Hourly Temperature Research Articles

Exploring the thermal limits of malaria transmission in the western Himalaya.

Environmental temperature is a key driver of malaria transmission dynamics. Using detailed temperature records from four sites: low elevation (1800), mid elevation (2200 m), and high elevation (2600–3200 m) in the western Himalaya, we model how temperature regulates parasite development rate (the inverse of the extrinsic incubation period, EIP) in the wild. Using a Briére parametrization of the EIP, combined with Bayesian parameter inference, we study the thermal limits of transmission for avian (Plasmodium relictum) and human Plasmodium parasites (P. vivax and P. falciparum) as well as for two malaria‐like avian parasites, Haemoproteus and Leucocytozoon. We demonstrate that temperature conditions can substantially alter the incubation period of parasites at high elevation sites (2600–3200 m) leading to restricted parasite development or long transmission windows. The thermal limits (optimal temperature) for Plasmodium parasites were 15.62–34.92°C (30.04°C) for P. falciparum, 13.51–34.08°C (29.02°C) for P. vivax, 12.56–34.46°C (29.16°C) for P. relictum and for two malaria‐like parasites, 12.01–29.48°C (25.16°C) for Haemoproteus spp. and 11.92–29.95°C (25.51°C) for Leucocytozoon spp. We then compare estimates of EIP based on measures of mean temperature versus hourly temperatures to show that EIP days vary in cold versus warm environments. We found that human Plasmodium parasites experience a limited transmission window at 2600 m. In contrast, for avian Plasmodium transmission was not possible between September and March at 2600 m. In addition, temperature conditions suitable for both Haemoproteus and Leucocytozoon transmission were obtained from June to August and in April, at 2600 m. Finally, we use temperature projections from a suite of climate models to predict that by 2040, high elevation sites (~2600 m) will have a temperature range conducive for malaria transmission, albeit with a limited transmission window. Our study highlights the importance of accounting for fine‐scale thermal effects in the expansion of the range of the malaria parasite with global climate change.

Case study and numerical modelling of heat transfer in a snow-covered building roof

We explored the heat transfer in a snow-covered building roof by setting a prototype field measurement in Harbin during the winter of 2018. Several sets of data were measured, including the meteorological elements and snow properties. Hence, a temperature-dependent constitutive model for a heterogeneous medium, the deposited snow on the roof, was accordingly proposed. To validate this, this model was adopted in a new proposed 3-D finite element modelling (FEM), in ANSYS software, to quantitatively analyze the heat transfer process. All heat transfer patterns, including roof heat-loss conduction, surrounding turbulent exchange, and net radiation, as well as the involved phase-changes in snow metamorphism, were considered. Using the birth and death element technique, the snowmelt and subsequent ice-layer formation can also be visually reproduced. The feasibility of the FEM was validated by comparing both the snow metamorphic features and the hourly temperature gradient in the snow between the FEM and the prototype measurement. In future work, the influence of water vapor transport in snow and the range of time-scales should be increased in further studies.

Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake

A reliable water level prediction in a lake system is crucial for water resources management, flood control, etc. The objective of this study is to propose a machine learning model which is able to achieve a considerably high level of accuracy in terms of water level prediction. Dongting Lake, which is the second-largest freshwater lake system in China, was selected as the study area. The hourly water level, flow rate, rainfall and temperature of the upstream water stations and rainfall of the downstream water stations were used as the input features, to predict the water level at the downstream stations. Multilayer perceptron neural network (MLP-NN), Elman neural network (ENN), and integration of particle swarm optimisation algorithm to Elman neural network (PSO-ENN) were selected as the model development techniques. The PSO-ENN model appears as the best performed model, as it records NSE of 0.929–0.988, RMSE of 0.129–0.322 and MAE of 0.151–0.359 at the downstream stations in Dongting Lake. The PSO-ENN model also shows its ability to provide better performance for the water level prediction of 36 h in advance. In terms of input variables sensitivity, the developed model is most sensitive to flow rate, followed by rainfall.

