Overestimation Tendency Research Articles

Daily satellite-based sunshine duration estimates over Brazil: validation and intercomparison

Abstract. The broad geographical coverage and high temporal and spatial resolution of geostationary satellite data provide an excellent opportunity to collect information on variables whose spatial distribution and temporal variability are not adequately represented by in situ networks. This study focuses on assessing the effectiveness of two geostationary satellite-based sunshine duration (SDU) datasets over Brazil, given the relevance of SDU to various fields, such as agriculture and the energy sector, to ensure reliable SDU data over the country. The analyzed datasets are the operational products provided by the Satellite Application Facility on Climate Monitoring (CMSAF) that uses data achieved with the Meteorological Satellite (Meteosat) series and by the Satellite and Meteorological Sensors Division of the National Institute for Space Research (DISSM–INPE) that employs Geostationary Operational Environmental Satellite (GOES) data. The analyzed period ranges from September 2013 to December 2017. The mean bias error (MBE), mean absolute error (MAE), root mean squared error (RMSE), correlation coefficient (r), and scatterplots between satellite products and in situ daily SDU measurements provided by the National Institute of Meteorology (INMET) were used to access the performance of the products. They were calculated on a monthly basis and grouped into climate regions. The statistical parameters exhibited a uniform spatial distribution, indicating homogeneity within a given region. Except for the tropical northeast oriental (TNO) region, there were no significant seasonal dependencies observed. The MBE values for both satellite products were generally low across most regions in Brazil, mainly between 0 and 1 h. The correlation coefficient (r) results indicated a strong agreement between the estimated values and the observed data, with an overall r value exceeding 0.8. Nevertheless, there were notable discrepancies in specific areas. The CMSAF product showed a tendency to overestimate observations in the TNO region, with the MBE consistently exceeding 1 h for all months, while the DISSM product exhibited a negative gradient of the MBE values in the west–east direction in the northern portion of Brazil. The scatterplots for the TNO region revealed that the underestimation pattern observed in the DISSM product was influenced by the sky condition, with more accurate estimations observed under cloudy skies. Additional analysis suggested that the biases observed might be attributed to the misrepresentation of clear-sky reflectance. In the case of the CMSAF product, the overestimation tendency observed in the TNO region appeared to be a result of systematic underestimation of the effective cloud albedo. The findings indicated that both satellite-based SDU products generally exhibited good agreement with the ground observations across Brazil, although their performance varied across different regions and seasons. The analyzed operational satellite products present a reliable source of data to several applications, which is an asset due to its high spatial resolution and low time latency.

중학생의 성별과 과학시험유형에 따른 더닝-크루거 효과와 과잉확신편향 분석 연구

Objectives This study aims to investigate potential gender differences in middle school students' self-assessment of their abilities during academic exam situations. Focusing on the potential emergence of overconfidence bias when predicting their own exam scores, we examined how the Dunning-Kruger effect interacts with an inherent trait-gender, and the illusion of competence based on the type of science exams (multiple-choice/shortanswer questions) as observed in prior research. The objective is to collectively ascertain whether such gender- based distinctions exist.
 Methods Individual data were collected through a survey of 2nd-grade students at a coeducational middle school located in Gyeongsangbuk-do, during the week preceding the midterm and final examinations. Utilizing the actual test scores achieved, the study employed t-tests and regression analysis to scrutinize the gender-based distribution of academic performance, the variation in performance between genders across different test types, and the gender-associated dissimilarities in perceptions of competence.
 Results The Dunning-Kruger effect, characterized by the illusion of competence, was observed as a negative phenomenon, evidenced by the bias score—a metric reflecting the disparity between actual scores and anticipated scores. Among both male and female students, a positive correlation was established between higher expected scores and higher actual scores. Additionally, a noteworthy negative correlation emerged between actual scores and biased scores, signifying that students achieving higher actual scores exhibited lower biased scores. Specifically, male students demonstrated an overestimation tendency within the ‘low’ and ‘middle’ quartile groups, whereas female students displayed an underestimation tendency within the ‘highest’ quartile group. Expected scores exhibited no dependency on either test type or gender, yet significant effects were discerned for actual scores and bias scores in relation to gender. This confirmed that the magnitude of overestimation was greater among male students than their female counterparts, whereas the extent of underestimation was comparatively less pronounced in female students compared to male students.
 Conclusions This study successfully validated the manifestation of the Dunning-Kruger effect based on students' gender. Male students exhibited a propensity for overestimation driven by overconfidence bias, and it was substantiated that this overestimation was more pronounced in short-answer type questions compared to multiple- choice questions, irrespective of gender. Recognizing the inherent gender distinctions among students, it is recommended to propose a self-directed learning approach aimed at enhancing metacognitive abilities. Additionally, future research endeavors should focus on mitigating test-related anxiety factors through follow-up investigations.

