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Uncrewed aircraft system (UAS) observations from the Lower Atmospheric Profiling Studies at Elevation–A Remotely-Piloted Aircraft Team Experiment (LAPSE-RATE) field campaign were assimilated into a high-resolution configuration of the Weather Research and Forecasting (WRF) Model. The impact of assimilating targeted UAS observations in addition to surface observations was compared to that obtained when assimilating surface observations alone using observing system experiments (OSEs) for a terrain-driven flow case and a convection initiation (CI) case observed within Colorado’s San Luis Valley (SLV). The assimilation of UAS observations in addition to surface observations results in a clear increase in skill for both flow regimes over that obtained when assimilating surface observations alone. For the terrain-driven flow case, the UAS observations improved the representation of thermal stratification across the northern SLV, which produced stronger upvalley flow over the eastern half of the SLV that better matched the observations. For the CI case, the UAS observations improved the representation of the pre-convective environment by reducing dry biases across the SLV and over the surrounding terrain. This led to earlier CI and more organized convection over the foothills that spilled outflows into the SLV, ultimately helping to increase low-level convergence and CI there. In addition, the importance of UAS capturing an outflow that originated over the Sangre de Cristo Mountains and triggered CI is discussed. These outflows and subsequent CI were not well captured in the simulation that assimilated surface observations alone. We report that observations obtained with a fleet of UAS are shown to notably improve high-resolution analyses and short-term predictions of two very different mesogamma-scale weather events.
The southeast Atlantic is home to well-defined smoke outflow from Africa coinciding vertically with extensive marine boundary-layer cloud decks, both reaching their climatological maxima in spatial extent around September. A framework is put forth for evaluating the performance of a range of global and regional aerosol models against observations made during the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) airborne mission in September 2016. The sparse airborne observations are first aggregated into 2o grid boxes and into three vertical layers: the cloud-topped marine boundary layer (MBL), the layer from cloud top to 3 km, and the 3-6 km layer. Aerosol extensive properties simulated for the entire study region for all September suggest that the 2016 ORACLES observations are reasonably representative of the regional monthly average, with systematic deviations of 30% or less. All six models typically place the bottom of the smoke layer at lower altitudes than do the airborne lidar observations by 300-1400 m, whereas model aerosol top heights are within 0-500 m of the observations. All but one of the models that report carbonaceous aerosol masses underestimate the ratio of particulate extinction to the masses, a proxy for mass extinction efficiency, in 3-6 km. Notable findings on individual models include that WRF-CAM5 predicts the mass of black carbon and organic aerosols with minor (~10% or less) biases. GEOS-5 overestimates the carbonaceous particle masses in the MBL by a factor of 3-6. Extinction coefficients in the free troposphere (FT) and above-cloud aerosol optical depth (ACAOD) are 10-30% lower in WRF-CAM5, 30-50% lower in GEOS-5, 10-40% higher in GEOS-Chem, 10-20% higher in EAM-E3SM except for the practically unbiased 3-6 km extinction, and 20-70% lower in the Unified Model, than the airborne in situ, lidar and sunphotometer measurements. ALADIN-Climate also underestimates the ACAOD, by 30%. GEOS-5 and GEOS-Chem predict carbon monoxide in the MBL with small (10% or less) negative biases, despite their overestimates of carbonaceous aerosol masses. Overall, this study highlights a new approach to utilizing airborne aerosol measurements for model diagnosis.
The blue economy is a dynamic and rapidly growing movement that captures the interplay between economic, social, and ecological sustainability of the ocean and encompasses numerous maritime sectors and activities (e.g., commerce and trade; living resources; renewable energy; minerals, materials, and freshwater; and ocean health and data). The demand for ocean data to inform scientific, risk reduction, and national security needs is leading to a large increase in the number of deployed ocean observation and monitoring systems, most of which require increased power. Because ocean observation systems are often placed in remote locations, they primarily rely on energy storage (or in some cases in situ energy generation) to power instruments and equipment, which imposes limits on sampling rates, deployment times, and spatiotemporal resolution of data. The U.S. Department of Energy Water Power Technologies Office is exploring the potential for marine renewable energy (MRE) devices (largely wave and tidal energy converters) to provide power to support multiple blue economy opportunities. A portion of these opportunities focus on power at sea markets for providing power in off-grid and offshore locations to support a variety of ocean-based activities, including ocean observation and navigation, underwater vehicle charging, marine aquaculture, marine algae farming, and seawater mining. Initially, research has focused on better understanding how and where MRE can provide a consistent source of reliable power to extend ocean observing missions, including operation of autonomous underwater vehicles. Online surveys as well as phone and inperson interviews were conducted with experts in the field of ocean observing systems and observatories to gather end-user requirements, determine energy needs, identify opportunities for codevelopment, and pinpoint constraints for MRE to meet those needs. The surveys and interviews provided feedback on the potential for powering devices and vehicles using MRE, including identifying common themes and challenges that will inform foundational research and development steps needed to advance the integration of MRE with ocean observing systems. In most cases, additional power generation on the order of watts was identified as significantly beneficial to enhancing ocean observations capabilities.
