Search NASA⌕ Search

SEARCH · Search NASA

Results for “GROUND BASED”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A Deep Study of an Intermediate-age Open Cluster SAI 35 (Juchert 20) Using Ground-based Imaging and Gaia EDR3 Astrometry

We present a CCD UBVI photometric study of poorly studied intermediate-age open cluster SAI 35 (Juchert 20) for the first time. To accomplish this study, we also used LAMOST DR5, Two Micron All Sky Survey, and Gaia EDR3 databases. We identified 214 most probable cluster members with membership probability higher than 50%. The mean proper motion of the cluster is found as μ{sub α}cos δ=1.10±0.01 and μ {sub δ} = −1.66 ± 0.01 mas yr{sup −1}. We find the normal interstellar extinction law using the various two-color diagrams. The age, distance, reddening, and radial velocity of the cluster are estimated to be 360 ± 40 Myr, 2.9 ± 0.15 kpc, 0.72 ± 0.05 mag, and −91.62 ± 6.39 km s{sup −1}, respectively. The overall mass function slope for main-sequence stars is found to be 1.49 ± 0.16 within the mass range 1.1–3.1 M {sub ⊙}, which is in agreement with Salpeter’s value within uncertainty. The present study demonstrates that SAI 35 is dynamically relaxed. Galactic orbital parameters are determined using Galactic potential models. We found that this object follows a circular path around the Galactic center.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Occurrence of seeding multi-layer clouds in the Arctic from ground-based observations

Studies of Arctic clouds often focus on low-level single-layer clouds (SLCs). Here, we use combined observations of soundings and cloud radar during the MOSAiC, ACSE, and AO2018 research cruises as well as from long-term observations at Ny-Ålesund, Svalbard and Utqiagvik, Alaska to investigate the occurrence of SLCs and multi-layer clouds (MLCs) in the Arctic and to assess the rate of ice-crystal seeding in cold MLCs. MOSAiC observations show cloudy conditions in between 70 % and 90 % of sounding-radar cases. SLCs show occurrence rates of 30 % to 40 % with the highest value of 45 % during October. Cold MLCs are most abundant from November to June (40 % to 55 % of cases). Seeding occurs in about half to two thirds of the identified cold MLCs during MOSAiC for which the sub-saturated layer extends between 100 and 1000 m. The seeding rate increases by as much as 20 percentage points as the assumed size of the falling ice crystals is increased from 100 to 400 µm. The observations reveal a stable rate of cloud-free conditions of around 20 % over the covered latitude range. Cloud occurrence during MOSAiC and at Ny-Ålesund in July, when the geographical distance between observations was minimal, shows reasonable agreement. Comparisons of MOSAiC and other research cruises to the central Arctic also indicate consistent occurrence rates of different cloud types despite the likely effect of year-to-year variability. The comparison of data from ship campaigns and land sites suggests that the latter are not necessarily a good indicator of cloud occurrence in the high Arctic.

Achtert, Peggy [Leipzig Univ. (Germany); Meteorolo↗
DOE OSTI2020

Ground-based observations of cloud and drizzle liquid water path in stratocumulus clouds

Abstract. The partition of cloud and drizzle water path in precipitating clouds plays a key role in determining the cloud lifetime and its evolution. A technique to quantify cloud and drizzle water path by combining measurements from a three-channel microwave radiometer (23.8, 30, and 90 GHz) with those from a vertically pointing Doppler cloud radar and a ceilometer is presented. The technique is showcased using 1 d of observations to derive precipitable water vapor, liquid water path, cloud water path, drizzle water path below the cloud base, and drizzle water path above the cloud base in precipitating stratocumulus clouds. The resulting cloud and drizzle water path within the cloud are in good qualitative agreement with the information extracted from the radar Doppler spectra. The technique is then applied to 10 d each of precipitating closed and open cellular marine stratocumuli. In the closed-cell systems only ~20 % of the available drizzle in the cloud falls below the cloud base, compared to ~40 % in the open-cell systems. In closed-cell systems precipitation is associated with radiative cooling at the cloud top <-100W m -2 and a liquid water path >200 g m -2 . However, drizzle in the cloud begins to exist at weak radiative cooling and liquid water path >~150 g m-2. Our results collectively demonstrate that neglecting scattering effects for frequencies at and above 90 GHz leads to overestimation of the total liquid water path of about 10 %–15 %, while their inclusion paves the path for retrieving drizzle properties within the cloud.

Cadeddu, Maria P.↗
DOE OSTI2020

Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Retrieval of temperature and humidity profiles from ground-based high-resolution infrared observations using an adaptive fast iterative algorithm

Various retrieval algorithms have been developed for retrieving temperature and water vapor profiles from Atmospheric Emitted Radiance Interferometer (AERI) observations. The physical retrieval algorithm, named AERI Optimal Estimation (AERIoe), outperforms other retrieval algorithms in many aspects except the retrieval time, which is significantly increased due to the complex radiative transfer process. The calculation of the Jacobian matrix is the most computationally intensive step of the physical retrieval algorithm. Interestingly, an analysis of the change in AERI observations' information content with respect to Jacobians revealed that the AERIoe algorithm's performance presents negligible dependence on these metrics. Thus, the Jacobian matrix could remain unchanged when the variation in the atmospheric state is small in the retrieval process to reduce the most time-consuming computation. On the basis of the above findings, a fast physical–iterative retrieval algorithm was proposed by adaptively recalculating Jacobians in keeping with the changes in the atmospheric state. Experiments with synthetic observations demonstrate that the proposed method experiences an average reduction in retrieval time by an impressive 59 % compared to the original AERIoe algorithm while achieving maximum root-mean-square errors of less than 0.95 K and 0.22 log(ppmv) for heights below 3 km for the temperature and water vapor profile, respectively. Further analyses revealed that the fast-retrieval algorithm reached an acceptable convergence rate of 98.7 %, marginally lower than AERIoe's 99.9 % convergence rate for the 826 cases used in this study.

54 ENVIRONMENTAL SCIENCES↗