Simulation Of Dust Storms Research Articles

Effects of Dust Storm and Wildfire Events on Phytoplankton Growth and Carbon Sequestration in the Tasman Sea, Southeast Australia

Dust storms and wildfires occur frequently in south-eastern Australia. Their effects on the ecology, environment and population exposure have been the focus of many studies recently. Dust storms do not emit ground-sequestered carbon, but wildfires emit significant quantities of carbon into the atmosphere. However, both natural events promote phytoplankton growth in water bodies because carbon, and other trace elements such as iron, deposit on the surface water of oceans. Carbon dioxide is reabsorbed by phytoplankton via photosynthesis. The carbon balance cycle due to dust storms and wildfires is not well known. Recent studies on the carbon emission of the 2019–2020 summer wildfires in eastern Australia indicated that this megafire event emitted approximately 715 million tonnes of CO2 (195 Tg C) into the atmosphere from burned forest areas. This study focusses on the association of dust storms and wildfires in southeastern Australia with phytoplankton growth in the Tasman Sea due to the February 2019 dust storm event and the 2019–2020 Black Summer wildfires. Central Australia and western New South Wales were the sources of the dust storm emission (11 to 16 February 2019), and the Black Summer wildfires occurred along the coast of New South Wales and Victoria (from early November 2019 to early January 2020). The WRF-Chem model is used for dust storm simulation with the AFWA (Air Force Weather Agency of the US) dust emission version of the GOCART model, and the WRF-Chem model is used for wildfire simulation with FINN (Fire Emission Inventory from NCAR) emission data. The results show the similarities and differences in the deposition of particulate matter, phytoplankton growth and carbon reabsorption patterns in the Tasman Sea from these events. A higher rate of deposition of PM2.5 on the ocean surface corresponds to a higher rate of phytoplankton growth. Using the WRF-Chem model, during the 5-day dust storm event in February 2019, approximately ~1230 tons of total dust was predicted to have been deposited in the Tasman Sea, while ~132,000 tons of PM10 was deposited in the early stage of the wildfires from 1 to 8 November 2019.

Improvement in Dust Storm Simulation by Considering Stone Coverage Effects for Stony Deserts in East Asia

AbstractApproximately 50% of the Earth's deserts are covered with stony surfaces, not dunes. The stony surfaces often block or diminish mineral dust aerosol emissions through area fraction and roughness element effects. Incorporating these stone coverage effects is crucial for climate and environmental modeling research. Based on our field observations, this study combined the stone coverage effects into a dust simulation model for East Asia using two regression formulas and some constants. The double regression scheme assumed that the stone roughness density could be derived from the coarse fragment fraction of the SoilGrids 2.0 data set. According to the data set, the stone coverage is higher in Western Mongolia and Dzungaria and lower in the Chinese Gobi Desert and the Loess Plateau. Consequently, the model reproduced fewer dust aerosols in the higher coverage areas and more in the lower coverage areas. This simulation result was consistent with the World Meteorological Organization's current weather reports and satellite aerosol observations. The improved model reproduced the diversity of soil erodibility and was well balanced in performance statistics. This study is the first successful investigation of stone coverage effects on dust storm simulation using a realistic stone coverage map to the authors' best knowledge.

Dust storm simulation over the Sahara Desert (Moroccan and Mauritanian regions) using HYSPLIT

