An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region

To date, more than half a dozen merged rainfall data sets are available to the research community. These data sets use different approaches for rainfall retrieval and combine different satellites or/and ground-based rainfall observations. However, these data sets appear to differ among themselves and deviate from in situ observations at regional and seasonal scales. Hence, it is becoming difficult to choose a suitable data set from these products for regional rainfall analyses. In the present study, four independently developed multisatellite high-resolution precipitation products (HRPPs), namely Climate Prediction Center Morphing (CMORPH) version 1.0, Naval Research Laboratory (NRL)–blended, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)–3B42 version 7 are compared with quality-controlled gridded rain gauge data over India developed by the India Meteorological Department (IMD). A preliminary analysis is carried out for a 6 year period from 2004 to 2009 at daily scale for the summer monsoon season of June to September. Comparison of all-India seasonal (June to September) mean rainfall with rain gauge data shows a considerable underestimation by all HRPPs, although the underestimation is comparatively less for TMPA. Moreover, all the HRPPs are able to capture the important characteristic features of the summer monsoon rainfall such as intra-seasonal (active/break spells) and inter-annual (excess/deficient) variabilities reasonably well. Regional differences between observed rainfall and the HRPPs are also analysed. Results suggest that TMPA is comparatively closer to the ground-truth, possibly due to the incorporation of rain gauge observations. Furthermore, all the HRPPs show high probability of detection, low false alarm ratio, and high threat score in detection of rainfall events over most parts of India. It is also observed that all these HRPPs have certain issues in rainfall detection over the rain-shadow region of southeast peninsular India, semi-arid northwest parts of India, and hilly northern parts. Hence, results of the 6 year analysis over a region with a dense network of surface observations of rainfall suggest that the TMPA merged rainfall product is better than the other HRPPs due to (1) lower underestimation of rainfall, (2) higher correlation and lower root-mean-square error (RMSE), and (3) better performance over the west coast. Therefore, TMPA can be used with confidence as compared to other HRPPs for monsoon studies, particularly over the Indian land region with a considerable rain gauge network. This study also clarifies the fact that the merged satellite rainfall products with sufficient ground-truths can be the ideal product for monsoon and hydrological studies.     

The assessment of different vegetation indices for spatial disaggregating of thermal imagery over the humid agricultural region

ABSTRACT

Land surface temperature (LST) plays a significant role in surface water circulation and energy balance at both global and regional scales. Thermal disaggregation technique, which relies on vegetation indices, has been widely used due to its advantage in producing relatively high resolution LST data. However, the spatial enhancement of satellite LST using soil moisture delineated vegetation indices has not gained enough attention. Here we compared the performances of temperature vegetation dryness index (TVDI), normalized difference vegetation index (NDVI), and fractional vegetation coverage (FVC), in disaggregating LST over the humid agriculture region. The random forest (RF) regression was used to depict the relationship between LST and vegetation indices in implementing thermal disaggregating. To improve the model performance, we used the thin plate spline (TPS) approach to calibrate the RF residual estimation. Results suggested that the models based on TVDI performed better than those based on NDVI and FVC, with a reduced average root mean square error and mean absolute error of 0.20 K and 0.16 K, respectively. Moreover, based on the surface energy balance model, we found the surface evapotranspiration (ET) derived with the TVDI disaggregated LST as inputs achieved higher accuracy than those derived with NDVI and FVC disaggregated LST. It is indicated that TVDI, a soil moisture delineated vegetation indices, can improve the performance of LST enhancement and ET estimation over the humid agriculture region, when combining random forest regression and TPS calibration. This work is valuable for terrestrial hydrology related research.     

Variability and mechanisms of vertical distribution of aerosols over the Indian region

The present study investigates the seasonal variability in the vertical distribution of aerosol over the Indian region and its surroundings, and the possible mechanisms in the atmosphere that give rise to vertical transport of the aerosols. During boreal summer months, the aerosols reach a higher altitude of above 5 km over the Indian region. In the winter season, especially during December, January, and February, the aerosols remain at low levels of the atmosphere, extending to about 3 km. The low-level atmospheric conditions are favourable for lifting of aerosols associated with the organized convection in the atmosphere during the months from May to September. The shifting of the Inter Tropical Convergence Zone (ITCZ) towards the northern hemisphere and the monsoon activity associated with it makes the atmosphere turbulent over the region during the period. The vorticity and convergence patterns are favourable for the vertical transport of aerosols during the period from May to November. High vertical wind shear, which leads to the generation of turbulence during the monsoon season, enhances the mixing of aerosols in the atmosphere and supports the lifting motion. Over the Arabian Sea, during the summer months, the aerosols reach a higher altitude of about 6 km. The production of marine aerosols is increased by the monsoon winds over the sea, and the turbulent atmosphere lifts the particles to high altitudes. The transportation of dust aerosols from west and northwest parts is found at high altitudes towards the destination regions in north and south India. This also dominates the total aerosol content over the region.     

