Application Of Atmospheric Models Research Articles

An implementation of the particle flow filter in an atmospheric model

Abstract The particle flow filter (PFF) shows promise for fully nonlinear data assimilation (DA) in high dimensional systems. However, its application in atmospheric models has been relatively unexplored. In this study, we develop a new algorithm, PFF-DART, in order to conduct DA for high-dimensional atmospheric models. PFF-DART combines the PFF and the two-step ensemble filtering algorithm in the Data Assimilation Research Testbed (DART), exploiting the highly parallel structure of DART. To evaluate the performance of PFF-DART, we conduct an Observing System Simulation Experiment (OSSE) in a simplified atmospheric general circulation model, and compare the performance of PFF-DART with an existing linear and Gaussian DA method. Using the PFF-DART algorithm, we demonstrate, for the first time, the capability of the PFF to yield stable results in a year-long cycling DA OSSE. Moreover, PFF-DART retains the important ability of the PFF to improve the assimilation of nonlinear and non-Gaussian observations. Finally, we emphasize that PFF-DART is a versatile algorithm that can be integrated with numerous other non-Gaussian DA techniques. This quality makes it a promising method for further investigation within a more sophisticated numerical weather prediction model in the future.

A 1.46–2.48 μm spectroscopic atlas of a T6 dwarf (1060 K) atmosphere with IGRINS: first detections of H2S and H2, and verification of H2O, CH4, and NH3 line lists

ABSTRACT We present Gemini South/IGRINS observations of the 1060 K T6 dwarf 2MASS J08173001−6155158 with unprecedented resolution ($R\equiv \lambda /\Delta \lambda =45\, 000$) and signal-to-noise ratio (S/N > 200) for a late-type T dwarf. We use this benchmark observation to test the reliability of molecular line lists used up-to-date atmospheric models. We determine which spectroscopic regions should be used to estimate the parameters of cold brown dwarfs and, by extension, exoplanets. We present a detailed spectroscopic atlas with molecular identifications across the H and K bands of the near-infrared. We find that water (H2O) line lists are overall reliable. We find the most discrepancies amongst older methane (CH4) line lists, and that the most up-to-date CH4 line lists correct many of these issues. We identify individual ammonia (NH3) lines, a hydrogen sulfide (H2S) feature at 1.5900 $\mu$m, and a molecular hydrogen (H2) feature at 2.1218 $\mu$m. These are the first unambiguous detections of H2S and H2 absorption features in an extra-solar atmosphere. With the H2 detection, we place an upper limit on the atmospheric dust concentration of this T6 dwarf: at least 500 times less than the interstellar value, implying that the atmosphere is effectively dust-free. We additionally identify several features that do not appear in the model spectra. Our assessment of the line lists is valuable for atmospheric model applications to high-dispersion, low-S/N, high-background spectra, such as an exoplanet around a star. We demonstrate a significant enhancement in the detection of the CH4 absorption signal in this T6 dwarf with the most up-to-date line lists.

Errors and uncertainties in a gridded carbon dioxide emissions inventory

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.

Random Force Perturbations: A New Extension of the Cell Perturbation Method for Turbulence Generation in Multiscale Atmospheric Boundary Layer Simulations

AbstractCoupling between mesoscale and large eddy simulation (LES) is critically important for many atmospheric model applications, from predictions of wind energy to fire propagation. The grid‐nesting technique enables bridging between vastly different scales without incurring prohibitive computational costs. However, the transition from coarser to finer resolutions often requires a large number of grid points from inflow boundaries for the development of fine‐scale turbulence features in the LES domain. Recently, the cell perturbation method (CPM) was developed to reduce the turbulence development region with high computational efficiency. Herein, we explore a new method based on the CPM that uses force perturbations in both the horizontal and vertical directions (Force Cell Perturbation Method) instead of the potential temperature perturbations in the original CPM, as an attempt to further explore the performance of the random perturbation techniques. This approach is tested for a neutral and a convective atmospheric boundary layer under idealized conditions. Overall, similar performance is found between the optimal configurations of the CPM and the Force Cell Perturbation Method pointing to the robustness of this family of perturbation methods in accelerating turbulence generation in nested domains. Vertical force perturbations performed better than horizontal force perturbations for both atmospheric stability conditions. The CPM performed best under convective stability conditions. The combination of the force and potential temperature perturbations is found to provide no additional performance improvement over the stand‐alone application of the individual methods.

