Objective Analysis System Research Articles

Objective analysis of corneal subbasal nerve tortuosity and its changes in patients with dry eye and diabetes

Objective To construct an objective analysis system of corneal nerve tortuosity and detect the changes of corneal subbasal nerve tortuosity in patients with dry eye and diabetes. Methods Grade Ⅰ to Ⅳ nerve tortuosity were evaluated and 80 photos of each grade were randomly chosen from the in vivo confocal microscopy library.Nerve fibers were extracted, segmented and then analyzed by 6 tortuosity related parameters including L_C, Seg_L_C_mean, Cur_mean, Specific_p, ICM and SCC_mean.After verifying the validaty of parameters above, a cross-sectional study was conducted.Subjects were collected from June, 2018 to February, 2019 in Peking University Third Hospital, and were divided into healthy control group (28 persons 56 eyes), dry eye without diabetes group (28 patients 56 eyes), diabetes without dry eye group(24 patients 48 eyes), diabetes with dry eye group (23 patients 46 eyes). Basic and dry eye information includes sex, age, ocular surface disease index (OSDI), tear film break-up time (TBUT), Schirmer Ⅰ test (SⅠt) and corneal fluorescence staining (CFS) score.Fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) were detected in diabetic patients.Cochet-Bonnet examination (C-BE) was detected to evaluate corneal sensation and 2 corneal subbasal nerve photos of each eye were selected for effective tortuosity and density related parameters analysis.Data was analyzed by SPSS and diagnostic test were perfomed by MedCalc.This study followed the Declaration of Helsinki.This study protocol was approved by Ethic Committee of Peking University Third Hospital(No.IR6761-M2017354). Written informed consent was obtained from each subject prior to entering study cohort. Results L_C, Seg_L_C_mean, Cur_mean, Specific_p, ICM and SCC_mean increased as the nerve tortuosity increased from Grade Ⅰ to Grade Ⅳ, with an overall significance among 4 groups (F=39.100, 36.367, 57.743, 4.043, 6.818, 33.493; all at P<0.01). Among the above 6 parameters, Cur_mean and L_C of any two groups were of significant difference (all at P<0.01). Twenty three to twenty eight persons were enrolled in each group of the cross-sectional study.Sex and age were comparable among 4 groups.Diagnostic criteria were met in dry eye and diabetes.Corneal sensation parameter C-BE decreased in diabetes without dry eye group and diabetes with dry eye group compared with healthy control group(all at AdjP<0.05), other than in dry eye without diabetes group (AdjP≥0.05). Nerve density of diabetes without dry eye group and diabetes with dry eye group was lower compared with healthy control group(all at P<0.001), while no significant difference between dry eye without diabetes group and healthy control group(P≥0.05). Among the effective parameters of tortuosity, L_C, Cur_mean, Seg_L_C_mean and SCC_mean of dry eye without diabetes group, diabetes without dry eye group, diabetes with dry eye group were higher compared with healthy control group(all at P<0.05). Diagnostic tests of tortuosity related parameters all showed an area under curve (AUC) from 0.5 to 0.7. Conclusions L_C and Cur_mean can be used to analyze corneal nerve curvature more reliably.Compared with normal volunteers, patients of dry eye or diabetes show higher corneal subbasal nerve tortuosity. Key words: Corneal subbasal nerve; Tortuosity; Dry eye; Diabetes

A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada

ABSTRACTAn objective analysis is one of the main components of data assimilation. By combining observations with the output of a predictive model we combine the best features of each source of information: the complete spatial and temporal coverage provided by models, with a close representation of the truth provided by observations. The process of combining observations with a model output is called an analysis. To produce an analysis requires the knowledge of observation and model errors, as well as its spatial correlation. This paper is devoted to the development of methods of estimation of these error variances and the characteristic length-scale of the model error correlation for its operational use in the Canadian objective analysis system. We first argue in favor of using compact support correlation functions, and then introduce three estimation methods: the Hollingsworth–Lönnberg (HL) method in local and global form, the maximum likelihood method (ML), and the diagnostic method. We perform one-dimensional (1D) simulation studies where the error variance and true correlation length are known, and perform an estimation of both error variances and correlation length where both are non-uniform. We show that a local version of the HL method can capture accurately the error variances and correlation length at each observation site, provided that spatial variability is not too strong. However, the operational objective analysis requires only a single and globally valid correlation length. We examine whether any statistics of the local HL correlation lengths could be a useful estimate, or whether other global estimation methods such as by the global HL, ML, or should be used. We found in both 1D simulation and using real data that the ML method is able to capture physically significant aspects of the correlation length, while most other estimates give unphysical and larger length-scale values.Implications: This paper describes a proposed improvement of the objective analysis of surface pollutants at Environment and Climate Change Canada (formerly known as Environment Canada). Objective analyses are essentially surface maps of air pollutants that are obtained by combining observations with an air quality model output, and are thought to provide a complete and more accurate representation of the air quality. The highlight of this study is an analysis of methods to estimate the model (or background) error correlation length-scale. The error statistics are an important and critical component to the analysis scheme.

