Anu Rani Sharma

Fog Over Indo-Gangetic Plains—A Study Using Multisatellite Data and Ground Observations

Every year, fog formation over the Indo-Gangetic Plains (IGP) of Indian region during the winter months of December-January is believed to create numerous health hazards, economic loss, and cross-country transportation of aerosols. It has attracted the global scientific communitys attention to address the uncertainties pertaining to its formation and physico-chemical properties. In this paper, we made an attempt to study the fog conditions that occurred over the north Indian region and long-range transport of aerosols from the fog region towards the southern region during November 2008, using multisatellite data sets and ground-based observations on aerosol properties and solar irradiance in the urban region of Hyderabad, India. Ground measurements showed a considerable increase in aerosol optical depth (AOD) at 500 nm ( ~ 30%) and a decrease in total solar irradiance ( ~ 7%) over Hyderabad, India, during the fog period compared to a normal day corresponding to 04 November 2008.

Forest fire monitoring and burnt area mapping using satellite data: a study over the forest region of Kerala State, India

The present study describes the night-time active forest fire detection capabilities of Defense Meteorological Satellite Program – Operational Linescan System (DMSP-OLS) satellite data over the forest region of Kerala State, India in 2004. Kerala State had a high incidence of forest fires during 2004 because of the extended dry season, with the unusual absence of intermittent rainfall from January to April. DMSP-OLS data were processed to detect active night-time forest fires over Kerala State and validated with ground data and fine-resolution Indian Remote Sensing (IRS)-P6 Advanced Wide Field Sensor (AWiFS) satellite imagery. DMSP-OLS-derived night-time fire products were compared with synchronous Moderate Resolution Imaging Spectroradiometer (MODIS)-derived daytime fire products to check for the spatial agreement and continuity of fires. To estimate the burnt areas, different atmospheric correction algorithms were applied to the IRS-P6 AWiFS dataset; these included the cosine approximation model (COST), ATCOR2 in ERDAS Imagine and the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code. Atmospheric corrections to the satellite data indicated significant improvement in burnt area estimates. The results of the study suggest a good correlation between AWiFS data-derived burnt areas, DMSP-OLS-derived fire counts and MODIS-derived fire products. The fire occurrences derived from DMSP-OLS and MODIS data were validated with field records on fire occurrences over the study area.

Soil moisture variations in remotely sensed and reanalysis datasets during weak monsoon conditions over central India and central Myanmar

Variation of soil moisture during active and weak phases of summer monsoon JJAS (June, July, August, and September) is very important for sustenance of the crop and subsequent crop yield. As in situ observations of soil moisture are few or not available, researchers use data derived from remote sensing satellites or global reanalysis. This study documents the intercomparison of soil moisture from remotely sensed and reanalyses during dry spells within monsoon seasons in central India and central Myanmar. Soil moisture data from the European Space Agency (ESA)—Climate Change Initiative (CCI) has been treated as observed data and was compared against soil moisture data from the ECMWF reanalysis-Interim (ERA-I) and the climate forecast system reanalysis (CFSR) for the period of 2002–2011. The ESA soil moisture correlates rather well with observed gridded rainfall. The ESA data indicates that soil moisture increases over India from west to east and from north to south during monsoon season. The ERA-I overestimates the soil moisture over India, while the CFSR soil moisture agrees well with the remotely sensed observation (ESA). Over Myanmar, both the reanalysis overestimate soil moisture values and the ERA-I soil moisture does not show much variability from year to year. Day-to-day variations of soil moisture in central India and central Myanmar during weak monsoon conditions indicate that, because of the rainfall deficiency, the observed (ESA) and the CFSR soil moisture values are reduced up to 0.1xa0m3/m3 compared to climatological values of more than 0.35xa0m3/m3. This reduction is not seen in the ERA-I data. Therefore, soil moisture from the CFSR is closer to the ESA observed soil moisture than that from the ERA-I during weak phases of monsoon in the study region.

Assessment of regional climatic changes in the Eastern Himalayan region: a study using multi-satellite remote sensing data sets.

