Differences In Aerosol Optical Depth Research Articles

High‐Latitude Stratospheric Aerosol Geoengineering Can Be More Effective if Injection Is Limited to Spring

AbstractStratospheric aerosol geoengineering focused on the Arctic could substantially reduce local and worldwide impacts of anthropogenic global warming. Because the Arctic receives little sunlight during the winter, stratospheric aerosols present in the winter at high latitudes have little impact on the climate, whereas stratospheric aerosols present during the summer achieve larger changes in radiative forcing. Injecting SO2 in the spring leads to peak aerosol optical depth (AOD) in the summer. We demonstrate that spring injection produces approximately twice as much summer AOD as year‐round injection and restores approximately twice as much September sea ice, resulting in less increase in stratospheric sulfur burden, stratospheric heating, and stratospheric ozone depletion per unit of sea ice restored. We also find that differences in AOD between different seasonal injection strategies are small compared to the difference between annual and spring injection.

Model physics and chemistry causing intermodel disagreement within the VolMIP-Tambora Interactive Stratospheric Aerosol ensemble

Abstract. As part of the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP), several climate modeling centers performed a coordinated pre-study experiment with interactive stratospheric aerosol models simulating the volcanic aerosol cloud from an eruption resembling the 1815 Mt. Tambora eruption (VolMIP-Tambora ISA ensemble). The pre-study provided the ancillary ability to assess intermodel diversity in the radiative forcing for a large stratospheric-injecting equatorial eruption when the volcanic aerosol cloud is simulated interactively. An initial analysis of the VolMIP-Tambora ISA ensemble showed large disparities between models in the stratospheric global mean aerosol optical depth (AOD). In this study, we now show that stratospheric global mean AOD differences among the participating models are primarily due to differences in aerosol size, which we track here by effective radius. We identify specific physical and chemical processes that are missing in some models and/or parameterized differently between models, which are together causing the differences in effective radius. In particular, our analysis indicates that interactively tracking hydroxyl radical (OH) chemistry following a large volcanic injection of sulfur dioxide (SO2) is an important factor in allowing for the timescale for sulfate formation to be properly simulated. In addition, depending on the timescale of sulfate formation, there can be a large difference in effective radius and subsequently AOD that results from whether the SO2 is injected in a single model grid cell near the location of the volcanic eruption, or whether it is injected as a longitudinally averaged band around the Earth.

Correction of a lunar-irradiance model for aerosol optical depth retrieval and comparison with a star photometer

Abstract. The emergence of Moon photometers is allowing measurements of lunar irradiance over the world and increasing the potential to derive aerosol optical depth (AOD) at night-time, which is very important in polar areas. Actually, new photometers implement the latest technological advances that permit lunar-irradiance measurements together with classical Sun photometry measurements. However, a proper use of these instruments for AOD retrieval requires accurate time-dependent knowledge of the extraterrestrial lunar irradiance over time due to its fast change throughout the Moon's cycle. This paper uses the RIMO (ROLO Implementation for Moon's Observation) model (an implementation of the ROLO – RObotic Lunar Observatory – model) to estimate the AOD at night-time assuming that the calibration of the solar channels can be transferred to the Moon by a vicarious method. However, the obtained AOD values using a Cimel CE318-T Sun–sky–Moon photometer for 98 pristine nights with low and stable AOD at the Izaña Observatory (Tenerife, Spain) are not in agreement with the expected (low and stable) AOD values estimated by linear interpolations from daytime values obtained during the previous evening and the following morning. Actually, AOD calculated using RIMO shows negative values and with a marked cycle dependent on the optical air mass. The differences between the AOD obtained using RIMO and the expected values are assumed to be associated with inaccuracies in the RIMO model, and these differences are used to calculate the RIMO correction factor (RCF). The RCF is a proposed correction factor that, multiplied by the RIMO value, gives an effective extraterrestrial lunar irradiance that provides AOD closer to the expected values. The RCF varies with the Moon phase angle (MPA) and with wavelength, ranging from 1.01 to 1.14, which reveals an overall underestimation of RIMO compared to the lunar irradiance. These obtained RCF values are modelled for each photometer wavelength to a second-order polynomial as a function of MPA. The AOD derived by this proposed method is compared with the independent AOD measurements obtained by a star photometer at Granada (Spain) for 2 years. The mean of the Moon–star AOD differences is between −0.015 and −0.005, and the standard deviation (SD) is between 0.03 and 0.04 (which is reduced to about 0.01 if 1 month of data affected by instrumental issues is not included in the analysis) for 440, 500, 675, and 870 nm; however, for 380 nm, the mean and standard deviation of these differences are higher. The Moon–star AOD differences are also analysed as a function of MPA, showing no significant dependence.

