Estimation of particulate matter from satellite- and ground-based observations over Hyderabad,India

Long-term trends in surface-level particulate matter of dynamic diameter ≤2 µm (PM₂) in regard to air quality observations over Greater Hyderabad Region (GHR), India are estimated by the synergy of ground-based measurements and satellite observations during the period 2001–2013 (satellite) and July 2009–Dec 2013 (ground-based). Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical thickness (AOT) (MODIS-AOTs) was validated against that measured from Microtops-II Sunphotometer (MTS) AOTs (MTS-AOTs) and then utilized to estimate surface-level PM₂ concentrations over GHR using regression analysis between MODIS-AOTs, MTS-AOTs, and measured PM₂. In general, the MODIS-estimated PM₂ concentrations fell within the uncertainty of the measurements, thus allowing the estimate of PM₂ from MODIS, although in some cases they differed significantly due to vertical heterogeneity in aerosol distribution and the presence of distinct elevated aerosol layers of different origin and characteristics. Furthermore, significant spatial and temporal heterogeneity in the AOT and PM₂ estimates is observed in urban environments, especially during the pre-monsoon and monsoon seasons, which reduces the accuracy of the PM₂ estimates from MODIS. The estimates of PM₂ using MTS or MODIS-AOT exhibit a root mean square deference (RMSD) of about 8–16% against measured PM₂ on a seasonal basis. Furthermore, a tendency of increasing PM₂ concentrations is observed, which however is difficult to quantify for urban areas due to uncertainties in PM₂ estimations and gaps in the data set. Examination of surface and columnar aerosol concentrations, along with meteorological parameters from radiosonde observations on certain days, reveals that changes in local emissions and boundary-layer dynamics, and the presence or arrival of distinct aerosol plumes aloft, are major concerns in the accurate estimation of PM₂ from MODIS, while the large spatial distribution of aerosol and pollutants in the urban environment makes such estimates a considerable challenge.     

Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO2 column

Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO₂ columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO₂ columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NO_x emission data to expand the density of NO₂ columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO₂ columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m^–2 year^–1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.     

Estimation of snow water equivalent over first-year sea ice using AMSR-E and surface observations

A SWE retrieval algorithm developed in-situ using passive microwave surface based radiometer data is applied to the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E). Snow water equivalent is predicted from two pixels located in Canadian Arctic Shelf Exchange Study (CASES) overwintering study area in Franklin Bay, N.W.T., Canada. Results show that the satellite SWE predictions are statistically valid with measured in-situ snow thickness data in both smooth and rough ice environments where predicted values range from 15 to 25 mm. Stronger correlation between measured and predicted data is found over smooth ice with R² value of 0.75 and 0.73 for both pixels respectively. Furthermore, a qualitative study of sea ice roughness using both passive and active microwave satellite data shows that the two pixels are rougher than the surrounding areas, but the SWE predictions do not seem to be affected significantly.     

The impact of assimilation of satellite derived wind observations for the prediction of a monsoon depression over India using a mesoscale model

The Penn State/NCAR mesoscale model (MM5) has been used in this study to ingest and assimilate the INSAT‐CMV (Indian National Satellite System‐Cloud Motion Vector) wind observations using analysis nudging (four‐dimensional data assimilation, FDDA) to improve the prediction of a monsoon depression which occurred over the Bay of Bengal, India during 28 July 2005 to 31 July 2005. To determine the impact of assimilation of INSAT‐CMV winds on the prediction of a monsoon depression, three sets of numerical experiments (NOFDDA, FDDA and FDDA CMV) were designed. While the FDDA CMV run assimilated satellite derived winds only, the FDDA run assimilated both satellite and conventional observations. The NOFDDA run used neither satellite nor conventional observations. The results of the study indicate that the simulated sea level pressure field from the FDDA run is more consistent with the sea level pressure field from NCEP‐FNL compared to the FDDA CMV and NOFDDA runs. The highest correlation and lowest rms error of the sea level pressure field are associated with the FDDA run, and this provides a quantitative verification of the improvement due to the assimilation of satellite derived winds and the conventional upper air observations for the prediction of monsoon depression. All the three model simulated winds are in good agreement with the analysis winds at 850 hPa, 500 hPa and 200 hPa levels. The simulated structure of the spatial precipitation pattern for the assimilation experiments (FDDA and FDDA CMV) are closer to the TRMM observations with more rainfall simulated over the east coast regions in the assimilation experiments. The rms errors of the wind speed for the FDDA run show lower values at 500 hPa for all the three model runs, with a reduction in all three levels of up to 0.8–1.4 m s^?1 for the FDDA run and 0.5–1.9 m s^?1 for the FDDA CMV run with respect to the NOFDDA run. The statistical significance of the sea level pressure and the precipitation differences between the FDDA and the NOFDDA as well as the differences between the FDDA CMV and the NOFDDA have been calculated using the two‐tailed Student's t‐test and were found to be statistically significant. The influence of varying the nudging coefficients in the FDDA experiment has been studied.     

