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Results for “differential absorption lidar”

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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11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Extending water vapor measurement capability of photon-limited differential absorption lidars through simultaneous denoising and inversion

Abstract. The micropulse differential absorption lidar (MPD) was developed at Montana State University (MSU) and the National Center for Atmospheric Research (NCAR) to perform range-resolved water vapor (WV) measurements using low-power lasers and photon-counting detectors. The MPD has proven to produce accurate WV measurements up to 6 km altitude. However, the MPD's ability to produce accurate higher-altitude WV measurements is impeded by the current standard differential absorption lidar (DIAL) retrieval methods. These methods are built upon a fundamental methodology that algebraically solves for the WV using the MPD forward models and noisy observations, which exacerbates any random noise in the lidar observations. The work in this paper introduces the adapted Poisson total variation (PTV) specifically for the MPD instrument. PTV was originally developed for a ground-based high spectral resolution lidar, and this paper reports on the adaptations that were required in order to apply PTV on MPD WV observations. The adapted PTV method, coined PTV-MPD, extends the maximum altitude of the MPD from 6 to 8 km and substantially increases the accuracy of the WV retrievals starting above 2 km. PTV-MPD achieves the improvement by simultaneously denoising the MPD noisy observations and inferring the WV by separating the random noise from the non-random WV. An analysis with 130 radiosonde (RS) comparisons shows that the relative root-mean-square difference (RRMSE) of WV measurements between RS and PTV-MPD exceeds 100 % between 6 and 8 km, whereas the RRMSE between RS and the standard method exceeds 100 % near 3 km. In addition, we show that by employing PTV-MPD, the MPD is able to extend its useful range of WV estimates beyond that of the ARM Southern Great Plains Raman lidar (RRMSE exceeding 100 % between 3 and 4 km); the Raman lidar has a power-aperture product 500 times greater than that of the MPD.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Micropulse Differential Absorption Lidar (MPD) Network Demonstration Field Campaign Report

The National Center for Atmospheric Research (NCAR) and Montana State University (MSU) are nearing completion of a National Science Foundation (NSF) major research instrumentation grant to build five micropulse differential absorption lidars (MPDs) for water vapor profiling. The first demonstration of the MPD configured as a five-unit network occurred at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) field sites near Lamont, Oklahoma from 17 April–21 July 2019 for a total of 96 days of operation. The field deployment was funded in part by DOE (which provided support for the MPD installation, power at the field sites, personnel to launch 3-hourly radiosondes from the Central Facility, and the cost of the radiosondes themselves), internal NCAR funds (to support the MPD shipping costs and NCAR staff travel expenses), and NSF/Major Research Instrumentation (MRI) funds (to support the MSU travel expenses to participate in the instrument setup).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Remote sensing of planetary boundary layer from ground and space: Structure, thermodynamics, and boundary layer clouds

The planetary boundary layer (PBL) is a critical interface between Earth's surface and atmosphere, influencing atmospheric convection, weather patterns, and air quality. Recognized by the 2017 National Academies of Sciences, Engineering, and Medicine Earth Science Decadal Survey as an Incubation Targeted Observable, high-quality and effective observation of the PBL has become a priority. This paper reviews surface-based and satellite remote sensing techniques for characterizing PBL features, including PBL height (PBLH), boundary layer thermodynamics, turbulence, and boundary layer clouds (BLCs). These elements are treated as interconnected aspects of the PBL system, while recognizing that different instruments retrieve different physical manifestations of the PBL. The review summarizes recent advances and limitations in Micro-Pulse Lidar (MPL), Doppler lidar, Raman lidar, Differential Absorption Lidar (DIAL), ceilometers, wind profilers, GNSS Radio Occultation, radar, and hyperspectral sounders. These observations help trace dynamic processes within the PBL and link PBL structure to broader weather and climate processes. The review also highlights persistent observational gaps over oceans, remote land, and polar regions, where continuous surface-based profiling is sparse. By comparing instrument capabilities, practical limitations, and interpretation issues, this review emphasizes the need for integrated remote sensing approaches and careful definition of the retrieved PBL quantities.