Assessment of Temperature and Precipitation Forecasts of the WRF Model in the Bahía de Banderas Region (Mexico)

The Centro de Ciencias de la Atmósfera at UNAM, in Mexico, uses the Water Research and Forecasting model to provide weather forecasts to the country. In this study, we downloaded the mean temperature and precipitation forecasts of the first 24 h generated by the WRF model in the center of the country. Only the time series of our study region (Bahía de Banderas) was processed from this database, from June to October 2010, and these data were compared with the data recorded in six stations to evaluate the performance of the model at a local level. Data from 12 stations were used to construct the observed temperature and precipitation maps for spatial validation. The results show that the model performance was partially acceptable. The correlation coefficient for hourly temperatures was an average of r=0.84. Errors were less than 2 °C with a BIAS of ±1 °C. For the accumulated 24 h precipitation, however, the results were not satisfactory (r=0.26). The model predicted only 25.7% of the rainy days observed. In terms of spatial distribution, ~2.3 times more rain was observed than had been predicted by the model.

The effects of night-time warming on mortality burden under future climate change scenarios: a modelling study

The health impacts of climate warming are usually quantified based on daily average temperatures. However, extra health risks might result from hot nights. We project the future mortality burden due to hot nights. We selected the hot night excess (HNE) to represent the intensity of night-time heat, which was calculated as the excess sum of high temperature during night time. We collected historical mortality data in 28 cities from three east Asian countries, from 1981 to 2010. The associations between HNE and mortality in each city were firstly examined using a generalised additive model in combination with a distributed lag non-linear model over lag 0-10 days. We then pooled the cumulative associations using a univariate meta-regression model at the national or regional levels. Historical and future hourly temperature series were projected under two scenarios of greenhouse-gas emissions from 1980-2099, with ten general circulation models. We then projected the attributable fraction of mortality due to HNE under each scenario. Our dataset comprised 28 cities across three countries (Japan, South Korea, and China), including 9 185 598 deaths. The time-series analyses showed the HNE was significantly associated with increased mortality risks, the relative mortality risk on days with hot nights could be 50% higher than on days with non-hot nights. Compared with the rise in daily mean temperature (lower than 20%), the frequency of hot nights would increase more than 30% and the intensity of hot night would increase by 50% by 2100s. The attributable fraction of mortality due to hot nights was projected to be 3·68% (95% CI 1·20 to 6·17) under a strict emission control scenario (SSP126). Under a medium emission control scenario (SSP245), the attributable fraction of mortality was projected to increase up to 5·79% (2·07 to 9·52), which is 0·95% (-0·39 to 2·29) more than the attributable fraction of mortality due to daily mean temperature. Our study provides evidence for significant mortality risks and burden in association with night-time warming across Japan, South Korea, and China. Our findings suggest a growing role of night-time warming in heat-related health effects in a changing climate. The National Natural Science Foundation of China, Shanghai International Science and Technology Partnership Project.

Study on Correlation between Wind Speed and Temperature using Information Entropy

This project investigated temperature and wind speed correlation using information entropy. The hourly temperature and wind speed were retrieved over two weeks for two different regions of the United States, Boston, Massachusetts and Lincoln, Nebraska. Differential entropy was used to calculate mutual information between the hourly change in each variable. Findings revealed that mutual information shared between the two variables was greater in Lincoln than in Boston, indicating change in temperature and wind speed had a stronger correlation in Lincoln than in Boston.

Prediction of hourly air temperature based on CNN–LSTM

The prediction accuracy of hourly air temperature is generally poor because of random changes, long time series, and the nonlinear relationship between temperature and other meteorological elements, such as air pressure, dew point, and wind speed. In this study, two deep-learning methods—a convolutional neural network (CNN) and long short-term memory (LSTM)—are integrated into a network model (CNN–LSTM) for hourly temperature prediction. The CNN reduces the dimensionality of the time-series data, while LSTM captures the long-term memory of the massive temperature time-series data. Training and validation sets are constructed using 60,133 hourly meteorological data (air temperature, dew point, air pressure, wind direction, wind speed, and cloud amount) obtained from January 2000 to October 2020 at the Yinchuan meteorological station in China. Mean absolute error (MAE), mean absolute percentage error (MAPE), and goodness of fit are used to compare the performances of the CNN, LSTM, and CNN–LSTM models. The results show that MAE, MAPE, RMSE, and PBIAS from the CNN–LSTM model for hourly temperature prediction are 0.82, 0.63, 2.05, and 2.18 in the training stage and 1.02, 0.8, 1.97, and −0.08 in the testing stage. Average goodness of fit from the CNN–LSTM model is 0.7258, higher than the CNN (0.5291), and LSTM (0.5949) models. The hourly temperatures predicted by the CNN–LSTM model are highly consistent with the measured values, especially for long time series of hourly temperature data.

