Aerosol Optical Depth Correlation Research Articles

Intercomparison of aerosol optical depths from four reanalyses and their multi-reanalysis consensus

Abstract. The emergence of aerosol reanalyses in recent years has facilitated a comprehensive and systematic evaluation of aerosol optical depth (AOD) trends and attribution over multi-decadal timescales. Notable multi-year aerosol reanalyses currently available include NAAPS-RA from the US Naval Research Laboratory, the NASA MERRA-2, JRAero from the Japan Meteorological Agency (JMA), and CAMSRA from Copernicus/ECMWF. These aerosol reanalyses are based on differing underlying meteorology models, representations of aerosol processes, as well as data assimilation methods and treatment of AOD observations. This study presents the basic verification characteristics of these four reanalyses versus both AERONET and MODIS retrievals in monthly AOD properties and identifies the strength of each reanalysis and the regions where divergence and challenges are prominent. Regions with high pollution and often mixed fine-mode and coarse-mode aerosol environments, such as South Asia, East Asia, Southeast Asia, and the Maritime Continent, pose significant challenges, as indicated by higher monthly AOD root mean square error. Moreover, regions that are distant from major aerosol source areas, including the polar regions and remote oceans, exhibit large relative differences in speciated AODs and fine-mode versus coarse-mode AODs among the four reanalyses. To ensure consistency across the globe, a multi-reanalysis consensus (MRC, i.e., ensemble mean) approach was developed similarly to the International Cooperative for Aerosol Prediction Multi-Model Ensemble (ICAP-MME). Like the ICAP-MME, while the MRC does not consistently rank first among the reanalyses for individual regions, it performs well by ranking first or second globally in AOD correlation and RMSE, making it a suitable candidate for climate studies that require robust and consistent assessments.

Aerosol radiative forcing of forest fires unprecedented in South Korea (2022) captured by Korean geostationary satellites, GK-2A AMI and GK-2B GEMS

The worst forest fires in Korean history broke out on March 4, 2022 and lasted for ten days. In order to monitor the catastrophic forest fires, Geostationary Korea Multi-Purpose Satellite (GK)-2 A Advanced Meteorological Imager (AMI) and GK-2B Geostationary Environment Monitoring Spectrometer (GEMS) data were used in this study. Aerosol optical depth (AOD) irretrievable for the biomass-burning aerosols produced with water vapor classified as could-contaminated, was reconstructed by ultraviolet aerosol index (UVAI). Afterward, aerosol radiative forcing (ARF) at TOA was finally estimated by the correlation of AOD and surface albedo with ARF. Most of the aerosols drifted toward the East Sea by the prevailing westerly winds, and caused a cooling effect on the atmosphere with a maximum daily average radiative forcing of −69.28 Wm-2. Furthermore, the fire-prone conditions for the unprecedented forest fires were discussed in detail as following aspects; 1) the most severe drought caused by a “triple-dip” La Niña; 2) pressure patterns and topographical features that generate strong winds; 3) coniferous forests prone to fires; and 4) increased human activity following the nationwide COVID-19 vaccination. This study demonstrated that the rapid and effective ARF estimation based on the satellite remote sensing can contribute to a better understanding of ARF in the Earth's radiation budget for the global forest fires that will be more frequent, intense, and longer-lasting due to the human-caused climate and environment changes.

Analysis of aerosol optical depth and relation to covariates during pre-monsoon season (2002–2019) over Pakistan using ARIMAX model and cross-wavelet analysis

