Horizontal Visibility Data Research Articles

Dust Events over the Urmia Lake Basin, NW Iran, in 2009–2022 and Their Potential Sources

Nowadays, dried lake beds constitute the largest source of saline dust storms, with serious environmental and health issues in the surrounding areas. In this study, we examined the spatial–temporal distribution of monthly and annual dust events of varying intensity (dust in suspension, blowing dust, dust storms) in the vicinity of the desiccated Urmia Lake in northwestern (NW) Iran, based on horizontal visibility data during 2009–2022. Dust in suspension, blowing dust and dust storm events exhibited different monthly patterns, with higher frequencies between March and October, especially in the southern and eastern parts of the Urmia Basin. Furthermore, the intra-annual variations in aerosol optical depth at 500 nm (AOD550) and Ångström exponent at 412/470 nm (AE) were investigated using Terra/Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data over the Urmia Lake Basin (36–39°N, 44–47°E). Monthly distributions of potential coarse aerosol (AE < 1) sources affecting the lower troposphere over the Urmia Basin were reconstructed, synergizing Terra/Aqua MODIS AOD550 for AE < 1 values and HYSPLIT_4 backward trajectories. The reconstructed monthly patterns of the potential sources were compared with the monthly spatial distribution of Terra MODIS AOD550 in the Middle East and Central Asia (20–70°E, 20–50°N). The results showed that deserts in the Middle East and the Aral–Caspian arid region (ACAR) mostly contribute to dust aerosol load over the Urmia Lake region, exhibiting higher frequency in spring and early summer. Local dust sources from dried lake beds further contribute to the dust AOD, especially in the western part of the Urmia Basin during March and April. The modeling (DREAM8-NMME-MACC) results revealed high concentrations of near-surface dust concentrations, which may have health effects on the local population, while distant sources from the Middle East are the main controlling factors to aerosol loading over the Urmia Basin.

Evaluating the Computational Values of Suspended Particles Below 10 μm With Their Actual Concentration

Background & Aims of the Study: Dust as a phenomenon is among the significant problems in numerous regions of Iran and other countries. Empirical equations can be used to estimate the concentration of particulate matter. This study evaluated computational values of suspended particles less than 10 μm with their actual concentration in Ahvaz city based on a linear regression model. Materials and Methods: This descriptive cross-sectional study was conducted from April 2016 to March 2018. The dust images were obtained from the MODIS sensor of the Aqua satellite. The meteorological data included 3-hour horizontal visibility data recorded in m. PM10 concentration data were obtained from the Environment Organization. Accordingly, 5 empirical equations between horizontal visibility and PM10 have been used in this case. The calculated values of particulate matter less than 10 μm in Ahvaz City, Iran, were evaluated with actual values of PM10 based on the linear regression model. Results: The study results suggested that the computational R index for empirical equations had higher accuracy in 2017 data. The coefficients of determination using D’Almeida (1986), Dayan (1986), Chung (2003), Jugder et al. (2014), and Camino et al. (2015) indicators were calculated as 0.87, 0.806, 0.745, 0.873, and 0.866, respectively. Conclusion: Jugder et al. (2014) index with R² equal to 0.548667 was the best index for estimating PM10 in this region. The study results showed that empirical equations could estimate particulate matter and dust in southwestern Iran.

Aridity Index Variations and Dust Events in Iran from 1990 to 2018

This study was carried out to determine whether the changes in dust concentration (DC) in Iran were attributed to changes in aridity index (AI) from 1990 to 2018. Long-term precipitation and potential evapotranspiration were used to compute the United Nations Environment Program AI and horizontal visibility data were used to calculate the DC in the study area. The DC spatial distribution maps were prepared to better identify areas affected by sand-dust events. Pearson’s correlation analysis was performed to explore the association between DC and AI in Iran and study stations during the monitoring period. Spatiotemporal variations in DC showed that more than 60 percent of Iran’s area experienced severe wind erosion events in 2001, 2008, and 1990 when AI was less than 0.2. The results also showed that approximately 45 percent of Iran’s area has suffered from the average DC over the study period (565 µg/m3). The incremental changes in DC were observed in almost half of the entire area of Iran during the second period (2000–2008) and one third of this area during the third period (2009–2018). Areas affected by decreasing changes in AI during the second and third periods were 90 percent and 73 percent of the area affected during the previous period, respectively. Overall, there was a significant negative correlation between AI and DC in Iran over the monitoring period (r = −0.51, p value < 0.01). Insignificant positive correlations were observed in humid regions, whereas negative correlations were mostly detected in arid and semiarid regions of Iran.

