Kumaresh Singh

Intercontinental source attribution of ozone pollution at western U.S. sites using an adjoint method

[1] We use the GEOS-Chem chemical transport model and its adjoint to quantify source contributions to ozone pollution at two adjacent sites on the U.S. west coast in spring 2006: Mt. Bachelor Observatory (MBO) at 2.7 km altitude and Trinidad Head (TH) at sea level. The adjoint computes the sensitivity of ozone concentrations at the receptor sites to ozone production rates at 2° x 2.5° resolution over the history of air parcels reaching the site. MBO experiences distinct Asian ozone pollution episodes; most of the ozone production in these episodes takes place over East Asia with maxima over northeast China and southern Japan, adding to a diffuse background production distributed over the extratropical northern hemisphere. TH shows the same Asian origins for ozone as MBO but no distinct Asian pollution episodes. We find that transpacific pollution plumes transported in the free troposphere are diluted by a factor of 3 when entrained into the boundary layer, explaining why these plumes are undetectable in U.S. surface air.

Spatially Refined Aerosol Direct Radiative Forcing Efficiencies

Global aerosol direct radiative forcing (DRF) is an important metric for assessing potential climate impacts of future emissions changes. However, the radiative consequences of emissions perturbations are not readily quantified nor well understood at the level of detail necessary to assess realistic policy options. To address this challenge, here we show how adjoint model sensitivities can be used to provide highly spatially resolved estimates of the DRF from emissions of black carbon (BC), primary organic carbon (OC), sulfur dioxide (SO(2)), and ammonia (NH(3)), using the example of emissions from each sector and country following multiple Representative Concentration Pathway (RCPs). The radiative forcing efficiencies of many individual emissions are found to differ considerably from regional or sectoral averages for NH(3), SO(2) from the power sector, and BC from domestic, industrial, transportation and biomass burning sources. Consequently, the amount of emissions controls required to attain a specific DRF varies at intracontinental scales by up to a factor of 4. These results thus demonstrate both a need and means for incorporating spatially refined aerosol DRF into analysis of future emissions scenario and design of air quality and climate change mitigation policies.

A Practical Method to Estimate Information Content in the Context of 4D-Var Data Assimilation

Data assimilation obtains improved estimates of the state of a physical system by dynamically combining imperfect model results with sparse and noisy observations of reality. Not all observations u...

Information Theoretic Metrics to Characterize Observations in Variational Data Assimilation

Abstract Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. Not all observations used in data assimilation are equally valuable. The ability to characterize the usefulness of different observation locations is important for analyzing the effectiveness of the assimilation system, for data pruning, and for the design of future sensor systems. This paper proposes a new approach to characterizes the usefulness of different observation in four dimensional variational (4D-Var) data assimilation. Metrics from information theory are used to quantify the contribution of observations to decreasing uncertainty with which the system state is known. We derive ensemble based, computationally feasible procedures to estimate the information content of various observations.

Variational chemical data assimilation with approximate adjoints

Data assimilation obtains improved estimates of the state of a physical system by combining imperfect model results with sparse and noisy observations of reality. In the four-dimensional variational (4D-Var) framework, data assimilation is formulated as an optimization problem, which is solved using gradient-based optimization methods. The 4D-Var gradient is obtained by forcing the adjoint model with observation increments. The construction of the adjoint model requires considerable development effort. Moreover, the computation time associated with the adjoint model is significant (typically, a multiple of the time needed to run the forward model). In this paper we investigate the use of approximate gradients in variational data assimilation. The approximate gradients need to be sufficiently accurate to ensure that the numerical optimization algorithm makes progress toward the maximum likelihood solution. The approximate gradients are obtained through simplified adjoint models; this decreases the adjoint development effort, and reduces the CPU time and the storage requirements associated with the computation of the 4D-Var gradient. The resulting approach, named quasi-4D-Var (Q4D-Var), is illustrated on a global chemical data assimilation problem using satellite observations and the GEOS-Chem chemical transport model.

Chemical Data Assimilation with CMAQ: Continuous vs. Discrete Advection Adjoints

The Community Multiscale Air Quality (CMAQ) system is the Environmental Protection Agencys main modeling tool for atmospheric pollution studies. CMAQ-ADJ, the adjoint model of CMAQ, offers new capabilities such as receptor-oriented sensitivity analysis and chemical data assimilation. This paper presents the construction of discrete advection adjoints in CMAQ. The new adjoints are thoroughly validated against finite differences. We assess the performance of discrete and continuous advection adjoints in CMAQ on sensitivity analysis and 4D-Var data assimilation applications. The results show that discrete adjoint sensitivities better agree with finite difference value than their continuous counterparts. However, continuous adjoints result in a faster convergence of the numerical optimization in 4D-Var data assimilation. Similar conclusions apply to modified discrete adjoints.

Development and acceleration of parallel chemical transport models

Improving chemical transport models for atmospheric simulations relies on future developments of mathematical methods and parallelization methods. Better mathematical methods allow simulations to more accurately model realistic processes and/or to run in a shorter amount of time. Parallelization methods allow simulations to run in less time, allowing scientists to use more accurate or more detailed simulations (higher resolution grids, smaller time steps). The STEM chemical transport model provides a large scale end-to-end application to experiment with running chemical integration methods and transport methods on GPUs. GPUs provide high computational power at a fairly cheap cost. The CUDA programming environment simplifies the GPU development process by providing access to powerful functions to execute parallel code. This work demonstrates the acceleration of a large scale end-to-end application on GPUs showing significant speedups. This is achieved by implementing all relevant kernels on the GPU using CUDA. Nevertheless, further improvements to GPUs are needed to allow these applications to fully exploit the power of GPUs.

Improving GEOS-Chem Model Tropospheric Ozone through Assimilation of Pseudo Tropospheric Emission Spectrometer Profile Retrievals

4D-variational or adjoint-based data assimilation provides a powerful means for integrating observations with models to estimate an optimal atmospheric state and to characterize the sensitivity of that state to the processes controlling it.In this paper we present the improvement of 2006 summer time distribution of global tropospheric ozone through assimilation of pseudo profile retrievals from the Tropospheric Emission Spectrometer (TES) into the GEOS-Chem global chemical transport model based on a recently-developed adjoint model of GEOS-Chem v7. We are the first to construct an adjoint of the linearized ozone parameterization (linoz) scheme that can be of very high importance in quantifying the amount of tropospheric ozone due to upper boundary exchanges. Tests conducted at various geographical levels show that the mismatch between adjoint values and their finite difference approximations could be up to 87% if linoz module adjoint is not used, leading to a divergence in the quasi-Newton approximation algorithm (L-BFGS) during data assimilation. We also present performance improvements in this adjoint model in terms of memory usage and speed. With the parallelization of each science process adjoint subroutine and sub-optimal combination of checkpoints and recalculations, the improved adjoint model is as efficient as the forward GEOS-Chem model.