ECAWsoft: A Web based Climate and Weather Data Visualization for Big Data Analysis

Purpose: In Tanzania, data for climate and weather are normally analyzed by Meteorological Agency and then are published through TV, website and radio. Different stakeholders normally obtain the weather and climate data / information in a generalized way. This calls for a need of a system which allows data to be shared openly to different stakeholders so that they can analyze those data as per their specific needs. The paper presents the overview of the developed system, ECAWsoft. Also, it gives some few interfaces showing different outputs from the system. Findings: The goal of this paper has been attained by developing a working data visualization tool for climate and weather called ECAWsoft. The system is current operational and is providing open data for different stakeholders. It is user friendly and interactive with capability of displaying visualization of data as per fine granularity required by user. Development of open data system for data visualization has lead to a transparency system which is helping farmers, researchers, policy makers (etc.) to make informed decision on weather and climate. Practical implications: The system presented in this paper need to be scaled up so that more data from all weather stations in Tanzania can be populated in real time. Originality/value: The development and adoption of open systems for visualizing weather and climate data remains seriously lacking in many countries including Tanzania. This paper provides an overview of some initiative to fill such a research gap

Weather, climate, and hydrologic forecasting for the US Southwest: A survey

As part of a regional integrated assessment of climate vulnerability, a survey was conducted from June 1998 to May 2000 of weather, climate, and hydrologic forecasts with coverage of the US Southwest and an emphasis on the Colorado River Basin. The survey addresses the types of forecasts that were issued, the organizations that provided them, and techniques used in their generation. It reflects discussions with key personnel from organizations involved in producing or issuing forecasts, providing data for making forecasts, or serving as a link for communicating forecasts. During the survey period, users faced a complex and constantly changing mix of forecast products available from a variety of sources. The abundance of forecasts was not matched in the provision of corresponding interpretive materials, documentation about how the forecasts were generated, or reviews of past performance. Potential existed for confusing experimental and research products with others that had undergone a thorough review process, including official products issued by the National Weather Service. Contrasts between the state of meteorologic and hydrologic forecasting were notable, especially in the former's greater operational flexibility and more rapid incorporation of new observations and research products. Greater attention should be given to forecast content and communication, including visualization, expression of probabilistic forecasts and presentation of ancillary information. Regional climate models and use of climate forecasts in water supply forecasting offer rapid improvements in predictive capabilities for the Southwest. Forecasts and production details should be archived, and publicly available forecasts should be accompanied by performance evaluations that are relevant to users

Exploring the potential impacts of climate change on North America\u27s Laurentian Great Lakes tourism sector

Climate change is one of the major challenges facing the global hospitality and tourism sector in the coming century and, given the important role that weather and climate play in all aspects of the tourism experience, tourism businesses owners need to start thinking about and enacting climate change adaptation strategies now. This work has utilized a combination of social science and physical science methods to (1) understand how the Great Lakes tourism sector could be impacted by climate change and (2) provide some insights into how researchers can help business owners prepare for these potential impacts. Overall, the results of this work illustrate the challenges that tourism managers face in terms of adapting to climate change despite their high awareness of the importance of weather and climate to their businesses; however, creative methods of communicating climate change science, such as through the use of data visualization techniques and scenario planning, could help overcome some of these barriers. In addition, the results of the analysis of atmospheric-ocean general circulation models (AOGCMs) and Variable Infiltration Capacity (VIC) model simulations show that climate change could lead to significant changes in winter weather and extreme weather in the Great Lakes region and, subsequently, impact the region\u27s tourism sector. Future research can build on these findings by continuing to explore the best means of quantifying climate change impacts for the tourism sector, evaluate the best way of translating those findings into actionable science for tourism business owners, and expand the dialogue around weather preparedness and long-term sector sustainability

TreeWatch.net : a water and carbon monitoring and modeling network to assess instant tree hydraulics and carbon status

TreeWatch.net is an initiative that has been developed to watch trees grow and function in real-time. It is a water- and carbon-monitoring and modeling network, in which high quality measurements of sap flow and stem diameter variation are collected on individual trees. Automated data processing using a cloud service enables instant visualization of water movement and radial stem growth. This can be used to demonstrate the sensitivity of trees to changing weather conditions, such as drought, heat waves, or heavy rain showers. But TreeWatch.net's true innovation lies in its use of these high precision harmonized data to also parameterize process-based tree models in real-time, which makes displaying the much needed mechanisms underlying tree responses to climate change possible. Continuous simulation of turgor to describe growth processes and long-term time series of hydraulic resistance to assess drought-vulnerability in real-time are only a few of the opportunities our approach offers. TreeWatch.net has been developed with the view to be complementary to existing forest monitoring networks and with the aim to contribute to existing dynamic global vegetation models. It provides high-quality data and real-time simulations in order to advance research on the impact of climate change on the biological response of trees and forests. Besides its application in natural forests to answer climate-change related scientific and political questions, we also envision a broader societal application of TreeWatch.net by selecting trees in nature reserves, public areas, cities, university areas, schoolyards, and parks to teach youngsters and create public awareness on the effects of changing weather conditions on trees and forests in this era of climate change

A Statistical, Data-driven Assessment of Climate Extremes and Trends for the Continental U.S.

