Recent Submissions

  • Load-Altering Attacks Against Power Grids Under COVID-19 Low-Inertia Conditions

    Lakshminarayana, Subhash; Ospina, Juan; Konstantinou, Charalambos (IEEE, 2023-07-16) [Presentation]
    The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studies have shown that the low demand conditions caused by COVID-19 lockdowns combined with large renewable generation have resulted in extremely low-inertia grid conditions. In this work, we examine how an attacker could exploit these scenarios to cause unsafe grid operating conditions by executing load-altering attacks (LAAs) targeted at compromising hundreds of thousands of IoT-connected high-wattage loads in low-inertia power systems. Our study focuses on analyzing the impact of the COVID-19 mitigation measures on U.S. regional transmission operators (RTOs), formulating a plausible and realistic least-effort LAA targeted at transmission systems with low-inertia conditions, and evaluating the probability of these large-scale LAAs. Theoretical and simulation results are presented based on the WSCC 9-bus and IEEE 118-bus test systems. Results demonstrate how adversaries could provoke major frequency disturbances by targeting vulnerable load buses in low-inertia systems and offer insights into how the temporal fluctuations of renewable energy sources, considering generation scheduling, impact the grid’s vulnerability to LAAs.
  • Laser-Induced Engineering of Nanomaterial Phase and Shape for 3D Light Control at the Nanoscale

    Elizarov, Maxim; Li, Ning; Fratalocchi, Andrea (IEEE, 2023-06-26) [Presentation]
    The control of light at the nanoscale through new nanofabrication techniques has garnered significant attention in research [1]. Recent advancements in fabrication methodologies have focused on processing specific geometrical patterns in a given material. However, a technique that allows point-to-point control over both material phase and geometrical shape is currently unavailable. Addressing this problem can open new pathways for controlling materials' optical response, leading to new levels of performance of photonic devices.
  • Two-Photon Lensless Endoscopes with Multicore Fibers

    Moussawi, Fatima El; Hofer, Matthias; Sivankutty, Siddharth; Bertoncini, Andrea; Labat, Damien; Cassez, Andy; Bouwmans, Geraud; Cossart, Rosa; Vanvincq, Olivier; Liberale, Carlo; Rigneault, Herve; Andresen, Esben Ravn (IEEE, 2023-06-26) [Presentation]
    The lensless endoscope represents the ultimate limit in miniaturization of imaging tools: an image can be transmitted by numerical or physical inversion of the mode scrambling process through a bare optical fiber. Lensless endoscopes featuring multicore fibers and spatial light modulators are well adapted for nonlinear imaging as they minimally distort ultrashort pulses in the time domain as opposed to multimode fibers [1]. And in earlier works, we had addressed the issues of imaging artifacts and bending sensitivity with an helically twisted multicore fiber with a sparse and aperiodic core layout in the transverse plane [2]. However sufficiently irradiating the sample plane remained a major challenge - particularly for the imaging of dim and challenging samples such as neurons in scattering media.
  • Classification Of Environmental Micro-Fibres Using Stimulated Raman Microspectroscopy

    Laptenok, Siarhei; Genchi, Luca; Martin, C.; Balkhair, Fadia; Duarte, Carlos M.; Liberale, Carlo (IEEE, 2023-06-26) [Presentation]
    The mass usage of plastic materials in daily life has exponentially increased the amount of plastic waste in the environment. Various environmental factors degrade bigger plastic debris into smaller microplastic particles (less than 5 mm in size). Those particles have been detected in every possible environment, from oceans to fresh bottled water, from deserts to agricultural soils, food and air, and human blood. Due to its long degradation time and high surface-to-value ratio, microplastic can become an efficient vehicle for various pollutants that can accumulate in time. Therefore, accumulation in the human tissue will likely have a negative long-term effect. Microfibers are considered the most abundant microparticle type in the environment - their size (small, often < 15 μm in diameter, and relatively long length) and light weight allow easy and fast distribution even using aerial pathways.
  • Design Optimization of On-Chip III-V/SiN Quantum Well/Dot Lasers

