Browsing by Autor "Juan Marcos Calle"

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Development of Hourly Resolution Air Temperature Across Titicaca Lake on Auxiliary ERA5 Variables and Machine Learning-Based Gap-Filling
(Multidisciplinary Digital Publishing Institute, 2025) J. W. Sirpa-Poma; Juan Marcos Calle; Elvis Uscamayta-Ferrano; Jorge Molina‐Carpio; Frédéric Satgé; Osmar Cuentas Toledo; Ricardo Duran; Paula Pacheco Mollinedo; Rizuana Iqbal Hussain; Ramiro Pillco Zolá
This article presents an innovative procedure that combines advanced quality control (QC) methods with machine learning (ML) techniques to produce reliable, continuous, high-resolution meteorological data. The approach was applied to hourly air temperature records from six automatic weather stations located around Lake Titicaca in the Altiplano region of South America. The raw dataset contained time gaps, inconsistencies, and outliers. To address these, the QC stage employed Interquartile Range, Biweight, and Local Outlier Factor (LOF) statistics, resulting in a clean dataset. Two gap-filling methods were implemented: a spatial approach using time series from nearby stations and a temporal approach based on each station's time series and selected variables from the ERA5-Land reanalysis. Several ML models were also employed in this process: Random Forest (RF), Support Vector Machine (SVM), Stacking (STACK), and AdaBoost (ADA). Model performance was evaluated on a validation subset (30% of station data). The RF model achieved the best results, with R<sup>2</sup> values up to 0.9 and Root Mean Square Error (RMSE) below 1.5 °C. The spatial approach performed best when stations were strongly correlated, while the temporal approach was more suitable for locations with low inter-station correlation and high local variability. Overall, the procedure substantially improved data reliability and completeness, and it can be extended to other meteorological variables.
Effects of undetected data quality issues on climatological analyses
(2017) Stefan Hunziker; Stefan Brönnimann; Juan Marcos Calle; Isabel Moreno; Marcos Andrade; Laura Ticona; Adrian Huerta; Waldo Lavado‐Casimiro
Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature, and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control method strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets, and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.
Effects of undetected data quality issues on climatological analyses
(Copernicus Publications, 2018) Stefan Hunziker; Stefan Brönnimann; Juan Marcos Calle; Isabel Moreno; Marcos Andrade; Laura Ticona; Adrian Huerta; Waldo Lavado‐Casimiro
Abstract. Systematic data quality issues may occur at various stages of the data generation process. They may affect large fractions of observational datasets and remain largely undetected with standard data quality control. This study investigates the effects of such undetected data quality issues on the results of climatological analyses. For this purpose, we quality controlled daily observations of manned weather stations from the Central Andean area with a standard and an enhanced approach. The climate variables analysed are minimum and maximum temperature and precipitation. About 40 % of the observations are inappropriate for the calculation of monthly temperature means and precipitation sums due to data quality issues. These quality problems undetected with the standard quality control approach strongly affect climatological analyses, since they reduce the correlation coefficients of station pairs, deteriorate the performance of data homogenization methods, increase the spread of individual station trends, and significantly bias regional temperature trends. Our findings indicate that undetected data quality issues are included in important and frequently used observational datasets and hence may affect a high number of climatological studies. It is of utmost importance to apply comprehensive and adequate data quality control approaches on manned weather station records in order to avoid biased results and large uncertainties.
Establishing glacier proximal meteorological and glacier ablation stations in different climatic zones along the South American Andes.
(2024) Owen King; Tom Matthews; Marcos Andrade; Juan‐Luis García; Claudio Bravo; Wouter Buytaert; Juan Marcos Calle; Alejandro Dussaillant; Tamsin Edwards; Iñigo Irarrázaval
Climate change has had a significant impact on the behaviour of the high mountain cryosphere, with widespread glacier retreat and mass loss now occurring in most of the planet&#8217;s glacierised mountain ranges over multi-decadal timescales. If we are to accurately understand the impacts of deglaciation on freshwater availability to communities downstream, robust modelling of future glacier meltwater yield is paramount. Meteorological observations at glacierised elevations are essential to drive simulations of the energy balance at glacier surfaces, and therefore glacier melt, although such records are sparse in most high mountain regions due to the logistical challenges associated with making even short-term measurements. The scarcity of high-altitude meteorological observations has resulted in only limited understanding of factors such as the spatial and temporal variability of temperature lapse rates, precipitation amounts and phase, and the prevalence of conditions suited to sublimation, all of which have an important influence on glacier mass loss rates at high elevation.Here we summarise the installation of meteorological and glacier ablation stations in different climatic zones of the South American Andes - the Tropical Andes of Peru (Nevado Ausangate basecamp, 4800 m, (13&#176;48'45.96"S, 71&#176;12'53.18"W) and Bolivia (Laguna Glaciar, 5300 m, 15&#176;50'10.59"S, 68&#176;33'11.30"W), the Subtropical Andes (Glaciar Universidad, Chile, 2540 m, 34&#176;43'10.07"S, 70&#176;20'44.98"W) and Patagonian Andes (Lago Tranquillo, Chile, 280 m, 46&#176;35'47.00"S, 72&#176;47'38.91"W) &#8211; as part of the NERC-funded Deplete and Retreat Project. Meteorological station records include time series of air temperature and pressure, relative humidity, wind speed and direction, incoming and outgoing short- and longwave radiation, precipitation amount and phase. Coincident glacier ablation is monitored at each site using &#8216;Smart Stakes&#8217;, recording surface elevation change on-glacier. We describe station situation, installation and preliminary measurements, along with aims and objectives of analyses using the meteorological time series.
Identifying, attributing, and overcoming common data quality issues of manned station observations
(Wiley, 2017) Stefan Hunziker; Stefanie Gubler; Juan Marcos Calle; Isabel Moreno; Marcos Andrade; Fernando Velarde; Laura Ticona; Gualberto Carrasco; Yaruska Castellón; Clara Oria
ABSTRACT In situ climatological observations are essential for studies related to climate trends and extreme events. However, in many regions of the globe, observational records are affected by a large number of data quality issues. Assessing and controlling the quality of such datasets is an important, often overlooked aspect of climate research. Besides analysing the measurement data, metadata are important for a comprehensive data quality assessment. However, metadata are often missing, but may partly be reconstructed by suitable actions such as station inspections. This study identifies and attributes the most important common data quality issues in Bolivian and Peruvian temperature and precipitation datasets. The same or similar errors are found in many other predominantly manned station networks worldwide. A large fraction of these issues can be traced back to measurement errors by the observers. Therefore, the most effective way to prevent errors is to strengthen the training of observers and to establish a near real‐time quality control ( QC ) procedure. Many common data quality issues are hardly detected by usual QC approaches. Data visualization, however, is an effective tool to identify and attribute those issues, and therefore enables data users to potentially correct errors and to decide which purposes are not affected by specific problems. The resulting increase in usable station records is particularly important in areas where station networks are sparse. In such networks, adequate selection and treatment of time series based on a comprehensive QC procedure may contribute to improving data homogeneity more than statistical data homogenization methods.