AbstractSaharan dust storms are major events that occur normally in the summer and affect the air quality in various regions of the world. In particular, Saharan regions in Morocco and Mauritania actively contribute to dust storms. The Saharan outbreak that took place between 14 and 19 of June 2020 was one of the most severe Saharan dust storms in recent years. This paper investigates the PM10 emissions and concentrations during the 4 days of the dust storm in the region of Western Sahara of Morocco and Mauritania and the transport of the PM10 from the area of study to the Caribbean Sea and the Gulf of Mexico using Hybrid Single‐Particle Lagrangian Integrated Trajectory Model (HYSPLIT) software with PM10 emission model and cluster analysis. We also analyse the effect of the transported PM10 particles on the concentration level in the Southern parts of the United States and the Martinique islands. The results showed that the average PM10 concentration below the altitude of 100 m during the dust storm was higher than 100 μg/m3 in most of the regions such as Dakhla in Morocco, Nouakchott, Adrar and Tiris Zemmour in Mauritania. This is confirmed by Aerosol Optical Depth (AOD) values between 0.7 and 1 retrieved by MODIS‐Aqua for those areas. Furthermore, PM10 particles transported across the Atlantic Ocean affected the concentrations observed in the Caribbean Basin, where hourly PM10 reached 372 μg/m3 and the dust top layer was found between 4 and 4.5 km above ground level. In addition, HYSPLIT cluster analysis results revealed several PM10 particle source areas in Western Sahara such as Bir Anzerane in Morocco, Nouakchott and Tichit in Mauritania that contributed to the increase of PM10 concentrations to an Unhealthy level in the Texas and Florida States in the United States.

Numerical simulations of dust storms originated from dried lakes in central and southwest Asia: The case of Aral Sea and Sistan Basin

Ephemeral and dried lakes in southwest and central Asia, such as the Aral Sea and Hamouns in east Iran, are major sources of dust storms with an increasing tendency during the last decades due to anthropogenic influence and climate change. This study examines two characteristic dust-storm events originated from these areas on 12–15 July 2016 and 27–28 May 2018. Thermal low-pressure systems over topographic-low areas and the Caspian Sea High, along with pressure gradients and intense surface winds over the dust sources facilitated the dust outbreaks and transport. Three models (DREAM-NMME-MACC, CAMS and WRF-Chem) are synergized to simulate the spatial distribution of AOD and surface dust concentrations during the dust events. The results show that DREAM-NMME-MACC and WRF-Chem exhibit the highest discrepancies (R = 0.15–0.58; RMSE = 83%–125%) in representing the spatial and temporal distribution of dust compared to Terra-MODIS AODs, while CAMS reveals the best performance (R = 0.29–0.77; RSME = 67%–124%). All models significantly underestimate the high MODIS AODs, especially near the source areas, due to different dust schemes, soil conditions, meteorology and dynamic processes for dust emissions that they comprise. Furthermore, notable differences between the models are revealed in simulations of the PM 10 concentrations in Zabol, east Iran, as the models fail to reproduce the temporal evolution and high intensity of the dust event. In general, all models represent better the dust storm originated from Sistan (13–14 July 2016) rather than the Aralkum dust storm (27–28 May 2018), indicating an incapability in representing the soil characteristics in a progressively drying terrain.

A geospatial hybrid cloud platform based on multi-sourced computing and model resources for geosciences

ABSTRACTCloud computing has been considered as the next-generation computing platform with the potential to address the data and computing challenges in geosciences. However, only a limited number of geoscientists have been adapting this platform for their scientific research mainly due to two barriers: 1) selecting an appropriate cloud platform for a specific application could be challenging, as various cloud services are available and 2) existing general cloud platforms are not designed to support geoscience applications, algorithms and models. To tackle such barriers, this research aims to design a hybrid cloud computing (HCC) platform that can utilize and integrate the computing resources across different organizations to build a unified geospatial cloud computing platform. This platform can manage different types of underlying cloud infrastructure (e.g., private or public clouds), and enables geoscientists to test and leverage the cloud capabilities through a web interface. Additionally, the platform also provides different geospatial cloud services, such as workflow as a service, on the top of common cloud services (e.g., infrastructure as a service) provided by general cloud platforms. Therefore, geoscientists can easily create a model workflow by recruiting the needed models for a geospatial application or task on the fly. A HCC prototype is developed and dust storm simulation is used to demonstrate the capability and feasibility of such platform in facilitating geosciences by leveraging across-organization computing and model resources.

