Doppler Lidar Motion-Correction Wind Profiles (DLMCPROF-WIND) Value-Added Product Report

To better understand the complicated web of processes governing convective cloud life cycle and aerosol-convection interactions, the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) user facility supported deployment of a variety of advanced atmospheric measurement systems to the greater Houston, Texas, area from 1 October 2021 to 30 September 2022 as part of the Tracking Aerosol Convection Interactions Experiment (TRACER). Houston was selected as a study area because isolated convection and a variety of aerosol conditions are common in this region. This one-year ARM Mobile Facility (AMF) deployment featured a four-month intensive operational period (IOP) during summer 2022 (1 June–30 September). The ARM instrumentation was deployed at three sites along an east-west transect from La Porte, Texas to an ancillary site in a less-polluted rural region southwest of downtown Houston (Figure 1). At the La Porte Site, which is located near the Houston ship channel in an area that experiences significant pollution, the first ARM Mobile Facility (AMF1) was deployed. During the IOP, the ARM tethered balloon system (TBS) operated at the ancillary site. The second-generation C-Band Scanning ARM Precipitation Radar (CSAPR) operated near Pearland, Texas, roughly halfway between the Laporte and ancillary sites. As part of the TRACER- Coastal Urban Boundary-Layer Interactions with Convection (CUBIC) project, three boundary-layer profiling systems) were deployed along a north-south transect spanning from the University of Houston Coastal Center to the Aldine site north of downtown Houston (also blue dot in Figure 1) during the TRACER IOP. These systems included the National Oceanic and Atmospheric Administration (NOAA) National Severe Storms Laboratory CLAMPS2 (C2), which was deployed at the UHCC, the University of Wisconsin SPARC, which was deployed at the ARM CSAPR site near Pearland (orange diamond in middle of map in Figure 1), and the University of Oklahoma CLAMPS1 (C1), which was deployed at Aldine. These three systems have been successfully operated in various field campaigns, providing data sets that collectively offer new insights into atmospheric-boundary-layer (ABL) processes, sea-breeze (SB) circulations, and convection initiation (CI). For the TRACER IOP window, these systems ran continuously between 1 June and 26 September, 2022. Due to commitments to NOAA projects, the Doppler lidar at the UHCC site was not available until 24 June 2022. The CLAMPS and SPARC profiling systems are self-contained platforms that have benefited from several years of development and deployment. Instruments and data processing were maintained remotely, which made the 4-month deployment for the TRACER-CUBIC IOP period possible. The same basic instrument configuration comprises each system: a scanning Doppler wind lidar for flow characterization and passive profiler(s) for characterizing planetary-boundary-layer (PBL) thermodynamic properties. Each platform includes a Halo Streamline Doppler wind lidar, an Atmospheric Emitted Radiance Interferometer (AERI), and a surface meteorology station. CLAMPS1 and CLAMPS2 each also include a microwave radiometer (MWR, Figure 1d). The TRACER-CUBIC hypotheses included (i) Interactions of SB and urban circulations and how they affect the PBL structure in the Houston environment, causing spatially (horizontally and vertically) and temporally highly variable flow patterns, (ii) heat, moisture, and aerosol transport and mixing depend on these flow dynamics, and (iii) an improved understanding of the flow patterns and PBL structure are critical for investigating the processes leading to CI. To test these hypotheses, the project aimed at (i) characterizing SB circulations and their impacts on the diurnal evolution of the structure of the ABL, (ii) studying the evolution of Houston’s complex urban boundary layer, and (iii) identifying effects of urban-induced circulations on pre-convective environments. TRACER-CUBIC observations generally provide good coverage during the summer IOP. Initial screening of the data indicates a good number of cases with bay-breeze (BB) and/or SB signatures, local CI, and interesting boundary-layer features such as strong nocturnal low-level jets (LLJs, Table 1). The numbers listed in rows 3-5 in this table will be further updated as part of ongoing in-depth analyses and systematic identification of local circulations and CI events. More detailed information about the data availability and quality for each instrument is provided in the “readme” files that were submitted to the ARM Data Center along with each archived data sets. These “readme” files also provide instrument descriptions, information about the data collection and processing procedures, data formats, and any additional information relevant for further data analysis.

The acquisition of scanning cloud radars by the Atmospheric Radiation Measurement (ARM) program and research institutions around the world generates the need for developing operational scan strategies for cloud radars. Here, the first generation of sampling strategies for the scanning ARM cloud radars (SACRs) is presented. These scan strategies are designed to address the scientific objectives of ARM; however, they introduce an initial framework for operational scanning cloud radars. While the weather community uses scan strategies that are based on a sequence of scans at constant elevations, the SACR scan strategies are based on a sequence of scans at constant azimuth. This is attributed to the cloud geometrical properties, which are vastly different from the rain and snow shafts that are the primary targets of precipitation radars; the need to cover the cone of silence; and the scanning limitations of the SACRs. A “cloud surveillance” scan strategy is introduced that is based on a sequence of horizon-to-horizon range–height indicator (RHI) scans that sample the hemispherical sky (HS) every 30° azimuth (HSRHI). The HSRHI scan strategy is complimented with a low-elevation plan position indicator (PPI) scan. The HSRHI and PPI are repeated every 30 min to provide a static view of the cloud conditions around the SACR location. Between the HSRHI and PPI scan strategies, other scan strategies are introduced depending on the cloud conditions. In the future, information about the atmospheric cloud state will be used in a closed-loop process to optimize the selection of the SACR scan strategy.

Ceilometer measurements of aerosol backscatter profiles have been widely used to provide continuous PBLHT estimations. To investigate the robustness of ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using long term U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) ceilometer and balloon-borne sounding data at three ARM fixed-location atmospheric observatories and from three ARM mobile observatories deployed around the world for various field campaigns, which cover from Tropics to Polar regions and over both ocean and land surfaces. Statistical comparisons of ceilometer-estimated PBLHTs from the Vaisala CL31 ceilometer data with radiosonde-estimated PBLHTs from the ARM PBLHT-SONDE Value-added Product (VAP) are performed under different atmospheric conditions including stable and unstable atmospheric boundary layer, low-level cloud-free, and cloudy conditions at these ARM observatories. Under unstable atmospheric boundary layer conditions, good comparisons are found between ceilometer- and radiosonde-estimated PBLHTs at ARM low- and mid-latitude land observatories. However, it is still challenging to obtain reliable PBLHT estimations over ocean surfaces even using radiosonde data. Under stable atmospheric boundary layer conditions, ceilometer- and radiosonde-estimated PBLHTs have weak correlations. Among different PBLHT estimations utilizing the Heffter, the Liu-Liang, and the bulk Richardson number methods in the ARM PBLHT-SONDE VAP, ceilometer-estimated PBLHTs have better comparisons with the Liu-Liang method under unstable and with the bulk Richardson number method under stable atmospheric boundary layer conditions.

<strong class="journal-contentHeaderColor">Abstract.</strong> Physics-Informed neuronal networks (PINN) is a research field where a neuronal network is trained to solve an incorporated partial differential equation that describes some physical phenomenon. This work describes the coupling of the Navier Stokes (NS) equation with data from a 3D scanning pulsed Doppler lidar to reconstruct blanked sectors with radial velocities in a plan position indicator (PPI) scan. For the reconstruction, only the adjacent line of sight (LOS) measurements were used as input data for the neuronal network. Almost one year of collected lidar data were used to analyze the wind field sector reconstruction algorithm. The results show that the reconstruction of 35&deg; azimuth sectors feature mean square errors of less than 1 m²/s² and absolute errors of less than 2 m/s in 99 % and 98 %, respectively, of all cases. The runtime is about 0.1 minutes on average with commercial off-the-shelve CPU hardware. The reconstructed wind field of radial velocities can be used either to fill in sectors where the lidar is blocked e.g. by an obstacle or to extend the maximum operational range by measuring only a few lines-of-sight with increased pulse accumulation time. An example of a range extension PPI provided here demonstrates that the range can be extended to 25 km while maintaining the total recording time of 30 s as for the reference PPI scan featuring only a maximum range of approximately 12 km.

Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with air-mass hysteresis. Here, we describe ARMTRAJ (https://doi.org/10.5439/2309851, Silber, 2024a; https://doi.org/10.5439/2309849, Silber, 2024b; https://doi.org/10.5439/2309850, Silber, 2024c; https://doi.org/10.5439/2309848, Silber, 2024d), a set of multipurpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25°) and are initialized using ARM datasets. The trajectory datasets include information about air-mass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported air-mass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM's Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ will soon become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.

The Atmospheric Radiation Measurement (ARM) user facility was established in 1989 by the U.S Department of Energy (DOE) Office of Biological and Environmental Research (BER) to provide an observational basis for studying the Earth’s climate. ARM began collecting observations in 1992 and was designated a user facility in 2003. The facility includes a network of extensively instrumented long-term fixed-location observatories and mobile facilities. The ARM facility also includes an aerial component to augment these ground-based measurements. Because of the diversity of in situ and remotely obtained observations, ARM’s data management infrastructure is designed to collect, process, and deliver data to the research community.

Abstract. Surface precipitation measurements are essential for Earth system model (ESM) evaluation and understanding cloud processes. An ever-growing need for robust, temporally evolving, and easy-to-use statistical datasets provides motivation for a baseline ground-based precipitation properties data product. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility operates an extensive suite of precipitation instruments with various sensitivities and operating mechanisms, which render the decision of which instrument to use based on one or more fixed thresholds challenging and prone to errors and bias. Using a long-term instrument inter-comparison from a unique per-precipitation event perspective, rather than instantaneous sample comparison, we demonstrate that ARM rainfall-measuring instruments are generally consistent with each other at the statistical level. Inter-instrument deviations at the single event level can be large, especially for specific rainfall event properties such as maximum precipitation rates. A machine-learning (ML) analysis using a random forest regressor indicates that in some cases, depending on instrument, local site climatology, and/or specific deployment configuration, certain atmospheric state variables influence the measured quantities in an unpredictable manner. Thus, a-priori weighting of different instruments does not necessarily lead to more accurate and less biased synthesis of instrument data. These results motivate the design of the ARM precipitation best-estimate (PrecipBE) value-added product, which incorporates all valid precipitation data while considering data quality and other instrument limitations. PrecipBE consists of time series and tabular statistics datasets in an easy-to-use and insightful per-precipitation event format. It provides a large set of precipitation event properties supplemented with ancillary data from ARM datasets that correspond to the detected precipitation events. We describe the PrecipBE algorithm and demonstrate its use via the examination of a single-day output as well as a long-term trend analysis of precipitation events at the ARM Southern Great Plains (SGP) site, covering more than 30 years of data. The trend analysis tentatively suggests a long-term temporal tendency for mainly shorter and less intense precipitation events at the SGP site, but a long-term increase in annual rainfall by more than 36 mm (5 %) per decade. This rainfall trend is catalyzed primarily by more extreme event properties of relatively rare, intense precipitation events, with event total and 1 min maximum precipitation rate at a 1 year timeframe increasing up to 5 mm and 9 mm h−1 (several percent) per decade, respectively. While the currently available PrecipBE datasets (at https://adc.arm.gov/discovery/, last access: 8 December 2025) cover rainfall from multiple ARM deployments up to March 2025, PrecipBE is planned to be expanded to include solid-phase precipitation and will soon become an operational product with a several-day lag from real-time. We invite the ARM user community to leverage this new product and welcome user feedback to further enhance the dataset.

The Ka-band ARM Zenith Radar Active Remote Sensing of CLouds CloudSat-aligned (KAZRARSCL-CLOUDSAT) Value-Added Product (VAP) applies satellite-based reflectivity calibrations to KAZRARSCL data sets. The Atmospheric Radiation Measurement (ARM) user facility has primarily used radar subsystem calibration monitoring to track cloud radar reflectivity drift over time, since reliable external calibration sources or other absolute references (such as corner reflectors) have historically been unavailable or impracticable. A study by Kollias et al. (2019) examined cloud reflectivity profiles observed with a well-characterized spaceborne downward-pointing millimeter cloud radar, operating as part of NASA’s CloudSat satellite mission (Tanelli et al. 2008). The Kollias team derived monthly statistical reflectivity offsets between CloudSat and the various generations of ARM cloud radars (millimeter wavelength cloud radar [MMCR], W-Band ARM Cloud Radar [WACR[, and Ka-band ARM Zenith Radar [KAZR]) for many, but not all, months at most fixed and mobile ARM sites over the period 2007-2017. These offsets, when available, are applied to the existing KAZRARSCL VAP products using the KAZRARSCL-CLOUDSAT VAP.

Aerosol light absorption and scattering of solar radiation play an important role in the earth’s atmosphere in terms of direct and semi-direct radiative forcing. Optical parameters of importance to the US Department of Energy (DOE) climate models include absorption and scattering coefficients, single scattering albedo (SSA), absorption Angstrom exponents (AAE), and the asymmetry parameter (g). These parameters depend on aerosol size, shape and composition (refractive index), and are spectrally sensitive in the shortwave region. Additionally, these parameters have a complex dependency on the emission source, especially for carbonaceous aerosols. The DOE Atmospheric Radiation Measurement (ARM) user facility has deployed aerosol observing systems (AOS) containing several filter-based instruments to measure and constrain aerosol optical properties and related parameters at multiple sites worldwide. For measurement of aerosol light absorption, the AOS includes filter-based instruments (particle soot absorption photometer and tricolor absorption photometer) that infer particle-phase aerosol absorption coefficients at nominal red, green, and blue wavelength bands from the attenuation (ATN) of light passing through a particulate filter on which aerosols are deposited. Measurement of aerosol scattering is done in situ using nephelometers. By combining inferred absorption coefficients from filter-based ATN measurements and in situ scattering coefficients, value-added products (VAPs) such as SSA, AAE, and g are derived.

Nocturnal Low-Level Jets (NLLJ) are known to be a crucial process in the long-range transport of atmospheric pollutants with a positive or negative impact on air quality. In recent years, the study of NLLJ that occur usually at the top of the atmospheric boundary layer (ABL) has been greatly enhanced by recent technological and methodological advances in ground-based remote sensing instruments. They are now able to provide continuously high-quality profiles of ABL parameters that can be obtained from automatic LIDAR-ceilometers (ALC) providing information about clouds, precipitations, aerosols (including aerosol characteristics and types with depolarization measurements) and from wind Doppler LIDAR (WDL) providing information about wind profile characteristics.At the Royal Meteorological Institute of Belgium (RMI), we have been developing a new pioneering algorithm (CONIOPOL: CONIOlogy + POLarization) based only on ALC measurements with a depolarization function (VAISALA CL61) to provide in real-time automatic identification of cloud phase, precipitation type and aerosol type. By combining the output of CONIOPOL based on measurements from a CL61 installed in Uccle (Brussels) with measurements from a WDL located at Brussels Airport (15km away from the CL61), it is possible to monitor continuously the transport of aerosols by NLLJ.The synergy between both instruments will be illustrated by an interesting case study, showing the transport of marine aerosols by NLLJ into the ABL characterized in the presence of dust. The characteristic and the evolution of the synoptic situation will be used to highlight the geographical origin of aerosols. In order to better characterize the type identification of aerosols by CONIOPOL, ground measurements concerning the physical properties of aerosols carried out close to the CL61 will also be presented.

The eddy covariance technique (EC) is used worldwide to measure surface fluxes of greenhousegases and energy balance components. Nevertheless, in the scientific community it is well-knownthat EC presents an imbalance of the energy components. In this regard, the atmospheric boundarylayer (ABL) directly influences the mass and energy transfer between surface and atmosphere.Thus, knowledge and characterization of the ABL might be essential to disentangling the driverscausing the imbalance of the energy components measured by EC. This work aims to relate ABLcharacteristics to the accuracy of the surface energy balance closure (SEBC) obtained by EC.The study was carried out in an irrigated olive orchard (Olea europaea var. europaea L.) in theSoutheastern Iberian Peninsula (37.9427&amp;#186; N, 3.3002&amp;#186; W and 370 m asl) during the intensiveBLOOM (turBulence and oLea pOllen prOperties experiMent) campaign, from May 19th to June20th 2022. In order to characterise ABL dynamics, remote sensing techniques are commonly used.One of them is via Doppler lidar, which provides measurements of wind components andturbulence-related products at high spatial and temporal resolutions. In particular, a Doppler lidarStream Line (HALO photonics) with a temporal resolution of 2 seconds and 10 minutes (for verticaland scanning measurements, respectively) and 30 m of vertical spatial resolution was used toretrieve the turbulent kinetic energy dissipation rate (&amp;#949;) and wind shear (sh) with a commonresolution of 3 minutes as indicators of convective and mechanical sources of turbulence,respectively. To assess the SEBC, we used (1) a three-axis sonic anemometer (CSAT-3, CampbellScientific, Logan, UT, USA) and an enclosed path infrared gas analyser (IRGA, Li-Cor 7200;Lincoln, NE, USA) on a tower 9 m tall to measure latent heat (&amp;#955;E) and sensible heat (H) fluxes; (2)an incoming and outgoing short- and long-wave 4-component radiometer (CNR-4, Kipp &amp; Zonen,Delft, Netherlands) to measure net radiation (Rn); and (3) two each: soil moisture probes (CS616,CSI), thermocouples (TCAV, CSI) and heat flux plates (HFP01, Hukseflux, Delft, the Netherlands)at 0.10 m, 0.04 m and 0.08 m depth, respectively to calculate the soil heat flux (G).Preliminary results show that SEBC enhances under turbulent conditions (slope and R2 from 0.49and 0.92 to 0.81 and 0.94, respectively). However, when the source of turbulence is mechanical theSEBC is less accurate (slope and R2 from 0.82 and 0.94 to 0.77 and 0.9, respectively). A moredetailed study based on principal component analysis of the ABL height, skewness of the verticalwind velocity and vertical profile of horizontal wind, together with &amp;#949; and sh, among other Dopplerlidar-derived products is expected to offer reliable information that is highly relevant regarding theinfluence of the ABL on the SEBC.ACKNOWLEDGMENTS: This work was supported by theSpanish Ministry of Science and Innovation through project PID2020-117825GB-C21(INTEGRATYON3), the Andalusian regional Development through the projects B-RNM-60-UGR20 (OLEAGEIs) and P18-RT-3629 (ICAERSA), including European Union ERDF funds.

The turbulent energy dissipation rate (EDR) is a quantitative measure of turbulence intensity, and it is widely used across various fields. Accurate estimation of EDR using Doppler lidar depends on the choice of estimation technique and scanning strategy. Therefore, a comparison of the techniques is still required to achieve an accurate estimation. However, the effect of the choice on estimation accuracy remains uncertain. This study systematically evaluates the accuracy of EDR estimation techniques by utilizing two distinct scanning strategies: a vertically pointing scan (EDRVP) and a Plan Position Indicator scan (EDRVAD). We assess four different EDRVP estimation techniques and assess the accuracy of EDRVAD for each elevation angle by comparing it with the measurements from sonic anemometers on a 300 m tall meteorological tower. EDRVAD shows a positive correlation coefficient exceeding 0.5 with the sonic anemometers. EDRVAD demonstrates dependency on the elevation angle, with lower angles resulting in higher EDR values. Conversely, all of the EDRVP techniques exhibit high agreement, with correlation coefficients above 0.9. This study provides a comprehensive assessment of the accuracy of each technique, highlighting their respective characteristics and practical considerations.

An all‐fiber pulsed coherent Doppler LIDAR (CDL) system is described. It uses a fiber laser as a light source at a 1.54‐μm wavelength, producing 200 μJ pulses at 10 kHz. The local oscillator signal is mixed with the backscattered light (of different frequency) in the fiber. The atmospheric wind speed is determined through the fast Fourier transform applied to the difference frequency signal acquired by an analog‐to‐digital converter card. This system was used to measure the atmospheric wind above the upper‐air meteorological observatory in Rongcheng (37.10°N, 122.25°E) of China between January 7 and 19, 2015. The CDL data are compared with sounding‐ and pilot‐balloon measurements to assess the CDL performance. The results show that the correlation coefficient of the different wind‐speed measurements is 0.93 and their discrepancy 0.64 m/s; the correlation coefficient for wind‐direction values is 0.92 and their discrepancy 5.8 deg. A time serial of the wind field, which benefits the understanding of atmospheric dynamics, is presented after the comparisons between data from CDL and balloons. The CDL system has a compact structure and demonstrates good stability, reliability, and a potential for application to wind‐field measurements in the atmospheric boundary layer.

To better provide U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility’s disdrometer deployment strategy and support ARM precipitation-related projects, this study analyzes 18 months of drop size distribution (DSD) observations from six collocated disdrometers deployed at the ARM Southern Great Plains (SGP) site. Emphasis is placed on quantifying the uncertainties related to DSD and rainfall properties using different types of disdrometers and different pairing concepts. The instrument sampling errors are also discussed, which improves our understanding of instrument-specific impacts on rainfall measurements. The key findings are as follows: 1) All the disdrometers show consistent behaviors overall regarding accumulated precipitation, mean rainfall rate, DSD parameters, and radar reflectivity values; and 2) Strong agreement is shown between three disdrometer types in terms of their DSDs for mid-size drop range (D = 1 mm-4 mm). The two-dimensional video disdrometer performance meets expectations as the most accurate unit among all. The paired disdrometer shows a reduced statistical sampling error in terms of drop counts and other DSD properties. A concept of deploying two or more collocated disdrometers side by side is recommended for future ARM field campaigns.

The Atmospheric Radiation Measurement (ARM) Program was created in 1989 with funding from the U.S. Department of Energy (DOE) to develop several highly instrumented ground stations to study cloud-formation processes and their influence on radiative transfer. This scientific infrastructure provides for fixed sites, mobile facilities, an aerial facility, and a data archive available for use by scientists worldwide through the ARM Climate Research Facility—a scientific user facility. The ARM Climate Research Facility currently operates more than 300 instrument systems that provide ground-based observations of the atmospheric column. To keep ARM at the forefront of climate observations, the ARM infrastructure depends heavily on instrument scientists and engineers, known as Mentors. Mentors must have an excellent understanding of instrumentation theory and operation for their instrument areas and have comprehensive knowledge of critical scale-dependent atmospheric processes. They must also possess the technical and analytical skills to develop new data retrievals that provide innovative approaches for creating research-quality data sets. The ARM Facility seeks the best overall qualified candidate, or team when appropriate, that can fulfill Mentor requirements in a timely manner. The roles and responsibilities of the ARM Instrument Operations Manager are provided in Appendix A. The key role and responsibilities and detailed responsibilities of ARM Lead Mentors are provided in Appendix B and Appendix C, respectively.