An effective method for improving the accuracy of Argo objective analysis

Based on the optimal interpolation objective analysis of the Argo data, improvements are made to the empirical formula of a background error covariance matrix widely used in data assimilation and objective analysis systems. Specifically, an estimation of correlation scales that can improve effectively the accuracy of Argo objective analysis has been developed. This method can automatically adapt to the gradient change of a variable and is referred to as “gradient-dependent correlation scale method”. Its effect on the Argo objective analysis is verified theoretically with Gaussian pulse and spectrum analysis. The results of one-dimensional simulation experiment show that the gradient-dependent correlation scales can improve the adaptability of the objective analysis system, making it possible for the analysis scheme to fully absorb the shortwave information of observation in areas with larger oceanographic gradients. The new scheme is applied to the Argo data objective analysis system in the Pacific Ocean. The results are obviously improved.

Verification of RUC 0–1-h Forecasts and SPC Mesoscale Analyses Using VORTEX2 Soundings

Abstract This study uses radiosonde observations obtained during the second phase of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) to verify base-state variables and severe-weather-related parameters calculated from Rapid Update Cycle (RUC) analyses and 1-h forecasts, as well as those calculated from the operational surface objective analysis system used at the Storm Prediction Center (the SFCOA). The rapid growth in temperature, humidity, and wind errors from 0 to 1 h seen at all levels in a past RUC verification study by Benjamin et al. is not seen in the present study. This could be because the verification observations are also assimilated into the RUC in the Benjamin et al. study, whereas the verification observations in the present study are not. In the upper troposphere, the present study shows large errors in relative humidity, mostly related to a large moist bias. The planetary boundary layer tends to be too shallow in the RUC analyses and 1-h forecasts. Wind speeds tend to be too fast in the lowest 1 km and too slow in the 2–4-km layer. RUC and SFCOA 1-h forecast errors for many important severe weather parameters are large relative to their potential impact on convective evolution. However, the SFCOA significantly improves upon the biases seen in most of the 1-h RUC forecasts for the base-state surface variables and most of the other severe-weather-related parameters, indicating that the SFCOA has a more significant impact in reducing the biases in the 1-h RUC forecasts than on the root-mean-squared errors.

Analysis on MM5 predictions at Sriharikota during northeast monsoon 2008

The Indian northeast monsoon is inherently chaotic in nature as the rainfall realised in the peninsular India depends substantially on the formation and movement of low-pressure systems in central and southwest Bay of Bengal and on the convective activity which is mainly due to the moist north-easterlies from Bay of Bengal. The objective of this study is to analyse the performance of the PSU-NCAR Mesoscale Model Version 5 (MM5), for northeast monsoon 2008 that includes tropical cyclones – Rashmi, Khai-Muk and Nisha and convective events over Sriharikota region, the rocket launch centre. The impact of objective analysis system using radiosonde observations, surface observations and Kalpana-1 satellite derived Atmospheric Motion Wind Vectors (AMV) is also studied. The performance of the model is analysed by comparing the predicted parameters like mean sea level pressure (MSLP), intensity, track and rainfall with the observations. The results show that the model simulations could capture MSLP and intensity of all the cyclones reasonably well. The dependence of the movement of the system on the environmental flow is clearly observed in all the three cases. The vector displacement error and percentage of improvement is calculated to study the impact of objective data analysis on the movement and intensity of the cyclone.

