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Wilczak, James M.

Publications and source records attributed to Wilczak, James M..

8 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Observations of surface energy fluxes and meteorology in the seasonally snow-covered high-elevation East River watershed during SPLASH, 2021–2023

Abstract. From autumn 2021 through summer 2023, scientists from the National Oceanic and Atmospheric Administration (NOAA) and partners conducted the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign in the East River watershed of Colorado. One objective of SPLASH was to observe the transfer of energy between the atmosphere and the surface, which was done at several locations. Two remote sites were chosen that did not have access to power utilities. These were along the valley floor near the East River in the vicinity of the unincorporated town of Gothic, Colorado. Energy balance measurements were made at these locations using autonomous, single-level flux towers referred to as atmospheric surface flux stations (ASFSs). The ASFSs were deployed on 28 September 2021 at the Kettle Ponds Annex site and on 12 October 2021 at the Avery Picnic site and operated until 19 July and 21 June 2023, respectively. Measurements included basic meteorology; upward and downward longwave and shortwave radiative fluxes and subsurface conductive flux, each at 1 min resolution; 3-D winds from a sonic anemometer and H2O/CO2 from an open-path gas analyzer, both at 20 Hz from which sensible, latent heat, and CO2 fluxes were derived; and profiles of soil properties in the upper 0.5 m (both sites) and temperature profiles through the snow (at Avery Picnic), each reported between 10 min and 6 h. The system uptime was 97 % (Kettle Ponds) and 90 % (Avery Picnic), and collectively 1184 d of data was obtained between the stations. The purpose of this article is to document the ASFS deployment at SPLASH, to document the data acquisition and post-processing of measurements, and to serve as a guide for interested users of the data sets, which are archived at Zenodo (https://doi.org/10.5281/zenodo.10313363, Cox et al., 2023b; https://doi.org/10.5281/zenodo.10327409, Cox et al., 2023c; https://doi.org/10.5281/zenodo.10313894, Cox et al., 2023d; https://doi.org/10.5281/zenodo.10307825, Cox et al., 2023e; https://doi.org/10.5281/zenodo.10310520, Cox et al., 2023f) with the Creative Commons Attribution 4.0 International license.

Cox, Christopher J. (ORCID:0000000322037173)↗
DOE OSTI2024

Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models

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 above-mentioned 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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Impact of Seasonal Snow-Cover Change on the Observed and Simulated State of the Atmospheric Boundary Layer in a High-Altitude Mountain Valley

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model

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.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Scientific challenges to characterizing the wind resource in the marine atmospheric boundary layer

Abstract. With the increasing level of offshore wind energy investment, it is correspondingly important to be able to accurately characterize the wind resource in terms of energy potential as well as operating conditions affecting wind plant performance, maintenance, and lifespan. Accurate resource assessment at a particular site supports investment decisions. Following construction, accurate wind forecasts are needed to support efficient power markets and integration of wind power with the electrical grid. To optimize the design of wind turbines, it is necessary to accurately describe the environmental characteristics, such as precipitation and waves, that erode turbine surfaces and generate structural loads as a complicated response to the combined impact of shear, atmospheric turbulence, and wave stresses. Despite recent considerable progress both in improvements to numerical weather prediction models and in coupling these models to turbulent flows within wind plants, major challenges remain, especially in the offshore environment. Accurately simulating the interactions among winds, waves, wakes, and their structural interactions with offshore wind turbines requires accounting for spatial (and associated temporal) scales from O(1 m) to O(100 km). Computing capabilities for the foreseeable future will not be able to resolve all of these scales simultaneously, necessitating continuing improvement in subgrid-scale parameterizations within highly nonlinear models. In addition, observations to constrain and validate these models, especially in the rotor-swept area of turbines over the ocean, remains largely absent. Thus, gaining sufficient understanding of the physics of atmospheric flow within and around wind plants remains one of the grand challenges of wind energy, particularly in the offshore environment. This paper provides a review of prominent scientific challenges to characterizing the offshore wind resource using as examples phenomena that occur in the rapidly developing wind energy areas off the United States. Such phenomena include horizontal temperature gradients that lead to strong vertical stratification; consequent features such as low-level jets and internal boundary layers; highly nonstationary conditions, which occur with both extratropical storms (e.g., nor'easters) and tropical storms; air–sea interaction, including deformation of conventional wind profiles by the wave boundary layer; and precipitation with its contributions to leading-edge erosion of wind turbine blades. The paper also describes the current state of modeling and observations in the marine atmospheric boundary layer and provides specific recommendations for filling key current knowledge gaps.

17 WIND ENERGY↗
DOE OSTI2022

A Hybrid Bulk Algorithm to Predict Turbulent Fluxes over Dry and Wet Bare Soils

Measurements made in the Columbia River basin (Oregon) in an area of irregular terrain during the second Wind Forecast Improvement Project (WFIP2) field campaign are used to develop an optimized hybrid bulk algorithm to predict the surface turbulent fluxes from readily measured or modeled quantities over dry and wet bare or lightly vegetated soil surfaces. The hybrid (synthetic) algorithm combines (i) an aerodynamic method for turbulent flow, which is based on the transfer coefficients (drag coefficient and Stanton number), roughness lengths, and Monin–Obukhov similarity; and (ii) a modified Priestley–Taylor (P-T) algorithm with physically based ecophysiological constraints, which is essentially based on the surface energy budget (SEB) equation. Soil heat flux in the latter case was estimated from measurements of soil temperature and soil moisture. In the framework of the hybrid algorithm, bulk estimates of the momentum flux and the sensible heat flux are derived from a traditional aerodynamic approach, whereas the latent heat flux (or moisture flux) is evaluated from a modified P-T model. Direct measurements of the surface fluxes (turbulent and radiative) and other ancillary atmospheric/soil parameters made during WFIP2 for different soil conditions (dry and wet) are used to optimize and tune the hybrid bulk algorithm. The bulk flux estimates are validated against the measured eddy-covariance fluxes. We also discuss the SEB closure over dry and wet surfaces at various time scales based on the modeled and measured fluxes. Although this bulk flux algorithm is optimized for the data collected during the WFIP2, a hybrid approach can be used for similar flux-tower sites and field campaigns.

54 ENVIRONMENTAL SCIENCES↗