Climate extremes are meteorological events that can have significant impacts on human and natural systems. Weather hazards, such as heat waves, drought, heavy thunderstorms, floods, hurricanes, occur frequently, and are a threat to human lives and property. Climate data observations spanning over 100 years are an important asset in understanding climate extremes and trends. This research uses daily climate data observations from more than 3000 climate stations in the continental U.S. to assess the climate trends and extremes, including temperature, precipitation, and snowfall. The climate data measurement sites were grouped by climate divisions and each climate division was statistically assessed for the following elements: maximum and minimum temperature, precipitation and snowfall. Furthermore, by dividing the climate data time series into 2 time intervals (1946-1980 and 1981-2015). Application of a host of non-parametric, statistical tests, provided insights on whether the recent time period is experiencing increased, decreased or similar frequencies of the climate extremes threshold being analyzed. The study also analyzed trends of climate extremes on a regional basis by breaking up the continental US into western, high plains, southern, midwestern, northeast and southeast regions. A data visualization system was also developed to assess and analyze the results from this data-intensive study. The visualization system includes intuitive choropleth maps and charts that depict climate trends

Visualizing urban microclimate and quantifying its impact on building energy use in San Francisco

Weather data at nearby airports are usually used in building energy simulation to estimate energy use in buildings or evaluate building design or retrofit options. However, due to urbanization and geography characteristics, local weather conditions can differ significantly from those at airports. This study presents the visualization of 10-year hourly weather data measured at 27 sites in San Francisco, aiming to provide insights into the urban microclimate and urban heat island effect in San Francisco and how they evolve during the recent decade. The 10-year weather data are used in building energy simulations to investigate its influence on energy use and electrical peak demand, which informs the city's policy making on building energy efficiency and resilience. The visualization feature is implemented in CityBES, an open web-based data and computing platform for urban building energy research

GR-182 - IoT Clusters Platform for Data Collection, Analysis, and Visualization Use Case

Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season\u27s unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part of the world. Since we have completed the first part, we are implementing a study case for a field used by the platform. Thus, we are implementing an environment monitoring technology base on weather data. For this study, the platform will collect real-time environmental data using sensors (Temperature, humidity, light and ultraviolet sensors, and other sensors). We are setting those sensors in relatively limited superficies due to resources problem. Next, we will use this data to find patterns in weather changes using Machine and Deep learning techniques since these environmental data come from a designated area. The main objective of this part is to find a weather pattern using collected data specific to this area. Data collected during this research and the IoT platform are available on campus for students to use for their class projects or future research. Currently, we are in the data collection process. We also evaluate the degradation and environmental effects on devices and sensors used. This study case is a needed step in the IoT Clusters Platform for Data Collection, Analysis, and Visualization research project. At the end of the project, the data collection framework will be efficient and cost less

IoT Clusters Platform for Data Collection, Analysis, and Visualization Use Case

Climate change is happening, and many countries are already facing devastating consequences. Populations worldwide are adapting to the season\u27s unpredictability they relay to lands for agriculture. Our first research was to develop an IoT Clusters Platform for Data Collection, analysis, and visualization. The platform comprises hardware parts with Raspberry Pi and Arduino\u27s clusters connected to multiple sensors. The clusters transmit data collected in real-time to microservices-based servers where the data can be accessed and processed. Our objectives in developing this platform were to create an efficient data collection system, relatively cheap to implement and easy to deploy in any part of the world. Since we have completed the first part, we are implementing a study case for a field used of the platform. Thus, we are implementing an environment monitoring technology base on weather data. For this study, the platform will collect real-time environmental data using sensors (Temperature, humidity, light and ultraviolet sensors, and other sensors). We are setting those sensors in relatively limited superficies due to resources problem. Next, we will use this data to find patterns in weather changes using Machine and Deep learning techniques since these environmental data come from a designated area. The main objective of this part is to find a weather pattern using collected data specific to this area. Data collected during this research and the IoT platform are available on campus for students to use for their class projects or future research. Currently, we are in the data collection process. We also evaluate the degradation and environmental effects on devices and sensors used. This study case is a needed step in the IoT Clusters Platform for Data Collection, Analysis, and Visualization research project. At the end of the project, the data collection framework from it will be efficient and cost less