The pre-monsoon season heavily influences the precipitation amount in Pakistan. When hydrometeorological parameters interact with aerosols from multiple sources, a radiative climatic response is observed. In this study, aerosol optical depth (AOD) space-time dynamics were analyzed in relation to meteorological factors and surface parameters during the pre-monsoon season in the years 2002–2019 over Pakistan. Level-3 (L3) monthly datasets from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-Angle Imaging Spectroradiometer (MISR) were used. Tropical Rainfall Measuring Mission (TRMM) derived monthly precipitation, Atmospheric Infrared Sounder (AIRS) derived air temperature, after moist relative humidity (RH) from Modern-Era Retrospective analysis for Research and Applications, Version-2 (MERRA-2), near-surface wind speed, and soil moisture data derived from Global Land Data Assimilation System (GLDAS) were also used on a monthly time scale. For AOD trend analysis, Mann-Kendall (MK) trend test was applied. Moreover, Autoregressive Integrated Moving Average with Explanatory variable (ARIMAX) technique was applied to observe the actual and predicted AOD trend, as well as test the multicollinearity of AOD with covariates. The periodicities of AOD were analyzed using continuous wavelet transformation (CWT) and the cross relationships of AOD with prevailing covariates on a time-frequency scale were analyzed by wavelet coherence analysis. A high variation of aerosols was observed in the spatiotemporal domain. The MK test showed a decreasing trend in AOD which was most significant in Baluchistan and Punjab, and the overall trend differs between MODIS and MISR datasets. ARIMAX model shows the correlation of AOD with varying meteorological and soil parameters. Wavelet analysis provides the abundance of periodicities in the 2–8 months periodic cycles. The coherency nature of the AOD time series along with other covariates manifests leading and lagging effects in the periodicities. Through this, a notable difference was concluded in space-time patterns between MODIS and MISR datasets. These findings may prove useful for short-term and long-term studies including oscillating features of AOD and covariates.

Evaluation of Aerosol Optical Depth Forecasts from NOAA’s Global Aerosol Forecast Model (GEFS-Aerosols)

Abstract The NWS/NCEP recently implemented a new global deterministic aerosol forecast model named the Global Ensemble Forecast Systems Aerosols (GEFS-Aerosols), which is based on the Finite Volume version 3 GFS (FV3GFS). It replaced the operational NOAA Environmental Modeling System (NEMS) GFS Aerosol Component version 2 (NGACv2), which was based on a global spectral model (GSM). GEFS-Aerosols uses aerosol modules from the GOCART previously integrated in the WRF Model with Chemistry (WRF-Chem), FENGSHA dust scheme, and several other updates. In this study, we have extensively evaluated aerosol optical depth (AOD) forecasts from GEFS-Aerosols against various observations over a timespan longer than one year (2019–20). The total AOD improvement (in terms of seasonal mean) in GEFS-Aerosols is about 40% compared to NGACv2 in the fall and winter season of 2019. In terms of aerosol species, the biggest improvement came from the enhanced representation of biomass burning aerosol species as GEFS-Aerosols is able to capture more fire events in southern Africa, South America, and Asia than its predecessor. Dust AODs reproduce the seasonal variation over Africa and the Middle East. We have found that correlation of total AOD over large regions of the globe remains consistent for forecast days 3–5. However, we have found that GEFS-Aerosols generates some systematic positive biases for organic carbon AOD near biomass burning regions and sulfate AOD over prediction over East Asia. The addition of a data assimilation capability to GEFS-Aerosols in the near future is expected to address these biases and provide a positive impact to aerosol forecasts by the model. Significance Statement The purpose of this study is to quantify improvements associated with the newly implemented global aerosol forecast model at NWS/NCEP. The monthly and seasonal variations of AOD forecasts of various aerosol regimes are overall consistent with the observations. Our results provide a guide to downstream regional air quality models like CMAQ that will use GEFS-Aerosols to provide lateral boundary conditions.

Correlation analyses between downward longwave radiation particulate matters, aerosol optical depth, and metrological variables under non-dusty and cloudless conditions

This study used downward longwave (LW) radiation measurements, air temperature (T), particulate matter (PM) concentrations of fine (PM 2.5) and coarse (PM10) particles, aerosol optical depth (AOD), wind speed (WS), and precipitable water vapor (PWV) data from Riyadh, an arid site in central Saudi Arabia, between 2014 and 2019 to characterize their variations and investigate the influence of these variables on the measured downward LW radiation under non-dusty, clear sky conditions. The LW radiation and air temperature, it was found, attain their maximum in summer and minimum in winter. Conversely, the PM mean concentrations and AOD showed their maximum in spring and minimum in winter. PWV features an increasing trend during spring, summer, and fall, whereas it features a decreasing trend during the winter. The monthly variation of WS shows low monthly values during fall and winter and considerably higher monthly mean variation during spring and summer. Apart from wind speed, which does not affect the LW radiation, correlation analyses demonstrated that the LW correlates positively with the remainder of the considered variables. The strength and degree of association between the LW radiation and these variables differ from one variable to another, with air temperature having the strongest correlation (correlation coefficient = 0.98) with the LW radiation, followed by the AOD (correlation coefficient = 0.69), with a correlation coefficient of 0.36 with the PWV. Similarly, the relationships between the LW radiation and both PM10 and PM2.5 had the same correlation coefficient of 0.32. The effect of the meteorological variables (mainly air temperature, relative humidity, and wind speed) on the PM concentrations was investigated using the regression analyses. The results showed a significant positive relationship with air temperature and wind speed and a negative relationship with RH.

