Vijay Natraj

Space‐based near‐infrared CO2 measurements: Testing the Orbiting Carbon Observatory retrieval algorithm and validation concept using SCIAMACHY observations over Park Falls, Wisconsin

Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO_2. The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO_2 (X_(CO)_2) with the precision and accuracy needed to quantify CO_2 sources and sinks on regional scales (∼1000 × 1000 km^2) and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve (X_(CO)_2) and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O_2 A band at 0.76 μm and the 1.58 μm CO_2 band for Park Falls, Wisconsin. Even after accounting for a systematic error in our representation of the O_2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS (X_(CO)_2) retrievals of ∼3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O_2 A band region for the SCIAMACHY (X_(CO)_2) retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS (X_(CO)_2) retrievals. We compared the seasonal cycle of (X_(CO)_2)at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-Ames-Stanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO_2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

Information Content of Exoplanetary Transit Spectra: An Initial Look

It has been shown that spectroscopy of transiting extrasolar planets can potentially provide a wealth of information about their atmospheres. Herein, we set up the inverse problem in spectroscopic retrieval. We use nonlinear optimal estimation to retrieve the atmospheric state (pioneered for Earth sounding by Rodgers). The formulation quantifies the degrees of freedom and information content of the spectrum with respect to geophysical parameters; herein, we focus specifically on temperature and composition. First, we apply the technique to synthetic near-infrared spectra and explore the influence of spectral signal-to-noise ratio and resolution (the two important parameters when designing a future instrument) on the information content of the data. As expected, we find that the number of retrievable parameters increases with increasing signal-to-noise ratio and resolution, although the gains quickly level off for large values. Second, we apply the methods to the previously studied dayside near-infrared emission spectrum of HD 189733b and compare the results of our retrieval with those obtained by others.

RAYLEIGH SCATTERING IN PLANETARY ATMOSPHERES: CORRECTED TABLES THROUGH ACCURATE COMPUTATION OF X AND Y FUNCTIONS

Tables that have been used as a reference for nearly 50 years for the intensity and polarization of reflected and transmitted light in Rayleigh scattering atmospheres have been found to be inaccurate, even to four decimal places. We convert the integral equations describing the X and Y functions into a pair of coupled integrodifferential equations that can be efficiently solved numerically. Special care has been taken in evaluating Cauchy principal value integrals and their derivatives that appear in the solution of the Rayleigh scattering problem. The new approach gives results accurate to eight decimal places for the entire range of tabulation (optical thicknesses 0.02–1.0, surface reflectances 0–0.8, solar and viewing zenith angles 0 ◦ –88.85 ◦ , and relative azimuth angles 0 ◦ –180 ◦ ), including the most difficult case of direct transmission in the direction of the sun. Revised tables have been created and stored electronically for easy reference by the planetary science and astrophysics community.

Retrieval of from simulated Orbiting Carbon Observatory measurements using the fast linearized R‐2OS radiative transfer model

In a recent paper, we introduced a novel technique to compute the polarization in a vertically inhomogeneous, scattering-absorbing medium using a two orders of scattering (2OS) radiative transfer (RT) model. The 2OS computation is an order of magnitude faster than a full multiple scattering scalar calculation and can be implemented as an auxiliary code to compute polarization in operational retrieval algorithms. In this paper, we employ the 2OS model for polarization in conjunction with a scalar RT model (Radiant) to simulate backscatter measurements in near infrared (NIR) spectral regions by space-based instruments such as the Orbiting Carbon Observatory (OCO). Computations are performed for six different sites and two seasons, representing a variety of viewing geometries, surface and aerosol types. The aerosol extinction (at 13000 cm^−1) was varied from 0 to 0.3. The radiance errors using the Radiant/2OS (R-2OS) RT model are an order of magnitude (or more) smaller than errors arising from the use of the scalar model alone. In addition, we perform a linear error analysis study to show that the errors in the retrieved column-averaged dry air mole fraction of CO2 (XCO2) using the R-2OS model are much lower than the “measurement” noise and smoothing errors appearing in the inverse model. On the other hand, we show that use of the scalar model alone induces X CO2 errors that could dominate the retrieval error budget.