Meteorological Variables That Affect Visibility Degradation and Their Seasonal Trends in the Niger Delta Region of Nigeria

The study of visibility in the Niger Delta region is necessary because it reflects the atmospheric changes caused by economic expansion in Nigeria. Cities in the Niger Delta (especially Port Harcourt) are the most polluted cities in the country and therefore visibility degradation has become one of the major environmental challenge in Nigeria. Analysis of a 31 years (1981-2012) monthly mean horizontal visibility data and monthly mean datasets of meteorological parameters such as relative humidity and wind direction obtained from Nigerian Meteorological Agency (NIMET) and the National Centre for Environmental Prediction (NCEP) for Calabar, Uyo, Port Harcourt, Owerri, Warri and Akure was done using statistical techniques. A correlation analysis was done and the annual visibility variability indexes from (NIMET) shows significant correlation with the (NCEP) datasets for R/humidity at r=0.1334 and Wind direction at r=0.1210 respectively at 90% confidence level from t-test. This study concluded that the relationship of the atmospheric visibility and meteorological factors are closely related. The results showed that visibility is more correlated with Relative humidity in places with high hydrocarbon activities leading to excess aerosol loading like Port Harcourt while it is better correlated with wind direction in places with less hydrocarbon activities like Calabar and Akure. The results of this study can assist policy makers and operators in establishing positive strategies to improve the air quality.

The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia

Abstract. In the southern Amazon relationships have been established among drought, human activities that cause forest loss, fire, and smoke emissions. We explore the impacts of recent drought on fire, forest loss, and atmospheric visibility in lowland Bolivia. To assess human influence on fire, we consider climate, fire, and vegetation dynamics in an area largely excluded from human activities since 1979, Noel Kempff Mercado National Park (NK) in northeastern Bolivia. We use data from five sources: the Moderate Resolution Imaging Spectroradiometer Collection 6 active fire product (2001–2015) (MODIS C6), Global Fire WEather Database (GFWED) data (1982–2015), MODIS land cover data (2001–2010), MODIS forest loss data (2000–2012), and the regional extinction coefficient for the southwestern Amazon (i.e., Bext), which is derived from horizontal visibility data from surface stations at the World Meteorological Organization (WMO) level (1973–2015). The Bext is affected by smoke and acts as a proxy for visibility and regional fire emissions. In lowland Bolivia from 2001 to 2015, interannual Drought Code (DC) variability was linked to fire activity, while from 1982 to 2015, interannual DC variability was linked to Bext. From 2001 to 2015, the Bext and MODIS C6 active fire data for lowland Bolivia captured fire seasonality, and covaried between low- and high-fire years. Consistent with previous studies, our results suggest Bext can be used as a longer-term proxy of regional fire emissions in southwestern Amazonia. Overall, our study found drought conditions were the dominant control on interannual fire variability in lowland Bolivia, and fires within NK were limited to the Cerrado and seasonally inundated wetland biomes. Our results suggest lowland Bolivian tropical forests were susceptible to human activities that may have amplified fire during drought. Human activities and drought need to be considered in future projections of southern Amazonian fire, in regard to carbon emissions and global climate.

A revisit to decadal change of aerosol optical depth and its impact on global radiation over China

Global radiation over China decreased between the 1960s and 1990, since when it has remained stable. As the total cloud cover has continued to decrease since the 1960s, variations in aerosols were suggested in previous studies to be the primary cause for variations in global radiation over China. However, the effect of aerosols on global radiation on a decadal scale has not been physically quantified over China. In this study, aerosol optical depth (AOD) data since 1980 are estimated by combining horizontal visibility data at stations in China and AOD observed by the moderate resolution imaging spectroradiometer (MODIS). It is found that the AOD exhibits decadal changes, with two decreasing periods (before the end of 1980s and after 2006) and one increasing period (from 1990 to 2006). With the derived AOD, a clear-sky model is then applied to quantify the role of aerosols in the variations in global radiation over China. The results show that aerosol direct effect cannot fully explain the decadal variations in the global radiation over China between 1980 and 2010, though it has a considerable effect on global radiation climatology. There are significant differences between the trends of clear-sky global radiation impacted by aerosols and those of all-sky global radiation impacted by aerosols and clouds, and the correlation coefficient for the comparison is very low. Therefore, the variations in all-sky global radiation over China are likely to be due to changes in cloud properties and to interactions between clouds and aerosols.