Sensitivity studies of the forth-generation regional climate model simulation of dust storms in the Sistan plain, Iran

Dust storm phenomenon is one of the main features over the Sistan plain, eastern Iran. To examine of dust storm structure in the Sistan area, an experiment was conducted by forth-generation regional climate model in an area between 20°–48°N and 42°–78°E during the cold period of 2000 and 2002 years with two horizontal resolutions (20 and 80 km) in 6-h time step. Validation of satellite data and NCEP/NCAR re-analysis were used to stimulate model outputs. In order to have information pertaining to the dust storms, model is coupled with chemical scheme of the atmosphere, showing dust emission based on various factors. There is a systematic underestimation of dust emission especially over Sistan plain when compared with analyzed data in 80 km. However vectors and streamlines show the same direction in both resolutions but they are show better and near the fact in 20 km horizontal grid spacing compared with NCEP/NCAR re-analysis results, while 20 km will be shown the complex-terrain conditions in studied region. An additional experiment was showed that by using different resolutions and running model to calculate the dust storm factors over the Sistan plain, in 20 km resolution all land surface coverage and functions are better than 80 km resolution and this feature affected on the model outputs. This item led to obtain a clear insight regarding to dust storms when it is indicated in wind velocity cross section and sea level pressure while they are influenced by the Sistan situation.

A view-dependent spatiotemporal saliency-driven approach for time varying volumetric data in geovisualization

Geospatial datasets from satellite observations and model simulations are becoming more accessible. These spatiotemporal datasets are relatively massive for visualization to support advanced analysis and decision making. A challenge to visualizing massive geospatial datasets is identifying critical spatial and temporal changes reflected in the data while maintaining high interactive rendering speed, even when data are accessed remotely. We propose a view-dependent spatiotemporal saliency-driven approach that facilitates the discovery of regions showing high levels of spatiotemporal variability and reduces the rendering intensity of interactive visualization. Our method is based on a novel definition of data saliency, a spatiotemporal tree structure to store visual saliency values, as well as a saliency-driven view-dependent level-of-detail (LOD) control. To demonstrate its applicability, we have implemented the approach with an open-source remote visualization package and conducted experiments with spatiotemporal datasets produced by a regional dust storm simulation model. The results show that the proposed method may not be outstanding in some specific situations, but it consistently performs very well across different settings according to different criteria.

Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation.

Dust storm has serious disastrous impacts on environment, human health, and assets. The developments and applications of dust storm models have contributed significantly to better understand and predict the distribution, intensity and structure of dust storms. However, dust storm simulation is a data and computing intensive process. To improve the computing performance, high performance computing has been widely adopted by dividing the entire study area into multiple subdomains and allocating each subdomain on different computing nodes in a parallel fashion. Inappropriate allocation may introduce imbalanced task loads and unnecessary communications among computing nodes. Therefore, allocation is a key factor that may impact the efficiency of parallel process. An allocation algorithm is expected to consider the computing cost and communication cost for each computing node to minimize total execution time and reduce overall communication cost for the entire simulation. This research introduces three algorithms to optimize the allocation by considering the spatial and communicational constraints: 1) an Integer Linear Programming (ILP) based algorithm from combinational optimization perspective; 2) a K-Means and Kernighan-Lin combined heuristic algorithm (K&K) integrating geometric and coordinate-free methods by merging local and global partitioning; 3) an automatic seeded region growing based geometric and local partitioning algorithm (ASRG). The performance and effectiveness of the three algorithms are compared based on different factors. Further, we adopt the K&K algorithm as the demonstrated algorithm for the experiment of dust model simulation with the non-hydrostatic mesoscale model (NMM-dust) and compared the performance with the MPI default sequential allocation. The results demonstrate that K&K method significantly improves the simulation performance with better subdomain allocation. This method can also be adopted for other relevant atmospheric and numerical modeling.