Evaluation of atmospheric empirical model based on TIMED/SABER satellite temperature data

A total of 15 years of global temperature data from 2002–2016 as measured by TIMED/SABER were taken as standard to compare with those of USSA-76 and NRLMSISE-00. The accuracy of the two atmospheric models was analyzed by calculating daily mean and monthly mean temperature deviations and standard deviations of temperature deviations at different altitudes. The confidence of the model under different temperature deviations is calculated through setting the deviation between the models and the satellite observation temperature. The results show that the temperature data calculated by the two atmospheric models are in good agreement with the temperature observed by satellite. The daily mean and monthly mean accuracy of the NRLMSISE-00 are higher than those of the USSA-76. In the UMLT region, the two atmospheric models have yet to be improved to characterize the true atmosphere at the mid-high latitudes of upper mesosphere in summer and winter. The overall ability of the NRLMSISE-00 to represent the true atmosphere is superior to the USSA-76. In addition, the confidence decreases with the increase of altitude in the same latitude area and temperature deviation requirements. The confidence decreases with increasing latitude in the same altitude and temperature deviation requirements. By analyzing the differences between the atmospheric models and satellite observations. This paper provides a reference for the application of atmospheric models in engineering and provides the basis for the subsequent correction of atmospheric modes.

A novel mechanism for the isomerization of N2O4 and its implication for the reaction with H2O and acid rain formation

AbstractWe have discovered, by high‐level quantum‐chemical calculations, a new and predominant isomerization mechanism for N2O4 → ONONO2 via a roaming‐like transition state occurring unimolecularly or bimolecularly during collision with H2O. The new mechanism allows N2O4 to react with H2O with a significantly lower barrier (< 13.1 kcal/mol) than the commonly known tight transition state (∼30‐45 kcal/mol) by concurrent stretching of the NN bond and rotation of one of the NO2 groups to form trans‐ONONO2, which then undergoes a rapid metathetical reaction with H2O in the gas phase and in aqueous solution. The results have a significant implication for the hydrolysis of N2O4 in water to produce HONO and HNO3. Rate constants for the isomerization and hydrolysis reactions have been predicted for atmospheric modeling applications.

Can we define an asymptotic value for the ice active surface site density for heterogeneous ice nucleation?

AbstractThe immersion freezing behavior of droplets containing size‐segregated, monodisperse feldspar particles was investigated. For all particle sizes investigated, a leveling off of the frozen droplet fraction was observed reaching a plateau within the heterogeneous freezing temperature regime (T >− 38°C). The frozen fraction in the plateau region was proportional to the particle surface area. Based on these findings, an asymptotic value for ice active surface site density ns, which we named , could be determined for the investigated feldspar sample. The comparison of these results with those of other studies not only elucidates the general feasibility of determining such an asymptotic value but also shows that the value of strongly depends on the method of the particle surface area determination. However, such an asymptotic value might be an important input parameter for atmospheric modeling applications. At least it shows that care should be taken when ns is extrapolated to lower or higher temperature.

