A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. I: Methodology

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. This paper focuses on the four dimensional variational (4D-Var) data assimilation framework. Metrics from information theory are used to quantify the contribution of observations to decreasing the uncertainty with which the system state is known. We establish an interesting relationship between different information-theoretic metrics and the variational cost function/gradient under Gaussian linear assumptions. Based on this insight we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. The approach is illustrated on linear and nonlinear test problems. In the companion paper [Singh et al.(2011)] the methodology is applied to a global chemical data assimilation problem

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper (Sandu et al., 2012) we derive an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify the signal and degrees of freedom for signal information metrics of satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content yields an analysis comparable in quality with the one obtained using the entire data set

EFFECT OF MUCUNA COCHINCHINENSIS EXTRACTS ON EFFLUX PUMP IN FUNGAL STRAINS

Objective: To study the efflux pump inhibition activity of Mucunacochinchinensis extracts Methods: Sensitivity assay was performed by Kirby Bauer technique. Effluxing ability of microbe was done by accretion, accumulation and efflux pump assays. Results: Resistant fungal strains were subjected to efflux ability studies. Maximum effluxing ability was determined from the experimental data and it was found to be 105 min and 75 min by Candida albicans for methanolic extract of M. cochinchinenesis (MMC) and ethanolic extract of M. cochinchinensis (EMC) respectively and 45 min and 105 min by Aspergillus niger for MMC and EMC respectively. Profound synergistic effect of inhibition was observed with combined MMC and standard Clotrimoxazole when compared to the standard alone; against C. albicansindicates the modulating efficacy of MMC. Conclusion: M. cochinchinensis extracts showed efflux pump modulating activity

Content in the Context of 4D-Var Data Assimilation. II: Application to Global Ozone Assimilation

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different data points is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. In the companion paper [Sandu et al.(2011)] we derived an ensemble-based computational procedure to estimate the information content of various observations in the context of 4D-Var. Here we apply this methodology to quantify two information metrics (the signal and degrees of freedom for signal) for satellite observations used in a global chemical data assimilation problem with the GEOS-Chem chemical transport model. The assimilation of a subset of data points characterized by the highest information content, gives analyses that are comparable in quality with the one obtained using the entire data set

Cotton Cultivating Marginalised Farmers’ Climate Change Perceptions, Impacts and Adaptation Strategies in Vidarbha Region, Central India

This study aimed to assess cotton cultivating marginalised farmers' climate change perceptions, impacts, and adaptation strategies in the Vidarbha region of Central India. Purposive sampling was carried out to identify 70 marginalised farmers from the study area in the year 2020. A specially designed and developed questionnaire was used as a tool to elicit information from the respondent. From the identified sample population, 14.28% are illiterate and 34.28% with primary education and 42.85% don't use a cell phone. Climate change perceptions of these farmers are well understood and clear and reported rain pattern change (100%), wind profile change (81.42%) and high atmospheric temperature (75.71%). Of the different causes responsible for climate change crop residue burning is considered as a major (97.14%). Impacts of climate change on agriculture in general is reported as crop production reduced (95.71%) > crop growth reduction (85.71%) > reduction in soil fertility (81.42%) > irrigation water scarcity (65.71%). Impacts on cotton cultivation in particular include increase in insect/pest attack (90%) > production reduced (62.85%) > crop quality deteriorated (32.85%). The cost of insecticide/pesticide use is increased by 21-40% whereas profit received is decreased (71.42%). Heatstroke is identified as a major (66.66%) impact on livestock and death due to it (23.33%). Farmers willingness for adaptation to new methods is in the order of irrigation (82.85%) > harvest (72.85%) > during sowing (67.14%) > crop growth (51.42%). Future climate change adaptation strategies include high yielding crop varieties (100%) > early maturing crop varieties (60%) > use of organic manure (52.85%) > irrigated crops (48.57%) > drought-resistant crop varieties (45.71%) > crop diversification (37.14%) > water use change (30%). The climate change-induced problems faced by these marginalised farmers are well defined and different from other farmer categories and needs a holistic approach to overcome them. Sustainable adaptation strategies emphasize on climate-smart agriculture is the need of the hour to pave the way for sustainable agriculture and sustainable livelihood. This may be perhaps the first study with this aim from the region. Keywords: Central India, Chandrapur, climate change, climate smart agriculture, cotton, marginalised farmers, Vidarbh