scholarly journals Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution

2016 ◽  
Author(s):  
Xueling Liu ◽  
Arthur P. Mizzi ◽  
Jeffrey L. Anderson ◽  
Inez Fung ◽  
Ronald C. Cohen
Keyword(s):  
Data Assimilation ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Weather Forecast ◽  
Point Sources ◽  
Short Time Scale ◽  
Strongly Coupled ◽  
Meteorological Observations ◽  
High Space ◽  
Short Time

Abstract. Observations of trace gases from space based instruments offer the opportunity to constrain chemical and weather forecast and reanalysis models using the tools of data assimilation. To date, attempts at assimilation of nitrogen dioxide (NO2) satellite remote sensing have focused on updating emissions and concentrations. These initial efforts evaluated updates at length scales of ~ 100 km using once a day measurements from satellites with ground pixels of 13 km × 24 km or larger. In the boundary layer, NO2 has a lifetime on the order of five hours and corresponding 1/e concentration variations near urban and point sources occur on spatial scales on the order of 50–75 km. Accurate observations and modeling of these variations require spatial resolution of order 4 km. In addition, because of the short lifetime, NO2 variations are more strongly coupled to short time scale meteorological parameters than longer lived chemicals such as CO or CO2. In the next few years, we anticipate the launch of several instruments with ~ 3 km spatial resolution. In addition, some of these instruments will be in geostationary orbits and thus have hourly revisit times. In anticipation of these instruments, we investigate the potential of high space and time resolution column measurements to serve as constraints on urban NOx emissions using a geostationary observation simulator coupled to a data assimilation system. We find that constraints on emissions are strongest in regions with high emissions and are most effective when coupled to hourly assimilation of meteorological observations. We find that errors in the meteorological fields result in unrecoverable biases in the updated emissions confirming a conjecture that simultaneous meteorology and chemical assimilation is essential to accurate description of the emissions and chemistry.

scholarly journals Discovering novae in early-type galaxies with MUSE: A chance find in NGC 1404, and 12 more candidates from an archival search

2020 ◽  
Vol 494 (1) ◽  
pp. L1-L5
Author(s):  
Russell J Smith
Keyword(s):  
Elliptical Galaxy ◽  
Point Sources ◽  
Short Time Scale ◽  
Early Type ◽  
X Ray ◽  
Type Ia ◽  
Hα Emission ◽  
Integral Field Spectrograph ◽  
Short Time ◽  
Nova Outburst

ABSTRACT I report the discovery of a transient broad-Hα point source in the outskirts of the giant elliptical galaxy NGC 1404, discovered in archival observations taken with the Multi-Unit Spectroscopic Explorer (MUSE) integral field spectrograph. The Hα line width of 1950 km s−1 full width at half-maximum, and luminosity of (4.1 ± 0.1) × 1036 erg s−1, are consistent with a nova outburst, and the source is not visible in MUSE data obtained 9 months later. A transient soft X-ray source was detected at the same position (within &lt;1 arcsec), 14 yr before the Hα transient. If the X-ray and Hα emission are from the same object, the source may be a short-time-scale recurrent nova with a massive white dwarf accretor, and hence a possible Type-Ia supernova progenitor. Selecting broad-Hα point sources in MUSE archival observations for a set of nearby early-type galaxies, I discovered 12 more nova candidates with similar properties to the NGC 1404 source, including five in NGC 1380 and four in NGC 4365. Multi-epoch data are available for four of these twelve sources; all four are confirmed to be transient on ∼1 yr time-scales, supporting their identification as novae.

scholarly journals Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz ’96 system

2021 ◽  
Author(s):  
Zofia Stanley ◽  
Ian Grooms ◽  
William Kleiber
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Background Error ◽  
Strongly Coupled ◽  
Localization Function ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Coupled Data Assimilation ◽  
Lorenz 96 ◽  
The Impact

