Multi-angle Imaging SpectroRadiometer Research Articles

Enhancing Global Simulation of Smoke Injection Height for Intense Pyro‐Convection Through Coupling an Improved One‐Dimensional Plume Rise Model in CAM‐chem

AbstractThe impact of wildfire smoke is largely determined by the height where they are injected into the atmosphere. Current plume rise models tend to underestimate the high smoke injection heights because the previous models and configurations were mainly constrained and validated by the plume height observation from Multi‐angle Imaging SpectroRadiometer (MISR), of which most cases inject low within the planetary boundary layer (PBL). Here we retrieve smoke injection heights from intense pyro‐convections based on pyrocumulonimbus satellite images in PYROCAST data set alongside meteorological reanalysis. It largely augments the MISR data set with smoke injection heights up to the upper troposphere and lower stratosphere (UTLS). Constrained by both MISR and PYROCAST, we show that a scaling down of factor 0.2 to the entrainment efficiency parameterized in the one‐dimensional plume‐rise model (1‐D PRM, Freitas et al. (2010, https://doi.org/10.5194/acp‐10‐585‐2010)) significantly improves the model performance for high injection cases without compromising the accuracy of low injection cases. We also found that the fire intensity input can be obtained through a simplified dependence on the biome and biomass burning emission flux. While being unable to represent high cases before, the improved 1‐D PRM model predicts similarly well in injection heights both low near the PBL height and high into the UTLS. The improved 1‐D PRM is then coupled into Community Atmosphere Model with Chemistry (CAM‐chem). The coupled CAM‐chem‐PRM, when predicting injection heights in tests imitating real BB emission, exhibited consistent predictive capabilities with the standalone 1‐D PRM while saw a mere 15% increase of computation time.

Substantial Underestimation of Fine-Mode Aerosol Loading from Wildfires and Its Radiative Effects in Current Satellite-Based Retrievals over the United States.

Wildfires generate abundant smoke primarily composed of fine-mode aerosols. However, accurately measuring the fine-mode aerosol optical depth (fAOD) is highly uncertain in most existing satellite-based aerosol products. Deep learning offers promise for inferring fAOD, but little has been done using multiangle satellite data. We developed an innovative angle-dependent deep-learning model (ADLM) that accounts for angular diversity in dual-angle observations. The model captures aerosol properties observed from dual angles in the contiguous United States and explores the potential of Greenhouse gases Observing Satellite-2's (GOSAT-2) measurements to retrieve fAOD at a 460 m spatial resolution. The ADLM demonstrates a strong performance through rigorous validation against ground-based data, revealing small biases. By comparison, the official fAOD product from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS), and the Multiangle Imaging Spectroradiometer (MISR) during wildfire events is underestimated by more than 40% over western USA. This leads to significant differences in estimates of aerosol radiative forcing (ARF) from wildfires. The ADLM shows more than 20% stronger ARF than the MODIS, VIIRS, and MISR estimates, highlighting a greater impact of wildfire fAOD on Earth's energy balance.

