Hai Nguyen

Spatial Statistical Data Fusion for Remote Sensing Applications

Aerosols are tiny solid or liquid particles suspended in the atmosphere; examples of aerosols include windblown dust, sea salts, volcanic ash, smoke from wildfires, and pollution from factories. The global distribution of aerosols is a topic of great interest in climate studies since aerosols can either cool or warm the atmosphere depending on their location, type, and interaction with clouds. Aerosol concentrations are important input components of global climate models, and it is crucial to accurately estimate aerosol concentrations from remote sensing instruments so as to minimize errors “downstream” in climate models. Currently, space-based observations of aerosols are available from two remote sensing instruments on board NASAs Terra spacecraft: the Multiangle Imaging SpectroRadiometer (MISR), and the MODerate-resolution Imaging Spectrometer (MODIS). These two instruments have complementary coverage, spatial support, and retrieval characteristics, making it advantageous to combine information from both sources to make optimal inferences about global aerosol distributions. In this article, we predict the true aerosol process from two noisy and possibly biased datasets, and we also estimate the uncertainties of these estimates. Our data-fusion methodology scales linearly and bears some resemblance to Fixed Rank Kriging (FRK), a variant of kriging that is designed for spatial interpolation of a single, massive dataset. Our spatial statistical approach does not require assumptions of stationarity or isotropy and, crucially, allows for change of spatial support. We compare our methodology to FRK and Bayesian melding, and we show that ours has superior prediction standard errors compared to FRK and much faster computational speed compared to Bayesian melding.

Spatio-Temporal Data Fusion for Very Large Remote Sensing Datasets

Developing global maps of carbon dioxide (CO2) mole fraction (in units of parts per million) near the Earth’s surface can help identify locations where major amounts of CO2 are entering and exiting the atmosphere, thus providing valuable insights into the carbon cycle and mitigating the greenhouse effect of atmospheric CO2. Existing satellite remote sensing data do not provide measurements of the CO2 mole fraction near the surface. Japan’s Greenhouse gases Observing SATellite (GOSAT) is sensitive to average CO2 over the entire column, and NASA’s Atmospheric InfraRed Sounder (AIRS) is sensitive to CO2 in the middle troposphere. One might expect that lower-atmospheric CO2 could be inferred by differencing GOSAT column-average and AIRS mid-tropospheric data. However, the two instruments have different footprints, measurement-error characteristics, and data coverages. In addition, the spatio-temporal domains are large, and the AIRS dataset is massive. In this article, we describe a spatio-temporal data-fusion (STDF) methodology based on reduced-dimensional Kalman smoothing. Our STDF is able to combine the complementary GOSAT and AIRS datasets to optimally estimate lower-atmospheric CO2 mole fraction over the whole globe. Further, it is designed for massive remote sensing datasets and accounts for differences in instrument footprint, measurement-error characteristics, and data coverages. This article has supplementary material online.

Multivariate Spatial Data Fusion for Very Large Remote Sensing Datasets

Global maps of total-column carbon dioxide (CO2) mole fraction (in units of parts per million) are important tools for climate research since they provide insights into the spatial distribution of carbon intake and emissions as well as their seasonal and annual evolutions. Currently, two main remote sensing instruments for total-column CO2 are the Orbiting Carbon Observatory-2 (OCO-2) and the Greenhouse gases Observing SATellite (GOSAT), both of which produce estimates of CO2 concentration, called profiles, at 20 different pressure levels. Operationally, each profile estimate is then convolved into a single estimate of column-averaged CO2 using a linear pressure weighting function. This total-column CO2 is then used for subsequent analyses such as Level 3 map generation and colocation for validation. In principle, total-column CO2 in these applications may be more efficiently estimated by making optimal estimates of the vector-valued CO2 profiles and applying the pressure weighting function afterwards. These estimates will be more efficient if there is multivariate dependence between CO2 values in the profile. In this article, we describe a methodology that uses a modified Spatial Random Effects model to account for the multivariate nature of the data fusion of OCO-2 and GOSAT. We show that multivariate fusion of the profiles has improved mean squared error relative to scalar fusion of the column-averaged CO2 values from OCO-2 and GOSAT. The computations scale linearly with the number of data points, making it suitable for the typically massive remote sensing datasets. Furthermore, the methodology properly accounts for differences in instrument footprint, measurement-error characteristics, and data coverages.

