Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations

Li, Jing; Li, Xichen; Carlson, Barbara E.; Kahn, Ralph A.; Lacis, Andrew A.; Dubovik, Оleg; Nakajima, Teruyuki

doi:10.1002/2016jd025469

Cited by 25 publications

(39 citation statements)

References 72 publications

(85 reference statements)

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“…They found that the new 4.4 km product performs better in comparison with the DRAGON data, which they attribute to the higher-resolution algorithm being better able to capture the true spatial variability of aerosols. Li et al (2016) have studied the AERONET locations using multi-sensor satellite data and an ensemble Kalman filter approach. They analyzed the spatial representativeness of individual AERONET sites and found that this depends on the season and the dominant aerosol type.…”

Section: T H Virtanen Et Al: Collocation Mismatch Uncertaintiesmentioning

confidence: 99%

Collocation mismatch uncertainties in satellite aerosol retrieval validation

et al. 2018

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Abstract. Satellite-based aerosol products are routinely validated against ground-based reference data, usually obtained from sun photometer networks such as AERONET (AEROsol RObotic NETwork). In a typical validation exercise a spatial sample of the instantaneous satellite data is compared against a temporal sample of the point-like ground-based data. The observations do not correspond to exactly the same column of the atmosphere at the same time, and the representativeness of the reference data depends on the spatiotemporal variability of the aerosol properties in the samples. The associated uncertainty is known as the collocation mismatch uncertainty (CMU). The validation results depend on the sampling parameters. While small samples involve less variability, they are more sensitive to the inevitable noise in the measurement data. In this paper we study systematically the effect of the sampling parameters in the validation of AATSR (Advanced Along-Track Scanning Radiometer) aerosol optical depth (AOD) product against AERONET data and the associated collocation mismatch uncertainty. To this end, we study the spatial AOD variability in the satellite data, compare it against the corresponding values obtained from densely located AERONET sites, and assess the possible reasons for observed differences.We find that the spatial AOD variability in the satellite data is approximately 2 times larger than in the ground-based data, and the spatial variability correlates only weakly with that of AERONET for short distances. We interpreted that only half of the variability in the satellite data is due to the natural variability in the AOD, and the rest is noise due to retrieval errors. However, for larger distances (∼ 0.5 • ) the correlation is improved as the noise is averaged out, and the day-to-day changes in regional AOD variability are well captured. Furthermore, we assess the usefulness of the spatial variability of the satellite AOD data as an estimate of CMU by comparing the retrieval errors to the total uncertainty estimates including the CMU in the validation. We find that accounting for CMU increases the fraction of consistent observations.

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Section: T H Virtanen Et Al: Collocation Mismatch Uncertaintiesmentioning

confidence: 99%

Collocation mismatch uncertainties in satellite aerosol retrieval validation

et al. 2018

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“…On average, out of the 135 sites, 122 sites show bias decreases, 128 show RMSE decreases, and 110 show correlation increases. The greatest changes are found in South America and Southeast Asia, corresponding well to places with high spatial representativeness ( (Li et al, 2016)). The United States and Europe also show moderate changes, mainly due to the high site density there.…”

Section: Validation Resultsmentioning

confidence: 99%

“…We therefore further develop an ensemble Kalman filter (EnKF)-based technique to effectively combine satellite and ground observations. This work is a direct follow-up of (Li et al, 2016). In that paper, we constructed a multidata set ensemble and examined the changes in the background error covariance after assimilating ground-based observations using the EnKF technique.…”

