Thunderstorms can lead to heavy or extreme heavy rainfall events and impact many sectors, such as aviation, urban infrastructure, and power systems. In Aviation Meteorology, The main objective of weather radar1 is to identify thunderstorm cells on the flight route and issue warning messages with the traces of wind motions, rainfall intensity and possible turbulence. These attributes contribute significantly to how air navigation is performed safely and efficiently against high-risk weather hazardous zones. To improve the severe thunderstorm forecast, develop an automated thunderstorm warning system application that detects the thunderstorm cells from the radar images by using image processing techniques. It recognizes the size of the thunderstorm cells and measures the gauge between the airport and each cell. Moreover, estimating the velocity of cell movement towards the airport is an added advantage. It is an added-value product of the Aviation Weather Decision Support System2 (AWDSS). This application utilizes the two main Python packages OpenCV3 and Wradlib4 .   Keywords: Aviation Meteorology, Decision Support System, Image Processing Technique, Weather radar, OpenCV, Thunderstorm, Radar Imaging.     References 1) Theodore Fujita, T., McCarthy, J. (1990). The Application of Weather Radar to Aviation Meteorology. In: Atlas, D. (eds) Radar in Meteorology. American Meteorological Society, Boston, MA. https://doi.org/10.1007/978-1-935704-15-7_43. 2) Eilts, Michael & Shaw, Brent & Barrere, Charles & Fritchie, Robert & Carpenter, Richard & Spencer, Phillip & Li, Yanhong & Ladwig, William & Mitchell, Dewayne & Johnson, J. & Conway, J. (2015). THE AVIATION WEATHER DECISION SUPPORT SYSTEM: DATA INTEGRATION AND TECHNOLOGIES IN SUPPORT OF AVIATION OPERATIONS. 3) Open Souce Computer Vision (OpenCV),https://docs.opencv.org/4.x/. 4) wradlib: An Open Source Library for Weather Radar Data Processing, https://docs.wradlib.org/en/latest/.
Thresholding is the most widely used change detection technique for identifying the changes in remote sensing images. However, most of the thresholding methods would generate isolated spots in the final change map, which are reduced by applying postprocessing step. This paper proposes a novel thresholding technique to address the aforesaid problem without applying the postprocessisng operation. The proposed technique uses two thresholds simultaneously to generate the change map, which is the key point of this method. Above-stated thresholds are calculated by initial indicators that are generated by local neighborhood mutual information. In addition, this approach compares the local statistics of the interested pixel with the derived thresholds instead of the pixel itself, which enhances the robustness of this technique. Particularly, the method resides on three things: generation of initial indicators, use of two thresholds simultaneously, and comparison of local statistics with thresholds. Finally, the method is tested on multitemporal multispectral images of different sensors, and experimental results validate the effectiveness of the proposed method.
Rainfall is an extremely variable parameter in both space and time. Rain gauge density is very crucial in order to quantify the rainfall amount over a region. The level of rainfall accuracy is highly dependent on density and distribution of rain gauge stations over a region. Indian Space Research Organisation (ISRO) have installed a number of Automatic Weather Station (AWS) rain gauges over Indian region to study rainfall. In this paper, the effect of rain gauge density over daily accumulated rainfall is analyzed using ISRO AWS gauge observations. A region of 50 km × 50 km box over southern part of Indian region (Bangalore) with good density of rain gauges is identified for this purpose. Rain gauge numbers are varied from 1–8 in 50 km box to study the variation in the daily accumulated rainfall. Rainfall rates from the neighbouring stations are also compared in this study. Change in the rainfall as a function of gauge spacing is studied. Use of gauge calibrated satellite observations to fill the gauge station value is also studied. It is found that correlation coefficients (CC) decrease from 82% to 21% as gauge spacing increases from 5 km to 40 km while root mean square error (RMSE) increases from 8.29 mm to 51.27 mm with increase in gauge spacing from 5 km to 40 km. Considering 8 rain gauges as a standard representative of rainfall over the region, absolute error increases from 15% to 64% as gauge numbers are decreased from 7 to 1. Small errors are reported while considering 4 to 7 rain gauges to represent 50 km area. However, reduction to 3 or less rain gauges resulted in significant error. It is also observed that use of gauge calibrated satellite observations significantly improved the rainfall estimation over the region with very few rain gauge observations.
In nature chromium exists in two forms viz., tri and hexavalent.Cr (VI) is highly recalcitrant and carcinogenic.It is frequently and extensively being used in variety of industries particularly Leather, Electroplating and Mining.Its removal is immensely required.Present investigation was an attempt to find out the optimum conditions for the removal of chromium by saw dusts as a result of adsorption.Study revealed that Teak (Tecton agrandis) is the best adsorbent of chromium followed by Sakhu (Shorea robusta), Eucalyptus (Eucalyptus globules), Sheesham (Dalbergia sisso), Neem (Azadirachta indica) and Mango (Mangifera indica).The conditions at which there was maximum adsorption was pH 6, temperature 27 o C, 50 g adsorbent concentration and 50 ppm adsorbate concentration with 20-minute retention time
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