A countrywide analysis of 27 solar power plants installed at different climates

The world is still heavily using nonconventional energy sources, which are worryingly based on carbon. The step is now alternative energy sources hoping that they will be more environmentally friendly. One of the important energy conversion forms by using these sources is photovoltaic solar systems. These type of power plants is on the increase in everyday on the world. Before investment a solar power plant in a specified region, a techno-economic analyse is performed for that power plant by using several meteorological data like solar irradiance and ambient temperature. However, this analyses generally lacks evaluation on effects of climatic and geographical conditions. In this work, 5 years of data of 27 grid-connected photovoltaic power plants are investigated, which are installed on seven different climate types in Turkey. Firstly, the power plants are categorized considering the tilt angles and Köppen–Gieger climate classification. The performance evaluations of the plants are mainly conducted using monthly average efficiencies and specific yields. The monthly average efficiencies, which were classified using the tilts and climate types were from 12 to 17%, from 12 to 16% and from 13 to 15% for tilts 30°/10°, 25° and 20°, respectively. The variation in the specific yields decrease with elevation as y(x) = − 0.068x + 1707.29 (kWh/kWp). As the performances of photovoltaic systems for some locations within the Csb climatic regions may relatively lower than some other regions with same climate type. Thus, techno-economic performance for PVPP located in this climate classification should be carefully treated.

Changes in Rainfall and Climate Classification in South Sulawesi

The study discuss about Changes in Rainfall and Climate Classification in South Sulawesi. The climate of the Earth is determined by the location of the sun in relation to the earth's surface. Geographical location influences the categorization of climate on our planet. The results of the study (1) Rainfall in Bone Regency has been classified as high rainfall intensity for the last 10 years; (2) Determination of climate classification can be done by processing rainfall data obtained from data before weighting, after weighting, ranking, and opportunity; (3) The climate classification according to Schmidt-Ferguson for Bone Regency has a B climate type, which is a humid subtropical climate; and (4) The climate classification according This is based on a comparison of the number of dry months (BK) and wet months (BB), from which the Q value is obtained, which is then used to determine the type of climate according to Schmidt-Ferguson; (4) Oldeman's climate classification for Bone Regency has a C1 climate type, which has the characteristics of planting lowland rice once a year and secondary crops twice a year; (5) Oldeman's climate classification for Bone Regency has a This is based on the number of Wet Months (BB) and Dry Months (BK) in a given year

Association with Temperature Variability and Physical Activity, Sedentary Behavior, and Sleep in a Free-Living Population

The present study examines the temperature variability in physical activity (PA), sedentary behavior (SB), and sleep in a free-living population. A representative sample of 1235 adults (ages 21–70) from Iowa, U.S.A., wore a SenseWear Mini Armband (SWA) for a randomly assigned day. Koppen’s weather climate classification was used to precisely classify the temperature: cold (−13 to 32 °F), cool (32 to 50 °F), mild (50 to 64 °F), warm (64 to 73 °F), and hot (73 to 95 °F). The main effect of three-way ANOVA (age × gender × temperature) had differences for SB and sleep, with older adults having higher levels than younger adults (p < 0.05). However, moderate to vigorous PA (MVPA) did not vary systematically by age or gender, and contrary to expectations, the main effect of the weather was not significant for MVPA (p > 0.05). Participants spent more time participating in PA at cold than at hot temperatures. The results clarify the impact of temperature on shaping PA and SB patterns in adults. The variable impacts and differential patterns by age suggest that weather should be considered when interpreting differences in PA patterns in research or surveillance applications.

