Thermal infrared dust optical depth and coarse-mode effective diameter retrieved from collocated MODIS and CALIOP observations

In this study, we developed a novel algorithm based on the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared (TIR) observations and dust vertical profiles from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) to simultaneously retrieve dust aerosol optical depth at 10 &micro;m (DAOD10&mu;m) and the coarse-mode dust effective diameter (Deff) over global oceans. The accuracy of the Deff retrieval is assessed by comparing retrieved Deff with the in-situ measured dust particle size distributions (PSDs) from the AER-D, SAMUM-2 and SALTRACE field campaigns through case studies. The new DAOD10&mu;m retrievals were evaluated first through comparisons with the collocated DAOD10.6&mu;m retrieved from the combined Imaging Infrared Radiometer (IIR) and CALIOP observations from our previous study (Zheng et al. 2022). The pixel-to-pixel comparison of the two retrievals indicates a good agreement (R~0.7) and a significant reduction of (~50 %) retrieval uncertainties largely thanks to the better constraint on dust size. In a climatological comparison, the seasonal and regional (5&deg;&times;2&deg;) mean DAOD10um retrievals based on our combined MODIS and CALIOP method are in good agreement with the two independent Infrared Atmospheric Sounding Interferometer (IASI) products over three dust transport regions (i.e., North Atlantic (NA; R = 0.9), Indian Ocean (IO; R = 0.8) and North Pacific (NP; R = 0.7)). Using the new retrievals from 2013 to 2017, we performed a climatological analysis of coarse mode dust Deff over global oceans. We found that dust Deff over IO and NP are up to 20 % smaller than that over NA. Over NA in summer, we found a ~50 % reduction of the number of retrievals with Deff &gt; 5 &mu;m from 15&deg; W to 35&deg; W and a stable trend of Deff average at 4.4 &mu;m from 35&deg; W throughout the Caribbean Sea (90&deg; W). Over NP in spring, only ~5 % of retrieved pixels with Deff &gt; 5 &mu;m are found from 150&deg; E to 180&deg;, while the mean Deff remains stable at 4.0 &mu;m throughout eastern NP. To our best knowledge, this study is the first to retrieve both DAOD and coarse-mode dust particle size over global oceans for multiple years. This retrieval dataset provides insightful information for evaluating dust long-wave radiative effects and coarse mode dust particle size in models.</p

Intercomparison of desert dust optical depth from satellite measurements

This work provides a comparison of satellite retrievalsof Saharan desert dust aerosol optical depth (AOD)during a strong dust event through March 2006. In this event,a large dust plume was transported over desert, vegetated,and ocean surfaces. The aim is to identify the differencesbetween current datasets. The satellite instruments consideredare AATSR, AIRS, MERIS, MISR, MODIS, OMI,POLDER, and SEVIRI. An interesting aspect is that the differentalgorithms make use of different instrument characteristicsto obtain retrievals over bright surfaces. These includemulti-angle approaches (MISR, AATSR), polarisationmeasurements (POLDER), single-view approaches using solarwavelengths (OMI, MODIS), and the thermal infraredspectral region (SEVIRI, AIRS). Differences between instruments,together with the comparison of different retrievalalgorithms applied to measurements from the same instrument,provide a unique insight into the performance andcharacteristics of the various techniques employed. As wellas the intercomparison between different satellite products,the AODs have also been compared to co-located AERONETdata. Despite the fact that the agreement between satellite andAERONET AODs is reasonably good for all of the datasets,there are significant differences between them when comparedto each other, especially over land. These differencesare partially due to differences in the algorithms, such as assumptionsabout aerosol model and surface properties. However,in this comparison of spatially and temporally averageddata, it is important to note that differences in sampling, relatedto the actual footprint of each instrument on the heterogeneousaerosol field, cloud identification and the qualitycontrol flags of each dataset can be an important issue

Satellite-based time-series of sea-surface temperature since 1981 for climate applications

A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 10^12 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km^2 and 45 km^2. The mean density of good-quality observations is 13 km^−2 yr^−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr^−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics

Dust source identification using MODIS: a comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069–17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557–569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10–11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy

Working with the enemy? Social work education and men who use intimate partner violence

This article examines service user involvement in social work education. It discusses the challenges and ethical considerations of involving populations who may previously have been excluded from user involvement initiatives, raising questions about the benefits and challenges of their involvement. The article then provides discussion of an approach to service user involvement in social work education with one of these populations, men who use violence in their intimate relationships, and concludes by considering the implications of their involvement for the social work academy

