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.

The role of cyclonic activity in tropical temperature-rainfall scaling

The attribution of changing intensity of rainfall extremes to global warming is a key challenge of climate research. From a thermodynamic perspective, via the Clausius-Clapeyron relationship, rainfall events are expected to become stronger due to the increased water-holding capacity of a warmer atmosphere. Here, we employ global, 1-hourly temperature and 3-hourly rainfall data to investigate the scaling between temperature and extreme rainfall. Although the Clausius-Clapeyron scaling of +7% rainfall intensity increase per degree warming roughly holds on a global average, we find very heterogeneous spatial patterns. Over tropical oceans, we reveal areas with consistently strong negative scaling (below −40%∘C−1). We show that the negative scaling is due to a robust linear correlation between pre-rainfall cooling of near-surface air temperature and extreme rainfall intensity. We explain this correlation by atmospheric and oceanic dynamics associated with cyclonic activity. Our results emphasize that thermodynamic arguments alone are not enough to attribute changing rainfall extremes to global warming. Circulation dynamics must also be thoroughly considered.

Estimation of the terms acting on local 1 h surface temperature variations in Paris region: the specific contribution of clouds

Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.

BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model’s excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios.

Decarbonization of Summer Cooling Energy Demands of Buildings Employing Absorption Systems in the Framework of Climate Change in Italy

Temperatures in the Mediterranean area have gradually risen in the last decades due to climate change, especially in the Italian Peninsula. This phenomenon has increased the cooling needs to ensure thermal comfort in buildings and, consequently, the use of refrigeration machines. Summer air conditioning is carried out mainly using compression machines powered by electricity supplied by the national network. All this contributes to the emission of climate-changing gases. To avoid this disadvantageous chain, compression machines could be replaced by absorption cooling systems powered by solar energy. The energy needs of the buildings in a time are directly proportional to the sum of positive differences between the outdoor air temperature and the indoor set point of the systems (equal to 26°C). The annual sum of hourly temperature differences defined above can be computed for each grid cell thanks to a numerical weather prediction model, namely the Weather Research and Forecasting model, that simulates the hourly temperatures on high-resolution computation grids and over fairly large extents. Maps of cooling consumption for buildings are thus produced. Choosing absorption solar energy-powered systems instead of vapor compression refrigeration systems leads to a drop in electrical energy consumption and therefore in emissions of greenhouse gases. In this work, different hypothetical scenarios of penetration of this technology have been considered. And the subsequent consumption of electricity withdrawn from the national grid has been estimated together with the reduction of greenhouse gas emissions.

Evaluation of Long-Term Degree-Days Estimated with Several Methods for Corn in Nebraska, USA

The concept of thermal time, measured in degree-days, is widely used among the agricultural community in Nebraska to make decisions in corn (Zea Mays L.) production. Instead of the real-time temperatures that are experienced by corn plants, most of the widely available temperature data are limited to daily timescale observations from standard meteorological stations. And a variety of equations are used by different agricultural groups (e.g., researchers, advisors, farmers, and seed companies) to estimate thermal time for corn. Two problems could arise: a) the estimation method is lacking in accuracy; and b) different estimation methods are used for the same purpose by different groups. Consequently, citing these inaccurate and maybe inherently different thermal time results could lead to biased decisions in corn production. The goal of this study is to evaluate six commonly used estimation methods by comparing the estimated thermal time with the hourly-temperature approximated thermal time. We analyzed the root mean square error and mean absolute error for six metrics of total growing season (from May through September) degree-days based on the temperature data from a total of 14 long-term observing locations in Nebraska. In particular, we selected four location-extreme year cases to demonstrate the six methods’ estimation performance on a daily timescale. We found that the most commonly used adjusted Tmax and Tmin rectangle method provided poor estimation in the study area. Instead, single-sine, double-sine, or Tavg-based method was more superior depending on the metric of degree-days.