Polarized Light Reflected and Transmitted by Thick Rayleigh Scattering Atmospheres

Accurate values for the intensity and polarization of light reflected and transmitted by optically thick Rayleigh scattering atmospheres with a Lambert surface underneath are presented. A recently reported new method for solving integral equations describing Chandrasekhars X- and Y-functions is used. The results have been validated using various tests and techniques, including the doubling-adding method, and are accurate to within one unit in the eighth decimal place. Tables are stored electronically and expected to be useful as benchmark results for the (exo)planetary science and astrophysics communities. Asymptotic expressions to obtain Stokes parameters for a thick layer from those of a semi-infinite atmosphere are also provided.

Accounting for aerosol scattering in the CLARS retrieval of column averaged CO2 mixing ratios

The California Laboratory for Atmospheric Remote Sensing Fourier transform spectrometer (CLARS‐FTS) deployed at Mount Wilson, California, has been measuring column abundances of greenhouse gases in the Los Angeles (LA) basin in the near‐infrared spectral region since August 2011. CLARS‐FTS measures reflected sunlight and has high sensitivity to absorption and scattering in the boundary layer. In this study, we estimate the retrieval biases caused by aerosol scattering and present a fast and accurate approach to correct for the bias in the CLARS column averaged CO2 mixing ratio product, X_(CO2). The high spectral resolution of 0.06 cm^(−1) is exploited to reveal the physical mechanism for the bias. We employ a numerical radiative transfer model to simulate the impact of neglecting aerosol scattering on the CO_2 and O_2 slant column densities operationally retrieved from CLARS‐FTS measurements. These simulations show that the CLARS‐FTS operational retrieval algorithm likely underestimates CO_2 and O_2 abundances over the LA basin in scenes with moderate aerosol loading. The bias in the CO_2 and O_2 abundances due to neglecting aerosol scattering cannot be canceled by ratioing each other in the derivation of the operational product of X_(CO2). We propose a new method for approximately correcting the aerosol‐induced bias. Results for CLARS X_(CO2) are compared to direct‐Sun X_(CO2) retrievals from a nearby Total Carbon Column Observing Network (TCCON) station. The bias‐correction approach significantly improves the correlation between the X_(CO2) retrieved from CLARS and TCCON, demonstrating that this approach can increase the yield of useful data from CLARS‐FTS in the presence of moderate aerosol loading.

Information Content of Visible and Mid‐Infrared Radiances for Retrieving Tropical Ice Cloud Properties

Hyperspectral instruments such as AIRS have spectrally dense observations effective for ice cloud retrievals. However, due to the large number of channels, only a small subset is typically used. It is crucial that this subset of channels be chosen to contain the maximum possible information about the retrieved variables. This study describes an information content analysis designed to select optimal channels for ice cloud retrievals. To account for variations in ice cloud properties, we perform channel selection over an ensemble of cloud regimes, extracted with a clustering algorithm, from a multi-year database at a tropical ARM site. Multiple satellite viewing angles over land and ocean surfaces are considered to simulate the variations in observation scenarios. The results suggest that AIRS channels near wavelengths of 14, 10.4, 4.2, and 3.8 μm contain the most information. With an eye toward developing a joint AIRS-MODIS retrieval, the analysis is also applied to combined measurements from both instruments. While application of this method to MODIS yields results consistent with previous channel sensitivity studies, the analysis shows that this combination may yield substantial improvement in cloud retrievals. MODIS provides most information on optical thickness and particle size, aided by a better constraint on cloud vertical placement from AIRS. An alternate scenario where cloud top boundaries are supplied by the active sensors in the A-Train is also explored. The more robust cloud placement afforded by active sensors shifts the optimal channels towards the window region and SWIR, further constraining optical thickness and particle size.

A review of fast radiative transfer techniques

Atmospheric radiative transfer involves gas absorption coupled with molecular Rayleigh scattering, in addition to scattering and absorption by clouds and aerosols. Further, computation of heating rates are dependent on absorption and emission of radiation, processes that have a complex dependence on various quantities. Typically, spectral regions contain several overlapping lines with intensities varying over many orders of magnitude. The most accurate method for computing the radiative terms in a molecular atmosphere involves a detailed line-by-line (LBL) calculation of the absorption coefficient versus wavenumber. However, direct numerical solution of the radiative transfer equation over frequency is in most cases too computationally expensive to be used on a routine basis. Therefore a variety of approximations have been developed to accelerate the computational process. This chapter discusses several of these techniques.