Global variability of midtropospheric carbon dioxide as measured by the Atmospheric Infrared Sounder

Abstract The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua spacecraft provides accurate and consistent measurements of midtropospheric carbon dioxide ( CO 2 ) with global monthly coverage. The data are widely used for studies of vertical transport of CO 2 due to large-scale dynamics (e.g., ENSO, MJO, and the Walker Circulation). The purpose of this paper is to characterize the response of CO 2 in the midtroposphere, at the altitudes where AIRS is most sensitive, to geophysical changes at the surface across the globe. Our findings confirm that surface factors, as well as weather and climate patterns, impact the global variability of midtropospheric CO 2 as observed by AIRS. Despite a phase lag and a reduction in the seasonal amplitude observed in AIRS CO 2 relative to surface CO 2 measurements in the Northern Hemisphere, a significant correlation is observed between regional variability of CO 2 from AIRS and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Gross Primary Productivity at the surface, primarily in the high-latitude boreal forests during the peak of the growing season (July). A video of global AIRS CO 2 and MODIS vegetation index clearly shows the seasonal drawdown of CO 2 from the midtroposphere over highly vegetated areas in the northern latitudes. In the Southern Hemisphere, we see higher amplitude in the seasonal cycle, with the phase leading that of the surface. Both are indicative of interhemispheric transport.

Monthly Representations of Mid-Tropospheric Carbon Dioxide from the Atmospheric Infrared Sounder

The Atmospheric Infrared Sounder (AIRS) on NASAs Earth Observing System Aqua spacecraft was launched in May of 2002 and acquires hyperspectral infrared spectra used to generate a wide range of atmospheric products including temperature, water vapor, and trace gas species including carbon dioxide. Here we present monthly representations of global concentrations of mid-tropospheric carbon dioxide produced from 8 years of data obtained by AIRS between the years of 2003 and 2010. We define them as representations rather than climatologies to reflect that the files are produced over a relatively short time period and represent summaries of the Level 3 data. Finally, they have not yet been independently validated. The representations have a horizontal resolution of 2.0° × 2.5° (Latitude × Longitude) and faithfully reproduce the original 8 years of monthly L3 CO2 concentrations with a standard deviation of 1.48 ppm and less than 2% outliers. The representations are intended for use in studies of the global general circulation of CO2 and identification of anomalies in CO2 typically associated with atmospheric transport. The seasonal variability and trend found in the AIRS CO2 data are discussed.

Spatial data compression via adaptive dispersion clustering

Adaptive Spatial Dispersion Clustering (ASDC), a new method of spatial data compression, is specifically designed to reduce the size of a spatial dataset in order to facilitate subsequent spatial prediction. Unlike traditional data and image compression methods, the goal of ASDC is to create a new dataset that will be used as input into spatial-prediction methods, such as traditional kriging or Fixed Rank Kriging, where using the full dataset may be computationally infeasible. ASDC can be classified as a lossy compression method and is based on spectral clustering. It aims to produce contiguous spatial clusters and to preserve the spatial-correlation structure of the data so that the loss of predictive information is minimal. An extensive simulation study demonstrates the predictive performance of these adaptively compressed datasets for several scenarios. ASDC is compared to two other data-reduction schemes, one using local neighborhoods and one using simple binning. An application to remotely sensed sea-surface-temperature data is also presented, and computational costs are discussed.

Global and regional seasonal variability of mid-tropospheric CO2 as measured by the Atmospheric Infrared Sounder (AIRS)

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral infrared instrument on the Earth Observing System (EOS) Aqua Spacecraft, launched on May 4, 2002 into a near polar sun-synchronous orbit. AIRS has 2378 infrared channels ranging from 3.7 μm to 15.4 μm and a 13.5 km footprint at nadir. AIRS, in conjunction with the Advanced Microwave Sounding Unit (AMSU), produces temperature profiles with 1K/km accuracy on a global scale, as well as water vapor profiles and trace gas amounts for CO2, CO, SO2, O3 and CH4. AIRS CO2 climatologies have been shown to be useful for identifying anomalies associated with geophysical events such as El Niño-Southern Oscillation or Madden–Julian oscillation. In this study, monthly representations of mid-tropospheric CO2 are constructed from 10 years of AIRS Version 5 monthly Level 3 data. We compare the AIRS mid-tropospheric CO2 representations to ground-based measurements from the Scripps and National Oceanic and Atmospheric Administration Climate Modeling and Diagnostics Laboratory (NOAA CMDL) ground networks to better understand the phase lag of the CO2 seasonal cycle between the surface and middle troposphere. Results show only a small phase lag in the tropics that grows to approximately two months in the northern latitudes.