Assessment of global warming in Al Buraimi, sultanate of Oman based on statistical analysis of NASA POWER data over 39 years, and testing the reliability of NASA POWER against meteorological measurements

The microbiome of the lichen Lobaria pulmonaria varies according to climate on a subcontinental scale

The Lobaria pulmonaria holobiont comprises algal, fungal, cyanobacterial and bacterial components. We investigated L. pulmonaria's bacterial microbiome in the adaptation of this ecologically sensitive lichen species to diverse climatic conditions. Our central hypothesis posited that microbiome composition and functionality aligns with subcontinental-scale (a stretch of ~1100 km) climatic parameters related to temperature and precipitation. We also tested the impact of short-term weather dynamics, sampling season and algal/fungal genotypes on microbiome variation. Metaproteomics provided insights into compositional and functional changes within the microbiome. Climatic variables explained 41.64% of microbiome variation, surpassing the combined influence of local weather and sampling season at 31.63%. Notably, annual mean temperature and temperature seasonality emerged as significant climatic drivers. Microbiome composition correlated with algal, not fungal genotype, suggesting similar environmental recruitment for the algal partner and microbiome. Differential abundance analyses revealed distinct protein compositions in Sub-Atlantic Lowland and Alpine regions, indicating differential microbiome responses to contrasting environmental/climatic conditions. Proteins involved in oxidative and cellular stress were notably different. Our findings highlight microbiome plasticity in adapting to stable climates, with limited responsiveness to short-term fluctuations, offering new insights into climate adaptation in lichen symbiosis.

Data-driven techniques for temperature data prediction: big data analytics approach

For extrapolation, climate change and other meteorological analysis, a study of past and current weather events is a prerequisite. NASA (National Aeronautics and Space Administration) has been able to develop a model capable of predicting various weather data for any location on the Earth, including locations lacking weather stations, weather satellite coverage, and other weather measuring instruments. This paper evaluates the prediction accuracy of the NASA temperature data with respect to NiMet (Nigerian Meteorological Agency) ground truth measurement, using Akwa Ibom Airport as a case study. Exploratory data analysis (descriptive and diagnostic analyses) of temperature retrieved from NiMet and NASA was performed to give a clear path to follow for predictive and prescriptive analyses. Using 2783 days of weather data retrieved from NiMet as ground truth, the accuracy of NASA predictions with the corresponding resolution was calculated. Mean absolute error (MAE) of 2.184 °C and root mean square error (RMSE) of 2.579 °C, with a coefficient of determination (R²) of 0.710 for maximum temperature, then MAE of 0.876 °C, RMSE of 1.225 °C with a coefficient of determination (R²) of 0.620 for minimum temperature was discovered. There is a good correlation between the two datasets; hence, a model can be developed to generate more accurate predictions, using the NASA data as input. Predictive and prescriptive analyses were performed by employing five prediction algorithms: decision tree regression, XGBoost regression and MLP (multilayer perceptron) with LBFGS (limited-memory Broyden-Fletcher-Goldfarb-Shanno) optimizer, MLP with SGD (stochastic gradient) optimizer and MLP with Adam optimizer. The MLP LBFGS algorithm performed best, by significantly reducing the MAE by 35.35% and RMSE by 31.06% for maximum temperature, accordingly, MAE by 10.05% and RMSE by 8.00% for minimum temperature. Results obtained show that given sufficient data, plugging NASA predictions as input to an LBFGS-MLP model gives more accurate temperature predictions for the study area.

Changes in the pattern of heat waves and the impacts on Holstein cows in a subtropical region

This study aimed to evaluate the change in the air temperature and the impacts of heat waves using Climate Change Indexes on the physiological and productive responses of lactating Holstein cows. Daily data of maximum and minimum air temperature for 1981-2021 were used. Heat waves were determined using six Climate Change Indexes. Individual data on respiratory rate, rectal temperature, and milk yield were collected in the summers of 2018, 2019, and 2021. The temperature trend analysis showed a significant (p < 0.0001) increase in maximum temperature, minimum temperature, and days in a heat wave. All six indexes increased significantly (p > 0.01). The increase in warm nights (> 20 °C) and the hottest days (> 35 °C) was the highest since 2010. Heat waves were classified into short (< 5 days) and long (> 5 days) of greater (> 36 °C) or lesser (< 36 °C) intensity. During the long and short heat waves of greater intensity, the respiratory rate increased (p < 0.05) until the fourth day. On the other hand, rectal temperature was higher (p < 0.05) from the fourth day until the end of the long heat waves. Therefore, the decrease in milk yield was significantly greater from the fourth or fifth day onwards. Finally, the evaluation method based on indexes was efficient to demonstrate the negative effects on physiological parameters and milk yield and can be indicated to evaluate heat stress in lactating cows.

Assessment of solar radiation resource from the NASA-POWER reanalysis products for tropical climates in Ghana towards clean energy application

In order to expand the output of solar power systems for efficient integration into the national grid, solar energy resource assessment at site is required. A major impediment however, is the widespread scarcity of radiometric measurements, which can be augmented by satellite observation. This paper assessed the suitability of satellite-based solar radiation resource retrieved from the NASA-POWER archives at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.5^\circ \times 0.5^\circ$$\end{document}0.5∘×0.5∘ spatial resolution over Ghana–West Africa, to develop a long-term source reference. The assessment is based on the criteria of comparison with estimations from sunshine duration measurement for 22 synoptic stations. Overall, the satellite-based data compared well with ground-based estimations by r = 0.6–0.94 ± 0.1. Spatiotemporally, the agreement is strongest over the northern half Savannah-type climate during March–May, and weakest over the southern half Forest-type climate during June–August. The assessment provides empirical framework to support solar energy utilization in the sub-region.