The atmospheric response to the solar eclipse of 8 April 2024 in North America is investigated with a specific focus on the marine atmospheric boundary layer (MABL). We leverage measurements collected during the Third Wind Forecast Improvement Project (WFIP3), including Doppler lidars, sonic anemometers, and thermodynamic profiler data to investigate the atmospheric response across sites that experienced partial eclipse conditions with nearly 90 1.2^∘C to 1.4^∘C in coastal regions and from 0.3^∘C to 0.5^∘C over the ocean. This analysis suggests that the MABL’s higher thermal inertia compared to coastal regions moderates the temperature decrease during the eclipse. Wind speed exhibits a more complex behavior, as it is influenced by both the MABL and preexisting synoptic conditions. Although a reduction in wind speed is observable up to approximately 140 m above ground level (AGL) at more inland sites, at other locations closer to the coast, this reduction is constrained to the lowest 100 m AGL. Turbulence parameters retrieved from sonic anemometers, such as turbulence kinetic energy, turbulent heat flux, and friction velocity, decrease during the eclipse at coastal sites, accompanied by a brief transition of atmospheric stability from unstable to neutral or weakly stable conditions. For the open-ocean sites, the variability in turbulence statistics and atmospheric stability is minimal during the occurrence of the eclipse.

In the hyper-arid Namib Desert, fog serves as the only regular source of moisture, vital for sustaining local ecosystems. While fog occurrence in the region is typically associated with the advection of marine stratus clouds and their interaction with topography, its spatial distribution is strongly influenced by cloud base height, which remains poorly understood. To address this gap, this study utilizes ground-based remote sensing and in-situ observations to analyze systematic spatial and temporal patterns of cloud base height. Our results reveal clear seasonality and a diurnal cycle, with cloud base lowering moderately (10-50 m h-1) during the evening and early night, and lifting rapidly (30-150 m h-1) after sunrise, especially inland. Additionally, the findings indicate that these rates are influenced by horizontal gradients in cloud thickness. Quantile regression highlights the tight relationship between cloud base height and near-surface relative humidity (r approximate to-0.76) that is expected in well-mixed boundary layer, which can therefore be employed to estimate cloud base height across FogNet sites. In a case study, the potential value of the estimated cloud base height for separating fog from low clouds in satellite-based products is shown. In the future, a full integration of the estimated cloud base height with a satellite-based fog and low-cloud product can enable a spatially continuous mapping of fog in the region for the first time, which would facilitate fog ecological impact studies.

Thermodynamic profiles, especially in the atmospheric boundary layer (ABL), are essential for many research and operational applications. Ground-based infrared spectrometers (IRS) are commercially available, and thermodynamic profiles in the ABL can be retrieved from these observations at 5 min resolution or better. This study deployed seven IRS systems within 5 m of each other in Boulder, Colorado, USA, in September-October 2023, providing an opportunity to evaluate the relative accuracy of the measured radiances from these systems as well as the retrieved thermodynamic profiles. The analysis demonstrates that the observed radiances from the seven instruments agree within 1 % of the ambient radiance in both opaque and more transparent channels. The differences in the spectral calibration between the instruments were smaller than 0.11 cm-1, relative to the nominal effective wavenumber of the metrology laser of 15 799 cm-1 (i.e., better than 7.1 ppm). Further, the retrieved temperature and humidity profiles agree with each other well within the uncertainty of the retrieved profiles, and quantities derived from these thermodynamic profiles such as precipitable water vapor and height of the convective boundary layer also agree within their uncertainties. These results demonstrate a high degree of repeatability and precision, and that if these instruments were deployed as part of a network, any differences larger than the retrieval uncertainty would be associated with real environmental differences and not an artifact of the instrument calibration or retrieval.