Интегральная оценка нуклидного состава и тепловыделения потенциальных ядерных отходов, образованных топливом в бассейне выдержки энергоблока 1 АЭС Фукусима Дайичи

Assuming an accident that may damage spent fuel assemblies at an NPP, nuclear fuel in the spent fuel pool can be considered as radioactive waste. Data on its nuclide composition and heat generation is seen important for the safety assessment of its storage and potential radiological consequences in case of an accident. The SOCRAT/V3 integral code designed to simulate severe accidents at nuclear power plants with VVER power units is able to estimate the nuclide inventory of irradiated fuel based on a one-through calculation and a built-in model. This code was used for a computational analysis of the spent fuel from the Fukushima Daiichi Unit 1, namely, evaluated was its nuclide composition and its decay heat release. The results were compared with the reference data from JAEA (Japan). The calculations demonstrate that in case of severe accident simulations, the use of an integral approach causes no large errors in the estimated nuclide inventory. For most dose-forming radionuclides in the spent fuel, relative deviation of activity from the reference data does not exceed 15%, which appears to be lower than the typical uncertainty under source term predictions. Despite certain overestimation tendency that can be noticed, the decay heat levels calculated for the spent fuel pool appear to be quite similar to those presented in the JAEA report differing by no more than 16% in the first years after the accident, and by 5% ten years after the accident.

Snowfall and snow accumulation during the MOSAiC winter and spring seasons

Abstract. Data from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition allowed us to investigate the temporal dynamics of snowfall, snow accumulation and erosion in great detail for almost the whole accumulation season (November 2019 to May 2020). We computed cumulative snow water equivalent (SWE) over the sea ice based on snow depth and density retrievals from a SnowMicroPen and approximately weekly measured snow depths along fixed transect paths. We used the derived SWE from the snow cover to compare with precipitation sensors installed during MOSAiC. The data were also compared with ERA5 reanalysis snowfall rates for the drift track. We found an accumulated snow mass of 38 mm SWE between the end of October 2019 and end of April 2020. The initial SWE over first-year ice relative to second-year ice increased from 50 % to 90 % by end of the investigation period. Further, we found that the Vaisala Present Weather Detector 22, an optical precipitation sensor, and installed on a railing on the top deck of research vessel Polarstern, was least affected by blowing snow and showed good agreements with SWE retrievals along the transect. On the contrary, the OTT Pluvio2 pluviometer and the OTT Parsivel2 laser disdrometer were largely affected by wind and blowing snow, leading to too high measured precipitation rates. These are largely reduced when eliminating drifting snow periods in the comparison. ERA5 reveals good timing of the snowfall events and good agreement with ground measurements with an overestimation tendency. Retrieved snowfall from the ship-based Ka-band ARM zenith radar shows good agreements with SWE of the snow cover and differences comparable to those of ERA5. Based on the results, we suggest the Ka-band radar-derived snowfall as an upper limit and the present weather detector on RV Polarstern as a lower limit of a cumulative snowfall range. Based on these findings, we suggest a cumulative snowfall of 72 to 107 mm and a precipitation mass loss of the snow cover due to erosion and sublimation as between 47 % and 68 %, for the time period between 31 October 2019 and 26 April 2020. Extending this period beyond available snow cover measurements, we suggest a cumulative snowfall of 98–114 mm.