Estimation of global solar radiation using ANN over Turkey

The main objective of the present study is to develop an artificial neural network (ANN) model based on multi-nonlinear regression (MNLR) method for estimating the monthly mean daily sum global solar radiation at any place of Turkey. For this purpose, the meteorological data of 31 stations spread over Turkey along the years 2000-2006 were used as training (27 stations) and testing (4 stations) data. Firstly, all independent variables (latitude, longitude, altitude, month, monthly minimum atmospheric temperature, maximum atmospheric temperature, mean atmospheric temperature, soil temperature, relative humidity, wind speed, rainfall, atmospheric pressure, vapor pressure, cloudiness and sunshine duration) were added to the Enter regression model. Then, the Stepwise MNLR method was applied to determine the most suitable independent (input) variables. With the use of these input variables, the results obtained by the ANN model were compared with the actual data, and error values were found within acceptable limits. The mean absolute percentage error (MAPE) was found to be 5.34% and correlation coefficient (R) value was obtained to be about 0.9936 for the testing data set.     

Determination of the cloud fraction in the SCIAMACHY ground scene using MERIS spectral measurements

SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) is a passive remote sensing spectrometer observing backscattered radiation from the atmosphere and the Earth's surface, in the wavelength range between 240 and 2380 nm. The instrument is onboard ENVironmental SATellite (ENVISAT) which was launched on 1 March 2002. The Medium Resolution Imaging Spectrometer (MERIS) is also one of the 10 instruments onboard the ENVISAT satellite. MERIS is a 68.5° field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). It obtains a global coverage of the Earth in three days. Its main objective is to measure sea colour and quantify ocean chlorophyll content and sediment, thus providing information on the ocean carbon cycle and thermal regime. It is also used to derive the cloud top height, aerosol and cloud optical thickness, and water vapour column. The ground spatial resolution of the instrument is 260 m × 290 m. This paper is aimed at determining the cloud fraction in SCIAMACHY pixels (typically, 30 km × 60 km ground scenes) using MERIS observations and number of thresholds for MERIS top-of-atmosphere reflectances and their ratios. Thresholds utilize the fact that clouds are bright white objects having similar reflectances in the blue and red. The MERIS cloud fraction has been derived for a number of SCIAMACHY states with area of 916 km × 400 km. The results are compared with correspondent cloud fractions obtained using SCIAMACHY polarization measurement devices (PMDs). Large differences are found between cloud fractions derived using SCIAMACHY and MERIS measurements. It is recommended to use highly spatially resolved MERIS observations instead of SCIAMACHY PMD measurements to retrieve cloud fractions in SCIAMACHY pixels. The improvements advised will enhance SCIAMACHY trace gas and cloud retrievals in the presence of broken cloud fields.     

Inferences on the lifetime performance index for Weibull distribution based on censored observations using the max p-value method

In the service (or manufacturing) industries, process capability indices (PCIs) are utilised to assess whether product quality meets the required level. And the lifetime performance index (or larger-the-better PCI) C_L is frequently used as a means of measuring product performance, where L is the lower specification limit. Hence, this study first uses the max p-value method to select the optimum value of the shape parameter β of the Weibull distribution and β is given. Second, we also construct the maximum likelihood estimator (MLE) of C_L based on the type II right‐censored sample from the Weibull distribution. The MLE of C_L is then utilised to develop a novel hypothesis testing procedure provided that L is known. Finally, we give one practical example to illustrate the use of the testing procedure under given significance level α.     

Estimation of regional terrestrial water cycle using multi-sensor remote sensing observations and data assimilation

An integrated data assimilation system is implemented over the Red-Arkansas river basin to estimate the regional scale terrestrial water cycle driven by multiple satellite remote sensing data. These satellite products include the Tropical Rainfall Measurement Mission (TRMM), TRMM Microwave Imager (TMI), and Moderate Resolution Imaging Spectroradiometer (MODIS). Also, a number of previously developed assimilation techniques, including the ensemble Kalman filter (EnKF), the particle filter (PF), the water balance constrainer, and the copula error model, and as well as physically based models, including the Variable Infiltration Capacity (VIC), the Land Surface Microwave Emission Model (LSMEM), and the Surface Energy Balance System (SEBS), are tested in the water budget estimation experiments. This remote sensing based water budget estimation study is evaluated using ground observations driven model simulations. It is found that the land surface model driven by the bias-corrected TRMM rainfall produces reasonable water cycle states and fluxes, and the estimates are moderately improved by assimilating TMI 10.67 GHz microwave brightness temperature measurements that provides information on the surface soil moisture state, while it remains challenging to improve the results by assimilating evapotranspiration estimated from satellite-based measurements.     

