scholarly journals Toward Estimating Climatic Trends in SST. Part II: Random Errors

2006 ◽  
Vol 23 (3) ◽  
pp. 476-486 ◽  
Author(s):  
Elizabeth C. Kent ◽  
Peter G. Challenor
Keyword(s):  
Spatial Variability ◽  
Error Estimates ◽  
Statistical Technique ◽  
Random Errors ◽  
Observational Error ◽  
Data Set ◽  
Spatial Error ◽  
Semivariogram Analysis ◽  
Merchant Ship ◽  
Method Of Measurement

Abstract Random observational errors for sea surface temperature (SST) are estimated using merchant ship reports from the International Comprehensive Ocean–Atmosphere Data Set (ICOADS) for the period of 1970–97. A statistical technique, semivariogram analysis, is used to isolate the variance resulting from the observational error from that resulting from the spatial variability in a dataset of the differences of paired SST reports. The method is largely successful, although there is some evidence that in high-variability regions the separation of random and spatial error is not complete, which may have led to an overestimate of the random observational error in these regions. The error estimates are robust to changes in the details of the regression method used to estimate the spatial variability. The resulting error estimates are shown to vary with region, time, the quality control applied, the method of measurement, the recruiting country, and the source of the data. SST data measured using buckets typically contain smaller random errors than those measured using an engine-intake thermometer. Errors are larger in the 1970s, probably because of problems with data transmission in the early days of the Global Telecommunications System. The best estimate of the global average random error in ICOADS ship SST for the period of 1970–97 is 1.2°C if the estimates are weighted by ocean area and 1.3°C if the estimates are weighted by the number of observations.

scholarly journals Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

2019 ◽  
Vol 13 (11) ◽  
pp. 3045-3059 ◽  
Author(s):  
Nick Rutter ◽  
Melody J. Sandells ◽  
Chris Derksen ◽  
Joshua King ◽  
Peter Toose ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Layer Thickness ◽  
Snow Water Equivalent ◽  
Estimation Accuracy ◽  
Snow Layer ◽  
Data Set ◽  
Snow Water ◽  
Retrieval Errors ◽  
Snow Microstructure ◽  
The Impact

Abstract. Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across nine trenches) collected over two winters at Trail Valley Creek, NWT, Canada, was applied in synthetic radiative transfer experiments. This allowed for robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability in total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were less than a metre for all layers. Depth hoar was consistently ∼30 % of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7 % under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6 m, depth hoar SSA estimates of ±5 %–10 % around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ∼30 cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

scholarly journals Combining Multiple Organizational-level Databases: An Empirical Evaluation of Different Matching Methods

2021 ◽  
pp. 004912412098618
Author(s):  
Tim de Leeuw ◽  
Steffen Keijl
Keyword(s):  
Empirical Evaluation ◽  
Organizational Level ◽  
Random Errors ◽  
Data Set ◽  
Future Studies ◽  
Matching Methods ◽  
Matching Process ◽  
Significant Relationships ◽  
Research Findings ◽  
Insight Into

Although multiple organizational-level databases are frequently combined into one data set, there is no overview of the matching methods (MMs) that are utilized because the vast majority of studies does not report how this was done. Furthermore, it is unclear what the differences are between the utilized methods, and it is unclear whether research findings might be influenced by the utilized method. This article describes four commonly used methods for matching databases and potential issues. An empirical comparison of those methods used to combine regularly used organizational-level databases reveals large differences in the number of observations obtained. Furthermore, empirical analyses of these different methods reveal that several of them produce both systematic and random errors. These errors can result in erroneous estimations of regression coefficients in terms of direction and/or size as well as an issue where truly significant relationships might be found to be insignificant. This shows that research findings can be influenced by the MM used, which would argue in favor of the establishment of a preferred method as well as more transparency on the utilized method in future studies. This article provides insight into the matching process and methods, suggests a preferred method, and should aid researchers, reviewers, and editors with both combining multiple databases and describing and assessing them.

