Quantifying the impact of aerosol scattering on the retrieval of methane from airborne remote sensing measurements

As a greenhouse gas with strong global warming potential, atmospheric methane (CH4) emissions have attracted a great deal of attention. Although remote sensing measurements can provide information about CH4 sources and emissions, accurate retrieval is challenging due to the influence of atmospheric aerosol scattering. In this study, imaging spectroscopic measurements from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) in the shortwave infrared are used to compare two retrieval techniques – the traditional matched filter (MF) method and the optimal estimation (OE) method, which is a popular approach for trace gas retrievals. Using a numerically efficient radiative transfer model with an exact single-scattering component and a two-stream multiple-scattering component, we also simulate AVIRIS-NG measurements for different scenarios and quantify the impact of aerosol scattering in the two retrieval schemes by including aerosols in the simulations but not in the retrievals. The presence of aerosols causes an underestimation of CH4 in both the MF and OE retrievals; the biases increase with increasing surface albedo and aerosol optical depth (AOD). Aerosol types with high single-scattering albedo and low asymmetry parameter (such as water-soluble aerosols) induce large biases in the retrieval. When scattering effects are neglected, the MF method exhibits lower fractional retrieval bias compared to the OE method at high CH4 concentrations (2–5 times typical background values) and is suitable for detecting strong CH4 emissions. For an AOD value of 0.3, the fractional biases of the MF retrievals are between 1.3 % and 4.5 %, while the corresponding values for OE retrievals are in the 2.8 %–5.6 % range. On the other hand, the OE method is an optimal technique for diffuse sources (<1.5 times typical background values), showing up to 5 times smaller fractional retrieval bias (8.6 %) than the MF method (42.6 %) for the same AOD scenario. However, when aerosol scattering is significant, the OE method is superior since it provides a means to reduce biases by simultaneously retrieving AOD, surface albedo, and CH4. The results indicate that, while the MF method is good for plume detection, the OE method should be employed to quantify CH4 concentrations, especially in the presence of aerosol scattering.

Observing the timescales of aerosol-cloud interactions in snapshot satellite images

The response of cloud processes to an aerosol perturbation is one of the largest uncertainties in the anthropogenic forcing of the climate. It occurs at a variety of timescales, from the near-instantaneous Twomey effect, to the longer timescales required for cloud adjustments. Understanding the temporal evolution of cloud properties following an aerosol perturbation is necessary to interpret the results of so-called "natural experiments" from a known aerosol source, such as a ship or industrial site. This work uses reanalysis windfields and ship emission information matched to observations of shiptracks to measure the timescales of cloud responses to aerosol in instantaneous (or "snapshot") images taken by polar-orbiting satellites. As found in previous studies, the local meteorological environment is shown to have a strong impact on the occurrence and properties of shiptracks, but there is a strong time dependence in their properties. The largest droplet number concentration (Nd) responses are found within three hours of emission, while cloud adjustments continue to evolve over periods of ten hours or more. Cloud fraction is increased within the early life of shiptracks, with the formation of shiptracks in otherwise clear skies indicating that around 5–10 % of clear-sky cases in this region may be aerosol-limited. The liquid water path (LWP) enhancement and the Nd-LWP sensitivity are also time dependent and strong functions of the background cloud and meteorological state. The near-instant response of the LWP within shiptracks may be evidence of a retrieval bias in previous estimates of the LWP response to aerosol derived from natural experiments. These results highlight the importance of temporal development and the background cloud field for quantifying the aerosol impact on clouds, even in situations where the aerosol perturbation is clear.

Direct Retrieval Bias for Categoric and Specific Memories for Negatively-Valenced Cues in Major Depression