Abstract. The High-Resolution Rapid Refresh (HRRR) model is run operationally by the National Oceanic and Atmospheric Administration to provide high-resolution short-range forecasts for the continental United States. The evaluation of the HRRR model off of the U.S. coasts has been challenged by the lack of suitable continuous profile observations in the marine boundary layer in the past. State-of-the art remote sensing instruments were recently deployed along the coast of New England in the northeastern United States for the multi-year Third Wind Forecast Improvement Project and provide a unique opportunity for the evaluation of temperature and wind in the marine boundary layer in the HRRR model. We used 1 year of data at three sites, two of which were on islands, to document the seasonal characteristics of the marine boundary layer and its representation in the HRRR model for different forecast hours. Overall, the HRRR model captured the seasonal and diurnal evolution of temperature and wind very well. However, low-level horizontal wind shear and static stability were too weak in the model, especially during the warmer months, which might be partly linked to errors in sea surface temperature. Low-level jets (LLJs) occurred in approximately 20 % of the hourly profiles with a maximum frequency during spring and summer. Up to 60 % of the LLJ profiles during peak seasons were correctly predicted, using the critical success index as a measure. Systematic model errors in wind and temperature were found during LLJs, when the HRRR model frequently underestimated wind speed at nose height and shear below nose height, often accompanied by static stability that was too weak. These errors resulted in low-level Bulk Richardson numbers that were consistently too large at all three sites, indicating an overestimation of dynamic stability in the boundary layer in the model. Such systematic errors in low-level wind shear and stability were largely absent during correct rejections, that is, when an LLJ was neither observed nor simulated, indicating that LLJs were responsible for a large part of the model errors.

Thermally driven upvalley (UV) wind in the upper East River Valley in the Colorado Rocky Mountains often unexpectedly stops in midmorning and reverses back to downvalley (DV) wind. We use a comprehensive observational data set for a nearly two‐year long period to analyze the wind system and boundary layer evolution in this high‐altitude valley and determine the reason for this early wind reversal. Days with short UV wind predominantly occur during the warm season when the valley floor is free of snow and the convective boundary layer (CBL) grows well above the height of the surrounding ridges. UV wind persists throughout the day only on a few days during the warm season. We link differences in valley wind evolution to wind direction at upper levels at and above ridge height and propose forced channeling mechanisms to describe coupling between valley and upper‐level wind when the CBL grows above ridge height. The frequency distribution of upper‐level wind direction is such that channeling in the DV direction is favored, which explains the predominance of days with short UV wind. The deep CBL is supported by the presence of a deep weakly stably stratified residual layer with high aerosol content, which is regularly present over the mountain range during the warm season. On days when the CBL does not grow above ridge height, for example, when the valley floor is covered by snow, thermally driven UV wind is able to persist throughout the day independent of upper‐level wind direction.

Comprehensive atmospheric measurements were conducted in the East River Valley in Colorado for a nearly 2-year period from 2021 through 2023 in the framework of the NOAA Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) and the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) program Surface Atmosphere Integrated Field Laboratory (SAIL) campaigns. The main focus of these research initiatives is to enhance weather and water prediction capabilities by measuring, evaluating, and understanding integrated atmospheric and hydrologic processes relevant to water resources. The East River Valley is embedded in the East River Watershed which is a representative mountainous headwater catchment of the Colorado River Basin and is a primary source of water for much of the southwestern United States. The valley floor is located at more than 2500 m above mean sea level and the surrounding ridges extend above 4000 m.In this study, we used temperature, humidity, and wind profiles from ground-based remote sensing instruments and radiosondes in the upper part of the valley, as well as near-surface meteorological observations from 5 sites distributed along the valley axis. Temperature and humidity profiles with high temporal resolution were retrieved from infrared spectrometer radiances with the optimal estimation physical retrieval TROPoe. The data set allows one to investigate the seasonal and diurnal cycle of the boundary layer and to investigate the impact of varying spatial snow coverage including the melt period. We show that the diurnal cycle of the boundary layer conditions on many days is very different from a typical thermally driven wind system, especially when snow coverage is low, and we discuss possible factors contributing to the boundary evolution.