In this study, an attempt has been made to capture the sensitivity of a mountainous region to elevation-dependent warming and the response of a glacier-laden surface to increasing greenhouse gases (GHGs) and aerosol concentration. Some of the changes Sikkim has undergone due to urban sprawl are as follows: an increase of ~0.7u2009±u20090.46xa0°C temperature in the past 40xa0years at an altitude of 5.5xa0km; a 2.21xa0km2/year rate of loss of glacierised area in the past 33xa0years; an increase in absorbed longwave radiation (6u2009±u20092.41xa0W/m2); an increase in heat fluxes (2u2009±u20090.97xa0W/m2); a decrease in albedo during the last 30xa0years; an increase in the concentrations of carbon dioxide (4.42xa0%), methane (0.61xa0%), ozone (0.67xa0%) and black carbon column optical depth (7.19xa0%); a decrease in carbon monoxide (2.61xa0%) and an increase in aerosol optical depth (19.16xa0%) during the last decade; a decrease in precipitation, water yield, discharge and groundwater; and an increase in evapotranspiration during 1971–2005. Detection of three climate signals (1976, 1997 and 2005) in the entire analysis is the quantification of the fact that the climate of Sikkim is moving away from its inter-annual variability. An increase in temperature (0.23xa0°C/decade) at higher altitude (~5.5xa0km), suppression of precipitation, decreasing water availability and rapid loss of glacierised area are the evidences of the fact that air pollution is playing a significant role in bringing about regional climatic changes in Sikkim. In this study, change detection method has been used for the first time for the estimation of change in a glacierised area of the region.

Long-Term (1951–2007) Rainfall Trends around Six Indian Cities: Current State, Meteorological, and Urban Dynamics

The present study focuses on analyzing the precipitation trends over six Indian cities during the summer monsoon (June–September) covering the period 1951–2007 and also attempting to investigate possible urban forcing and dynamics by examining the variation in precipitation in the upwind and downwind directions. The analysis shows negative trends in the total number of rainy days over Hyderabad (−10.4%), Kanpur (−7.1%), Jaipur (−10.5%), and Nagpur (−4.8%) and positive trends over Delhi (7.4%) and Bangalore (22.9%). On the other hand, decreases of −21.3%, −5.9%, −14.2%, and −14.6% in seasonal rainfall are found over Delhi, Hyderabad, Jaipur, and Kanpur, respectively, whereas Bangalore and Nagpur show 65.8% and 13.5% increase. The lesser rainfall and rainy days, along with the mostly declining trend, in the downwind directions of the cities may imply an urban influence in precipitation associated with the increased anthropogenic emissions due to expansion of the urban areas and the increase of population. However, the large spatiotemporal variability of precipitation and the lack of statistical significance in the vast majority of the trends do not allow the extraction of safe conclusion concerning the aerosol-precipitation interactions around Indian cities.

Intraseasonal Variability of Summer Monsoon Rainfall and Droughts over Central India

Rainfall over Madhya Pradeshxa0(MP) in central India has large intra-seasonal variability causing droughts and floods in many years. In this study, rainfall variability in daily and monthly scale over central India has been examined using observed data. Consistency among various datasets such as rainfall, surface temperature, soil moisture and evapotranspiration has been examined. These parameters are from various different sources and critical for drought monitoring and prediction. It is found that during weak phases of monsoon, central India receives deficit rainfall with weaker monsoon circulation. This phase is characterized by an anticyclonic circulation at 850xa0hPa centered on MP. The EOF analysis of daily rainfall suggests that the two leading modes explain about 23–24% of rainfall variability in intraseasonal timescale. These two modes represent drought/flood conditions over MP. Relationship of weak phases of rainfall over central India with real-time multivariate (RMM) indices of Madden Julian Oscillation (MJO) has been examined. It is found that RMM-6, RMM-7, RMM-1 and RMM-2 describe the weak monsoon conditions over central India. However, frequency of drought occurrence over MP is more during RMM-7 phase. Surface temperature increases by about 0.5°–1° during weak phases of rainfall over this region. Soil moisture and evapotranspiration gradually reduce when rainfall reduces over the study region. Soil moisture and evapotranspiration anomalies have positive pattern during good rainfall events over central India and gradually reduce and become negative anomalies during weak phases.