Boundary layer clouds↗
DOE OSTI2022

Simultaneous Observations of Surface Layer Profiles of Humidity, Temperature, and Wind Using Scanning Lidar Instruments

We demonstrate the combination of three Doppler lidars (DLs), a water vapor differential absorption lidar and a temperature rotational Raman lidar for the investigation of the interactions between the land-surface and the atmospheric boundary layer. This combination of scanning lidars was operated for the first time during the Land-Atmosphere Feedback Experiment at the Atmospheric Radiation Measurement program's Southern Great Plain site, Oklahoma, USA, in August 2017, and provided simultaneous surface layer profiles of horizontal wind, humidity and temperature. The horizontal wind profiles were determined using the dual-Doppler method with two DLs. The scans were performed above four towers providing atmospheric variables and fluxes. These combined lidar data allowed for the estimation of the friction velocity as well as the surface latent and sensible heat fluxes. For this purpose, profiles calculated with Monin-Obukhov similarity theory were fitted with the Levenberg-Marquardt nonlinear least squares curve-fitting method to the measured surface-layer lidar profiles. We present case studies of three 50-min periods to illustrate the new method. The measurements resulted in friction velocities of 0.31, 0.29, and 0.38 ms –1 , sensible surface heat fluxes of 312.1, 234.1, and 183.3 Wm –2 and latent surface heat fluxes of 251.8, 227.3, and 274.4 Wm –2 . These values were compared with the in-situ measurements of the towers. Considering the sampling differences and the error analyses, the results agree within 25.1%, 42.5%, and 28.8%, respectively, demonstrating that this synergy of scanning active remote sensing systems can be used to derive surface fluxes with reasonable accuracy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

WFIP3 DIAL Lidar / Raw Data

This dataset contains raw Differential Absorption Lidar (DIAL) data from Nantucket. The instrument was installed by UND and features a WXT534 Vaisala Weather Transmitter for near-ground measurements.

17 WIND ENERGY↗
DOE OSTI2021

MicroPulse DIAL (MPD) – a diode-laser-based lidar architecture for quantitative atmospheric profiling

Abstract. Continuous water vapor and temperature profiles are critically needed for improved understanding of the lower atmosphere and potential advances in weather forecasting skill. Ground-based, national-scale profiling networks are part of a suite of instruments to provide such observations; however, the technological method must be cost-effective and quantitative. We have been developing an active remote sensing technology based on a diode-laser-based lidar technology to address this observational need. Narrowband, high-spectral-fidelity diode lasers enable accurate and calibration-free measurements requiring a minimal set of assumptions based on direct absorption (Beer–Lambert law) and a ratio of two signals. These well-proven quantitative methods are known as differential absorption lidar (DIAL) and high-spectral-resolution lidar (HSRL). This diode-laser-based architecture, characterized by less powerful laser transmitters than those historically used for atmospheric studies, can be made eye-safe and robust. Nevertheless, it also requires solar background suppression techniques such as narrow-field-of-view receivers with an ultra-narrow bandpass to observe individual photons backscattered from the atmosphere. We discuss this diode-laser-based lidar architecture's latest generation and analyze how it addresses a national-scale profiling network's need to provide continuous thermodynamic observations. The work presented focuses on general architecture changes that pertain to both the water vapor and the temperature profiling capabilities of the MicroPulse DIAL (MPD). However, the specific subcomponent testing and instrument validation presented are for the water vapor measurements only. A fiber-coupled seed laser transmitter optimization is performed and shown to meet all of the requirements for the DIAL technique. Further improvements – such as a fiber-coupled near-range receiver, the ability to perform quality control via automatic receiver scanning, advanced multi-channel scalar capabilities, and advanced processing techniques – are discussed. These new developments increase narrowband DIAL technology readiness and are shown to allow higher-quality water vapor measurements closer to the surface via preliminary intercomparisons within the MPD network itself and with radiosondes.

Spuler, Scott M.↗
DOE OSTI2021

Evaluation and Applications of Multi-Instrument Boundary-Layer Thermodynamic Retrievals

Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. Furthermore, we also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗
DOE OSTI2021

Ground-based temperature and humidity profiling: combining active and passive remote sensors

Abstract. Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Multi-channel Imager Algorithm (MIA): A novel cloud-top phase classification algorithm

The current Geostationary Operational Environmental Satellites (GOES-16 and 17) cloud-top phase classification algorithm is based primarily on empirical thresholds at multiple wavelengths that have varying absorption capabilities for water and ice. The performance of current GOES-16 cloud-top phase product largely depends on the accuracy of the selection of reflectance ratios. Here this study aims at presenting a novel cloud-top phase classification algorithm (the Multi-channel Imager Algorithm, MIA) that provides a more judicious selection of relationships between channels using a supervised K-mean clustering method on multi-channel Red-Green-Blue images. The K-mean clustering method works analogously to how human eyes separate different colors in a microphysical color rendering set of satellite images, which differentiates water, ice and unclassified thin clouds. For water phase, cloud-top temperature information is used to further distinguish supercooled water. To evaluate the performance of the MIA, an extensive comparison with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer, and current GOES-16 cloud-top phase products is conducted, using CALIOP as the benchmark. Compared to the current GOES-16 cloud-top phase product, MIA demonstrates a substantial improvement in phase classification, where hit rate increases from 69% to 76% over the Continental United States and 58% to 66% over the full disk domain.

54 ENVIRONMENTAL SCIENCES↗