Observing Profiles of Derived Kinematic Field Quantities Using a Network of Profiling Sites

Observations of thermodynamic and kinematic parameters associated with derivatives of the thermodynamics and wind fields, namely advection, vorticity, divergence, and deformation, can be obtained by applying Green’s Theorem to a network of observing sites. The five nodes that comprise the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) profiling network, spaced 50 -80 km apart, are used to obtain measurements of these parameters over a finite region. To demonstrate the applicability of this technique at this location, it is first applied to gridded model output from the High Resolution Rapid Refresh (HRRR) numerical weather prediction model, using profiles from the locations of ARM network sites, so that values calculated from this method can be directly compared to finite difference calculations. Good agreement is found between both approaches as well as between the model and values calculated from the observations. Uncertainties for the observations are obtained via a Monte Carlo process in which the profiles are randomly perturbed in accordance with their known error characteristics. The existing size of the ARM network is well-suited to capturing these parameters, with strong correlations to model values and smaller uncertainties than a more closely-spaced network, yet it is small enough that it avoids the tendency for advection to go to zero over a large area.

Schließungsstudien von Aerosol/Wolkenwechselwirkungen anhand von Lidar- und Radarbeobachtungen während MOASiC

<p>Während MOSAiC (Multidisciplinary drifting Observatory for the Study of Arctic Climate) wurden verschiedene Aerosol- und Wolkentypen mit einem Mehrwellenlängen-Polarisations-Raman-Lidar (Polly-XT) der OCEANET-Atmosphere-Plattform und mit dem KAZR-Wolkenradar der ARM (Atmospheric Radiation Measurement user facility) an Bord des Eisbrechers POLARSTERN beobachtet. Im Winterhalbjahr (2019/20) wurden dafür in der zentralen Arktis regelmäßig Aerosole in Oberflächennähe bis in 4-6 km Höhe (arktischer Dunst) und in der oberen Troposphäre und unteren Stratosphäre (Waldbrandrauch, bis in 18 km Höhe) beobachtet. Neu entwickelte Methoden der Fernerkundung ermöglichen die Bestimmung der Konzentrationen von Wolkenkondensationskernen (CCNC), der Wolkentröpfchenanzahl (CDNC), der eiskeimbildenden Partikel (INPC) und sogar, mit Hilfe von Dopplerradarbeobachtungen, der Eiskristallzahl (ICNC). Gleichzeitig sind Profile der relativen Luftfeuchtigkeit und der Temperatur aus Raman-Lidar, Mikrowellen-Radiometer und Radiosondierungen verfügbar. Mit Hilfe dieses einzigartigen Datensatzes präsentieren wir eine Aerosol-Wolkenschlussstudie, in der wir zeigen, dass CCNC und CDNC sowie INPC und ICNC miteinander verknüpft werden können. Die Ergebnisse können verwendet werden, um zu testen, welche CCN- und INP-Parametrisierungen (aus idealisierten Labormessungen) im arktischen Regime am besten zutreffen. <br>In Anlehnung an diese Methoden werden im zukünftigen Projekt SCiAMO (Smoke Cirrus interaction in the Arctic during MOSAiC) etwa 65 beobachtete Zirren im Hinblick auf Eisnukleationsprozesse in Abhängigkeit vom Auftreten von Rauchpartikeln in der Winter- und Sommersaison analysiert und verglichen.</p>

Spatial Interpolation of Air Pollutant and Meteorological Variables in Central Amazonia

The Amazon Rainforest is highlighted by the global community both for its extensive vegetation cover that constantly suffers the effects of anthropic action and for its substantial biodiversity. This dataset presents data of meteorological variables from the Amazon Rainforest region with a spatial resolution of 0.001° in latitude and longitude, resulting from an interpolation process. The original data were obtained from the GoAmazon 2014/5 project, in the Atmospheric Radiation Measurement (ARM) repository, and then processed through mathematical and statistical methods. The dataset presented here can be used in experiments in the field of Data Science, such as training models for predicting climate variables or modeling the distribution of species.