Monsoon-seasonal validation of MODIS aerosol optical depth and characterization using AERONET observation retrieve over Italy.

This study used Angstrom Exponent (AE) relationship with Aerosol Optical Depth (AOD) obtained from space-based direct sky-radiometer of Moderate Resolution Imaging Spectroradiometer (MODIS) and direct Sun algorithm surface-based AERONET network (level 2.0 version 3) to evaluate monsoon season (June-September) aerosol optical depth and characterization at 7 Italy sites: IMAA_POTENZA (40.60N, 15.72E), ISPRA (45.80N, 8.62E), LAMPEDUSA (35.51N, 12.63E), MESSINA (38.19N, 15.56E), MODENA (44.63N, 10.94E), ROME_TOR_VERGATA (41.83N, 12.64E) and VENISE (45.31N, 12.50E) from 2010 to 2019. Standardized anomaly and the standard deviation ratio method of analysis to address the robustness of AE were identified to classify aerosols typing. The extracted monsoon AOD correlation between MODIS and AERONET is (r=0.95) which is plausible to determine discrepancy in data handling. In order to remove large influence of annual cycle, the data were first detrend. The results show that standard deviation value>1 indicates monthly dominance than climatology. The standardized anomaly records (-0.22±0.13) for MODIS and AERONET AODs with corresponding correlation of (r=0.96) in June. There is disparity in AOD data handling from MODIS in some periods, which could attribute that space-based interpretation, should be validated with ground-base observation over Italy. The fine mode aerosols due to high AE values interestingly present the characteristic of AOD dominance, but experience trans-seasonal change, where MODIS has weak correlation.

The spatiotemporal relationship between PM<sub>2.5</sub> and aerosol optical depth in China: influencing factors and implications for satellite PM<sub>2.5</sub> estimations using MAIAC aerosol optical depth

Abstract. Satellite aerosol retrievals have been a popular alternative to monitoring the surface-based PM2.5 concentration due to their extensive spatial and temporal coverage. Satellite-derived PM2.5 estimations strongly rely on an accurate representation of the relationship between ground-level PM2.5 and satellite aerosol optical depth (AOD). Due to the limitations of satellite AOD data, most studies have examined the relationship at a coarse resolution (i.e., ≥ 10 km); thus, more effort is still needed to better understand the relationship between “in situ” PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Therefore, considerable uncertainty still exists in satellite-derived PM2.5 estimations due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD as well as its plausible impact factors, including meteorological parameters and topography, in mainland China during 2019, at various spatial and temporal scales. The coefficient of variation, the Pearson correlation coefficient and the slope of the linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China, and the linear slope was larger on average in northern inland regions than in other areas. Temporally, the PM2.5–AOD correlation peaked at noon and in the afternoon, and reached a maximum in winter. Simultaneously, considering relative humidity (RH) and the planetary boundary layer height (PBLH) in the relationship can improve the correlation, but the effect of RH and the PBLH on the correlation varied spatially and temporally with respect to both strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as the Sichuan Basin, the Shanxi–Shaanxi basins and the Junggar Basin. MAIAC 1 km AOD can better represent the ground-level fine particulate matter in most domains with exceptions, such as in very high terrain (i.e., Tibetan Plateau) and northern central China (i.e., Qinghai and Gansu). The findings of this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status of mainland China.

Retrieving spatial variation of aerosol level over urban mixed land surfaces using Landsat imageries: Degree of air pollution in Dhaka Metropolitan Area