Evaluation of Radiative Transfer Models With Clouds

Data from hyperspectral infrared sounders are routinely ingested worldwide by the National Weather Centers. The cloud-free fraction of this data is used for initializing forecasts which include temperature, water vapor, water cloud, and ice cloud profiles on a global grid. Although the data from these sounders are sensitive to the vertical distribution of ice and liquid water in clouds, this information is not fully utilized. In the future, this information could be used for validating clouds in National Weather Center models and for initializing forecasts. We evaluate how well the calculated radiances from hyperspectral Radiative Transfer Models (RTMs) compare to cloudy radiances observed by AIRS and to one another. Vertical profiles of the clouds, temperature, and water vapor from the European Center for Medium-Range Weather Forecasting were used as input for the RTMs. For nonfrozen ocean day and night data, the histograms derived from the calculations by several RTMs at 900 cm 1 have a better than 0.95 correlation with the histogram derived from the AIRS observations, with a bias relative to AIRS of typically less than 2 K. Differences in the cloud physics and cloud overlap assumptions result in little bias between the RTMs, but the standard deviation of the differences ranges from 6 to 12 K. Results at 2,616 cm 1 at night are reasonably consistent with results at 900 cm . Except for RTMs which use full scattering calculations, the bias and histogram correlations at 2,616 cm 1 are inferior to those at 900 cm 1 for daytime calculations. Plain Language Summary Getting the right clouds of the right type, at the right time and location in Global Circulation Models, is key to getting the local energy balance right. This is key to an accurate forecast. If the clouds are of the wrong type or at the wrong location or time, the accuracy of the forecast is degraded. We evaluate the accuracy of the best currently available cloud description (produced by the European Center for Medium-Range Weather Forecasting) by comparing the radiances calculated using Radiative Transfer Models (RTMs) from six major development teams to cloudy radiances observed by the Atmospheric Infrared Sounder at the same location and time. The better RTMs fit statistically reasonably well in the 11-μm atmospheric window area, with little latitude (zonal) and day/night cloud-type related bias. None of the RTMs fit well in the 4-μm atmospheric window area during daytime, unless the calculations use full scattering. With the current state of art, all major RTMs would be suitable to start the validation of cloud effects in the National Weather Center models using just one 11-μm atmospheric window channel.

The Geostationary Carbon Process Mapper

The Geostationary Carbon Process Mapper (GCPM) is an earth science mission to measure key atmospheric trace gases and process tracers related to climate change and human activity. The measurement strategy delivers a process based understanding of the carbon cycle that is accurate and extensible from city to regional and continental scales. This understanding comes from contiguous maps of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and chlorophyll fluorescence (CF) collected up to 10 times per day at high spatial resolution (~4km × 4km) from geostationary orbit (GEO). These measurements will capture the spatial and temporal variability of the carbon cycle across diurnal, synoptic, seasonal and interannual time scales. The CO2/CH4/CO/CF measurement suite has been specifically selected because their combination provides the information needed to disentangle natural and anthropogenic contributions to atmospheric carbon concentrations and to minimize key uncertainties in the flow of carbon between the atmosphere and surface since they place constraints on both biogenic uptake and release as well as on combustion emissions. Additionally, GCPMs combination of high-resolution mapping and high measurement frequency provide quasi-continuous monitoring, effectively eliminating atmospheric transport uncertainties from source/sink inversion modeling. GCPM uses a single instrument, the “Geostationary Fourier Transform Spectrometer (GeoFTS)” to make measurements in the near infrared spectral region at high spectral resolution. The GeoFTS is a half meter cube size instrument designed to be a secondary “hosted” payload on a commercial GEO satellite. NASA and other government agencies have adopted the hosted payload implementation approach because it substantially reduces the overall mission cost. This paper presents a hosted payload implementation approach for measuring the major carbon-containing gases in the atmosphere from the geostationary vantage point, to affordably advance the scientific understating of carbon cycle processes and climate change.