ESTIMATION AND ADAPTIVE ONLINE CORRECTION OF SYSTEMATIC ERRORS IN THE GLOBAL FORECAST SYSTEM (GFS) USING ANALYSIS INCREMENTS

Numerical Weather prediction models have improved drastically in the last few decades with advances in data assimilation, improved parameterization, and ensemble forecasting. Despite these developments, the performance of numerical weather prediction models like the Global Forecast System (GFS) is still limited by errors in the model forecasts. These errors arise from inaccuracies in the initial condition and model’s inability to accurately represent physics, dynamics, and chemical processes. Operation centers generally use an offline correction scheme that corrects the forecast error after the forecast is generated. Past research has shown that another class of correction schemes, the online correction schemes that correct for the forecast errors during the model integration have certain advantages over offline schemes. However, the online schemes tested so far are prohibitive for operation use. The goal of this work is to introduce and test an ``adaptive online correction scheme” based on the methodology developed by (Danforth et al., 2007) that is suitable for operational use is introduced and implemented. As a first step towards correcting the tendency equation, the model errors are estimated using the 6-hr Analysis Increments (AIs). Assuming initial linear error growth and absence of observation bias in the analysis, 6-hr AIs provide a measure of model errors that can later be used to estimate model tendency errors. Seasonal means of 6-hr AIs during the period from 2012-2016 indicate robust model biases despite the changes in the model and data assimilation during that period. Apart from the season means, GFS also has significant periodic errors that are dominated by errors in the diurnal and semi-diurnal cycle. An adaptive online correcting scheme that uses 6-hr AIs, averaged over a moving training period to compute the bias correction term to be added in the model integration equation is then implemented with GFS. The scheme is tested using training periods of different lengths ranging from past 7 to 28 days. This scheme is remarkably stable and reduces the forecasts errors significantly in forecasts all over the globe at lead times of 1 day and shorter and over the tropics at longer lead times. An offline correction scheme was also tested but found to be less effective than the online correction scheme especially at lead times longer than 1-day

A Review Paper on Video De-Interlacing Multiple Techniques

In this paper present video interlacing de-interlacing and various techniques. Focus on the different techniques of video De- Interlacing that are Intra Field, Inter Field, Motion Adaptive, Motion Compensated De- interlacing and Spatio-Temporal Interpolation. De- Interlaced video use the full resolution of each scan so produced high quality image and remove flicker problem. Techniques are work on the scan line of object Intra Field techniques use pixels of the moving object, Inter Field works on stationary regions of object, Motion Adaptive works on the edge of the Object and Motion Compensation focus video sequence and brightness variation. Advantage of using De-interlacing technique is: Better Moving object image, no flickers and high vertical resolution

Wireless Sensor Based Data Analytics for Precision Farming

With advances in the Internet of Things, the use of Wireless Sensor Networks (WSN) has been widely proposed for monitoring and automation of farm processes under the umbrella of Precision Farming. In conventional WSN systems, data gathered by sensors is transmitted to remote cloud servers for analysis. These systems, however, incur delay in getting insights into the processes due to the high volume of data generated on the farms coupled with the poor Internet connectivity. This negatively affects the delay-sensitive applications that require immediate response. The Fog Computing paradigm suggests a shift in intelligence from the cloud towards the network edges to cater to the requirements of delay-sensitive applications. It proposes the use of compute, memory and networking resources available at edge devices such as gateways, routers and sensors to reduce dependency on cloud and, thereby, improve the responsiveness of the system. In this work, we focus our attention on the development of on-board intelligence for sensor devices in the context of Precision Farming. Firstly, we identify gaps in the current WSN-based Precision Farming technologies and examine the suitability of Edge Mining, an instance of Fog Computing, for real-time event detection in farm processes. In addition, we propose an extension of the Edge Mining approach to allow for context-aware operation of sensor devices in farms. A WSN prototype consisting of a plug-n-play universal sensor device and gateway node has been designed to validate the performance of these algorithms. Next, we develop two cooperative frameworks - Collaborative Edge Mining and Iterative Edge Mining, to represent the analytic problems as a set of cooperative Edge Mining-based tasks for parallel and sequential analysis respectively within WSN. The cooperation between tasks allows for scaling of analysis within and across devices to improve computational capability of the network. Finally, we discuss resource management through cooperative computing within WSN. Cooperation between devices is considered to improve accuracy and timeliness of in-network analytics while optimizing the use of energy resources of sensor devices for improved network longevity