1. MOTIVATION The last decade has witnessed an unprecedented growth in the availability of devices equipped with location-tracking sensors. Examples of such devices include cellphones, in-car navigation systems and weather monitoring gadgets. The widespread usage of these devices has resulted in an abundance of data that are in the form of trajectories. Querying and mining these trajectories is...

Ocean surface temperatures and wind speeds tend to vary together across ocean basins away from tropical regions. Typically, stronger surface winds are seen over cooler and weaker winds over warmer water, because strong winds cool the ocean surface by evaporation and by stirring up cold subsurface water. But is this always the case? Aside from warm tropical oceans, where moist air ascends high i...

The Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), a National Science Foundation Engineering Research Center, is creating a new type of weather observation system featuring networks of low-power, low-cost radars that adaptively and collaboratively collect high resolution data in the lowest 3 km of the atmosphere, a region which is under sampled by current technology. These ...

Turbulence is a major aviation hazard for both commercial and private aircraft. Currently, the clear-air turbulence forecasting tool Graphical Turbulence Guidance (GTG) is used by airline meteorologists and dispatchers for flight planning, and in part to determine operational Airman’s Meteorological Information (AIRMET) turbulence advisories; however, GTG has much higher resolution and intensit...

Big data brings new challenges and opportunities in many scientific areas today. Characterized by the high volume, velocity, and variety (3Vs) model, big data is valuable in many knowledge discovery applications, whereas requires new methodologies and technologies to manage and make use of the data. In this dissertation, a fundamental methodology and an emerging application of big data are pres...

Probabilistic weather forecasts are amongst the most popular ways to quantify numerical forecast uncertainties. The analog regression method can quantify uncertainties and express them as probabilities. The method comprises the analysis of errors from a large database of past forecasts generated with a specific numerical model and observational data. Current visualization tools based on this me...

In this article, we summarize current forecasting practice, the performance of the forecasting enterprise, and the impacts of tropical cyclones from a meteorological perspective. In the past, a forecast was considered successful if it predicted the hurricane’s position and intensity 12–72 h into the future. By the 1990s, forecast users came to expect more specific details such as spatial distri...

Despite decades of work on climate change biology, the scientific community remains uncertain about where and when most species distributions will respond to altered climates. A major barrier is the spatial mismatch between the size of organisms and the scale at which climate data are collected and modeled. Using a meta-analysis of published literature, we show that grid lengths in species dist...

Meteorologists and other scientists rely heavily on remotely sensed data collected from instruments aboard orbiting satellites. The design of such instruments requires technical and economic trade-offs that results in certain desirable data not being directly available. One way to mitigate the lack of availability of this data is to use machine learning techniques to estimate the values that ca...

This paper presents a study developed at SIMEPAR (Paraná state weather service) using lightning data for electrical thunderstorm nowcasting. Thunderstorm electrical data collected at SIMEPAR such as lightning location, time of occurrence, current intensity, polarity, etc, are stored in real-time in a relational database. As a first step of this study, Microsoft Business Intelligence bundled wit...