    Alkhazraji, Emad; Chow, Weng W.; Grillot, Frederic; Bowers, John E.; Madaras, Scott E.; Gehl, Michael; Skogen, Erik; Wan, Yating (IEEE, 2023-06-26) [Presentation]
    We performed a parametric study of the design of an integrated III-V/SiN distributed feedback (DFB) quantum well (QW) and quantum dot (QD) lasers involving detailed and comprehensive modeling and multi-objective performance optimization. The study aims to maximize the potential laser linewidth reduction in InP/Si lasers coupled with SiN microring resonators. The design of the complex structures in such devices requires a large parameter space to explore for design engineering. This investigation and the formulated theory serve as an analytical tool for parametric studies to produce timely results for design engineering and optimization.
  • Metasurface Light Encoders Enable Real-Time Hyperspectral Imaging and Video Understanding

    Makarenko, Maksim; Burguete-Lopez, A.; Wang, Qizhou; Getman, Fedor; Giancola, Silvio; Ghanem, Bernard; Fratalocchi, Andrea (IEEE, 2023-06-26) [Presentation]
    Hyperspectral imaging has emerged as a powerful tool for identifying and remotely sensing complex materials in a wide range of applications, including medical diagnostics, security, food safety, and precision agriculture [1], [2]. Despite significant advances in this field, there remain a number of challenges that must be addressed in order to fully realize the potential of hyperspectral imaging in real-world applications. One major challenge is the high cost and slow acquisition time of current state-of-the-art hyperspectral imaging systems, which can exceed 20.000 USD for a single camera and take up to a minute to acquire a single image[3]. Additionally, existing systems are often limited by low spatial resolution and require large amounts of memory storage.
  • Dual-Comb Spectroscopy in the Water-Transparent 8-12 μm Region

    Moretti, L.; Walsh, M.; Gatti, D.; Lamperti, M.; Genest, J.; Farooq, Aamir; Marangoni, M. (IEEE, 2023-06-26) [Presentation]
    Dual-comb spectroscopy (DCS) is emerging as a technique of choice to measure gas absorption spectra over broad bands at high temporal resolution [1]. The extension of this technique to the mid-infrared is crucial for chemical kinetic studies in combustion environments, where a typically short absorption path length must be compensated by strong absorption features to enhance the signal-to-noise ratio over a short measurement time. It was only very recently that DCS has been demonstrated at full resolution [2], i.e. without apodization [3], in the spectroscopically rich water-free 8–12 μm absorption region. As a drawback, this was achieved with a rather complex apparatus driven by self-referenced Cromium combs at a repetition frequency (frep) of only 80 MHz.
  • Machine Learning Empowers Large-Scale Optical Sensors for Ultrasensitive Detection

    Li, Ning; Wang, Qizhou; He, Zhao; Burguete-Lopez, A.; Xiang, Fei; Fratalocchi, Andrea (IEEE, 2023-06-26) [Presentation]
    Optical sensors are stirring broad interests in disease diagnostics, food safety, and environment monitoring [1]––[3]. Several criteria assess the performance of a sensor, including the analytical detection speed, cost, sensitivity, and reproducibility [4], [5]. Traditionally, optical sensing leverages localized spectral features such as e.g., resonance peaks shift, intensity variations, and widths. This approach, while straightforward in implementation, results in a weak detection limit for analytes, and needs improvement for enabling practical applications. Recent pioneering work focuses on artificial intelligence (AI) to address this issue, leveraging sparse features in broad amounts of data to enhance the sensor detection sensitivity [6]. However, most of these approaches rely on post-processing data collected with complex equipment, such as spectrum analyzers. These systems are significantly expensive, not integrated, and compete poorly with traditional sensing based on localized features.
  • Record Efficient and Stable Si-Based Photoanodes Enabled by Ultrathin Transition-Metal Alloy Film for Solar-Assisted Water Splitting

    Xiang, Fei; Li, Ning; Burguete-Lopez, A.; He, Zhao; Elizarov, Maxim; Fratalocchi, Andrea (IEEE, 2023-06-26) [Presentation]
    Photoelectrochemical (PEC) water splitting is attracting tremendous research interest as a promising energy conversion and storage route to ease the reliance on fossil fuels and contribute to sustainable development [1]–[3]. It produces clean hydrogen fuels from solar energy and water with zero carbon emissions and requires much less external bias input than the traditional pure electrolyzing system [4], [5]. Since the sluggish kinetics of water oxidation reaction at the anode side bottleneck the overall performance of the entire device, current research focuses on enhancing the catalytic performance at the photoanodes [4], [6].
  • Metrology System Based on Metasurface Implementation of Artificial Inteligence