Detection and emission estimates of NOx sources over China North Plain using OMI observations

We designed a fast procedure to detect the nitrogen oxides (NO_x) sources in the China North Plain and to estimate their NO_x emissions through a two-dimensional Gaussian fitting method applied to averaged Ozone Monitoring Instrument (OMI) observations of nitrogen dioxide (NO₂) column concentration. The Northern China Plain is a region that has one of the highest densities of anthropogenic NO_x sources in the world and therefore the sources are difficult to distinguish. With our procedure we still found 94 individual NO_x emission sources. Of these sources Tangshan city has the strongest NO_x emission rate (92 Gg N year^–1), while the weakest that we are still able to detect is Zhangjiakou city, with a NO_x emission rate of 0.4 Gg N year^–1. Using the fitting results, we reconstruct the NO₂ column concentration distribution map, which matches the OMI observations with an R² = 0.85 and a slope of 0.78. The derived NO_x emission rates for cities and provinces level show good agreement with former studies.     

Estimation of large scale monthly rainfall over Indian region using minimal INSAT-VHRR data

In this paper, we have examined the possibility of minimizing the number of geostationary Very High Resolution Radiometer (VHRR) images required for the estimation of rainfall on large time and space scales using the Arkin's approach. In the selection of appropriate images we are guided by our knowledge of the pattern of diurnal variability of cloudiness/rainfall over the region of interest. For the present work, INSAT-VHRR thermal band images over the Indian region for the month of June 1986 are utilized. Monthly average brightness temperatures (T_b) over 2·5° by 2·5° regions, derived from afternoon (0900 UTC) and post-midnight (2100UTC) INSAT-VHRR thermal infrared band images, separately and in conjunction, have been compared with the Arkin et at. monthly average rainfall based on 3–hourly INSAT images, as well as with ground based measurements.. The analysis indicates that even one image taken at 0900 UTC daily is able to locate the regions of high convection almost as well as depicted by Arkin's analysis based on 3–hourly images. Inclusion of 2100 UTC images results in marginal improvement in the spatial distribution of rainfall. It is also observed that the present results based on 0900 and 2100 UTC VHRR data are somewhat better correlated with groundbased rainfall measurements than the results from Arkin et al.     

Regional forecast of the UV index with optimized total ozone prediction using satellite observations over East Asia

A regional forecast model of the ultraviolet (UV) index has been developed by using a radiative transfer model and a multiple linear regression model to forecast total ozone over East Asia. This is a difficult and challenging task because of frequent cloud cover and atmospheric aerosols. The new, improved total ozone forecast model was constructed over each grid point in East Asia based on extensive investigation of the correlation between the total ozone and predictors related to the variation in total ozone. Root mean square errors (RMSEs) in the UV index between the forecast and satellite observations from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) for clear sky conditions range from 0.27 to 1.50 with an average of 0.79 in monthly statistics. Although the patterns of the corrected UV index when applying the cloud modification factor (CMF) and the aerosol modification factor (AMF) compare reasonably well with those of the UV index measured with the Ozone Monitoring Instrument (OMI), the performance depends on the accuracy of forecast for cloud and aerosol optical depth (AOD). Additional consideration of surface albedo and cloud optical depth are required to further refine and improve the accuracy of the prediction.     

Impact of convection over the equatorial trough on summer monsoon activity over India

Causes of disruption of rainfall (break in monsoon conditions) over the Indian subcontinent during the monsoon months for the period 1979–1998 are investigated using pentad rainfall data from the Global Precipitation Climatology Project (GPCP). Most (about 73%) of the break in monsoon (BM) events were associated with convective activity (rainfall more than 30 mm/pentad) over the equatorial trough (ET) region. The association between these events and the convective activity over the western (WET) and eastern equatorial trough (EET) regions of the tropical Indian Ocean were further explored. These relationships were tested for different (deficit, normal and excess) monsoon conditions over the Indian subcontinent and the El Niño conditions in the Pacific Ocean. There appears to be a negative and significant correlation between the Central Indian Region (CIR) rainfall and EET during deficit and non‐El Niño years. During deficit and El Niño years (1982 and 1987), both CIR and all India rainfall (AIR) exhibited a negative correlation with WET. In the case of years with no breaks, EET was negatively (positively) correlated during the years 1982 and 1992 (1994 and 1997) with AIR. The convective activity was more intense over EET than WET during prolonged BM and also in a deficit and non‐El Niño year (1979).     

