Chemical network problems solved on NASA/Goddard's massively parallel processor computer

The single instruction stream, multiple data stream Massively Parallel Processor (MPP) unit consists of 16,384 bit serial arithmetic processors configured as a 128 x 128 array whose speed can exceed that of current supercomputers (Cyber 205). The applicability of the MPP for solving reaction network problems is presented and discussed, including the mapping of the calculation to the architecture, and CPU timing comparisons

Tropospheric ozone in the western Pacific Rim: Analysis of satellite and surface-based observations along with comprehensive 3-D model simulations

Tropospheric ozone production and transport in mid-latitude eastern Asia is studied. Data analysis of surface-based ozone measurements in Japan and satellite-based tropospheric column measurements of the entire western Pacific Rim are combined with results from three-dimensional model simulations to investigate the diurnal, seasonal and long-term variations of ozone in this region. Surface ozone measurements from Japan show distinct seasonal variation with a spring peak and summer minimum. Satellite studies of the entire tropospheric column of ozone show high concentrations in both the spring and summer seasons. Finally, preliminary model simulation studies show good agreement with observed values

Autoregressive Models of Background Errors for Chemical Data Assimilation

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. Data assimilation (DA) is the process of adjusting the states or parameters of a model in such a way that its outcome (prediction) is close, in some distance metric, to observed (real) states. It is widely accepted that a key ingredient of successful data assimilation is a realistic estimation of the background error distribution. This paper introduces a new method for estimating the background errors which are modeled using autoregressive processes. The proposed approach is computationally inexpensive and captures the error correlations along the flow lines

An analysis of the impacts of global climate and emissions changes on regional tropospheric ozone

Many of the synergistic impacts resulting from future changes in emissions as well as changes in ambient temperature, moisture, and UV flux have not been quantified. A three-dimensional regional-scale photo-chemical model (STEM-2) is used in this study to evaluate these perturbations to trace gas cycles over the eastern half of the United States of America. The model was successfully used to simulate a regional-scale ozone episode (base case - June 1984) and four perturbations scenarios - viz., perturbed emissions, temperature, water vapor column, and incoming UV flux cases, and a future scenario (for the year 2034). The impact of these perturbation scenarios on the distribution of ozone and other major pollutants such as SO2 and sulfates were analyzed in detail. The spatial distribution and the concentration of ozone at the surface increased by about 5-15 percent for most cases except for the perturbed water vapor case. The regional scale surface ozone concentration distribution for the year 2034 (future scenario) showed an increase of non-attainment areas. The rural areas of Pennsylvania, West Virginia, and Georgia showed the largest change in the surface ozone field for the futuristic scenario when compared to the base case

Ensemble-based Chemical Data Assimilation III: Filter Localization

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we implement and assess the performance of a localized ``perturbed observations'' ensemble Kalman filter (LEnKF). We analyze different settings of the ensemble localization, and investigate the joint assimilation of the state, emissions and boundary conditions. Results with a real model and real observations show that LEnKF is a promising approach for chemical data assimilation. The results also point to several issues on which future research is necessary

Ensemble-based chemical data assimilation I: An idealized setting

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF). Results in an idealized setting show that EnKF is promising for chemical data assimilation

Ensemble-based chemical data assimilation II: Real observations

Data assimilation is the process of integrating observational data and model predictions to obtain an optimal representation of the state of the atmosphere. As more chemical observations in the troposphere are becoming available, chemical data assimilation is expected to play an essential role in air quality forecasting, similar to the role it has in numerical weather prediction. Considerable progress has been made recently in the development of variational tools for chemical data assimilation. In this paper we assess the performance of the ensemble Kalman filter (EnKF) and compare it with a state of the art 4D-Var approach. We analyze different aspects that affect the assimilation process, investigate several ways to avoid filter divergence, and investigate the assimilation of emissions. Results with a real model and real observations show that EnKF is a promising approach for chemical data assimilation. The results also point to several issues on which further research is necessary

Assessment of Biomass Burning Smoke Influence on Environmental Conditions for Multi-Year Tornado Outbreaks by Combining Aerosol-Aware Microphysics and Fire Emission Constraints

We use the WRF system to study the impacts of biomass burning smoke from Central America on several tornado outbreaks occurring in the US during spring. The model is configured with an aerosol-aware microphysics parameterization capable of resolving aerosol-cloud-radiation interactions in a cost-efficient way for numerical weather prediction (NWP) applications. Primary aerosol emissions are included and smoke emissions are constrained using an inverse modeling technique and satellite-based AOD observations. Simulations turning on and off fire emissions reveal smoke presence in all tornado outbreaks being studied and show an increase in aerosol number concentrations due to smoke. However, the likelihood of occurrence and intensification of tornadoes is higher due to smoke only in cases where cloud droplet number concentration in low level clouds increases considerably in a way that modifies the environmental conditions where the tornadoes are formed (shallower cloud bases and higher low-level wind shear). Smoke absorption and vertical extent also play a role, with smoke absorption at cloud-level tending to burn-off clouds and smoke absorption above clouds resulting in an increased capping inversion. Comparing these and WRF-Chem simulations configured with a more complex representation of aerosol size and composition and different optical properties, microphysics and activation schemes, we find similarities in terms of the simulated aerosol optical depths and aerosol impacts on near-storm environments. This provides reliability on the aerosol-aware microphysics scheme as a less computationally expensive alternative to WRFChem for its use in applications such as NWP and cloud-resolving simulations