Thermodynamic profiles in the atmospheric boundary layer can be retrieved from ground-based passive remote sensing instruments like infrared spectrometers and microwave radiometers with optimal-estimation physical retrievals. With a high temporal resolution on the order of minutes, these thermodynamic profiles are a powerful tool to study the evolution of the boundary layer and to evaluate numerical models. In this study, we describe three recent modifications to the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) retrieval framework, which improve the availability of valid solutions for different atmospheric conditions and increase the temporal consistency of the retrieved profiles. We present methods to enhance the availability of valid solutions retrieved from infrared spectrometers by preventing overfitting and by adding information from an additional spectral band in high-moisture environments. We show that the characterization of the uncertainty of the input and the choice of spectral infrared bands are crucial for retrieval performance. Since each profile is retrieved independently from the previous one, the time series of the thermodynamic variables contain random uncorrelated noise, which may hinder the study of diurnal cycles and temporal tendencies. By including a previous retrieved profile as input to the retrieval, we increase the temporal consistency between subsequent profiles without suppressing real mesoscale atmospheric variability. We demonstrate that these modifications work well at midlatitudes, polar and tropical sites, and for retrievals based on infrared spectrometer and microwave radiometer measurements.

Offshore wind energy development in the United States is accelerating, with projects currently representing 40 gigawatts of proposed installed capacity. However, there are still substantial, unsolved challenges with forecasting winds and turbulence over the ocean. To help overcome the challenges, the US Department of Energy (DOE) and the National Oceanic and Atmospheric Administration (NOAA) are currently conducting a multi-seasonal offshore field campaign off the coast of New England in the Eastern United States. In collaboration with public and private partners, WFIP3 aims to boost offshore wind generation through better forecasting for existing, constructed, and planned wind farms in the area. WFIP3 builds on the success of the first and second wind forecast improvement projects (WFIP1 and WFIP2) which collected data to improve the accuracy of short-term wind forecasts over land. WFIP3 seeks to improve the understanding of the physical phenomena in the marine atmospheric boundary layer that impact wind and turbulence within turbine rotor planes that are critical for wind energy. Since November 2023, a comprehensive set of remote sensing and in situ meteorological instruments have been installed at several sites at the coast and on islands. These continuous land-based observations are complemented by observations on a barge and ship during several multi-week-long periods. The observations will be used to evaluate and improve NOAA’s currently operational High-Resolution-Rapid-Refresh model as well as its successor the Rapid Refresh Forecast System. We present an overview of the campaign, research questions, and measurement strategy and will show some preliminary results from the ongoing campaign.

ABSTRACTAs offshore wind energy development accelerates in the United States, it is important to assess the accuracy of hub‐height wind forecasts from numerical weather prediction models over the ocean. Leveraging approximately 2 years of Doppler lidar observations from buoys in the New York Bight, we evaluate 80‐m wind speed forecasts from two weather models: the High‐Resolution Rapid Refresh (HRRR) atmospheric model and the Global Forecast System (GFS) coupled atmosphere‐ocean model. These models have different horizontal grid spacing, vertical layering, initialization methods, and parameterizations of boundary layer mixing and surface–atmosphere interactions. Despite these differences, the models demonstrate similar and highly skillful short‐term forecasts at three measurement sites. At the Hudson Southwest location that provides a full year of data, their performance is statistically indistinguishable: root mean square error (RMSE) = 2.1 m/s and the Pearson correlation coefficient for 24‐h forecasts of both models, and RMSE = 2.6 m/s and 0.83 for 48‐h forecasts. Twenty‐four‐hour forecasts also exhibit skill in predicting quiescent winds and winds associated with maximum turbine power. By Day 10, GFS forecasts on average have almost no skill. The short‐term forecast skill by the HRRR and GFS does not strongly depend on season or time of day, yet we find some dependence of the models' performance on near‐surface stability. Additionally, 4‐ to 14‐day forecasts by the GFS exhibit lower RMSE during summer relative to other seasons. The high skill of the HRRR and GFS short‐term forecasts establishes confidence in their utility for offshore wind energy maintenance and operation.