Assessment of the AMSR-E soil moisture product over India

Soil moisture is a very important boundary parameter in numerical weather prediction at different spatial and temporal scales, controlling the exchange of water and energy between the atmosphere and land surface. Satellite-based microwave radiometric observations are considered to be the best for soil moisture remote sensing because of their high sensitivity, as well as their all-weather and day–night observation capabilities with high repeativity. In this study, an attempt has been made to assess the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-EOS) soil moisture product over India. The AMSR-E soil moisture product has been assessed using in situ soil moisture observations made by the India Meteorological Department (IMD) during the monsoon period (May–August) for the years 2002–2006 over 18 meteorological stations. Apart from assessing AMSR-E soil moisture retrieval accuracy, this study also investigates the effect of vegetation, topography and coastal water contamination, and determines the regions where the AMSR-E soil moisture product could be useful for different applications.     

Control of observations over random processes fluxes

The construction of the iteration procedure for synthesizing the law of control of observation over the processes, appeared one after another according to random flux regularities, is considered. The general approach to solution of the problem and the approximate algorithm for synthesizing the law of observations control are given for the case of a simple Poissonian flux. The example is presented in the paper.     

Validation of satellite and model aerosol optical depth and precipitable water vapour observations with AERONET data over Pune,India

ABSTRACT

The present work concerns with a detailed study of the validation of the Moderate Resolution Imaging Spectroradiometer (MODIS) and model products, and investigates the spatial and temporal variations in the correlation coefficient of the validation results obtained from the analysis of Aerosol Robotic Network (AERONET) sun–sky radiometer data archived at Pune during 2005–2015. Combining the confidence intervals and prediction levels, the ground-based AERONET aerosol optical depth (AOD) at 550 nm and precipitable water vapour (PWV) have been used to validate the MODIS, model AOD (550 nm), and PWV (cm) observations. The correlation coefficients (r) of AOD for the linear regression fits are 0.73, 0.75, and 0.79, and of PWV are 0.88, 0.89, and 0.97 for Terra, Aqua, and model simulations, respectively. Month-to-month/seasonal variation of AOD (550 nm) and PWV observations of satellite and model observations are also compared with AERONET observations. Additionally, various statistical metrics, including the root mean square error, mean absolute error, and root mean bias values were calculated using AERONET, satellite, and model simulations data. Furthermore, a frequency distribution of AOD (550 nm) and PWV observations are studied from AERONET, satellite, and model data. The study emphasizes that the globally distributed AERONET observations help to improve the satellite retrievals and model predictions to enrich our knowledge of aerosols and their impact on climate, the hydrological cycle, and air quality.     

Estimation of air temperature and reference evapotranspiration using MODIS land surface temperature over Greece

Moderate Resolution Imaging Spectroradiometer (MODIS), land surface temperature data, during daytime (LST_day) or night-time (LST_night), were employed for predicting maximum (T_max) or minimum (T_min) air temperature measured at ground stations, respectively, in order to be used as alternative inputs in minimum data-based reference evapotranspiration (ET) models in 28 stations in Greece during the growing season (May–October). The deviations between daily LST_night and T_min were found to be small, but they were greater between LST_day and T_max. Furthermore, the temperature vegetation index (TVX) method was employed for achieving more accurate T_max values from LST_day, after determining the normalized difference vegetation index of a full canopy (NDVI_max). The TVX method was validated on ‘temporal’ basis, but when the method was tested spatially, the improvement on the T_max estimates from LST_day was not encouraging, for being used operationally over Greece. Thus, LST_day or LST_night MODIS data were used as inputs in three ET models [Hargreaves–Samani, Droogers–Allen, and Reference Evapotranspiration Model for Complex Terrains (REMCT)] and their estimations, as compared with ground-based Penman–Monteith estimates, indicated that the REMCT model achieved the most accurate ET predictions (r = 0.93, mean bias error = 0.44 mm day^–1 and root mean square error = 0.74 mm day^–1), which can allow the spatial analysis of ET at higher spatial resolutions in areas with lack of ground temperature data.     

