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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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NetCDF L2 data files from the NOAA SHIP ceilometer contain level 2 (L2) data that have gone through the precalculation service and averaging. The profile is set to 4500 m (14,764 ft).
NOAA SHIP ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description: L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined
CACO ceilometer: netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values, and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description: L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined
NetCDF L2 data files from the CACO ceilometer contain level 2 (L2) data that have gone through the precalculation service and averaging. The profile is set to 4500 m (14,764 ft).
This dataset consists of CACO Site netCDF L1 data files containing level 1 (L1) data from the ceilometer.
Ceilometer (CEIL): cloud-base heights and polarimetric variables
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Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined
Site A1 netCDF L3 data files have level 3 (L3) data that have gone through the calculation service and contain all the data from the algorithms, including mixing layer height values and quality index data. L3 default files contain L3 data that use the default preset for a live plot. File naming schema: L3_DEFAULT_ _YYYYMMDDHHMM_ _ .nc Name Description L3 Identification of the data level DEFAULT Identification of the L3 file type CUSTOM OFFLINE STATION_NUMBER WMO station number, if defined YYYYMMDDHHMM UTC time ParameterKey Identification of the advanced algorithm settings. See the table below for an explanation. FREE_FORMAT File suffix, if defined
This dataset contains UND Vaisala CL31 data for measuring the cloud base height and aerosol backscatter profiles.
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Air quality dispersion modeling is performed for the Savannah River Site (SRS) to demonstrate compliance with applicable regulations. The AMS/EPA Regulatory Model (AERMOD) modeling system is an EPA recommended model for air quality applications with a data preprocessor (AERMET) to incorporate meteorological data collected on site. AERMET parameterizes or calculates meteorological variables that are not directly measured onsite. One of the parameters estimated by AERMET is the atmospheric mixing height. While the mixing height is not currently a measurement input into AERMET, SRS has the capability to measure the local mixing height. The Savannah River National Laboratory (SRNL) operates a Vaisala CL31 Lidar Ceilometer which estimates mixing height from aerosol backscatter. This study compares the parameterized mixing height from AERMET to the ceilometer estimated mixing height for the current regulatory period at SRS incorporating data from 2015-2019. Results from this study showed the average daily minimum values (morning) from AERMET were an order of magnitude lower than the commonly used Holzworth (1972) method and the ceilometer estimated mixing heights. Additionally, on average, the ceilometer exhibited a daily maximum mixing height value that occurred 1-3 hours later than the AERMET estimated maximum. This difference is likely due to the nighttime atmospheric mixing height assumptions and calculations used by AERMET. The AERMET algorithm cuts off mixing height growth at sunset while the ceilometer data show ongoing evening convection typical of the southeastern United States. These results suggest that the AERMET parametrization scheme assumptions may not be representative of a forested landscape and evening convection which could account for more mixing overnight. The results obtained in this study are significant for air dispersion modeling applications for regulatory purposes and worker safety. Mixing height can impact model estimated pollutant concentrations. A greater mixing height will provide more volume for pollutant dispersion. This report documents efforts to quantify the dependence of mixing height inputs toward a conservative estimated pollutant concentration.
The mixing layer height (MLH) is the top layer of turbulent mixing within the lower atmosphere, above the Earth’s surface. Estimates of the mixing layer height allow us to determine the volume available for the dispersion of pollutants throughout the atmosphere. Our goal is to identify the most suitable mixing layer height input for our dispersion modeling tool (AERMOD). For this project we evaluated two different methods of estimating the local mixing layer height. First, we use 3 estimates of the mixing layer height obtained from a ceilometer, each height estimate corresponds to a different gradient in aerosol backscatter which is used as a proxy for mixing layer height identification. Second, we use AERMET, our AERMOD modeling system preprocessor for meteorological data, which estimates the mixing layer height using several equations combined with measured meteorological data. We evaluate the ceilometer and AERMET estimates over a 5-year period (2015-2019) to see how well aligned the estimated mixing layer heights are. Our results suggest that our model, AERMET, is on average, aligned with ceilometer estimates during the daytime hours. However, daily maximum MLH estimates by AERMET occur earlier in the day than those estimated by the ceilometer. We believe that the ceilometer struggles with accurately measuring the MLH during nighttime hours as a result of sensor limitations.