Atmospheric aerosol influence on the Brazilian solar energy assessment: Experiments with different horizontal visibility bases in radiative transfer model

The radiative transfer model BRASIL-SR is used by Brazilian Institute for Space Research for the assessment of the solar irradiation in Brazil. The model parameterizes the influence of aerosols in the solar radiation transmittance using climate averages of horizontal visibility, which does not represent the actual atmospheric condition in Brazil, especially during dry season. In clear sky conditions, aerosols are a major source of bias in solar radiation models. Their concentration have large spatial and temporal variability particularly in the Brazilian Midwestern region from April until October, due to forest fires, and in Southeastern region due to pollution from megacities. In this study, meteorological data from METAR comprising the years of 2006, 2007 and 2008 were analyzed to evaluate the seasonal variability of the horizontal visibility in Brazil to better represent the influence of aerosols on the model estimations of surface solar irradiation. New horizontal visibility values was generated to each month simulated, to provide input data to the BRASIL-SR model and site specific ground data were used to validate the model estimates. The global, direct beam and diffuse solar irradiation estimates obtained by making use of the new horizontal visibility data presented an overall lower BIAS and RMSE deviations.

A long-term record of aerosol index from TOMS observations and horizontal visibility in sub-Saharan West Africa

Aerosol index (AI) observations from the Total Ozone Mapping Spectrometers (TOMS) on board the Nimbus-7 (1979–1992) and Earth Probe (1997–2004) satellites are used to investigate the annual and seasonal variations of dust haze in Sahelian West Africa. Eight synoptic stations, namely Port Harcourt (PHA), Lagos, Ilorin, Makurdi, Minna, Yola, Maiduguri and Kano, representing the four major climatic zones (tropical rainforest and Guinea, Sudan and Sahel savannahs) of Nigeria were considered in this study. Twenty-nine years (1979–2007) of horizontal visibility data are also analysed in order to investigate the effect of dust haze on visibility in the stations under study. Results show that there are general decreasing trends in the visibility with corresponding increases in TOMS AI trends in all the climatic zones of Nigeria. This clearly suggests that there had been an increase in the integrated column dust aerosols over the years, especially during the harmattan dusty months. The average visibility was highest in Ilorin (11363.2 ± 1644.4 m) and the lowest in Maiduguri (9017.9 ± 1021.3 m) when the dust-prone months of November to March of the following year were considered. Logarithmic equations with inverse relationships are developed between TOMS AI and horizontal visibility for each station with correspondingly high coefficients of determination (R 2) in all the locations. It is concluded that TOMS AI can reasonably be used to reproduce visibility measurement in sub-Saharan West Africa where sparse visibility data are available for meteorological uses.

Inter-annual variability and long-term trend of UV-absorbing aerosols during Harmattan season in sub-Saharan West Africa

The inter-annual variability and long-term trend of UV-absorbing aerosols (mainly soil dust and smoke from biomass burning) during Harmattan seasons in the Sahel zone of Nigeria have been analyzed using the total ozone mapping spectrometer aerosol index (TOMS AI) and horizontal visibility data from synoptic stations in the zone. The relationship between daily TOMS AI and horizontal visibility ( V x ) during the Harmattan seasons in the zone is also investigated. Analyses of 25-year TOMS AI data and 30-year horizontal visibility data reveal that in addition to inter-annual variability, there was a general decreasing trend which suggests increasing dustiness over the period. The Harmattan season mean horizontal visibility for the 1990–1999 decade was 18.9% lower than the mean value for the corresponding seasons in 1970–1979 decade. Also the mean TOMS AI for 2001–2004 Harmattan seasons was 24% higher than that of the corresponding seasons of 1979–1984. These results suggest that in the Sahel zone of Nigeria, the average atmospheric dust loadings in recent Harmattan seasons were generally higher than those of preceding three decades. It has also been shown that in the Sahel zone of Nigeria the relationship between mean daily TOMS AI and horizontal visibility ( Vx ¯ ) is of the form: AI = - k ln ( Vx ¯ ) + C , where k and C are constants. The strong correlation coefficient, r = - 0.92 , between AI and Vx ¯ indicates that TOMS AI captures the variability of atmospheric dust concentration in the region nearly as well as horizontal visibility from surface observation. Thus, TOMS AI and surface visibility data could complement each other for monitoring dust and smoke events in the West African Sahel zone.

