Search NASA⌕ Search

Engineering topics

Emily Berndt

Publications and source records attributed to Emily Berndt.

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Transforming Satellite Data into Weather Forecasts

A NASA project spans the gap between research and operations, introducing new composites of satellite imagery to weather forecasters to prepare for the next generation of satellites. Satellite imagery has been an immense benefit to weather forecasters. With it, they can assess aviation hazards such as low clouds, monitor thunderstorms, and track the evolution of dust plumes. Satellite sensors are continually evolving to provide ever greater imaging capabilities, and researchers continue to develop advanced techniques to identify hazards in satellite imagery. However, barriers can prevent experimental products from reaching forecasters in the operational environment. A NASA project has developed an interactive process whereby weather forecasters learn to interpret the latest satellite imagery and apply it to their operations. Forecasters then provide feedback to the researchers who are developing algorithms and products to further improve future products. This effort has taken on greater importance with the anticipated launch of a new series of satellites that will provide substantially greater amounts of data than are currently available.

Emily Berndt↗
NASA NTRSDate not supplied

NASA SPoRT Suite of Legacy and Current Satellite Products in Support of Tropical Analysis and Forecasting

The NASA Short-term Prediction, Research, and Transition (SPoRT) Program works closely with NOAA/NWS weather forecasters to transition unique satellite data and capabilities into operations in order to assist with nowcasting and short-term forecasting issues. SPoRT has applied data and capabilities from a variety of research-oriented missions to improve the operational analysis and short-term forecasting of the tropical environment and tropical cyclones(TC).Toward researching new products, SPoRT is examining the diurnal cycle of TC intensity by observing changes in precipitation, winds, and midlevel moisture, including the use of NUCAPS soundings to assess the moisture and temperature environment around TCs, as well as examining changes in sounding profiles associated with the TC diurnal cycle. SPoRT has developed capabilities to analyze the evolution and diurnal cycle of GPM/IMERG rain rates and GLM lightning characteristics associated with TC, by compass and up-/down-shear quadrants relative to the cyclone center. As Early Adopters in the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) mission, SPoRT is also in the process of developing new experimental products utilizing TROPICS data. Furthermore, SPoRT is investigating the relationship between TC intensity and lightning flash size and optical energy. MSFC hosts a GOES ground rebroadcast station which enables the potential for very low latency GLM products. These products provide the ability to monitor tropical regions/systems in data-void oceanic regions and to fill the gaps in traditional observational systems. SPoRT seeks to expand our collaborations with operational centers and other stakeholders to provide new tools to aid in tropical analysis and forecasting.

satellite remote sensing↗
NASA NTRSDate not supplied

Standardized RGB Composite Imagery for Global Applications

NASA’s MODIS instruments on the Terra and Aqua satellites have been providing imaging capabilities from polar orbit for the last 20 years allowing for twice daily sampling of mesoscale atmospheric features with multispectral or Red, Green, Blue (RGB) composite imagery. With the launch of the next generation of geostationary sensors the capability exists for expanded coverage of RGB imagery with the ability to track features over long distances and study the processes that contribute to the development of fast evolving features. However, the use of RGB imagery on a global basis is not without limitations due to the impact of limb-effects at high viewing angles and subtle spectral channel differences between satellite sensors.

Emily Berndt↗
NASA NTRSDate not supplied

Application of Advanced Earth Observations and Model Simulations to Improve Air Quality Monitoring in the Hindu-Kush-Himalayan Region

Air pollution is a serious environmental health concern in the Hindu Kush Himalayan (HKH) region of south-central Asia, as rapid industrialization and population growth have led to increased anthropogenic emissions from transportation, residential, industrial, energy, and biomass burning sources. Natural emissions from dust and forest fires are additional sources of air pollutants that can exacerbate air quality in the region. The combination of the complex pollutant mixtures and atmospherically stable weather conditions during the winter monsoon can visibility reductions and hazardous air quality from persistent haze episodes. The Kathmandu Valley is especially vulnerable to extreme haze issues due to the surrounding mountains that restrict air movement and retains pollutants in the atmosphere. This study uses state-of-the-art satellite observations and modeling capabilities in conjunction with ground-based networks to provide a comprehensive data toolkit for advancing air quality monitoring and forecasting decisions in the HKH region. The toolkit includes new generation satellite observations from the TROPOspheric Monitoring Instrument (TROPOMI), Geostationary Environment Monitoring Spectrometer (GEMS), and Advanced Meteorological Imager, which provide high spatiotemporal information on NO2, HCHO, SO2, O3, and aerosol optical depth (AOD). Particulate matter with diameters less than 2.5 micrometers (PM2.5) are derived from the satellite-retrieved AOD using ground-based observations and forecast model data. The satellite observations are also used to initialize and constrain forecast model systems designed for the HKH region. This talk will highlight the performance of the air quality toolkit for enhancing decision-making processes during exceptional air quality events in the region. Note: Presentation includes additional attachment of full presentation with sound and animation (best when viewed as slide show) with runtime of 15 min 32 secs

Aaron Naeger↗
NASA NTRSDate not supplied

Dust Machine Learning Probability and Assessment

- NASA SPoRT introduced the "Dust RGB" via NASA satellites to demonstrate GOES-R ABI capabilities and then evaluated the impact in operations (Fuell et al. 2016) - The Dust RGB allows for continued dust detection at night, but the cooling ground surface limits the effectiveness as night progresses. - SPoRT has developed a 'Machine Learning' (ML) model using a physically-based approach which can correctly label 85% of dust pixels and 99% of no-dust pixels

Dust↗