Deep Q-Value Neural Network (DQN) Reinforcement Learning for the Techno-Economic Optimization of a Solar-Driven Nanofluid-Assisted Desalination Technology

A solar-driven desalination system, featuring a single-slope solar still is studied here. For this design, Al2O3 nanofluid is utilized, and the condition achieving the highest efficiency and cost-effectiveness is found using a reinforcement learning called a deep Q-value neural network (DQN). The results of optimization are implemented for the built experimental setup. Experimental data obtained under the climatic conditions of Tehran, Iran, are employed to compare the enhancement potential of the optimized solar still system with nanofluid (OSTSWNF) with the solar still system with water (STSWWA). The hourly fluid temperatures in the basin as well as the hourly and cumulative freshwater production (HFWP and CFWP) are discussed. A number of other parameters, including daily water production and efficiency in addition to the cost per liter (CPL) of the resulting desalinated water, are also taken into account. The results reveal that annual water production increases from 1326.8 L to 1652.4 L, representing ~25% growth. Moreover, the annual average efficiency improves by ~32%, rising from 41.6% to 54.7%. A great economic enhancement is seen as well, with the CPL decreasing by ~8%, i.e., from USD 0.0258/L to USD 0.0237/L.

Role of submesoscale processes in the isopycnal mixing associated with subthermocline eddies in the Philippine Sea

Submesoscale processes are very typical in the ocean interior with a front formed by mesoscale eddies, and contribute to ocean mixing. However, the role of submesoscale processes in the isopycnal mixing associated with ocean eddies has never been explored from observations. Based on hourly velocity and temperature data from the mooring at 8.5°N, 130°E between 26 September 2015 and 22 December 2016, this study provides the horizontal eddy diffusivity using a mixing-length framework and a parameterization related to the Rhines scale, and estimates the contribution of submesoscale processes to isopycnal mixing for the first time. The subthermocline eddies in the Philippine Sea exist between 200 m and 800 m depth, have characteristic speeds of 0.1–0.5 m/s with a typical period of 40–100 days, and can cause the temperature fluctuation of ∼0.5 °C. The horizontal diffusivity due to subthermocline eddies is about 4000–11,000 m2/s, and has a significant maximum near ∼400 m depth but the horizontal diffusivity associated with mixing length gradually increases below ∼700 m. The submesoscale processes can change the current velocity and temperature by ∼5 cm/s and ∼0.1 °C, respectively. The submesoscale processes in the ocean interior can increase the isopycnal mixing associated with subthermocline eddies by about 6–7%. The profile of horizontal eddy diffusivity derived in this study is useful for parameterization of ocean eddies and submesoscale processes in the Philippine Sea in coarse-resolution ocean models.

Generating a 2-km, all-sky, hourly land surface temperature product from Advanced Baseline Imager data

