scholarly journals The Characteristics of Key Analysis Errors. Part II: The Importance of the PV Corrections and the Impact of Balance

2007 ◽  
Vol 135 (2) ◽  
pp. 267-280 ◽  
Author(s):  
Jean-François Caron ◽  
M. K. Yau ◽  
Stéphane Laroche ◽  
Peter Zwack
Keyword(s):  
Short Range ◽  
Vertical Motion ◽  
Forecast Errors ◽  
Divergent Part ◽  
Essential Information ◽  
Original Analysis ◽  
Analysis Error ◽  
Motion Corrections ◽  
The Impact ◽  
Analysis Errors

Abstract This study examines a few approaches to isolate the balanced component of the initial corrections from the Canadian Meteorological Centre energy-norm-based key analysis error algorithm, in an attempt to capture the part of the key analysis errors responsible for short-range forecast errors. The best results were obtained with the nonlinear balance potential vorticity (PV) inversion technique. It was shown that the PV component of the initial corrections contains the essential information for reducing short-range forecast errors. The remaining imbalance part of the initial corrections does not grow in time and does not contribute to the improvement of the forecast. The removal of the imbalance part of the initial corrections makes the corrected analysis slightly closer to the observations, but remains systematically farther away as compared with the original analysis. Thus the balanced part of the key analysis errors cannot justifiably be associated to analysis errors. A methodology to balance the divergent part of the initial corrections, which reduces significantly the spinup in the vertical motion corrections, is also presented. Finally, in light of the results presented in this paper, some recommendations to improve the key analysis error algorithm are proposed.

scholarly journals The Characteristics of Key Analysis Errors. Part I: Dynamical Balance and Comparison with Observations

2007 ◽  
Vol 135 (2) ◽  
pp. 249-266 ◽  
Author(s):  
Jean-François Caron ◽  
M. K. Yau ◽  
Stéphane Laroche ◽  
Peter Zwack
Keyword(s):  
Forecast Error ◽  
Forecast Errors ◽  
Control Analysis ◽  
Initial Condition ◽  
Initial State ◽  
Mass Correction ◽  
Current Algorithm ◽  
Dynamical Balance ◽  
Analysis Error ◽  
Analysis Errors

Abstract The characteristics of the initial corrections obtained from the Canadian Meteorological Centre (CMC) energy-norm-based key analysis error algorithm that minimizes short-range (24 h) forecast errors were investigated for four specific CMC operational analyses. The results show that both the rotational and the divergent components of the initial corrections are strongly out of balance. Some dispersive modes are also present in the mass component of the initial corrections. The results from one experiment where the initial state errors were known suggest that the current algorithm always selects a set of unbalanced initial corrections with more mass correction than wind correction, regardless of the characteristics of the real initial condition errors. Comparison with observational data showed that the corrected analysis is systematically farther away from the observations than the control analysis even in large forecast error events where most of the forecast errors are believed to have originated from errors in the initial state.

scholarly journals Estimation and calibration of observation impact signals using the Lanczos method in NOAA/NCEP data assimilation system

2012 ◽  
Vol 19 (5) ◽  
pp. 541-557 ◽  
Author(s):  
M. Wei ◽  
M. S. F. V. De Pondeca ◽  
Z. Toth ◽  
D. Parrish
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Lanczos Method ◽  
Forecast Errors ◽  
Observation Data ◽  
Error Covariance ◽  
Observation Impact ◽  
Analysis Error ◽  
The Tropics ◽  
The Impact

Abstract. Despite the tremendous progress that has been made in data assimilation (DA) methodology, observing systems that reduce observation errors, and model improvements that reduce background errors, the analyses produced by the best available DA systems are still different from the truth. Analysis error and error covariance are important since they describe the accuracy of the analyses, and are directly related to the future forecast errors, i.e., the forecast quality. In addition, analysis error covariance is critically important in building an efficient ensemble forecast system (EFS). Estimating analysis error covariance in an ensemble-based Kalman filter DA is straightforward, but it is challenging in variational DA systems, which have been in operation at most NWP (Numerical Weather Prediction) centers. In this study, we use the Lanczos method in the NCEP (the National Centers for Environmental Prediction) Gridpoint Statistical Interpolation (GSI) DA system to look into other important aspects and properties of this method that were not exploited before. We apply this method to estimate the observation impact signals (OIS), which are directly related to the analysis error variances. It is found that the smallest eigenvalue of the transformed Hessian matrix converges to one as the number of minimization iterations increases. When more observations are assimilated, the convergence becomes slower and more eigenvectors are needed to retrieve the observation impacts. It is also found that the OIS over data-rich regions can be represented by the eigenvectors with dominant eigenvalues. Since only a limited number of eigenvectors can be computed due to computational expense, the OIS is severely underestimated, and the analysis error variance is consequently overestimated. It is found that the mean OIS values for temperature and wind components at typical model levels are increased by about 1.5 times when the number of eigenvectors is doubled. We have proposed four different calibration schemes to compensate for the missing trailing eigenvectors. Results show that the method with calibration for a small number of eigenvectors cannot pick up the observation impacts over the regions with fewer observations as well as a benchmark with a large number of eigenvectors, but proper calibrations do enhance and improve the impact signals over regions with more data. When compared with the observation locations, the method generally captures the OIS over regions with more observation data, including satellite data over the southern oceans. Over the tropics, some observation impacts may be missed due to the smaller background errors specified in the GSI, which is not related to the method. It is found that a large number of eigenvectors are needed to retrieve impact signals that resemble the banded structures from satellite observations, particularly over the tropics. Another benefit from the Lanczos method is that the dominant eigenvectors can be used in preconditioning the conjugate gradient algorithm in the GSI to speed up the convergence.