Identification of Drought Occurrences Using Ensemble Predictions up to 20-Days in Advance

In the present study, skill of an extended range forecast system has been evaluated for identifying droughts over central India 20-days in advance. Rainfall forecasts from 44 ensemble members of the forecast system developed Indian Institute of Tropical Meteorology (IITM), Pune have been used to prepare probabilistic rainfall forecasts. It is seen that the uncertainties in the forecasts (in terms of ensemble spread) increases from day-5 to day 20. As the focus of the study is for drought predictions, forecasts in the bins 0-5xa0mm/5xa0day and 5-25xa0mm/5xa0day (no rain or less rain) were studied in detail. It is found that the modeling system has a tendency to over-forecast rainfall probabilities. With bias correction, the forecasts become more reliable. Various drought indices were computed using the mean of the forecast distribution up to 20-days in advance. Standardized precipitation index (SPI) computed using Gamma and Pearson type-III distributions are similar in the study region. It was found that these are in reasonable agreement with those from observations. Probabilistic forecasts of standardized precipitation index (SPI) were made and the relative operating characteristics (ROC) scores indicate that the forecasted SPI values are suitable for application.

The DSSAT model simulations of soil moisture and evapotranspiration over central India and comparison with remotely-sensed data

In this study, the decision support system for agrometeorology transfer (DSSAT) v4.6 model has been used to simulate soil moisture and evapotranspiration over central India (Madhya Pradesh) for the period 1990–2011 during drought years. Drought years were identified using observed gridded rainfall datasets for the monsoon months June, July and August for selected stations viz., Balaghat, Jabalpur, Narsinghpur and Seoni in Madhya Pradesh. Remote sensing data from the European Space Agency (ESA) derived soil moisture and moderate-resolution imaging spectroradiometer (MODIS) evapotranspiration have been used to compare the model simulated soil moisture and evapotranspiration at daily scale. It is found that the ESA derived soil moisture and MODIS evapotranspiration are closer to model simulated soil moisture and evapotranspiration respectively. RMS error of 0.07–0.17xa0m3xa0m−u20093 is noted in model simulated soil moisture for each station and RMS error of 60–100xa0mm is noticed in model simulated evapotranspiration over each station. Therefore, the DSSAT model simulated soil moisture and evapotranspiration during drought years are useful parameters for drought monitoring and prediction during drought occurrences.

Probabilistic predictions of the Indian monsoon rainfall in below-normal category using NMME global model products

Seasonal predictions of rainfall during the Indian monsoon from the North American Multi-Model Ensemble (NMME) have been used to compute the prediction skill. Observed and model-predicted rainfall have been put in three categories (namely below-normal, above-normal, and near-normal) based on the value of interannual standard deviation. In this study, focus is on the rainfall in below-normal rainfall category over central India. Forecast products were evaluated against the observed rainfall data for the period from 1982 to 2009. Simple model averaging and singular value decomposition (SVD) methods have been used to prepare the multi-model ensemble (MME) predictions. The root mean square error over central India is very less in the MME predictions compared to the member models. The weighted MME scheme using SVD method only marginally improves the skill over the simple MME scheme. The deterministic forecast skill is low (the correlation coefficient is non-significant) over central India even when the SVD scheme is employed. Both parametric and non-parametric methods have been applied to prepare probabilistic forecasts using multi-model ensemble for seasonal prediction. The probabilistic forecasts are reliable and usable as the hit rate is more than the false alarm rate for below-normal rainfall category.

Health risk assessment due to biomass smoke exposure in Indian indoor environment: An empirical approach using lung deposition model

The paper subsumes a framework that assesses health risk due to exposure to different fuel combustion through articulation of modern microscopic techniques, empirical equations, lung diagnostic tools and a pre-existing model that has been extrapolated to futuristic aspects (within controlled conditions). The framework was tested on 132 household cooks belonging to different age groups and using different types of fuel. The inhalable fraction released during fuel combustion varied in morphological characteristics and deposition site. Micrographs obtained using Scanning Electron Microscope (SEM) analysis of (biomass smoke) soot indicates aggregate formation attributing to a higher level of health risk. Further, abnormal ventilatory function along with higher risk (RRu202f>u202f1) was more evident within biomass fuel users. The condition further exacerbates while using dung cakes due to high levels of emissions (294.3 particles/liter) that deposit in the upper respiratory tract (0.0899). Further, the population attributable risk percent (79%) calculated on the basis of cooking behavior suggests a rural culture health determinant as clean fuel usage is not practiced as an outcome of low literacy and poor income in the region. These preliminary findings highlight the drudgery of impuissant women who are exposed to high particulate emissions on a regular basis which results in reduced lung function. Nevertheless, further cogitation is required to eliminate the limitations in this study and explore further linkages between exposure and vulnerable group to generate meaningful policy recommendations.