    Burguete-Lopez, A.; Makarenko, Maksim; Wang, Qizhou; Getman, Fedor; Fratalocchi, Andrea (IEEE, 2023-06-26) [Presentation]
    Optical instrumentation is ubiquitous across scientific disciplines and industrial settings for its ability to deliver non-destructive and high accuracy mesurements [1]. However, as global manufacturing transitions an automated industry paradigm, the need for highly integrated metrology systems for autonomous machines cannot be addressed traditional bulk optics based equipment [2]. Metasurfaces provide a possible solution to this challenge, enabling near-arbitrary light control functionality in a compact form factor [3]–[5].
  • Novel approaches for in situ extraction of heterologous metabolites from living microalgae cultures

    Overmans, Sebastian; Lauersen, Kyle J. (2023-06-14) [Presentation]
    Genetic engineering of microbes is an established technology used to enhance the production of heterologous metabolites which opens new opportunities for the sustainable production of valuable compounds. These technologies have recently been applied to microalgae, especially in the model Chlamydomonas reinhardtii. At small scale, petroleum-based hydrophobic solvents such as dodecane can be added on top of microbial cultures to capture hydrocarbon products like isoprenoids. However, this approach has considerable downsides at larger scales, such as the difficulty of handling organic solvents, potential contamination with unwanted byproducts, and the formation of micelles at the culture-solvent interface during gas sparging. Here, we present three promising novel methods to extract the C. reinhardtii-produced heterologous isoprenoid patchoulol from living culture. This work includes the use of a hydrophobic hollow-fiber membrane contactor to provide a large surface area between algae culture and a dodecane phase. Subsequent continuous membrane extraction of patchoulol from dodecane enables product concentration in a methanol stream and dodecane recovery for its reuse. Perfluorocarbon liquids (FCs) and functionalized silica microparticles can also act as physical sinks for microbially produced isoprenoids during cultivation, but unlike dodecane, they are stable, inert, and amenable to direct liquid-liquid/solid-liquid extraction with alcohols for rapid product isolation. The ability of FCs to form distinct high-density underlays enables the cultivation of microbes at the FC-culture medium interface, which further extends their application to filamentous or mat-forming organisms. The three approaches presented here are all promising alternatives to traditional dodecane-overlay extractions that can open new avenues in bioprocess design for engineered microalgal chassis heterologous metabolite milking.
  • Hybrid 2D/CMOS Microchips for Memristive Technologies

    Lanza, Mario (IEEE, 2023-05-31) [Presentation]
    Two-dimensional layered materials (2D-LMs) materials have outstanding physical, chemical and thermal properties that make them attractive for the fabrication of solid-state micro/nano-electronic devices and circuits. However, synthesizing high-quality 2D-LMs at the wafer scale is difficult, and integrating them in semiconductor production lines brings associated multiple challenges. Nevertheless, in the past few years substantial progress has been achieved and leading companies like TSMC, Samsung and Imec have started to work more intensively on the fabrication of devices using 2D-LMs. In this invited talk, I will present our hybrid 2D/CMOS microchips for memristive operations. I will show state-of-the-art performance as electronic memory, as well as other novel properties that traditional microchips don't exhibit, and that enable different innovative applications.
  • Inverse Modeling of the Initial Stage of the 1991 Pinatubo Volcanic Cloud Accounting for Radiative Feedback of Volcanic Ash

    Ukhov, Alexander; Stenchikov, Georgiy L.; Osipov, Sergey; Krotkov, Nickolay; Gorkavyi, Nick; Li, C.; Dubovik, Oleg; Lopatin, Anton (Copernicus GmbH, 2023-02-26) [Presentation]
    The evolution of volcanic clouds is sensitive to the initial three-dimensional (3D) distributions of volcanic material, which are often unknown. Here, we conduct inverse modeling of the fresh Mt. Pinatubo cloud to estimate the time-dependent emissions profiles and initial 3D spatial distributions of volcanic ash and SO2. We account for aerosol radiative feedback and dynamic lofting of volcanic ash. It results in a lower (by 1 km for ash) injection height than that without ash radiative feedback. The solution captures the elevated ash layer between 14 and 24 km and the meridional height gradient during the first two days after an eruption. A significant fraction of the emissions (i.e., 6/16.6 Mt of SO2 and 34/64.22 Mt of fine ash) did not reach the stratosphere. The results demonstrate that the Pinatubo eruption ejected ~78% of fine ash at 12 to 23 km, ~64% of SO2 at 17 to 23 km, and most of the ash and SO2 mass for the first two days after the eruption resides in the 15- to 22- km layer. 6 Mt of tropospheric SO2 oxidized into sulfate aerosol within a week. This outcome might help to explain the discrepancies between the observations and model simulations recently discussed in the literature. The long-term evolution of the Pinatubo aerosol optical depth simulated using the obtained ash and SO2 initial distributions converges with the available stratospheric aerosol and gas experiment (SAGE) observations a month after the eruption when the tropospheric aerosol cloud dissipated.
  • A Preliminary Green Function Database for Global 3-D Centroid Moment Tensor Inversions