scholarly journals An OSSE-Based Evaluation of Hybrid Variational–Ensemble Data Assimilation for the NCEP GFS. Part I: System Description and 3D-Hybrid Results

2015 ◽  
Vol 143 (2) ◽  
pp. 433-451 ◽  
Author(s):  
Daryl T. Kleist ◽  
Kayo Ide
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Forecast Errors ◽  
Weather Forecasts ◽  
Global Data Assimilation System ◽  
Analysis Error ◽  
Environmental Prediction ◽  
The Impact

Abstract An observing system simulation experiment (OSSE) has been carried out to evaluate the impact of a hybrid ensemble–variational data assimilation algorithm for use with the National Centers for Environmental Prediction (NCEP) global data assimilation system. An OSSE provides a controlled framework for evaluating analysis and forecast errors since a truth is known. In this case, the nature run was generated and provided by the European Centre for Medium-Range Weather Forecasts as part of the international Joint OSSE project. The assimilation and forecast impact studies are carried out using a model that is different than the nature run model, thereby accounting for model error and avoiding issues with the so-called identical-twin experiments. It is found that the quality of analysis is improved substantially when going from three-dimensional variational data assimilation (3DVar) to a hybrid 3D ensemble–variational (EnVar)-based algorithm. This is especially true in terms of the analysis error reduction for wind and moisture, most notably in the tropics. Forecast impact experiments show that the hybrid-initialized forecasts improve upon the 3DVar-based forecasts for most metrics, lead times, variables, and levels. An additional experiment that utilizes 3DEnVar (100% ensemble) demonstrates that the use of a 25% static error covariance contribution does not alter the quality of hybrid analysis when utilizing the tangent-linear normal mode constraint on the total hybrid increment.

scholarly journals The THORPEX Observation Impact Intercomparison Experiment

2010 ◽  
Vol 138 (11) ◽  
pp. 4009-4025 ◽  
Author(s):  
Ronald Gelaro ◽  
Rolf H. Langland ◽  
Simon Pellerin ◽  
Ricardo Todling
Keyword(s):  
Short Range ◽  
Forecast Error ◽  
Detailed Comparison ◽  
Forecast Errors ◽  
Commercial Aircraft ◽  
Forecast System ◽  
Small Individual ◽  
Forecast Models ◽  
Combined Impact ◽  
The Impact

Abstract An experiment is being conducted to directly compare the impact of all assimilated observations on short-range forecast errors in different forecast systems using an adjoint-based technique. The technique allows detailed comparison of observation impacts in terms of data type, location, satellite sounding channel, or other relevant attributes. This paper describes results for a “baseline” set of observations assimilated by three forecast systems for the month of January 2007. Despite differences in the assimilation algorithms and forecast models, the impacts of the major observation types are similar in each forecast system in a global sense. However, regional details and other aspects of the results can differ substantially. Large forecast error reductions are provided by satellite radiances, geostationary satellite winds, radiosondes, and commercial aircraft. Other observation types provide smaller impacts individually, but their combined impact is significant. Only a small majority of the total number of observations assimilated actually improves the forecast, and most of the improvement comes from a large number of observations that have relatively small individual impacts. Accounting for this behavior may be especially important when considering strategies for deploying adaptive (or “targeted”) components of the observing system.

scholarly journals The Characteristics of Key Analysis Errors. Part III: A Diagnosis of Their Evolution

2007 ◽  
Vol 135 (7) ◽  
pp. 2754-2777 ◽  
Author(s):  
Jean-François Caron ◽  
M. K. Yau ◽  
Stéphane Laroche
Keyword(s):  
Level Structure ◽  
Potential Temperature ◽  
Initial Time ◽  
Forecast Errors ◽  
Diagnostic Study ◽  
Low Level ◽  
Diabatic Processes ◽  
Upper Level ◽  
Analysis Error ◽  
Analysis Errors