Abstract Previous observational studies have indicated that mesoscale convective systems (MCSs) contribute the majority of precipitation over the Bay of Bengal (BoB) during the summer monsoon season, yet their initiation and propagation remain incompletely understood. To fill this knowledge gap, we conducted a comprehensive study using a combination of 20-yr satellite observations, MCS tracking, reanalysis data, and a theoretical linear model. Satellite observations reveal clear diurnal propagation signals of MCS initiation frequency and rainfall from the west coast of the BoB toward the central BoB, with the MCS rainfall propagating slightly slower than the MCS initiation frequency. Global reanalysis data indicate a strong association between the offshore-propagating MCS initiation frequency/rainfall and diurnal low-level wind perturbations, implying the potential role of gravity waves. To verify the hypothesis, we developed a 2D linear model that can be driven by realistic meteorological fields from reanalysis. The linear model realistically reproduces the characteristics of offshore-propagating diurnal wind perturbations. The wind perturbations, as well as the offshore propagation signals of MCS initiation frequency and rainfall, are associated with diurnal gravity waves emitted from the coastal regions, which in turn are caused by the diurnal land–sea thermal contrast. The ambient wind speed and vertical wind shear play crucial roles in modulating the timing, propagation, and amplitude of diurnal gravity waves. Using the linear model and satellite observations, we further show that the stronger monsoonal flows lead to faster offshore propagation of diurnal gravity waves, which subsequently control the offshore propagation signals of MCS initiation and rainfall. Significance Statement Rainfall over the Bay of Bengal (BoB) is primarily contributed by large and organized rainfall systems in the summer monsoon season. During this season, these systems are commonly observed over the east coast of India around midnight, the western BoB in the morning, and the central BoB in the afternoon. This eastward rainfall propagation is confirmed by observations, reanalysis data, and a theoretical model to have a strong association with atmospheric diurnal gravity waves. These waves are caused by land–sea thermal contrast and can trigger rainfall systems over the offshore regions. We also found that the diurnal gravity waves, as well as their triggered rainfall systems, can be greatly modulated by the large-scale monsoonal flows. All the above findings improve our understanding of diurnal rainfall cycle over the BoB and may contribute to the future improvement of rainfall forecast over the region.
This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar datasets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective up- and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size-intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ~ 0.5), and increases in draft intensity with altitude. A model-observational intercomparison for draft properties (core width, intensity, mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (?x) that range from 4 km to 250 m. It is shown that the simulations performed at ?x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.
Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.
The Dark Energy Spectroscopic Instrument Legacy Surveys, a combination of three ground-based imaging surveys, have mapped 16,000 deg{sup 2} in three optical bands (g, r, and z) to a depth 1–2 mag deeper than the Sloan Digital Sky Survey. Our work addresses one of the major challenges of wide-field imaging surveys conducted at ground-based observatories: the varying depth that results from varying observing conditions at Earth-bound sites. To mitigate these effects, the Legacy Surveys (the Dark Energy Camera Legacy Survey, or DECaLS; the Mayall z-band Legacy Survey, or MzLS; and the Beiijing-Arizona Sky Survey, or BASS) employed a unique strategy to dynamically adjust the exposure times as rapidly as possible in response to the changing observing conditions. We present the tiling and observing strategies used by the first two of these surveys. We demonstrate that the tiling and dynamic observing strategies jointly result in a more uniform-depth survey that has higher efficiency for a given total observing time compared with the traditional approach of using fixed exposure times.