A stochastic model for predicting aerosol optical depth over the north Indian region

A simplistic model to forecast aerosol optical depth (AOD) over north India is presented in this study. The forecasts are generated by integrating the available high-resolution AOD data using time series modelling techniques. The forecasts are done using the autoregressive integrated moving average (ARIMA) method. It is found that the modelled values show good fit with the multiangle imaging spectroradiometer data during the years 2000–2010. This long-term statistical dependence shows that AOD over the north Indian region exhibits a long memory. The forecasts for the next 12 months were done at a 95% level of confidence. Our analysis confirms that using time series models prediction of AOD is possible, particularly during the summer months when the region is dominated by dust aerosols. The results obtained using the chosen ARIMA model suggest that this model proposes a simple and efficient method for determining the future values of AOD compared to more complex deterministic models.     

Verification of dust forecast over the Indian region with satellite and ground based observations

This article presents the verification results of the dust forecast by a numerical model over India and neighbouring regions. National Centre for Medium Range Weather Forecasting Unified Model (NCUM) is a global numerical weather prediction (NWP) model with a prognostic dust scheme. Evaluation of the performance of dust forecast by NCUM is carried out in this study. Model forecast of dust optical depth (DOD) at 550 nm is validated against ground-based and satellite observations since optical depth measurements in mid-visible wavelength are easily available. Daily 5-day forecast based on 00 UTC initial condition during dust dominated pre-monsoon season (April–May) of 2014 is used in this study. Location specific and geographical distribution of dust forecast is validated against Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite retrieved DOD observation at 532 nm, Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD), Ozone Monitoring Instrument (OMI), aerosol index, and Aerosol Robotic Network (AERONET) station data of total and coarse mode AOD. The verification results indicate that NCUM dust forecast generally gives good representation of large scale geographical distribution of dust over the western region of India. DOD forecasts show good correlation with co-located CALIPSO DOD over the western part (0.71) compared to central (0.58) and eastern (0.61) part of India in April while it show similar trend in May with slightly improved correlation (0.68) over the eastern part of India. Results also show that DOD forecasts are better correlated to AERONET coarse mode AOD observations over Jaipur in April and over Kanpur in May. Vertical distribution of dust concentrations in the forecast show reasonably good agreement with attenuated backscatter and depolarization ratio from CALIPSO observations. The model is also able to simulate spatiotemporal distribution of dust during a major dust event as observed by CALIPSO, MODIS, and OMI.     

On the variation of the tropospheric ozone over Indian region in relation to the meteorological parameters

Using monthly mean satellite measurements of TOMS/SBUV tropospheric ozone residual (TOR) data and meteorological parameters (tropopause height (TPH), 200 hPa geopotential height (GPH) and outgoing longwave radiation (OLR)) during 1979–2001, seasonal variability of TOR data and their association with meteorological parameters are outlined over the Indian region. Prominent higher values of TOR (44–48 DU, which is higher than the globally averaged 31.5 DU) are observed over the northern parts of the country during the summer monsoon season (June–September). Similar to the TOR variation, meteorological parameters (tropopause height, 200 hPa geopotential height and outgoing longwave radiation) also show higher values during the summer monsoon season, suggesting an in phase relationship and strong association between them because of deep convection present during summer monsoon time. The monthly trends in TOR values are found to be positive over the region. TOR has significant positive correlations (5% level) with GPH, and negative correlations with OLR and TPH for the month of September. The oxidation chains initiated by CH₄ and CO show the enhanced photochemical production of ozone that would certainly become hazardous to the ecological system. Interestingly, greenhouse gases (GHG) emissions were found to have continuously increased over the Indian region during the period 1990–2000, indicating more anthropogenic production of ozone precursor gases causing higher level of tropospheric ozone during this period.     