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

Prediction for cloud spacing confirmed using stereo cameras

Using three years of the Clouds Optically Gridded by Stereo (COGS) product, the mean cloud base, cloud top, cloud width, and cloud spacing are described with respect to their seasonal and/or diurnal evolution at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. In addition to confirming and extending prior results, the data show that the effective diameter of shallow cumuli are approximately equal to the height above ground of the lifting condensation level (LCL). Furthermore, the cloud spacing is found to closely match a prediction by Thuburn and Efstathiou for the horizontal scale of the largest unstable eddies in an unsheared convective boundary layer.

Relative importance and interactions of primary and secondary ice production in the Arctic mixed-phase clouds

A discrepancy of up to 5 orders of magnitude between ice crystal and ice nucleating particle (INP) number concentrations was found in the measurements, indicating the potential important role of secondary ice production (SIP) in the clouds. However, the relative importance and interactions between primary and SIP processes remain unexplored. In this study, we implement five different ice nucleation schemes as well as physical representations of SIP processes (i.e., droplet shattering during rain freezing, ice-ice collisional break-up, and rime splintering) in the Community Earth System Model version 2 (CESM2). We run CESM2 in the single column mode for model comparisons with the DOE Atmospheric Radiation Measurement (ARM) Mixed-Phase Arctic Cloud Experiment (M-PACE) observations. We find that the model experiments with aerosol-aware ice nucleation schemes and SIP processes yield the best simulation results for the M-PACE single-layer mixed-phase clouds. We further investigate the relative importance of ice nucleation and SIP to ice number and cloud phase as well as interactions between ice nucleation and SIP in the M-PACE single-layer mixed-phase clouds. Our results show that SIP contributes 80 % to the total ice formation and transforms ~ 30 % of pure liquid-phase clouds simulated in the model experiments without considering SIP into mixed-phase clouds. We find that SIP is not only a result of ice crystals produced from ice nucleation, but also competes with the ice nucleation. Conversely, strong ice nucleation also suppresses SIP by glaciating mixed-phase clouds.

Evaluation of a land-atmosphere coupling metric computed from a ground-based infrared interferometer

Land-atmosphere feedbacks are a critical component of the hydrologic cycle. Vertical profiles of boundary layer temperature and moisture, together with information about the land surface, are used to compute land-atmosphere coupling metrics. Ground based remote sensing platforms, such as the Atmospheric Emitted Radiance Interferometer (AERI), can provide high temporal resolution vertical profiles of temperature and moisture. When co-located with soil moisture, surface flux, and surface meteorological observations, such as at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site, it is possible to observe both the terrestrial and atmospheric legs of land-atmosphere feedbacks. In this study, we compare a commonly used coupling metric computed from radiosonde-based data to that obtained from the AERI to characterize the accuracy and uncertainty in the metric derived from the two distinct platforms. This approach demonstrates the AERI’s utility where radiosonde observations are absent in time and/or space. Radiosonde and AERI based observations of the Convective Triggering Potential and Low-Level Humidity Index (CTP-HIlow) were computed during the 1200 UTC observation time and displayed good agreement during both 2017 and 2019 warm seasons. Radiosonde and AERI derived metrics diagnosed the same atmospheric preconditioning based upon the CTP-HIlow framework a majority of the time. When retrieval uncertainty was considered, even greater agreement was found between radiosonde and AERI derived classification. The AERI’s ability to represent this coupling metric well enabled novel exploration of temporal variability within the overnight period in CTP and HIlow. Observations of CTP-HIlow computed within a few hours of 1200 UTC were essentially equivalent, however with greater differences in time arose greater differences in CTP and HIlow.