Section: Introductionmentioning

confidence: 99%

Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

Kahn

Wei

et al. 2020

JGR Atmospheres

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Satellite‐ and ground‐based remote sensing are two widely used techniques to measure aerosol properties. However, neither is perfect in that satellite retrievals suffer from various sources of uncertainties, and ground observations have limited spatial coverage. In this study, focusing on improving estimates of aerosol information on large scale, we develop a data synergy technique based on the ensemble Kalman filter (EnKF) to effectively combine these two types of measurements and yield a monthly mean aerosol optical depth (AOD) product with global coverage and improved accuracy. We first construct a 474‐member ensemble using 11 monthly mean AOD data sets to represent the variability of the AOD field. Then Moderate Resolution Imaging Spectroradiometer AOD retrievals are selected as the background field into which ground‐based measurements from 135 Aerosol Robotic Network sites are assimilated using the EnKF. Compared with satellite data, the bias and root‐mean‐square errors of the combined field are greatly reduced, and correlation coefficients are greatly improved. Moreover, cross validation shows that at locations where surface observations were not assimilated, the reduction in root‐mean‐square error and bias and the increase in correlation can still reach ~20%. Locations where the spatial representativeness of AOD is large or the site density is high are where the greatest changes are typically found. This study shows that the EnKF technique effectively extends the information obtained at surface sites to a larger area, paving the way for combining information from different types of measurements to yield better estimates of aerosol properties as well as their space‐time variability.

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“…Newly developed dust optical depth products such as those from infrared high-spectral sensors (e.g., Infrared Atmospheric Sounding Interferometer (IASI); Klüser et al, 2012;Peyridieu et al, 2013;Capelle et al, 2014) or those produced with the Generalized Retrieval of Aerosol and Surface Properties (GRASP) algorithm (Chen et al, 2018) are promising but have more limited space-time coverage. In addition, location of AERONET stations closer to source regions (as discussed in Li et al, 2016) would allow evaluation of models and satellite retrievals near the source (e.g., the short-term deployment during the Fennec field campaign; , and retrievals from such observations should in future account for particles with diameters exceeding 30 µm (Ryder et al, 2013).…”

Section: User Requirements For Desert Mineral Dust Emissionsmentioning

confidence: 99%

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

et al. 2018

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Abstract. Numerical prediction of aerosol particle properties has become an important activity at many research and operational weather centers. This development is due to growing interest from a diverse set of stakeholders, such as air quality regulatory bodies, aviation and military authorities, solar energy plant managers, climate services providers, and health professionals. Owing to the complexity of atmospheric aerosol processes and their sensitivity to the underlying meteorological conditions, the prediction of aerosol particle concentrations and properties in the numerical weather prediction (NWP) framework faces a number of challenges. The modeling of numerous aerosol-related parameters increases computational expense. Errors in aerosol prediction concern all processes involved in the aerosol life cycle including (a) errors on the source terms (for both anthropogenic and natural emissions), (b) errors directly dependent on the meteorology (e.g., mixing, transport, scavenging by precipitation), and (c) errors related to aerosol chemistry (e.g., nucleation, gas-aerosol partitioning, chemical transformation and growth, hygroscopicity). Finally, there are fundamental uncertainties and significant processing overhead in the diverse observations used for verification and assimilation within these systems. Indeed, a significant component of aerosol forecast development consists in streamlining aerosol-related observations and reducing the most important errors through model development and data assimilation. Aerosol particle observations from satellite-and groundbased platforms have been crucial to guide model development of the recent years and have been made more readily available for model evaluation and assimilation. However, for the sustainability of the aerosol particle prediction activities around the globe, it is crucial that quality aerosol observations continue to be made available from different platforms (space, near surface, and aircraft) and freely shared. This paper reviews current requirements for aerosol observations in the context of the operational activities carried out at various global and regional centers. While some of the requirements are equally applicable to aerosol-climate, the focus here is on global operational prediction of aerosol properties such as mass concentrations and optical parameters. It is also recognized that the term "requirements" is loosely used here given the diversity in global aerosol observing systems and that utilized data are typically not from operational sources. Most operational models are based on bulk schemes that do not predict the size distribution of the aerosol particles. Others are based on a mix of "bin" and bulk schemes with limited capability of simulating the size information. However the next generation of aerosol operational models will output both mass and number density concentration to provide a more complete description of the aerosol population. A brief overview of the state of the art is provided with an introduction on the importance of ...

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Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations

Cited by 25 publications

References 72 publications

Collocation mismatch uncertainties in satellite aerosol retrieval validation

Collocation mismatch uncertainties in satellite aerosol retrieval validation

Synergy of Satellite‐ and Ground‐Based Aerosol Optical Depth Measurements Using an Ensemble Kalman Filter Approach

Status and future of numerical atmospheric aerosol prediction with a focus on data requirements

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