Abstract Gravity waves (GWs) are among the key drivers of the meridional overturning circulation in the mesosphere and upper stratosphere. Their representation in climate models suffers from insufficient resolution and limited observational constraints on their parameterizations. This obscures assessments of middle atmospheric circulation changes in a changing climate. This study presents a comprehensive analysis of stratospheric GW activity above and downstream of the Andes from 1 to 15 August 2019, with special focus on GW representation ranging from an unprecedented kilometer-scale global forecast model (1.4 km ECMWF IFS), ground-based Rayleigh lidar (CORAL) observations, modern reanalysis (ERA5), to a coarse-resolution climate model (EMAC). Resolved vertical flux of zonal GW momentum (GWMF) is found to be stronger by a factor of at least 2–2.5 in IFS compared to ERA5. Compared to resolved GWMF in IFS, parameterizations in ERA5 and EMAC continue to inaccurately generate excessive GWMF poleward of 60°S, yielding prominent differences between resolved and parameterized GWMFs. A like-to-like validation of GW profiles in IFS and ERA5 reveals similar wave structures. Still, even at ∼1 km resolution, the resolved waves in IFS are weaker than those observed by lidar. Further, GWMF estimates across datasets reveal that temperature-based proxies, based on midfrequency approximations for linear GWs, overestimate GWMF due to simplifications and uncertainties in GW wavelength estimation from data. Overall, the analysis provides GWMF benchmarks for parameterization validation and calls for three-dimensional GW parameterizations, better upper-boundary treatment, and vertical resolution increases commensurate with increases in horizontal resolution in models, for a more realistic GW analysis. Significance Statement Gravity wave–induced momentum forcing forms a key component of the middle atmospheric circulation. However, complete knowledge of gravity waves, their atmospheric effects, and their long-term trends are obscured due to limited global observations, and the inability of current climate models to fully resolve them. This study combines a kilometer-scale forecast model, modern reanalysis, and a coarse-resolution climate model to first compare the resolved and parameterized momentum fluxes by gravity waves generated over the Andes, and then evaluate the fluxes using a state-of-the-art ground-based Rayleigh lidar. Our analysis reveals shortcomings in current model parameterizations of gravity waves in the middle atmosphere and highlights the sensitivity of the estimated flux to the formulation used.
The theory of Rotunno et al. (“RKW” theory) addresses the behavior of squall-line cold pools in vertically sheared flows. It predicts that, within a given thermodynamic environment, a balance between baroclinic vorticity generation by the cold pool and low-level environmental vertical wind shear induces an upright updraft along the gust front that maximizes the initiation of new convective cells. Although this theory has been evaluated numerically, its applicability to observed systems remains unclear and is limited by a lack of critical measurements, including high-frequency thermodynamic and wind profiles across the gust front. Herein, observations from the Atmospheric Radiation Measurement Southern Great Plains (ARM-SGP) observatory near Lamont, Oklahoma, are used to evaluate RKW theory for 10 well-observed squall lines over a 11-yr period. For this evaluation, RKW parameters including cold-pool intensity (c), low-level ambient, line-normal vertical shear (ΔV n ), subcloud and cloud-layer updraft tilts, and multiple measures of system intensity are estimated. Furthermore, the c estimates rely on thermodynamic retrievals from the Atmosphere Emitted Radiance Interferometer (AERI), which are uncertain but verify reasonably well against independent observations. As predicted by the theory, for c/ΔV n ≥ 1, c/ΔV n correlates positively with updraft tilt and negatively with system intensity, but these results are not always statistically significant and are also sensitive to the method by which ΔV n is evaluated. Specifically, ΔV n evaluations that extend above the cold-pool top yield greater consistency with RKW predictions. Also, some measures of intensity correlate more strongly with standard moist instability metrics than with RKW parameters.
Profiles of radiative fluxes simulated from thermodynamic and cloud observations made at the Atmospheric Radiation Measurement (ARM) eastern North Atlantic (ENA) site for a 6-yr period are termed as ENARad. ENARad radiative fluxes are compared to those from the Clouds and the Earth’s Radiant Energy System (CERES) 1°-resolution synoptic product (SYN1deg)-simulated radiative flux profiles as well as the CERES instrument observed top-of-the-atmosphere (TOA) fluxes and ground site broadband radiometer measurements. Monthly average differences between ENARad and surface radiometer reported fluxes and differences between ENARad, SYN1deg, and observed fluxes at TOA were statistically insignificant. SYN1deg significantly overestimated surface downwelling shortwave flux by 12 ± 52 W m −2 and surface downwelling longwave flux by 5 ± 20 W m −2 on monthly time scales. Such overestimations were traced to a moister and warmer subcloud layer, a drier cloud layer, and a moister and colder above-cloud-free troposphere in the ancillary thermodynamic and cloud properties used by SYN1deg than observed. Similarly, low-cloud coverage, boundaries, and liquid water paths utilized by SYN1deg were also significantly higher than observed. Intramodel differences in the hourly values of shortwave fluxes exceeded 100 W m−2 at the TOA and the surface. These differences were also due to inaccuracies in the representation of low-cloud properties within the SYN1deg product relative to those determined by ENA ARM instrumentation and used as ENARad ancillary data. Results presented are relevant to investigations employing the CERES SYN1deg data product, studies that estimate radiative fluxes from surface-based or satellite-borne observations, and comparative analyses of radiative fluxes derived using different methodological approaches.