ExtSym: a program to aid space-group determination from powder diffraction data

2008 ◽  
Vol 41 (6) ◽  
pp. 1177-1181 ◽  
Author(s):  
Anders J. Markvardsen ◽  
Kenneth Shankland ◽  
William I. F. David ◽  
John C. Johnston ◽  
Richard M. Ibberson ◽  
...  
Keyword(s):  
Error Estimates ◽  
Powder Diffraction ◽  
Diffraction Data ◽  
National Laboratory ◽  
Powder Diffraction Data ◽  
Acta Cryst ◽  
Data Set ◽  
Crystal System ◽  
Wide Range ◽  
Integrated Intensities

Once unit-cell dimensions have been determined from a powder diffraction data set and therefore the crystal system is known (e.g.orthorhombic), the method presented by Markvardsen, David, Johnson & Shankland [Acta Cryst.(2001), A57, 47–54] can be used to generate a table ranking the extinction symbols of the given crystal system according to probability. Markvardsenet al.tested a computer program (ExtSym) implementing the method against Pawley refinement outputs generated using theTF12LSprogram [David, Ibberson & Matthewman (1992). Report RAL-92-032. Rutherford Appleton Laboratory, Chilton, Didcot, Oxon, UK]. Here, it is shown thatExtSymcan be used successfully with many well known powder diffraction analysis packages, namelyDASH[David, Shankland, van de Streek, Pidcock, Motherwell & Cole (2006).J. Appl. Cryst.39, 910–915],FullProf[Rodriguez-Carvajal (1993).Physica B,192, 55–69],GSAS[Larson & Von Dreele (1994). Report LAUR 86-748. Los Alamos National Laboratory, New Mexico, USA],PRODD[Wright (2004).Z. Kristallogr.219, 1–11] andTOPAS[Coelho (2003). Bruker AXS GmbH, Karlsruhe, Germany]. In addition, a precise description of the optimal input forExtSymis given to enable other software packages to interface withExtSymand to allow the improvement/modification of existing interfacing scripts.ExtSymtakes as input the powder data in the form of integrated intensities and error estimates for these intensities. The output returned byExtSymis demonstrated to be strongly dependent on the accuracy of these error estimates and the reason for this is explained.ExtSymis tested against a wide range of data sets, confirming the algorithm to be very successful at ranking the published extinction symbol as the most likely.

scholarly journals The day-of-the-week effect: South African stock market indices

2018 ◽  
Vol 9 (2) ◽  
pp. 197-212 ◽  
Author(s):  
Elda du Toit ◽  
John Henry Hall ◽  
Rudra Prakash Pradhan
Keyword(s):  
South African ◽  
Stock Exchange ◽  
Garch Model ◽  
Statistical Technique ◽  
Data Set ◽  
Content Type ◽  
Johannesburg Stock Exchange ◽  
Time Period ◽  
Day Of The Week ◽  
Micro Level

Purpose The presence of a day-of-the-week effect has been investigated by many researchers over many years, using a variety of financial data and methods. However, differences in methodology between studies could have led to conflicting results. The purpose of this paper is to expand on an existing study to observe whether an analysis of the same data set with some added years and using a different statistical technique provide the same results. Design/methodology/approach The study examines the presence of a day-of-the-week effect on the Johannesburg Stock Exchange (JSE) indices for the period March 1995-2016, using a GARCH model. Findings The findings show that, contrary to the original study, the day-of-the week effect is present in both volatility and return equations. The highest and lowest returns are observed on Monday and Friday, respectively, while volatility is observed on all five days from Monday to Friday. Originality/value This study adds to the existing literature on day-of-the-week effect of JSE indices, where different patterns or, in some cases, no pattern have been noted. Few previous studies on the day-of-the-week effect observed the effect at micro-level for separate industries or made use of a GARCH model. The present study thus expands on the study of Mbululu and Chipeta (2012), by adding four additional observation years and using a different statistical technique, to observe differences that arise from a different time period and statistical technique. The results indicate that a day-of-the-week effect is mostly a function of the statistical technique applied.

scholarly journals An Icelandic freshwater radiocarbon reservoir effect: Implications for lacustrine 14C chronologies

The Holocene ◽  
2011 ◽  
Vol 21 (7) ◽  
pp. 1073-1080 ◽  
Author(s):  
P.L. Ascough ◽  
G.T. Cook ◽  
H. Hastie ◽  
E. Dunbar ◽  
M.J. Church ◽  
...  
Keyword(s):  
Stable Isotope ◽  
Spatial Variability ◽  
Spatial Variation ◽  
Age Differences ◽  
Trophic Levels ◽  
Data Set ◽  
Reservoir Effect ◽  
Age Correction ◽  
Freshwater System ◽  
Lake Mývatn