Thermodynamic profiles in the atmospheric boundary layer can be retrieved from ground-based passive remote sensing instruments with an optimal estimation physical retrieval such as Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe). The retrieval combines measurements, prior information, and corresponding uncertainties to find an optimal solution of the atmospheric state. TROPoe permits combining passively sensed radiances from infrared spectrometers and microwave radiometers with thermodynamic profiles from Raman lidars, Differential Absorption lidars, Radio Acoustic Sounding Systems, radio soundings, or numerical weather prediction models. After more than 10 years of development, the TROPoe retrieval code was recently converted to Python and put into a Docker container to facilitate its usage for both operations and research. It is currently used operationally by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program and by the Swiss weather service MeteoSwiss as part of the EUMETNET. With a high temporal resolution on the order of minutes, the retrieved thermodynamic profiles are a powerful tool to study the temporal evolution of the boundary layer.Since each profile is retrieved independently from the previous one, the time series of thermodynamic variables contain random uncorrelated noise, which may hinder the study of diurnal cycles and temporal tendencies. In this work, we investigate how the temporal consistency of thermodynamic profiles retrieved with TROPoe can be improved by including information from a previous retrieved profile as input to the retrieval. We demonstrate that this method works well in mid-latitudes, polar and tropical sites and for retrievals based on measurements from infrared spectrometers and microwave radiometers. We further present methods to enhance the availability of valid profiles retrieved from infrared spectrometers by preventing overfitting and by adding information from an additional infrared band in high moisture environments when the typically used spectral bands are saturated.

Over heterogeneous, mountainous terrain, the determination of spatial heterogeneity of any type of a turbulent layer has been known to pose substantial challenges in mountain meteorology. In addition to the combined effect in which buoyancy and shear contribute to the turbulence intensity of such layers, it is well known that mountains add an additional degree of complexity via non-local transport mechanisms, compared to flatter topography. It is therefore the aim of this study to determine the vertical depths of both daytime convectively and shear-driven boundary layers within a fairly wide and deep Alpine valley during summertime. Specifically, three Doppler lidars deployed during the CROSSINN (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) campaign within a single week in August 2019 are used to this end, as they were deployed along a transect nearly perpendicular to the along-valley axis. To achieve this, a bottom-up exceedance threshold method based on turbulent Doppler spectrum width sampled by the three lidars has been developed and validated against a more traditional bulk Richardson number approach applied to radiosonde profiles obtained above the valley floor. The method was found to adequately capture the depths of convective turbulent boundary layers at a 1 min temporal and 50 m spatial resolution across the valley, with the degree of ambiguity increasing once surface convection decayed and upvalley flows gained in intensity over the course of the afternoon and evening hours. Analysis of four intensive observation period (IOP) events elucidated three regimes of the daytime mountain boundary layer in this section of the Inn Valley. Each of the three regimes has been analysed as a function of surface sensible heat flux H, upper-level valley stability Γ, and upper-level subsidence wL estimated with the coplanar retrieval method. Finally, the positioning of the three Doppler lidars in a cross-valley configuration enabled one of the most highly spatially and temporally resolved observational convective boundary layer depth data sets during daytime and over complex terrain to date.

Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in situ surface platforms, and numerical weather prediction models. Among the employed instruments are microwave radiometers (MWRs), infrared spectrometers (IRSs), radio acoustic sounding systems (RASSs), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud-base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWR and/or IRS instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the abovementioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (a.g.l.) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.