Evolving Particles in the 2022 Hunga Tonga—Hunga Ha'apai Volcano Eruption Plume

AbstractThe Multi‐angle Imaging SpectroRadiometer (MISR) aboard NASA's Terra satellite observed the Hunga Tonga—Hunga Ha'apai (HTHH) 15 January eruption plume on eight occasions between 15 and 23 January 2022. From the MISR multi‐angle, multi‐spectral imagery we retrieve aerosol plume height geometrically, along with plume‐level motion vectors, and derive radiometrically constraints on particle effective size, shape, and light‐absorption properties. Parts of two downwind aerosol layers were observed in different places and times, one concentrated in the upper troposphere (11–18 km ASL), and a mid‐stratosphere layer ∼23–30+ km ASL. After the initial day (1/15), the retrievals identified only spherical, non‐light‐absorbing particles, typical of volcanic sulfate/water particles. The near‐tropopause plume particles show constant, medium‐small (several tenths of a micron) effective size over 4 days. The mid‐stratosphere particles were consistently smaller, but retrieved effective particle size increased between 1/17 and 1/23, though they might have decreased slightly on 1/22. As a vast amount of water was also injected into the stratosphere by this eruption, models predicted relatively rapid sulfate particle growth from the modest amounts of SO2 gas injected by the eruption to high altitudes along with the water (Zhu et al., 2022, https://doi.org/10.5194/acp‐22‐10267‐2022). MISR observations up to 10 days after the eruption are consistent with these model predictions. The possible decrease in stratospheric particle size after initial growth was likely caused by evaporation, as the plume mixed with drier, ambient air. Particles in the lower‐elevation plume observed on 1/15 were larger than all the downwind aerosols and contained significant non‐spherical (likely ash) particles.

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Observations of the macrophysical properties of cumulus cloud fields over the tropical western Pacific and their connection to meteorological variables

Abstract. The poor representation of the macrophysical properties of shallow oceanic cumuli in climate models contributes to the large uncertainty in cloud feedback. These properties are also difficult to measure because it requires high-resolution satellite imagery that is seldomly collected over ocean. Here, we examine cumulus cloud macrophysical properties, their size, shape, and spatial distributions, over the tropical western Pacific using 170 15 m resolution scenes from Terra's Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) collected during the 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) mission. The average cloud fraction (CF) was 0.12, half of which was contributed by clouds less than 1.6 km in area-equivalent diameter. This compared well to Terra's Multi-angle Imaging SpectroRadiometer (MISR) resolution-corrected CF of 0.14 but less than the 0.19 measured by Terra's Moderate Resolution Imaging Spectroradiometer (MODIS). The cloud size distribution exhibited a power law form with an exponent of 2.93 and an area–perimeter power law with a dimension of 1.25. ASTER, MISR, and CAMP2Ex aircraft lidar showed excellent agreement in the cloud top height (CTH) distribution peak altitude of ∼ 750 m. We examined cumulus properties in relation to meteorological variables and found that the variation in mean CTH is controlled most by the total column water vapor, lower-tropospheric stability (LTS), and estimated inversion strength (EIS). The variation in CF is most controlled by surface wind speed and near-cloud relative humidity instead of LTS/EIS, suggesting the need to improve low-cloud parameterizations in climate models that use LTS/EIS based on stratocumulus studies.

Improved estimates of smoke exposure during Australia fire seasons: importance of quantifying plume injection heights

Abstract. Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM2.5, especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88, normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM2.5 in several key cities near the wildfire source regions, with smoke PM2.5 accounting for 5 %–52 % of total PM2.5 during fire seasons from 2009 to 2020.

Satellite Multi‐Angle Observations of Wildfire Smoke Plumes During the CalFiDE Field Campaign: Aerosol Plume Heights, Particle Property Evolution, and Aging Timescales

AbstractWildfire‐related aircraft field campaigns frequently offer opportunities to validate remote‐sensing retrievals of aerosol properties and other quantities derived from satellite‐borne‐instrument observations. Satellite instruments often provide regional context‐imagery for more sparsely sampled aircraft and surface‐based measurements. However, aerosol amount, particle type, aerosol plume height and the associated wind vector products retrieved from the NASA Earth Observing System's Multi‐angle Imaging SpectroRadiometer (MISR) instrument have matured sufficiently that these quantities can also contribute substantially to a campaign data set, in regional context. This is especially useful when such measurements are not acquired at all from the suborbital platforms. During NOAA's California Fire Dynamics Experiment (CalFiDE), aircraft operations were coordinated with MISR overpasses on two occasions: for the Rum Creek fire on 30 August 2022, and for the Mosquito fire on 08 September. MISR‐retrieved aerosol properties show distinctly different patterns of black and brown smoke particle distributions and inferred plume evolution in the two cases. This paper presents the satellite‐retrieved results that complement the field observations, demonstrating what such measurements can offer, and contributing material for detailed fire dynamics and chemistry studies when combined with the CalFiDE suborbital observations and models in continuing studies.

