Browsing by Subject "Retrieval"
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Item Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies(AGU, 2016-04-18) Sayer, Andrew; Hsu, N. C.; Bettenhausen, C.; Lee, J.; Redemann, J.; Schmid, B.; Shinozuka, Y.Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the “Deep Blue” AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty ~25–50% (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty ~10–20%, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.Item Multivariate Retrieval of Carbon Monoxide(2011-01-01) Wilson, Robert Christopher; Hoff, Raymond M; Physics; Physics, AtmosphericA new technique is presented here to retrieve carbon monoxide (CO) profiles from Atmospheric Emitted Radiance Interferometer (AERI) spectra. This retrieval version deviates from the previous AERI CO retrieval method, which utilized signal processing to determine a constant CO mixing ratio representative of the entire troposphere. Instead, this retrieval version utilizes linear mapping to ascertain an estimate of the CO profile. A detailed analysis is conducted to estimate the error from all aspects of the linear mapping procedure including measurements, forward modeling of atmospheric radiation, and uncertainty from inputs to the forward model. It was found that the dominant sources of error were from cloud contaminated spectra and uncertainty in absorption line strengths inside the forward model. A new cloud flagging technique that uses a neural network to identify spectra affected by clouds was tested and compared to the previously used version based on brightness temperature contrast. The neural network method decreased uncertainty between AERI and forward model spectra by 30 percent when compared with the previously used version. First guess CO profiles to the AERI retrieval were from two different sources. One source was an a priori rofile calculated as the mean profile from 57 individual measurements where each CO profile encompasses tower, aircraft, and a satellite CO measurement. The other first guess CO profile came from the AIRS version 5 (AIRSv5) retrieved CO product. Incorporating the AIRS CO profile to the AERI retrieval provided a better estimate of free tropospheric CO when compared with the a priori ile. Using a better upper tropospheric CO estimate resulted in more accurate results from the AERI retrieval below 2 km, thus revealing that an AERI plus AIRS retrieved CO product is superior to either instrument's own CO retrieval working alone. The combined retrieval product is shown to have an RMSE of 10% in the first 2 km of the atmosphere.