Immersion freezing by natural dust based on a soccer ball model with the Community Atmospheric Model version 5: climate effects

We introduce a simplified version of the soccer ball model (SBM) developed by Niedermeier et al (2014 Geophys. Res. Lett. 41 736–741) into the Community Atmospheric Model version 5 (CAM5). It is the first time that SBM is used in an atmospheric model to parameterize the heterogeneous ice nucleation. The SBM, which was simplified for its suitable application in atmospheric models, uses the classical nucleation theory to describe the immersion/condensation freezing by dust in the mixed-phase cloud regime. Uncertain parameters (mean contact angle, standard deviation of contact angle probability distribution, and number of surface sites) in the SBM are constrained by fitting them to recent natural dust (Saharan dust) datasets. With the SBM in CAM5, we investigate the sensitivity of modeled cloud properties to the SBM parameters, and find significant seasonal and regional differences in the sensitivity among the three SBM parameters. Changes of mean contact angle and the number of surface sites lead to changes of cloud properties in Arctic in spring, which could be attributed to the transport of dust ice nuclei to this region. In winter, significant changes of cloud properties induced by these two parameters mainly occur in northern hemispheric mid-latitudes (e.g., East Asia). In comparison, no obvious changes of cloud properties caused by changes of standard deviation can be found in all the seasons. These results are valuable for understanding the heterogeneous ice nucleation behavior, and useful for guiding the future model developments.

Massively parallel solvers for elliptic partial differential equations in numerical weather and climate prediction

The demand for substantial increases in the spatial resolution of global weather and climate prediction models makes it necessary to use numerically efficient and highly scalable algorithms to solve the equations of large‐scale atmospheric fluid dynamics. For stability and efficiency reasons, several of the operational forecasting centres, in particular the Met Office and the European Centre for Medium‐Range Weather Forecasts (ECMWF) in the UK, use semi‐implicit semi‐Lagrangian time‐stepping in the dynamical core of the model. The additional burden with this approach is that a three‐dimensional elliptic partial differential equation (PDE) for the pressure correction has to be solved at every model time step and this often constitutes a significant proportion of the time spent in the dynamical core. In global models, this PDE must be solved in a thin spherical shell.To run within tight operational time‐scales, the solver has to be parallelized and there seems to be a (perceived) misconception that elliptic solvers do not scale to large processor counts and hence implicit time‐stepping cannot be used in very high‐resolution global models. After reviewing several methods for solving the elliptic PDE for the pressure correction and their application in atmospheric models, we demonstrate the performance and very good scalability of Krylov subspace solvers and multigrid algorithms for a representative model equation with more than 1010 unknowns on 65 536 cores on the High‐End Computing Terascale Resource (HECToR), the UK's national supercomputer. For this, we tested and optimized solvers from two existing numerical libraries (the Distributed and Unified Numerics Environment (DUNE) and Parallel High Performance Preconditioners (hypre)) and implemented both a conjugate gradient solver and a geometric multigrid algorithm based on a tensor‐product approach, which exploits the strong vertical anisotropy of the discretized equation. We study both weak and strong scalability and compare the absolute solution times for all methods; in contrast to one‐level methods, the multigrid solver is robust with respect to parameter variations.

On the attribution of contributions of atmospheric trace gases to emissions in atmospheric model applications

Abstract. We present an improved tagging method, which describes the combined effect of emissions of various species from individual emission categories, e.g. the impact of both, nitrogen oxides and non-methane hydrocarbon emissions on ozone. This method is applied to two simplified chemistry schemes, which represent the main characteristics of atmospheric ozone chemistry. Analytical solutions are presented for this tagging approach. In the past, besides tagging approaches, sensitivity methods were used, which estimate the contributions from individual sources based on differences in two simulations, a base case and a simulation with a perturbation in the respective emission category. We apply both methods to our simplified chemical systems and demonstrate that potentially large errors (factor of 2) occur with the sensitivity method, which depend on the degree of linearity of the chemical system. This error depends on two factors, the ability to linearise the chemical system around a base case, and second the completeness of the contributions, which means that all contributions should principally add up to 100%. For some chemical regimes the first error can be minimised by employing only small perturbations of the respective emission, e.g. 5%. The second factor depends on the chemical regime and cannot be minimized by a specific experimental set-up. It is inherent to the sensitivity method. Since a complete tagging algorithm for global chemistry models is difficult to achieve, we present two error metrics, which can be applied for sensitivity methods in order to estimate the potential error of this approach for a specific application.