    Sawade, Lucas; Ding, Liang; Peter, Daniel; Gharti, Hom Nath; Liu, Qinya; Nettles, Meredith; Ekström, Göran; Tromp, Jeroen (Copernicus GmbH, 2023-02-26) [Presentation]
    Currently, the accuracy of synthetic seismograms used for Global CMT inversion, which are based on modern 3D Earth models, is limited by the validity of the path-average approximation for mode summation and surface-wave ray theory. Inaccurate computation of the ground motion’s amplitude and polarization as well as other effects that are not modeled may bias inverted earthquake parameters. Synthetic seismograms of higher accuracy will improve the determination of seismic sources in the CMT analysis, and remove concerns about this source of uncertainty. Strain tensors, and databases thereof, have recently been implemented for the spectral-element solver SPECFEM3D (Ding et al., 2020) based on the theory of previous work (Zhao et al., 2006) for regional inversion of seismograms for earthquake parameters. The main barriers to a global database of Green functions have been storage, I/O, and computation. But, compression tricks and smart selection of spectral elements, fast I/O data formats for high-performance computing, such as ADIOS, and wave-equation solution on GPUs, have dramatically decreased the cost of storage, I/O, and computation, respectively. Additionally, the global spectral-element grid matches the accuracy of a full forward calculation by virtue of Lagrange interpolation. Here, we present our first preliminary database of stored Green functions for 17 seismic stations of the global seismic networks to be used in future 3-D centroid moment tensor inversions. We demonstrate the fast retrieval and computation of seismograms from the database.
  • Quantifying lifetime water scarcity

    Vanderkelen, Inne; Davin, Édouard; Keune, Jessica; Miralles, Diego G.; Wada, Yoshihide; Müller-Schmied, Hannes; Gosling, Simon; Pokhrel, Yadu; Satoh, Yusuke; Hanasaki, Naota; Burek, Peter; Ostberg, Sebastian; Grant, Luke; Taranu, Sabin; Mengel, Matthias; Volkholz, Jan; Thiery, Wim (Copernicus GmbH, 2023-02-25) [Presentation]
    Water scarcity is a growing concern in many regions worldwide, as demand for clean water increases and supply becomes increasingly uncertain under climate change. Already today, more than 4 billion people experience water scarcity at least one month per year (Mekonnen and Hoekstra, 2016). Developing socio-economic conditions and growing population increase water demands, while climate change leads to changes in freshwater availability. Most studies quantify water scarcity in discrete time windows, with fixed population and climate change signals (e.g., 30 years or long-term averages). Recently, however, Thiery et al. (2021) proposed a novel approach, in which climate change impacts are integrated over a person's lifetime. In this cohort perspective, lifetime impact values are comparable across generations and regions. Evaluating this perspective for natural hazards, they showed, for example, that a newborn will experience a sixfold increase in drought exposure compared to a 60-year-old (Thiery et al., 2021). In this study, we use this cohort perspective to study how much water scarcity a person experiences during their lifetime. Based on monthly fluctuations in water demand and availability, we estimate the total amount of water demand not met and refer to it as 'lifetime water deficit'. To this end, we use an ensemble of four global hydrological models (MATSIRO, CWatM, LPJmL and H08), each forced by four GCMs and two RCP scenarios from the InterSectoral Impact Model Intercomparison Project (ISIMIP2b). The simulations account for varying population and socio-economic conditions in the historical and future period, following the SSP2 scenario. Combined with country-based population, cohort distribution and life expectancies, lifetime water deficits are quantified for different generations on a country level. Our findings reveal high water lifetime deficit values for regions that are already water scarce today, such as the Mediterranean, North Africa and the Middle East. In these regions, more than 70% of the lifetime water demand is not met when needed. Further comparison reveals differences in spatial, intergenerational and climate change scenarios, and provides information on different scenarios. Overall, this study provides a new perspective on quantifying water scarcity and the climate and population impacts.
  • Simulation of ground motions with high frequency components obtained from Fourier neural operators

