Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach

The Orbiting Carbon Observatories-2 and -3 make space-based measurements in the oxygen A-band and the weak and strong carbon dioxide (CO2) bands using the Atmospheric Carbon Observations from Space (ACOS) retrieval. Within ACOS, a Bayesian optimal estimation approach is employed to retrieve the column-averaged CO2 dry air mole fraction from these measurements. This retrieval requires a large number of polarized, multiple-scattering radiative transfer calculations for each iteration. These calculations take up the majority of the processing time for each retrieval and slow down the algorithm to the point that reprocessing data from the mission over multiple years becomes especially time consuming. To accelerate the radiative transfer model and, thereby, ease this bottleneck, we have developed a novel approach that enables modeling of the full spectra for the three OCO-2/3 instrument bands from radiances calculated at a small subset of monochromatic wavelengths. This allows for a reduction of the number of monochromatic calculations by a factor of 10, which can be achieved with radiance errors of less than 0.01% with respect to the existing algorithm and is easily tunable to a desired accuracy-speed trade-off. For the ACOS retrieval, this speeds up the over-retrievals by about a factor of two. The technique may be applicable to similar retrieval algorithms for other greenhouse gas sensors with large data volumes, such as GeoCarb, GOSAT-3, and CO2M

Combined multispectral/hyperspectral remote sensing of tropospheric aerosols for quantification of their direct radiative effect

2013 Fall.Includes bibliographical references.Scattering and absorption of solar radiation by aerosols in the atmosphere has a direct radiative effect on the climate of the Earth. Unfortunately, according to the IPCC the uncertainties in aerosol properties and their effect on the climate system represent one of the largest uncertainties in climate change research. Related to aerosols, one of the largest uncertainties is the fraction of the incident radiation that is scattered rather than absorbed, or their single scattering albedo. In fact, differences in single scattering albedo have a significant impact on the magnitude of the cooling effect of aerosols (opposite to that of greenhouse gasses) which can even have a warming effect for strongly absorbing aerosols. Satellites provide a unique opportunity to measure aerosol properties on a global scale. Traditional approaches use multispectral measurements of intensity at a single view angle to retrieve at most two aerosol parameters over land but it is being realized that more detail is required for accurate quantification of the direct effect of aerosols, in particular its anthropogenic component, and therefore more measurement information is required. One approach to more advanced measurements is to use not only intensity measurements but also polarimetric measurements and to use multiple view angles. In this work we explore another alternative: the use of hyperspectral measurements in molecular absorption bands. Our study can be divided into three stages the first of which is the development of a fast radiative transfer model for rapid simulation of measurements. Our approach is matrix operator based and uses the Padé approximation for the matrix exponential to evaluate the homogeneous solution. It is shown that the method is two to four times faster than the standard and efficient discrete ordinate technique and is accurate to the 6th decimal place. The second part of our study forms the core and is divided into two chapters the first of which is a rigorous sensitivity and optimal estimation based information content study that explores the use of measurements made by a MODIS type instrument combined with measurements made by an instrument similar to GOSAT TANSO-FTS which supplies hyperspectral measurements of intensity and polarization in the O2 A-band and the 1.61- and 2.06-μm CO2 bands. It is found that the use of the hyperspectral bands provides a means to separate the effects of the surface and aerosol absorption from effects related to aerosol single scattering parameters. The amount of information increases significantly when the CO2 bands are included rather than just the more traditional O2 A-band, when polarization measurements are included, and when measurements are made at multiple view angles. We then present a retrieval using co-located observations of MODIS and GOSAT TANSO-FTS which are both also co-located with AERONET sites for validation purposes. We introduce an optimal estimation retrieval and perform this retrieval on our co-located observations. We choose a complete state vector to maximize the use of the information in our measurements and use an a priori constraint and regularization to arrive at a stable solution. In addition to the retrieved parameters, we also calculate a self contained estimation of the retrieval error. Validation with AERONET, for retrievals using MODIS plus TANSO-FTS measurements of intensity and polarization in all three bands indicate accuracies within 15% for optical thickness, 10% for fine mode mean radius, 35% for coarse mode mean radius, 15% for the standard deviation of fine mode mean radius, 25% for the standard deviation of the coarse mode mean radius, 0.04 for the real part of the index of refraction, and 0.05 for single scattering albedo. In addition to the retrieved parameters, we also validate the estimated retrieval error and find that the estimations have distributions that are tighter and within the broader distributions of real errors relative to AERONET. The third part of our study uses the retrieval results to calculate radiative fluxes, errors, and sensitivities at solar wavelengths along with aerosol radiative effect and effect efficiency. In addition, we outline how to propagate the errors in the retrieval through the flux calculations to provide an error estimation of the fluxes. These results are then validated against the corresponding AERONET products. It was found that the flux results were most sensitive to single scattering albedo while the size distribution and real part of the index of refraction also have significant effects. Relative to AERONET our fluxes are less accurate than an independent AERONET validation, due to uncertainties in our satellite based retrieval with accuracies within 13 Wm-2 for TOA upward, 9 Wm-2 for BOA upward, and 30 Wm-2 for BOA downward. The estimated errors also contained uncertainties but were in fact more conservative than the actual errors

The Community Cloud retrieval for CLimate (CC4CL). Part II: The optimal estimation approach

The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model which, includes the 5 clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), the “fast” radiative transfer solution (which includes a multiple scattering treatment) All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modelling errors become more significant. The retrieval method is then presented describing 10 optimal estimation in general, the non-linear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10% for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors ranging up to 20%

Evaluating the consistency between OCO-2 and OCO-3 XCO₂ estimates derived from the NASA ACOS version 10 retrieval algorithm

Abstract. The version 10 (v10) Atmospheric Carbon Observations from Space (ACOS) Level 2 full-physics (L2FP) retrieval algorithm has been applied to multiyear records of observations from NASA's Orbiting Carbon Observatory 2 and 3 sensors (OCO-2 and OCO-3, respectively) to provide estimates of the carbon dioxide (CO2) column-averaged dry-air mole fraction (XCO2). In this study, a number of improvements to the ACOS v10 L2FP algorithm are described. The post-processing quality filtering and bias correction of the XCO2 estimates against multiple truth proxies are also discussed. The OCO v10 data volumes and XCO2 estimates from the two sensors for the time period of August 2019 through February 2022 are compared, highlighting differences in spatiotemporal sampling but demonstrating broad agreement between the two sensors where they overlap in time and space. A number of evaluation sources applied to both sensors suggest they are broadly similar in data and error characteristics. Mean OCO-3 differences relative to collocated OCO-2 data are approximately 0.2 and −0.3 ppm for land and ocean observations, respectively. Comparison of XCO2 estimates to collocated Total Carbon Column Observing Network (TCCON) measurements shows root mean squared errors (RMSEs) of approximately 0.8 and 0.9 ppm for OCO-2 and OCO-3, respectively. An evaluation against XCO2 fields derived from atmospheric inversion systems that assimilated only near-surface CO2 observations, i.e., did not assimilate satellite CO2 measurements, yielded RMSEs of 1.0 and 1.1 ppm for OCO-2 and OCO-3, respectively. Evaluation of uncertainties in XCO2 over small areas, as well as XCO2 biases across land–ocean crossings, also indicates similar behavior in the error characteristics of both sensors. Taken together, these results demonstrate a broad consistency of OCO-2 and OCO-3 XCO2 measurements, suggesting they may be used together for scientific analyses