Daytime and nighttime aerosol optical depth implementation in CÆLIS

Abstract. The University of Valladolid (UVa, Spain) has managed a calibration center of the AErosol RObotic NETwork (AERONET) since 2006. The CÆLIS software tool, developed by UVa, was created to manage the data generated by AERONET photometers for calibration, quality control and data processing purposes. This paper exploits the potential of this tool in order to obtain products like the aerosol optical depth (AOD) and Ångström exponent (AE), which are of high interest for atmospheric and climate studies, as well as to enhance the quality control of the instruments and data managed by CÆLIS. The AOD and cloud screening algorithms implemented in CÆLIS, both based on AERONET version 3, are described in detail. The obtained products are compared with the AERONET database. In general, the differences in daytime AOD between CÆLIS and AERONET are far below the expected uncertainty of the instrument, ranging in mean differences between -1.3×10-4 at 870 nm and 6.2×10-4 at 380 nm. The standard deviations of the differences range from 2.8×10-4 at 675 nm to 8.1×10-4 at 340 nm. The AOD and AE at nighttime calculated by CÆLIS from Moon observations are also presented, showing good continuity between day and nighttime for different locations, aerosol loads and Moon phase angles. Regarding cloud screening, around 99.9 % of the observations classified as cloud-free by CÆLIS are also assumed cloud-free by AERONET; this percentage is similar for the cases considered cloud-contaminated by both databases. The obtained results point out the capability of CÆLIS as a processing system. The AOD algorithm provides the opportunity to use this tool with other instrument types and to retrieve other aerosol products in the future.

Spectral Aerosol Optical Depth Retrievals by Ground-Based Fourier Transform Infrared Spectrometry

Aerosol Optical Depth (AOD) and the Ångström Exponent (AE) have been calculated in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions over a period of one year (May 2019–May 2020) at the high-mountain Izaña Observatory (IZO) from Fourier Transform Infrared (FTIR) solar spectra. The high-resolution FTIR measurements were carried out coincidentally with Cimel CE318-T photometric observations in the framework of the Aerosol Robotic Network (AERONET). A spectral FTIR AOD was generated using two different approaches: by means of the selection of seven narrow FTIR micro-windows (centred at 1020.90, 1238.25, 1558.25, 1636.00, 2133.40, 2192.00, and 2314.20 nm) with negligible atmospheric gaseous absorption, and by using the CE318-AERONET’s response function in the near-coincident bands (1020 nm and 1640 nm) to degrade the high-resolution FTIR spectra. The FTIR system was absolutely calibrated by means of a continuous Langley–Plot analysis over the 1-year period. An important temporal drift of the calibration constant was observed as a result of the environmental exposure of the FTIR’s external optical mirrors (linear degradation rate up to 1.75% month−1). The cross-validation of AERONET-FTIR databases documents an excellent agreement between both AOD products, with mean AOD differences below 0.004 and root-mean-squared errors below 0.006. A rather similar agreement was also found between AERONET and FTIR convolved bands, corroborating the suitability of low-resolution sunphotometers to retrieve high-quality AOD data in the NIR and SWIR domains. In addition, these results demonstrate that the methodology developed here is suitable to be applied to other FTIR spectrometers, such as portable and low-resolution FTIR instruments with a potentially higher spatial coverage. The spectral AOD dependence for the seven FTIR micro-windows have been also examined, observing a spectrally flat AOD behaviour for mineral dust particles (the typical atmospheric aerosols presented at IZO). A mean AE value of 0.53 ± 0.08 for pure mineral dust in the 1020–2314 nm spectral range was retrieved in this paper. A subsequent cross-validation with the MOPSMAP (Modeled optical properties of ensembles of aerosol particles) package has ensured the reliability of the FTIR dataset, with AE values between 0.36 to 0.60 for a typical mineral dust content at IZO of 100 cm−3 and water-soluble particle (WASO) content ranging from 600 to 6000 cm−3. The new database generated in this study is believed to be the first long-term time series (1-year) of aerosol properties generated consistently in the NIR and SWIR ranges from ground-based FTIR spectrometry. As a consequence, the results presented here provide a very promising tool for the validation and subsequent improvement of satellite aerosol products as well as enhance the sensitivity to large particles of the existing databases, required to improve the estimation of the aerosols’ radiative effect on climate.