Estimation of air quality degradation due to Saharan dust at Nouakchott, Mauritania, from horizontal visibility data

It is now irrefutable that air pollution caused by large amounts of Total Suspended Particulates (TSP) and respiratory particulates or Particulate Matter less than 10 μm in aerodynamic diameter (PM10) has numerous undesired consequences on human health. Air quality degradation far from the African continent, in the US and in Europe, caused by high concentrations of African dust, is seen as a major threat even though most of these countries are very distant from the Sahara. Surprisingly, no estimates of TSP or PM10 levels near the Saharan dust source are available. Based on horizontal visibility observations which are reduced by the presence of dust in the atmosphere, TSP and PM10 levels are estimated throughout the year 2000 at Nouakchott-Airport, Mauritania, using relations found in the literature. It appears that concentrations of particles are significant both in terms magnitude and frequency, as the 24-hour PM10 thresholds established by the US EPA National Ambient Air Quality Standards and the EU Limits Values for Air Quality were exceeded 86 and 137 times, respectively. The average annual concentration is far above air quality standards and estimated at 159 μg m−3 for TSP and 108 μg m−3 for PM10. These very high particulate levels are likely to represent an important public health hazard and should be considered as a major environmental risk.

Retrieval of atmospheric horizontal visibility by statistical regression from NOAA/AVHRR satellite data

Based on the atmospheric horizontal visibility data from forty-seven observational stations along the eastern coast of China near the Taiwan Strait and simultaneous NOAA/AVHRR multichannel satellite data during January 2001 to December 2002, the spectral characters associated with visibility were investigated. Successful retrieval of visibility from multichannel NOAA/AVHRR data was performed using the principal component regression (PCR) method. A sample of retrieved visibility distribution was discussed with a sea fog process. The correlation coefficient between the observed and retrieved visibility was about 0.82, which is far higher than the 99.9% confidence level by statistical test. The rate of successful retrieval is 94.98% of the 458 cases during 2001–2002. The error distribution showed that high visibilities were usually under-estimated and low visibilities were over-estimated and the relative error between the observed and retrieved visibilities was about 21.4%.

Ground Fog Detection from Space Based on MODIS Daytime Data—A Feasibility Study

Abstract The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).

Estimating UV Erythemal Irradiance by Means of Neural Networks¶

ABSTRACTIn recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill‐defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000–2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of −0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.

Estimating UV erythemal irradiance by means of neural networks.

In recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000-2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of -0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.

A modified method of estimating Ångström’s turbidity coefficient for solar radiation models

The method by Pinazo et al. (Pinazo JM, Cañada J, Boscá JV. A new method to determine Ångström’s turbidity coefficient: its application for Valencia. Solar Energy 1995;54(4):219–226) to determine the Ångström turbidity coefficient is modified. Two new correlations are developed that link Pinazo’s method with model ‘C’ by Iqbal (Iqbal M. An introduction to solar radiation. New York: Academic Press, 1983), and are derived using measured horizontal visibility data. The modified model is used to compute Ångström turbidity coefficient for Brunei Darussalam using measured data on solar radiation for a period of 5 years (1990–1994). The data were collected from the Meteorological Department of Civil Aviation, Ministry of Communication, Brunei Darussalam. A comparison of the modified model is made with those by Pinazo and Iqbal. The preliminary calculations reveal that the modified model is capable of computing Ångström turbidity coefficient more accurately than that developed by Pinazo and it can be used to compute Ångström turbidity coefficient for any location irrespective of the climatological zones.