scholarly journals Preliminary results from the total lightning detector-cum-mini weather station installed at the Calcutta University

2018 ◽  
Author(s):  
Subrata Kumar Midya ◽  
Sujay Pal ◽  
Reetambhara Dutta ◽  
Prabir Kumar Gole ◽  
Upal Saha ◽  
...  
Keyword(s):  
West Bengal ◽  
Monsoon Season ◽  
Severe Weather ◽  
Lightning Activity ◽  
Weather Data ◽  
Weather Station ◽  
Withdrawal Time ◽  
Preliminary Results ◽  
Daily Weather Data ◽  
Total Lightning

Abstract. We report preliminary results derived from the total lightning detector-cum-mini weather station installed at the Calcutta University during 2016. This detector is a part of Earth Networks Total Lightning Network (ENTLN) operated globally for ground-based monitoring of total lightning activity and forecasting of localized storm alert and severe weather conditions. This set up provides improved measurement of in-cloud (IC) lightning as well as cloud-to-ground (CG) lightning in addition to daily weather data. Severe weather such as thunder squall, Nor'wester, hailstorm, cyclone over the Gangetic West Bengal can be studied in details based on total lightning activity along with other atmospheric and meteorological research using the weather data. Here we present some initial results from the analysis of total lightning measurements during the recent Nor'wester events occurred in and around Kolkata. We also present variation of wet component of atmospheric refractivity index during the monsoon season which can be used to declare the onset and withdrawal time of monsoon over Gangetic West Bengal.

scholarly journals Preliminary Development and Evaluation of Lightning Jump Algorithms for the Real-Time Detection of Severe Weather

2009 ◽  
Vol 48 (12) ◽  
pp. 2543-2563 ◽  
Author(s):  
Christopher J. Schultz ◽  
Walter A. Petersen ◽  
Lawrence D. Carey
Keyword(s):  
Performance Metrics ◽  
Severe Weather ◽  
Probability Of Detection ◽  
Lightning Activity ◽  
Convective System ◽  
Severe Thunderstorms ◽  
Mesoscale Convective ◽  
Tennessee Valley ◽  
Total Lightning ◽  
Jump Algorithm

Abstract Previous studies have demonstrated that rapid increases in total lightning activity (intracloud + cloud-to-ground) are often observed tens of minutes in advance of the occurrence of severe weather at the ground. These rapid increases in lightning activity have been termed “lightning jumps.” Herein, the authors document a positive correlation between lightning jumps and the manifestation of severe weather in thunderstorms occurring across the Tennessee Valley and Washington D.C. A total of 107 thunderstorms from the Tennessee Valley; Washington, D.C.; Dallas, Texas; and Houston, Texas, were examined in this study. Of the 107 thunderstorms, 69 thunderstorms fall into the category of nonsevere and 38 into the category of severe. From the dataset of 69 isolated nonsevere thunderstorms, an average, peak, 1-min flash rate of 10 flashes per minute was determined. A variety of severe thunderstorm types were examined for this study, including a mesoscale convective system, mesoscale convective vortex, tornadic outer rainbands of tropical remnants, supercells, and pulse severe thunderstorms. Of the 107 thunderstorms, 85 thunderstorms (47 nonsevere, 38 severe) were from the Tennessee Valley and Washington, D.C., and these 85 thunderstorms tested six lightning jump algorithm configurations (Gatlin, Gatlin 45, 2σ, 3σ, Threshold 10, and Threshold 8). Performance metrics for each algorithm were then calculated, yielding encouraging results from the limited sample of 85 thunderstorms. The 2σ lightning jump algorithm had a high probability of detection (POD; 87%), a modest false-alarm rate (FAR; 33%), and a solid Heidke skill score (0.75). These statistics exceed current NWS warning statistics with this dataset; however, this algorithm needs further testing because there is a large difference in sample sizes. A second and more simplistic lightning jump algorithm named the Threshold 8 lightning jump algorithm also shows promise, with a POD of 81% and a FAR of 41%. Average lead times to severe weather occurrence for these two algorithms were 23 min. The overall goal of this study is to advance the development of an operationally applicable jump algorithm that can be used with either total lightning observations made from the ground, or in the near future from space using the Geostationary Operational Environmental Satellite Series R (GOES-R) Geostationary Lightning Mapper.

scholarly journals Impact Assessment of Block Level Agro Advisories for Saving Input Cost of Farmers under Old Gangetic Plains of West Bengal - A Case Study in Malda