Aerosols, or suspended particles in the atmosphere, are critical in the production of cloud nuclei, climate change, poor air quality and reduced ambient visibility, as well as public health and disease. Since there are limited ground monitoring stations in the Dhaka Metropolitan Area (DMA), calculating the degree of Aerosol Optical Depth (AOD) and PM2.5 is thus a challenge. The research aimed to retrieve the AOD at 30 m spatial resolution using the Simplified Aerosol Retrieval Algorithm (SARA) based on Landsat imageries. Landsat 4–5 and 8 cloud-free images at 30 m spatial resolution were obtained from 1999 to 2019 at 5 years interval to retrieve AOD and estimate Particulate Matter 2.5 (PM2.5) concentrations. The results showed significant increase in PM2.5 concentrations and the average PM2.5 were estimated for the year of 1999, 2004, 2009, 2014, 2019 were 48.41, 55.26, 66.42, 72.9 and 90.13 μg/m3, respectively. The AOD, mostly generated from the built-up area was 2.6 in 2019, which was 3 times higher than in 1999. Besides, to find out the relationship among AOD, LST (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index), results revealed a strong positive correlation of AOD with LST but negative correlation with NDVI. Based on AERONET and NASA SEDAC data, the AOD and PM2.5 were validated using statistically significant (p value 0.05) correlation analysis. By presenting a growing pattern and coverage of air pollution over time, the study will assist urban planners, policymakers, and health sectors.

Relationship between cloud condensation nuclei (CCN) concentration and aerosol optical depth in the Arctic region

To determine the direct and indirect effects of aerosols on climate, it is important to know the spatial and temporal variations in cloud condensation nuclei (CCN) concentrations. Although many types of CCN measurements are available, extensive CCN measurements are challenging because of the complexity and high operating cost, especially in remote areas. As aerosol optical depth (AOD) can be readily observed by remote sensing, many attempts have been made to estimate CCN concentrations from AOD. In this study, the CCN–AOD relationship is parameterized based on CCN ground measurements from the Zeppelin Observatory (78.91° N, 11.89° E, 474 m asl) in the Arctic region. The AOD measurements were obtained from the Ny-Ålesund site (78.923° N, 11.928° E) and Modern-Era Retrospective Analysis for Research and Applications, Version 2 reanalysis. Our results show a CCN–AOD correlation with a coefficient of determination R2 of 0.59. Three additional estimation models for CCN were presented based on the following data: (i) in situ aerosol chemical composition, (ii) in situ aerosol optical properties, and (iii) chemical composition of AOD obtained from reanalysis data. The results from the model using in situ aerosol optical properties reproduced the observed CCN concentration most efficiently, suggesting that the contribution of BC to CCN concentration should be considered along with that of sulfate.

Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations

The scope of this work was to evaluate simulated carbon monoxide (CO) and aerosol optical depth (AOD) from the CAM-chem model against observed satellite data and additionally explore the empirical relationship of CO, AOD and fire radiative power (FRP). The simulated seasonal global concentrations of CO and AOD were compared, respectively, with the Measurements of Pollution in the Troposphere (MOPITT) and the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite products for the period 2010–2014. The CAM-chem simulations were performed with two configurations: (A) tropospheric-only; and (B) tropospheric with stratospheric chemistry. Our results show that the spatial and seasonal distributions of CO and AOD were reasonably reproduced in both model configurations, except over central China, central Africa and equatorial regions of the Atlantic and Western Pacific, where CO was overestimated by 10–50 ppb. In configuration B, the positive CO bias was significantly reduced due to the inclusion of dry deposition, which was not present in the model configuration A. There was greater CO loss due to the chemical reactions, and shorter lifetime of the species with stratospheric chemistry. In summary, the model has difficulty in capturing the exact location of the maxima of the seasonal AOD distributions in both configurations. The AOD was overestimated by 0.1 to 0.25 over desert regions of Africa, the Middle East and Asia in both configurations, but the positive bias was even higher in the version with added stratospheric chemistry. By contrast, the AOD was underestimated over regions associated with anthropogenic activity, such as eastern China and northern India. Concerning the correlations between CO, AOD and FRP, high CO is found during March–April–May (MAM) in the Northern Hemisphere, mainly in China. In the Southern Hemisphere, high CO, AOD, and FRP values were found during August–September–October (ASO) due to fires, mostly in South America and South Africa. In South America, high AOD levels were observed over subtropical Brazil, Paraguay and Bolivia. Sparsely urbanized regions showed higher correlations between CO and FRP (0.7–0.9), particularly in tropical areas, such as the western Amazon region. There was a high correlation between CO and aerosols from biomass burning at the transition between the forest and savanna environments over eastern and central Africa. It was also possible to observe the transport of these pollutants from the African continent to the Brazilian coast. High correlations between CO and AOD were found over southeastern Asian countries, and correlations between FRP and AOD (0.5–0.8) were found over higher latitude regions such as Canada and Siberia as well as in tropical areas. Higher correlations between CO and FRP are observed in Savanna and Tropical forests (South America, Central America, Africa, Australia, and Southeast Asia) than FRP x AOD. In contrast, boreal forests in Russia, particularly in Siberia, show a higher FRP x AOD correlation than FRP x CO. In tropical forests, CO production is likely favored over aerosol, while in temperate forests, aerosol production is more than CO compared to tropical forests. On the east coast of the United States, the eastern border of the USA with Canada, eastern China, on the border between China, Russia, and Mongolia, and the border between North India and China, there is a high correlation of CO x AOD and a low correlation between FRP with both CO and AOD. Therefore, such emissions in these regions are not generated by forest fires but by industries and vehicular emissions since these are densely populated regions.