Climatological sensitivities of shallow-cumulus bulk entrainment in continental and oceanic locations

Cumulus entrainment is a complex process that has long challenged conceptual understanding and atmospheric prediction. To investigate this process observationally, two retrievals are used to generate multi-year climatologies of shallow-cumulus bulk entrainment (ϵ) at two Atmospheric Radiation Measurement cloud observatories, one in the US southern Great Plains (SGP) and the other in the Azores archipelago in the eastern North Atlantic (ENA). The statistical distributions of ϵ thus obtained, as well as certain environmental and cloud-related sensitivities of ϵ, are consistent with previous findings from large-eddy simulations. The retrieved ϵ robustly increases with cloudlayer relative humidity and decreases in wider clouds and cloud ensembles with larger cloud-base mass fluxes. While ϵ also correlates negatively with measures of cloud-layer vigor (e.g., maximum in-cloud vertical velocity and cloud depth), the extent to which these metrics actually regulate ϵ (or vice-versa) is unclear. Novel sensitivities of ϵ include a robust decrease of ϵ with increasing subcloud wind speed in oceanic flows, as well as a decrease of ϵ with increasing cloud-base mass flux in individual cumuli. A strong land–ocean contrast in ϵ is also found, with median values of 0.5-0.6 km−1 at the continental SGP site and and 1.0-1.1 km−1 at the oceanic ENA site. This trend is associated with drier and deeper cloud layers, along with larger cloud-base mass fluxes, at SGP, all of which favor reduced ϵ. The flow-dependence of retrieved ϵ implies that its various sensitivities should be accounted for in cumulus parameterization schemes.

Ground-based temperature and humidity profiling: combining active and passive remote sensors

Thermodynamic profiles in the planetary boundary layer (PBL) are important observations for a range of atmospheric research and operational needs. These profiles can be retrieved from passively sensed spectral infrared (IR) or microwave (MW) radiance observations or can be more directly measured by active remote sensors such as water vapor differential absorption lidars (DIALs). This paper explores the synergy of combining ground-based IR, MW, and DIAL observations using an optimal-estimation retrieval framework, quantifying the reduction in the uncertainty in the retrieved profiles and the increase in information content as additional observations are added to IR-only and MW-only retrievals. This study uses ground-based observations collected during the Perdigão field campaign in central Portugal in 2017 and during the DIAL demonstration campaign at the Atmospheric Radiation Measurement Southern Great Plains site in 2017. The results show that the information content in both temperature and water vapor is higher for the IR instrument relative to the MW instrument (thereby resulting in smaller uncertainties) and that the combined IR + MW retrieval is very similar to the IR-only retrieval below 1.5 km. However, including the partial profile of water vapor observed by the DIAL increases the information content in the combined IR + DIAL and MW + DIAL water vapor retrievals substantially, with the exact impact vertically depending on the characteristics of the DIAL instrument itself. Furthermore, there is a slight increase in the information content in the retrieved temperature profile using the IR + DIAL relative to the IR-only; this was not observed in the MW + DIAL retrieval.

Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment

The Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign was designed to improve understanding of orographic cloud life cycles in relation to surrounding atmospheric thermodynamic, flow, and aerosol conditions. The deployment to the Sierras de Córdoba range in north-central Argentina was chosen because of very frequent cumulus congestus, deep convection initiation, and mesoscale convective organization uniquely observable from a fixed site. The C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar was deployed for the first time with over 50 ARM Mobile Facility atmospheric state, surface, aerosol, radiation, cloud, and precipitation instruments between October 2018 and April 2019. An intensive observing period (IOP) coincident with the RELAMPAGO field campaign was held between 1 November and 15 December during which 22 flights were performed by the ARM Gulfstream-1 aircraft.A multitude of atmospheric processes and cloud conditions were observed over the 7-month campaign, including: numerous orographic cumulus and stratocumulus events; new particle formation and growth producing high aerosol concentrations; drizzle formation in fog and shallow liquid clouds; very low aerosol conditions following wet deposition in heavy rainfall; initiation of ice in congestus clouds across a range of temperatures; extreme deep convection reaching 21-km altitudes; and organization of intense, hail-containing supercells and mesoscale convective systems. These comprehensive datasets include many of the first ever collected in this region and provide new opportunities to study orographic cloud evolution and interactions with meteorological conditions, aerosols, surface conditions, and radiation in mountainous terrain.