An official website of the United States government
Abstract. Ground-based instruments offer unique capabilities such as detailed atmospheric, thermodynamic, cloud, and aerosol profiling at a high temporal sampling rate. The U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility provides comprehensive datasets from key locations around the globe, facilitating long-term characterization and process-level understanding of clouds, aerosol, and aerosol–cloud interactions. However, as with other ground-based datasets, the fixed (Eulerian) nature of these measurements often introduces a knowledge gap in relating those observations with air-mass hysteresis. Here, we describe ARMTRAJ (https://doi.org/10.5439/2309851, Silber, 2024a; https://doi.org/10.5439/2309849, Silber, 2024b; https://doi.org/10.5439/2309850, Silber, 2024c; https://doi.org/10.5439/2309848, Silber, 2024d), a set of multipurpose trajectory datasets that helps close this gap in ARM deployments. Each dataset targets a different aspect of atmospheric research, including the analysis of surface, planetary boundary layer, distinct liquid-bearing cloud layers, and (primary) cloud decks. Trajectories are calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model informed by the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis dataset at its highest spatial resolution (0.25°) and are initialized using ARM datasets. The trajectory datasets include information about air-mass coordinates and state variables extracted from ERA5 before and after the ARM site overpass. Ensemble runs generated for each model initialization enhance trajectory consistency, while ensemble variability serves as a valuable uncertainty metric for those reported air-mass coordinates and state variables. Following the description of dataset processing and structure, we demonstrate applications of ARMTRAJ to a case study and a few bulk analyses of observations collected during ARM's Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field deployment. ARMTRAJ will soon become a near real-time product accompanying new ARM deployments and an augmenting product to ongoing and previous deployments, promoting reaching science goals of research relying on ARM observations.
more »
« less
The Lake-Effect Snow Ensemble Reanalysis Version 1.0 Dataset
Abstract of Purpose and Method: The Lake-Effect Snow Ensemble Reanalysis version 1.0 dataset contains hourly gridded atmospheric variables for the Great Lakes region, focusing on events during the NSF OWLeS field campaign, which took place in December 2013 and January 2014. A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. This reanalysis uses the Penn State University Ensemble Kalman Filter (PSU EnKF) for data assimilation with Weather Research and Forecasting (WRF) model. Observations that are assimilated include conventional surface and atmospheric observations from NOAA. The dataset includes gridded fields of temperature, wind, surface pressure, and precipitation fields, and is downloadable as netCDF files. Companion papers, cited below, further describe this dataset as well as apply it to scientific studies.
more »
« less
- Award ID(s):
- 1745243
- PAR ID:
- 10481342
- Publisher / Repository:
- Penn State Data Commons
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Land surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2and higher by 14.86 W·m−2in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.more » « less
-
Abstract Forecasting Antarctic atmospheric, oceanic, and sea ice conditions on subseasonal to seasonal scales remains a major challenge. During both the freezing and melting seasons current operational ensemble forecasting systems show a systematic overestimation of the Antarctic sea-ice edge location. The skill of sea ice cover prediction is closely related to the accuracy of cloud representation in models, as the two are strongly coupled by cloud radiative forcing. In particular, surface downward longwave radiation (DLW) deficits appear to be a common shortcoming in atmospheric models over the Southern Ocean. For example, a recent comparison of ECMWF reanalysis 5th generation (ERA5) global reanalysis with the observations from McMurdo Station revealed a year-round deficit in DLW of approximately 50 Wm−2in marine air masses due to model shortages in supercooled cloud liquid water. A comparison with the surface DLW radiation observations from the Ocean Observatories Initiative mooring in the South Pacific at 54.08° S, 89.67° W, for the time period January 2016–November 2018, confirms approximately 20 Wm−2deficit in DLW in ERA5 well north of the sea-ice edge. Using a regional ocean model, we show that when DLW is artificially increased by 50 Wm−2in the simulation driven by ERA5 atmospheric forcing, the predicted sea ice growth agrees much better with the observations. A wide variety of sensitivity tests show that the anomalously large, predicted sea-ice extent is not due to limitations in the ocean model and that by implication the cause resides with the atmospheric forcing.more » « less
-
Abstract Reanalysis datasets are frequently used in the study of atmospheric variability owing to their length of record and gridded global coverage. In the midlatitudes, much of the day-to-day atmospheric variability is associated with atmospheric fronts. These fronts are also responsible for the majority of precipitation in the midlatitudes, and are often associated with extreme weather, flooding, and wildfire activity. As such, it is important that identification of fronts and their associated rainfall remains as consistent as possible between studies. Nevertheless, it is often the case that only one reanalysis dataset and only one objective diagnostic for the detection of atmospheric fronts is used. By applying two different frontal identification methods across the shared time period of eight reanalysis datasets (1980–2001), it is found that the individual identification of fronts and frontal precipitation is significantly affected by both the choice of identification method and dataset. This is shown to subsequently impact the climatologies of both frontal frequency and frontal precipitation globally with significant regional differences as well. For example, for one diagnostic, the absolute multireanalysis range in the global mean frontal frequency and the proportion of precipitation attributed to atmospheric fronts are 12% and 69%, respectively. A percentage reduction of 77% and 81%, respectively, in these absolute multireanalysis ranges occurs, however, upon regridding all datasets to the same coarser grid. Therefore, these findings have important implications for any study on precipitation variability and not just those that consider atmospheric fronts.more » « less
-
An assessment of early 20th century Antarctic pressure reconstructions using historical observationsAbstract While gridded seasonal pressure reconstructions poleward of 60°S extending back to 1905 have been recently completed, their skill has not been assessed prior to 1958. To provide a more thorough evaluation of the skill and performance in the early 20th century, these reconstructions are compared to other gridded datasets, historical data from early Antarctic expeditions, ship records, and temporary bases. Overall, the comparison confirms that the reconstruction uncertainty of 2–4 hPa (evaluated after 1979) over the Southern Ocean is a valid estimate of the reconstruction error in the early 20th century. Over the interior and near the coast of Antarctica, direct comparisons with historical data are challenged by elevation‐based reductions to sea level pressure. In a few cases, a simple linear adjustment of the reconstruction to sea level matches the historical data well, but in other cases, the differences remain greater than 10 hPa. Despite these large errors, comparisons with continuous multi‐season observations demonstrate that aspects of the interannual variability are often still captured, suggesting that the reconstructions have skill representing variations on this timescale, even if it is difficult to determine how well they capture the mean pressure at these higher elevations. Additional comparisons with various 20th‐century reanalysis products demonstrate the value of assimilating the historical observations in these datasets, which acts to substantially reduce the reanalysis ensemble spread, and bring the reanalysis ensemble mean within the reconstruction and observational uncertainty.more » « less
