Validation of ERA5-Land temperature and relative humidity on four Peruvian glaciers using on-glacier observations

Weather and climate conditions drive the evolution of tropical glaciers which play an important role as water reservoirs for Peruvian inhabitants in the arid coast and semi-arid Andean region. The scarcity of long-term high-quality observations over Peruvian glaciers has motivated the extensive use of reanalysis data to describe the climatic evolution of these glaciers. However, the representativeness and uncertainties of these reanalysis products over these glaciers are still poorly constrained. This study evaluates the ability of the ERA5-Land reanalysis (ERA5L) to reproduce hourly and monthly 2 m air temperature and relative humidity (T2m and Rh2m, respectively) over several Peruvian glaciers. We compared the ERA5L with data from four on-glacier automatic weather stations (AWS), whose hourly time series were completed with nearby stations, for the period January 2017 to December 2019. Results indicates a better performance of the reanalysis for T2m (r >0.80) than for Rh2m (similar to 0.4<

Effects of Land Cover Variability on Evapotranspiration in the Llanganuco Valley

A process-based understanding of the effect land cover has on evapotranspiration (ET) in alpine valleys is lacking. It is conventionally assumed that ET is a negligible part of the water cycle in the Peruvian Andes, a critical source of water for the local inhabitants, due to lack of precipitation during the 6-month dry season and high humidity during the 6-month wet season. However, recent research findings from an embedded sensor network indicate that ET is in fact an important part of the Andean water cycle in Peru. Water resources within Peru are affected by the glaciers within the Cordillera Blanca, where the Llanganuco Valley is located, which will severely impact people away from the valley. This project incorporated GIS, remote sensing, meteorological data and ET modeling to further show that ET is affected by the valley’s terrain and land cover and it varies according to seasonal and daily time scales

Air temperature measurements using autonomous self-recording dataloggers in mountainous and snow covered areas

High mountain areas are poorly represented by official weather observatories. It implies that new instruments must be evaluated over snow-covered and strongly insolated environments (i.e. mid-latitude mountain areas). We analyzed uncertainty sources over snow covered areas including: 1) temperature logger accuracy and bias of two widely used temperature sensors (Tinytag and iButton); 2) radiation shield performance under various radiation, snow, and wind conditions; 3) appropriate measurement height over snow covered ground; and 4) differences in air temperature measured among nearby devices over a horizontal band. The major results showed the following. 1) Tinytag performance device (mean absolute error: MAE≈ 0.1–0.2°C in relation to the reference thermistor) was superior to the iButton (MAE≈ 0.7°C), which was subject to operating errors. 2) Multi-plate radiation shield showed the best performance under all conditions (> 90% samples has bias between ±0.5°C). The tube shield required wind (> 2.5ms⁠−1) for adequate performance, while the funnel shield required limited radiation (< 400Wm⁠−2). Snow cover causes certain overheating. 3) Air temperatures were found to stabilize at 75–100cm above the snow surface. Air temperature profile was more constant at night, showing a considerable cooling on near surface at midday. 4) Horizontal air temperature differences were larger at midday (0.5°C). These findings indicate that to minimize errors air temperature measurements over snow surfaces should be carried out using multi-plate radiation shields with high-end thermistors such as Tinytags, and be made at a minimum height above the snow covered ground.This study was funded by the research projects “El papel de la nieve en la hidrología de la peninsula ibérica y su respuesta a procesos de cambio global-HIDROIBERNIEVE-CGL2017-82216-R” and CLIMPY “Characterization of the evolution of climate and provision of information for adaptation in the Pyrenees” (FEDER-POCTEFA)

Estimating adaptive setpoint temperatures using weather stations

Reducing both the energy consumption and CO 2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.Spanish Ministry of Science, Innovation and Universities 00064742/ITC-20133094Spanish Ministry of Economy, Industry and Competitiveness BIA 2017-85657-

Estimating Adaptive Setpoint Temperatures Using Weather Stations

[Abstract] Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probe