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Junhua Liu

Publications and source records attributed to Junhua Liu.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy Data Fusion to Map Global Surface Ozone Concentration and Associated Uncertainty

Estimates of ground-level ozone concentrations have been improved through data fusion of observations and atmospheric chemistry models. Our previous global ozone estimates for the Global Burden of Disease study corrected for bias uniformly across continents and then corrected near monitoring stations using the Bayesian Maximum Entropy (BME) framework for data fusion. Here, we use the Regionalized Air Quality Model Performance (RAMP) framework to correct model bias over a much larger spatial range than BME can, accounting for the spatial inhomogeneity of bias and nonlinearity as a function of modeled ozone. RAMP bias correction is applied to a composite of 9 global chemistry-climate models, based on the nearest set of monitors. These estimates are then fused with observations using BME, which matches observations at measurement stations, with the influence of observations declining with distance in space and time. We create global ozone maps for each year from 1990 to 2017 at fine spatial resolution. RAMP is shown to create unrealistic discontinuities due to the spatial clustering of ozone monitors, which we overcome by applying a weighting for RAMP based on the number of monitors nearby. Incorporating RAMP before BME has little effect on model performance near stations, but strongly increases R 2 by 0.15 at locations farther from stations, shown through a checkerboard cross-validation. Corrections to estimates differ based on location in space and time, confirming heterogeneity. We quantify the likelihood of exceeding selected ozone levels, finding that parts of the Middle East, India, and China are most likely to exceed 55 parts per billion (ppb) in 2017. About 96% of the global population was exposed to ozone levels above the World Health Organization guideline of 60 µg m −3 (30 ppb) in 2017. Our annual fine-resolution ozone estimates may be useful for several applications including epidemiology and assessments of impacts on health, agriculture, and ecosystems.

Ozone↗
NASA NTRS2022

Change in Tropospheric Ozone in the Recent Decades and its Contribution to Global Total Ozone

Tropospheric ozone is a key chemically active trace gas and radiative forcer. Understanding its long-term changes is important to properly interpret observed changes in total column ozone and stratospheric ozone recovery. We investigate global and regional tropospheric ozone changes and their impact on total column ozone during 2005-2018 using satellite measurements and the NASA Goddard Earth Observing System Chemistry Climate Model (GEOSCCM). Global total ozone increased ~4 DU during 2005-2018 (+0.28±0.06 DU yr(exp -1)) as inferred from Ozone Monitoring Instrument (OMI). Consistent with previous studies, the OMI/MLS (Microwave Limb Sounder) derived global tropospheric ozone increased 2.2 DU during this period, 60% of the global total column ozone increase. While GEOSCCM reproduces reasonably well the total column increase, it overestimates the stratospheric ozone increase and underestimates the tropospheric ozone increase. We find that the tropospheric ozone increases are likely attributed to a growth of regional emissions of key ozone precursors, especially volatile organic compounds (VOCs) as reflected by the positive trends in formaldehyde (CH2O). Although carbon monoxide (CO) has been decreasing everywhere around the globe, it has relatively small impact on the tropospheric ozone trend. Trends in nitrogen dioxide (NO2) vary with regions, and these changes counteract or reinforce the positive effects of CH2O on the tropospheric ozone increases. The model underestimates the observed tropospheric ozone increase, especially over the US and Europe, because of underestimated NO2 emissions changes used in the model. The stratospheric ozone contribution increases during this period in the Northern Hemisphere and contributes to the tropospheric ozone increase.

Atmospheric chemistry↗
NASA NTRS2021

Description of the NASA GEOS Composition Forecast Modeling System GEOS-CF v1.0

The Goddard Earth Observing System composition forecast (GEOS-CF) system is a high-resolution (0.25 degree) global constituent prediction system from NASA’s Global Modeling and Assimilation Office (GMAO). GEOS-CF offers a new tool for atmospheric chemistry research, with the goal to supplement NASA’s broad range of space-based and in-situ observation sand to support flight campaign planning, support of satellite observations, and air quality research. GEOS-CF expands on the GEOS weather and aerosol modeling system by introducing the GEOS-Chem chemistry module to provide analyses and 5-day forecasts of atmospheric constituents including ozone (O3), carbon monoxide (CO), nitrogen dioxide (NO2), and fine particulate matter (PM2.5). The chemistry module integrated in GEOS-CF is identical to the offline GEOS-Chem model and readily benefits from the innovations provided by the GEOS-Chem community.Evaluation of GEOS-CF against satellite, ozone sonde and surface observations show realistic simulated concentrations of O3, NO2, and CO, with normalized mean biases of -0.1 to -0.3, normalized root mean square errors (NRMSE) between 0.1-0.4, and correlations between 0.3-0.8. Comparisons against surface observations highlight the successful representation of air pollutants under a variety of meteorological conditions, yet also highlight current limitations, such as an over prediction of summertime ozone over the Southeast United States. GEOS-CFv1.0 generally overestimates aerosols by 20-50% due to known issues in GEOS-Chem v12.0.1 that have been addressed in later versions.The 5-day hourly forecasts have skill scores comparable to the analysis. Model skills can be improved significantly by applying a bias-correction to the surface model output using a machine-learning approach.