Abstract This paper presents a diagnostic study of the evolution of initial corrections obtained from the key analysis error algorithm that minimizes the short-range (24 h) forecast errors for four specific events poorly forecasted over the eastern part of North America. A potential vorticity (PV) perspective is employed. It is shown that the modification to the low-level structure at the initial time is mainly attributed to the modification of the low-level PV distribution, while changes in the upper-level structure are attributed to the modification of the upper-level PV distribution. The low-level corrections grow mainly through background surface potential temperature advection by the wind corrections attributable to the interior PV corrections. Changes in the diabatic processes and the vertical alignment of low-level PV corrections by differential PV advection also increase the magnitude of the low-level corrections with time. The upper-level corrections grow by advection of background PV from wind corrections. However, the cause of these latter wind corrections responsible for upper-level background PV advection varies from case to case. An investigation of the relative importance of the low-level and of the upper-level initial corrections to produce the final-time corrections also reveals strong variability between cases. Finally, comparison of two cases in which the key analysis errors propagate vertically with two others without significant vertical propagation shows how the relative position of the key analysis errors with respect to the structure of the background flow can influence the evolution of the initial corrections.

Cyclogenetic Perturbations and Analysis Errors Decomposed into Singular Vectors

2005 ◽  
Vol 62 (7) ◽  
pp. 2234-2247 ◽  
Author(s):  
Chris Snyder ◽  
Gregory J. Hakim
Keyword(s):  
Potential Vorticity ◽  
Vertical Structure ◽  
Energy Norm ◽  
Vertical Shear ◽  
Initial Time ◽  
Forecast Errors ◽  
Ensemble Forecasts ◽  
Singular Vectors ◽  
Analysis Error ◽  
Analysis Errors

Abstract Singular vectors (SVs) have been applied to cyclogenesis, to initializing ensemble forecasts, and in predictability studies. Ideally, the calculation of the SVs would employ the analysis error covariance norm at the initial time or, in the case of cyclogenesis, a norm based on the statistics of initial perturbations, but the energy norm is often used as a more practical substitute. To illustrate the roles of the choice of norm and the vertical structure of initial perturbations, an upper-level wave with no potential vorticity perturbation in the troposphere is considered as a typical cyclogenetic perturbation or analysis error, and this perturbation is then decomposed by its projection onto each energy SV. All calculations are made, for simplicity, in the context of the quasigeostrophic Eady model (i.e., for a background flow with constant vertical shear and horizontal temperature gradient). Viewed in terms of the energy SVs, the smooth vertical structure of the typical perturbation, as well as its evolution, results from strong cancellation between the growing and decaying SVs, most of which are highly structured and tilted in the vertical. A simpler picture, involving less cancellation, follows from decomposition of the typical perturbation into SVs using an alternative initial norm, which is based on the relation between initial norms and the statistics of initial perturbations together with the empirical assumption that the initial perturbations are not dominated by interior potential vorticity. Differences between the energy SVs and those based on the alternative initial norm can be understood by noting that the energy norm implicitly assumes initial perturbations with second-order statistics given by the covariance matrix whose inverse defines the energy norm. Unlike the “typical” perturbation, perturbations with those statistics have large variance of potential vorticity in the troposphere and fine vertical structure. Finally, a brief assessment is presented of the extent to which the upper wave, and more generally the alternative initial norm, is representative of cyclogenetic perturbations and analysis errors. There is substantial evidence supporting deep perturbations with little vertical structure as frequent precursors to cyclogenesis, but surrogates for analysis errors are less conclusive: operational midlatitude analysis differences have vertical structure similar to that of the perturbations implied by the energy norm, while short-range forecast errors and analysis errors from assimilation experiments with simulated observations are more consistent with the alternative norm.

Particularities of the independent auditor's report on reliability of accounting (financial) statements for 2019: COVID-19 as qualitatively essential information (Part 2)

2020 ◽  
Vol 23 (7) ◽  
pp. 777-799
Author(s):  
O.I. Shvyreva ◽  
Z.I. Kruglyak ◽  
A.V. Petukh
Keyword(s):  
Financial Reporting ◽  
Financial Statements ◽  
Economic Situation ◽  
Risk Disclosure ◽  
Essential Information ◽  
Logical Method ◽  
Annual Reporting ◽  
The Face ◽  
Key Aspects ◽  
The Impact

