constituents. Evaluation of the reanalysis against aircraft data and balloon-borne frost point hygrometers indicates a faithful representation of small-scale structures in the assimilated water vapor, HNO3 and ozone fields near the tropopause. Comparisons with independent observations and a process-based analysis of the consistency of the assimilated constituent fields with the MERRA-2 dynamics and with large-scale stratospheric processes demonstrate the utility of M2-SCREAM for scientific studies of chemical and transport variability on time scales ranging from hours to decades. Analysis uncertainties and guidelines for data usage are provided.

A rare disturbance of the stratospheric Antarctic polar vortex in September 2019 led to a significantly higher than usual polar total ozone column. We use assimilation of ozone, HCl, and NO data from the Microwave Limb Sounder with the Global Earth Observing System Constituent Data Assimilation System driven by reanalysis meteorology to study the evolution of the 2019 Antarctic polar ozone. We find that the maximum 2019 ozone hole area was near 10 million km, and as little as 20% of that in 2018 in mid-September. However, the magnitude of vortex-averaged chemical ozone depletion was not significantly different between the two years despite earlier chlorine deactivation in 2019. The assimilation results show that most of the differences between 2018 and 2019 Antarctic ozone resulted from two factors: (1) the geometry of the 2019 vortex, with ozone-rich middle-stratospheric air masses overlying the lower portion of the vortex and leading to a significant reduction of the total column; (2) significantly reduced vortex volume. The anomalously small ozone hole of 2019 was comparable in size to the record breaking 2002 case and the mechanisms responsible were similar in the two cases. While the 2019 sudden stratospheric warming is classified as minor, its impact on ozone was very significant.