Abstract. Localization is widely used in data assimilation schemes to mitigate the impact of sampling errors on ensemble-derived background error covariance matrices. Strongly coupled data assimilation allows observations in one component of a coupled model to directly impact another component through inclusion of cross-domain terms in the background error covariance matrix. When different components have disparate dominant spatial scales, localization between model domains must properly account for the multiple length scales at play. In this work we develop two new multivariate localization functions, one of which is a multivariate extension of the fifth-order piecewise rational Gaspari-Cohn localization function; the within-component localization functions are standard Gaspari-Cohn with different localization radii while the cross-localization function is newly constructed. The functions produce non-negative definite localization matrices, which are suitable for use in variational data assimilation schemes. We compare the performance of our two new multivariate localization functions to two other multivariate localization functions and to the univariate analogs of all four functions in a simple experiment with the bivariate Lorenz '96 system. In our experiment the multivariate Gaspari-Cohn function leads to better performance than any of the other localization functions.

scholarly journals Propagating information from snow observations with CrocO ensemble data assimilation system: a 10-years case study over a snow depth observation network

2021 ◽  
Author(s):  
Bertrand Cluzet ◽  
Matthieu Lafaysse ◽  
César Deschamps-Berger ◽  
Matthieu Vernay ◽  
Marie Dumont
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Spatial Scales ◽  
Open Loop ◽  
Mountain Range ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Analysis Scheme ◽  
Meteorological Observations

Abstract. The mountainous snow cover is highly variable at all temporal and spatial scales. Snowpack models only imperfectly represent this variability, because of uncertain meteorological inputs, physical parameterisations, and unresolved terrain features. In-situ observations of the height of snow (HS), despite their limited representativeness, could help constrain intermediate and large scale modelling errors by means of data assimilation. In this work, we assimilate HS observations from an in-situ network of 295 stations covering the French Alps, Pyrenees and Andorra, over the period 2009–2019. In view of assimilating such observations into a spatialised snow cover modelling framework, we investigate whether such observations can be used to correct neighbouring snowpack simulations. We use CrocO, an ensemble data assimilation framework of snow cover modelling, based on a Particle Filter suited to the propagation of information from observed to unobserved areas. This ensemble system already benefits from meteorological observations, assimilated within SAFRAN analysis scheme. CrocO also proposes various localisation strategies to assimilate snow observations. These approaches are evaluated in a Leave-One-Out setup against the operational deterministic model and its ensemble open-loop counterpart, both running without HS assimilation. Results show that intermediate localisation radius of 35–50 km yield a slightly lower root mean square error (RMSE), and a better Spread-Skill than the strategy assimilating all the observations from a whole mountain range. Significant continuous ranked probability score (CRPS) improvements of about 13 % are obtained in the areas where the open-loop modelling errors are the largest, e.g. the Haute-Ariège, Andorra and the Extreme Southern Alps. Over these areas, weather station observations are generally sparser, resulting in more uncertain meteorological analyses, and therefore snow simulations. In-situ HS observations thus shows an interesting complementarity with meteorological observations to better constrain snow cover simulations over large areas.

scholarly journals ASCA Observations of LINERs

1997 ◽  
Vol 159 ◽  
pp. 54-55
Author(s):  
Y. Terashima ◽  
H. Kunieda ◽  
N. Iyomoto ◽  
K. Makishima ◽  
P. J. Serlemitsos
Keyword(s):  
Emission Line ◽  
Seyfert Galaxies ◽  
Point Sources ◽  
Photon Statistics ◽  
Short Time Scale ◽  
Spectral Slope ◽  
Iron Line ◽  
X Ray ◽  
Intrinsic Absorption ◽  
Short Time