A freshwater radiocarbon (14C) reservoir effect (FRE) is a 14C age offset between the atmospheric and freshwater carbon reservoirs. FREs can be on the order of 10 000 14C yr in extreme examples and are a crucial consideration for 14C dating of palaeoenvironmental and archaeological samples. Correction for a FRE may be possible, provided the FRE and the proportion of FRE-affected carbon within a sample can be accurately quantified. However, although such correction is desirable for affected samples, it is essential that such correction is accurate in order to produce useful chronological information. Accuracy of FRE correction can be limited by spatial variation in FRE within a freshwater system, but despite this there is currently a paucity of information to identify and quantify such variability within affected systems. Here we present results of a study that investigates the effects of spatial FRE variation upon dating accuracy within the freshwater system of Lake Mývatn, northern Iceland. A substantial FRE (>10 000 14C yr) has previously been identified in archaeological and modern samples from the region, which shows the potential for considerable spatial variability. The study also assesses the use of δ13C and δ15N in age correction of affected samples. The results show that benthic detritus and organisms at primary trophic levels from locations within the lake are affected by a FRE of at least 3500 14C yr, with clear spatial variation resulting in 14C age differences of up to 7670 14C yr between samples. There is a broad correlation between stable isotope values and FRE within the data set. However, large associated uncertainties currently preclude highly accurate and precise stable isotope-based quantification of the proportion of FRE-affected carbon within archaeological and palaeoenvironmental samples from Mývatn and the surrounding region.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2013 ◽  
Vol 6 (4) ◽  
pp. 7593-7631 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Synthetic Data ◽  
Analytic Solutions ◽  
The Other ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. EPA PMF version 5.0 and the underlying multilinear engine executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

scholarly journals Design, Analysis, and Interpretation of Method-Comparison Studies

2008 ◽  
Vol 19 (2) ◽  
pp. 223-234
Author(s):  
Sandra K. Hanneman
Keyword(s):  
Method Comparison ◽  
Design Analysis ◽  
Temperature Data ◽  
New Method ◽  
Clinical Use ◽  
Comparison Study ◽  
Data Set ◽  
Statistical Computation ◽  
Method Of Measurement ◽  
Method Comparison Study

Clinicians often need to know whether a new method of measurement is equivalent to an established one already in clinical use. This article reviews the methodology of a method-comparison study to assist the clinician with the conduct and evaluation of such studies. Temperature data from 1 subject are used to illustrate the procedures. Although one would not make decisions on the basis of the findings from 1 subject, the large number of paired measurements in the data set permits its use for illustrative purposes. Currently available software eliminates the need for tedious statistical computation but does not reduce the burden of understanding the concepts underlying a method-comparison study and accurate interpretation of the findings.

Extended Reconstruction of Global Sea Surface Temperatures Based on COADS Data (1854–1997)

2003 ◽  
Vol 16 (10) ◽  
pp. 1495-1510 ◽  
Author(s):  
Thomas M. Smith ◽  
Richard W. Reynolds
Keyword(s):  
Error Estimates ◽  
North Atlantic ◽  
Large Scale ◽  
Sea Surface ◽  
Data Set ◽  
The North ◽  
Reconstruction Methods ◽  
Control Procedures ◽  
Hadley Centre ◽  
Analysis Errors

Abstract A monthly extended reconstruction of global SST (ERSST) is produced based on Comprehensive Ocean–Atmosphere Data Set (COADS) release 2 observations from the 1854–1997 period. Improvements come from the use of updated COADS observations with new quality control procedures and from improved reconstruction methods. In addition error estimates are computed, which include uncertainty from both sampling and analysis errors. Using this method, little global variance can be reconstructed before the 1880s because data are too sparse to resolve enough modes for that period. Error estimates indicate that except in the North Atlantic ERSST is of limited value before 1880, when the uncertainty of the near-global average is almost as large as the signal. In most regions, the uncertainty decreases through most of the period and is smallest after 1950. The large-scale variations of ERSST are broadly consistent with those associated with the Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) reconstruction produced by the Met Office. There are differences due to both the use of different historical bias corrections as well as different data and analysis procedures, but these differences do not change the overall character of the SST variations. Procedures used here produce a smoother analysis compared to HadISST. The smoother ERSST has the advantage of filtering out more noise at the possible cost of filtering out some real variations when sampling is sparse. A rotated EOF analysis of the ERSST anomalies shows that the dominant modes of variation include ENSO and modes associated with trends. Projection of the HadISST data onto the rotated eigenvectors produces time series similar to those for ERSST, indicating that the dominant modes of variation are consistent in both.

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