Physics of planetary atmospheres. IV - Gas analysis by photoionization mass spectrometry
Photoionization mass spectrometer with 180 deg magnetic analyzer and use as gas analyzer
MAGNETIC INSTRUMENT↗
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Physics of the atmosphere: Response of the water vapor channel of the Meteosat satellite
An accurate model of the atmospheric transmission function is used to obtain the relationship between the cloudless radiances measured by the 6-7 microns Meteosat radiometer (water vapor channel) and the numerical parameters associated to each point of an image. This relationship is compared to the temporary calibration curve published by the European Space Agency.
Roulleau, M.↗
Physics of planetary atmospheres and ionospheres
The traditional atmospheric regions, the distinction between homosphere and heterosphere, and changing atmospheric composition are discussed. The validity of the barometric law based on a Maxwell-Boltzmann distribution, for the major part of a planetary atmosphere and its breakdown in the exosphere due to escape of atmospheric particles is considered. The formation and maintenance of photochemical and diffusion-controlled ionospheric layers are treated. Their applicability to planetary ionospheres is dealt with. The spatial extent of magnetic and nonmagnetic planet ionospheres is investigated. Thermal and nonthermal processes responsible for the mass loss of planetary atmospheres are surveyed.
Bauer, S. J.↗
Dimethylsulfide/cloud condensation nuclei/climate system - Relevant size-resolved measurements of the chemical and physical properties of atmospheric aerosol particles
The mass and number relationships occurring within the atmospheric dimethylsulfide/cloud condensation nuclei (CCN)/climate system, using simultaneous measurements of particulate phase mass size distributions of nss SO4(2-), methanesulfonic acid (MSA), and NH4(+); number size distributions of particles having diameters between 0.02 and 9.6 microns; CCN concentrations at a supersaturation of 0.3 percent; relative humidity; and temperature, obtained for the northeastern Pacific Ocean in April and May 1991. Based on these measurements, particulate nss SO4(2-), MSA, and NH4(+) mass appeared to be correlated with both particle effective surface area and number in the accumulation mode size range (0.16 to 0.5 micron). No correlations were found in the size range below 0.16 micron. A correlation was also found between nss SO4(2-) mass and the CCN number concentration, such that a doubling of the SO4(2-) mass corresponded to a 40 percent increase in the CCN number concentration. However, no correlation was found between MSA mass and CCN concentration.
Quinn, P. K.↗
Physical Processes Governing Atmospheric Trace Constituents Measured from Aircraft in Pem-Tropics
The MIT group participated in seven publications in the 1999 issues of JGR with sections devoted in PEM-Tropics A, and had two papers which may be considered offshoots of these studies combined with commercial aircraft trace constituents data, one in Nature and one in EOS. In the meteorological overview we contributed a set of 1000 hPa divergent wind maps which we calculated from ECMWF data, meridional wind cross-sections, velocity potential and divergent wind maps, maps of stream function and rotational wind components, and vertical velocity profiles computed from mass balance, these all constituting part of the overall climatology. We also contributed material for the "stalactite" case observed from the DC-8 on September 3, 1996. This included a map of potential vorticity on 350 K, a cross-section of lidaro O3 a cross-section of potential vorticity along 140 deg W, and a map of specific humidity showing extreme dryness accompanying the high potential vorticity values and the high ozone values, all suggesting subsidence from the local stratosphere. In the paper on chemical characteristics we contributed 12 hour values of the 1000 hPa divergent wind component for the full period of PEM Tropics A; these were used by David Westberg to establish the air mass boundaries and in turn used by Gerry Gregory to set up a table of quantitative values of trace constituents and their ratios both sides of the boundaries.
Newell, Reginald E.↗
The Physics of Flaring Atmospheres
The objective of this project is to study the structure of heated and flaring atmospheric models, including the corona, chromosphere, and photosphere, by means of both theoretical modeling and data analysis.
Fisher, George H.↗
Physical Processes Governing Atmospheric Trace Constituents Measured from an Aircraft on PEM-Tropics
Before the mission, the PI (principal investigator) was instrumental in securing real-time use of the new 51-level ECMWF (European Centre for Medium Range Weather Forecasts) meteorological data. During the mission, he provided flight planning and execution guidance as meteorologist for the P-3B. Mr. Yong Zhu computed and plotted meteorological forecast maps using the ECMWF data and transmitted them to the field from MIT (Massachusetts Institute of Technology). Dr. John Cho was in the field for the Christmas Island portion to extract data from the on-site NOAA (National Oceanic and Atmospheric Administration) radars for local wind profiles that were used at the flight planning meetings. When the power supply for the VHF radar failed, he assisted the NOAA engineer in its repair. After the mission, Mr. Zhu produced meteorological data memos, which were made available to the PEM (Pacific Exploratory Mission)-Tropics B science team on request. An undergraduate student, Ms. Danielle Morse, wrote memos annotating the cloud conditions seen on the aircraft external monitor video tapes. Dr. Cho and the PI circulated a memo regarding the status (and associated problems) of the meteorological measurement systems on the DC-8 and P-3B to the relevant people on the science team. Several papers by members of our project were completed and accepted by JGR (Journal of Geophysical Research) for the first special section on PEM-Tropics B. These papers included coverage of the following topics: 1) examination of boundary layer data; 2) water vapor transport; 3) tropospheric trace constituent layers; 4) summarizations of the meteorological background and events during PEM-Tropics B; 5) concomitant lidar measurements of ozone, water vapor, and aerosol.