SEVIRI Aerosol Optical Depth Validation Using AERONET and Intercomparison with MODIS in Central and Eastern Europe

This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.

Evaluating and improving the sand storm numerical simulation performance in Northwestern China using WRF-Chem and remote sensing soil moisture data

This paper aims to test the ability of the WRF-Chem model to simulate sand storms in Northwest China and improve the simulation results by introducing remote sensing soil moisture data into WRF-Chem. We conducted sensitivity tests, including parameterization scheme tests and soil moisture tests using WRF-Chem. By comparing various combinations of parameterization schemes, which have considerable impacts on sand emissions, the most suitable parameterization scheme for WRF-Chem to simulate sand storms in Northwest China was selected for establishing models in future research. Based on the optimal combination of parameterization schemes, the sensitivity of sand emissions to soil moisture was tested, indicating that soil moisture has a substantial influence on sand emissions and revealing a nonlinear relationship between sand emissions and soil moisture. Although sand simulations are sensitive to soil moisture, the sand emission volume does not always increase with decreasing soil moisture content. Moreover, the soil moisture content simulated by WRF-Chem is quite different from that observed by satellites. Therefore, we selected ten sand storms that occurred in Northwest China in 2009–2018, including weak, moderate and strong sand storms, to explore the responses of sand storms of different intensities to soil moisture. Some experiments were conducted in two scenarios: scenario A was simulated only by WRF-Chem, and scenario B was designed by replacing the initial soil moisture field in WRF-Chem with soil moisture satellite data from AMSR2 and AMSR-E. Comparing the simulation results with in-situ ground-based and satellite-based measurements indicates that WRF-Chem can capture strong sand storms and some moderate sand storms, but the ability to simulate weak sand storms needs to be greatly improved. After introducing remote sensing soil moisture data into WRF-Chem, the simulation of sand emissions becomes more accurate, substantially improving the simulation results. Among the ten simulated sand storms, the AOD (aerosol optical depth) and PM10 simulation accuracies are improved for five and seven sand storms, respectively. After this improvement, the AOD correlation coefficient can reach 0.63, and the PM10 correlation coefficient can reach 0.60.

Exploring analog-based schemes for aerosol optical depth forecasting with WRF-Chem

We implement and test an analog-based post-processing method to improve short range forecasts of aerosol optical depth (AOD) using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Model postprocessing of AOD is performed using historical analog forecasts and a Kalman Filter (KF). Analog forecasts are selected from WRF-Chem simulations based on a set of environmental predictors (AOD, wind speed, precipitable water, and particulate matter) that exhibit past values similar to the current forecasts. Space-borne AOD from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard Terra and Aqua satellites corresponding to the analogs are used to build the analog ensemble. This study focuses on a spatial domain covering the AERONET sites in contiguous United States. We use the analog ensemble weighted mean (AN) and Kalman filter analog (KFAN) algorithms, which are both trained using WRF-Chem AOD forecasts for the months of June to August during 2008–2011 and tested during the same months for 2012. Overall, the AOD forecast are more skillful when the forecast errors are corrected using a combination of analogs and Kalman filter in KFAN. This is especially true for the western US where the correlation of AOD with PM2.5, PM10, and surface horizontal wind speed are higher than those for other predictors. In fact, the overall biases in AOD are significantly reduced close to zero, with KFAN AOD being statistically indistinguishable to MODIS. However, both methods show mixed results (albeit still showing overall improvements) in eastern and central U.S., where AOD and its variability are highest. We find that, during the summer, PM is not the only predominant factor driving AOD in these regions, unlike western United States (U.S.) (except New Mexico and Arizona). We note, however, that the quality of the analogs depends on the model's capability to accurately simulate total precipitable water, which in turn influences aerosol sources and sinks.