The role of aerodynamic resistance in thermal remote sensing-based evapotranspiration models

Aerodynamic resistance (hereafter ra) is a preeminent variable in evapotranspiration (ET) modelling. The accurate quantification of ra plays a pivotal role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models. It used data from nine Australian OzFlux eddy covariance sites of contrasting aridity in conjunction with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the sensible heat flux (H) and the latent heat flux (LE, the energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated against flux observations in water-limited (arid and semi-arid) and energy-limited (mesic) ecosystems from 2011 to 2019.Our results revealed an overestimation tendency of instantaneous LE by all three models in the water-limited shrubland, woodland and grassland ecosystems by up to 50% on average, which was caused by an underestimation of H. Overestimation of LE was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which uncertainties in LST (expressed as the difference between MODIS LST and in-situ LST) apparently played a minor role. On the other hand, a positive difference in LST coincided with low ra (high wind speeds) and caused a slight underestimation of LE at the water-limited sites. The impact of ra on the LE residual error was found to be of the same magnitude as the influence of LST uncertainties in the semi-arid ecosystems as indicated by variable importance in projection (VIP) coefficients from partial least squares regression above unity. In contrast, our results for the mesic forest ecosystems indicated minor dependency on ra for modelling LE (VIP < 0.4), which was due to a higher roughness length and lower LST resulting in the dominance of mechanically generated turbulence, thereby diminishing the importance of buoyancy production for the determination of ra.

Biases in morphological landscape features: challenges for environmental purposes in GIScience and related fields

During the last two decades, a wide range of geographical tools including the calculation of landscape metrics were transposed to ecological studies to build models for land-use dynamics. Currently, few studies have evaluated the biases which can occur during the rasterization step which could influence the results. The purpose of this study was to evaluate the influence of dataset rasterization on area and perimeter variables, which are frequently used to calculate landscape indices, according to (i) the rasterization cell size and (ii) the shape of geographic features. The Urban Atlas 2006 dataset focused on Bas-Rhin department (France) was used as a vector reference layer. Rasterization was performed for various cell sizes to evaluate the influence of spatial resolution on the errors injected into shape descriptors. Five morphological metrics were calculated for all geographic features. For the first time, a UMAP algorithm was performed to relate the rasterization relative errors at all spatial resolutions with morphological attributes. Results showed that low values of area errors were obtained for cell sizes lower than 5 m ( 10%) with an overestimation tendency. For cell sizes greater to 10 m, overestimations and underestimations were occurring according to the shape of geographic features. This study showed that sensitivity analyses must be performed before any study carried out on landscape changes estimation to define the best raster cell size as function to the morphological attributes of the geographic features, the predefined error threshold.

Static dipole polarizabilities of polyacenes using self-interaction-corrected density functional approximations

Density functional approximations (DFAs) are known to significantly overestimate the polarizabilities of long chain-like molecules. We study the static electric dipole polarizabilities and the vertical ionization potentials of polyacenes from benzene to pentacene using the Fermi-Löwdin orbital-based self-interaction corrected (FLOSIC) density functional method. The orbital by orbital self-interaction correction corrects for the overestimation tendency of DFAs. The polarizabilities calculated with FLOSIC-DFA are, however, overly corrected. We also tested the recently developed locally scaled self-interaction correction (LSIC) method on polyacenes. The local-scaling method applies full SIC in the one-electron regions and restores the proper behavior of the SIC exchange-correlation functionals in the uniform density limit. The results show that LSIC removes the overcorrection tendency of the FLOSIC-DFA and produces results that are in excellent agreement with reference coupled-cluster single and double values. The vertical ionization potentials with LSIC also show good agreement with available experimental values.

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions.