ASCA observations have revealed the presence of low-luminosity AGN in ~10 LINERs as a hard point sources at the nucleus. The X-ray continuum shapes (photon indices Γ ≈ 1.8) are very similar to those of Seyfert galaxies (Makishima et al. 1997, Serlemitsos et al. 1996). An iron emission line is observed from heavily absorbed low-luminosity AGNs (Makishima et al. 1997), while M81 is the only object among those with small intrinsic absorption from which an iron line detected (Ishisaki et al. 1996) on account of limited photon statistics for other objects.We summed up the ASCA spectra of 5 LINERs which host low-luminosity AGN of low intrinsic absorption to search for an iron emission line. We used 7 observations of 5 objects (NGC 1097, NGC 3310, NGC 3998, NGC 4450, and NGC 4594) to make a composite spectrum. All the objects have very similar X-ray characteristics (spectral slope, intrinsic absorption, no short time-scale variability; Iyomoto et al. 1996, Serlemitsos et al. 1996).

scholarly journals Multivariate localization functions for strongly coupled data assimilation in the bivariate Lorenz 96 system

2021 ◽  
Vol 28 (4) ◽  
pp. 565-583
Author(s):  
Zofia Stanley ◽  
Ian Grooms ◽  
William Kleiber
Keyword(s):  
Data Assimilation ◽  
Spatial Scales ◽  
Background Error ◽  
Strongly Coupled ◽  
Localization Function ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Coupled Data Assimilation ◽  
Lorenz 96 ◽  
The Impact

Abstract. Localization is widely used in data assimilation schemes to mitigate the impact of sampling errors on ensemble-derived background error covariance matrices. Strongly coupled data assimilation allows observations in one component of a coupled model to directly impact another component through the inclusion of cross-domain terms in the background error covariance matrix. When different components have disparate dominant spatial scales, localization between model domains must properly account for the multiple length scales at play. In this work, we develop two new multivariate localization functions, one of which is a multivariate extension of the fifth-order piecewise rational Gaspari–Cohn localization function; the within-component localization functions are standard Gaspari–Cohn with different localization radii, while the cross-localization function is newly constructed. The functions produce positive semidefinite localization matrices which are suitable for use in both Kalman filters and variational data assimilation schemes. We compare the performance of our two new multivariate localization functions to two other multivariate localization functions and to the univariate and weakly coupled analogs of all four functions in a simple experiment with the bivariate Lorenz 96 system. In our experiments, the multivariate Gaspari–Cohn function leads to better performance than any of the other multivariate localization functions.

scholarly journals Assimilation of satellite NO<sub>2</sub> observations at high spatial resolution using OSSEs

2017 ◽  
Vol 17 (11) ◽  
pp. 7067-7081 ◽  
Author(s):  
Xueling Liu ◽  
Arthur P. Mizzi ◽  
Jeffrey L. Anderson ◽  
Inez Y. Fung ◽  
Ronald C. Cohen
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Nox Emissions ◽  
Chemical Transport Model ◽  
Model Framework ◽  
Simulation Experiments ◽  
High Space

Abstract. Observations of trace gases from space-based instruments offer the opportunity to constrain chemical and weather forecast and reanalysis models using the tools of data assimilation. In this study, observing system simulation experiments (OSSEs) are performed to investigate the potential of high space- and time-resolution column measurements as constraints on urban NOx emissions. The regional chemistry–meteorology assimilation system where meteorology and chemical variables are simultaneously assimilated is comprised of a chemical transport model, WRF-Chem, the Data Assimilation Research Testbed, and a geostationary observation simulator. We design OSSEs to investigate the sensitivity of emission inversions to the accuracy and uncertainty of the wind analyses and the emission updating scheme. We describe the overall model framework and some initial experiments that point out the first steps toward an optimal configuration for improving our understanding of NOx emissions by combining space-based measurements and data assimilation. Among the findings we describe is the dependence of errors in the estimated NOx emissions on the wind forecast errors, showing that wind vectors with a RMSE below 1 m s−1 allow inference of NOx emissions with a RMSE of less than 30 mol/(km2  ×  h) at the 3 km scale of the model we use. We demonstrate that our inference of emissions is more accurate when we simultaneously update both NOx emissions and NOx concentrations instead of solely updating emissions. Furthermore, based on our analyses, we recommend carrying out meteorology assimilations to stabilize NO2 transport from the initial wind errors before starting the emission assimilation. We show that wind uncertainties (calculated as a spread around a mean wind) are not important for estimating NOx emissions when the wind uncertainties are reduced below 1.5 m s−1. Finally, we present results assessing the role of separate vs. simultaneous chemical and meteorological assimilation in a model framework without covariance between the meteorology and chemistry.