2020 ◽  
pp. 86-96
Author(s):  
Debjyoti Majumder ◽  
Rakesh Roy ◽  
F. H. Rahman ◽  
B. C. Rudra
Keyword(s):  
West Bengal ◽  
Monsoon Season ◽  
India Meteorological Department ◽  
Weather Forecast ◽  
Economic Benefits ◽  
Weather Data ◽  
New Delhi ◽  
Medium Range Weather Forecast ◽  
Net Returns ◽  
Block Level

Biweekly block level Agromet bulletins were disseminated based on medium range weather forecast with an objective to assess the effectiveness and usefulness of Block level Agro Advisory Services (AAS) and quantify the economic benefits through adopting the micro scale agromet advisory in their day to day agricultural operations at Malda, West Bengal. Two farmers groups were considered for the study on the basis of adoption and non-adoption of the agro-met advisories. Crop situation of these farmers were compared with nearby fields having the same crops where forecast were not adopted among non AAS farmers. The entire cost incurred along with yield and net returns were calculated from sowing to marketing of goods. Similarly, the weather forecast and actual weather data received from India Meteorological Department, New Delhi were compared to verify the accuracy of rainfall forecast for the year 2019-20 at GKMS centre, Malda KVK, West Bengal. It was apparent that the value of ratio score was higher during winter (84%) than pre-monsoon (80%), post-monsoon (79%) and monsoon (74%). However, the value of threat score was also found maximum during pre-monsoon season (79%). Statistical analysis like correlation coefficient, RMSE values of wind direction were found too high in all the four seasons to accept any homogeneity in the predicted and observed values. Blockwise verification of rainfall over the year showed the range of accuracy forecast for rainfall in between 67–76%. This forecast directly had a significant role in profit generation among the AAS adaptive farmers whose additional profit enhancement for maize cultivation was between 12% and 19% only towards cost of irrigation as compared to non-adaptive farmers. The study also showcased that the AAS adaptive farmers had a better livelihood as compared to non-AAS adaptive farmers.

scholarly journals Gridded daily weather data for North America with comprehensive uncertainty quantification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peter E. Thornton ◽  
Rupesh Shrestha ◽  
Michele Thornton ◽  
Shih-Chieh Kao ◽  
Yaxing Wei ◽  
...  
Keyword(s):  
North America ◽  
Cross Validation ◽  
Snow Water Equivalent ◽  
Soil Health ◽  
High Elevation ◽  
Day Length ◽  
Shortwave Radiation ◽  
Weather Data ◽  
Weather Station ◽  
Daily Weather Data

AbstractAccess to daily high-resolution gridded surface weather data based on direct observations and over long time periods is essential for many studies and applications including vegetation, wildlife, soil health, hydrological modelling, and as driver data in Earth system models. We present Daymet V4, a 40-year daily meteorological dataset on a 1 km grid for North America, Hawaii, and Puerto Rico, providing temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset includes an objective quantification of uncertainty based on strict cross-validation analysis for temperature and precipitation results. The dataset represents several improvements from a previous version, and this data descriptor provides complete documentation for updated methods. Improvements include: reductions in the timing bias of input reporting weather station measurements; improvement to the three-dimensional regression model techniques in the core algorithm; and a novel approach to handling high elevation temperature measurement biases. We show cross-validation analyses with the underlying weather station data to demonstrate the technical validity of new dataset generation methods, and to quantify improved accuracy.

scholarly journals Distribution and incidence of viruses in Irish seed potato crops

2015 ◽  
Vol 54 (2) ◽  
pp. 98-106 ◽  
Author(s):  
F. Hutton ◽  
J.H. Spink ◽  
D. Griffin ◽  
S. Kildea ◽  
D. Bonner ◽  
...  
Keyword(s):  
Potato Virus ◽  
Significant Proportion ◽  
Potato Production ◽  
Weather Data ◽  
Management Approach ◽  
Potato Seed ◽  
Virus Diseases ◽  
Aphid Control ◽  
Daily Weather Data ◽  
Potato Crops

Abstract Virus diseases are of key importance in potato production and in particular for the production of disease-free potato seed. However, there is little known about the frequency and distribution of potato virus diseases in Ireland. Despite a large number of samples being tested each year, the data has never been collated either within or across years. Information from all known potato virus testing carried out in the years 2006–2012 by the Department of Agriculture Food and Marine was collated to give an indication of the distribution and incidence of potato virus in Ireland. It was found that there was significant variation between regions, varieties, years and seed classes. A definition of daily weather data suitable for aphid flight was developed, which accounted for a significant proportion of the variation in virus incidence between years. This use of weather data to predict virus risk could be developed to form the basis of an integrated pest management approach for aphid control in Irish potato crops.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Impact of Extreme Weather on Healthcare Utilization by People with HIV in Metropolitan Miami

