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Stephen E Cohn

Publications and source records attributed to Stephen E Cohn.

15 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS Data With MERRA-2 Transport

MERRA-2 Stratospheric Composition Reanalysis of Aura Microwave Limb Sounder (M2-SCREAM) is a new reanalysis of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO 3 ) and nitrous oxide (N 2 O) between 2004 and the present (with a latency of several months). The assimilated fields are provided at a 50-km horizontal resolution and at a three-hourly frequency. M2-SCREAM assimilates version 4.2 Microwave Limb Sounder (MLS) profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. Dynamics and tropospheric water vapor are constrained by the MERRA-2 reanalysis. The assimilated species are in excellent agreement with the MLS observations, except for HNO 3 in polar night, where data are not assimilated. Comparisons against independent observations show that the reanalysis realistically captures the spatial and temporal variability of all the assimilated constituents. In particular, the standard deviations of the differences between M2-SCREAM and constituent mixing ratio data from The Atmospheric Chemistry Experiment Fourier Transform Spectrometer are much smaller than the standard deviations of the measured constituents. Evaluation of the reanalysis against aircraft data and balloon-borne frost point hygrometers indicates a faithful representation of small-scale structures in the assimilated water vapor, HNO 3 and ozone fields near the tropopause. Comparisons with independent observations and a process-based analysis of the consistency of the assimilated constituent fields with the MERRA-2 dynamics and with large-scale stratospheric processes demonstrate the utility of M2-SCREAM for scientific studies of chemical and transport variability on time scales ranging from hours to decades. Analysis uncertainties and guidelines for data usage are provided.

MERRA-2↗
NASA NTRS2022

Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems

Motivated by the spurious variance loss encountered during covariance propagation in atmospheric and other large-scale data assimilation systems, we consider the problem for state dynamics governed by the continuity and related hyperbolic partial differential equations. This loss of variance has been attributed to reduced-rank representations of the covariance matrix, as in ensemble methods for example, or else to the use of dissipative numerical methods. Through a combination of analytical work and numerical experiments, we demonstrate that significant variance loss, as well as gain, typically occurs during covariance propagation, even at full rank. The cause of this unusual behavior is a discontinuous change in the continuum covariance dynamics as correlation lengths become small, for instance in the vicinity of sharp gradients in the velocity field. This discontinuity in the covariance dynamics arises from hyperbolicity: the diagonal of the kernel of the covariance operator is a characteristic surface for advective dynamics. Our numerical experiments demonstrate that standard numerical methods for evolving the state are not adequate for propagating the covariance, because16they do not capture the discontinuity in the continuum covariance dynamics as correlations lengths tend to zero. Our analytical and numerical results show that this leads to significant, spurious variance loss in certain regions, and gain in others. The results suggest that developing local covariance propagation methods designed specifically to capture covariance evolution near the diagonal may prove a useful alternative to current methods of covariance propagation.

covariance propagation↗
NASA NTRS2020

The Anomalous 2019 Antarctic Ozone Hole in the GEOS Constituent Data Assimilation System With MLS Observations

A rare disturbance of the stratospheric Antarctic polar vortex in September 2019 led to a significantly higher than usual polar total ozone column. We use assimilation of ozone, HCl, and N2O data from the Microwave Limb Sounder with the Global Earth Observing System (GEOS) Constituent Data Assimilation System driven by reanalysis meteorology to study the evolution of the 2019 Antarctic polar ozone. We find that the maximum 2019 ozone hole area was near 10 × 10(exp 6) ksq.m, and as little as 20% of that in 2018 in mid‐September. However, the magnitude of vortex‐averaged chemical ozone depletion was not significantly different between the 2 years despite earlier chlorine deactivation in 2019. The assimilation results show that most of the differences between 2018 and 2019 Antarctic ozone resulted from two factors: (1) the geometry of the 2019 vortex, with ozone‐rich middle‐stratospheric air masses overlying the lower portion of the vortex and leading to a significant reduction of the total column, and (2) significantly reduced vortex volume. The anomalously small ozone hole of 2019 was comparable in size to the record breaking 2002 case and the mechanisms responsible were similar in the two cases. While the 2019 sudden stratospheric warming is classified as minor, its impact on ozone was very significant.

Krzysztof Wargan↗
NASA NTRSDate not supplied

Continuum Covariance Propagation for Understanding Variance Loss in Advective Systems

We demonstrate for state dynamics governed by the continuity equation and related hyperbolic partial differential equations that significant, spurious variance loss occurs during covariance propagation by traditional methods used in data assimilation, even at full rank. This inaccurate variance evolution is caused not by numerical dissipation, but rather by a discontinuous change in the continuum covariance dynamics as correlation lengths tend to zero.