A review of dispersion modelling and its application to the dispersion of particles: An overview of different dispersion models available

This paper provides the first review of the application of atmospheric models for particle dispersion. The different types of dispersion models available, from simple box type models to complex fluid dynamics models are outlined and the suitability of the different approaches to dispersion modelling within different environments, in regards to scale, complexity of the environment and concentration parameters is assessed. Finally, several major commercial and non-commercial particle dispersion packages are reviewed, detailing which processes are included and advantages and limitations of their use to modelling particle dispersion. The models reviewed included: Box models (AURORA, CPB and PBM), Gaussian models (CALINE4, HIWAY2, CAR-FMI, OSPM, CALPUFF, AEROPOL, AERMOD, UK-ADMS and SCREEN3), Lagrangian/Eulerian Models (GRAL, TAPM, ARIA Regional), CFD models (ARIA Local, MISKAM, MICRO-CALGRID) and models which include aerosol dynamics (GATOR, MONO32, UHMA, CIT, AERO, RPM, AEROFOR2, URM-1ATM, MADRID, CALGRID and UNI-AERO).

An Intercomparison of Three Snow Schemes of Varying Complexity Coupled to the Same Land Surface Model: Local-Scale Evaluation at an Alpine Site

The Interactions between Soil, Biosphere, and Atmosphere land surface scheme is currently used coupled both to atmospheric models and to a distributed hydrological model. There are two snow-scheme options available for hydrological modeling: the baseline force-restore approach, which uses a composite snow-soil-vegetation energy budget and a single snow layer; and a multilayer detailed internal-process snow model. Only the force-restore method is routinely used in atmospheric modeling applications. Recent studies have shown that hydrological simulations for mountainous catchments within the Rhone basin, France, are significantly improved using the detailed snow scheme. The main drawback is that the scheme is computationally expensive, and it is not currently feasible for routine application in atmospheric models. For these reasons, a third new intermediate-complexity scheme has been developed that includes certain key physical processes from the complex model for improved snowpack realism and hydrological depiction while attemping to keep computational requirements similar to those of the simple default scheme. In the current study, the new scheme is described, evaluated, and compared with the results from the two other schemes at a local scale at an alpine site located within the Rhone basin for two contrasting (weather) years. All schemes are able to model the basic features of the snow cover with similar errors averaged over the 2-yr period; however, there are important differences on shorter timescales. When compared with the more complex scheme, it was found that differing surface energy budget parameter-izations (turbulent transfer, albedo) were the cause for the largest differences in total snowpack snow water equivalent (SWE) simulated by the models. When compared with the simple scheme, the ability for the intermediate model to simulate snow ripening resulted in the largest differences in simulated SWE and snow temperature during melt and runoff.

Urban Emissions Measured with Aircraft

ABSTRACT Detailed knowledge of the quantity and composition of urban emissions is a prerequisite for successful application of atmospheric models to predict transport and distribution of primary and secondary air pollutants in the troposphere. We investigate the prospects and limitations of aircraft measurements in the determination of emission fluxes from urban areas. Our analysis focuses on data collected in September 1994 in and around Athens, Greece. Generally, emission fluxes from cities can be quantified with aircraft and with the minimum acceptable precision (uncertainty better than a factor of 2) only under very favorable meteorological conditions, namely in a homogeneous flow field in a well-mixed boundary layer. Better accuracy can be achieved only through ensemble averaging of repeated measurements. From our measurements in the Athens area, we deduced relative emission ratios of pollutant gases. With the support of ground-based measurements in a street canyon, the emission ratios NOx/CO, SO2/CO, and volatile organic compounds/CO (34 individual VOCs) could be determined with high precision. These results are very useful in analyzing differences between various existing emission inventories. Our data for VOCs reveal that the non-traffic emissions are of the same magnitude as the emissions originating from traffic.