scholarly journals Evaluation of Analysis by Cross-Validation. Part I: Using Verification Metrics

2018 ◽  
Author(s):  
Richard Menard ◽  
Martin Deshaies-Jacques
Keyword(s):  
Air Quality ◽  
Cross Validation ◽  
Error Variance ◽  
Quality Analysis ◽  
Second Moment ◽  
Error Margin ◽  
Analysis Scheme ◽  
Analysis Error ◽  
First Moment

We examine how observations can be used to evaluate an air quality analysis by verifying against passive observations (i.e. cross-validation) that are not used to create the analysis and we compare these verifications to those made against the same set of (active) observations that were used to generate the analysis. The results show that both active and passive observations can be used to evaluate of first moment metrics (e.g. bias) but only passive observations are useful to evaluate second moment metrics such as variance of observed-minus-analysis and correlation between observations and analysis. We derive a set of diagnostics based on passive observation–minus-analysis residuals and we show that the true analysis error variance can be estimated, without relying on any statistical optimality assumption. This diagnostic is used to obtain near optimal analyses that are then used to evaluate the analysis error using several different methods. We compare the estimates according to the method of Hollingsworth Lonnberg, Desroziers, a diagnostic we introduce, and the perceived analysis error computed from the analysis scheme, to conclude that as long as the analysis is optimal, all estimates agrees within a certain error margin. The analysis error variance at passive observation sites is also obtained.

scholarly journals Evaluation of Analysis by Cross-Validation. Part II: Diagnostic and Optimization of Analysis Error Covariance

2017 ◽  
Author(s):  
Richard Ménard ◽  
Martin Deshaies-Jacques
Keyword(s):  
Air Quality ◽  
General Theory ◽  
Cross Validation ◽  
Error Variance ◽  
Geometric Interpretation ◽  
Quality Analysis ◽  
Error Margin ◽  
Error Covariance ◽  
Analysis Scheme ◽  
Analysis Error

We present a general theory of estimation of analysis error covariances based on cross-validation as well as a geometric interpretation of the method. In particular we use the variance of passive observation–minus-analysis residuals and show that the true analysis error variance can be estimated, without relying on the optimality assumption. This approach is used to obtain near optimal analyses that are then used to evaluate the air quality analysis error using several different methods at active and passive observation sites. We compare the estimates according to the method of Hollingsworth-Lönnberg, Desroziers et al., a new diagnostic we developed, and the perceived analysis error computed from the analysis scheme, to conclude that, as long as the analysis is near optimal, all estimates agree within a certain error margin.

scholarly journals Assimilation of GOSAT Methane in the Hemispheric CMAQ; Part II: Results Using Optimal Error Statistics

2022 ◽  
Vol 14 (2) ◽  
pp. 375
Author(s):  
Sina Voshtani ◽  
Richard Ménard ◽  
Thomas W. Walker ◽  
Amir Hakami
Keyword(s):  
Kalman Filter ◽  
Cross Validation ◽  
Error Variance ◽  
Error Statistics ◽  
Optimal Error ◽  
Meaningful Improvement ◽  
Aircraft Observations ◽  
Variance Parameters ◽  
Analysis Error ◽  
Or Modelling

We applied the parametric variance Kalman filter (PvKF) data assimilation designed in Part I of this two-part paper to GOSAT methane observations with the hemispheric version of CMAQ to obtain the methane field (i.e., optimized analysis) with its error variance. Although the Kalman filter computes error covariances, the optimality depends on how these covariances reflect the true error statistics. To achieve more accurate representation, we optimize the global variance parameters, including correlation length scales and observation errors, based on a cross-validation cost function. The model and the initial error are then estimated according to the normalized variance matching diagnostic, also to maintain a stable analysis error variance over time. The assimilation results in April 2010 are validated against independent surface and aircraft observations. The statistics of the comparison of the model and analysis show a meaningful improvement against all four types of available observations. Having the advantage of continuous assimilation, we showed that the analysis also aims at pursuing the temporal variation of independent measurements, as opposed to the model. Finally, the performance of the PvKF assimilation in capturing the spatial structure of bias and uncertainty reduction across the Northern Hemisphere is examined, indicating the capability of analysis in addressing those biases originated, whether from inaccurate emissions or modelling error.

Air quality analysis in critical zones of Mexico using unsupervised machine learning

2020 ◽  
Author(s):  
Eli G. Pale-Ramon ◽  
Luis J. Morales-Mendoza ◽  
Sonia L. Mestizo-Gutierrez ◽  
Mario Gonzalez-Leee ◽  
Rene F. Vazquez-Bautista ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Quality Analysis ◽  
Unsupervised Machine Learning ◽  
Critical Zones

scholarly journals Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones

2007 ◽  
Vol 135 (12) ◽  
pp. 4006-4029 ◽  
Author(s):  
C. A. Reynolds ◽  
M. S. Peng ◽  
S. J. Majumdar ◽  
S. D. Aberson ◽  
C. H. Bishop ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Energy ◽  
Tropical Cyclones ◽  
Error Variance ◽  
Naval Research ◽  
Error Statistics ◽  
Background Error ◽  
Analysis Error ◽  
The Impact ◽  
Perturbation Growth

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.

scholarly journals Air quality analysis through measurement of pb levels in blood using atomic absorption spectrophotometer

2021 ◽  
Vol 1 (2) ◽  
pp. 89-95
Author(s):  
Uitumen Erdenezul
Keyword(s):  
Air Quality ◽  
Atomic Absorption ◽  
Threshold Value ◽  
Daily Activities ◽  
Atomic Absorption Spectrophotometer ◽  
Quality Analysis ◽  
Observational Method ◽  
The Poor ◽  
Pb Exposure ◽  
High Level

Air pollution is a problem that needs attention, especially pollution by heavy metals such as lead (Pb). This research was conducted to measure the levels of Pb in the blood of people who do a lot of daily activities on the highway in the Ulaanbaatar region, Mongolia, so that an overview of the level of exposure to Pb in the air is obtained. The study was conducted using an observational method by measuring the blood directly from the participants using an atomic absorption spectrophotometer. The participants involved were 20 people who met the criteria. The results showed that the average level of Pb in the blood of people who had daily activities on the highway was 8.97 ppm. Where the smallest level is 5.12 ppm and the highest level is 12.06 ppm. This value is far above the threshold value determined by WHO, which is 0.05 ppm. Therefore, it can be concluded that the air quality in the Ulaanbaatar area is in the poor category with a high level of Pb exposure.

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