Effect of Ocean Warming on Cloud Properties Over India and Adjoining Oceanic Regions

Changes in precipitation pattern have been associated with global warming and is of more importance particularly for monsoon dependent regions such as India, which receives maximum rainfall from south-west monsoon. Indian land mass is surrounded by ocean from three sides named Arabian Sea (AS), Bay of Bengal (BOB) and rest of the Indian Ocean (IO) which makes its climate more sensitive. To understand the effect of global warming, long term (1960–2017) annually averaged in-situ sea surface temperature (SST) is studied which shows an increasing trend (~ 0.11 °C/decade; P < 0.05) with higher variations (r2AS = 0.46; r2BOB = 0.43) over AS and BOB whereas comparatively lower in magnitude (~ 0.14 °C/decade; P < 0.05) with less variation (r2IO = 0.74) over IO. Rise in SST could vary evaporation rate, moisture content, cloud temperature and initial conditions required for cloud formation. To understand this heterogeneity in conjunction with seasonal variation, present study correlates cloud microphysical properties such as cloud effective radius (CER) with SST and aerosol optical depth (AOD) at high-resolution (1° × 1°) using linear interpolation method during 2001–2016. Features of north-east monsoon captures with high (~ 0.006–0.012 kg/kg) specific humidity at 850 hPa, positive correlation (~ 0.1–0.8) of SST-CER and negative correlation (~ − 0.1 to ~ − 0.8) of AOD–CER over BOB which may imply formation of bigger droplets due to presence of more moisture and less AOD. Though these patches show prominent results, it also shows scattered interpolation signifying role of other parameters on CER. Findings would be promising with more parameters, which can be used as an input data in climate models to understand regional climate variability.

Contrasting changes in cloud optical properties and the influence of aerosols, meteorology and radiation feedback in the Himalaya Karakoram region

The spatio-temporal variations in cloud optical properties and the influence of aerosols, radiation feedback and meteorological parameters were investigated at high altitudes of Diamir, Gilgit and Skardu in the Himalaya Karakoram (HK) region of northern Pakistan from 2004 to 2016. The aerosol optical depth (AOD) depicted significant variation with high values during spring months as compared to other months. The results revealed high values of cloud fraction (CF), cloud water path (CWP) and cloud optical depth (COD) in winter months, while contrasting response was observed in summer months. Due to Twomey effect, clouds become thicker and brighter with maximum COD and liberation of latent heat, which modifies temperature gradient of vertical profile. Similarly, an uneven distribution of cloud effective radius (CER) was observed throughout the study period. An increasing trend of cloud top temperature (CTT) from February to September was observed, while an opposite response was found from September to January during the study period. Likewise, minimum and maximum cloud top pressure (CTP) was found in February and November, respectively. AOD is positively correlated with CF and COD due to high humidity and the existence of hydrophilic aerosols. The results of AOD and CER correlation were in good agreement with Twomey effect. However, AOD showed contrasting relationship with CWP over Diamir and Gilgit. The negative relationships of AOD with CTP and CTT indicated the suppression of precipitation process and variability of meteorological parameters. In addition, a positive correlation was observed between COD and CTP against top of the atmosphere shortwave cloud radiative forcing (TOASWCRF), which indicates the cooling effect due to increasing COD and CTP. With TOASWCRF, a variation in CWP and CER may also lead to cooling effect due to reflection of solar radiations. While the meteorology depicted an uneven relationship with cloud optical properties at observation sites.