A method for evaluating the modes of variability in general circulation models

The seasonal mean of an atmospheric climate variable is considered to be a statistical random variable with two components: a slow component related to slowly varying forcing from external and internal atmospheric sources (time scale of a season or more), and an intraseasonal component related to forcing from weather variability with time scale less than a season. Here, a method is proposed to compare the modes of variability obtained from eigenvalue decomposition of the slow and intraseasonal covariance matrices estimated from reanalysis data with modes of variability estimated from a set of coupled general circulation models. As an example, the method is applied to the Southern Hemisphere summer 500hPa geopotential height for the period 1951--2000. The method is applicable to many other atmospheric climate variables and datasets. References G. P. Cressman. An operational objective analysis system. Mon. Wea. Rev. , 87:367--374, 2.0.CO;2>doi:10.1175/1520-0493(1959)087 2.0.CO;2 , 1959. C. S. Frederiksen and X. Zheng. Variability of seasonal-mean fields arising from intraseasonal variability. part 2, application to nh winter circulations. Climate Dynamics , 23:193--206, doi:10.1007/s00382-004-0429-6 , 2004. C. S. Frederiksen and X. Zheng. Coherent Structures of Interannual Variability of the Atmospheric Circulation: The Role of Intraseasonal Variability. Frontiers in Turbulence and Coherent Structures, World Scientific Lecture Notes in Complex Systems , Vol. 6, Eds Jim Denier and Jorgen Frederiksen, World Scientific Publications, 87--120, 2007. C. S. Frederiksen and X. Zheng. Variability of seasonal-mean fields arising from intraseasonal variability: Part 3: Application to SH winter and summer circulations. Climate Dynamics , 28:849--866, doi:10.1007/s00382-006-0214-9 , 2007. N. Higham. Computing the nearest correlation matrix---a problem from finance. I.M.A. J. Numerical Analysis , 22:329--343, doi:10.1093/imanum/22.3.329 , 2002. E. Kalnay et al. The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. , 77:437--471, 2.0.CO;2>doi:10.1175/1520-0477(1996)077 2.0.CO;2 , 1996. G. A. Meehl et al. The WCRP CMIP3 multimodel dataset: A new era in climate change research. Bull. Amer. Meteor. Soc. , 88:1383--1394, doi:10.1175/BAMS-88-9-1383 , 2007. H. von Storch and F. W. Zwiers. Statistical Analysis in Climate Research. Cambridge University Press , 484pp, 1999. K. E. Taylor. Summarizing multiple aspects of model performance in a single diagram. J. Geophys. Res. , 106:7183--7192, http://www.agu.org/pubs/crossref/2001/2000JD900719.shtml , 2001. X. Zheng and C. S. Frederiksen. Variability of seasonal-mean fields arising from intraseasonal variability. Part 1, methodology. Climate Dynamics , 23:177--191, doi:10.1007/s00382-004-0428-7 , 2004. X. Zheng, M. Sugi and C. S. Frederiksen. Interannual variability and predictability in an ensemble of climate simulations with the MRI-JMA AGCM. J. Met. Soc. Jap. , 82:1--18, doi:10.2151/jmsj.82.1 , 2004.

Local Adjustment of the Background Error Correlation for Surface Analyses over Complex Terrain

Abstract The terrain between grid points is used to modify locally the background error correlation matrix in an objective analysis system. This modification helps to reduce the influence across mountain barriers of corrections to the background field that are derived from surface observations. This change to the background error correlation matrix is tested using an analytic case of surface temperature that encapsulates the significant features of nocturnal radiation inversions in mountain basins, which can be difficult to analyze because of locally sharp gradients in temperature. Bratseth successive corrections, optimal interpolation, and three-dimensional variational approaches are shown to yield exactly the same surface temperature analysis. Adding the intervening terrain term to the Bratseth approach led to solutions that match more closely the specified analytic solution. In addition, the convergence of the Bratseth solutions to the best linear unbiased estimation of the analytic solution is faster. The intervening terrain term was evaluated in objective analyses over the western United States derived from a modified version of the Advanced Regional Prediction System Data Assimilation System. Local adjustment of the background error correlation matrix led to improved surface temperature analyses by limiting the influence of observations in mountain valleys that may differ from the weather conditions present in adjacent valleys.

Distributed Parallelization of a Global Atmospheric Data Objective Analysis System

It is difficult to parallelize a subsistent sequential algorithm. Through analyzing the sequential algorithm of a Global Atmospheric Data Objective Analysis System, this article puts forward a distributed parallel algorithm that statically distributes data on a massively parallel processing (MPP) computer. The algorithm realizes distributed parailelization by extracting the analysis boxes and model grid point Iatitude rows with leaped steps, and by distributing the data to different processors. The parallel algorithm achieves good load balancing, high parallel efficiency, and low parallel cost. Performance experiments on a MPP computer arc also presented.