Comparison of V4 and V3 CALIOP (L3) aerosol products: A global perspective

Gridded satellite products provide (computationally less-intense) opportunities to compare and contrast parameters from different versions/sensors. In the present study, Level-3 (L3) Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol products from the most recent release (version 4, V4) are compared with its earlier version (version 3, V3) products. The various improvements in V4 are detailed and the resultant changes in aerosol optical depth (AOD) and aerosol vertical extinction profiles for various aerosol species (total aerosol, dust, polluted dust, smoke) over various regions are discussed. Broadly, V4 global AOD is higher than the V3 AOD (except for polluted dust). Significant differences in V4 and V3 AODs are observed over Africa, East Asia, Atlantic Ocean, Arabian Sea, Bay of Bengal and South America. Seasonal-wise global mean percentage difference in V4 and V3 total-AODs (with respect to V4 AODs) ranged between 9% and 15%. The change in the spatial distribution of polluted dust over oceanic regions is one of the significant differences between V4 and V3. V4 total-AODs are closer to Moderate Resolution Imaging Spectroradiometer (MODIS) AODs than V3 total-AODs are. The shape of the aerosol extinction profiles also differed between V4 and V3. The altitudinal differences in the V4 and V3 extinction values varied spatio-temporally.

Aerosol pattern changes over the dead sea from west to east - Using high-resolution satellite data

The area of the Dead Sea, a terminal lake on the border between Israel and Jordan, has gone through extensive environmental changes in recent decades, stemming from several processes including extreme evaporation and land degradation. Our study explored the distribution and the long-term high-resolution climatology of the Dead Sea aerosols, using satellite-retrieved Aerosol Optical Depth (AOD) measurements. We used the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm that provides AOD measurements at a 1 km resolution. MAIAC retrievals for the entire study period (2000–2016) have shown a very good agreement (r = 0.8) with the nearby AERONET station. For the first time, the AOD temporal distribution and its variability, on both Dead Sea coasts, were calculated on a monthly/seasonal basis. Three main aerosol pollution patterns (i.e. high-levels of AOD) emerged as follows: western focalized (75 and 59% of the months examined for Aqua and Terra, respectively), eastern focalized (14 and 24%), and similar levels of pollution on both coasts (11 and 17%, respectively). Using the Normalized AOD Difference (NAODD) metric, we studied how the AOD spatial patterns changed and when. Following the negative trends in most of the months, our analysis has demonstrated a shift towards unexplained increasing pollution levels over the Jordanian eastern side. For March–April, this shift had already occurred, and the potential reasons including changes in synoptic regimes are discussed. Local and global implications are of much regional interest because of the Dead Sea deterioration and the need for urgent environmental policy changes.

Aerosol retrievals from the EKO MS-711 spectral direct irradiance measurements and corrections of the circumsolar radiation

Abstract. Spectral direct UV–visible normal solar irradiance (DNI) has been measured with an EKO MS-711 grating spectroradiometer, which has a spectral range of 300–1100 nm, and 0.4 nm step, at the Izaña Atmospheric Observatory (IZO, Spain). It has been used to determine aerosol optical depth (AOD) at several wavelengths (340, 380, 440, 500, 675, and 870 nm) between April and September 2019, which has been compared with synchronous AOD measurements from a reference Cimel and Aerosol RObotic NETwork (AERONET) sun photometer. The EKO MS-711 has been calibrated at the Izaña Atmospheric Observatory by using the Langley plot method during the study period. Although this instrument has been designed for spectral solar DNI measurements, and therefore has a field of view (FOV) of 5∘ that is twice the recommended amount in solar photometry for AOD determination, the AOD differences compared to the AERONET–Cimel reference instrument (FOV ∼1.2∘) are fairly small. A comparison of the results from the Cimel AOD and EKO MS-711 AOD presents a root mean square (rms) of 0.013 (24.6 %) at 340 and 380 nm, and 0.029 (19.5 %) for longer wavelengths (440, 500, 675, and 870 nm). However, under relatively high AOD, near-forward aerosol scattering might be significant because of the relatively large circumsolar radiation (CSR) due to the large EKO MS-711 FOV, which results in a small but significant AOD underestimation in the UV range. The AOD differences decrease considerably when CSR corrections, estimated from libRadtran radiative transfer model simulations, are performed and obtain an rms of 0.006 (14.9 %) at 340 and 380 nm, and 0.005 (11.1 %) for longer wavelengths. The percentage of 2 min synchronous EKO AOD–Cimel AOD differences within the World Meteorological Organization (WMO) traceability limits were ≥96 % at 500, 675, and 870 nm with no CSR corrections. After applying the CSR corrections, the percentage of AOD differences within the WMO traceability limits increased to >95 % for 380, 440, 500, 675, and 870 nm, while for 340 nm the percentage of AOD differences showed a poorer increase from 67 % to a modest 86 %.

Aerosol and Its Radiative Effects during the Aeroradcity 2018 Moscow Experiment

During the AeroRadCity-2018 spring aerosol experiment at the Moscow State University Meteorological Observatory the aerosol properties of the atmosphere and radiative aerosol effects were analyzed using a wide complex of measurements and model COSMO-ART simulations over Moscow domain. The program of measurements consisted of columnar aerosol AERONET retrievals, surface PM10, black carbon (BC) and aerosol gas precursors mass concentrations, as well as radiative measurements under various meteorological conditions. We obtained a positive statistically significant dependence of total and fine aerosol optical depth (AOD) mode (R2 ~0.4) with PM concentrations. This dependence has revealed a pronounced bifurcation point around PM10=0.04 mgm-3. The modelled BC concentration is in agreement with the observations and has a pronounced correlation with PM, but not with the AODs. The analysis of radiative effects of aerosol has revealed up to 30% loss for UV irradiance and 15% - for shortwave irradiance at high AOD in Moscow. Much intensive radiation attenuation is observed in the afternoon when remote pollution sources may affect solar fluxes at elevated boundary layer conditions. Negative (cooling) radiative forcing effect at the top of the atmosphere from -18 Wm-2 to -4 Wm-2 has been evaluated. Mean difference in visible AOD between urban and background conditions in Moscow and Zvenigorod was about 0.01 according to measurements and model simulations, while in some days the difference may increase up to 0.05. The generation of urban aerosol was shown to be more favorable in conditions with low intensity of pollutant dispersion, when mean deltaAOD550 was doubled from 0.01 to 0.02.

Evaluation and improvement of MODIS aerosol optical depth products over China

In this study, we use the level-1.5 and level-2.0 aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) Version 3 dataset at 12 sites in China to evaluate the MODIS Collection 6.1 (C6.1) AOD products retrieved using three distinct algorithms: Dark Target (DT), Deep Blue (DB), and DTB merged from DT and DB from Terra and Aqua. The performance of the three algorithms is evaluated. Based on these evaluations, a simple and efficient AOD retrieval algorithm at a high spatial resolution of 1.0 km for China is proposed. AODs at this spatial resolution over China are then retrieved using the methods of DT and DB, respectively. The evaluation results showed that: (1) there is little difference in the AOD products derived from Terra and Aqua. The differences in the determination coefficient (R2) between the two platforms at most sites are less than 0.05. (2) There are relatively large differences in AODs between the AERONET level-1.5 dataset and MODIS dataset while these differences are mostly filtered out by the quality assurance process of the AERONET level-2.0 dataset. The statistical tests indicate that the MODIS DTB AOD product is generally better than those from the other two algorithms. Using the new algorithm developed in this study, the AODs at a high spatial resolution of 1 km in the whole of China are determined and the results are compared with the MODIS DTB product. The results show that the AODs determined using the new method agree reasonably well with those of the MODIS DTB dataset though the new results have slightly negative biases in the wintertime. However, these negative biases may not be a negative sign due to the fact that the MODIS AODs are subject to a positive bias relative to the AERONET AODs.

How should we aggregate data? Methods accounting for the numerical distributions, with an assessment of aerosol optical depth

