Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ceilomete”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Title NWTC Site 3.2 - NREL ASSIST (SN11) / Thermodynamic retrievals TROPoe

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a Vaisala CL51 ceilometer. The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data were not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Denver, CO.

17 WIND ENERGY↗
DOE OSTI2025

NREL ASSIST Barge / Thermodynamic retrievals TROPoe v0.19

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.19 (Turner and Löhnert, 2014; Turner and Blumberg, 2019; Turner and Löhnert, 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height from a collocated NREL Vaisala CL51 ceilometer (when available) or vertically staring Halo XR lidar, and surface temperature, relative humidity, and pressure from the collocated Oregon State University met tower. The full pipeline to run the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al., 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2025

Site B - NREL ASSIST (SN11) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 11) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site G - NREL ASSIST (SN10) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 10) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2025

Site C1a - NREL ASSIST (SN12) Thermodynamic Retrievals TROPoe / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe v0.12 (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous observations from the NREL ASSIST-II (SN 12) infrared spectrometer. Observations are noise-filtered but not averaged in time to minimize errors due to non-uniform clouds. Additional input data in TROPoe are cloud base height (CBH), which is a combined data product that uses data from ceilometers at sites A1 and H and scanning lidars from ARM sites C1 and E37. The CBH is weighted inversely proportionally to the distance to the respective site to take into account the spatial variability of clouds (see https://github.com/StefanoWind/ASSIST_analysis/blob/main/awaken_processing/combine_cbh.py). The full pipeline for running the retrieval is available at https://github.com/StefanoWind/TROPoe_processor. Met data was not ingested. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at ARM SGP, OK.

17 WIND ENERGY↗
DOE OSTI2026

BLOC Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Block Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2026

NANT Site - ASSIST Thermodynamic Retrievals TROPoe v0.18 / Derived Data

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). This is a post-processed dataset and recommended for use. The profiles are retrieved every 10 minutes from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Michaud-Belleau et al. 2025) operated by NOAA Physical Sciences Laboratory (PSL) on Nantucket Island for WFIP3. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm-1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a collocated surface tower operated by NOAA PSL. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY. The TROPoe docker container (version 0.18) is available from Docker Hub at https://hub.docker.com/r/davidturner53/tropoe/tags, and the source code code is available in the GitHub repository https://github.com/OAR-atmospheric-observations/TROPoe.

17 WIND ENERGY↗
DOE OSTI2026

Microwave Radiometer

This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 minutes from instantaneous brightness temperature measurements at 35 channels observed with a microwave radiometer MP3000A operated by UND on the Barge for WFIP3. Additional input data in TROPoe are cloud base height from a collocated ceilometer operated by NOAA GML and temperature, water vapor mixing ratio, and pressure from a sensor attached to the MWR housing. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) that provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see, e.g., Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. A monthly prior was computed from operational radiosonde launches at Upton, NY.

17 WIND ENERGY↗
DOE OSTI2024

Deep-learning-derived planetary boundary layer height from conventional meteorological measurements

Abstract. The planetary boundary layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, which can estimate PBLH by integrating the morning temperature profiles and surface meteorological observations. The DNN model is developed by leveraging a rich dataset of PBLH derived from long-standing radiosonde records augmented with high-resolution micro-pulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden-layer structures, which collectively yield a robust 27-year PBLH dataset over the southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micro-pulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (Green Ocean Amazon; tropical rainforest) and CACTI (Cloud, Aerosol, and Complex Terrain Interactions; middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary layer processes with implications for improving the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Profiles of Radiative Fluxes at ENA