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model's built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a "basic" traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model's LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

GEDI: Gammachirp envelope distortion index for predicting intelligibility of enhanced speech

In this study, we propose a new concept, the gammachirp envelope distortion index (GEDI), based on the signal-to-distortion ratio in the auditory envelope, SDRenv, to predict the intelligibility of speech enhanced by nonlinear algorithms. The objective of GEDI is to calculate the distortion between enhanced and clean-speech representations in the domain of a temporal envelope extracted by the gammachirp auditory filterbank and modulation filterbank. We also extend GEDI with multi-resolution analysis (mr-GEDI) to predict the speech intelligibility of sounds under non-stationary noise conditions. We evaluate GEDI in terms of the speech intelligibility predictions of speech sounds enhanced by a classic spectral subtraction and a Wiener filtering method. The predictions are compared with human results for various signal-to-noise ratio conditions with additive pink and babble noises. The results showed that mr-GEDI predicted the intelligibility curves better than short-time objective intelligibility (STOI) measure, extended-STOI (ESTOI) measure, and hearing-aid speech perception index (HASPI) under pink-noise conditions, and better than HASPI under babble-noise conditions. The mr-GEDI method does not present an overestimation tendency and is considered a more conservative approach than STOI and ESTOI. Therefore, the evaluation with mr-GEDI may provide additional information in the development of speech enhancement algorithms.

Validation of GOES-Based Surface Insolation Retrievals and Its Utility for Model Evaluation

AbstractIncident solar radiation at Earth’s surface, also called surface insolation, plays an important role in the Earth system as it affects surface energy balance, weather, climate, water supply, biochemical emissions, photochemical reactions, etc. The University of Alabama in Huntsville (UAH) and the NASA Short-term Prediction Research and Transition Center (SPoRT) have been generating and archiving several products, including insolation, from the Geostationary Operational Environmental Satellite (GOES) Imager for over a decade. The NASA/UAH insolation product has been used in studies to improve air quality simulations, biogenic emission estimates, correcting surface energy balance, and for cloud assimilation, but has not been thoroughly evaluated. In this study, the NASA/UAH insolation product is compared to surface pyranometer measurements from the Surface Radiation Budget Network (SURFRAD) and the U.S. Climate Reference Network (USCRN) for a 12-month period from March 2013 to February 2014. The insolation product has normalized bias values within 6% of the mean observation, a root-mean-square error between 6% and 16%, and correlation coefficients greater than 0.96 for hourly insolation estimates. It also shows better performance without the presence of clouds. However, erroneous estimates may be produced for persistent snow-covered surfaces. Further, this study attempts to demonstrate the use of such a satellite-based insolation product for model evaluation. The NASA/UAH insolation product is compared to the downward shortwave radiation from the Rapid Refresh, version 1 (RAPv1), and successfully captures the overestimation tendency in surface energy input as mentioned in previous studies. Finally, future plans for improving the retrieval algorithm and developing a GOES-16 insolation product are discussed.

Reliability of single breath hold three-dimensional cine kat-ARC for the assessment of biventricular dimensions and function

PurposeTo assess the accuracy and reproducibility of 3D-cine k-adaptative-t-autocalibrating reconstruction for cartesian sampling (3D cine kat-ARC) for quantification of biventricular volumes, ejection fraction and LV mass in clinical practice. Method74 patients underwent cardiac magnetic resonance for clinical indications. In the whole population 3D cine kat-ARC and 2D cine bSSFP images were acquired on short axis view. Subsequently, the population was divided in three subgroups (dilated, hypetrophic, other phenotypes).Two experienced observers performed analysis of volumes, biventricular function and left ventricular mass in the overall population and subgroups using an off-line workstation.Statistical analysis was performed using Student's t-test, linear regression and Bland-Altman plot, correlation coefficient η2 and the intraclass correlation coefficient (ICC). A cut-off value of p < 0.05 was considered statistically significant. ResultsBiventricular volumes, function and left ventricular mass evaluated with 3D cine kat-ARC sequences did not show any significant difference compared to 2D bSSFP sequences in the overall population (p > 0.05). Bland-Altman analysis showed limited bias and narrow limits of the agreement for all measurements in overall population. Subgroup analysis showed a statistically significant difference (p = 0.04) for left ventricular ejection fraction (LVEF) in patients with a dilated phenotype; showing a minimum overestimation tendency for 3D cine kat ARC (2D cine bSSFP LVEF = 46.44 ± 15.83% vs 3D cine kat-ARC LVEF = 48.36 ± 16.50 %). Conclusions3D cine kat-ARC 3D sequences allow an accurate evaluation of biventricular volumes and function in a single breath hold.

