Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer at Barge. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer on the barge. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer on the barge. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Standardized Data
This dataset contains raw data from the laser disdrometer at Nantucket. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Standardized Data
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Standardized Data
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Laser Disdrometer / Daily Standardized Files
This dataset contains raw data from the laser disdrometer at Nantucket. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
17 WIND ENERGY↗
Laser Disdrometer / Daily Standardized Files
This dataset contains standardized data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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ASIT Laser Disdrometer / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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ASIT Laser Disdrometer 2 / Raw Data
This dataset contains raw data from the laser disdrometer at ASIT. The disdrometer measures droplet size distribution, hydrometer type, and precipitation rate.
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Solid-State Transformation of an Additive Manufactured Inconel 625 Alloy at 700 °C
Inconel 625, a nickel-based superalloy, has drawn much attention in the emerging field of additive manufacturing (AM) because of its excellent weldability and resistance to hot cracking. The extreme processing condition of AM often introduces enormous residual stress (hundreds of MPa to GPa) in the as-fabricated parts, which requires stress-relief heat treatment to remove or reduce the internal stresses. Typical residual stress heat treatment for AM Inconel 625, conducted at 800 °C or 870 °C, introduces a substantial precipitation of the δ phase, a deleterious intermetallic phase. In this work, we used synchrotron-based in situ scattering and diffraction methods and ex situ electron microscopy to investigate the solid-state transformation of an AM Inconel 625 at 700 °C. Our results show that while the δ phase still precipitates from the matrix at this temperature, its precipitation rate and size at a given time are both smaller when compared with their counterparts during typical heat treatment temperatures of 800 °C and 870 °C. A comparison with thermodynamic modeling predictions elucidates these experimental findings. Our work provides the rigorous microstructural kinetics data required to explore the feasibility of a promising lower-temperature stress-relief heat treatment for AM Inconel 625. The combined methodology is readily extendable to investigate the solid-state transformation of other AM alloys.
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Machine Learning Models for Mapping Groundwater Pollution Risk: Advancing Water Security and Sustainable Development Goals in Georgia, USA
The widespread use of pesticides, such as atrazine and malathion, in agricultural systems raises significant concerns regarding the contamination of groundwater, which serves as a critical resource for drinking water. This study applies machine learning techniques to predict the concentrations of atrazine and malathion in groundwater across Georgia, USA, using 2019 data. A Random Forest classifier was employed to integrate various environmental and demographic factors, including pesticide application rates, precipitation, lithology, and population density, to predict pesticide contamination in groundwater. The models demonstrated high training accuracies of 100% and moderate average testing accuracy of 55% for atrazine and 60% for malathion across five iterations. The low test accuracy of the model, ranging from 50% to 75%, is likely due to overfitting, which can be attributed to the small dataset size and the complex nature of pesticide-contamination patterns, making it challenging for the model to generalize to unseen data. Feature importance analysis revealed that average pesticide usage emerged as the most influential factor for atrazine, while aquifer lithology and precipitation played crucial roles in both models. These results provide valuable insights into the dynamics of pesticide contamination, highlighting areas at greater risk of contamination. The findings underscore the importance of integrating environmental, geological, and agricultural variables for more effective groundwater management and sustainable agricultural practices, contributing to the protection of water resources and public health.
54 ENVIRONMENTAL SCIENCES↗
Cloud regimes over the Amazon Basin: perspectives from the GoAmazon2014/5 campaign
Abstract. Radiosonde observations collected during the GoAmazon2014/5 campaign are analyzed to identify the primary thermodynamic regimes accompanying different modes of convection over the Amazon. This analysis identifies five thermodynamic regimes that are consistent with traditional Amazon calendar definitions of seasonal shifts, which include one wet, one transitional, and three dry season regimes based on a k-means cluster analysis. A multisensor ground-based approach is used to project associated bulk cloud and precipitation properties onto these regimes. This is done to assess the propensity for each regime to be associated with different characteristic cloud frequency, cloud types, and precipitation properties. Additional emphasis is given to those regimes that promote deep convective precipitation and organized convective systems. Overall, we find reduced cloud cover and precipitation rates to be associated with the three dry regimes and those with the highest convective inhibition. While approximately 15 % of the dataset is designated as organized convection, these events are predominantly contained within the transitional regime.
54 ENVIRONMENTAL SCIENCES↗
The prevalence of precipitation from polar supercooled clouds
Abstract. Supercooled clouds substantially impact polar surface energy budgets, but large-scale models often underestimate their occurrence, which motivates accurately establishing metrics of basic processes. An analysis of long-term measurements at Utqiaġvik, Alaska, and McMurdo Station, Antarctica, combines lidar-validated use of soundings to identify supercooled cloud layers and colocated ground-based profiling radar measurements to quantify cloud base precipitation. We find that more than 85 % (75 %) of sampled supercooled layers are precipitating over the Arctic (Antarctic) site, with more than 75 % (50 %) precipitating continuously to the surface. Such high frequencies can be reconciled with substantially lesser spaceborne estimates by considering differences in radar hydrometeor detection sensitivity. While ice precipitation into supercooled clouds from aloft is common, we also find that the great majority of supercooled cloud layers without ice falling into them are themselves continuously generating precipitation. Such sustained primary ice formation is consistent with continuous activation of immersion-mode ice-nucleating particles (INPs), suggesting that supercooled cloud formation is a principal gateway to ice formation at temperatures greater than ∼-38 ∘C over polar regions. The prevalence of weak precipitation fluxes is also consistent with supercooled cloud longevity and with well-observed and widely simulated case studies. An analysis of colocated microwave radiometer retrievals suggests that weak precipitation fluxes can be nonetheless consequential to moisture budgets for supercooled clouds owing to small liquid water paths. The results here also demonstrate that the observed abundance of mixed-phase clouds can vary substantially with instrument sensitivity and methodology. Finally, we suggest that these ground-based precipitation rate statistics offer valuable guidance for improving the representation of polar cloud processes in large-scale models.