Abstract. Many applications of geophysical data – whether from surface observations, satellite retrievals, or model simulations – rely on aggregates produced at coarser spatial (e.g. degrees) and/or temporal (e.g. daily and monthly) resolution than the highest available from the technique. Almost all of these aggregates report the arithmetic mean and standard deviation as summary statistics, which are what data users employ in their analyses. These statistics are most meaningful for normally distributed data; however, for some quantities, such as aerosol optical depth (AOD), it is well-known that distributions are on large scales closer to log-normal, for which a geometric mean and standard deviation would be more appropriate. This study presents a method of assessing whether a given sample of data is more consistent with an underlying normal or log-normal distribution, using the Shapiro–Wilk test, and tests AOD frequency distributions on spatial scales of 1∘ and daily, monthly, and seasonal temporal scales. A broadly consistent picture is observed using Aerosol Robotic Network (AERONET), Multiangle Imaging SpectroRadiometer (MISR), Moderate Resolution Imagining Spectroradiometer (MODIS), and Goddard Earth Observing System Version 5 Nature Run (G5NR) data. These data sets are complementary: AERONET has the highest AOD accuracy but is sparse, and MISR and MODIS represent different satellite retrieval techniques and sampling. As a model simulation, G5NR is spatiotemporally complete. As timescales increase from days to months to seasons, data become increasingly more consistent with log-normal than normal distributions, and the differences between arithmetic- and geometric-mean AOD become larger, with geometric mean becoming systematically smaller. Assuming normality systematically overstates both the typical level of AOD and its variability. There is considerable regional heterogeneity in the results: in low-AOD regions such as the open ocean and mountains, often the AOD difference is small enough (<0.01) to be unimportant for many applications, especially on daily timescales. However, in continental outflow regions and near source regions over land, and on monthly or seasonal timescales, the difference is frequently larger than the Global Climate Observation System (GCOS) goal uncertainty in a climate data record (the larger of 0.03 or 10 %). This is important because it shows that the sensitivity to an averaging method can and often does introduce systematic effects larger than the total goal GCOS uncertainty. Using three well-studied AERONET sites, the magnitude of estimated AOD trends is shown to be sensitive to the choice of arithmetic vs. geometric means, although the signs are consistent. The main recommendations from the study are that (1) the distribution of a geophysical quantity should be analysed in order to assess how best to aggregate it, (2) ideally AOD aggregates such as satellite level 3 products (but also ground-based data and model simulations) should report a geometric-mean or median AOD rather than (or in addition to) arithmetic-mean AOD, and (3) as this is unlikely in the short term due to the computational burden involved, users can calculate geometric-mean monthly aggregates from widely available daily mean data as a stopgap, as daily aggregates are less sensitive to the choice of aggregation scheme than those for monthly or seasonal aggregates. Furthermore, distribution shapes can have implications for the validity of statistical metrics often used for comparison and evaluation of data sets. The methodology is not restricted to AOD and can be applied to other quantities.

Climate Forcing and Trends of Organic Aerosols in the Community Earth System Model (CESM2)

AbstractThe Community Earth System Model version 2 (CESM2) includes three main atmospheric configurations: the Community Atmosphere Model version 6 (CAM6) with simplified chemistry and a simplified organic aerosol (OA) scheme, CAM6 with comprehensive tropospheric and stratospheric chemistry representation (CAM6‐chem), and the Whole Atmosphere Community Climate Model version 6 (WACCM6). Both, CAM6‐chem and WACCM6 include a more comprehensive secondary organic aerosols (SOA) approach using the Volatility Basis Set (VBS) scheme and prognostic stratospheric aerosols. This paper describes the different OA schemes available in the different atmospheric configurations of CESM2 and discusses differences in aerosol burden and resulting climate forcings. Derived OA burden and trends differ due to differences in OA formation using the different approaches. Regional differences in Aerosol Optical Depth with larger values using the comprehensive approach occur over SOA source regions. Stronger increasing SOA trends between 1960 and 2015 in WACCM6 compared to CAM6 are due to increasing biogenic emissions aligned with increasing surface temperatures. Using the comprehensive SOA approach further leads to improved comparisons to aircraft observations and SOA formation of ≈143 Tg/yr. We further use WACCM6 to identify source contributions of OA from biogenic, fossil fuel, and biomass burning emissions, to quantify SOA amounts and trends from these sources. Increasing SOA trends between 1960 and 2015 are the result of increasing biogenic emissions aligned with increasing surface temperatures. Biogenic emissions are at least two thirds of the total SOA burden. In addition, SOA source contributions from fossil fuel emissions become more important, with largest values over Southeast Asia. The estimated total anthropogenic forcing of OA in WACCM6 for 1995–2010 conditions is −0.43 W/m2, mostly from the aerosol direct effect.

Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

The classification of aerosol types using remote sensing tools is crucial for improving our knowledge on the impacts of aerosol species on global and regional climate. Ground-based classification methods have proven useful though they are limited by discontinuous spatial observations of aerosol optical properties. As such, there is a need to develop methods in aerosol species identification using satellite data. In this study, a 2D-space model suitable for MODIS AOD bands is developed to distinguish aerosol particle species based on aerosol optical depth (AOD) and aerosol relative optical depth (AROD). The model can be used to classify five aerosol types, including biomass burning (BB: AOD470 ≥ 0.45, AROD660/470 < 0.58), urban industry (UI: AOD470 ≥ 0.45, 0.68 > AROD660/470 ≥ 0.58), sub-continental (SB: AOD470 ≥ 0.45, 0.91 > AROD660/470 ≥ 0.68), desert dust (DD: AROD660/470 ≥ 0.91) and continental (CO: AOD470 < 0.45, AROD660/470 < 0.91). AROD values corresponding to different types of aerosols are calculated through Mie scattering theory using the aerosol particle size distribution parameters and spectral complex refractive index. The accuracy and applicability of the model are verified in three steps: 1) we compare aerosol discrimination results of model with ground-based data and the local atmospheric conditions over different regions, 2) we compare aerosol identification results from Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrieval and ground-based data, and 3) we test our model to validate its use in individual cases studies and regional observations. Our results demonstrate that aerosol species classification for each study region is consistent with the characteristics of their atmospheric conditions, and the classification results using MODIS AOD retrieval are similar to the ground-based observation results. The differences in MODIS versus AERONET AOD, caused by MODIS observation errors, is the major contributor to the differences between ground-based and satellite-based recognition results. Our three-steps validation results indicate that the 2D-space model developed in this paper can accurately identify different types of aerosols and has strong applicability. This provides a basis for the use of satellite data for continuous spatial identification of aerosol type, which greatly promotes research in related fields.

Aerosol optical depth comparison between GAW-PFR and AERONET-Cimel radiometers from long-term (2005–2015) 1 min synchronous measurements

Abstract. A comprehensive comparison of more than 70 000 synchronous 1 min aerosol optical depth (AOD) data from three Global Atmosphere Watch precision-filter radiometers (GAW-PFR), traceable to the World AOD reference, and 15 Aerosol Robotic Network Cimel radiometers (AERONET-Cimel), calibrated individually with the Langley plot technique, was performed for four common or “near” wavelengths, 380, 440, 500 and 870 nm, in the period 2005–2015. The goal of this study is to assess whether, despite the marked technical differences between both networks (AERONET, GAW-PFR) and the number of instruments used, their long-term AOD data are comparable and consistent. The percentage of data meeting the World Meteorological Organization (WMO) traceability requirements (95 % of the AOD differences of an instrument compared to the WMO standards lie within specific limits) is &gt;92 % at 380 nm, &gt;95 % at 440 nm and 500 nm, and 98 % at 870 nm, with the results being quite similar for both AERONET version 2 (V2) and version 3 (V3). For the data outside these limits, the contribution of calibration and differences in the calculation of the optical depth contribution due to Rayleigh scattering and O3 and NO2 absorption have a negligible impact. For AOD &gt;0.1, a small but non-negligible percentage (∼1.9 %) of the AOD data outside the WMO limits at 380 nm can be partly assigned to the impact of dust aerosol forward scattering on the AOD calculation due to the different field of view of the instruments. Due to this effect the GAW-PFR provides AOD values, which are ∼3 % lower at 380 nm and ∼2 % lower at 500 nm compared with AERONET-Cimel. The comparison of the Ångström exponent (AE) shows that under non-pristine conditions (AOD &gt;0.03 and AE &lt;1) the AE differences remain &lt;0.1. This long-term comparison shows an excellent traceability of AERONET-Cimel AOD with the World AOD reference at 440, 500 and 870 nm channels and a fairly good agreement at 380 nm, although AOD should be improved in the UV range.

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

The thermal environment is closely related to human well-being. Determinants of surface urban heat islands (SUHIs) have been extensively studied. Nevertheless, some research fields remain blank or have conflicting findings, which need to be further addressed. Particularly, few studies focus on drivers of SUHIs in massive cities with different sizes under various contexts at large scales. Using multisource data, we explored 11 determinants of surface urban heat island intensity (SUHII) for 1449 cities in different ecological contexts throughout China in 2010, adopting the Spearman and partial correlation analysis and machine learning method. The main results were as follows: (1) Significant positive partial correlations existed between daytime SUHII and the differences in nighttime light intensity and built-up intensity between cities and their corresponding villages except in arid or semiarid western China. The differences in the enhanced vegetation index were generally partially negatively correlated with daytime and nighttime SUHII. The differences in white sky albedo were usually partially negatively correlated with nighttime SUHII. The mean air temperature was partially positively correlated with nighttime SUHII in 40% of cases. Only a few significant partial relationships existed between SUHII and urban area, total population, and differences in aerosol optical depth. The explanation rates during daytime were larger than during nighttime in 72% of cases. The largest and smallest rates occurred during summer days in humid cold northeastern China (63.84%) and in southern China (10.44%), respectively. (2) Both the daytime and nighttime SUHII could be well determined by drivers using the machine learning method. The RMSE ranged from 0.49°C to 1.54°C at a national scale. The simulation SUHII values were always significantly correlated with the actual SUHII values. The simulation accuracies were always higher during nighttime than daytime. The highest accuracies occurred in central-northern China and were lowest in western China during both daytime and nighttime.