Evaluation of the TRMM rainfall product accuracy over the central Mediterranean during a 20-year period (1998–2017)

This study presents the first comprehensive evaluation of the performance of the monthly TRMM rainfall product (3B43-v7) in capturing the rainfall patterns, on the geographic area of the central Mediterranean during the period 1998–2017. Monthly rainfall data from a network of ground-based stations were used to compute a series of spatiotemporal evaluation statistics for the satellite measurement accuracy. These statistics showed a satisfactory accuracy of the examined satellite products in capturing seasonal and annual rainfall patterns, having rather high correlation coefficients (0.75 to 0.96). Nevertheless, a slight overestimation of the rainfall values (with a mean value lower than 15 mm) implied by the TRMM was found at seasonal basis with the exception of winter when a small underestimation (with a mean value amounting to 8 mm) was detected. The overestimation tendency was mainly detected over the northern and the western parts of the studied domain, where the effect of the earth surface altitude seems to be important. An underestimation tendency prevails over the marine regions and seems to be associated with the rain rate (in the sense that heavy rainfall episodes are underestimated more). The overall biases of the TRMM rainfall product in respect to the control ground-based station sample found to be within ± 0.2 of the local rainfall standard deviation and nonstatistically significant almost in half of the cases in all seasons except winter. The possible causes of the detected diversity of the seasonal error statistics behavior were further explored and discussed.

Accuracy Comparison of Coastal Wind Speeds between WRF Simulations Using Different Input Datasets in Japan

In order to improve the accuracy of the wind speed simulated by a mesoscale model for the wind resource assessment in coastal areas, this study evaluated the effectiveness of using the Japan Meteorological Agency (JMA)’s latest and finest (2 km × 2 km) grid point value (GPV) data, produced from the local forecast model (LFM) as input data to the mesoscale model. The evaluation was performed using wind lidar measurements at two sites located on the coasts of the Sea of Japan and Pacific Ocean. The accuracy of the LFM–GPV was first compared with that of two products from the JMA Meso Scale Model (MSM) (5 km × 5 km): MSM-GPV and mesoscale analysis (MANAL). Consequently, it was shown that LFM–GPV exhibited the most accurate wind speeds against lidar measurements. Next, dynamical downscaling simulations were performed using the weather research and forecasting model (WRF) forced by the three datasets above, and their results were compared. As compared to the GPVs, it was found that the WRF dynamical downscaling simulation using them as input can improve the accuracy of the coastal wind speeds. This was attributed to the advantage of the WRF simulation to improve the negative bias from the input data, especially for the winds blowing from the sea sectors. It was also found that even if the LFM–GPV is used as an input to the WRF simulation, it does not always reproduce more accurate wind speeds, as compared to the simulations using the other two datasets. This result is partly owing to the tendency of WRF to overestimate the wind speed over land, thus obscuring the higher accuracy of the LFM–GPV. It was also shown that the overestimation tendency cannot be improved by only changing the nudging methods or the planetary boundary layer schemes in WRF. These results indicate that it may be difficult to utilize the LFM–GPV in the WRF wind simulation, unless the overestimation tendency of WRF itself is improved first.