By characterizing high-frequency surface thermal dynamics at a medium spatial scale, hourly land surface temperatures (LST), retrieved from geostationary satellite thermal infrared (TIR) observations, shows great potential to be used across a range of scientific applications; however, cloud cover typically leads to data gaps and degraded retrieval accuracy in TIR LST products, such as the official Advanced Baseline Imager (ABI) LST product. Studies have focused on the LST gap reconstruction; however, most interpolation-based methods only work for a short-term cloud duration and are unable to adequately compensate for cloud effects, and traditional surface energy balance (SEB)-based methods are able to handle cloud coverage while they are not feasible for use at night. Moreover, few studies have concentrated on recovering the abnormal retrievals of partial cloud pixels. In this study, an all-sky diurnal, hourly LST estimation method based on SEB theory was proposed; the proposed method involved three major steps: 1) an original spatiotemporal dynamic model of LST was constructed from ECMWF Reanalysis v.5 (ERA5); 2) clear-sky ABI LST was then assimilated to the dynamic model to generate a continuous LST series; 3) the diurnal cloud effects were superimposed on cloudy time estimated by an innovative optimization method from satellite radiation products. A 2-km, all-sky, hourly LST product was produced over the contiguous US and Mexico from July 2017 to June 2021. Validation was conducted using ground measurements at 18 sites from Surface Radiation (SURFRAD) and core AmeriFlux networks, and produced an overall root-mean-square error (RMSE) of 2.44 K, a bias of −0.19 K, and an R2 of 0.97 based on 408,300 samples. For clear-sky samples, the RMSE values were 2.37 and 2.24 K for day and nighttime, respectively, which was a notable improvement over the corresponding values of the official ABI LST product (2.73 and 2.86 K, respectively). The RMSE values on cloudy-sky were 2.78 and 2.23 K for day and nighttime, respectively. The daily mean LST by aggregating all-sky, hourly LST had an RMSE of 1.13 K and R2 of 0.99. Overall, this product showed reliability under consistent cloud durations, although it was slightly affected by surface elevation. The diurnal temperature cycle climatology of major land cover types was also characterized. The product is freely available at: http://glass.umd.edu/allsky_LST/ABI/.

Comparison of three machine learning models for the prediction of hourly PV output power in Saudi Arabia

The optimum integration of photovoltaic (PV) technologies into existing power systems necessitates accurate PV performance planning, which is critical for both plant operators and the grid. This study investigates the application of different machine learning (ML) models to predict the PV power output at Jubail Industrial City, Kingdom of Saudi Arabia. Specifically, three techniques have been explored which include k nearest neighbour (kNN), Multiple regression and decision tree regression each with its own set of hyper-parameters & functions. Using the Ostwald’s technique, large dataset comprising the hourly solar irradiance and temperature covering a three-year period, i.e. 2016 to 2019, has been initially used to estimate the PV power for Jubail. Then, the PV power was predicted using the aforementioned ML models. The kNN outperformed the other models, with root mean square error, mean absolute error and normalized root mean square error of 18.68%, 80.6%, and 13.2%, respectively. The other two ML techniques i.e. MLR and the DTR performed reasonably well. As a further contribution, the kNN was applied to forecast the day ahead PV output power for Jubail and the results shows a good agreement between the predicted the actual values. The results imply that using ML techniques, it is possible to predict PV output power across Saudi Arabia, and that this data may be used as a reference for predicting PV output power in different regions of the country.

Effect of Urban Expansion Indices Change by (R.S.) on Height of Convective Radix Layer around Baghdad Airport (Iraq)

Change in structure of land surface can affect atmospheric boundary layer and formation of internal boundary layer. This will change shear stresses and turbulent boundary layer. Change in low level wind shear and turbulent can affect air craft performance and has potentially adverse effects on flight safety during landing and taking-off stages. In this study, change in land area around Baghdad Airport and its effects on structure of boundary layer and radix layer through summer season (July from years 1985 and 2014) is examined. The examination is done through atmospheric data of radiosonde (at altitude more than 1500 m) and remote sensing by Landsat 5 and 8 images (circular region around airport has radius 3.250 km and the airport is considered the center of this circle). From analysis of hourly wind speed and temperature profile with height observed by radiosonde, change in convective radix layer can be determined at this period resulted from change structure of boundary layer depending on characteristic of landcover used in supervised classification. Building area increases from 18% in 1985 to 41% in 2014. These results enforced by positive values of built up index BI, that showed a decreases in 2014. All these elements changed convective radix layer from less than 500 m in summer 1985 to more than 700 m. There is a large fluctuation in radix layer height depended on convective and friction velocity at 2014 data. Thus indirect change in structure of convective layer will affect navigation movement of Baghdad International Airport.