Assessment of atmospheric aerosols from two reanalysis products over Australia

Assessments of atmospheric aerosols from reanalysis are important for understanding uncertainty in model simulations, and ultimately predictions, such as for solar power or air quality forecasts and assessments. This study intercompares total aerosol optical depth (AOD) and dust AOD (DAOD) from two global reanalyses datasets, the European Centre for Medium-Range Weather Forecasts (ECMWF) Monitoring Atmospheric Composition and Climate (MACC) and the NASA Modern-Era Retrospective Analysis for Research-2 (MERRA-2). These are evaluated against AeroSpan (Aerosol characterisation via Sun photometry: Australian Network) ground observations which forms part of the Aerosol Robotic Network (AERONET) over the Australian continent for the 2002–2012 period. During dust storms, AeroSpan/AERONET AOD measurements were missing due to cloud screening. To overcome validation limitations in sun photometry for dust events, a nephelometer's scattering coefficient is qualitatively compared against reanalysis of DAOD at a key dust storm activation site, Tinga Tingana in South Australia (~200 km east of Lake Eyre). A specific extreme event that occurred in 2009 originating from the Lake Eyre basin, a major dust source covering one-sixth of Australia, was studied. The results show that MERRA-2 reanalysis overestimates monthly total AOD twice as much compared to AeroSpan/AERONET ground observations but seems better correlated against AeroSpan/AERONET than ECMWF/MACC. Mean data of MERRA-2 time series over 10 years provide lower DAOD values and lower dust aerosol estimates than ECMWF/MACC reanalysis (over the Lake Eyre basin with spatial averaging). Specifically at Tinga Tingana, the correlation from MERRA-2 (0.45 correlation) and ECMWF/MACC (0.43 correlation) against AeroSpan/AERONET's AOD were similar. Between MERRA-2 and ECMWF/MACC decade long daily gridded DAOD, the correlation coefficient was high at 0.73, again indicating similarity between the datasets. MERRA-2 total AOD correlation is significantly higher (by 0.26) against AeroSpan/AERONET than ECMWF/MACC. MERRA-2 also provides higher AOD values in extreme cases which may correspond to dust storms. During dust storms, a hybrid strategy using nephelometers and hourly reanalysis from MERRA-2 is able to identify dust storms better than AeroSpan/AERONET. Overall, this work can enable and inform better aerosol data assimilation into forecast models such as for solar energy, agriculture or air quality over Australia.

Mitigating Satellite‐Based Fire Sampling Limitations in Deriving Biomass Burning Emission Rates: Application to WRF‐Chem Model Over the Northern sub‐Saharan African Region

AbstractLargely used in several independent estimates of fire emissions, fire products based on MODIS sensors aboard the Terra and Aqua polar‐orbiting satellites have a number of inherent limitations, including (a) inability to detect fires below clouds, (b) significant decrease of detection sensitivity at the edge of scan where pixel sizes are much larger than at nadir, and (c) gaps between adjacent swaths in tropical regions. To remedy these limitations, an empirical method is developed here and applied to correct fire emission estimates based on MODIS pixel level fire radiative power measurements and emission coefficients from the Fire Energetics and Emissions Research (FEER) biomass burning emission inventory. The analysis was performed for January 2010 over the northern sub‐Saharan African region. Simulations from WRF‐Chem model using original and adjusted emissions are compared with the aerosol optical depth (AOD) products from MODIS and AERONET as well as aerosol vertical profile from CALIOP data. The comparison confirmed an 30–50% improvement in the model simulation performance (in terms of correlation, bias, and spatial pattern of AOD with respect to observations) by the adjusted emissions that not only increases the original emission amount by a factor of two but also results in the spatially continuous estimates of instantaneous fire emissions at daily time scales. Such improvement cannot be achieved by simply scaling the original emission across the study domain. Even with this improvement, a factor of two underestimations still exists in the modeled AOD, which is within the current global fire emissions uncertainty envelope.

Synergy of MODIS and AATSR for better retrieval of aerosol optical depth and land surface directional reflectance

This paper presents a new algorithm to simultaneously retrieve Aerosol Optical Depth (AOD) and land surface Bidirectional Reflectance Distribution Function (BRDF) from Advanced Along-Track Scanning Radiometer (AATSR) by adopting gradient optimization method. Different from traditional method the approach presented here can perform simultaneous retrieval from each individual AATSR swath rather than multiple days. A theoretical sensitivity study proves the proposed method is insensitive to the distortion of initial BRDF. The presented algorithm is tested on AATSR data around four different Aerosol Robotic Network (AERONET) sites representing various types of land surface. Compared with the four selected AERONET sites' AOD and BRDF-derived albedo from AERONET-based Surface Reflectance Validation Network (ASRVN) data in corresponding four AERONET sites, the presented algorithm proves considerable accuracy for various type of land surface with correlation of AOD ranging from 0.647 to 0.911 and correlation of BRDF-derived albedo ranging from 0.483 to 0.944. The intersensor comparison with Moderate Resolution Imaging Spectroradiometer (MODIS) 3km AOD dark target product reveals high coverage rate of the presented method especially in bright surface or nonvegetation area and the correlation between the two sensors reaches up to 0.967. The improved estimation of BRDF from AATSR retrieval in AERONET Beijing site is compared with MODIS MCD43B1 product. The relative differences in hemispherical albedo calculated from average BRDF shape function parameters between AATSR and MODIS product are 1.33%, 1.52%, 2.60% and 4.28% at 550nm, 670nm, 870nm and 1600nm respectively.

