Insights from the study of Arabic reading

© 2020 John Wiley & Sons Ltd. Current reading models were largely designed to explain findings from experiments of the reading of English and other European languages (Reichle, 2020, Computational models of reading: A handbook). Recent evidence from studies of other languages and writing systems (e.g., Chinese) has demonstrated the need to critically evaluate the assumptions of these models, and whether they are sufficient to explain the full range of findings related to reading, as required, for example, to understand the universal and specific cognitive principles that support reading. In this article, we review the recent behavioural and cognitive-neuroscience research on the reading of Arabic, a world language that until recently has received scant attention despite the fact that its writing system poses fundamental challenges for current models of reading. We also highlight the points of convergence and difference between what has been learned about the reading of Arabic and the reading of another, more widely studied Semitic language, Hebrew. We then discuss the theoretical implications of these findings for existing models of reading

Assessment and enhancement of MERRA land surface hydrology estimates

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979 present. This study introduces a supplemental and improved set of land surface hydrological fields ("MERRA-Land") generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies

Randomized Benchmarking of Quantum Gates

A key requirement for scalable quantum computing is that elementary quantum gates can be implemented with sufficiently low error. One method for determining the error behavior of a gate implementation is to perform process tomography. However, standard process tomography is limited by errors in state preparation, measurement and one-qubit gates. It suffers from inefficient scaling with number of qubits and does not detect adverse error-compounding when gates are composed in long sequences. An additional problem is due to the fact that desirable error probabilities for scalable quantum computing are of the order of 0.0001 or lower. Experimentally proving such low errors is challenging. We describe a randomized benchmarking method that yields estimates of the computationally relevant errors without relying on accurate state preparation and measurement. Since it involves long sequences of randomly chosen gates, it also verifies that error behavior is stable when used in long computations. We implemented randomized benchmarking on trapped atomic ion qubits, establishing a one-qubit error probability per randomized pi/2 pulse of 0.00482(17) in a particular experiment. We expect this error probability to be readily improved with straightforward technical modifications.Comment: 13 page

Land-Focused Changes in the Updated GEOS FP System (Version 5.25)

Many of the changes imposed in the January 2020 upgrade from Version 5.22 to 5.25 of the Goddard Earth Observing System (GEOS) Forward Processing (FP) analysis system were designed to increase the realism of simulated land variables. The changes, which consist of both land model parameter updates and improvements to the physical treatments employed for various land processes, have generally positive or neutral impacts on the character of the FP product, as documented here

Predicting Hydrological Drought: Relative Contributions of Soil Moisture and Snow Information to Seasonal Streamflow Prediction Skill

in this study we examine how knowledge of mid-winter snow accumulation and soil moisture conditions contribute to our ability to predict streamflow months in advance. A first "synthetic truth" analysis focuses on a series of numerical experiments with multiple sophisticated land surface models driven with a dataset of observations-based meteorological forcing spanning multiple decades and covering the continental United States. Snowpack information by itself obviously contributes to the skill attained in streamflow prediction, particularly in the mountainous west. The isolated contribution of soil moisture information, however, is found to be large and significant in many areas, particularly in the west but also in region surrounding the Great Lakes. The results are supported by a supplemental, observations-based analysis using (naturalized) March-July streamflow measurements covering much of the western U.S. Additional forecast experiments using start dates that span the year indicate a strong seasonality in the skill contributions; soil moisture information, for example, contributes to kill at much longer leads for forecasts issued in winter than for those issued in summer

Assessment of MERRA-2 Land Surface Energy Flux Estimates

In the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) system the land is forced by replacing the model-generated precipitation with observed precipitation before it reaches the surface. This approach is motivated by the expectation that the resultant improvements in soil moisture will lead to improved land surface latent heating (LH). Here we assess aspects of the MERRA-2 land surface energy budget and 2 m air temperatures (T(sup 2m)). For global land annual averages, MERRA-2 appears to overestimate the LH (by 5 W/sq m), the sensible heating (by 6 W/sq m), and the downwelling shortwave radiation (by 14 W/sq m), while underestimating the downwelling and upwelling (absolute) longwave radiation (by 10-15 W/sq m each). These results differ only slightly from those for NASA's previous reanalysis, MERRA. Comparison to various gridded reference data sets over Boreal summer (June-July-August) suggests that MERRA-2 has particularly large positive biases (>20 W/sq m) where LH is energy-limited, and that these biases are associated with evaporative fraction biases rather than radiation biases. For time series of monthly means during Boreal summer, the globally averaged anomaly correlations (R(sub anom)) with reference data were improved from MERRA to MERRA-2, for LH (from 0.39 to 0.48 vs. GLEAM data) and the daily maximum T(sup 2m) (from 0.69 to 0.75 vs. CRU data). In regions where T(sup 2m) is particularly sensitive to the precipitation corrections (including the central US, the Sahel, and parts of south Asia), the changes in the T(sup 2m) R(sub anom) are relatively large, suggesting that the observed precipitation influenced the T(sup 2m) performance