Quality assurance of meteorological data is crucial for ensuring the reliability of applications and models that use such data as input variables, especially in the field of environmental sciences. ...Spatial validation of meteorological data is based on the application of quality control procedures using data from neighbouring stations to assess the validity of data from a candidate station (the station of interest). These kinds of tests, which are referred to in the literature as spatial consistency tests, take data from neighbouring stations in order to estimate the corresponding measurement at the candidate station. These estimations can be made by weighting values according to the distance between the stations or to the coefficient of correlation, among other methods. The test applied in this study relies on statistical decision-making and uses a weighting based on the standard error of the estimate. This paper summarizes the results of the application of this test to maximum, minimum and mean temperature data from the Agroclimatic Information Network of Andalusia (southern Spain). This quality control procedure includes a decision based on a factor
f
, the fraction of potential outliers for each station across the region. Using GIS techniques, the geographic distribution of the errors detected has been also analysed. Finally, the performance of the test was assessed by evaluating its effectiveness in detecting known errors.
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•ET0 projection maps were calculated using a temperature-based approach up to 2100.•The models were trained using 122 Automated Weather Station from the RIA network.•The ET0 estimates ...were more accurate when using RCP8.5 compared to RCP4.5.•The ET0 is expected to increase from 1300 to 1600 mm to 1900 mm using the RCP8.5.•The highest Sen slopes from 2023 to 2100 are in Andalusia's south coast.
This study firstly examines the performance of temperature-based machine learning models for estimating reference evapotranspiration (ET0), an essential parameter for water management in agriculture, ecosystems, and hydrology. Data from 122 Automated Weather Stations (AWS) across different regions in Southern Spain has been studied and four machine learning models have been developed and assessed: Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM). The results indicate that all machine learning models outperform the traditional Hargreaves-Samani method in estimating ET0. Specifically, ELM performed, on average, as the best in terms of Global Performance Indicator (GPI) for those locations situated in the first region (GPI = 0.1860), while MLP outperformed the rest for those located in the second (GPI = 0,1162). Besides, the configuration using minimum, mean and maximum air temperature (Tx, Tm, Tn, respectively), the diurnal temperature range (DTR), and Extraterrestrial solar radiation features (Ra) was found to be the fittest for the second region (GPI = 0.0734) and that using Tx, Tn, Tm and Ra in the first one in (GPI = 0.1938). Once the models were validated, they were applied to future 5 km gridded projection datasets, using different Representative Concentration Pathway (RCP) scenarios, in order to estimate ET0 up to the year 2100. In general, the projected ET0 was found to increase significantly in the future, with Mann Kendall Z values that ranged from 7.11 to 10.37 in the RCP4.5 scenario and from 10.84 to 12.57 in the RCP8.5 scenario. Thus, the ET0 is expected to increase from 1300 to 1600 mm to 1500–1700 mm using the RCP4.5 and to 1900 mm using the RCP8.5 in Andalusia, with the highest increase occurring in the south coastal region. This study provides important insights into the application of machine learning models to estimate ET0 and its implications for future water management strategies.
Context.
Future astrophysical surveys such as J-PAS will produce very large datasets, the so-called “big data”, which will require the deployment of accurate and efficient machine-learning (ML) ...methods. In this work, we analyze the miniJPAS survey, which observed about ∼1 deg
2
of the AEGIS field with 56 narrow-band filters and 4
u
g
r
i
broad-band filters. The miniJPAS primary catalog contains approximately 64 000 objects in the
r
detection band (mag
A
B
≲ 24), with forced-photometry in all other filters.
Aims.
We discuss the classification of miniJPAS sources into extended (galaxies) and point-like (e.g., stars) objects, which is a step required for the subsequent scientific analyses. We aim at developing an ML classifier that is complementary to traditional tools that are based on explicit modeling. In particular, our goal is to release a value-added catalog with our best classification.
Methods.
In order to train and test our classifiers, we cross-matched the miniJPAS dataset with SDSS and HSC-SSP data, whose classification is trustworthy within the intervals 15 ≤
r
≤ 20 and 18.5 ≤
r
≤ 23.5, respectively. We trained and tested six different ML algorithms on the two cross-matched catalogs: K-nearest neighbors, decision trees, random forest (RF), artificial neural networks, extremely randomized trees (ERT), and an ensemble classifier. This last is a hybrid algorithm that combines artificial neural networks and RF with the J-PAS stellar and galactic loci classifier. As input for the ML algorithms we used the magnitudes from the 60 filters together with their errors, with and without the morphological parameters. We also used the mean point spread function in the
r
detection band for each pointing.
Results.
We find that the RF and ERT algorithms perform best in all scenarios. When the full magnitude range of 15 ≤
r
≤ 23.5 is analyzed, we find an area under the curve AUC = 0.957 with RF when photometric information alone is used, and AUC = 0.986 with ERT when photometric and morphological information is used together. When morphological parameters are used, the full width at half maximum is the most important feature. When photometric information is used alone, we observe that broad bands are not necessarily more important than narrow bands, and errors (the width of the distribution) are as important as the measurements (central value of the distribution). In other words, it is apparently important to fully characterize the measurement.