ABSTRACT Joint multimessenger observations with gravitational waves and electromagnetic (EM) data offer new insights into the astrophysical studies of compact objects. The third Advanced LIGO and Advanced Virgo observing run began on 2019 April 1; during the 11 months of observation, there have been 14 compact binary systems candidates for which at least one component is potentially a neutron star. Although intensive follow-up campaigns involving tens of ground and space-based observatories searched for counterparts, no EM counterpart has been detected. Following on a previous study of the first six months of the campaign, we present in this paper the next five months of the campaign from 2019 October to 2020 March. We highlight two neutron star–black hole candidates (S191205ah and S200105ae), two binary neutron star candidates (S191213g and S200213t), and a binary merger with a possible neutron star and a ‘MassGap’ component, S200115j. Assuming that the gravitational-wave (GW) candidates are of astrophysical origin and their location was covered by optical telescopes, we derive possible constraints on the matter ejected during the events based on the non-detection of counterparts. We find that the follow-up observations during the second half of the third observing run did not meet the necessary sensitivity to constrain the source properties of the potential GW candidate. Consequently, we suggest that different strategies have to be used to allow a better usage of the available telescope time. We examine different choices for follow-up surveys to optimize sky localization coverage versus observational depth to understand the likelihood of counterpart detection.
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Ensemble Forecast Sensitivity to Observation (EFSO) is a technique that can efficiently identify the beneficial/detrimental impacts of every observation in ensemble-based data assimilation (DA). While EFSO has been successfully employed on atmospheric DA, it has never been applied to ocean or coupled DA due to the lack of a suitable error norm for oceanic variables. This study introduces a new density-based error norm incorporating sea temperature and salinity forecast errors, making EFSO applicable to ocean DA for the first time. We implemented the oceanic EFSO on the CFSv2-LETKF and investigated the impact of ocean observations under a weakly coupled DA framework. By removing the detrimental ocean observations detected by EFSO, the CFSv2 forecasts were significantly improved, showing the validation of impact estimation and the great potential of EFSO to be extended as a data selection criterion.
Abstract As climate change continues, the likelihood of passing critical thresholds or tipping points increases. Hence, there is a need to advance the science for detecting such thresholds. In this paper, we assess the needs and opportunities for Earth Observation (EO, here understood to refer to satellite observations) to inform society in responding to the risks associated with ten potential large-scale ocean tipping elements: Atlantic Meridional Overturning Circulation; Atlantic Subpolar Gyre; Beaufort Gyre; Arctic halocline; Kuroshio Large Meander; deoxygenation; phytoplankton; zooplankton; higher level ecosystems (including fisheries); and marine biodiversity. We review current scientific understanding and identify specific EO and related modelling needs for each of these tipping elements. We draw out some generic points that apply across several of the elements. These common points include the importance of maintaining long-term, consistent time series; the need to combine EO data consistently with in situ data types (including subsurface), for example through data assimilation; and the need to reduce or work with current mismatches in resolution (in both directions) between climate models and EO datasets. Our analysis shows that developing EO, modelling and prediction systems together, with understanding of the strengths and limitations of each, provides many promising paths towards monitoring and early warning systems for tipping, and towards the development of the next generation of climate models.
Fire poses a substantial threat to concrete structures because calcium silicate hydrate (C-S-H) is not thermally stable at high temperatures. Herein, in situ TEM at temperatures from 20 to 800 °C was conducted to study the thermal-induced structural changes in C-S-H. We captured C-S-H shrinks at an average rate of 0.02 μm{sup 2}/{sup o}C upon heating with three stages observed, including induction, constant, and rapid periods. Our observation revealed that an 800 nm pore could be healed during pore structure evolution owing to the reconstruction and deformation among C-S-H blocks. The Ca/Si ratio was dropped at higher temperatures because of the leakage of calcium ions from the C-S-H structure to form CaO precipitates. The temperature-driven phase transformation and degradation were also detected using electron diffraction that C-S-H was transformed into metastable calcium silicate minerals above 800 °C. This work provides insights into the nanoscale transformation of C-S-H at high temperatures.
A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.
Abstract The explicit coupling at meter and second scales of vegetation's responses to the atmospheric‐boundary layer dynamics drives a dynamic heterogeneity that influences canopy‐top fluxes and cloud formation. Focusing on a representative day during the Amazonian dry season, we investigate the diurnal cycle of energy, moisture and carbon dioxide at the canopy top, and the transition from clear to cloudy conditions. To this end, we compare results from a large‐eddy simulation technique, a high‐resolution global weather model, and a complete observational data set collected during the GoAmazon14/15 campaign. The overall model‐observation comparisons of radiation and canopy‐top fluxes, turbulence, and cloud dynamics are very satisfactory, with all the modeled variables lying within the standard deviation of the monthly aggregated observations. Our analysis indicates that the timing of the change in the daylight carbon exchange, from a sink to a source, remains uncertain and is probably related to the stomata closure caused by the increase in vapor pressure deficit during the afternoon. We demonstrate quantitatively that heat and moisture transport from the subcloud layer into the cloud layer are misrepresented by the global model, yielding low values of specific humidity and thermal instability above the cloud base. Finally, the numerical simulations and observational data are adequate settings for benchmarking more comprehensive studies of plant responses, microphysics, and radiation.