2021 ◽  
Vol 18 (5) ◽  
pp. 2442
Author(s):  
Daniel Samano ◽  
Shubhayu Saha ◽  
Taylor Corbin Kot ◽  
JoNell E. Potter ◽  
Lunthita M. Duthely
Keyword(s):  
Relative Risk ◽  
The United States ◽  
Extreme Weather ◽  
South Florida ◽  
Heat Index ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Steady Increase ◽  
Daily Weather Data ◽  
The Impact

Extreme weather events (EWE) are expected to increase as climate change intensifies, leaving coastal regions exposed to higher risks. South Florida has the highest HIV infection rate in the United States, and disruptions in clinic utilization due to extreme weather conditions could affect adherence to treatment and increase community transmission. The objective of this study was to identify the association between EWE and HIV-clinic attendance rates at a large academic medical system serving the Miami-Dade communities. The following methods were utilized: (1) Extreme heat index (EHI) and extreme precipitation (EP) were identified using daily observations from 1990–2019 that were collected at the Miami International Airport weather station located 3.6 miles from the studied HIV clinics. Data on hurricanes, coastal storms and flooding were collected from the National Oceanic and Atmospheric Administration Storms Database (NOAA) for Miami-Dade County. (2) An all-HIV clinic registry identified scheduled daily visits during the study period (hurricane seasons from 2017–2019). (3) Daily weather data were linked to the all-HIV clinic registry, where patients’ ‘no-show’ status was the variable of interest. (4) A time-stratified, case crossover model was used to estimate the relative risk of no-show on days with a high heat index, precipitation, and/or an extreme natural event. A total of 26,444 scheduled visits were analyzed during the 383-day study period. A steady increase in the relative risk of ‘no-show’ was observed in successive categories, with a 14% increase observed on days when the heat index was extreme compared to days with a relatively low EHI, 13% on days with EP compared to days with no EP, and 10% higher on days with a reported extreme weather event compared to days without such incident. This study represents a novel approach to improving local understanding of the impacts of EWE on the HIV-population’s utilization of healthcare, particularly when the frequency and intensity of EWE is expected to increase and disproportionately affect vulnerable populations. More studies are needed to understand the impact of EWE on routine outpatient settings.

scholarly journals A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

2015 ◽  
Vol 127 (3-4) ◽  
pp. 573-585 ◽  
Author(s):  
G. Duveiller ◽  
M. Donatelli ◽  
D. Fumagalli ◽  
A. Zucchini ◽  
R. Nelson ◽  
...  
Keyword(s):  
Climate Change ◽  
Weather Data ◽  
Climate Change Scenarios ◽  
Crop Modelling ◽  
Daily Weather ◽  
Daily Weather Data

scholarly journals A Total Lightning Trending Algorithm to Identify Severe Thunderstorms

2010 ◽  
Vol 27 (1) ◽  
pp. 3-22 ◽  
Author(s):  
Patrick N. Gatlin ◽  
Steven J. Goodman
Keyword(s):  
Severe Weather ◽  
Lightning Flash ◽  
Severe Thunderstorm ◽  
Critical Success ◽  
Severe Thunderstorms ◽  
Weather Events ◽  
Total Lightning ◽  
Jump Algorithm ◽  
Early Indication ◽  
Critical Success Index

Abstract An algorithm that provides an early indication of impending severe weather from observed trends in thunderstorm total lightning flash rates has been developed. The algorithm framework has been tested on 20 thunderstorms, including 1 nonsevere storm, which occurred over the course of six separate days during the spring months of 2002 and 2003. The identified surges in lightning rate (or jumps) are compared against 110 documented severe weather events produced by these thunderstorms as they moved across portions of northern Alabama and southern Tennessee. Lightning jumps precede 90% of these severe weather events, with as much as a 27-min advance notification of impending severe weather on the ground. However, 37% of lightning jumps are not followed by severe weather reports. Various configurations of the algorithm are tested, and the highest critical success index attained is 0.49. Results suggest that this lightning jump algorithm may be a useful operational diagnostic tool for severe thunderstorm potential.

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