Anthropogenic aerosol fraction over the Indian region: model simulations versus multi-satellite data analysis

Anthropogenic aerosols play a crucial role in our environment, climate, and health. Assessment of spatial and temporal variation in anthropogenic aerosols is essential to determine their impact. Aerosols are of natural and anthropogenic origin and together constitute a composite aerosol system. Information about either component needs elimination of the other from the composite aerosol system. In the present work we estimated the anthropogenic aerosol fraction (AF) over the Indian region following two different approaches and inter-compared the estimates. We espouse multi-satellite data analysis and model simulations (using the CHIMERE Chemical transport model) to derive natural aerosol distribution, which was subsequently used to estimate AF over the Indian subcontinent. These two approaches are significantly different from each other. Natural aerosol satellite-derived information was extracted in terms of optical depth while model simulations yielded mass concentration. Anthropogenic aerosol fraction distribution was studied over two periods in 2008: pre-monsoon (March–May) and winter (November–February) in regard to the known distinct seasonality in aerosol loading and type over the Indian region. Although both techniques have derived the same property, considerable differences were noted in temporal and spatial distribution. Satellite retrieval of AF showed maximum values during the pre-monsoon and summer months while lowest values were observed in winter. On the other hand, model simulations showed the highest concentration of AF in winter and the lowest during pre-monsoon and summer months. Both techniques provided an annual average AF of comparable magnitude (~0.43 ± 0.06 from the satellite and ~0.48 ± 0.19 from the model). For winter months the model-estimated AF was ~0.62 ± 0.09, significantly higher than that (0.39 ± 0.05) estimated from the satellite, while during pre-monsoon months satellite-estimated AF was ~0.46 ± 0.06 and the model simulation estimation ~0.53 ± 0.14. Preliminary results from this work indicate that model-simulated results are nearer to the actual variation as compared to satellite estimation in view of general seasonal variation in aerosol concentrations.     

A satellite-based operational system for upper air analysis in the Australian region

The Australian Bureau of Meteorology has had the capacity since May 1980 to receive, process, and utilize remotely sensed meteorological data from the TIROS-N/NOAA A series of satellites. This paper describes the hardware established for data reception, the methods used in the extraction of meteorological data, initial assessments of these data, and the final configuration of the operational system.     

Long-term trends and decadal solar variability in ozone near the tropopause over the Indian region

To investigate the long-term trends and effects of decadal solar variability in the upper tropospheric ozone, data obtained from the Stratospheric Aerosol and Gas Experiment II (SAGE II) aboard the Earth Radiation Budget Satellite (ERBS) during the period 1985–2005 were analysed using a multifunctional regression model over the Indian region (8–40° N; 65–100° E). Analysis of time series spanning these years shows statistically insignificant trends (at the two-sigma level (95% confidence level)) at upper tropospheric pressure levels (10?16 km). This period covers two solar cycles, one lasting from 1985 to 1995 and the other from 1996 to 2005; these are referred to as decade I and decade II, respectively. Since temporal variation in ozone number density indicates 11 year periodicity, trends are statistically significant when calculated separately during each solar cycle. Trend analysis indicates statistically significant positive trends (0.7 ± 1.7% to 3.9 ± 2.9% year^?1 during decade I, and 2.2 ± 1.6% to 4.5 ± 3.0% year^?1 during decade II). In general, higher ozone trends are observed during decade II. Seasonal variation in trends during decade II shows increasing trends during the pre-monsoon (0.8?3.8% year^?1), monsoon (0.8?7.1% year^?1), and post-monsoon (2.8?8.0% year^?1) seasons. The annually averaged solar signal in ozone is found to be of the order of around??5 ± 4.3% to??13.8 ± 6.7%/(100 sfu). Results obtained in the present study are also compared with those obtained by other researchers.     

A new approach to satellite-based estimation of precipitation over snow cover

Current satellite-based remote-sensing approaches are largely incapable of estimating precipitation over snow cover. This note reports a proof-of-concept study of a new satellite-based approach to the estimation of precipitation over snow-covered surfaces. The method is based on the principle that precipitation can be inferred from the changes in the snow water equivalent of the snowpack. Using satellite-based snow water equivalent measurements, we derived daily precipitation amounts for the northern hemisphere for three snow-accumulation seasons, and evaluated these against independent reference datasets. The new precipitation estimates captured realistic-looking storm events over largely un-instrumented regions. However, the data are noisy and, on a seasonal scale, the amount of precipitation is believed to be underestimated. Nevertheless, current uncertainty in snow measurements, albeit large (50–100%), is still lower than direct precipitation measurements over snow (100–140%) and therefore this approach is still useful. The method will become more feasible as the quality of remotely sensed snow measurements improves.     

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.     