Shay Gilpin↗
NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗
NASA NTRSDate not supplied

M2-SCREAM: A Stratospheric Composition Reanalysis of Aura MLS

Stratospheric composition and transport patterns are changing because of increasing greenhouse gas concentrations and declining levels of ozone depleting substances. Superimposed on forced trends are: dynamical variability at a range of time scales and unforeseen perturbations such as those resulting from injections of volcanic material and smoke from PyroCb events. Chemical reanalyses can be powerful tools for studying the composition of the stratosphere and disentangling forced responses from internal variability. This presentation introduces a new chemical reanalysis of stratospheric constituents developed and produced at NASA’s Global Modeling and Assimilation Office (GMAO) using the recently developed GMAO Constituent Data Assimilation System. Called the MERRA-2 Stratospheric Composition Reanalysis with Aura MLS (M2-SCREAM), this reanalysis consists of assimilated global three-dimensional fields of stratospheric ozone, water vapor, hydrogen chloride (HCl), nitric acid (HNO3) and nitrous oxide (N2O) mixing ratios and covers the period since the beginning of MLS observations in September 2004 to the present. M2-SCREAM assimilates version 4.2 MLS profiles of the five constituents alongside total ozone column from the Ozone Monitoring Instrument. The dynamics and tropospheric water vapor are constrained by assimilated meteorological fields from MERRA-2. The assimilated constituent fields and meteorology are provided to users at a 50-km horizontal resolution and a three-hourly frequency. Monthly analysis uncertainties and flags are also provided to the users. M2-SCREAM is an accurate and dynamically consistent high-resolution data record of the five constituents, all of which are of primary importance to stratospheric chemistry and transport studies. We will present a description of M2-SCREAM and selected results of a process-based evaluation of this product using independent data. As an illustration, we will discuss the perturbation to stratospheric composition from the eruption of Hunga Tonga–Hunga Ha'apai in January 2022 as seen in the reanalysis. We will also propose potential scientific applications of M2-SCREAM and outline plans for an upcoming comprehensive composition reanalysis that is being developed at NASA GMAO.

MERRA-2↗
NASA NTRSDate not supplied

A Generalized, Compactly-Supported Correlation Function for Data Assimilation Applications

Correlation functions play an essential role in modern data assimilation, where they are used to model covariances given a set of tunable parameters or applied as tapering functions to localize covariances in ensemble-based schemes. One of the most widely-used correlation functions in data assimilation is the Gaspari and Cohn (1999) piecewise-rational, compactly-supported parametric correlation function (hereafter referred to as GC99). The GC99 correlation function is useful due to its tunable cut-off parameter c and Gaussian-like shape achieved when the parameter a is set to one-half. These properties are attractive for tapering functions in data assimilation applications. However, the GC99 correlation function is homogeneous over Euclidean 3-space and isotropic when restricted to the sphere, properties that may be less than ideal for some geophysical applications. GC99 is also compactly-supported on a sphere of fixed radius, which requires tuning of the cut-off parameter c that can depend on the specific application. This work presents a generalization of the GC99 correlation function that allows the cut-off parameter c and shape parameter a to vary over space to gain more flexibility in shape while maintaining its compact support property. The function, which we call the Generalized Gaspari Cohn (GenGC) correlation function, introduces inhomogeneity in Euclidean 3-space and anisotropy when restricted to the sphere by allowing both parameters c and a to vary, as functions, over the spatial domain. The GC99 correlation function is a special case of GenGC where the functions c and a are held constant, as fixed parameters rather than functions. The GenGC correlation function also generalizes the follow-on to the work of Gaspari and Cohn (1999) presented in Gaspari et al. (2006), which allowed a to vary while keeping c fixed. We illustrate through simple one- and two-dimensional examples the variety of inhomogeneous and anisotropic correlation functions GenGC can produce by varying c and a over space, and suggest applications where they may be useful in data assimilation, such as covariance modeling or localization. In particular, we describe how the GenGC correlation function can be used to construct covariances using correlation length and variance fields derived from dynamics. For example, the correlation length field for advective dynamics is governed by a partial differential equation (PDE) in N spatial dimensions, where N is the number of space dimensions of the state. Correlation length fields can be determined from this PDE and used with GenGC to construct the corresponding correlations. We can then approximate the full covariance by rescaling by the variance, which also satisfies a PDE in N spatial dimensions for advective dynamics. Thus we can approximate the full covariance without solving the covariance PDE, which is in 2N spatial dimensions, by solving just two PDEs each in only N spatial dimensions. This approach to evolving the correlation length and variance fields, then reconstructing the correlations using GenGC, is suggested as an alternative to current methods of covariance modeling in data assimilation algorithms.

GC99↗