Spatial and temporal variability of soil moisture over India using IRS P4 MSMR data

The brightness temperature data measured by the multi‐frequency scanning microwave radiometer (MSMR) data has been analysed over the Indian subcontinent to deduce the seasonal and monthly variations of soil moisture. The present results show the spatial variations of soil moisture over the Indian region which is affected by the monsoon and show strong variability over different geological terrains.     

Thermal inertia mapping over the Brahmaputra basin,India using NOAA-AVHRR data and its possible geological applications

Thermal inertia is a volume property and shows the resistance power of the material against changes in its temperature. The thermal inertia of a surficial feature of interest cannot be directly measured. Hence, a proper modelling is required for its estimation. The objective of the project is to develop a technique to generate thermal inertia images using available National Oceanic and Atmospheric Administration (NOAA) satellite data to detect thermal anomalies and oilfield signature over a known producing basin. The Brahmaputra valley in Upper Assam is selected for this study.

NOAA-Advanced Very High Resolution Radiometer (AVHRR) thermal data were converted to temperature, based on the look-up table (LUT) given in the NOAA-AVHRR CD and by using split-window atmospheric attenuation correction models. The thermal inertia imagery is constructed with the help of the albedo imagery generated from the daytime and with the knowledge of the surface temperature change between the daytime and night-time data. The thermal inertia values are computed for all pixels common to both daytime and night-time and the thermal inertia imagery generated for the study area. The thermal inertia of a surface cannot be measured directly; so another model is also used to estimate apparent thermal inertia (ATI). The images from both the models have shown similar results.

The geological map when draped over the ATI image shows good correlation of gross lithology and thermal inertia. The metamorphics/basement and the sediments are well differentiated by their tonal and textural characters. The Mikir massif shows conspicuously brighter signature than the featureless darker signatures of the surrounding valley. Within the valley, the river water exhibits bright tone, whereas the present-day sandbars within the river exhibit darker tone than the alluvial plains of the valley. This is in agreement with the available published data. Major thrusts can be mapped as bright linear tone, and their geometry coincides well with those mapped in the field. Exposed cross faults can also be mapped in Arunachal foothills and faults in Mikir massif. The isoneotectonic map when draped over the ATI image shows that the identified isoneotectonic units can be well differentiated in the image on the basis of tonal characters. The prominent lineaments mapped in Mikir massif can be traced in the valley part also.

The producing and dry structures in the valley show very few signatures on the thermal inertia images, possibly due to poor spectral and spatial resolution of the NOAA data. It is planned to use the developed technique to generate thermal inertia maps using higher spatial and spectral resolution satellite data (e.g. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)), which may provide better oilfield signatures.     

Seasonal variability in aerosol optical depth over India: a spatio-temporal analysis using the MODIS aerosol product

Aerosols are one of the key components of climate systems. They absorb and scatter both solar and terrestrial radiation and produce strong surface as well as atmospheric radiative forcing effects. Aerosol climatology includes the measurement of light extinction by aerosol scattering and absorption, by procedures such as aerosol optical depth (AOD), angstrom exponent (α), single-scattering albedo (ω), and size distribution. This article analyses the dynamics of seasonal AOD over the Indian subcontinent from 2001 to 2009 using the MODIS level 2 data set. The analysis carried out for winter, pre-monsoon, monsoon, and post-monsoon seasons is based on 8 days’ composite AOD data for selected months representative of each season. The spatial variability of AOD has been shown to be 0.47 μm and 0.66 μm for fine- and coarse-mode aerosols, respectively, which illustrates the principle of relative difference. The dynamics of seasonally averaged AOD over the period under study represent an increasing tendency from 0.20 to 0.37 at 0.47 μm and from 0.16 to 0.26 at 0.66 μm during winter (2003–2009), whereas AOD in the pre-monsoon season ranged from 0.24 to 0.16 at 0.47 μm and from 0.24 to 0.16 at 0.66 μm (2005–2009). The monsoon season yielded an AOD of less than 0.15 throughout the study period, and the post-monsoon season recorded an increasing tendency from 0.18 to 0.29 at 0.47 μm and from 0.16 to 0.19 at 0.66 μm (2005–2009), reflecting a similar trend to that of the winter AOD curve. The spatial distribution of AOD shows that the northern part of India – especially the Indo-Gangetic plain – remains most affected by high AOD throughout the year. Such high AOD can be attributed to increasing anthropogenic emission due to an ever-increasing population, and urban, industrial, and other economic activities causing high concentrations of fine-mode organic and inorganic aerosol particles, along with coarse soil and mineral dust over the Indo-Gangetic plain.