Occupational workload of Indian agricultural workers

The occupational workload of 13 agricultural workers was determined during a summer season, on the basis of cardio respiratory responses and individual capacity to perform work. Thirty different agricultural operations were observed during the actual working season. V02maxof the workers was 34 8cm³min^-1 kg^-1, ranging from 28.6 to 41.5cm³ min^-1 kg^-1. Pulmonary ventilation during the operations varied from 14 to 411 min^-1; only water lifting, bund trimming in dry-land and pedal threshing operations demanded more than 301 min^-1, and these were found to be the heaviest jobs in agricultural work. About 29% of total man-hours are involved in light work, 64% in moderate work and only 6% in heavy work. Daily energy expenditure of the workers varied from 10.3 to 1l.7MJ,ofwhich 53% to 56% energy was expended during the working day (i.e. the time-weighted work demand was about 30 to 40% of [Vdot]O₂ max) and about one-fifth of total heat production of the body was external thermal load.     

Influence of continuous satellite-based SST distribution on heavy snowfall events over the Korean Peninsula

To understand the mechanism for the development of heavy snowfall and the influence of the variation in the satellite-based Sea Surface Temperature (SST) distribution on the evolution of snow convection cells, synoptic characteristics of heavy snowfall over the Honam District, south-western Korean Peninsula, were analysed during December 2005; several numerical experiments were also conducted. New Generation Sea Surface Temperature (NGSST) data based on satellite observation are used in this study, with Penn State University/National Center for Atmospheric Research (PSU/NCAR) 5^th- generation Mesoscale Model (MM5) used as the numerical model.

Since the cold air from the arctic centre spreads strongly along the planetary wave, the Siberian high pressure matured earlier than normal in 2005. The analysis of the Arctic Oscillation Index (AOI) also indicated the development of a cold Siberian high.

Snow convection cells occurred primarily as a result of air–sea interactions and orographic forcing over the Korean Peninsula and, in this case, two major convection cells appeared on 21 December 2005. Because of the decrease in the SST with time, the intensity of the convection cells also decreased with time. The two-dimensional distribution of the SST was also strongly associated with the amount of inland snowfall. Therefore, satellite-based observation is a useful method to detect the change in detail of SST distribution, and the exact estimation of the SST gradient in the central Yellow Sea and near the coast is also an important factor in forecasting the intensity of snowfall over the south-western Korean Peninsula.     

Evaluation of INSAT-3D derived TPW with AIRS retrievals and GNSS observations over the Indian region

ABSTRACT

Precipitable water vapor is an important and highly atmospheric variable in temporal and spatially; the knowledge of its variability is important for meteorological and climatological studies. The main objective of this paper, an evaluation of Total Precipitable Water (TPW) retrieved from the Indian National Satellite System (INSAT-3D) data provided by the INSAT-3D Meteorological Data Processing System (IMDPS) at National Satellite Meteorological Centre (NSMC), New Delhi along with collocated Atmospheric Infrared Sounder (AIRS) L3 Standard Physical Retrievals (AIRS-only) during a one year period 2017 over the Indian region. The spatiotemporal distribution of seasonal mean and monthly dependency of the correlation coefficient, bias and root mean square error (RMSE) was computed between INSAT-3D TPW and AIRS retrievals during both daytime and nighttime. The results of the intercomparison reveal that TPW from the INSAT-3D is in very good agreement with the AIRS, that is seasonally distribution of TPW larger in warm seasons (June, July, August) and smaller in the cold season (December, January, February) and monthly dependency of correlation coefficient (> 0.8), bias (2–3 mm) and RMSE (< 5 mm) in all months during both daytime and nighttime, except June, July, August over coastal regions of Arabian Sea and Bay of Bengal shows degradation performance. However, the statistical analysis between INSAT-3D with respect to AIRS TPW retrieval during both daytime and nighttime shows that more reliable except during cloudy days. In addition to it, a similar analysis is carried out to assess the relative performance of INSAT-3D retrieved TPW with respect to 10 Global Navigational Satellite System (GNSS) over the Indian subcontinent obtained from the NSMC the period from 1^st January to 30 June 2017 on hourly. In this analysis, for each station time series, diurnal variations of TPW and monthly, seasonal distribution of the Taylor diagram was carried out between INSAT-3D and GNSS retrievals. INSAT-3D and 10 GNSS stations gave comparable accuracies during the months of March to June whereas the quality degrades in January and February months resulting in slight error. It might be caused by the in the winter season the surface emissivity could be one region for more degraded performance under the drier condition it brings in more uncertainties in surface emissivity. Overall, these results give good confidence in the quality and potential of INSAT-3D over the Indian region and can be used in weather forecasting and nowcasting applications.     