An assessment of the extremes and impacts of the February 2021 South-Central U.S. Arctic outbreak, and how climate services can help

In February 2021, a widespread cold-air outbreak, with two associated winter storm systems, impacted the South-Central United States. After a comprehensive summary of the synoptic setup and a day-by-day analysis of the event, we assess the significance of the storm from a climatological perspective. Concerning winter precipitation, there were isolated instances of record snowfall accumulations. While freezing rain and freezing drizzle both occurred, total freezing precipitation accumulations did not exceed a one-in-50 year event. The duration of the cold was notable — many stations across the region broke records for the highest number of consecutive days below freezing. When analyzing hourly temperature observations, we found that the February 2021 event was the record longest duration of hours below freezing for 12 stations. Nearly 6,000 daily temperature records were broken by this event. We next summarize significant impacts of this event. While we find that this event was extreme, most aspects of this storm were not unprecedented. Even in the context of a warming climate, cold events such as this should be considered when assessing risk and hazard mitigation planning. The magnitude of impacts associated with this event suggests a lack of preparedness that needs to be addressed. We discuss the importance of using climate services in planning for future extreme events. While there are documented benefits to users engaging with climate service providers and integrating climate information into their decision-making, the February 2021 event serves as an example of the failures that can occur when climate services have not been integrated into planning. We recommend the use of climate services when assessing risk and planning for future climate and weather extremes.

Mathematical Model of the Thermal Performance of Double-Pass Solar Collector for Solar Energy Application in Sierra Leone

The primary aim of this study was to utilize thermal energy for drying applications on March 21 (day of the year, n = 80) for the climatic weather conditions of Freetown, Sierra Leone. We evaluated the heat absorption of a double-pass solar air collector based on its configuration and exterior input variables before it was designed and mounted at the location of interest. This study considered a steady-state heat transfer using the thermal network procedure for thermodynamic modeling of a double-pass solar collector developed for drying and heating purposes. A mathematical model defining the thermophysical collector properties and many heat transfer coefficients is formed and numerically solved for this purpose. Indeed, this helped us generate the hourly temperature of different heat collector components, which aided in the performance evaluation of the system. The impact of the fluid flowing inside the collector on the temperature of the exit air was analyzed. It was observed that a flow rate of 0.02 kg/s produced an output of 91.72°C. The system's thermal efficiency improves with increased flow rate at various solar radiation intensities. It was observed that the thermal efficiency of the collector increases from 29% to 67% at flow rates of 0.01–0.3 kg/s. Collector lengths of 1.4 and 2.4 m are observed to be economically viable. An increase in the flow rate caused an increase on the efficiency. The hourly outputs for the collector components were represented graphically, and the curve patterns were similar to those of previous studies.

A New Model to Predict the Global Solar Radiation GSR of Souk-Ahras City

The value of the global solar radiation GSR reaching the earth is very important because it is an essential variable for different applications. Unfortunately, solar radiation measurements are not available, most of the time, in developing countries because of the lack of measurement means. Moreover, these measurements are difficult to obtain under complicated weather conditions. Thus, solar radiation evaluation models are used. In this study, a new semi-empirical model for the estimation and prediction of the global solar radiation of Souk Ahras area in Algeria is proposed. The model developed is based on meteorological data such as daily hourly temperatures and average relative humidity required from several stations and databases, over a period of four years. The values of the regression coefficients a, b and c calculated are 0.0142, -10.6206 and 57.8367 respectively. To set the modal valid, we have applied it to 10 Algerian cities and we calculated the H/H0 ratio for each site from our model. They have been then compared with the values from the (CDER). We can conclude that our new model gives a good estimate of the average daily global solar radiation (H) for the studied regions (error between 2.49% and 8.93%) and can also be used elsewhere in areas with the same climatic conditions.

Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method

The forecast and estimation of total electric power demand of a residential community, its baseload, and its heating ventilation and air-conditioning (HVAC) power component, which represents a very large portion of a community electricity usage, are important enablers for optimal energy controls and utility planning. This paper proposes a method that employs machine learning in a multi-step integrated approach. An LSTM model for total electric power at the main circuit feeder is trained using historic multi-year hourly data, outdoor temperature, and solar irradiance. New key temperature indicators, TmHAVC, corresponding to the standby zero-power operation for HVAC systems for summer cooling and winter heating are introduced using a V-shaped hourly total load curve. The trained LTSM model is additionally run with TmHVAC and zero irradiance inputs yielding an estimated baseload, which is representative of typical occupancy patterns. The HVAC power component is disaggregated as the difference between total and baseload power. Total power forecasts of an aggregated residential community as seen by major distribution lines are experimentally validated with a satisfactory MAPE error below 10% based on a 4-year dataset from a representative suburban community with more than 1800 homes in Kentucky, U.S. Discussions regarding the validity of the separation method based on combined considerations of fundamental physics, statistics, and human behavior are also included.