Profiles of radiative fluxes observed at the Atmospheric Radiation Measurement (ARM)’s Eastern North Atlantic (ENA) observatory along with the ancillary measurements are reported. The below-cloud drizzle properties were derived by combining the data from the ceilometer and Ka-band ARM Zenith Radar (KAZR) following the technique explained by Ghate et al. (2021 JAMC). The cloud and drizzle water path values were derived from the brightness temperatures reported by the microwave radiometer following the technique of Cadeddu et al. (2020 AMT). The cloud water path was then scaled to the KAZR-reported radar reflectivity to calculate profiles of liquid water content (LWC). Following the analysis from Ghate et al. (2023 JGR), cloud droplet effective radius was calculated using the number concentration value of 100 cm-3. The cloud properties, along with the thermodynamic properties, served as an input to the Rapid Radiative Transfer Model (RRTM) to yield profiles of radiative fluxes at a 1-minute temporal and 50-m vertical resolution. The fluxes were then averaged to hourly temporal resolution for analysis. In Mitra et al. (2025 JClim), the calculated profiles were compared against those derived from the satellite measurements (SYN1deg). Flux profiles from the SYN1deg and the thermodynamic and cloud properties used for deriving them are also reported here. Both all-sky and clear-sky radiative flux profiles were calculated. Due to the large data volume, the surface and top-of-atmosphere (TOA) radiative fluxes for the six-year period, and the hourly profiles of the radiative fluxes for January 2018, are submitted here. Full profiles of radiative fluxes calculated from the thermodynamic and cloud properties measured at the ENA site at 1-minute temporal and 50-m vertical resolution for a six-year period are available from the authors. Six files here correspond to the following data: 1_ENARAD_CERES_with_cld_amount_timeseries.nc: Time-series of hourly values of RRTM-simulated values of upwelling and downwelling fluxes at the surface and TOA, observed boundary-layer cloud fractions, and upwelling and downwelling fluxes from the SYN1deg from July 2015 to January 2022. 2_CERES_2018_at_CERES_levels.nc: SYN1deg radiative fluxes at six levels for the year 2018. 3_ENARad_2018_at_CERES_levels.nc: RRTM calculated fluxes at the SYN1deg vertical levels for the year 2018. 4_ENARad_rrtminputs_hourly_201801.nc: Thermodynamic and cloud properties used as an input to the RRTM for January 2018. 5_CERES_inputs_hourly_201801.nc: Thermodynamic and cloud properties utilized by SYN1deg algorithm for January 2018. 6_ENARAD_hourly_201801.nc: Full profiles of hourly averaged radiative fluxes from the RRTM simulations for January 2018.

Atmosphere↗
NASA NTRS1971

The pointing errors of geosynchronous satellites

A study of the correlation between cloud motion and wind field was initiated. Cloud heights and displacements were being obtained from a ceilometer and movie pictures, while winds were measured from pilot balloon observations on a near-simultaneous basis. Cloud motion vectors were obtained from time-lapse cloud pictures, using the WINDCO program, for 27, 28 July, 1969, in the Atlantic. The relationship between observed features of cloud clusters and the ambient wind field derived from cloud trajectories on a wide range of space and time scales is discussed.

Sikdar, D. N.↗
NASA NTRS1980

Ceiling and visibility instrumentation within government agencies

The key systems requirements for ceilometer systems are described. The following items are included: range must be 10,000 ft.; laser emission must conform to the bureau of radiological health class I performance; system must detect two lowest cloud layers; display must be in either English or metric units; and system must be capable of self monitoring and testing performance. Based upon the requirements competitive prototype cloud height indicator systems are to be built.

Bonner, R. S.↗
NASA NTRS1990

An eight-month climatology of marine stratocumulus cloud fraction, albedo, and integrated liquid water

As part of the FIRE/Extended Time Observations (ETO) program, extended time observations were made at San Nicolas Island (SNI) from March to October, 1987. Hourly averages of air temperature, relative humidity, wind speed and direction, solar irradiance, and downward longwave irradiance were recorded. The radiation sensors were standard Eppley pyranometers (shortwave) and pyrgeometers (longwave). The SNI data were processed in several ways to deduce properties of the stratocumulus covered marine boundary layer (MBL). For example, from the temperature and humidity the lifting condensation level, which is an estimate of the height of the cloud bottom, can be computed. A combination of longwave irradiance statistics can be used to estimate fractional cloud cover. An analysis technique used to estimate the integrated cloud liquid water content (W) and the cloud albedo from the measured solar irradiance is also described. In this approach, the cloud transmittance is computed by dividing the irradiance measured at some time by a clear sky value obtained at the same hour on a cloudless day. From the transmittance and the zenith angle, values of cloud albedo and W are computed using the radiative transfer parameterizations of Stephens (1978). These analysis algorithms were evaluated with 17 days of simultaneous and colocated mm-wave (20.6 and 31.65 GHz) radiometer measurements of W and lidar ceilometer measurements of cloud fraction and cloudbase height made during the FIRE IFO. The algorithms are then applied to the entire data set to produce a climatology of these cloud properties for the eight month period.