Newell, Reginald E.↗
Chemistry and Physics of the Atmospheric Boundary Layer Experiment (CAPABLE)
Presentation explains the role of the CAPABLE site in satellite validation activities.
CAPABLE↗
Clouds and aerosols in Saturn's atmosphere
The physical properties and spatial distribution of aerosol and cloud particles in Saturn's atmosphere are discussed based on data from remote measurement of scattered solar radiation and thermal radiation emitted from Saturn's atmosphere. A brief overview of the relations between particle properties and the scattered and emitted radiation field is given, and observations of Saturn at wavelengths from the ultraviolet to the thermal infrared which bear on the atmosphere's aerosol and cloud properties are reviewed. The single-scattering properties of the particles deduced from observations are discussed, commenting on the possible composition of the particles. The implications of the observations for the vertical and horizontal distribution of the clouds and aerosols are reviewed, and current information and areas for further research on the clouds and aerosols are summarized.
Tomasko, M. G.↗
Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay
In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.
54 ENVIRONMENTAL SCIENCES↗
Theoretical studies of the physics of the solar atmosphere
Significant advances in our theoretical basis for understanding several physical processes related to dynamical phenomena on the sun were achieved. We have advanced a new model for spicules and fibrils. We have provided a simple physical view of resonance absorption of MHD surface waves; this allowed an approximate mathematical procedure for obtaining a wealth of new analytical results which we applied to coronal heating and p-mode absorption at magnetic regions. We provided the first comprehensive models for the heating and acceleration of the transition region, corona, and solar wind. We provided a new view of viscosity under coronal conditions. We provided new insights into Alfven wave propagation in the solar atmosphere. And recently we have begun work in a new direction: parametric instabilities of Alfven waves.
Hollweg, Joseph V.↗
Small-Scale Variability of Large Cloud Drops
Cloud droplet size distribution is one of the most fundamental subjects in cloud physics. Understanding of spatial distribution and small-scale fluctuations of cloud droplets is essential for both cloud physics and atmospheric radiation. For cloud physics, it relates to the coalescence growth of raindrops while for radiation, it has a strong impact on a cloud's radiative properties. Most of the existing cloud radiation and precipitation formation models assume that the mean number of drops with a given radius varies proportionally to volume. The analysis of microphysical data on liquid water drop sizes shows that, for sufficiently small volumes, the number is proportional to the drop size dependent power of the volume. For abundant small drops present, the exponent is 1 as assumed in the conventional approach. However, for rarer large drops, the exponents fall below unity. At small scales, therefore, the mean number of large drops decreases with volume at a slower rate than the conventional approach assumes, suggesting more large drops at these scales than conventional models account for; their impact is consequently underestimated. Size dependent models of spatial distribution of cloud drops that simulate the observed power laws show strong drop clustering, the more so the larger the drops. The degree of clustering is determined by the observed exponents. The strong clustering of large drops arises naturally from the observed power-law statistics. Current theories of photon-cloud interaction and warm rain formation will need radical revision in order to produce these statistics; their underlying equations are unable to yield the observed power law.
Marshak, Alexander↗
Interpolating Fields of Carbon Monoxide Data Using a Hybrid Statistical-Physical Model
Atmospheric Carbon Monoxide (CO) is a pollutant gas of which the US congress has mandated regular monitoring, and satellite sensors can be used to retrieve regional concentrations of CO over several vertical layers. However, CO at cloudy locations cannot be observed and have to be estimated from the observed data set, resulting in an interpolation problem. The current state-of-the-art solution is to combine prior information, computed by a deterministic physical model, with observations. However, the deterministic model may introduce uncertainties that do not derive from the data. While sharing certain features with the physical model, this paper presents a Bayesian hierarchical model for interpolating CO on a 3-dimensional spatial grid, across time. To our knowledge such a model has not been considered before. The model is applied to a hypothetical air-quality monitoring scenario, and is compared to existing interpolation methods. The results provide motivation for the use of the statistical model for regional to local applications.