Evaluation of retrieved aerosol extinction profiles using as reference the aerosol optical depth differences between various heights

Aerosol extinction vertical profiles at Granada (Spain) are calculated with the GRASP (Generalized Retrieval of Aerosol and Surface Properties) code using as input Aerosol Optical Depth (AOD) and sky radiance measurements from AERONET (AEerosol RObotic NETwork) and ceilometer RCS (Range Corrected Signal) profiles, both corresponding to the Granada (Spain) station. This methodology is so called GRASPpac due to the combination of sun/sky photometer and ceilometer on GRASP. In order to evaluate the accuracy of these retrieved extinction profiles at Granada, two more nearby AERONET stations, located at different altitudes, are used. The AOD difference of the three choosen AERONET sun/sky photometers have been used to calculate the Integrated Aerosol Extinction (IAE) at different height layers. These three AERONET sun/sky photometers are used as a reference and compared against the integrated extinction at the same layers from the extinction profiles retrieved by GRASPpac. The differences between AERONET and GRASPpac retrieved IAE values indicate that GRASPpac aerosol extinction profiles are at least within the uncertainty of the sun/sky photometer measurements, but GRASPpac method overestimates the AERONET extinction at low altitudes and underestimates it at high levels. The most accurate and precise retrieved extinction correspond to the intermediate layer with a mean bias error (MBE ± standard deviation) of 0.00 ± 0.01 (0 ± 59%) for 1020 nm, and the worst integrated extinction results were obtained for the upper layers with a MBE of −0.01 ± 0.02 (28 ± 36%) for 1020 nm. In general these MBE values increases for shorter wavelengths. In order to obtain a complete characterization of this bias, the dependence of the obtained differences on the aerosol size and the solar zenith angle, among others, are analysed in detail. Finally, the behaviour of vertically-resolved aerosol extinction at Granada is evaluated using averages of the retrieved profiles from November of 2012 to December of 2017. The highest IAE values are found in Summer with mean values of 0.09 for the lower layers and 0.07 for the upper ones, both at 440 nm wavelength.

Spatial and Temporal Distribution of Aerosol Optical Depth and Its Relationship with Urbanization in Shandong Province

In the process of rapid urbanization, air environment quality has become a hot issue. Aerosol optical depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to monitor air pollution effectively. In this paper, the Spearman coefficient is used to analyze the correlations between AOD and urban development, construction factors, and geographical environment factors in Shandong Province. The correlation between AOD and local climatic conditions in Shandong Province is analyzed by geographic weight regression (GWR). The results show that in the time period from 2007 to 2017, the AOD first rose and then fell, reaching its highest level in 2012, which is basically consistent with the time when the national environmental protection decree was issued. In terms of quarterly and monthly changes, AOD also rose first and then fell, the highest level in summer, with the highest monthly value occurring in June. In term of the spatial distribution, the high-value area is located in the northwestern part of Shandong Province, and the low-value area is located in the eastern coastal area. In terms of social factors, the correlation between pollutant emissions and AOD is much greater the correlations between AOD and population, economy, and construction indicators. In terms of environmental factors, the relationship between digital elevation model (DEM), temperature, precipitation, and AOD is significant, but the regulation of air in coastal areas is even greater. Finally, it was found that there are no obvious differences in AOD among cities with different development levels, which indicates that urban development does not inevitably lead to air pollution. Reasonable development planning and the introduction of targeted environmental protection policies can effectively alleviate pollution-related problems in the process of urbanization.