Objective assessment of noise quality using standard measurement instrumentation

Objective assessment of noise (“noise quality”) can be thought of as a three-step process including data recording, signal analysis, and psychoacoustic evaluation. Standard measurement instrumentation may be used for recording and analysis, resulting in data suitable for a variety of present and future psychoacoustic relations. The discrete Fourier transform in its usual implementation, the short-time Fourier transform, and the variable-window Fourier transform can all be used for analysis of noise signals, However, these techniques have some limitations, such as linear frequency resolution, assumption of a locally stationary signal, and consequences of analysis windows. Real-time digital filtering is frequently more appropriate for the analysis of nonstationary noise signals, because it can provide logarithmic frequency resolution, inherently real-time analysis, and well-defined analysis windows. These two classes of methods will be compared. Analysis parameters of an objective analysis system using real-time digital filtering techniques will be discussed. A calibration procedure for the entire measurement chain, including a head and torso simulator used as transducer, will be described.

The verification of objective analyses: Diagnostics of analysis system performance

We use the spatial coherence of the fit of analyses to observation as the basis for new diagnostics of the performance of any linear objective analysis system. The diagnostics provide answers to such questions as: Up to now these questions could only be answered indirectly, by evaluation of the forecasts resulting from the analyses, or by study of the changes made by the initialisation. The lack of simple objective methods to address these questions has sometimes resulted in controversy about the interpretation of data impact studies. Suppose, in a practical analysis system, that the observation errors are uniform and uncorrelated, and the observations are homogeneously distributed. We show (for any practical linear analysis system) that if the Observation-minus-Analysis (OmA) auto-correlations are positive when extrapolated to zero separation, then the analysis has certainly not extracted all the information from the observations, and does not fit the data to within the observational error. If however the extrapolated OmA correlations are negative, then the analysis system does fit the data to within observational accuracy. If, in addition, the weights given to the observations in the analysis of the observed values are known, one can derive useful estimates of the analysis error at the observation points. This last result leads to an estimate of the effective data density. Along with the new verifications of mass and wind analyses, methods are developed to estimate the effectiveness with which data on mass-wind balance have been used. The results for practical analysis systems are used to interpret empirical determinations of the OmA auto-correlations in the ECMWF analysis system, for data with uncorrelated errors. We shall say that a practical analysis system is efficient if the data are fitted to within observational error, and is inefficient otherwise. The methods demonstrate that operational mid-tropospheric wind analyses over North America are quite efficient, but that the windshear and thickness analyses near the tropopause over North America are inefficient. The analysis of mass wind balance over North America is efficient for any single level, but the analysis of thermal wind balance for thin layers near the tropopause is inefficient, most notably on short horizontal scales. The analysis of the wind shear near the tropical tropopause is somewhat better than over North America, probably because of the use of sharper vertical structure functions.

Large‐scale analysis and forecast experiments with wind data from the Seasat A scatterometer

A series of data assimilation experiments is performed to assess the impact of Seasat A satellite scatterometer (SASS) wind data on Goddard Laboratory for Atmospheric Sciences (GLAS) model forecasts. The SASS data are dealiased as part of an objective analysis system utilizing a three‐pass procedure. The impact of the SASS data is evaluated with and without temperature soundings from the NOAA 4 Vertical Temperature Profile Radiometer (VTPR) instrument in order to study possible redundancy between surface wind data and upper air temperature data. In the northern hemisphere the SASS data are generally found to have a negligible effect on the forecasts. In the southern hemisphere the forecast impact from SASS data is somewhat larger and primarily beneficial in the absence of VTPR data. However, the inclusion of VTPR data effectively eliminates the positive impact over Australia and South America. This indicates that SASS data can be beneficial for numerical weather prediction in regions with large data gaps, but in the presence of satellite soundings the usefulness of SASS data is significantly reduced.

An Objective Limited-Area Analysis/Prognosis Experiment Using FGGE Data in the Australian Region

Abstract The data from a 9-day period of SOP-2 were analyzed at 6 h intervals using a fully automatic limited-area variational objective analysis system, with a primitive equations prognosis model providing guess fields for each analysis and thus maintaining time continuity between analyses. It is demonstrated that the scheme produced a set of analyses which showed a stable, consistent evolution of the synoptic systems throughout the period. This would not have been possible without manual intervention prior to FGGE. Twenty-four hour prognoses based on these analyses showed, on average, equal or slightly greater skill than the equivalent operational prognoses which had access to a similar (although somewhat less timely) data base, as well as having manual input. The entire experiment was repeated using updated rather than fixed boundary conditions during each 6 h prognosis, with the boundary tendencies being obtained from a hemispheric assimilation experiment. It is shown that this produced a small but si...