Spatiotemporal patterns of correlation between atmospheric nitrogen dioxide and aerosols over South Asia

An accurate knowledge is needed on the complex relation between atmospheric trace gasses and aerosol variability and their sources to explain trace gases–aerosols–climate interaction and next-generation modeling of climate change and air quality. In this regard, we have used tropospheric Nitrogen Dioxide (NO2), Aerosol Optical Depth (AOD) and Angstrom Exponent (AE) obtained from satellite-based Ozone Monitoring Instrument (OMI)/Aura and Moderate-Resolution Imaging Spectroradiometer (MODIS)/Aqua over South Asia. NO2–AOD correlation with coefficient r = 0.49 is determined over the landmass of South Asia during 2005–2015. Yearly mean NO2–AOD correlation over South Asia shows large variations ranging from r = 0.32 to 0.86 in 2008 and 2009, respectively. The highest correlation (r = 0.66) is seen over eastern regions of Bangladesh and India, as well as adjoining areas of western Myanmar mostly linked to anthropogenic activities. A significant correlation (r = 0.59) associated with natural causes is found over some parts of Sistan region, located at the borders of Iran, Pakistan and Afghanistan, and adjoining territory. We find significant positive correlations for monsoon and post-monsoon seasons with r = 0.50 and r = 0.61, respectively. A linear regression on the annual correlation coefficients data suggests that NO2–AOD correlation is strengthening with an increase of 12.9% over South Asia during the study period. The spatial distribution of data slopes reveals positive trends in NO2–AOD correlation over megacities Lahore, Dhaka, Mumbai and Kolkata linked to growing anthropogenic activities. Singrauli city (India) has the highest correlation (r = 0.62) and 35% increase in correlation coefficient value per year. A negative correlation is observed for megacity Karachi (r = −0.37) suggesting the non-commonality of NO2 and aerosols emission sources. AE has also been used to discuss its correlation with NO2 over the areas with dominance of fine-mode aerosols.

Evaluation of MODIS columnar aerosol retrievals using AERONET in semi-arid Nevada and California, U.S.A., during the summer of 2012

Satellite characterization of local aerosol pollution is desirable because of the potential for broad spatial coverage, enabling transport studies of pollution from major sources, such as biomass burning events. However, retrieval of quantitative measures of air pollution such as Aerosol Optical Depth (AOD) from satellite measurements is challenging over land because the underlying surface albedo may be heterogeneous in space and time. Ground-based sunphotometer measurements of AOD are unaffected by surface albedo and are crucial in enabling evaluation, testing, and further development of satellite instruments and retrieval algorithms. Columnar aerosol optical properties from ground-based sunphotometers (Cimel CE-318) as part of AERONET and MODIS aerosol retrievals from Aqua and Terra satellites were compared over semi-arid California and Nevada during the summer season of 2012. Sunphotometer measurements were used as a ‘ground truth’ to evaluate the current state of satellite retrievals in this spatiotemporal domain. Satellite retrieved (MODIS Collection 6) AOD showed the presence of wildfires in northern California during August. During the study period, the dark-target (DT) retrieval algorithm appears to overestimate AERONET AOD by an average factor of 3.85 in the entire study domain. AOD from the deep-blue (DB) algorithm overestimates AERONET AOD by an average factor of 1.64.Low AOD correlation was also found between AERONET, DT, and DB retrievals. Smoke from fires strengthened the aerosol signal, but MODIS versus AERONET AOD correlation hardly increased during fire events (r2∼0.1–0.2 during non-fire periods and r2∼0–0.31 during fire periods). Furthermore, aerosol from fires increased the normalized mean bias (NMB) of MODIS retrievals of AOD (NMB∼23%–154% for non-fire periods and NMB∼77%–196% for fire periods). Ångström Extinction Exponent (AEE) from DB for both Terra and Aqua did not correlate with AERONET observations. High surface reflectance and incorrect aerosol physical parametrizations may still be affecting the DT and DB MODIS AOD retrievals in the semi-arid western U.S.