Infrared Difference Dust Index Research Articles

Identification Method for Spring Dust Intensity Levels Based on Multiple Remote Sensing Parameters

The advancement of more precise remote sensing inversion technology for dust aerosols has long been a hot topic in the field of the atmospheric environment. In 2023, China experienced 18 dust-related weather events, predominantly in spring. These high-intensity and frequent dust events have attracted considerable attention. However, gridded observation data of dust intensity levels are not collected in current dust monitoring and forecasting operations. Based on the Himawari 9 geostationary satellite data, this study establishes a new method to identify spring dust events. This method integrates the brightness temperature difference method and the multiple infrared dust index, taking into account the response discrepancies of the multiple infrared dust index under various underlying surfaces. Furthermore, by obtaining dynamic background brightness temperature values eight times a day, threshold statistics are applied to analyze the correlation between the infrared difference dust index and ground-observed dust level, so as to establish a satellite-based near-surface dust intensity level identification algorithm. This algorithm aims to improve dust detection accuracy, and to provide more effective gridded observation support for dust forecasting and monitoring operations. The test results indicate that the algorithm can effectively identify the presence or absence of dust, with a misjudgment rate of less than 3%. With regard to dust intensity, the identification of blowing sand and floating dust aligns relatively well with ground-based observations, but notable uncertainties exist in determining a dust intensity of sand-storm level or above. Among these uncertainties, the differences between ground-based observations and satellite identification caused by non-grounded dust in the upper air, and the selection of dust identification thresholds, are two important error sources in the dust identification results of this study.

Sensitivity of surface albedo derived from METEOSAT data to drought episodes in a semi-arid region (M’Sila, north center of Algeria)

Drought is a natural phenomenon that affects ecological processes, land productivity, and human life. The phenomenon is slow to settle down and its impacts are not instantaneous, requiring an early monitoring of their signs of appearance. In order to track the effect drought on the land surface dynamics by means of surface albedo changes, we intend to exploit the VIS-channel reference image from the METEOSAT satellite data, which is an associated product of the new version of the Infrared Difference Dust Index (IDDI). The surface albedo is retrieved from the calibrated VIS-channel reference radiance, atmospherically corrected by the radiative transfer code 6S (Second Simulation of a Satellite Signal in the Solar Spectrum). In this study, we inspect the sensitivity of monthly surface albedo to drought events over a semi-arid region (M’Sila, center of northern Algeria) during the period covered by three METEOSAT satellites (5, 6, and 7). Two drought indices, SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index), were calculated at multiple timescales and were correlated with surface albedo over the study region. The results show that the correlation of surface albedo with the two drought indices at the timescale of 1 month (current month) is not necessarily the best indicator of the installation of a drought event. Additionally, we find that the sensitivity of surface albedo to drought indices at a medium timescales class (average of correlation coefficient at 6, 9, and 12 months), is more sensitive to SPEI index (principally 6-month timescale), during the growing season (April to September). It is also more sensitive to SPI index (principally 12-month timescale), during the supplying and charging period of water resources (October to March).

Dust Aerosol Optical Depth Retrieval and Dust Storm Detection for Xinjiang Region Using Indian National Satellite Observations

The Xinjiang Uyghur Autonomous Region (Xinjiang) is located near the western border of China. Xinjiang has a high frequency of dust storms, especially in late winter and early spring. Geostationary satellite remote sensing offers an ideal way to monitor the regional distribution and intensity of dust storms, which can impact the regional climate. In this study observations from the Indian National Satellite (INSAT) 3D are used for dust storm detection in Xinjiang because of the frequent 30-min observations with six bands. An analysis of the optical properties of dust and its quantitative relationship with dust storms in Xinjiang is presented for dust events in April 2014. The Aerosol Optical Depth (AOD) derived using six predefined aerosol types shows great potential to identify dust events. Cross validation between INSAT-3D retrieved AOD and MODIS AOD shows a high coefficient of determination (R2 = 0.92). Ground validation using AERONET (Aerosol Robotic Network) AOD also shows a good correlation with R2 of 0.77. We combined the apparent reflectance (top-of-atmospheric reflectance) of visible and shortwave infrared bands, brightness temperature of infrared bands and retrieved AOD into a new Enhanced Dust Index (EDI). EDI reveals not only dust extent but also the intensity. EDI performed very well in measuring the intensity of dust storms between 22 and 24 April 2014. A visual comparison between EDI and Feng Yun-2E (FY-2E) Infrared Difference Dust Index (IDDI) also shows a high level of similarity. A good linear correlation (R2 of 0.78) between EDI and visibility on the ground demonstrates good performance of EDI in estimating dust intensity. A simple threshold method was found to have a good performance in delineating the extent of the dust plumes but inadequate for providing information on dust plume intensity.