Sensitivity of meteorological input and soil properties in simulating aerosols (dust, PM10, and BC) using CHIMERE chemistry transport model

The objective of this study is to evaluate the ability of a European chemistry transport model, ‘CHIMERE’ driven by the US meteorological model MM5, in simulating aerosol concentrations [dust, PM10 and black carbon (BC)] over the Indian region. An evaluation of a meteorological event (dust storm); impact of change in soil related parameters and meteorological input grid resolution on these aerosol concentrations has been performed. Dust storm simulation over Indo-Gangetic basin indicates ability of the model to capture dust storm events. Measured (AERONET data) and simulated parameters such as aerosol optical depth (AOD) and Angstrom exponent are used to evaluate the performance of the model to capture the dust storm event. A sensitivity study is performed to investigate the impact of change in soil characteristics (thickness of the soil layer in contact with air, volumetric water, and air content of the soil) and meteorological input grid resolution on the aerosol (dust, PM10, BC) distribution. Results show that soil parameters and meteorological input grid resolution have an important impact on spatial distribution of aerosol (dust, PM10, BC) concentrations.

The Influence of Soil Moisture Cotent on the Numerical Simulation of Dust Storms

In this paper, the influence of the soil moisture content on the the dust emission proceed is discussed by using an integrated numerical simulation and forecast system of dust storms based on the physical mechanisms of sand and dust emissions by wind erosion. The results show that the influence of soil moisture content on the dust emission is very distinguished, and soil moisture content is one of the most important parameters as input conditions in the forcasting model systems of dust storm. The prediction capability of numerical dust model is improved by estimated the surface soil moisture content for dust model system.

Application of the Wind Erosion Prediction System in the AIRPACT Regional Air Quality Modeling Framework

<abstract> Abstract. Wind erosion of soil is a major concern of the agricultural community, as it removes the most fertile part of the soil and thus degrades soil productivity. Furthermore, dust emissions due to wind erosion degrade air quality, reduce visibility, and cause perturbations to regional radiation budgets. PM₁₀ emitted from the soil surface can travel hundreds of kilometers downwind before being deposited back to the surface. Thus, it is necessary to address agricultural air pollutant sources within a regional air quality modeling system in order to forecast regional dust storms and to understand the impact of agricultural activities and land-management practices on air quality in a changing climate. The Wind Erosion Prediction System (WEPS) is a new tool in regional air quality modeling for simulating erosion from agricultural fields. WEPS represents a significant improvement, in comparison to existing empirical windblown dust modeling algorithms used for air quality simulations, by using a more process-based modeling approach. This is in contrast with the empirical approaches used in previous models, which could only be used reliably when soil, surface, and ambient conditions are similar to those from which the parameterizations were derived. WEPS was originally intended for soil conservation applications and designed to simulate conditions of a single field over multiple years. In this work, we used the EROSION submodel from WEPS as a PM₁₀ emission module for regional modeling by extending it to cover a large region divided into Euclidean grid cells. The new PM₁₀ emission module was then employed within a regional weather and chemical transport modeling framework commonly used for comprehensive simulations of a wide range of pollutants to evaluate overall air quality conditions. This framework employs the Weather Research and Forecasting (WRF) weather model along with the Community Multi-scale Air Quality (CMAQ) model to treat ozone, particulate matter, and other air pollutants. To demonstrate the capabilities of the WRF/EROSION/CMAQ dust modeling framework, we present here results from simulations of dust storms that occurred in central and eastern Washington during 4 October 2009 and 26 August 2010. Comparison of model results with observations indicates that the modeling framework performs well in predicting the onset and timing of the dust storms and the spatial extent of their dust plumes. The regional dust modeling framework is able to predict elevated PM₁₀ concentrations hundreds of kilometers downwind of erosion source regions associated with the windblown dust, although the magnitude of the PM₁₀ concentrations are extremely sensitive to the assumption of surface soil moisture and model wind speeds. Future work will include incorporating the full WEPS model into the regional modeling framework and targeting field measurements to evaluate the modeling framework more extensively.

Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia

[1] Assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) total aerosol optical depth (AOD) retrieval products (at 550 nm wavelength) from both Terra and Aqua satellites have been developed within the National Centers for Environmental Prediction (NCEP) Gridpoint Statistical Interpolation (GSI) three-dimensional variational (3DVAR) data assimilation system. This newly developed algorithm allows, in a one-step procedure, the analysis of 3-D mass concentration of 14 aerosol variables from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) module. The Community Radiative Transfer Model (CRTM) was extended to calculate AOD using GOCART aerosol variables as input. Both the AOD forward model and corresponding Jacobian model were developed within the CRTM and used in the 3DVAR minimization algorithm to compute the AOD cost function and its gradient with respect to 3-D aerosol mass concentration. The impact of MODIS AOD data assimilation was demonstrated by application to a dust storm from 17 to 24 March 2010 over East Asia. The aerosol analyses initialized Weather Research and Forecasting/Chemistry (WRF/Chem) model forecasts. Results indicate that assimilating MODIS AOD substantially improves aerosol analyses and subsequent forecasts when compared to MODIS AOD, independent AOD observations from the Aerosol Robotic Network (AERONET) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, and surface PM10 (particulate matter with diameters less than 10 μm) observations. The newly developed AOD data assimilation system can serve as a tool to improve simulations of dust storms and general air quality analyses and forecasts.

Dust storm simulation with regional air quality model – Problems and results

Abstract On 24 March 2007 an extraordinary dust plume was observed in the troposphere over Central Europe. Satellite observations revealed its origins in a dust storm in Southern Ukraine, where large amounts of soil were resuspended from dried-out farmlands by wind gusts up to 30 m/s. Maximum particulate matter (PM 10 ) mass concentrations between 200 and 1400 μg m −3 occurred in Slovakia, the Czech Republic, Poland, and Germany along the pathway of the plume from East to West. In order to achieve a better understanding of the processes involved and their interaction the numerical simulation of this event was done by using a new parameterization of dust emission under certain conditions. A simplified dust emission scheme, proposed by Shao (2004), is implemented in the EURAD –Model (European Air Pollution Dispersion Model). A possible way for simulation of other similar dust events in the future is described. The obtained results are compared with the available measurement data and are commented.

Tropospheric aerosols over West Africa: highlights from the AMMA international program

AbstractWest Africa is the world's largest source of mineral dust and biomass burning aerosols. The AMMA (African Monsoon Multidisciplinary Analysis) project developed an integrated strategy combining field measurements, remote sensing and modelling studies to better quantify aerosol emissions, to extensively document their physico‐chemical properties and to describe their horizontal and vertical distributions and seasonal variability. Using this information in climate and numerical weather prediction models will lead to better climate projections and better simulations of dust storms, visibility, and air quality and the impacts upon human health. Copyright © 2011 Royal Meteorological Society

High-performance computing for the simulation of dust storms

Dust storm is a primary natural hazard that impact human activities. Previous work has simulated dust events with Eta model. This paper reports our effort to migrate a Dust Regional Atmospheric Model (DREAM) to the Nonhydrostatic Mesoscale Model (NMM), core of the Weather Research and Forecasting (WRF) system’s (WRF–NMM)-based dust model. The process is as follows: (1) the DREAM model is modified to fit into a high-performance computing environment by parallelizing the dust model based on the WRF–NMM weather forecasting model. We couple a dust module to the WRF–NMM model and parallelize the serial program using Message Passing Interface (MPI). (2) The performance of the parallel dust simulation model is tested using the southwestern United States as the experiment area and a dust event during 7 January 2008 for the case study. (3) The new dust model based on WRF–NMM is implemented and tested on a Linux cluster with 28 computing nodes and over 200 CPU cores. The new model can leverage high-performance computing clusters to reduce the execution time while at the same time enhancing the simulation resolution. As tested, when simulating a 72 h period, the WRF–NMM dust model took 1.8 h when using a resolution of 1/9 of a degree grid space, when 36 CPU cores were used for high-performance computing. The results show that the parallelized version of the dust simulation model can get a maximum speedup about 13.3. We also built a transformation component to export the model output to geographic information system (GIS) software through a Web Map Service (WMS).