Aerosol characteristics in CMIP6 models' global simulations and their evaluation with the satellite measurements

AbstractGlobal and regional trends of the Aerosol Optical Depth (AOD) from Coupled Model Intercomparison Project (CMIP) Phase 6 simulations for the study period 1971–2014 were compared against the satellite retrievals and the intermodel variations were analysed. The AOD from multimodel mean (MMM) of eight general circulation models (GCMs) has been evaluated against the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging Spectro Radiometer (MISR) AOD for the 2001–2014 period. Angstrom exponents (AE and its first derivative) that represent the size distribution of aerosols are estimated globally from the perturbed initial condition ensemble of MRI‐ESM2‐0 and MPI‐ESM‐1‐2‐HAM models to report the aerosol variations through their size distribution. We found that the global AOD obtained from the MMM8 showed an insignificant decreasing trend, while this trend is significantly positive over the northern tropical region. The MMM8 has overestimated the MODIS AOD over North Africa, India, China, and Australia while this overestimation is confined to North Africa and eastern China when compared against MISR AOD. The absolute percent bias of MMM8 is 28.1% and 24.1% over the globe when compared against MODIS and MISR AOD, respectively. The spatial pattern of AE showed the dominance of fine‐ and coarse‐mode particles during the boreal/austral winter and summer seasons, respectively, that replicate the seasonality of aerosols. The AE derived from MPI‐ESM‐1‐2‐HR demonstrated better agreement with AATSR SU's (Advanced Along Track Scanning Radiometer instrument series, with the algorithm developed by Swansea University) AE (550–870 nm). On the other hand, MRI‐ESM2‐0 consistently underestimated AE across different regions and wavelength ranges, suggesting an over representation of larger aerosol particles in the model's portrayal of aerosol size distribution compared to satellite observations.

Expanding the coverage of Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrievals over shallow, turbid, and eutrophic waters

Abstract. Shallow and coastal waters are often rich in nutrients (eutrophic), biologically productive, turbid from runoff, and located where the atmosphere above can be more aerosol-laden than over open-ocean waters due to proximity to aerosol sources on land. Although the NASA Earth Observing System's Multi-angle Imaging SpectroRadiometer (MISR) on board the Terra satellite has been monitoring global aerosols for over 23 years, the current operational retrieval algorithm (V23) is not applied over waters less than 50 m in depth or within 5 km of land, designated as “shallow water.” This is due to the simplicity of the Dark Water algorithm, applied operationally over deep waters, which assumes the surface is essentially black in the primarily used red and near-infrared spectral bands. In this work, we describe the implementation and validation of a “Shallow Water” aerosol retrieval algorithm for MISR, which takes advantage of all four available spectral bands and includes a Lambertian surface reflectivity term to account for water-leaving radiance. This algorithm compares well to independent, surface-based observations and demonstrates better performance over shallow waters than the operational Dark Water retrieval algorithm. Globally, aerosol retrievals over shallow waters increase the total number of MISR over-water measurements by more than 7 %, including new retrievals made over some of the most biologically productive parts of the ocean.

Diurnal time representation of MODIS, VIIRS, MISR, and AHI over Asia and Oceania

Retrievals of satellite aerosol optical depth (AOD) products are used for studies of air pollution, energy balance, and climate change. However, few studies have evaluated the diurnal time representation (DTR) of satellite AOD products. In this study, we proposed a statistically valid threshold method to investigate the DTR of Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), Multi-angle Imaging Spectroradiometer (MISR), and Advanced Himawari Imager (AHI) AOD products by comparing their retrievals to the daily AERONET AOD values and explored the ability of AHI and AERONET to capture daily aerosol variability at 54 sites in Asia and Oceania from July 2015 to December 2019. For polar-orbiting satellites, based on collocations with AERONET measurements, MISR-derived AOD showed high consistency with daily average AERONET AOD, with a correlation coefficient (R) of 0.94 and 80.85% matchups falling into the envelopes of expected error (EE, ± (0.05 + 0.15 × AODAERONET)). Compared with single MODIS products, a combination of satellite data from the Terra and Aqua MODIS dark target (DT) algorithm might obtain a dataset with better DTR (R = 0.94 and %Within EE = 72.10). The satellite-retrieved AOD did a good job to represent the daily mean AERONET AOD, because AERONET AOD averaged within a ± 30 min time window of different satellite overpasses closely mirrored the daily mean AERONET AOD. Furthermore, the seasonal average diurnal variations (SADV) of AERONET AOD were changing insignificant, except for the urban coverage area in the Southern Hemisphere and equatorial climate. For geostationary satellite, the DTR of AHI was relatively poorer in comparison to the other sensors (R = 0.84, %Within EE = 58.00), and AHI AOD was seriously underestimated in South Asia and overestimated in Oceania. Besides, the SADVs of AHI AOD presented several patterns summarized as wave type, unimodal type, stable fluctuation type, and other types, which meant that AHI AOD retrievals did not accurately capture the diurnal variability of aerosols. This study provides a reference for the aerosol community to select satellite AOD products with appropriate DTR.

Effect of viewing angle difference on spaceborne optical estimation of floating Ulva prolifera biomass in the Yellow Sea.

Optical remote sensing provides optimal technical support for the detection and quantification of floating macroalgae. Although the spatial scale effect on optical estimation of floating macroalgae coverage or biomass from different images has been clarified, the directional effect on them has not been investigated until now. In this study, synchronous multi-angle imaging spectroradiometer (MISR) and MODIS images were collected to investigate the multi-angle remote sensing of green tides. A dual thresholding method, based on the difference vegetation index (DVI) and scaled algae index, was employed to determine algae pixels. In addition, piecewise empirical models were developed for MISR and MODIS images to estimate the total biomass of green tides based on laboratory measurements and DVI values. Comparative analysis of DVI histograms and total biomass shows that the sensor zenith angle has a significant impact on the quantification of green tides. Under the same solar conditions, as the sensor zenith angle increases, the optical signals received from algae pixels weaken, resulting in a decrease in the quantification of green tides. In future research, the observation geometry (including the solar/sensor zenith angle and the solar/sensor azimuth angle) needs to be considered to improve the accuracy of optical remote detection and quantification of floating macroalgae.

Spatial distribution of aerosols burden and evaluation of changes in aerosol optical depth using multi-approach observations in tropical region

Understanding of Aerosol optical depth (AOD) parameter is important for air quality assessment. This study aims to evaluate and validate AOD measurements from combine datasets to improve air quality for a period 2005–2020 using Aerosol Robotic Network (AERONET) at Ilorin site (8.320° N, 4.340° E) in Nigeria. AOD outputs from Community Atmosphere Model Version 6 with chemistry (CAM6-chem) at 1° horizontal resolution and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) are investigated in addition to validation of two satellites AOD retrievals: Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR). Result of spatial distribution of AOD shows high values > 1 in the North and Western Sahara compared to Central Africa. Desert dust shows largest contribution in the North and Western Africa that is up to 2 magnitude larger than other aerosol types. Primary organic matter (POM) and secondary organic aerosols (SOAs) both presents high burdens with later been dominant at around 10° band, and black carbon (BC) largest burden (2.6 × 10 − 5 kgm − 2) is seen in the model from oil and gas exploration site in Nigeria. Inter-comparison of MERRA/MISR/MODIS and AERONET AOD using linear correlation of the seasonal dependence demonstrated high correlation (r = 0.864 − 0.973) subjected to Root Mean Square Error (RMSE = 0.069 − 0.211), suggesting good agreement between the datasets. When compared to seasonal mean maximum AERONET AOD value of 0.978 MERRA is ∼5%, MISR ∼28% and MODIS ∼29% lower with stronger correlations observed in the wet and pre-harmattan seasons. Similarly, MODEL AOD at 550 nm and dust burden were found to be ∼34% and ∼67% lower in context to AERONET AOD annual mean value of 0.627. Positive relationships that indicate an upward slope exist between all the computed datasets with moderate value of AERONET/CAM-chem spearman partial correlation, and MERRA/MODIS and MODIS/MISR showing strong and significant relationship with p-value less than 0.05. Low variance is observed with all measurements except in MERRA.

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.

Fusion of MISR Stereo Cloud Heights and Terra‐MODIS Thermal Infrared Radiances to Estimate Two‐Layered Cloud Properties

AbstractOur longest, stable record of cloud‐top pressure (CTP) and cloud‐top height (CTH) are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐Angle Imaging Spectroradiometer (MISR) on Terra. Because of single cloud‐layer assumptions in their standard algorithms, they provide only single CTP/CTH retrievals in multi‐layered situations. In the predominant multi‐layered regime of thin cirrus over low clouds, MODIS significantly overestimates cirrus CTP and emissivity, while MISR accurately retrieves low‐cloud CTH. Utilizing these complementary capabilities, we develop a retrieval algorithm for accurately determining both‐layer CTP and cirrus emissivity for such 2‐layered clouds, by applying the MISR low‐cloud CTH as a boundary condition to a modified MODIS CO2‐slicing retrieval. We evaluate our 2‐layered retrievals against collocated Cloud‐Aerosol Transport System (CATS) lidar observations. Relative to CATS, the mean bias of the upper cloud CTP and emissivity are reduced by ∼90% and ∼75% respectively in the new technique, compared to standard MODIS products. We develop an error model for the 2‐layered retrieval accounting for systematic and random errors. We find up to 87% of all residuals lie within modeled 95% confidence intervals, indicating a near‐closure of error budget. This reduction in error leads to a reduction in modeled atmospheric longwave radiative flux biases ranging between 5 and 40 W m−2, depending on the position and optical properties of the layers. Given this large radiative impact, we recommend that the pixel‐level 2‐layered MODIS + MISR fusion algorithm be applied over the entire MISR swath for the 22‐year Terra record, leading to a first‐of‐its‐kind 2‐layered cloud climatology from Terra's morning orbit.

Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models

This review was carried out to understand the retrieval of aerosol optical depth (AOD) datasets for estimating particle concentration and its influence on ambient air and surroundings by various models. Several studies have evaluated particle (PM10 and PM2.5) concentration profiles present in the lowest layers of the atmosphere by using AOD datasets. This study aimed at identifying consistent and precise particle estimation by various datasets retrieved by satellite‐based models for the ground‐level PM concentration. Extremities of satellite sensor data, like specific capabilities, as well as a few drawbacks are presented. Multi‐angle imaging spectroradiometer (MISR), visible infrared imaging radiometer suite (VIIRS) datasets, mixed‐effect model (MEM), and geographically weighted regression (GWR) models outperformed to estimate AOD and PM in comparison with the moderate resolution imaging spectroradiometer (MODIS) and other datasets. The improvised algorithms with higher resolution in the upcoming research would provide an even better estimation for AOD and PM.

Assessment of the impact of discontinuity in satellite instruments and retrievals on global PM2.5 estimates

Exposure to fine particulate matter (PM2.5) is the leading environmental risk factor for mortality globally. Satellite-derived estimates of surface PM2.5 developed from a combination of satellites, simulations, and ground monitor data are relied upon for health impact studies. The ability to develop satellite-derived PM2.5 estimates requires the continued availability of aerosol optical depth (AOD) sources. This work examines the impact of the addition or loss of satellite AOD data sources on global PM2.5 estimation and the impact of continuing the long-term record with AOD from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar orbiting Partnership (S-NPP) satellite after the loss of the MODIS (MODerate resolution Imaging Spectroradiometer) and MISR (Multi-angle Imaging Spectroradiometer) instruments on board the Terra and Aqua satellites. We find that the addition of VIIRS S-NPP AOD products to geophysical PM2.5 estimates from satellites and simulations causes regional differences that correspond to differences in the VIIRS and MODIS Deep Blue AOD algorithms and sampling. Changes in long-term trends and timeseries due to the addition or loss of AOD data sources are generally within their uncertainties. Statistical fusion with ground monitor data partially corrects for changes due to sampling differences when introducing the VIIRS AOD products, but uncertainty remains over desert regions where ground monitor coverage is sparse. This work provides promise for the sustained development of global satellite-derived PM2.5 estimates, despite discontinuities in instruments and retrieval methods.

Comparison of dust optical depth from multi-sensor products and MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) dust reanalysis over North Africa, the Middle East, and Europe

Abstract. Aerosol reanalysis datasets are model-based, observationally constrained, continuous 3D aerosol fields with a relatively high temporal frequency that can be used to assess aerosol variations and trends, climate effects, and impacts on socioeconomic sectors, such as health. Here we compare and assess the recently published MONARCH (Multiscale Online Non-hydrostatic AtmospheRe CHemistry) high-resolution regional desert dust reanalysis over northern Africa, the Middle East, and Europe (NAMEE) with a combination of ground-based observations and space-based dust retrievals and products. In particular, we compare the total and coarse dust optical depth (DOD) from the new reanalysis with DOD products derived from MODIS (MODerate resolution Imaging Spectroradiometer), MISR (Multi-angle Imaging SpectroRadiometer), and IASI (Infrared Atmospheric Sounding Interferometer) spaceborne instruments. Despite the larger uncertainties, satellite-based datasets provide a better geographical coverage than ground-based observations, and the use of different retrievals and products allows at least partially overcoming some single-product weaknesses in the comparison. Nevertheless, limitations and uncertainties due to the type of sensor, its operating principle, its sensitivity, its temporal and spatial resolution, and the methodology for retrieving or further deriving dust products are factors that bias the reanalysis assessment. We, therefore, also use ground-based DOD observations provided by 238 stations of the AERONET (AErosol RObotic NETwork) located within the NAMEE region as a reference evaluation dataset. In particular, prior to the reanalysis assessment, the satellite datasets were evaluated against AERONET, showing moderate underestimations in the vicinities of dust sources and downwind regions, whereas small or significant overestimations, depending on the dataset, can be found in the remote regions. Taking these results into consideration, the MONARCH reanalysis assessment shows that total and coarse-DOD simulations are consistent with satellite- and ground-based data, qualitatively capturing the major dust sources in the area in addition to the dust transport patterns. Moreover, the MONARCH reanalysis reproduces the seasonal dust cycle, identifying the increased dust activity that occurred in the NAMEE region during spring and summer. The quantitative comparison between the MONARCH reanalysis DOD and satellite multi-sensor products shows that the reanalysis tends to slightly overestimate the desert dust that is emitted from the source regions and underestimate the transported dust over the outflow regions, implying that the model's removal of dust particles from the atmosphere, through deposition processes, is too effective. More specifically, small positive biases are found over the Sahara desert (0.04) and negative biases over the Atlantic Ocean and the Arabian Sea (−0.04), which constitute the main pathways of the long-range dust transport. Considering the DOD values recorded on average there, such discrepancies can be considered low, as the low relative bias in the Sahara desert (< 50 %) and over the adjacent maritime regions (< 100 %) certifies. Similarly, over areas with intense dust activity, the linear correlation coefficient between the MONARCH reanalysis simulations and the ensemble of the satellite products is significantly high for both total and coarse DOD, reaching 0.8 over the Middle East, the Atlantic Ocean, and the Arabian Sea and exceeding it over the African continent. Moreover, the low relative biases and high correlations are associated with regions for which large numbers of observations are available, thus allowing for robust reanalysis assessment.

Dispersion and Aging of Volcanic Aerosols After the La Soufrière Eruption in April 2021

AbstractVolcanic aerosols change the atmospheric composition and thereby affect weather and climate. Aerosol dynamic processes such as nucleation, condensation, and coagulation modify the shape, size, and mass of aerosol particles, which influence their atmospheric lifetime and radiative properties. Nevertheless, most models omit these processes for ash particles. In this work, we explore the ash aerosol aging and sulfate production during the first 4 days following the 2021 La Soufrière (St. Vincent) eruption with the ICON‐ART model (ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases). Online coupling of ICON‐ART with a one‐dimensional volcanic plume model calculates volcanic emission, which makes it possible to resolve the different eruption phases of the noncontinuous La Soufrière eruption. We compared our simulated aerosol distribution and composition with observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, the Multiangle Imaging SpectroRadiometer (MISR) Research Aerosol (RA) Algorithm, and the Barbados Cloud Observatory (BCO). We show that online coupling is essential to adequately model the emissions and plume development close to the volcano. The modeled aerosol aging is in very good agreement with observations from MISR near the emission source and with CALIOP at larger distances. Furthermore, particle aging occurs faster in the troposphere than in the stratosphere due to the availability of water vapor and OH, but a layer of coated ash appears at the plume top due to faster oxidation of SO2 and lofting by aerosol‐radiation interaction. This paper gives the first direct comparison of aerosol aging in volcanic eruption plumes between simulations and observations.

Impacts of estimated plume rise on PM2.5 exceedance prediction during extreme wildfire events: a comparison of three schemes (Briggs, Freitas, and Sofiev)

Abstract. Plume height plays a vital role in wildfire smoke dispersion and the subsequent effects on air quality and human health. In this study, we assess the impact of different plume rise schemes on predicting the dispersion of wildfire air pollution and the exceedances of the National Ambient Air Quality Standards (NAAQS) for fine particulate matter (PM2.5) during the 2020 western United States wildfire season. Three widely used plume rise schemes (Briggs, 1969; Freitas et al., 2007; Sofiev et al., 2012) are compared within the Community Multiscale Air Quality (CMAQ) modeling framework. The plume heights simulated by these schemes are comparable to the aerosol height observed by the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). The performance of the simulations with these schemes varies by fire case and weather conditions. On average, simulations with higher plume injection heights predict lower aerosol optical depth (AOD) and surface PM2.5 concentrations near the source region but higher AOD and PM2.5 in downwind regions due to the faster spread of the smoke plume once ejected. The 2-month mean AOD difference caused by different plume rise schemes is approximately 20 %–30 % near the source regions and 5 %–10 % in the downwind regions. Thick smoke blocks sunlight and suppresses photochemical reactions in areas with high AOD. The surface PM2.5 difference reaches 70 % on the West Coast of the USA, and the difference is lower than 15 % in the downwind regions. Moreover, the plume injection height affects pollution exceedance (>35 µg m−3) predictions. Higher plume heights generally produce larger downwind PM2.5 exceedance areas. The PM2.5 exceedance areas predicted by the three schemes largely overlap, suggesting that all schemes perform similarly during large wildfire events when the predicted concentrations are well above the exceedance threshold. At the edges of the smoke plumes, however, there are noticeable differences in the PM2.5 concentration and predicted PM2.5 exceedance region. For the whole period of study, the difference in the total number of exceedance days could be as large as 20 d in northern California and 4 d in the downwind regions. This disagreement among the PM2.5 exceedance forecasts may affect key decision-making regarding early warning of extreme air pollution episodes at local levels during large wildfire events.

Breaking New Ground: Space Agencies and Epidemiologists Partner Up on Particulates

Breaking New Ground: Space Agencies and Epidemiologists Partner Up on Particulates