Radiative Effect of Mixed Mineral Dust and Biomass Burning Aerosol in the Thermal Infrared

This thesis treats the optical properties of mixed mineral dust and biomass burning aerosol in the thermal infrared (TIR) based on Fourier Transform infrared spectrometer (FTIR) measurements and radiative transfer simulations. The measurements were part of the Saharan Mineral Dust Experiment 2 (SAMUM-2) conducted from January to February 2008 at Praia, Cape Verde. The large amount of different instruments co-located at the main field site during the campaign resulted in a unique dataset comprising in-situ information and remote sensing data perfectly suited for column closure studies. The ultimate goal of this work is to investigate the consistency of microphysical and TIR remote sensing data. This is achieved by reproducing the measured radiances at top and bottom of the atmosphere (TOA, BOA) with a radiative transfer model, which assimilates the microphysical aerosol information gathered during SAMUM-2. The first part of the thesis describes several experimental efforts, including a novel calibration method and a drift correction algorithm for the ground-based FTIR instrument operated within the scope of SAMUM-2 by the author. The second part introduces the concurrent radiative transfer library PIRATES, which has been developed in the framework of this thesis for the analysis of TIR aerosol optical properties. The third and final part of the treatise compares measured and simulated spectra for various typical scenarios encountered during SAMUM-2. It is demonstrated in three case studies, that measured radiances in the TIR atmospheric window region (8-12 µm) can be reproduced at BOA and TOA by radiative transfer simulations assuming spheroidal model particles. Moreover, spherical particles are shown to be an inadequate model for mineral dust aerosol in this spectral region unless the aerosol optical depth is small

Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer

Aerosols in the Arctic cause radiative forcing and a variety of climatic feedbacks, which affect climate of both local and global scales. In order to assess the state of the Arctic climate, information on the aerosol type and amount is needed. Harsh conditions and remoteness of the Arctic region result in very few ground based measurements of aerosol optical thickness. Remote sensing has the potential to provide the necessary temporal and spatial coverage. A non-trivial task of aerosol retrieval over a very bright surface is being solved within the thesis; the developed retrieval consists of cloud screening over snow and two types of aerosol retrieval over snow - in the visible and infrared spectral regions. A number of validation and case studies has been performed to assess the quality of the retrieval. The developed algorithm applies to the data of Advanced Along Track Scanning Radiometer and produces maps of aerosol optical thickness over snow and ice

Studying Clouds and Aerosols with Lidar Depolarization Ratio and Backscatter Relationships

This dissertation consists of three parts, each devoted to a particular issue of significant importance for CALIPSO lidar observation of depolarization ratio (delta) and backscatter (gamma?) to improve current understanding of the microphysical properties of clouds and aerosols. The relationships between depolarization ratio and backscatter allow us to retrieve particle thermodynamic phase and shape and/or orientation of aerosols and clouds. The first part is devoted to the investigation of the relationships between lidar backscatter and the corresponding depolarization ratio for different cloud classifications and aerosol types. For each cloud and aerosol types, layer-averaged backscatter and backscattering depolarization ratio from the CALIPSO measurements are discussed. The present results demonstrate the unique capabilities of the CALIPSO lidar instrument for determining cloud phase and aerosols subtypes. In the second part, we evaluate the MODIS IR cloud phase with the CALIPSO cloud products. The three possible misclassifications of MODIS IR cloud phasealgorithm, which are studied by Nasiri and Kahn (2008) with radiative transfer modeling, are tested by comparing between MODIS IR phase and CALIOP observations. The current results support their hypotheses, which is that the MODIS phase algorithm may tend to classify thin cirrus clouds as water clouds or mixed phase clouds or unknown, and classify midlevel and/or mid-temperature clouds as mixed or unknown phase. In the third part, we present a comparison of mineral dust aerosol retrievals from two instruments, MODIS and CALIPSO lidar. And, we implement and evaluate a new mineral dust detection algorithm based on the analysis of thin dust radiative signature. In comparison, three commonly used visible and IR mineral dust detection algorithms, including BTD procedure, D parameter method, and multi-channel image algorithm, are evaluated with CALIPSO aerosol classification. The comparison reveals that those dust detection algorithms are not effective for optically thin dust layers, but for thick dust storm. The new algorithm using discriminant analysis with CALIPSO observation is much better in detecting thin dust layer of optical thickness between 0.1 and 2