The accurate forecast of persistent orographic cold-air pools in numerical weather prediction models is essential for the optimal integration of wind energy into the electrical grid during these events. Model development efforts during the second Wind Forecast Improvement Project (WFIP2) aimed to address the challenges related to this. We evaluated three versions of the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh model with two different horizontal grid spacings against in situ and remote sensing observations to investigate how developments in physical parameterizations and numerical methods targeted during WFIP2 impacted the simulation of a persistent cold-air pool in the Columbia River basin. Differences amongst model versions were most apparent in simulated temperature and low-level cloud fields during the persistent phase of the cold-air pool. The model developments led to an enhanced low-level cloud cover, resulting in better agreement with the observations. This removed a diurnal cycle in the near-surface temperature bias at stations throughout the basin by reducing a cold bias during the night and a warm bias during the day. However, low-level clouds did not clear sufficiently during daytime in the newest model version, which leaves room for further model developments. The model developments also led to a better representation of the decay of the cold-air pool by slowing down its erosion.

These files contain Surface Energy Balance data at the Brush Creek (CBC) site as part of NOAA's Global Monitoring Laboratory's deployment in the Sail-SPLASH Campaign between October 2021 through August 2023. NOTE: Version 2.1 contains one "zip" file containing all daily files for ease of download.

The structure and evolution of the atmospheric boundary layer (ABL) under clear-sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground-based infrared spectrometers deployed in the East River Valley in Colorado’s Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from snow-free to snow-covered ground. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval TROPoe. A nocturnal surface inversion formed in the valley during clear-sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer during snow-free periods. When the ground was snow covered, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA’s operational High-Resolution-Rapid-Refresh (HRRR) model and find large warm biases on clear-sky days resulting from the model’s inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. A possible explanation for these model shortcomings is the influence of the model’s relatively coarse horizontal grid spacing (3 km) and its impact on the model’s ability to represent well-developed thermally driven flows, specifically nighttime drainage flows.

The structure and evolution of the atmospheric boundary layer (ABL) under clear‐sky fair weather conditions over mountainous terrain is dominated by the diurnal cycle of the surface energy balance and thus strongly depends on surface snow cover. We use data from three passive ground‐based infrared spectrometers deployed in the East River Valley in Colorado's Rocky Mountains to investigate the response of the thermal ABL structure to changes in surface energy balance during the seasonal transition from low to high snow cover. Temperature profiles were retrieved from the infrared radiances using the optimal estimation physical retrieval Tropospheric Remotely Observed Profiling via Optimal Estimation. A nocturnal surface inversion formed in the valley during clear‐sky days, which was subsequently mixed out during daytime with the development of a convective boundary layer when snow cover was low. Over high snow cover, a very shallow convective boundary layer formed, above which the inversion persisted through the daytime hours. We compare these observations to NOAA's operational High‐Resolution‐Rapid‐Refresh model and find large warm biases on clear‐sky days resulting from the model's inability to form strong nocturnal inversions and to maintain the stable stratification in the valley during daytime when there was snow on the ground. We suggest several factors contributing to the large model errors. These are (a) the inability of the model to represent well‐developed thermally driven flows likely due to the too coarse horizontal grid spacing (3 km), (b) too much convective mixing during daytime, and (c) too strong vertical coupling between the valley atmosphere and the free troposphere.

As part of the Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa (DACCIWA) project, extensive in-situ measurements of the southern West African atmospheric boundary layer (ABL) have been performed at three supersites Kumasi (Ghana), Savè (Benin) and Ile-Ife (Nigeria) during the 2016 monsoon period (June and July). The measurements were designed to provide data for advancing our understanding of the relevant processes governing the formation, persistence and dissolution of nocturnal low-level stratus clouds and their influence on the daytime ABL in southern West Africa. An extensive low-level cloud deck often forms during the night and persists long into the following day strongly influencing the ABL diurnal cycle. Although the clouds are of a high significance for the regional climate, the dearth of observations in this region has hindered process understanding. Here, an overview of the measurements ranging from near-surface observations, cloud characteristics, aerosol and precipitation to the dynamics and thermodynamics in the ABL and above, and data processing is given. So-far achieved scientific findings, based on the dataset analyses, are briefly overviewed.