Rainfall retrieval and drought monitoring skill of satellite rainfall estimates in the Ethiopian Rift Valley Lakes Basin

Satellite-based rainfall products are essential for retrieving rainfall, particularly in data-scarce and drought-prone countries like Ethiopia. However, their quality needs to be validated prior to their use. Therefore, we evaluated the performance of the Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), the Tropical Applications of Meteorology using Satellite data version 3.0 (TAMSAT3), and the African Rainfall Climatology version 2 (ARC2) satellite rainfall estimates in the Ethiopian Rift Valley Lakes Basin. Their skill of retrieval was evaluated against ground-measured rainfall at dekadal, monthly, and seasonal scales across agroclimatic zones over 2001 to 2017. Finally, these satellite products have demonstrated different levels of agreement with the reference data, being the highest for CHIRPS and the lowest for ARC2. At all timescales and agroclimatic zones, ARC2 has severely underestimated the actual rainfall while TAMSAT3 has persistently overestimated it. However, TAMSAT3 has demonstrated better performance than ARC2. Generally, except for its slightly larger dekadal false alarm ratio, CHIRPS has achieved the highest and most consistent agreement with the reference data at all the timescales and agroclimatic classes. Consequently, CHIRPS was further assessed for its suitability of drought monitoring, and it has exhibited promising skill in detecting specific historical drought events. Therefore, to overcome the scarcity of ground-measured rainfall data in the study area, we recommend the CHIRPS rainfall estimate to be used as an alternative data source for drought monitoring. Conversely, owing to its overestimation tendency, TAMSAT3 could be used for flood monitoring in this region.

Estimation of surface downward shortwave radiation over China from AVHRR data based on four machine learning methods

Downward shortwave radiation (DSR) is one of the major driving forces of climate system. Knowledge of the Earth’s radiation budget is essential for improving our understanding of the Earth’s climate. Therefore, accurate estimation of DSR has great significance. Satellite remote sensing is a practical way to derive DSR with high spatial resolution and coverage. In this study, four machine learning methods, including gradient boosting regression tree (GBRT), random forest (RF), multivariate adaptive regression spline (MARS), and artificial neural network (ANN), were applied to estimate DSR at a spatial resolution of 5 km and a temporal resolution of 1 day using Advanced Very High Resolution Radiometer (AVHRR) data. The DSR estimates based on four machine learning methods were evaluated using ground measurements at 96 sites over China. The measurements were collected from the Climate Data Center of the Chinese Meteorological Administration (CDC/CMA) from 2001 to 2003. The evaluation results showed that the GBRT method performed best at both daily and monthly time scales under both clear and cloudy sky conditions. The validation results at the daily time scale showed an overall root mean square error (RMSE) of 30.34 W m−2 and an R value of 0.90 under clear sky conditions, whereas these values were 42.03 W m−2 and 0.86, respectively, under cloudy sky conditions. The DSR estimates had an overall RMSE value of 16.93 W m−2 and an R value of 0.97 at the monthly time scale. The Clouds and Earth's Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data sets were also used for comparison with the DSR estimates based on the GBRT method. The DSR estimates based on the GBRT method exhibited similar spatial distributions with those of the CERES-EBAF DSR product. Moreover, the DSR estimates based on the GBRT method did not show a clear overestimation tendency, as the CERES-EBAF DSR product did, at the CDC/CMA sites.

Assessment of the Subchannel Code CTF for Single- and Two-Phase Flows

As part of the Consortium for Advanced Simulation of Light Water Reactors, the subchannel code CTF is being used for single-phase and two-phase flow analysis under light water reactor operating conditions. Accurate determination of flow distribution, pressure drop, and void content is crucial for predicting margins to thermal crisis and ensuring more efficient plant performance. In preparation for the intended applications, CTF has been validated against data from experimental facilities comprising the General Electric (GE) 3 × 3 bundle, the boiling water reactor full-size fine-mesh bundle tests (BFBTs), the Risø tube, and the pressurized water reactor subchannel and bundle tests (PSBTs). Meanwhile, the licensed, well-recognized subchannel code VIPRE-01 was used to generate a baseline set of simulations for the targeted tests and solution parameters were compared to the CTF results.The flow split verification problem and single-phase GE 3 × 3 results are essentially in perfect agreement between the two codes. For the two-phase GE 3 × 3 cases, flow and quality discrepancies arise in the annular-mist flow regime, yet significant improvement is observed in CTF when void drift and two-phase turbulent mixing enhancement are considered. The BFBT pressure drop benchmark shows close agreement between predicted and measured results in general, although considerable overprediction by CTF is observed at relatively high void locations of the facility. This overestimation tendency is confirmed by the Risø cases. While overall statistics are satisfactory, both BFBT and PSBT bubbly-to-churn flow void contents are markedly overpredicted by CTF.The issues with two-phase closures such as turbulent mixing, interfacial and wall friction, and subcooled boiling heat transfer need to be addressed. Preliminary sensitivity studies are presented herein, but more advanced models and code stability analysis require further investigation.