Conclusions.
ML algorithms can compete with traditional star and galaxy classifiers; they outperform the latter at fainter magnitudes (
r
≳ 21). We use our best classifiers, with and without morphology, in order to produce a value-added catalog.
We report the development of a Si-based micro thermogenerator build from silicon-on-insulator by using standard CMOS processing. Ultrathin single-crystalline Si membranes, 100nm in thickness, with ...embedded n and p-type doped regions electrically connected in series and thermally in parallel, are active elements of the thermoelectric device that generate thermopower under various thermal gradients. This proof-of-concept device produces an output power density of 4.5µW/cm2, under a temperature difference of 5K, opening the way to envisage integration as wearable thermoelectrics for body energy scavenging.
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•We describe the microfabrication of a planar CMOS compatible Si-based generator.•The device contains a 100nm thick Si membrane with embedded n,p doped regions.•A power output of 4.5µW/cm2 is achieved for a temperature difference of 5.5K.•The chip could be suited for body-energy scavenging to feed low-power devices.
•A quality assurance system for validating meteorological data used to compute ET0 estimations is proposed.•Self-calibrated solar radiation models based on temperature for each weather stations have ...been carried out with an adequate performance.•The importance of applying quality control procedures for accurately estimating crop water requirements is revealed.
Validated meteorological data are required to make climate assessments, related decisions and to appropriately compute other important parameters such as reference evapotranspiration (ET0), vital to accurately estimate crops water requirements. In addition, quality meteorological datasets will increase the reliability of the results obtained by scientific or technical models that use them. In semiarid regions, with a structural water deficit as province of Mendoza (Argentina), the integrity and quality of these data are crucial to improve ET0 estimates, ensuring an adequate irrigation water management. In this work, several quality assurance procedures were applied to meteorological data—as a pre-requisite for ET0 computations—in order to detect erroneous and invalid data of each parameter from automated weather stations located in the three irrigated areas of province of Mendoza (Northern oasis, Western oasis and Southern oasis). Due to the lack and poor quality of solar radiation data, calibration of new based temperature solar radiation prediction models for each of the station are proposed. Results show the data flagged for each variable by range/limits, step, internal consistency and persistence tests, providing guidance of great value to end users. Finally, a simple comparison of ET0 estimations using original and validated meteorological datasets for each irrigated area in province of Mendoza is also reported.
•Regional frequency analysis of extreme annual rainfall data series has been done.•Several quality control methods were previously applied for validating the datasets.•A multifractal approach was ...used to characterize validated daily rainfall data.•Homogeneous regions were delimitated using the multifractal properties of rainfall.•This work shows a new tool based on multifractality to form homogeneous regions.
In this work, a regional frequency analysis of extreme annual rainfall data in Malaga (Southern Spain) has been performed. Rainfall records have been validated, applying various quality control tests as a pre-requisite before their use, ensuring their reliability and discarding anomalous data. For grouping the stations into potential homogeneous regions, the multifractal properties of daily rainfall data series recorded at 72 locations have been studied. The scaling of the rainfall moments has been analyzed and the empirical moments scaling exponent functions have been obtained. The corresponding multifractal values have been used to group stations into regions, resulting some of them homogeneous.
ABSTRACT
Tipping‐bucket rain gauges are convenient and reliable sensors of rainfall measurements; however, like all other field sensors, they are subject to different kinds of errors. Due to their ...location, rain gauges in this research can record accidental pulses produced by vibrations from works of farm machineries near the station, or may receive water from sprinkler irrigation systems. These spurious inputs are recorded as precipitation data, although they do not correspond to rain, so it is necessary to detect them in order to avoid their inclusion in the future soil–water balance. The main objective of this work is to design a simple quality control procedure to validate precipitation data generated in several stations of the Agroclimatic Information Network of Andalusia (southern Spain), and valid for similar agro‐meteorological station networks. The relationship between the degree of cloudiness through attenuation of solar radiation (atmospheric transmittance coefficient), relative humidity and rainfall measurements has been studied in order to separate true and false precipitation records. Meteorological data from 2002 to 2011 were used for the study.
Performance of engineering degree students often becomes an important concern for lectures. Using classical teaching methodologies in practical lectures may be behind the problem. The present work ...develops a couple of innovative student-response-system based methodologies designed to be implemented during engineering practical lessons in higher education (BSc level). A pretest–posttest design is developed to assess the evolution of student’s performance (third course in Civil Engineering degree at the University of Córdoba). In addition, a motivational questionnaire towards the use of the innovative methodologies has been filled by the students. The results of this pilot experience show an impact on performance by the rapid feedback linked to the in-class formative assessment, in which conceptual reminders at the beginning of the practical lessons boost the motivation, engagement, and enhancement. The motivational questionnaires depicted a favourable perception of the methodologies by the students, highlighting their recommendation to extend its application to other lecturers.
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