Changes in Aerosol Optical and Micro-Physical Properties over Northeast Asia from a Severe Dust Storm in April 2014

This study focuses on analyzing the changes to aerosol properties caused by the dust storm called “China’s Great Wall of Dust” that originated from the Taklimakan Desert in April 2014. IDDI (Infrared Difference Dust Index) images from FY-2E and true color composite images from FY-3C MERSI (Medium Resolution Spectral Imager) show the breakout and transport path of the dust storm. Three-hourly ground-based measurements from MICAPS (Meteorological Information Comprehensive Analysis and Process System) suggest that anticyclonic circulation occupying the Southern Xinjiang basin and cyclonic circulation in Mongolia form a dipole pressure system that leads to strong northwesterly winds (13.7–20 m/s), which favored the breakout of the dust storm. IDDI results indicate that the dust storm breakout occurred at ~2:00 UTC on 23 April in the Taklimakan Desert. Four-day forward air mass trajectories with the HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model gives the simulation results of the dust transport paths and dust vertical distributions, which are consistent with the corresponding aerosol vertical distributions derived from CALIPSO. The Aerosol Index (AI) data of TOU (Total Ozone Unit) aboard FY-3B are first used to study the areas affected by the dust storm. From the AI results, the dust-affected areas agree well with the synoptic meteorological condition analysis, which supports that the synoptic meteorological conditions are the main reason for the breakout and transport of the dust storm. Anomalies of the average MODIS (Moderate Resolution Imaging Spectroradiometer) AOD (Aerosol Optical Depth) distributions over northeast Asia during the dust storm to the average of the values in April between 2010 and 2014 are calculated as a percent. The results indicate high aerosol loading with a spatially-averaged anomaly of 121% for dusty days between 23 April and 25 April. Aerosol Robotic Network (AERONET) retrievals of VSD (Volume Size Distribution) and SSA (Single Scattering Albedo) show that while the aerosol properties in Dalanzadgad, which is closer to the dust source, were influenced primarily by coarse dust particles, the aerosol properties in Beijing were mostly contributed by fine dust particles that transported over longer distances and at high atmospheric levels.

Detection of Asian dust storms from geostationary satellite observations of the INSAT-3D imager

Asian dust storm outbreaks significantly influence air quality, weather, and climate. Therefore, it is desirable to have qualitative and quantitative information on the time, location, and coverage of these outbreaks at high spatial and temporal resolution. The imager on board the Indian metrological geostationary satellite INSAT-3D observes Asia at a temporal resolution of 30 min and a spatial resolution of 1, 4, 8, and 4 km in the visible, middle infrared (MIR), water vapour (WV), and thermal infrared (TIR) bands, respectively. In this article, an algorithm is described for detecting desert dust storms from INSAT-3D imager data. The algorithm described here is a combination of various pre-existing methods such as infrared split-window, MIR and TIR brightness temperature difference, and visible to MIR reflectance ratio, which are based on the fact that dust exhibits features of spectral dependence and contrast over the visible, MIR, and TIR spectrum that are different from clouds, surface, and clear-sky atmosphere. Using the Atmospheric Infrared Sounder (Aqua/AIRS) dust score as proxy, INSAT-3D dust storm products were tested under different scenarios such as dust storms and dust transport in Asia. TIR observations from the geostationary platform of INSAT-3D allows computation of the infrared difference dust index (IDDI), which gives a quantitative measure of dust loading relative to clear atmosphere. Moreover, due to the high temporal resolution (30 min) of INSAT-3D observations, INSAT-3D-derived dust products allow more precise monitoring of dust transportation as compared with dust products derived from polar satellite observations.

