Ragu: a free tool for the analysis of EEG and MEG event-related scalp field data using global randomization statistics

We present a program (Ragu; Randomization Graphical User interface) for statistical analyses of multichannel event-related EEG and MEG experiments. Based on measures of scalp field differences including all sensors, and using powerful, assumption-free randomization statistics, the program yields robust, physiologically meaningful conclusions based on the entire, untransformed, and unbiased set of measurements. Ragu accommodates up to two within-subject factors and one between-subject factor with multiple levels each. Significance is computed as function of time and can be controlled for type II errors with overall analyses. Results are displayed in an intuitive visual interface that allows further exploration of the findings. A sample analysis of an ERP experiment illustrates the different possibilities offered by Ragu. The aim of Ragu is to maximize statistical power while minimizing the need for a-priori choices of models and parameters (like inverse models or sensors of interest) that interact with and bias statistics.

Observation errors in early historical upper-air observations

Upper-air observations are a fundamental data source for global atmospheric data products, but uncertainties, particularly in the early years, are not well known. Most of the early observations, which have now been digitized, are prone to a large variety of undocumented uncertainties (errors) that need to be quantified, e.g., for their assimilation in reanalysis projects. We apply a novel approach to estimate errors in upper-air temperature, geopotential height, and wind observations from the Comprehensive Historical Upper-Air Network for the time period from 1923 to 1966. We distinguish between random errors, biases, and a term that quantifies the representativity of the observations. The method is based on a comparison of neighboring observations and is hence independent of metadata, making it applicable to a wide scope of observational data sets. The estimated mean random errors for all observations within the study period are 1.5 K for air temperature, 1.3 hPa for pressure, 3.0 ms−1for wind speed, and 21.4° for wind direction. The estimates are compared to results of previous studies and analyzed with respect to their spatial and temporal variability.

Global ocean storage of anthropogenic carbon

The global ocean is a significant sink for anthropogenic carbon (Cant), absorbing roughly a third of human CO2 emitted over the industrial period. Robust estimates of the magnitude and variability of the storage and distribution of Cant in the ocean are therefore important for understanding the human impact on climate. In this synthesis we review observational and model-based estimates of the storage and transport of Cant in the ocean. We pay particular attention to the uncertainties and potential biases inherent in different inference schemes. On a global scale, three data-based estimates of the distribution and inventory of Cant are now available. While the inventories are found to agree within their uncertainty, there are considerable differences in the spatial distribution. We also present a review of the progress made in the application of inverse and data assimilation techniques which combine ocean interior estimates of Cant with numerical ocean circulation models. Such methods are especially useful for estimating the air–sea flux and interior transport of Cant, quantities that are otherwise difficult to observe directly. However, the results are found to be highly dependent on modeled circulation, with the spread due to different ocean models at least as large as that from the different observational methods used to estimate Cant. Our review also highlights the importance of repeat measurements of hydrographic and biogeochemical parameters to estimate the storage of Cant on decadal timescales in the presence of the variability in circulation that is neglected by other approaches. Data-based Cant estimates provide important constraints on forward ocean models, which exhibit both broad similarities and regional errors relative to the observational fields. A compilation of inventories of Cant gives us a "best" estimate of the global ocean inventory of anthropogenic carbon in 2010 of 155 ± 31 PgC (±20% uncertainty). This estimate includes a broad range of values, suggesting that a combination of approaches is necessary in order to achieve a robust quantification of the ocean sink of anthropogenic CO2.

Risk factors for patient-reported medical errors in eleven countries

OBJECTIVES The aim of this study was to identify common risk factors for patient-reported medical errors across countries. In country-level analyses, differences in risks associated with error between health care systems were investigated. The joint effects of risks on error-reporting probability were modelled for hypothetical patients with different health care utilization patterns. DESIGN Data from the Commonwealth Fund's 2010 lnternational Survey of the General Public's Views of their Health Care System's Performance in 11 Countries. SETTING Representative population samples of 11 countries were surveyed (total sample = 19,738 adults). Utilization of health care, coordination of care problems and reported errors were assessed. Regression analyses were conducted to identify risk factors for patients' reports of medical, medication and laboratory errors across countries and in country-specific models. RESULTS Error was reported by 11.2% of patients but with marked differences between countries (range: 5.4-17.0%). Poor coordination of care was reported by 27.3%. The risk of patient-reported error was determined mainly by health care utilization: Emergency care (OR = 1.7, P < 0.001), hospitalization (OR = 1.6, P < 0.001) and the number of providers involved (OR three doctors = 2.0, P < 0.001) are important predictors. Poor care coordination is the single most important risk factor for reporting error (OR = 3.9, P < 0.001). Country-specific models yielded common and country-specific predictors for self-reported error. For high utilizers of care, the probability that errors are reported rises up to P = 0.68. CONCLUSIONS Safety remains a global challenge affecting many patients throughout the world. Large variability exists in the frequency of patient-reported error across countries. To learn from others' errors is not only essential within countries but may also prove a promising strategy internationally.

Mapping spectroscopic uncertainties into prospective methane retrieval errors from Sentinel-5 and its precursor

Sentinel-5 (S5) and its precursor (S5P) are future European satellite missions aiming at global monitoring of methane (CH4) column-average dry air mole fractions (XCH4). The spectrometers to be deployed onboard the satellites record spectra of sunlight backscattered from the Earth's surface and atmosphere. In particular, they exploit CH4 absorption in the shortwave infrared spectral range around 1.65 mu m (S5 only) and 2.35 mu m (both S5 and S5P) wavelength. Given an accuracy goal of better than 2% for XCH4 to be delivered on regional scales, assessment and reduction of potential sources of systematic error such as spectroscopic uncertainties is crucial. Here, we investigate how spectroscopic errors propagate into retrieval errors on the global scale. To this end, absorption spectra of a ground-based Fourier transform spectrometer (FTS) operating at very high spectral resolution serve as estimate for the quality of the spectroscopic parameters. Feeding the FTS fitting residuals as a perturbation into a global ensemble of simulated S5- and S5P-like spectra at relatively low spectral resolution, XCH4 retrieval errors exceed 0.6% in large parts of the world and show systematic correlations on regional scales, calling for improved spectroscopic parameters.