Do Children Accurately Estimate Their Performance of Fundamental Movement Skills?

An inaccurate perception of motor competence might compromise the engagement of children in physical activities and might be a problem in terms of safety in physical education classes or at playgrounds. The relationship between estimation and actual performance in children with different levels of performance in fundamental movement skills (FMS) was analyzed. Three hundred and three children (aged 6–10 years) were ranked according to their performance in FMS tasks: jumping, kicking, throwing, and walking backward (WB) on a balance beam. Tertiles were created for each task according to children’s performance. Before performing the tasks, children estimated their maximum performance. Absolute percent errors (i.e., deviation percentage from accurate estimations) and error tendency (i.e., frequency of underestimations, right judgments, or overestimations) were calculated. All performance groups tended to overestimate their skills at all tasks, except for the upper tertile group at the WB task (underestimation tendency). After controlling for age, children in the lower tertiles were consistently less accurate than children in the upper tertiles, exhibiting greater absolute percent errors for all the tasks. The overestimation tendency that was found might positively influence children’s engagement in physical activities, but unrealistic estimations might be a problem in terms of safety.

Upscaling instantaneous to daily evapotranspiration using modelled daily shortwave radiation for remote sensing applications: an artificial neural network approach

Abstract. Upscaling instantaneous evapotranspiration retrieved at any specific time-of-day (ETi) to daily evapotranspiration (ETd) is a key challenge in mapping regional ET using polar orbiting sensors. Various studies have unanimously cited the shortwave incoming radiation (RS) to be the most robust reference variable explaining the ratio between ETd and ETi. This study aims to contribute in ETi upscaling for global studies using the ratio between daily and instantaneous incoming shortwave radiation (RSd ∕ RSi) as a factor for converting ETi to ETd.This paper proposes an artificial neural network (ANN) machine-learning algorithm first to predict RSd from RSi followed by using the RSd ∕ RSi ratio to convert ETi to ETd across different terrestrial ecosystems. Using RSi and RSd observations from multiple sub-networks of the FLUXNET database spread across different climates and biomes (to represent inputs that would typically be obtainable from remote sensors during the overpass time) in conjunction with some astronomical variables (e.g. solar zenith angle, day length, exoatmospheric shortwave radiation), we developed the ANN model for reproducing RSd and further used it to upscale ETi to ETd. The efficiency of the ANN is evaluated for different morning and afternoon times of day, under varying sky conditions, and also at different geographic locations. RS-based upscaled ETd produced a significant linear relation (R2 = 0.65 to 0.69), low bias (−0.31 to −0.56 MJ m−2 d−1; approx. 4 %), and good agreement (RMSE 1.55 to 1.86 MJ m−2 d−1; approx. 10 %) with the observed ETd, although a systematic overestimation of ETd was also noted under persistent cloudy sky conditions. Inclusion of soil moisture and rainfall information in ANN training reduced the systematic overestimation tendency in predominantly overcast days. An intercomparison with existing upscaling method at daily, 8-day, monthly, and yearly temporal resolution revealed a robust performance of the ANN-driven RS-based ETi upscaling method and was found to produce lowest RMSE under cloudy conditions. Sensitivity analysis revealed variable sensitivity of the method to biome selection and high ETd prediction errors in forest ecosystems are primarily associated with greater rainfall and cloudiness. The overall methodology appears to be promising and has substantial potential for upscaling ETi to ETd for field and regional-scale evapotranspiration mapping studies using polar orbiting satellites.