    Aquib, Tariq Anwar; Mai, Paul Martin (Copernicus GmbH, 2023-02-25) [Presentation]
    Seismic hazards analysis relies on accurate knowledge of ground motions arising from potential earthquakes to assess the risk of damage to buildings and infrastructure. It is necessary to perform ground motion simulations because recorded strong-motion data from specific combinations of earthquake magnitudes, epicentral distances, and site conditions are still limited. Physics-based simulations provide reliable ground motion estimates, but their application in practice is limited to frequency ranges f < 1Hz, largely due to limited computational resources and lack of information regarding earthquake sources and medium. While hybrid ground-motion computations combining deterministic low frequency components with stochastic high frequency components are often used, their stochastic high frequency components fail to correctly account for source and path effects and lead to inconsistent building responses. The large database of ground motion records from Japan lends itself to develop machine learning approaches to estimate high frequency ground motions. Applying state-of-the-art machine learning techniques, like Fourier neural operators (FNOs) and Generative Adversarial Networks (GANs), we estimate seismograms at higher frequencies from their low frequency counterparts. In our approach, the time and frequency properties of ground motions are estimated using two different FNO models. In the time domain, a relationship is established between normalised low pass filtered and broadband waveforms. Frequency domain analysis involves the learning of high frequency spectrum from low frequency spectrum. Finally, the time and frequency properties are combined to produce broadband ground motions. Source, site, and path aspects are naturally incorporated into the training process. We use ground motion data collected between 1996 and 2020 at 18 stations in the Ibaraki province of Japan to train our models and validate them on different events (Mw 4 to 7) around Japan. Using goodness of fit measures (GOFs), we show that the resulting ground motions match the recorded observations with good to acceptable GOF values for most of the predictions. To enhance our predictions, we include uncertainty estimation by utilizing a conditioned GAN approach. Lastly, to demonstrate the practicality of the approach, we compute high frequency components for a physics based simulated hypothetical Mw 5.0 earthquake in Japan.
  • Global biogeography of the glacier-fed stream microbiome

    Ezzat, Leila; Peter, Hannes; Bourquin, Massimo; Michoud, Grégoire; Fodelianakis, Stilianos; Kohler, Tyler; Lamy, Thomas; Busi, Susheel; Daffonchio, Daniele; Deluigi, Nicola; De Staercke, Vincent; Marasco, Ramona; Pramateftaki, Paraskevi; Schön, Martina; Styllas, Michail; Tolosano, Matteo; Battin, Tom (Copernicus GmbH, 2023-02-22) [Presentation]
    Glacier-fed streams (GFSs) serve as headwaters to many of the world’s largest river networks. Although being characterized by extreme environmental conditions (i.e., low water temperatures, oligotrophy) GFSs host an underappreciated microbial biodiversity, especially within benthic biofilms which play pivotal roles in downstream biogeochemical cycles. Yet, we still lack a global overview of the GFS biofilm microbiome. In addition, little is known on how environmental conditions shape bacterial diversity, and how these relationships drive global distribution patterns. This is particularly important as mountain glaciers are currently vanishing at a rapid pace due to global warming. Here, we used 16S rRNA gene sequencing data from the Vanishing Glaciers project to conduct a first comprehensive analysis of the benthic microbiome from 148 GFSs across 11 mountain ranges. Our analyses revealed marked biogeographic patterns in the GFS microbiome, mainly driven by the replacement of phylogenetically closely related taxa. Strikingly, the GFS microbiome was characterized by pronounced level of endemism, with >58% of the Amplicon Sequence Variants (ASVs) being specific to one mountain range. Consistent with the marked dissimilarities across mountain ranges, we found a very small taxonomic core including only 200 ASVs, yet accounting for >25% of the total relative abundance of the ASVs. Finally, we found that spatial effects such as dispersal limitation, isolation and spatially autocorrelated environmental conditions overwhelmed the effect of the environment by itself on benthic biofilm beta diversity. Our findings shed light on the previously unresolved global diversity and biogeography of the GFS microbiome now at risk across the world’s major mountain ranges because of rapidly shrinking glaciers.
  • The potential application of statistical post processing techniques on landslide early warning system

