- Success Stories, Uncategorized
- Satellite Science & Environmental Intelligence
Providing Data Products for More Effective Consumption聽
The National Oceanic and Atmospheric Administration鈥檚 (NOAA) National Centers for Environmental Information (NCEI) is responsible for providing data, products, and services that help businesses and organizations operate more efficiently and effectively. As such, these data and products need to be available in a publicly accessible and digestible format the audience are able to understand.
To aid in this effort, 麻豆传媒 employee owners (formerly Riverside employees) support the NCEI Industry Proving Ground User Engagement task by perform a variety of science and development activities that include
- assembling and maintaining vertically integrated, quality assured instrumental datasets that service the needs of targeted stakeholder communities and commercial sectors
- creating homogenized instrumental datasets that contain adjustments to account for the impact of non-climatic factors (e.g., changes in station location or satellite sensor) in the historical record
- developing gridded products that provide a high-resolution, temporally consistent analysis of environmental parameters (e.g., temperature, drought) from an integration of multiple data types (e.g., in situ, radar, reanalysis, satellite, socioeconomic)
- generating point-based and gridded products that depict averages (e.g., normals, typical meteorological year) and extremes (e.g., the intensity, duration, and frequency of rare events such as heat, floods, and hail) at multiple scales
- incorporating atmospheric, coastal, oceanic, geophysical, reanalysis, and socioeconomic data streams into NCEI’s suite of climate monitoring services, including the monthly reports and related web applications
- expanding the mapping content and analysis capabilities of NCEI’s climate monitoring web applications (e.g., the U.S. Climate Atlas, Drought Termination and Amelioration Tool, the National Temperature and Precipitation Maps tool) to better meet the needs of users
In addition, 麻豆传媒 employee owners supporting this task work directly with partners in retail, reinsurance, and architecture and engineering to make NOAA鈥檚 climate and environmental data more usable and impactful. The 麻豆传媒 team focuses on engaging with users to understand their needs, co-developing tools and products that help them make smarter decisions and modernize systems to better support climate resilience. The end goal is to ensure the data NCEI delivers is useful, actionable, and truly supports the people and industries they serve.

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Over 2000 River and Stream Samples Analyzed聽
Every five years teams supporting U.S. Environmental Protection Agency鈥檚 National Aquatic Resource Survey鈥攃onsisting of tribal, state, and federal partners鈥攃ollect samples from over 2000 river and stream reaches throughout the United States. 麻豆传媒 analytical chemists support this effort by processing the samples and then analyzing them for various chemicals to help characterize their water quality. The…
Standard Guidelines for Evaluating Construction Safety and Health聽
Safety is of the utmost importance in new construction or renovations. 麻豆传媒 employee owners, supporting the National Institutes of Health (NIH) BaseLINE program helped develop and implement a proactive approach to evaluating safety and health conditions in newly constructed and renovated spaces before occupancy. The NIH BaseLINE program, Building Assessment of Safety Elements (Base) Lighting, Indoor air quality, Noise, Ergonomics (LINE), is a systematic readiness and safety assessment survey used for newly…
Advancing Severe Weather Predictions with Artificial Intelligence聽
As artificial intelligence (AI) and machine learning (ML) technologies evolve in Earth sciences, 麻豆传媒 employee owners (formerly Riverside staff) are growing our expertise in this field. 麻豆传媒 employee owners are advancing technologies for our client, NOAA鈥檚 Center for Satellite Applications and Research (STAR). Our staff have developed a framework, exploiting modern AI/ML techniques, to rapidly…