Fairall, C. W.↗
NASA NTRS1990

Mesoscale variability of free tropospheric humidity near San Nicolas Island during FIRE

Humidity variability at the top of the marine boundary layer (MBL) and in the free troposphere was examined using a variety of measurements taken on and around San Nicolas Island (SNI) during the FIRE IFO in July, 1987. Doppler wind profiler reflectivity recorded at two minute time resolution has provided the most continuous record and detail of small scale humidity fluctuations. Rawinsonde data were available from both an island site and the research vessel Point Sur. The information extractable from these sources is somewhat limited due to the frequency of launches (3 to 4/day at SNI and 6/day on the Point Sur). Some additional data were available from instrumented aircraft although scheduling flights in the neighborhood of the island was difficult due to restrictions on the air space. Other relevant data were collected at SNI near the radar and rawinsonde launch sites. A continuous record of cloud base altitude was logged by a ceilometer. Doppler acoustic sounder (sodar) reflectivity data provided a good record of inversion height. The sodar also monitored turbulent temperature fluctuations in the MBL. A small ground station recorded hourly averages of solar irradiance and downward longwave irradiance. The analysis in progress of the various data sets for two adjacent two day periods from 11 July to 14 July is described. The earlier period was chosen because the marine inversion was unusually high and there was increased frequency of rawinsonde launches at SNI. The later period was chosen because of the significant descent with time of an elevated inversion indicated by the radar data. Throughout the four day period, but especially in the first half, the turbulent humidity structure calculated from Doppler radar reflectivity shows excellent agreement with humidity profiles evaluated from rawinsonde data.

White, A. B.↗
NASA NTRS1990

Analysis of diurnal variation of SCu layer using 2 days of CLASS soundings on San Nicolas Island

Sixty-nine soundings were taken at San Nicolas Island during the FIRE marine stratocumulus experiment. The archived thermodynamic data at 5mb vertical resolution were reduced to 25mb vertical resolution by averaging. Ceilometer data showed the time variation of cloud-base. A subset of 20 soundings taken during a 2 day period of light winds were analyzed. The soundings are grouped into 6 hr. averages (with 5 soundings in each category) to examine the diurnal variation of the thermodynamic structure. Consistent with the solar heating during the daytime, cloud-base rises during the daytime hours and the cloud layer thins. The subcloud layer also becomes more stable, suggesting some uncoupling of the cloud layer from the surface.

Betts, Alan K.↗
NASA NTRS1990

Analysis of tethered balloon data from San Nicolas Island on 8 July 1987

Analysis of the 8 July 1987 (Julian Day 189) tethered balloon flight from San Nicolas Island is summarized. The flight commenced at about 14:30 UTC (7:30 Pacific Daylight Time) and lasted six and one-half hours. The position of the Colorado State University (CSU) instrument package as a function of time is shown. For the purpose of presentation of results, researchers divided the flight into 13 legs. These legs consist of 20 minute constant level runs, with the exception of leg 1, which is a sounding from the surface to just above 930 mb. The laser ceilometer record of cloud base is also shown. The cloud base averaged around 970 mb during much of the flight but was more variable near the end. Before the tethered balloon flight commenced, a Communications Link Analysis and Simulation System (CLASS) sounding was released at 12:11 UTC (5:11 PDT). Temperature and moisture data below 927 mb for this sounding is shown. The sounding indicates a cloud top around 955 mb at this time.

Cox, Stephen K.↗
NASA NTRS1990

Winds and turbulence above San Nicolas Island during FIRE

Winds and turbulence above San Nicolas Island (SNI) were continuously monitored during the First ISCCP Regional Experiment (FIRE) with a Doppler sodar (1600 Hz) and an ultrahigh frequencies (UHF) (400 MHz) wind profiler. Individual profiles were logged from 1 through 19 July 1987 at 30 sec to 2 min intervals; preliminary processing of hourly averaged winds from 100 to 2300 m is complete. These wind profiles have been combined with intermittent radiosonde profiles to construct time-height sections of parameters such as the Richardson Number. Analysis now in progress includes interpretation of the sodar (C 2/T) and profiler (C 2/N, C 2/q) reflectivity profiles. These profiles are being combined with radiometric and ceilometer data for investigations of vertical transport and cloud base/top entrainment processes. Finally, by using the SNI data in conjunction with soundings made from the research vessel Pt. Sur, the relationship between changes in the SNI winds and mesoscale and synoptic-scale weather features is being investigated.

White, Allen B.↗
NASA NTRS1992

Dense fog on the highway: Visual range monitoring in cars?

This paper reports on the development of a new sensor. Laser range-finders are currently installed in cars and trucks to measure the distance to a proceeding car (LEICA). A modification of such a sensor to measure visibility was made. The problems that had to be solved were: (1) choice of wavelength with relation to the human eye for visibility measurements; (2) dependency of the wavelength on atmospheric turbidity; (3) laser eye-safety; and (4) influence of multiple scattering at visibilities smaller than 200 m. The wavelength used for lidar sensors in the near infrared presents no real problems because the object to be sensed is fog appearing white which means that scattering from fog is wavelength independent. There are however differences in backscatter-to-extinction ratio for different fog and weather situations. The two solutions to these problems are polarization and multiple scattering. As known from airport operations of a laser ceilometer, one can use this multiple scattering contribution to determine the visibility.

Hahn, W.↗