Subject. This article discusses the issues related to the practice of financial reporting in the face of uncertainties caused by the coronavirus contagion, as well as the specifics of the audit strategy and formation of an audit opinion on this reporting. Objectives. The article aims to identify the quality characteristics of financial reporting prepared in the context of the COVID-19 pandemic and justify the key aspects of assurance engagement completion in an extremely uncertain epidemiological and economic situation. Methods. For the study, we used an abstract-logical method, content analysis techniques, systematization, and classification. Results. Analyzing the impact of the extremely uncertain epidemiological and economic situation on financial statements, the article clarifies aspects of disclosure of events after the reporting date and threats to business continuity in the annual reporting of economic entities. The article identifies possible alternative procedures and algorithms to obtain proper evidence when it is insufficient in the face of the inability to meet certain audit standards requirements in a remote audit environment. The article defines the impact of COVID-19 risk disclosure on the structure of the audit report and opinion. Relevance. The results of the study can be used in the practical activities of economic entities that prepare financial statements in the face of significant uncertainty, as well as auditors and audit organizations.

scholarly journals Assessing the Risk of Democratic Reversal in the United States: A Reply to Kurt Weyland

2021 ◽  
pp. 1-6
Author(s):  
Matias López ◽  
Juan Pablo Luna
Keyword(s):  
United States ◽  
Comparative Analysis ◽  
Boolean Algebra ◽  
Qualitative Comparative Analysis ◽  
The United States ◽  
American Democracy ◽  
Institutional Dynamics ◽  
The Public ◽  
Original Analysis ◽  
The Impact

ABSTRACT By replying to Kurt Weyland’s (2020) comparative study of populism, we revisit optimistic perspectives on the health of American democracy in light of existing evidence. Relying on a set-theoretical approach, Weyland concludes that populists succeed in subverting democracy only when institutional weakness and conjunctural misfortune are observed jointly in a polity, thereby conferring on the United States immunity to democratic reversal. We challenge this conclusion on two grounds. First, we argue that the focus on institutional dynamics neglects the impact of the structural conditions in which institutions are embedded, such as inequality, racial cleavages, and changing political attitudes among the public. Second, we claim that endogeneity, coding errors, and the (mis)use of Boolean algebra raise questions about the accuracy of the analysis and its conclusions. Although we are skeptical of crisp-set Qualitative Comparative Analysis as an adequate modeling choice, we replicate the original analysis and find that the paths toward democratic backsliding and continuity are both potentially compatible with the United States.

scholarly journals Outcomes for Children and Adolescents With Cancer: Challenges for the Twenty-First Century

2010 ◽  
Vol 28 (15) ◽  
pp. 2625-2634 ◽  
Author(s):  
Malcolm A. Smith ◽  
Nita L. Seibel ◽  
Sean F. Altekruse ◽  
Lynn A.G. Ries ◽  
Danielle L. Melbert ◽  
...  
Keyword(s):  
United States ◽  
Childhood Cancer ◽  
Cancer Mortality ◽  
Lymphoblastic Leukemia ◽  
The United States ◽  
Mortality Rates ◽  
Mortality Data ◽  
Childhood Cancers ◽  
Essential Information ◽  
The Impact

Purpose This report provides an overview of current childhood cancer statistics to facilitate analysis of the impact of past research discoveries on outcome and provide essential information for prioritizing future research directions. Methods Incidence and survival data for childhood cancers came from the Surveillance, Epidemiology, and End Results 9 (SEER 9) registries, and mortality data were based on deaths in the United States that were reported by states to the Centers for Disease Control and Prevention by underlying cause. Results Childhood cancer incidence rates increased significantly from 1975 through 2006, with increasing rates for acute lymphoblastic leukemia being most notable. Childhood cancer mortality rates declined by more than 50% between 1975 and 2006. For leukemias and lymphomas, significantly decreasing mortality rates were observed throughout the 32-year period, though the rate of decline slowed somewhat after 1998. For remaining childhood cancers, significantly decreasing mortality rates were observed from 1975 to 1996, with stable rates from 1996 through 2006. Increased survival rates were observed for all categories of childhood cancers studied, with the extent and temporal pace of the increases varying by diagnosis. Conclusion When 1975 age-specific death rates for children are used as a baseline, approximately 38,000 childhood malignant cancer deaths were averted in the United States from 1975 through 2006 as a result of more effective treatments identified and applied during this period. Continued success in reducing childhood cancer mortality will require new treatment paradigms building on an increased understanding of the molecular processes that promote growth and survival of specific childhood cancers.

Balance properties of the short-range forecast errors in the ECMWF 4D-Var ensemble

2012 ◽  
Vol 139 (674) ◽  
pp. 1229-1238 ◽  
Author(s):  
N. Žagar ◽  
L. Isaksen ◽  
D. Tan ◽  
J. Tribbia
Keyword(s):  
Short Range ◽  
Forecast Errors
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