Validation of standard and alternative satellite ocean-color chlorophyll products off Western Iberia

Abstract Chlorophyll a concentration (Chl) product validation off the Western Iberian coast is here undertaken by directly comparing remote sensing data with in situ surface reference values. Both standard and recently developed alternative algorithms are considered for match-up data analysis. The investigated standard products are those produced by the MERIS (algal 1 and algal 2) and MODIS (OC3M) algorithms. The alternative data products include those generated within the CoastColour Project and Ocean Color Climate Change Initiative (OC-CCI) funded by ESA, as well as a neural net model trained with field measurements collected in the Atlantic off Portugal (MLP ATLP ). Statistical analyses showed that satellite Chl estimates tend to be larger than in situ reference values. The study also revealed that a non-uniform Chl distribution in the water column can be a concurring factor to the documented overestimation tendency when considering larger optical depth match-up stations. Among standard remote sensing products, MODIS OC3M and MERIS algal 2 yield the best agreement with in situ data. The performance of MLP ATLP highlights the capability of regional solutions to further improve Chl retrieval by accounting for environmental specificities. Results also demonstrate the relevance of oceanographic regions such as the Nazare area to evaluate how complex hydrodynamic conditions can influence the quality of Chl products.

Automated Image Analysis for the Detection of Benthic Crustaceans and Bacterial Mat Coverage Using the VENUS Undersea Cabled Network

The development and deployment of sensors for undersea cabled observatories is presently biased toward the measurement of habitat variables, while sensor technologies for biological community characterization through species identification and individual counting are less common. The VENUS cabled multisensory network (Vancouver Island, Canada) deploys seafloor camera systems at several sites. Our objective in this study was to implement new automated image analysis protocols for the recognition and counting of benthic decapods (i.e., the galatheid squat lobster, Munida quadrispina), as well as for the evaluation of changes in bacterial mat coverage (i.e., Beggiatoa spp.), using a camera deployed in Saanich Inlet (103 m depth). For the counting of Munida we remotely acquired 100 digital photos at hourly intervals from 2 to 6 December 2009. In the case of bacterial mat coverage estimation, images were taken from 2 to 8 December 2009 at the same time frequency. The automated image analysis protocols for both study cases were created in MatLab 7.1. Automation for Munida counting incorporated the combination of both filtering and background correction (Median- and Top-Hat Filters) with Euclidean Distances (ED) on Red-Green-Blue (RGB) channels. The Scale-Invariant Feature Transform (SIFT) features and Fourier Descriptors (FD) of tracked objects were then extracted. Animal classifications were carried out with the tools of morphometric multivariate statistic (i.e., Partial Least Square Discriminant Analysis; PLSDA) on Mean RGB (RGBv) value for each object and Fourier Descriptors (RGBv+FD) matrices plus SIFT and ED. The SIFT approach returned the better results. Higher percentages of images were correctly classified and lower misclassification errors (an animal is present but not detected) occurred. In contrast, RGBv+FD and ED resulted in a high incidence of records being generated for non-present animals. Bacterial mat coverage was estimated in terms of Percent Coverage and Fractal Dimension. A constant Region of Interest (ROI) was defined and background extraction by a Gaussian Blurring Filter was performed. Image subtraction within ROI was followed by the sum of the RGB channels matrices. Percent Coverage was calculated on the resulting image. Fractal Dimension was estimated using the box-counting method. The images were then resized to a dimension in pixels equal to a power of 2, allowing subdivision into sub-multiple quadrants. In comparisons of manual and automated Percent Coverage and Fractal Dimension estimates, the former showed an overestimation tendency for both parameters. The primary limitations on the automatic analysis of benthic images were habitat variations in sediment texture and water column turbidity. The application of filters for background corrections is a required preliminary step for the efficient recognition of animals and bacterial mat patches.