Evaluation of night-time aerosols measurements and lunar irradiance models in the frame of the first multi-instrument nocturnal intercomparison campaign

The first multi-instrument nocturnal aerosol optical depth (AOD) intercomparison campaign was held at the high-mountain Izaña Observatory (Tenerife, Spain) in June 2017, involving 2-min synchronous measurements from two different types of lunar photometers (Cimel CE318-T and Moon Precision Filter Radiometer, LunarPFR) and one stellar photometer. The Robotic Lunar Observatory (ROLO) model developed by the U.S. Geological Survey (USGS) was compared with the open-access ROLO Implementation for Moon photometry Observation (RIMO) model. Results showed rather small differences at Izaña over a 2-month time period covering June and July, 2017 (±0.01 in terms of AOD calculated by means of a day/night/day coherence test analysis and ± 2% in terms of lunar irradiance). The RIMO model has been used in this field campaign to retrieve AOD from lunar photometric measurements.No evidence of significant differences with the Moon's phase angle was found when comparing raw signals of the six Cimel photometers involved in this field campaign.The raw signal comparison of the participating lunar photometers (Cimel and LunarPFR) performed at coincident wavelengths showed consistent measurements and AOD differences within their combined uncertainties at 870 nm and 675 nm. Slightly larger AOD deviations were observed at 500 nm, pointing to some unexpected instrumental variations during the measurement period.Lunar irradiances retrieved using RIMO for phase angles varying between 0° and 75° (full Moon to near quarter Moon) were compared to the irradiance variations retrieved by Cimel and LunarPFR photometers. Our results showed a relative agreement within ± 3.5% between the RIMO model and the photometer-based lunar irradiances.The AOD retrieved by performing a Langley-plot calibration each night showed a remarkable agreement (better than 0.01) between the lunar photometers. However, when applying the Lunar-Langley calibration using RIMO, AOD differences of up to 0.015 (0.040 for 500 nm) were found, with differences increasing with the Moon's phase angle. These differences are thought to be partly due to the uncertainties in the irradiance models, as well as instrumental deficiencies yet to be fully understood.High AOD variability in stellar measurements was detected during the campaign. Nevertheless, the observed AOD differences in the Cimel/stellar comparison were within the expected combined uncertainties of these two photometric techniques. Our results indicate that lunar photometry is a more reliable technique, especially for low aerosol loading conditions.The uncertainty analysis performed in this paper shows that the combined standard AOD uncertainty in lunar photometry is dependent on the calibration technique (up to 0.014 for Langley-plot with illumination-based correction, 0.012–0.022 for Lunar-Langley calibration, and up to 0.1 for the Sun-Moon Gain Factor method). This analysis also corroborates that the uncertainty of the lunar irradiance model used for AOD calculation is within the 5–10% expected range.This campaign has allowed us to quantify the important technical difficulties that still exist when routinely monitoring aerosol optical properties at night-time. The small AOD differences observed between the three types of photometers involved in the campaign are only detectable under pristine sky conditions such as those found in this field campaign. Longer campaigns are necessary to understand the observed discrepancies between instruments as well as to provide more conclusive results about the uncertainty involved in the lunar irradiance models.

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA.

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30-210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

Comparison of aerosol optical depth between observation and simulation from MIROC-SPRINTARS: Effects of temporal inhomogeneous sampling

The global distribution of aerosol optical depth (AOD) is simulated using an aerosol transport model coupled to an atmospheric general circulation model with high spatial and temporal resolution. Daily representative AOD from model simulation is estimated after consideration of observation sampling in daytime (ground-based) and overpass time (satellite) after cloud masking. Large deviations in AOD are found after considering temporally inhomogeneous sampling, with positive differences over desert regions and negative differences over anthropogenic pollution and biomass burning regions. Mean difference in daily AOD of 5.33% (standard deviation of 8.02%), because of temporal inhomogeneous sampling, is identified based on observation time information from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Relative differences in AOD of >50% and >30% were found in 7.9% and 22.8% of the data, respectively. Based on the observation time information from the AERONET, relative root mean square error (rRMSE) of AOD due to temporal inhomogeneous sampling is estimated to be 4.30–18.66%. After correcting for temporal sampling inhomogeneity, the simulated global AOD was compared with AODs from MODIS and AERONET. The simulated AOD becomes lower than MODIS AOD because of emission and transport discrepancies related to dust, a limited accounting of nitrate processes, and limitation errors from MODIS AOD retrieval. A regional positive bias in SPRINTARS AOD was found in biomass burning regions, which is due to transport pattern errors related to the initial injection height of emissions. A weak correlation is found over the regions with multiple aerosol sources because of complex interactions of individual aerosol types.