Background and Objectives: Major depressive disorder (MDD) is associated with a tendency to retrieve general autobiographical memories, in particular more categoric memories and less specific memories. Autobiographical memories are retrieved via generative retrieval methods involving an effortful search, or direct retrieval methods whereby the memory immediately comes to mind. It has been argued that the tendency for general memories in depression occurs through a failure of generative retrieval, regardless of valence of cue word. However, we propose that categoric memories might be more likely to be recalled via direct retrieval, and direct retrieval is more likely for negatively-valenced cues. Methods: A large sample of individuals with MDD (N=298; M age=47.2, SD=12.7) completed the Autobiographical Memory Test (AMT) and indicated whether retrievals were generative or direct. Results: Categoric memories for negatively-valenced cues were more likely to be directly retrieved than generatively retrieved, and more likely than direct retrieval for positively-valenced cues. In addition, categoric memories for positively-valenced cues were more likely to be generatively retrieved relative to generative retrieval for negatively-valenced cues. For specific memories, the results followed the same pattern. Relative to non-clinical samples, direct retrieval for negative-valenced cues in MDD was high. Limitations: Future studies might include non-clinical groups and use alternate AMT instructionsConclusions: Negative categoric and specific memories were often direct representations of experiences in MDD. Retrieval method and valence may be important moderating processes in the type of memories that are recalled, and indicate a possible need to expand current theory on retrieval tendencies in MDD.

Sensitivity of Optimal Estimation Satellite Retrievals to Misspecification of the Prior Mean and Covariance, with Application to OCO-2 Retrievals

Optimal Estimation (OE) is a popular algorithm for remote sensing retrievals, partly due to its explicit parameterization of the sources of error and the ability to propagate them into estimates of retrieval uncertainty. These properties require specification of the prior distribution of the state vector. In many remote sensing applications, the true priors are multivariate and hard to characterize properly. Instead, priors are often constructed based on subject-matter expertise, existing empirical knowledge, and a need for computational expediency, resulting in a “working prior.” This paper explores the retrieval bias and the inaccuracy in retrieval uncertainty caused by explicitly separating the true prior (the probability distribution of the underlying state) from the working prior (the probability distribution used within the OE algorithm), with an application to Orbiting Carbon Observatory-2 (OCO-2) retrievals. We find that, in general, misspecifying the mean in the working prior will lead to biased retrievals, and misspecifying the covariance in the working prior will lead to inaccurate estimates of the retrieval uncertainty, though their effects vary depending on the state-space signal-to-noise ratio of the observing instrument. Our results point towards some attractive properties of a class of uninformative priors that is implicit for least-squares retrievals. Furthermore, our derivations provide a theoretical basis, and an understanding of the trade-offs involved, for the practice of inflating a working-prior covariance in order to reduce the prior’s impact on a retrieval (e.g., for OCO-2 retrievals). Finally, our results also lead to practical recommendations for specifying the prior mean and the prior covariance in OE.

Radiance-based retrieval bias mitigation for the MOPITT instrument: the version 8 product

The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument has been making nearly continuous observations of atmospheric carbon monoxide (CO) since 2000. Satellite observations of CO are routinely used to analyze emissions from fossil fuels and biomass burning, as well as the atmospheric transport of those emissions. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the version 8 (V8) product. V8 products benefit from updated spectroscopic data for water vapor and nitrogen used to develop the operational radiative transfer model and exploit a new method for minimizing retrieval biases through parameterized radiance bias correction. In situ datasets used for algorithm development and validation include the NOAA (National Oceanic and Atmospheric Administration) and HIPPO (HIAPER Pole-to-Pole Observations) datasets used for earlier MOPITT validation work in addition to measurements from the ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems – Cloud processes of the main precipitation systems in Brazil: A contribution to cloud resolving modeling and to the GPM (Global Precipitation Measurement)), KORUS-AQ (The Korea-United States Air Quality Study), and ATom (The Atmospheric Tomography Mission) programs. Validation results illustrate clear improvements with respect to long-term bias drift and geographically variable retrieval bias. For example, whereas bias drift for the V7 thermal-infrared (TIR)-only product exceeded 0.5 % yr−1 for levels in the upper troposphere (e.g., at 300 hPa), bias drift for the V8 TIR-only product is found to be less than 0.1 % yr−1 at all levels. Also, whereas upper-tropospheric (300 hPa) retrieval bias in the V7 TIR-only product exceeded 10 % in the tropics, corresponding V8 biases are less than 5 % (in terms of absolute value) at all latitudes and do not exhibit a clear latitudinal dependence.

Uncertainty of Reference Pixel Soil Moisture Averages Sampled at SMAP Core Validation Sites

Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.