Periodic Variations in the Coronal Green Line Intensity and their Connection with the White-light Coronal Structures

2000 ◽  
Vol 179 ◽  
pp. 197-200
Author(s):  
Milan Minarovjech ◽  
Milan Rybanský ◽  
Vojtech Rušin
Keyword(s):  
Time Resolution ◽  
White Light ◽  
Short Time Scale ◽  
High Time Resolution ◽  
Green Line ◽  
Periodic Intensity ◽  
Coronal Green Line ◽  
Significant Intensity ◽  
Short Time ◽  
Coronal Structures

AbstractWe present an analysis of short time-scale intensity variations in the coronal green line as obtained with high time resolution observations. The observed data can be divided into two groups. The first one shows periodic intensity variations with a period of 5 min. the second one does not show any significant intensity variations. We studied the relation between regions of coronal intensity oscillations and the shape of white-light coronal structures. We found that the coronal green-line oscillations occur mainly in regions where open white-light coronal structures are located.

High spatial resolution x-ray microanalysis of interfaces

1995 ◽  
Vol 53 ◽  
pp. 284-285
Author(s):  
J. R. Michael
Keyword(s):  
Spatial Resolution ◽  
Electron Probe ◽  
Solid Angle ◽  
Collection Efficiency ◽  
Spatial Scales ◽  
Energy Dispersive Spectrometer ◽  
X Rays ◽  
X Ray ◽  
Probe Size ◽  
Brightness Field

X-ray microanalysis in the analytical electron microscope (AEM) refers to a technique by which chemical composition can be determined on spatial scales of less than 10 nm. There are many factors that influence the quality of x-ray microanalysis. The minimum probe size with sufficient current for microanalysis that can be generated determines the ultimate spatial resolution of each individual microanalysis. However, it is also necessary to collect efficiently the x-rays generated. Modern high brightness field emission gun equipped AEMs can now generate probes that are less than 1 nm in diameter with high probe currents. Improving the x-ray collection solid angle of the solid state energy dispersive spectrometer (EDS) results in more efficient collection of x-ray generated by the interaction of the electron probe with the specimen, thus reducing the minimum detectability limit. The combination of decreased interaction volume due to smaller electron probe size and the increased collection efficiency due to larger solid angle of x-ray collection should enhance our ability to study interfacial segregation.

Nondiffusive Brownian Motion Studied by Diffusing-Wave Spectroscopy

1989 ◽  
Vol 177 ◽  
Author(s):  
D. J. Pine ◽  
D. A. Weitz ◽  
D. J. Durian ◽  
P. N. Pusey ◽  
R. J. A. Tough
Keyword(s):  
Autocorrelation Function ◽  
Colloidal Particles ◽  
Scattered Light ◽  
Short Time Scale ◽  
Velocity Autocorrelation Function ◽  
Velocity Autocorrelation ◽  
Power Law Decay ◽  
Brownian Particles ◽  
Short Time ◽  
Surrounding Fluid

ABSTRACTOn a short time scale, Brownian particles undergo a transition from initially ballistic trajectories to diffusive motion. Hydrodynamic interactions with the surrounding fluid lead to a complex time dependence of this transition. We directly probe this transition for colloidal particles by measuring the autocorrelation function of multiply scattered light and observe the effects of the slow power-law decay of the velocity autocorrelation function.

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