Dust-storm dynamics over Sistan region, Iran: Seasonality, transport characteristics and affected areas

Abstract The present work examines the seasonality, dust-plume altitudinal variation and affected areas for dust storms originated from the Sistan region, southeastern Iran during the summer (June–September) months of the period 2001–2012 synthesizing local meteorological records, satellite observations (TOMS, OMI, METEOSAT, MODIS) and HYSPLIT forward trajectories. Dust-storm days (356 in total) are associated with visibility below 1 km at Zabol, Iran meteorological station with higher frequency and intensity in June and July. Monthly-mean composite maps of TOMS and OMI AI show high (>3–3.5) values over Sistan and nearby downwind areas. HYSPLIT forward-trajectory analysis at 500 m for air masses originated from Sistan on the dust-storm days shows that they usually follow an anti-clockwise transport direction at elevations usually below 2 km, initially moving southwards and then shifting to east-northeast when they are approaching the Arabian Sea coast. This is the result of the influence of the local topography and formation of thermal low-pressure systems over the arid lands. It is found that in few cases the dust storms from Sistan affect central/south Arabian Sea and India, while they control the aerosol loading over northernmost Arabian Sea. The Infrared Difference Dust Index (IDDI) images, which represent brightness temperature reduction due to dust presence over land, are used at specific periods of persistent dust storms over Sistan, confirming the main pathways of the dust plumes and illustrating the importance of the region as one of the most active dust sources in southwest Asia.

Meteorological aspects associated with dust storms in the Sistan region, southeastern Iran

Dust storms are considered natural hazards that seriously affect atmospheric conditions, ecosystems and human health. A key requirement for investigating the dust life cycle is the analysis of the meteorological (synoptic and dynamic) processes that control dust emission, uplift and transport. The present work focuses on examining the synoptic and dynamic meteorological conditions associated with dust-storms in the Sistan region, southeastern Iran during the summer season (June–September) of the years 2001–2012. The dust-storm days (total number of 356) are related to visibility records below 1 km at Zabol meteorological station, located near to the dust source. RegCM4 model simulations indicate that the intense northern Levar wind, the high surface heating and the valley-like characteristics of the region strongly affect the meteorological dynamics and the formation of a low-level jet that are strongly linked with dust exposures. The intra-annual evolution of the dust storms does not seem to be significantly associated with El-Nino Southern Oscillation, despite the fact that most of the dust-storms are related to positive values of Oceanic Nino Index. National Center for Environmental Prediction/National Center for Atmospheric Research reanalysis suggests that the dust storms are associated with low sea-level pressure conditions over the whole south Asia, while at 700 hPa level a trough of low geopotential heights over India along with a ridge over Arabia and central Iran is the common scenario. A significant finding is that the dust storms over Sistan are found to be associated with a pronounced increase of the anticyclone over the Caspian Sea, enhancing the west-to-east pressure gradient and, therefore, the blowing of Levar. Infrared Difference Dust Index values highlight the intensity of the Sistan dust storms, while the SPRINTARS model simulates the dust loading and concentration reasonably well, since the dust storms are usually associated with peaks in model simulations.

Operational retrieval of Asian sand and dust storm from FY-2C geostationary meteorological satellite and its application to real time forecast in Asia

Abstract. This paper describes an operational retrieval algorithm for the sand/dust storm (SDS) from FY-2C/S-VISSR (Stretched-Visible and Infrared Spin-Scan Radiometer) developed at the National Satellite Meteorological Center (NSMC) of China. This algorithm, called Dust Retrieval Algorithm based on Geostationary Imager (DRAGI), is based on the optical and radiative physical properties of SDS in mid-infrared and thermal infrared spectral regions as well as the observation of all bands in the geostationary imager, which include the Brightness Temperature Difference (BTD) in split window channels, Infrared Difference Dust Index (IDDI) and the ratio of middle infrared reflectance to visible reflectance. It also combines the visible and water vapor bands observation of the geostationary imager to identify the dust clouds from the surface targets and meteorological clouds. The output product is validated by and related to other dust aerosol observations such as the synoptic weather reports, surface visibility, aerosol optical depth (AOD) and ground-based PM10 observations. Using the SDS-IDD product and a data assimilation scheme, the dust forecast model CUACE/Dust achieved a substantial improvement to the SDS predictions in spring 2006.

Dust absorption over the “Great Indian Desert” inferred using ground‐based and satellite remote sensing

Mineral dust is the single largest contributor of natural aerosols over land. Dust aerosols exhibit high variability in their radiative effects because their composition varies locally. This arises because of the regional distinctiveness of the soil characteristics as well as the accumulation of other aerosol species, such as black carbon, on dust while airborne. To accurately estimate the climate impact of dust, spatial and temporal distribution of its radiative properties are essential. However, this is poorly understood over many regions of the world, including the Indian region. In this paper, infrared (IR) radiance (10.5–12.5 μm) acquired from METEOSAT‐5 satellite (∼5‐km resolution) is used to retrieve dust aerosol characteristics over the “Great Indian Desert” and adjacent regions. The infrared radiance depression on account of the presence of dust in the atmosphere has been used as an index of dust load, called the Infrared Difference Dust Index (IDDI). Simultaneous, ground‐based spectral optical depths estimated at visible and near‐infrared wavelengths (using a multiwavelength solar radiometer) are used along with the IDDI to infer the dust absorption. The inferred single scattering albedo of dust was in the range of 0.88–0.94. We infer that dust over the Indian desert is of more absorbing nature (compared with African dust). Seasonally, the absorption is least in summer and most in winter. The large dust absorption leads to lower atmospheric warming of 0.7–1.2 K day−1.

Improvement of the detection of desert dust over the Sahel using METEOSAT IR imagery

Abstract. Desert dust over the arid regions of Africa is detected using the Infrared Difference Dust Index (IDDI) derived from the thermal infrared (TIR) channel of METEOSAT. However, the comparison with photometric aerosol optical thickness (AOT) of this dust index reveals some discrepancies. Using an instrumented site in Sahel where aerosol properties and meteorological conditions were monitored daily during the dry season, we performed radiative transfer computations with the MODTRAN 4.1 code to develop a method to improve the IDDI usefulness. We found that discrepancies between AOT and IDDI variations mostly come from changes in the surface temperature (Ts), which is an important parameter for radiative transfer computations in the TIR. We show that this temperature varies from day to day with the surface wind speed and during the course of the season with the solar elevation, and that it is possible, for the site considered, to correct Ts from these combined effect using a simple parameterization. We also observe that the dust layer itself has an impact on Ts by reducing the amount of solar radiation at the surface, and that this phenomenon can also be accounted for by adding an AOT-dependence to the above parameterization of Ts. We show that this parameterization allows improving the agreement between the IDDI and the photometric AOT.

Dust aerosols over India and adjacent continents retrieved using METEOSAT infrared radiance Part I: sources and regional distribution

Abstract. Mineral dust constitutes the single largest contributor to continental aerosols. To accurately assess the impact of dust aerosols on climate, the spatial and temporal distribution of dust radiative properties is essential. Regional characteristics of dust radiative properties, however, are poorly understood. The magnitude and even sign of dust radiative forcing is uncertain, as it depends on a number of parameters, such as vertical distribution of dust, cloud cover and albedo of the underlying surface. In this paper, infrared radiance (10.5-12.5 µm), acquired from the METEOSAT-5 satellite ( resolution), was used to retrieve regional characteristics of dust aerosols for all of 1999. The infrared radiance depression, due to the presence of dust in the atmosphere, has been used as an index of dust load, known as the Infrared Difference Dust Index (IDDI). There have been several studies in the past carried out over the Sahara using IDDI as a measure of dust load. Over the Indian region, however, studies on dust aerosols are sparse. Spatial and temporal variability in dust loading and its regional distribution over various arid and semiarid regions of India and adjacent continents (0-35° N; 30° E-100° E) (excluding Sahara) have been studied and the results are examined along with surface soil conditions (such as vegetation cover and soil moisture). The advantage of the IDDI method is that information on aerosol properties, such as chemical composition or microphysical properties, is not needed. A large day-to-day variation in IDDI was observed over the entire study region, with values ranging from 4 to 22 K. It was observed that dust activity starts by March over the Indian deserts, as well as over deserts of the Africa and Arabian regions. The IDDI reaches maximum during the period of May to August. Regional maps of IDDI, in conjunction with biomass burning episodes (using TERRA satellite fire pixel counts), suggest that large IDDI values observed during the winter months over Northern India could be due to a possible deposition of black carbon on larger dust aerosols. The IDDI values have been compared with another year (i.e. 2003), with a large number of dust storms reported by meteorological departments based on visibility data. During the dry season, the magnitude of the monthly average IDDI during 2003 was slightly higher than that of 1999. The monthly mean IDDI was in the range from 4 to 9 K over the Indian deserts, as well as over the deserts of Africa and Arabia. The maximum IDDI during a month was in the range from 6 to 18 K. Large IDDI values were observed even over vegetated regions (such as the vegetated part of Africa and central India), attributed to the presence of transported dust from nearby deserts.

Satellite detection of dust using the IR imagery of Meteosat: 1. Infrared difference dust index

The Infrared Difference Dust Index (IDDI) is a satellite dust product designed for climatological applications, designed specifically for dust remote sensing in arid regions such as the Sahel and Sahara. It is based on the atmospheric response to dust, extracted from midday Meteosat‐IR imagery, and takes advantage of the impact of dust aerosols on the thermal infra‐red radiance outgoing to space. Simulations show a quasi‐linear relationship between satellite response to dust and shortwave optical depth, with a sensitivity depending on particle size distribution and radiative surface properties. Comparison of measured satellite response with photometric optical depth agrees with the simulations. Water vapor significantly affects the satellite signal for cases of large columnar amounts and oceanic air masses advected inland. Hence apart from possible coastal effects, the water vapor effect can be neglected in the Sahelian‐Saharan zone north of the Intertropical Convergence Zone, coinciding with the major regions of African dust emission and transport. The construction of the IDDI involves the processing of reference images, theoretically representing the outgoing radiance obtaining under clear‐sky conditions. Errors may arise from (1) dust remaining in the reference images and (2) seasonal shifts of the reference level; however, the latter error will be offset by averaging used in climatological processing. An error budget is presented for the station of Gao. A statistical comparison of IDDI data with visibility measured at synoptic stations results in (1) a validation of the product, and (2) a climatologically relevant visibility‐IDDI relation, valid for the arid regions of northern Africa. The latter relation is consistent with both simulations and photometric measurements. IDDI maps over Africa compare successfully with optical depth over adjacent ocean regions derived from Meteosat‐VIS imagery. The observed continuity of dust plumes across the African coast demonstrates the consistency between both products.

Determination of the wind speed threshold for the emission of desert dust using satellite remote sensing in the thermal infrared

The Infrared Difference Dust Index (IDDI), derived from images obtained from the Meteosat 10.5‐ to 12.5‐μm channel, describes the dust distribution over the Saharan‐Sahelian region. This IDDI, associated with the 10‐m wind speed reanalyses from the European Centre for Medium‐Range Weather Forecasts (ECMWF), reveals whether or not the observed dust is associated with emission from an underlying source. This result allows one to determine the wind speed thresholds for dust emission from targets located in the western, central, and eastern Saharan‐Sahelian region, by means of satellite remote sensing. Threshold values determined for seven targets are presented. A comparison is carried out between such values and direct determinations obtained through the description of the soil texture and surface roughness of these targets. The agreement between these quite independent determinations is conclusive, with an average difference of 0.3 m s−1 and a rms difference of 0.35 m s−1.

Modeling the atmospheric dust cycle: 2. Simulation of Saharan dust sources

A soil‐derived dust emission scheme has been designed in order to provide simulation of mineral dust sources for atmospheric transport models [Marticorena and Bergametti, 1995]. This physical scheme considers the influence of surface features to compute the erosion threshold and the intensity of the dust emissions. It has been validated by comparison with relevant experimental data. However, it was necessary to extend its applicability and to test its capability to reproduce dust emissions over large arid areas. Specific methods have been developed to determine the parameters required by the dust production model for large‐scale applications. The surface features (dimensions of the roughness elements and soil mineralogy) and the wind velocity allow the computation of the roughness lengths of the surface, the size distribution of the erodible soils, and the wind friction velocity. A map of these surface characteristics has been established for the western part of the Sahara. This map coupled to the Europan Center for Medium‐Range Weather Forecast (ECMWF) surface wind fields are used to simulate dust emissions in this desert region. The simulated emissions have been compared to the Infrared Difference Dust Index (IDDI), determined by means of the Meteosat thermal infrared imagery. The simulated dust event frequencies are in good agreement with those observed by satellite. The comparison between the simulated fluxes and the satellite observations for 3 months of the year 1991 has revealed a linear relationship between the logarithm of the simulated flux and the IDDI. The annual and monthly dust emissions for 1991 and 1992 have been estimated and compared to those established by d'Almeida [1986]. Both the frequencies and the intensities of the emissions are well reproduced by the model associated with the surface features map.