Space Weather Database Of Notifications, Knowledge, Information (DONKI)

The Flight Awareness Collaboration Tool (FACT) user interface is a quad design with four areas. The Primary Map View shows the US with several traffic and weather overlays. The Surface Map View displays the selected airport with information on runway conditions and other factors. The Information View has specific data from various sources about the area of interest. This view also has a built-in algorithm that predicts the impact of the forecast winter weather on airport capacity. The Communication View supports messaging within the geographically-dispersed team that is using FACT. When an airport is selected in the Primary Map View, the information presented in the Surface Map and Information Views is focused on that choice. FACT is a web-based application using Node.js and MongoDB. It receives Java messages from the Federal Aviation Administration System Wide Information Management (SWIM) data repository. Data acquired from web pages and SWIM are tailored for FACTs Information View area. FACT is designed to reside on an existing workstation monitor to be put into use as needed.

Radiosondes are launched twice a day from different locations of the world and meteorological data is collected to plot the STUV diagram and determining CAPE (Cumulative Average Potential Energy) values. Radiosondes are not re-usable and used only at pre-determined locations around the globe. Moreover, a radiosonde can drift up to 125 miles from its release point. About 75,000 radiosondes are used every year. Given this unmet need, an inventor at NASA has developed an advanced airborne meteorological system which can provide meteorological parameters at any location at any desired time. In additional to routinely used meteorological sensors, an infrasonic sensor is also included to determine wind shear at local and regional levels. The airborne system may also be used in towns and cities to track drones and UAVs in the area. The airborne vehicle (UAV or drone) should be able to track seismic waves, magnetic storms, magneto-hydrodynamic waves, tornadoes, meteor, and lightning, etc. This technology can be use to measure environmental turbulence including wind shear, vortices as well as large and small eddies is an important factor in forecasting local and regional weather. It can also detect infrasound at ranges of many miles from the source and the shape of the acoustic power spectrum can be used to identify type of turbulence in the atmosphere.

NASA Goddard Space Flight Center now offers a new capability for meeting this Big Data challenge: MERRA Analytic Services (MERRA/AS). MERRA/AS combines the power of high-performance computing, storage-side analytics, and web APIs to dramatically improve customer access to MERRA data. It represents NASAs first effort to provide Climate Analytics-as-a-Service. Retrospective analyses (or reanalyses) such as MERRA have long been important to scientists doing climate change research. MERRA is produced by NASAs Global Modeling and Assimilation Office (GMAO), which is a component of the Earth Sciences Division in Goddards Sciences and Exploration Directorate. GMAOs research and development activities aim to maximize the impact of satellite observations in climate, weather, atmospheric, and land prediction using global models and data assimilation. These products are becoming increasingly important to application areas beyond traditional climate science. MERRA/AS provides a new cloud-based approach to storing and accessing the MERRA dataset. By combining high-performance computing, MapReduce analytics, and NASAs Climate Data Services API (CDS API), MERRA/AS moves much of the work traditionally done on the client side to the server side, close to the data and close to large compute power. This reduces the need for large data transfers and provides a platform to support complex server-side data analysesit enables Climate Analytics-as-a-Service. MERRA/AS currently implements a set of commonly used operations (such as avg, min, and max) over all the MERRA variables. Of particular interest to many applications is a core collection of about two dozen MERRA land variables (such as humidity, precipitation, evaporation, and temperature). Using the RESTful services of the Climate Data Services API, it is now easy to extract basic historical climatology information about places and time spans of interest anywhere in the world. Since the CDS API is extensible, the community can participate in MERRA/ASs development by contributing new and more complex analytics to the MERRA/AS service. MERRA/AS demonstrates the power of CAaaS and advances NASAs ability to connect data, science, computational resources, and expertise to the many customers and applications it serves.

This technology utilizes the U.S. high-voltage power transmission grid system as an extremely large antenna to extract unprecedented spatiotemporal space physical and geological information from distributed GIC observations. GICs are measured using differential a magnetometer technique involving one fluxgate magnetometer under the transmission line and another reference magnetometer station nearby. The reference station allows subtraction of the natural field from the line measurement, leaving only the GIC-related Biot-Savart field. This allows inversion of the GIC amplitude. The magnetometer stations are designed to operate autonomously. They are low-cost, enabling large scale application with a large number of measurement locations.

The Wind Event Warning System (WEWS) is high-energy Doppler LIDAR sensor that measures approaching changes of wind such as an oncoming variation of wind speed that will change the power output of a wind farm. Different from low-energy, the high-energy Doppler LIDAR has the energy to reach the long distances necessary to provide adequate warning time of a wind event. With the time provided by WEWS, the blades of a wind turbine could be feathered to prevent strong wind from damaging the turbine. In addition, airports could use WEWS to protect aircraft from sudden wind hazards.