An evaluation of satellite-based drought indices on a regional scale

We compared conventional and satellite-based drought indices from drought vulnerable sites in South Korea during 2004–2013. Satellite-based drought indices, the energy-based water deficit index (EWDI), and the standalone Moderate Resolution Imaging Spectroradiometer (MODIS)-based evaporative stress index (stMOD_ESI) were evaluated using MODIS imagery to assess its capability to analyse the complex topography of the Korean peninsula. Of the drought indices examined, the EWDI and stMOD_ESI were accurate when capturing moderate drought conditions, compared to the observed precipitation-based conventional drought indices (standardized precipitation index (SPI-3) and Palmer drought severity index (PDSI)). In addition, the satellite-basedsoil moisture index (SSMI) developed from the Advanced Microwave Scanning Radiometer (AMSR-E) and Advanced Scatterometer (ASCAT) soil moisture products were reasonably correlated with the EWDI and stMOD_ESI. These results suggest that the satellite-based drought indices (EWDI and stMOD_ESI) may be applicable on a regional scale.     

Monitoring forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System nighttime satellite data

Monitoring and management of forest fires is very important in countries like India where 55% of the total forest cover is prone to fires annually. The present study aims at effective monitoring of forest fires over the Indian region using Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) nighttime satellite data and to evaluate the active fire detection capabilities of the sensor. Nightly DMSP-OLS fire products were generated from February to May 2005 (peak fire season) and analyzed to study the occurrence and behavior of fires over different forest physiognomies in Indian region. Fire products generated from DMSP-OLS were validated with ground observations of fire records from state forest departments to evaluate the accuracy of fire products. Further, inter-comparison of the DMSP-OLS derived fire products with contemporary fire products from Moderate resolution Imaging Spectroradiometer (MODIS) (both daytime and nighttime products) in addition to fires and burnt areas derived from Indian Remote sensing Satellite (IRS-P6) Advanced Wide Field Sensor (AWiFS) data has been done to analyze spatial agreement of fire locations given by the above sensors.Results from the DMSP-OLS fire products (derived from February to May 2005) over Indian region showed high forest fires in southern dry deciduous forests during February-March; central Indian dry and mixed deciduous forests during March-April; northeastern tropical forests during February-April and northern pine forests during May. Spatial pattern in fires showed a typical seasonal shift in fire activity from the southern dry deciduous forests to the northern pine forests and temperate forests as the fire season progressed. Statistical evaluation of DMSP-OLS fire products with ground observations showed an over all accuracy of 98%. Comparison of DMSP-OLS derived fires with consecutive MODIS and AWiFS derived fires for individual days indicated that 69% of the fires continued from current day (DMSP-OLS pass around ∼ 7 pm to ∼ 10 pm local time) to the next day (MODIS and AWiFS pass ∼ 10:30 am local time). Comparison of DMSP-OLS derived fires with burnt areas estimated from AWiFS showed that 98% of DMSP-OLS derived fires on the current day fell within the burnt area of AWiFS on subsequent day. Since the worst forest fires are those that extend from the current to the consecutive days, DMSP-OLS derived fires provide a valuable augmentation to the fires derived from other sensors operating in daytime.     

Edge computing and the internet of things on agricultural green productivity

The Journal of Supercomputing - The purpose is to mitigate network congestion (NC) and high energy consumption (EC) in the traditional Internet of Things (IoT)-supported crop monitoring system...     

Comparative study of aircraft- and satellite-derived aerosol and cloud microphysical parameters during CAIPEEX-2009 over the Indian region

This article presents the spatial and vertical distribution of aerosols and cloud microphysical parameters from the combined data sets of aircraft and satellites. The aircraft-based Cloud Aerosol Interactions and Precipitation Enhancement Experiment (CAIPEEX) was conducted in India during May to September 2009. During the experimental period, 3 days were identified on which space-borne lidar (CALIPSO) and radar (CloudSat) were nearby/over passed the observational regions, which covered north, south central, and southern parts of the Indian subcontinent. The results obtained from these three cases are explored. Similar features of aerosol layering and water/ice cloud signatures are observed by both aircraft and CALIPSO. In addition, events where dust aerosols acting as ice nuclei and polluted aerosols increase the depth of warm rain initiation are observed. The CloudSat profiles of liquid water content, droplet number concentration, and effective radii are underestimated when compared with the corresponding aircraft profiles. The aircraft measurements are able to bring out fine variability in vertical distribution, which would be more useful for regional parameterization schemes and model evaluation.     

Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Agricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers.