The development of a microjoule-class pulsed Doppler lidar and deployment of this compact system on mobile platforms such as aircraft, ships, or trucks have opened a new opportunity to characterize the dynamics of complex mesoscale wind flows. The PickUp-based Mobile Atmospheric Sounder (PUMAS) truck-based lidar system was recently used during the American Wake Experiment (AWAKEN) to assess the general structure of boundary layer (BL) wind and turbulence around wind turbines in central Oklahoma.Wind speed profiles averaged over PUMAS transects influenced by the operating turbines (waked flow) show a 1-2 ms-1 reduction compared to mean undisturbed (free flow) wind speed profiles. Spatial variability in wind speed was observed in time-height cross-sections at different distances from turbines. The wind speeds were about 9-12 ms-1 at 6 km distance compared to 5-7 ms-1 at the transects near the turbines.The PUMAS dataset from AWAKEN demonstrated the capability of the mobile Doppler lidar system to document spatial variability in wind flows at different distances from wind turbines and obtain quantitative estimates of wind speed reduction in the waked flow. The high-frequency, simultaneous measurements of the horizontal and vertical winds provide a new approach for characterizing dynamic processes critical for wind farm wake analyses.

A simultaneous deployment of Doppler, temperature, and water-vapor lidars is able to provide profiles of molecular destruction rates and turbulent kinetic energy (TKE) dissipation in the convective boundary layer (CBL). Horizontal wind profiles and profiles of vertical wind, temperature, and moisture fluctuations are combined, and transversal temporal autocovariance functions (ACFs) are determined for deriving the dissipation and molecular destruction rates. These are fundamental loss terms in the TKE as well as the potential temperature and mixing ratio variance equations. These ACFs are fitted to their theoretical shapes and coefficients in the inertial subrange. Error bars are estimated by a propagation of noise errors. Sophisticated analyses of the ACFs are performed in order to choose the correct range of lags of the fits for fitting their theoretical shapes in the inertial subrange as well as for minimizing systematic errors due to temporal and spatial averaging and micro- and mesoscale circulations. We demonstrate that we achieve very consistent results of the derived profiles of turbulent variables regardless of whether 1 or 10 s time resolutions are used. We also show that the temporal and spatial length scales of the fluctuations in vertical wind, moisture, and potential temperature are similar with a spatial integral scale of ≈160 m at least in the mixed layer (ML). The profiles of the molecular destruction rates show a maximum in the interfacial layer (IL) and reach values of ϵm≃7×10-4 g2 kg−2 s−1 for mixing ratio and ϵθ≃1.6×10-3 K2 s−1 for potential temperature. In contrast, the maximum of the TKE dissipation is reached in the ML and amounts to ≃10-2 m2 s−3. We also demonstrate that the vertical wind ACF coefficient kw∝w′2‾ and the TKE dissipation ϵ∝w′2‾3/2. For the molecular destruction rates, we show that ϵm∝m′2‾w′2‾1/2 and ϵθ∝θ′2‾w′2‾1/2. These equations can be used for parameterizations of ϵ, ϵm, and ϵθ. All noise error bars are derived by error propagation and are small enough to compare the results with previous observations and large-eddy simulations. The results agree well with previous observations but show more detailed structures in the IL. Consequently, the synergy resulting from this new combination of active remote sensors enables the profiling of turbulent variables such as integral scales, variances, TKE dissipation, and the molecular destruction rates as well as deriving relationships between them. The results can be used for the parameterization of turbulent variables, TKE budget analyses, and the verification of large-eddy simulations.

The rapid change of wind speed and direction on 21 August 2017 is studied using Doppler lidar measurements at five sites of the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) facility in north-central Oklahoma. The Doppler lidar data were investigated along with meteorological variables such as temperature, humidity, and turbulence available from the large suite of instrumentation deployed at the SGP Central Facility (C1) during the Land-Atmosphere Feedback Experiment in August 2017. Lidar measurements at five sites, separated by 55–70 km, allowed us to document the development and evolution of the wind flow over the SGP area, examine synoptic conditions to understand the mechanism that leads to the ramp event, and estimate the ability of the High-Resolution Rapid Refresh model to reproduce this event. The flow feature in question is an atmospheric bore, a small-scale phenomenon that is challenging to represent in models, that was generated by a thunderstorm outflow northwest of the ARM SGP area. The small-scale nature of bores, its impact on power generation, and the modeling challenges associated with representing bores are discussed in this paper. The results also provide information about model errors between sites of different surface and vegetation types.

The social, economic, and ecological impacts of wildfires are increasing over much of the United States and globally, partially due to changing climate and build-up of fuels from past forest management practices. This creates a need to improve coupled fire-atmosphere forecast models. However, model performance is difficult to evaluate due to scarcity of observations for many key fire-atmosphere interactions, including updrafts and plume injection height, plume entrainment processes, fire intensity and rate -of -spread, and plume chemistry. Intensive observations of such fire-atmosphere interactions during active wildfires are rare due to the logistical challenges and scales involved. The California Fire Dynamics Experiment (CalFiDE) was designed to address these observational needs, using Doppler lidar, high -resolution multispectral imaging, and in situ air quality instruments on a NOAA Twin Otter research aircraft, and Doppler lidars, radar, and other instrumentation on multiple ground -based mobile platforms. Five wildfires were studied across northern California and southern Oregon over 16 flight days from 28 August to 25 September 2022, including a breadth of fire stages from large blow-up days to smoldering air quality observations. Missions were designed to optimize the observation of the spatial structure and temporal evolution of each fire from early afternoon until sunset during multiple consecutive days. The coordination of the mobile platforms enabled four-dimensional sampling strategies during CalFiDE that will improve understanding of fire-atmosphere dynamics, aiding in model development and prediction capability. Satellite observations contributed aerosol measurements and regional context. This article summarizes the scientific objectives, platforms and instruments deployed, coordinated sampling strategies, and presents first results.

Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two ver-sions of NOAA's 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deploy-ment into central Oregon-Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600-800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s21 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model "improvement," such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes de-grading model skill. Nesting consistently improved model performance, with version 1's nest producing the smallest errors overall. HRRR's ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.

Low-level jets (LLJs) are an important nocturnal source of wind energy in the U.S. Great Plains. An August 2017 lidar-based field-measurement campaign [the Land-Atmosphere Feedback Experiment (LAFE)] studied LLJs over the central SGP site in Oklahoma and found nearly equal occurrences of the usual southerly jets and postfrontal northeasterly jets -typically rare during this season -for an opportunity to compare the two types of LLJs during this month. Southerly winds were stronger than the northeasterlies by more than 4 m s-1 on average, reflecting a significantly higher frequency of winds stronger than 12 m s-1. The analysis of this dataset has been expanded to other SGP Dopplerlidar sites to quantify the variability of winds and LLJ properties between sites of different land use. Geographic variations of winds over the study area were noted: on southerly wind nights, the winds blew stronger at the highest, westernmost sites by 2 m s-1, whereas on the northeasterly flow nights, the easternmost sites had the strongest wind speeds. Lidar measurements at 5 sites during August 2017, contrasted to the 2016-21 summertime data, revealed unusual wind and LLJ conditions. Temporal hodographs using hourly averaged winds at multiple heights revealed unorganized behavior in the turbulent stable boundary layer (SBL) below the jet nose. Above the nose, some nights showed veering qualitatively similar to inertial oscillation (IO) behavior, but at amplitudes much smaller than expected for an IO, whereas other nights showed little veering. Vertical hodographs had a linear shape in the SBL, indicating little directional shear there, and veering above, resulting in a hook-shaped hodograph with height. SIGNIFICANCE STATEMENT: Doppler-lidar measurements at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) were used to quantify the variability of winds and low-level jet (LLJ) properties between five sites of different land use and wind regimes across this area. Knowledge of wind and LLJ structure and dynamics is important for many applications, and strong southerly LLJ winds at night are an important resource for wind energy. The analysis of multiyear (2016-21) summertime LLJ parameters provided insight into the LLJ climatology in this part of the Great Plains.

Model improvement efforts involve an evaluation of changes in model skill in response to changes in model physics and parameterization. When using wind measurements from various remote sensors to determine model forecast accuracy, it is important to understand the effects of measurement-uncertainty differences among the sensors resulting from differences in the methods of measurement, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. Here we quantify instrument measurement variability in 80-m wind speed during WFIP2 and its impact on the calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m. Model errors were found to be 2-3 m s(-1). Differences in errors as determined by various instruments at each site amounted to about 10% of this value, or 0.2-0.3 m s(-1). Changes in model skill due to physics or grid-resolution updates also differed depending on the instrument used to determine the errors; most of the instrument-to-instrument differences were similar to 0.1 m s(-1), but some reached 0.3 m s(-1). All instruments at a given site mostly showed consistency in the sign of the change in error. In two examples, though, the sign changed, illustrating a consequence of differences in measurements: errors determined using one instrument may show improvement in model skill, whereas errors determined using another instrument may indicate degradation. This possibility underscores the importance of having accurate measurements to determine the model error. Significance StatementTo evaluate model forecast accuracy using remote sensing instruments, it is important to understand the effects of measurement uncertainties due to differences in the methods of measurement and data processing techniques, the vertical and temporal resolution of the measurements, and the spatial variability of these differences. In this study, three types of collocated remote sensing systems are used to quantify the impact of measurement variability on the magnitude of calculated errors and the change in error from one model version to another. The model versions tested involved updates in model physics from HRRRv1 to HRRRv4, and reductions in grid interval from 3 km to 750 m.

The traditional surface energy balance (SEB) is investigated by applying the thermal energy balance based on total energy conservation. Based on total energy conservation, the thermal and the kinetic energy balances are connected through non‐hydrostatic energy transfer ( Q NH ) as a result of the hydrostatic imbalance caused by potential energy changes from vertical density fluxes in response to mechanical and thermal forcing in the stratified atmosphere. Field observations show that both Q NH and the surface energy imbalance (SEI) have the same diurnal variation; both are near zero under neutral conditions and increase with increasing atmospheric unstable stratification. Energy dissipation due to enhanced turbulent kinetic energy from Q NH is estimated to be the largest energy consumption for potentially explaining the largest SEI under free convective conditions. The relatively large value of energy dissipation in the atmospheric surface layer is due to its exponential increase toward the surface and its increase with atmospheric instability. The other two energy transfers that are traditionally missed are related to water mass flowing through the open soil and air layers, but are relatively small in comparison with the energy dissipation. The new understanding of the energy transfers provides not only a potential explanation for the temporal and magnitude variations of the SEI but also the explanation for different heat/moisture transferring efficiencies between thermally and mechanically generated turbulent mixing. Understanding the SEB is important not only for solving the SEI but also for understanding energy conservation in the atmosphere.

Transport of pollution into pristine wilderness areas is of concern for both federal and state agencies. Assessing such transport in complex terrain is a challenge when relying solely on data from standard federal or state air quality monitoring networks because of the sparsity of network monitors beyond urban areas. During the Front Range air quality study, conducted in the summer of 2008 in the vicinity of Denver, CO, research-grade surface air quality data, vertical wind profiles and mixing heights obtained by radar wind profilers, and ozone profile data obtained by an airborne ozone differential absorption lidar augmented the local regulatory monitoring networks. Measurements from this study were taken on 2 successive days at the end of July 2008. On the first day, the prevailing winds were downslope westerly, advecting pollution to the east of the Front Range metropolitan areas. On this day, chemistry measurements at the mountain and foothills surface stations showed seasonal background ozone levels of approximately 55–68 ppbv (nmol mol–1 by volume). The next day, upslope winds prevailed, advecting pollution from the Plains into the Rocky Mountains and across the Continental Divide. Mountain stations measured ozone values greater than 90 ppbv, comparable to, or greater than, nearby urban measurements. The measurements show the progression of the ozone-enriched air into the mountains and tie the westward intrusion into high-elevation mountain sites to the growth of the afternoon boundary layer. Thus, under deep upslope flow conditions, ozone-enriched air can be advected into wilderness areas of the Rocky Mountains. Our findings highlight a process that is likely to be an important ozone transport mechanism in mountainous terrain adjacent to ozone source areas when the right circumstances come together, namely a deep layer of light winds toward a mountain barrier coincident with a deep regional boundary layer.

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon-Washington, a significant wind-energy-generation region and the site of the Second Wind-Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid-Refresh (HRRR-version1)] to forecast wind-speed profiles for different regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. The other two included a hybrid regime and a non-conforming regime. For the large-scale pressure-gradient regimes, HRRR had wind-speed biases of ~1 m s −1 and RMSEs of 2-3 m s −1 . Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary-layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be aimed at determining which modeled processes produce the largest errors, so those processes can be improved and errors reduced.

Wintertime episodes of high aerosol concentrations occur frequently in urban and agricultural basins and valleys worldwide. These episodes often arise following development of persistent cold-air pools (PCAPs) that limit mixing and modify chemistry. While field campaigns targeting either basin meteorology or wintertime pollution chemistry have been conducted, coupling between interconnected chemical and meteorological processes remains an insufficiently studied research area. Gaps in understanding the coupled chemical-meteorological interactions that drive high pollution events make identification of the most effective air-basin specific emission control strategies challenging. To address this, a September 2019 workshop occurred with the goal of planning a future research campaign to investigate air quality in Western U.S. basins. Approximately 120 people participated, representing 50 institutions and 5 countries. Workshop participants outlined the rationale and design for a comprehensive wintertime study that would couple atmospheric chemistry and boundary-layer and complex-terrain meteorology within western U.S. basins. Participants concluded the study should focus on two regions with contrasting aerosol chemistry: three populated valleys within Utah (Salt Lake, Utah, and Cache Valleys) and the San Joaquin Valley in California. This paper describes the scientific rationale for a campaign that will acquire chemical and meteorological datasets using airborne platforms with extensive range, coupled to surface-based measurements focusing on sampling within the near-surface boundary layer, and transport and mixing processes within this layer, with high vertical resolution at a number of representative sites. No prior wintertime basin-focused campaign has provided the breadth of observations necessary to characterize the meteorological-chemical linkages outlined here, nor to validate complex processes within coupled atmosphere-chemistry models.

The spatiotemporal scales of turbulence in the boundary layer flow vary with the static stability of the atmosphere. For example, turbulence structures in unstable conditions are often found to be large and coherent, whereas the turbulent structures in stable conditions are relatively small and sporadic. To better understand the behavior of such turbulent structures under different atmospheric stability conditions in the lower atmosphere, herein, we use scanning lidar data collected over several months during the US Department of Energy's second Wind Forecast Improvement Project (WFIP 2) in 2016. The radial velocity data from scanning lidar were gathered along the conical surface (PPI mode) and were analyzed using proper orthogonal decomposition (POD) and spectral energy methods. The results from these analyses show that the turbulent structures in daytime unstable conditions are different from those present during nighttime stable conditions in terms of amount of energy and coherent structures. The POD energy is distributed more evenly in all the higher spatial modes in the daytime unstable case compared to the nighttime stable case. Besides characterizing the turbulence in the flow, the lidar data were also used to compare the simulated flow field generated from the WRF-LES framework (Weather Research and Forecasting - large-eddy simulation) with the scanning lidar data. The results from both simulated and lidar scanned data show similar structures - orienting the streak-like structure along the mean wind. This indicates that the streak-like structures can exist in the real atmosphere during unstable atmospheric condition cases.

The wind-energy (WE) industry relies on numerical weather prediction (NWP) forecast models as foundational or base models for many purposes, including wind-resource assessment and wind-power forecasting. During the Second Wind Forecast Improvement Project (WFIP2) in the Columbia River Basin of Oregon and Washington, a significant effort was made to improve NWP forecasts through focused model development, to include experimental refinements to the High Resolution Rapid Refresh (HRRR) model physics and horizontal grid spacing. In this study, the performance of an experimental version of HRRR that includes these refinements is tested against a control version, which corresponds to that of the operational HRRR run by National Oceanic and Atmospheric Administration/National Centers for Environmental Protection at the outset of WFIP2. The effects of horizontal grid resolution were also tested by comparing wind forecasts from the HRRR (with 3-km grid spacing) with those from a finer-resolution HRRR nest with 750-m grid spacing. Model forecasts are validated against accurate wind-profile measurements by three scanning, pulsed Doppler lidars at sites separated by a total distance of 71 km. Model skill and improvements in model skill, attributable to physics refinements and improved horizontal grid resolution, varied by season, by site, and during periods of atmospheric phenomena relevant to WE. In general, model errors were the largest below 150 m above ground level (AGL). Experimental HRRR refinements tended to reduce the mean absolute error (MAE) and other error metrics for many conditions, but degradation in skill (increased MAE) was noted below 150 m AGL at the two lowest-elevation sites at night. Finer resolution was found to produce the most significant reductions in the error metrics.