A numerical simulation of dust storms in China

Wind erosion occurs in many arid, semiarid and agricultural areas of the world. The desert areas of China, which occupy approximately 13% of China’s total surface area, are major sources of Asian dust. The major wind-erosion areas are the sandy lands in western and northwestern China together with the extensive regions of the Gobi desert in northern and northeastern China, especially along the basin of the Yellow River. In this paper, dust storms which occurred in China in the spring of 2002 were simulated using an integrated numerical modeling system. The purpose of the simulation is to produce quantitative predictions of wind erosion on regional scales. The integrated wind erosion modeling system used in this study coupled the following three major components: (1) An atmospheric prediction model, together with a land-surface model; (2) a wind-erosion model and (3) a geographic information database. The atmospheric model provides the necessary input data for the wind erosion scheme, including wind speed and precipitation. It also provides input data for the land-surface model that produces predictions for soil moisture. Dust transport and deposition are also considered in the atmospheric model. The wind-erosion model predicts streamwise saltation and dust emission rate for given atmospheric, soil and land surface conditions. The geographic information database provides spatially distributed parameters, such as soil type and vegetation coverage, for the atmospheric, land surface and wind erosion models. Dust storms in China occur mainly in spring and winter, but most frequently in April. In spring, surface soils frozen in the previous winter become especially loose, creating a favorable condition for wind erosion. As an example, the severe dust storms of 15–20 March were simulated. The results show the integrated modeling system can simulate the main characteristics of the dust storms. The system produced estimates of wind erosion intensity and patterns that are in agreement with observations. Such a system offers the possibility of determining wind erosion patterns on broad scales with high spatial resolution, as well as dust transport and deposition.

Height distortion and the sedimentation of dust on topographic scale models: Considerations and simulations

AbstractThe study focuses on the effect of height distortion of a scale model on the sedimentation of natural dust (loess) on that model, when dust storm simulation experiments are conducted in a wind tunel. A scale model of a very simple topographic feature (symmetrical hill) has been used first. Both the air‐flow field over the hill and the loess deposition pattern on the hill are examined in detail for four different height distortion cases. Later on, the effect of height distortion in complex topography in discussed.The results of the experiments indicate that height distortion has an important effect on both the air‐flow field and the loess sedimentation pattern. Height distortion must therefore, in principle, be avoided, unless it is necessary to meet the flow simulation requirements during the experiment or to intensity the contrasts in the sedimentation pattern on the scale model surface.For complex, hilly topography, a not‐too‐excessive height distortion may be allowable (especially in the case of very high horizontal scale reductions), but for open areas, where topographic features are situated relatively far from each other, great care must be taken in extrapolating wind tunnel results to the field.

Dust Storm Simulation for Accelerated Life Testing of Solar Collector Mirrors

A dust storm simulation chamber has been designed based on worst-case conditions to simulate the effects of blowing dust and sand on candidate solar concentrator materials. After evaluation with common glass mirrors, the system was used to simulate field exposure of Carolina glass mirrors in the West Texas environment in terms of loss of reflectivity. System calibration was achieved by comparing actual exposure data over a three-year period to results obtained in the simulation chamber. It was found that a twenty-year service life can be simulated by 80 hrs. exposure in the dust storm chamber. Degradation of the Carolina glass mirrors has been shown to be primarily dependent on momentum transport to and kinetic energy deposition on the mirror surface during dust storms: annual reflectivity losses of 2.4 percent and 1.4 percent may be expected, respectively, from dust storm and the sum of all other effects.