Toward snowpack runoff decision support

SummaryRain-on-snow (ROS) events are commonly linked to large historic floods in the United States. Projected increases in the frequency and magnitude of ROS multiply existing uncertainties and risks in operational decision making. Here, we introduce a framework for quality-controlling hourly snow water content, snow depth, precipitation, and temperature data to guide the development of an empirically based snowpack runoff decision support framework at the Central Sierra Snow Laboratory for water years 2006–2019. This framework considers the potential for terrestrial water input from the snowpack through decision tree classification of rain-on-snow and warm day melt events to aid in pattern recognition of prominent weather and antecedent snowpack conditions capable of producing snowpack runoff. Our work demonstrates how (1) present weather and (2) antecedent snowpack risk can be “learned” from hourly data to support eventual development of basin-specific snowpack runoff decision support systems aimed at providing real-time guidance for water resource management.

Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms

Wind energy is one of the fastest growing sources of energy worldwide. This is clear from the high volume of wind power applications that have been increased in recent years. However, the uncertain nature of wind speed induces several challenges towards the development of efficient applications that require a deep analysis of wind speed data and an accurate wind energy potential at a site. Therefore, wind speed forecasting plays a crucial rule in reducing this uncertainty and improving application efficiency. In this paper, we experimented with several forecasting models coming from both machine-learning and deep-learning paradigms to predict wind speed in a metrological wind station located in East Jerusalem, Palestine. The wind speed data were obtained, modelled, and forecasted using six machine-learning techniques, namely Multiple Linear Regression (MLR), lasso regression, ridge regression, Support Vector Regression (SVR), random forest, and deep Artificial Neural Network (ANN). Five variables were considered to develop the wind speed prediction models: timestamp, hourly wind speed, pressure, temperature, and direction. The performance of the models was evaluated using four statistical error measures: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The experimental results demonstrated that the random forest followed by the LSMT-RNN outperformed the other techniques in terms of wind speed prediction accuracy for the study site.

Evidence of horizontal urban heat advection in London using six years of data from a citizen weather station network

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)—a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ∘C–1.5 ∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ∘C, on average and negatively by down to −1.21 ∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

An hourly ground temperature dataset for 16 high-elevation sites (3493–4377 m a.s.l.) in the Bale Mountains, Ethiopia (2017–2020)

Abstract. Tropical mountains and highlands in Africa are under pressure because of anthropogenic climate and land-use change. To determine the impacts on the afro-alpine environment and to assess the potential socio-economic consequences, the monitoring of essential climate and environmental variables at high elevation is fundamental. However, long-term temperature observations on the African continent above 3000 m are very rare. Here we present a consistent multiannual dataset of hourly ground temperatures for the Bale Mountains in the southern Ethiopian Highlands, which comprise Africa's largest tropical alpine area. The dataset covers the period from January 2017 to January 2020. To characterise and continuously monitor the mountain climate and ecosystem of the Bale Mountains along an elevation gradient from 3493 to 4377 m, ground temperature data loggers have been installed at seven sites at 2 cm depth; at four sites at 10 cm depth; and at five sites at 2, 10, and 50 cm depth. The statistical analysis of the generated time series reveals that ground temperatures in the Bale Mountains are subject to large daily fluctuations of up to 40 ∘C and minor seasonal variations on the order of 5 to 10 ∘C. Besides incoming short-wave radiation, ground moisture, and clouds at night, slope orientation and the type of vegetation coverage seem to be the main factors controlling daily and seasonal ground temperature variations. On the central Sanetti Plateau above 3800–4000 m, the mean annual ground temperature ranges from 9 to 11 ∘C. However, nocturnal ground frost down to a depth of 5 cm occurs frequently during the dry season from November to February. At the five sites where ground temperature is measured at three depths, the monitoring will be continued to trace long-term changes. To promote the further use of the ground temperature dataset by the wider research community dealing with the climate and geo-ecology of tropical mountains in eastern Africa, it is made freely available via the open-access repository Zenodo: https://doi.org/10.5281/zenodo.6047457 (Groos et al., 2022).