Arellano, A. A.↗
Improving Time Step Convergence in an Atmosphere Model With Simplified Physics: The Impacts of Closure Assumption and Process Coupling
Convergence testing is a common practice in the development of dynamical cores of atmospheric models but is not as often exercised for the parameterization of sub-grid physics. An earlier study revealed that the stratiform cloud parameterizations in several predecessors of the Energy Exascale Earth System Model (E3SM) showed strong time-step sensitivity and slower-than-expected convergence when the model's time step was systematically refined. In this work, a simplified atmosphere model is configured that consists of the spectral-element dynamical core of the E3SM atmosphere model coupled with a large-scale condensation parameterization based on commonly used assumptions. This simplified model also resembles E3SM and its predecessors in the numerical implementation of process coupling and shows poor time-step convergence in short ensemble tests. We present a formal error analysis to reveal the expected time-step convergence rate and the conditions for obtaining such convergence. Numerical experiments are conducted to investigate the root causes of convergence problems. We show that revisions in the process coupling and closure assumption help to improve convergence in short simulations using the simplified model; the same revisions applied to a full atmosphere model lead to significant changes in the simulated long-term climate. This work demonstrates that causes of convergence issues in atmospheric simulations can be understood by combining analyses from physical and mathematical perspectives. Addressing convergence issues can help to obtain a discrete model that is more consistent with the intended representation of the physical phenomena.
54 ENVIRONMENTAL SCIENCES↗
Sources of Na for the Io atmosphere
The physics and geology of Io have been extensively studied, but there has been little discussion of the chemistry. Relatively little is known about Io chemistry, but there are constraints. Further, it will be a long time before improvements will result from direct observation, given the severe difficulties with the Galileo mission. Via laboratory simulation experiments, plausible thermochemical and photochemical processes which determine the nature and amounts of surface constituents of Io are explored. The well-known density of Io shows that the planet overall is rocky. Because the orbit of Io is well within the magnetosphere of Jupiter and because Io only has a thin, transient SO2 atmosphere, the surface is continually sputtered with magnetospheric ions. Complex processes ionize and accelerate the Io surface atoms to keV and MeV energies. Remarkably, only S, O, and Na ions were found by Voyager. Sputtering also produces an atomic cloud of Na and S (O not observable) with a trace of K. Both gaseous and solid SO2 are known from spectroscopic studies. A trace of H2S and possibly CO2 are present. Geologic features are interpreted in terms of elemental S, but there is no direct evidence for this constituent. We thus have a rocky planet which does not have rocks on the surface. Our general goal is to understand the cycling of Na, S, and O through the crust and atmosphere on present-day Io and to understand how Io evolved to this state. A specific objective was to determine the phases on the surface which are the source of the Na in the atmosphere of Io.
Burnett, D. S.↗
Data for publication Wan et al. (2020, JAMES), Improving time-step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling
This dataset contains simple condensation mdoel output and CAM4 model output that were used to produce figures in the following paper: Wan, H., Woodward, C. S., Zhang, S., Vogl, C. J., Stinis, P., Gardner, D. J., Rasch, P. J., Zeng, X., Larson, V. E., and Singh, B. (2020): Improving time-step convergence in an atmosphere model with simplified physics: the impacts of closure assumption and process coupling, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2019MS001982
54 ENVIRONMENTAL SCIENCES↗
Deciphering the incipient phases of ice-mineral interactions as a precursor of physical weathering
Atmospheric ice formation influences Earth’s global hydrological cycle and is widely studied. In contrast, the intricacies of freeze-thaw processes in natural soils, such as whether ice nucleation on mineral surfaces is affected by biological activity, needs additional insight. In-situ ice formation on unreacted grains and grains exposed to field and laboratory weathering conditions was studied at the submicron scale using an ice nucleation chamber interfaced with an environmental scanning electron microscope. We found that ice nucleation activity differed by treatment. Unreacted grains and fungal-mineral interfaces displayed ice formation along edges and fractures. Conversely, grains with mineral coatings, seemingly from biological activity, exhibited water uptake and ice nucleation on the coatings themselves. Our results suggest that in natural soils, mineral coatings can influence where and how ice forms. This ultimately affects weathering and nutrient release to the biosphere, with direct consequences for interactions between the atmosphere and geosphere.
Lybrand, Rebecca A.↗
Probabilistic predictions and uncertainty estimation for radiological and nuclear effects modeling
Better characterization of meteorological uncertainty is needed to predict the effects of radiological and nuclear releases in the atmosphere. Multi-physics ensembles of atmospheric simulations can estimate uncertainty of physical processes, but they are often limited for computational reasons. Because weather uncertainty is important for emergency response applications, but the conventional way of running multi-physics ensembles is infeasible, we have developed new machine learning-based methods that quickly estimate and identify key sources of meteorological uncertainty. Machine learning accelerates the analysis of weather uncertainty by iteratively learning about model physics options that affect plume predictions. We have demonstrated the power of machine learning by creating a large multi-physics weather and dispersion ensemble dataset containing 1,200 simulations for two hypothetical radiological release scenarios. By training on less than 10% of the ensemble, we can quickly and accurately predict different outcomes, including the mass of radiological material deposited and spatial distribution and extent of contaminated areas.
54 ENVIRONMENTAL SCIENCES↗