GEOS-CF↗
NASA NTRS2021

Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

Estimates of ground-level ozone concentrations are necessary to determine the human health burden of ozone. To support the Global Burden of Disease Study, we produce yearly fine resolution global surface ozone estimates from 1990 to 2017 through a data fusion of observations and models. As ozone observations are sparse in many populated regions, we use a novel combination of the M3Fusion and Bayesian Maximum Entropy (BME) methods. With M3Fusion, we create a multi-model composite by bias-correcting and weighting nine global atmospheric chemistry models based on their ability to predict observations (8,834 sites globally)in each region and year. BME is then used to integrate observations, such that estimates match observations at each monitoring site with the observational influence decreasing smoothly across space and time until the output matches the multi-model composite. After estimating at 0.5° resolution using BME, we add fine spatial detail from an additional model, yielding estimates at 0.1° resolution. Observed ozone is predicted more accurately (R2=0.81 at test point, 0.63 at 0.1°,0.62 at 0.5°) than the multi-model mean (R2=0.28 at 0.5°). Global ozone exposure is estimated to be increasing, driven by highly populated regions of Asia and Africa, despite decreases in the United States and Russia.

environmental modeling↗
NASA NTRS2020

Stratospheric Impact on the Northern Hemisphere Winter and Spring Ozone Interannual Variability in the Troposphere

In this study we use ozone and stratospheric ozone tracer simulations from the high-resolution (0.5°x0.5°) Goddard Earth Observing System, Version 5 (GEOS-5) in a replay mode to study the impact of stratospheric ozone on tropospheric ozone interannual variability (IAV). We use these simulations in conjunction with ozone sonde measurements from 1990 to 2016 during the winter and spring seasons. The simulations include a stratospheric ozone tracer (StratO3) to aid 15in the evaluation of the impact of stratospheric ozone IAV on the IAV of tropospheric ozone at different altitudes and location. The model is in good agreement with the observed interannual variation of tropospheric ozone, except for the post-Pinatubo period(1992-1994)over the region of North America. Ozone sonde data show a negative ozone anomaly in 1992-1994 following the Pinatubo eruption, with recovery thereafter. The simulated anomaly is only half the magnitude of that observed. Our analysis suggests that the simulated Stratosphere-troposphere exchange (STE)flux deduced from the analysis 20might be too strong over the North American (50°N-70°N) region after the Mt. Pinatubo eruption in the early 1990s,maskingthe impact of lower stratospheric ozone concentration on tropospheric ozone. European ozone sonde measurements show a similar but weaker ozone depletion after the Mt. Pinatubo eruption, which is fully reproduced by the model. Analysis based on the stratospheric ozone tracer (StratO3) identifies differences in strength and vertical extent of stratospheric ozone impact on the tropospheric ozone interannual variation (IAV)between North America and Europe. Over NorthAmerica25stations, the StratO3IAV has a significant impact on tropospheric ozone from the upper to lower troposphere and explains about 60% and 66% of the simulated ozone IAV at 400 hPa, and ~11% and 34% at 700 hPa in winter and spring respectively. Over European stations, the influence is limited to the middle to upper troposphere, and becomes much smaller at 700 hPa. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)assimilated fields exhibit strong longitudinal variations over northern hemisphere (NH) mid-high latitudes, with lower tropopause height and 30lower geopotential height over North America than Europe. These variations associated with the relevant variations in the location of tropospheric jet flows are responsible for the longitudinal differences in the stratospheric ozone impact, with stronger effects over North America than over Europe

tropospheric ozone IAV, Model, ozonesonde, stratos↗
NASA NTRSDate not supplied

Near Real-time Air Quality Forecasts Using the NASA GEOS Model

NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Future developments for the next GEOS-CF version will be discussed, including the assimilation system is being expanded to include chemically reactive trace gases, specifically using the capabilities of the GEOS Constituent Data Assimilation System (CoDAS).

CoDAS↗
NASA NTRSDate not supplied

NASA's High-Resolution GEOS Forecasting and Reanalysis Products: A Unified Tool from Local to Global Scales

NASA's GMAO produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric chemical composition at unprecedented horizontal resolution of 0.25 degrees (~25 km), freely available to the public. This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). GEOS-CF also assimilated satellite observations into the system for improved representation of weather and smoke. The assimilation system is currently being expanded to include chemically reactive trace gases. While the main focus of this new product is on tropospheric air quality information, the GEOS-Chem chemistry model used in this system includes the unified tropospheric stratospheric chemistry mechanism for improved forecasts of total column ozone during anomalous dynamical and chemical events. I will discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions for GEOS-CF and reanalysis products. In addition, I will show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.

Co-DAS↗