Engineering topics

William L Smith Jr

Publications and source records attributed to William L Smith Jr.

4 records

NASA NTRS2024

Long-Term Trends in Aerosols, Low Clouds, and Large-scale Meteorology over the Western North Atlantic from 2003 to 2020

A continuous decrease of aerosol over the western North Atlantic Ocean (WNAO) on decadal timescales provides a long-term experiment to evaluate how other natural and anthropogenic processes affect the manifestation of aerosol-cloud interactions in this region. Furthermore, the WNAO is a natural laboratory with diverse aerosol sources, marine boundary layer clouds that are more variable than marine stratocumulus deck regions, and unique flow regimes set up by the Gulf Stream and semi-permanent Bermuda High. We investigate how satellite-retrieved macrophysical and microphysical properties of low clouds and the surface shortwave irradiance changed from 2003 to 2020, in tandem with this aerosol decrease. The decadal changes in large-scale meteorology relating to the North Atlantic Oscillation (NAO) are also examined. We find no significant changes in low-cloud fraction but a widespread reduction in low-cloud optical depth attributed to fewer and larger cloud droplets with almost no change in cloud liquid water path. Despite robust signals in low-cloud optical properties together with aerosol decrease, a corresponding increase in the surface shortwave irradiance trends, also called surface brightening, is lacking. This absence of brightening is potentially due to concomitant changes found in large-scale meteorology associated with NAO— a Bermuda High strengthening, sea surface warming, and atmospheric moistening— as well as an increase in high cloud fraction that can counteract the surface brightening. Ultimately, our findings suggest that spatial patterns in the decadal meteorological variability, likely set up by NAO, contribute more to the surface cloud radiative effect over the WNAO than aerosol-cloud interactions.

J Minnie Park↗

NASA NTRS2023

Impact of COVID-19-related Air Traffic Reductions on the Coverage and Radiative Effects of Linear Persistent Contrails over Conterminous United States and Surrounding Oceanic Routes

The radiative effects of the large-scale air traffic slowdown during Apr and May 2020 due to the international response to the COVID-19 pandemic are estimated by comparing the coverage (CC), optical properties, and radiative forcing of persistent linear contrails over the conterminous United States and two surrounding oceanic air corridors during the slowdown period and a similar baseline period during 2018 and 2019 when air traffic was unrestricted. The detected CC during the slowdown period decreased by an area-averaged mean of 41% for the three analysis boxes. The retrieved contrail optical properties were mostly similar for both periods. Total shortwave contrail radiative forcings during the slowdown were 34 and 42% smaller for Terra and Aqua, respectively. The corresponding differences for longwave contrail radiative forcing were 33% for Terra and 40% for Aqua. To account for the impact of any changes in the atmospheric environment between baseline and slowdown periods on detected CC amounts, the contrail formation potential (CFP) was computed from reanalysis data. In addition, a filtered CFP (fCFP) was also developed to account for factors that may affect contrail formation and visibility of persistent contrails in satellite imagery. The CFP and fCFP were combined with air traffic density data to create empirical models that estimated CC during the baseline and slowdown periods and were compared to the detected CC. The models confirm that decreases in CC and radiative forcing during the slowdown period were mostly due to the reduction in air traffic, and partly due to environmental changes.

contrails↗

NASA NTRSDate not supplied

Cloud Climate Data Records from Geostationary Satellites to Support CERES: Challenges and Recent Progress

Cloud properties are critical for understanding the Earth’s radiation budget and cloud feedbacks. At NASA Langley Research Center, the Satellite ClOud and Radiative Property retrieval System (SatCORPS) provides real-time and historical analyses of clouds derived from Geostationary satellite (GEOsat) data for weather and climate applications. For the Clouds and the Earth’s Radiant Energy System (CERES) program, the global constellation of GEOsats has been analyzed since 2000 to help characterize and account for the diurnal cycle of clouds and their radiative impacts in the CERES climate data record. Obtaining consistent cloud properties over the GEOsat data record at all times of day during the CERES era is a major objective but a significant challenge considering the diversity of imaging capabilities deployed during that time. A brief overview of the current CERES/GEO cloud property data record constructed from three generations of GEOsat imagers for CERES Edition-4 is presented. In this version, different algorithms were applied to different satellites to take advantage of as much spectral information as possible for each satellite. Under this approach, the derived cloud properties were found to become accurate (and more similar to MODIS) for the GEOsat imagers with more spectral information. However, this approach also led to some discontinuities in cloud properties across platforms. Significant differences were also found between daytime and nighttime cloud properties. This paper will highlight the major issues in the Edition-4 record and discuss new strategies and early results from the next version of the SatCORPS GEOsat cloud property time series in development for CERES Edition-5 with the goal of improving day-night and cross-platform consistency during the CERES record.

clouds↗

NASA NTRSDate not supplied

Machine Learning Application for Improving Cloud Detection and Phase Determination Over Sunglint Regions for Geostationary Satellites

Cloud detection and phase determination over sunglint regions has been a challenge, especially for geostationary (GEO) satellites. Sunglint is observed when the sunlight specular reflection is at the same viewing angle of the satellite sensor. This intense reflection in the visible channels (VIS) is often comparable to that from optically thick clouds. It also contaminates the shortwave infrared channels (SWIR). Consequently, VIS and SWIR channels become less useful - or not useful- when they are saturated, hampering the detection of cloudy and clear-sky pixels. Sunglint contamination happens frequently and exists nearly in every daytime GEO full disk satellite images. However, sunglint intensity and region are difficult to model due to variable viewing geometry and ocean surface conditions. Moreover, existing physical models do not meet the accuracy required for operational GEO satellite cloud detection. We developed a machine learning algorithm to improve cloud detection in sunglint conditions for the NASA Langley’s Satellite ClOud and radiation Property retrieval System (SatCORPS). This poster presents our recent progress in the algorithm development, validation and applications. The algorithm is validated using collocated SatCORPS GOES-East and GOES-West cloud products. We demonstrate that the machine learning cloud detection in sunglint regions is superior to the traditional approach by improving temporal consistency between sunglint and non-sunglint conditions.

Machine Learning, Cloud detection, Sunglint, SatCO↗