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An ensemble-variational aerosol data assimilation (DA) system based on the Joint Effort for Data assimilation Integration (JEDI) was recently developed for global aerosol prediction at the National Oceanic and Atmospheric Administration (NOAA) in the U.S.. This system is currently evaluated in the near-real time (NRT) at NOAA/Global Systems Laboratory (GSL) that assimilate NOAA’s operational Visible Infrared Imaging Radiometer Suite (VIIRS) 550 nm aerosol optical depth (AOD) retrievals. Our online NRT evaluation (https://ruc.noaa.gov/projects/nrt/Aerosol-DA/) suggests the resulting AOD analysis significantly improves the agreement with independent AERONET AOD and AOD analyses from NASA and ECMWF. Collaborative efforts across NOAA, the Joint Center for Satellite Data Assimilation and the University of Iowa are established to accelerate the operational aerosol assimilation and prediction in NOAA’s Unified Forecast System. This JEDI-based aerosol DA system is currently extended to NOAA’s future operational aerosol forecast system UFS-Aerosols. It will be used to produce a 2018-2022 global aerosol reanalysis which will benefit the UFS development for the Medium-Range Weather and Subseasonal-Seasonal forecast. We are implementing assimilation of log-transformed AOD within JEDI. This will improve AOD assimilation through better satisfying Gaussian assumptions in the DA update. We recently implemented within JEDI and experimented on assimilation of nocturnal AOD along with NOAA’s operational VIIRS daytime AOD. Nocturnal AOD will complement most operational daytime AOD, offering an excellent opportunity to better capture the diurnal evolution of aerosol events. Results of our NRT aerosol assimilation experiments and impacts of the aforementioned advancements will be presented., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Current inverse modeling-based estimates of carbon dioxide (CO2) fluxes in urban areas typically use a network of 10-20 observation sites featuring high-accuracy gas analyzers that can cost over $100,000 each. Recently, commercially available, low-cost sensors to measure both traditional meteorological quantities and trace gases such as CO2 have become a focus of atmospheric science research. These flux estimations are an ill-posed problem in the sense that, depending on resolution, the mathematical model may be optimizing fluxes for hundreds or even thousands of grid points, with only relatively few observations to use as constraint. Theoretically, by introducing many more observations into the system, the result will better represent the true state of the surface fluxes. This work comprises of three related studies that evaluate the viability of using a low-cost CO2 sensor combined with a mesoscale meteorology model with online tracers, and an advanced ensemble data assimilation technique, to estimate surface fluxes of CO2 in an urban region. First, the SenseAir K30 sensor is evaluated compared to a reference gas analyzer to determine the accuracy and precision of the observations from this sensor. Next, a simulation of atmospheric CO2 is evaluated against observations to understand the error in simulated mole fractions from variations in existing emissions inventories. Finally, a series of observing system simulation experiments (OSSEs) are conducted to understand the sensitivity of estimated CO2 fluxes to the ensemble data assimilation system configuration. From this work, it is found that the K30 sensor can be useful for urban ambient monitoring of CO2 after corrections for environmental factors such as temperature and pressure. Additionally, the modeled CO2 results show that the error in simulated mole fractions is likely larger from meteorological error than it is from uncertainty in emissions. Finally, the OSSEs find that this ensemble data assimilation system using a dense network of lower-accuracy observations can achieve comparable CO2 flux estimation results to that of using a sparse network of high-accuracy observations. However, the configuration of the system, particularly the inflation technique used, can significantly affect the quality of the analyzed fluxes.

Non-dispersive infrared (NDIR) sensors are a low-cost way to observe carbon dioxide concentrations in air, but their specified accuracy and precision are not sufficient for some scientific applications. An initial evaluation of six SenseAir K30 carbon dioxide NDIR sensors in a lab setting showed that without any calibration or correction, the sensors have an individual root mean square error (RMSE) between ∼ 5 and 21 parts per million (ppm) compared to a research-grade greenhouse gas analyzer using cavity enhanced laser absorption spectroscopy. Through further evaluation, after correcting for environmental variables with coefficients determined through a multivariate linear regression analysis, the calculated difference between the each of six individual K30 NDIR sensors and the higher-precision instrument had an RMSE of between 1.7 and 4.3 ppm for 1 min data. The median RMSE improved from 9.6 for off-the-shelf sensors to 1.9 ppm after correction and calibration, demonstrating the potential to provide useful information for ambient air monitoring.