    Wang, Xuetong; Lombardo, Luigi; Tanyas, Hakan (Copernicus GmbH, 2023-02-22) [Presentation]
    With the increase of frequency and intensity of heavy precipitation in the future, rainfall triggered landslides (RTL) can be one of the major threat to human life and property security. Early warning systems of natural hazards are one of the most effective measure for reducing disaster losses and risks. However, the forecast of RTL in near-real-time (NRT) is extremely difficult since the quality of NRT precipitation data is relatively poor. Quantile regression forest (QRF), a state-of-the-art statistical postprocessing method, has been proved to reduce the difference existing between NRT satellite precipitation estimates and ground-based rainfall data. When predicted rainfall maps are put side by side with raw NRT satellite product, the pattern of the first matches much more closely the locations where landslide events have been mapped in a test site in North-Eastern Turkey. This leave an optimistic perspective on the application of statistical postprocessing techniques in the field of weather science and in general for natural hazard assessment. Ideally, by correcting the continuous information in space and time provided by satellite rainfall estimates, one could create a new operational tool for landslide early warning system, not bound to the financial and deployment requirement typical of rain gauge and terrestrial radar stations.
  • The effects of coarse dust in the models and observations in the dust source regions

    Stenchikov, Georgiy L.; Mostamandi, Suleiman; Shevchenko, Illia; Ukhov, Alexander (Copernicus GmbH, 2023-02-22) [Presentation]
    In dust source regions, such as the Middle East, dust is a major environmental factor affecting climate, air quality, and human health. Dust also hampers solar energy harvesting by weakening downward solar flux and depositing on optically active surfaces of solar energy devices. In this study, we combine fine-resolution WRF-Chem simulations with size-segregated measurements of dust deposition to quantify the contribution of coarse (2.5 um < r < 10 um) and giant (10 um <r < 100 um) dust particles in aerosols radiative forcing and deposition rates. Most up-to-date models do not represent the particles with r > 10 um. The absence of large particles in the models does not significantly affect the radiative fluxes, as their contribution to AOD is relatively small, but they comprise the most dust-deposited mass. We found that dust deposition rates calculated in WRF-Chem and reanalysis products are 2-3 times smaller than the observed. However, the deposition rate of particulate matter with a diameter smaller than 10 um (PM10) is in good agreement between the models and observations. In the Middle East, fine dust particles are predominantly responsible for the significant reduction (> 5 %) of the downward solar flux hampering solar energy production. Still, dust-deposited mass, primarily associated with coarse particles, causes about a 2% loss of PV panel efficiency daily due to soiling. As was suggested previously, WRF-Chem, like many other models, tends to overestimate the atmospheric concentration of fine (r < 2.5 um) dust particles and underestimate the concentration of coarse particles. As seen from the comparison of the size distribution of deposited dust in simulations and observations, the latter is caused not as much by too fast deposition of large particles but due to underestimating their emission in the models.
  • Sensitivity of African Easterly Waves to Dust Direct Radiative Forcing

    Bangalath, Hamza Kunhu; Raj, Jerry; Stenchikov, Georgiy L. (Copernicus GmbH, 2023-02-22) [Presentation]
    African Easterly Waves (AEWs) are the most important precipitation-producing dynamic systems in tropical Africa and Atlantic, where dust in the atmosphere is abundant. But the past studies lack consensus on the sign and magnitude of the dust radiative forcing impact on AEWs primarily because of the disagreement in calculating dust solar radiation absorption. The incapability of coarse-resolution models to represent various dust-AEW interactions is another source of uncertainty. The present study uses a high-resolution atmospheric general circulation model, HiRAM, to investigate the sensitivity of AEWs to the dust direct radiative forcing when dust shortwave absorption varies within the observed limits. Global simulations are conducted with the 25 km grid spacing to adequately simulate AEWs and associated circulation features. Four 10-year experiments are conducted: One control experiment without dust and three others with dust assuming dust is an inefficient, standard, and very efficient shortwave absorber. The results show that AEWs are highly sensitive to dust shortwave absorption. As dust shortwave absorption increases, AEW activity intensifies and broadens the wave track shifting it southward. The 6-9 day waves are more sensitive to dust shortwave absorption than the 3-5 day waves, where the response in the former has a stark land-sea contrast. The sensitivity of AEW to dust solar radiation absorption arises from a combination of energy conversion mechanisms. Although baroclinic energy conversion dominates the energy cycle, the responses to dust shortwave heating in barotropic and generation terms are comparable to that in baroclinic conversion.

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