Amy Braverman

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.

PARAGON: An Integrated Approach for Characterizing Aerosol Climate Impacts and Environmental Interactions

Aerosols exert myriad influences on the earths environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the long-term benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, inter-agency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality.

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.

Integrating and Interpreting Aerosol Observations and Models within the PARAGON Framework

Given the breadth and complexity of available data, constructing a measurement-based description of global tropospheric aerosols that will effectively confront and constrain global three-dimensional models is a daunting task. Because data are obtained from multiple sources and acquired with nonuniform spatial and temporal sampling, scales, and coverage, protocols need to be established that will organize this vast body of knowledge. Currently, there is no capability to assemble the existing aerosol data into a unified, interoperable whole. Technology advancements now being pursued in high-performance distributed computing initiatives can accomplish this objective. Once the data are organized, there are many approaches that can be brought to bear upon the problem of integrating data from different sources. These include data-driven approaches, such as geospatial statistics formulations, and model-driven approaches, such as assimilation or chemical transport modeling. Establishing a data interoperability fr...

A statistical complement to deterministic algorithms for the retrieval of aerosol optical thickness from radiance data

As a complement to the conventional deterministic geophysical algorithms, we consider a faster, but less accurate approach: training regression models to predict aerosol optical thickness (AOT) from radiance data. In our study, neural networks trained on a global data set are employed as a global retrieval method. Inverse distance spatial interpolation and region-specific neural networks trained on restricted, localized areas provide local models. We then develop two integrated statistical methods: local error correction of global retrievals and an optimal weighted average of global and local components. The algorithms are evaluated on the problem of deriving AOT from raw radiances observed by the Multi-angle Imaging SpectroRadiometer (MISR) instrument onboard NASAs Terra satellite. Integrated statistical approaches were clearly superior to global and local models alone. The best compromise between speed and accuracy was obtained through the weighted averaging of global neural networks and spatial interpolation. The results show that, while much faster, statistical retrievals can be quite comparable in accuracy to the far more computationally demanding deterministic methods. Differences in quality vary with season and model complexity.

A likelihood-based comparison of temporal models for physical processes

Many scientific and engineering problems involve physical modeling of complex processes. Sometimes multiple candidate models are available, and their performance can be compared by how well their outputs match observations. Various summary statistics can be used for this purpose, but no matter which statistics are chosen, it is important that comparisons based on them be considered in light of the inherent variability of the data used in their calculation. In this article, we consider the variability of a summary statistic through an empirical likelihood. The approach is nonparametric in the sense that a moving-block bootstrap procedure is used to obtain the empirical likelihood. Relative figures of merit for each candidate model are formed as the ratio of each candidate models likelihood to the largest likelihood. We use a small simulation study to show that our procedure can correctly distinguish between different time series models, and then we demonstrate how the method can be used to evaluate the output of 20 Intergovernmental Panel on Climate Change (IPCC) atmospheric models based on their agreement with the observations.

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.

Daytime Arctic Cloud Detection Based on Multi-Angle Satellite Data With Case Studies

Global climate models predict that the strongest dependences of surface air temperatures on increasing atmospheric carbon dioxide levels will occur in the Arctic. A systematic study of these dependences requires accurate Arctic-wide measurements, especially of cloud coverage. Thus cloud detection in the Arctic is extremely important, but it is also challenging because of the similar remote sensing characteristics of clouds and ice-and snow-covered surfaces. This article proposes two new operational Arctic cloud detection algorithms using Multiangle Imaging SpectroRadiometer (MISR) imagery. The key idea is to identify cloud-free surface pixels in the imagery instead of cloudy pixels as in the existing MISR operational algorithms. Through extensive exploratory data analysis and using domain knowledge, three physically useful features to differentiate surface pixels from cloudy pixels have been identified. The first algorithm, enhanced linear correlation matching (ELCM), thresholds the features with either fixed or data-adaptive cutoff values. Probability labels are obtained by using ELCM labels as training data for Fishers quadratic discriminant analysis (QDA), leading to the second (ELCM-QDA) algorithm. Both algorithms are automated and computationally efficient for operational processing of the massive MISR data set. Based on 5 million expert-labeled pixels, ELCM results are significantly in terms of both accuracy (92%%) and coverage (100%%) compared with two MISR operational algorithms, one with an accuracy of 80%% and coverage of 27%% and the other with an accuracy of 83%% and a coverage of 70%%. The ELCM-QDA probability prediction is also consistent with the expert labels and is more informative. In conclusion, ELCM and ELCM-QDA provide the best performance to date among all available operational algorithms using MISR data.

Ten years of MISR observations from Terra: Looking back, ahead, and in between

The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been collecting global Earth data from NASAs Terra satellite since February 2000. With its nine along-track view angles, four visible/near-infrared spectral bands, intrinsic spatial resolution of 275 m, and stable radiometric and geometric calibration, no instrument that combines MISRs attributes has previously flown in space. The more than 10-year (and counting) MISR data record provides unprecedented opportunities for characterizing long-term trends in aerosol, cloud, and surface properties, and includes 3-D textural information conventionally thought to be accessible only to active sensors.

Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO

Remote sensing of the atmosphere has provided a wealth of data for analyses and inferences in earth science. Satellite observations can provide information on the atmospheric state at fine spatial and temporal resolution while providing substantial coverage across the globe. For example, this capability can greatly enhance the understanding of the space-time variation of the greenhouse gas, carbon dioxide (