54 ENVIRONMENTAL SCIENCES↗
Influence of covariance of aerosol and meteorology on co-located precipitating and non-precipitating clouds over the Indo-Gangetic Plain
Aerosol–cloud–precipitation interaction (ACPI) plays a pivotal role in the global and regional water cycle and the earth's energy budget; however, it remains highly uncertain due to the different underlying physical mechanisms. Therefore, this study aims to systematically analyze the effects of aerosols and meteorological factors on ACPI in the co-located precipitating clouds (PCs) and non-precipitating clouds (NPCs) in winter and summer seasons by employing the long-term (2001–2021) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) coupled with the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis-II datasets over the Indo-Gangetic Plain (IGP). The results exhibit a decadal increase in aerosol optical depth (AOD) over Lahore (5.2 %), Delhi (9 %), Kanpur (10.7 %), and Gandhi College (22.7 %) and a decrease over Karachi (-1.9 %) and Jaipur (-0.5 %). The most stable meteorology with high values of lower-tropospheric stability (LTS) is found in both seasons over Karachi. In the summer season, the occurrence frequency of clouds is high (74 %) over Gandhi College, 60 % of which are PCs. Conversely, the lowest number of PCs is found over Karachi. Similarly, in the winter season, the frequency of cloud occurrence is low over Karachi and high over Lahore and Gandhi College. The analysis of cloud top pressure (CTP) and cloud optical thickness (COT) indicates high values of cloud fraction (CF) for thick and high-level clouds over all study areas except Karachi. The microphysical properties such as cloud effective radius (CER) and cloud droplet number concentration (CDNC) bear high values (CER ≳ 15 µm and CDNC ≳ 50 cm -3 ) for both NPCs and PCs in summer. The AOD–CER correlation is good (weak) for PCs (NPCs) in winter. Similarly, the sensitivity value of the first indirect effect (FIE) is high (ranging from 0.2 ± 0.13 to 0.3 ± 0.01 in winter and from 0.19 ± 0.03 to 0.32 ± 0.05 in summer) for PCs and low for NPCs. The sensitivity value for the second indirect effect (SIE) is relatively higher (such as 0.6 ± 0.14 in winter and 0.4 ± 0.04 in summer) than FIE. Sensitivity values of the aerosol-cloud interaction (ACI) are low (i.e., -0.06 ± 0.09) for PCs in summer. Furthermore, the precipitation rate (PR) exhibits high values in the summer season, primarily due to the significant contribution from optically thick clouds with lower CDNC (≲ 50 cm -3 ) and larger CER and the intermediate contribution from optically thick clouds with higher CDNC ( ≳ 50 cm -3 ).
54 ENVIRONMENTAL SCIENCES↗
VISSS Raw data from SAIL at Gothic from November 2022 to June 2023
A Video in situ Snowfall Sensor (VISSS) records shadow images of precipitating hydrometeors from two perspective at a 90 degree angle with a frequency of 140 Hz. From this, hydrometeor properties such as particle size, shape, complexity, degree of riming, and fall velocity can be derived. The VISSS was set up next to the ARM mobile facility during the SAIL project located in the East River Watershed near Crested Butte, Colorado from November 2022 to June 2023. The deployment was funded by the CORSIPP project (Characterization of orography-influenced riming and secondary ice production and their effects on precipitation rates using radar polarimetry and Doppler spectra) funded by the German Research Foundation (DFG). More information about the VISSS can be found in Maahn et al. 2023 ( https://dx.doi.org/10.5194/egusphere-2023-655 )
54 ENVIRONMENTAL SCIENCES↗
Back Trajectories during SAIL
This data set contains air mass back trajectories generated during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, Stein et al. 2015, Rolph et al. 2017). For each hour from January 1, 2021 to June 30, 2023, a 96-hour back trajectory was initiated at the SAIL sampling site, starting 100 m above ground level. The calculations used the GDAS meteorological data set with model vertical velocity. The files within this dataset show location and meteorological parameters of the back trajectory analysis. There are 21 columns within each data file. The first 2 columns pertain to back trajectory parameters: the trajectory number (which is always 1) and the meteorological grid number. The next 9 columns pertain to spatiotemporal information: the year, month, day, hour (0-23), and minute of the trajectory, the previous time of the trajectory (in negative hours), and the airmass location (latitude, longitude, and height in m AGL). The rest of the parameters within each file pertains to surface and meteorological characteristics of the airmass: pressure (in hPa), potential temperature (in K), temperature (in K), precipitation rate (in mm/hr), mixing depth (in m), relative humidity (in % relative to liquid water), specific humidity (in g/kg), H2O mixing ratio (in g/kg), underlying terrain height (in meters above sea level), and incoming radiation (in W/m^2).
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