This data package is associated with the publication “Radiative impact of record-breaking wildfires from integrated ground-based data” submitted to Nature Scientific Reports (Kassianov et al., 2024). Data from ground-based measurements of shortwave and spectrally resolved irradiance and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges were assessed to quantify the radiative impact of the September 2020 wildfires that occurred in the Western United States. Data were collected in September 2020 by several ground-based instruments at the Atmospheric Measurements Laboratory (AML) located in Richland, Washington (46.3451, -119.2792). These data include (1) Aerosol Optical Depth (AOD); (2) spectrally resolved and shortwave (SW) irradiances; (3) backscatter profiles; (4) total sky images; and (5) near-surface ambient air temperatures.The data package consists of five sub-directories: (1) “AML_Ceilometer_”; (2)” AML_CSPHOT_”; (3) “AML_MFRSR_irradiances_”; (4) “AML_SW_irradiances_and_Temp_”; (5) “AML_TSI_images_”; and 6 files stored at the directory level, including the readme, file-level metadata file, and data dictionary. The file-level metadata file (the file ending in “_flmd.csv”) lists all files contained in this data package and descriptions for each. The data dictionary (the file ending in “_dd.csv”) describes each tabular column header’s unit, definition, and structure. Below are descriptions of each sub-directory:“AML_Ceilometer_” includes ceilometer data collected at the AML. These files contain the corresponding narratives of data. Details related to the ceilometer data can be found in Morris (2016). “AML_CSPHOT_” includes ascii files with high-temporal resolution (about 10-15 min) AML CSPHOT data and their daily-averaged counterparts. These two files contain the corresponding narratives of data. Details related to the CSPHOT data can be found in Gregory (2011). “AML_MFRSR_irradiances_” includes ascii files with the AML MFRSR-measured diffuse, normal, and total spectrally resolved irradiance. Details related to the MFRSR data can be found in Hodges and Michalsky (2016) and Koontz et al. (2013). “AML_SW_irradiances_+_Temp_” includes near-surface ambient air temperature and SW irradiances, namely direct normal, diffuse hemispherical, and total hemispheric (global), measured at the AML. These files also incorporate the corresponding narratives of data. Details related to the SW irradiances can be found in Andreas et al. (2018). “AML_TSI_images_” includes Total Sky Images (TSIs) collected at the AML. Details related to the TSI data can be found in Morris (2005).
Infrared hyperspectral remote sensing has been widely used in the field of meteorology. Many scientists have carried out research on inversion methods of meteorological elements such as thermodynamic profile, boundary layer height, cloud base height, etc. In this study, a method based on machine learning for cloud detection using ground-based infrared hyperspectral radiation data is proposed. The features of outliers, the cloudy and cloud-free data of Atmospheric Emitted Radiance Interferometer (AERI) radiation are extracted. The “reference values” of cloudy and cloud-free are determined based on the observation data of Vaisala CL31 ceilometer within the time range of 8 min before the corresponding time of AERI. A support vector machine (SVM) algorithm is used for training. The dataset comes from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site and North Slope Alaska (NSA) site from 2015 to 2017, and the ARM West Antarctic Radiation Experiment (AWARE) site in 2016 is also analyzed. The instruments used in this paper include AERI, ceilometer, etc. The experimental results reveal that the agreement of cloud detection results between the proposed algorithm and ceilometer is about 93% at each site. However, for high clouds or optically thin clouds, the agreement will decrease.
Over the past five years (June 2017-current), the vertical electric field (E z ) as well as numerous cloud, precipitation and radiation properties have been monitored at the Department of Energy-Atmospheric Radiation Measurement (DOE ARM) North Slope of Alaska (NSA) field site. Comparisons between the composite diurnal averaged fair-weather E z , and composite ceilometer derived cloud base height during the polar night, reveal a significant correlation between the parameters (r = 0.62), supporting previous studies that there is high correlation between local electric field and cloud properties. With the use of extensive instrumentation at the site, such as the Micro Pulse Lidar (MPL), Ka-band Zenith Radar (KAZR), ceilometer, SKYRAD, Precipitation Imaging Package (PIP), among others, this study provides a more comprehensive examination of the diurnal cycle of cloud and precipitation properties along with the localized fair-weather return current of the larger Global Electric Circuit (GEC) system. Comparisons between the composite diurnal averaged fair-weather E z , and cloud thickness, maximum column backscatter, and precipitation particle counts all show similar diurnal variability during the polar night, indicating that during the largest magnitude fair weather E z time periods, clouds bases tend to be higher, clouds are thicker, have a larger column backscatter, and display more precipitating particles. Furthermore, a slight diurnal variability in the polar night surface temperature was found to be highly correlated (r = 0.87) to the longwave downwelling irradiance measured by SKYRAD, indicating that the variations in the physical properties of local clouds could modulate the diurnal polar night surface temperature variability on the order of 0.5 °C/day. In conclusion, these findings emphasize the importance and global nature of the GEC system, with the global aggregate of thunderstorms and electrified clouds potentially influencing polar night cloud properties as well as diurnal wintertime polar surface temperature variation.
Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.
This data file contains retrievals of cloud-base and below-cloud precipitation phase base for the MICRE campaign. The retrieved base is largely based on AAD depolarization lidar measurements. BOM cloud radar (BASTA), ARM ceilometer, and U. Canterbury ceilometer data are used to locate cloud base and below-cloud precipitation. Details on the algorithm and analysis of results are given in Tansey et al. 2023 (submitted DOI: 10.22541/essoar.168394768.89694625/v1). The data is limited to the period 20160406 to 201601120 during which both AAD lidar data and BoM radar data was collected.