The ARM Southern Great Plains (SGP) Site

Sky Image Classification Based on Transfer Learning Approaches

Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons' RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

Analysis of Random Forest Modeling Strategies for Multi-Step Wind Speed Forecasting

Although the random forest (RF) model is a powerful machine learning tool that has been utilized in many wind speed/power forecasting studies, there has been no consensus on optimal RF modeling strategies. This study investigates three basic questions which aim to assist in the discernment and quantification of the effects of individual model properties, namely: (1) using a standalone RF model versus using RF as a correction mechanism for the persistence approach, (2) utilizing a recursive versus direct multi-step forecasting strategy, and (3) training data availability on model forecasting accuracy from one to six hours ahead. These questions are investigated utilizing data from the FINO1 offshore platform and Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) C1 site, and testing results are compared to the persistence method. At FINO1, due to the presence of multiple wind farms and high inter-annual variability, RF is more effective as an error-correction mechanism for the persistence approach. The direct forecasting strategy is seen to slightly outperform the recursive strategy, specifically for forecasts three or more steps ahead. Finally, increased data availability (up to ∼8 equivalent years of hourly training data) appears to continually improve forecasting accuracy, although changing environmental flow patterns have the potential to negate such improvement. We hope that the findings of this study will assist future researchers and industry professionals to construct accurate, reliable RF models for wind speed forecasting.

Revisiting particle dry deposition and its role in radiative effect estimates

Dry deposition is a key sink of atmospheric particles, which impact human and ecosystem health, and the radiative balance of the planet. However, the deposition parameterizations used in climate and air-quality models are poorly constrained by observations. Dry deposition of submicron particles is the largest uncertainty in aerosol indirect radiative forcing. Our particle flux observations indicate that dry deposition velocities are an order of magnitude lower than models suggest. Our updated, observation-driven parameterizations should reduce uncertainty in modeled dry deposition. The scheme increases modeled accumulation mode aerosol number concentrations, and enhances the combined natural and anthropogenic aerosol indirect effect by −0.63 W m⁻², similar in magnitude to the total aerosol indirect forcing in the Intergovernmental Panel on Climate Change report.

Wet and dry deposition remove aerosols from the atmosphere, and these processes control aerosol lifetime and thus impact climate and air quality. Dry deposition is a significant source of aerosol uncertainty in global chemical transport and climate models. Dry deposition parameterizations in most global models were developed when few particle deposition measurements were available. However, new measurement techniques have enabled more size-resolved particle flux observations. We combined literature measurements with data that we collected over a grassland in Oklahoma and a pine forest in Colorado to develop a dry deposition parameterization. We find that relative to observations, previous parameterizations overestimated deposition of the accumulation and Aitken mode particles, and underestimated in the coarse mode. These systematic differences in observed and modeled accumulation mode particle deposition velocities are as large as an order of magnitude over terrestrial ecosystems. As accumulation mode particles form most of the cloud condensation nuclei (CCN) that influence the indirect radiative effect, this model-measurement discrepancy in dry deposition alters modeled CCN and radiative forcing. We present a revised observationally driven parameterization for regional and global aerosol models. Using this revised dry deposition scheme in the Goddard Earth Observing System (GEOS)-Chem chemical transport model, we find that global surface accumulation-mode number concentrations increase by 62% and enhance the global combined anthropogenic and natural aerosol indirect effect by −0.63 W m⁻². Our observationally constrained approach should reduce the uncertainty of particle dry deposition in global chemical transport models.

Evaluation of the Microphysical Assumptions within GPM-DPR Using Ground-Based Observations of Rain and Snow

The Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM-DPR) provides an opportunity to investigate hydrometeor properties. Here, an evaluation of the microphysical framework used within the GPM-DPR retrieval was undertaken using ground-based disdrometer measurements in both rain and snow with an emphasis on the evaluation of snowfall retrieval. Disdrometer measurements of rain show support for the two separate prescribed relations within the GPM-DPR algorithm between the precipitation rate (R) and the mass weighted mean diameter ( D m ) with a mean absolute percent error ( M A P E ) on R of 29% and 47% and a mean bias percentage ( M B P ) of − 6% and − 20% for the stratiform and convective relation, respectively. Ground-based disdrometer measurements of snow show higher MAPE and MBP values in the retrieval of R, at 77% and − 52% , respectively, compared to the stratiform rain relation. An investigation using the disdrometer-measured fall velocity and mass in the calculation of R and D m illustrates that the variability found in hydrometeor mass causes a poor correlation between R and D m in snowfall. The results presented here suggest that R − D m retrieval is likely not optimal in snowfall, and other retrieval techniques for R should be explored.

Differences in fine particle chemical composition on clear and cloudy days

Clouds are prevalent and alter PM_2.5 mass and chemical composition. Cloud-affected satellite retrievals are often removed from data products, hindering estimates of tropospheric chemical composition during cloudy times. We examine surface fine particulate matter (PM_2.5) chemical constituent concentrations in the Interagency Monitoring of PROtected Visual Environments network during Cloudy and Clear Sky times defined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud flags from 2010-2014 with a focus on differences in particle hygroscopicity and aerosol liquid water (ALW). Cloudy and Clear Sky periods exhibit significant differences in PM_2.5 and chemical composition that vary regionally and seasonally. In the eastern US, relative humidity alone cannot explain differences in ALW, suggesting emissions and in situ chemistry exert determining impacts. An implicit clear sky bias may hinder efforts to quantitatively to understand and improve model representation of aerosol-cloud interactions.