Coherence of global hydroclimate classification systems

Climate classification systems are useful for investigating future climate scenarios, water availability, and even socioeconomic indicators as they relate to climate dynamics. There are several classification systems that apply water and energy variables to create zone boundaries, although there has yet to be a simultaneous comparison of the structure and function of multiple existing climate classification schemes. Moreover, there are presently no classification frameworks that include evapotranspiration (ET) rates as a governing principle. Here, we developed a new system based on precipitation and potential evapotranspiration rates as well as three systems based on ET rates, which were all compared against four previously established climate classification systems. The within-zone similarity, or coherence, of several long-term hydroclimate variables was evaluated for each system based on the premise that the interpretation and application of a classification framework should correspond to the variables that are most coherent. Additionally, the shape complexity of zone boundaries was assessed for each system, assuming zone boundaries should be drawn efficiently such that shape simplicity and hydroclimate coherence are balanced for meaningful boundary implementation. The most frequently used climate classification system, Köppen–Geiger, generally had high hydroclimate coherence but also had high shape complexity. When compared to the Köppen–Geiger framework, the Water-Energy Clustering classification system introduced here showed overall improved or equivalent coherence for hydroclimate variables, yielded lower spatial complexity, and required only 2, compared to 24, parameters for its construction.

A 1 km global dataset of historical (1979–2013) and future (2020–2100) Köppen–Geiger climate classification and bioclimatic variables

The Köppen–Geiger classification scheme provides an effective and ecologically meaningful way to characterize climatic conditions and has been widely applied in climate change studies. Significant changes in the Köppen climates have been observed and projected in the last 2 centuries. Current accuracy, temporal coverage and spatial and temporal resolution of historical and future climate classification maps cannot sufficiently fulfill the current needs of climate change research. Comprehensive assessment of climate change impacts requires a more accurate depiction of fine-grained climatic conditions and continuous long-term time coverage. Here, we present a series of improved 1 km Köppen–Geiger climate classification maps for six historical periods in 1979–2013 and four future periods in 2020–2099 under RCP2.6, 4.5, 6.0, and 8.5. The historical maps are derived from multiple downscaled observational datasets, and the future maps are derived from an ensemble of bias-corrected downscaled CMIP5 projections. In addition to climate classification maps, we calculate 12 bioclimatic variables at 1 km resolution, providing detailed descriptions of annual averages, seasonality, and stressful conditions of climates. The new maps offer higher classification accuracy than existing climate map products and demonstrate the ability to capture recent and future projected changes in spatial distributions of climate zones. On regional and continental scales, the new maps show accurate depictions of topographic features and correspond closely with vegetation distributions. We also provide a heuristic application example to detect long-term global-scale area changes of climate zones. This high-resolution dataset of the Köppen–Geiger climate classification and bioclimatic variables can be used in conjunction with species distribution models to promote biodiversity conservation and to analyze and identify recent and future interannual or interdecadal changes in climate zones on a global or regional scale. The dataset referred to as KGClim is publicly available via http://glass.umd.edu/KGClim (Cui et al., 2021d)​​​​​​​ and can also be downloaded at https://doi.org/10.5281/zenodo.5347837 (Cui et al., 2021c) for historical climate and https://doi.org/10.5281/zenodo.4542076 (Cui et al., 2021b) for future climate.

Climatological water balance and climate classification of thornthwaite and mather for benin, west Africa, in 1970-2015 period

The climate is described by the predominant atmospheric conditions in a particular region and influences several human activities. In agriculture, water availability defines the growth and yield of crops and can be obtained by the water balance. The climate classification also aids to identify suitable areas for agricultural crops. Thus, the aim of this work was to elaborate the water balance and perform the climate classification through the method of Thornthwaite and Mather (1955) for six weather stations (Bohicon, Cotonou-Airport, Kandi-Airport, Natitingou, Parakou-Airport and Savè) located in Benin, Western Africa. For the execution of this work, monthly series of precipitation and potential evapotranspiration from 1970 to 2015 were used. Once the monthly water balance of the six seasons was elaborated, it was observed that the rainy (dry) period decreases (increases) from the coast (Cotonou-Airport) to the north of Benin (Kandi-Airport) and, coincides with Boreal summer and part of autumn (Boreal winter and part of spring). Regarding the climate classification, the Cotonou-Airport station was characterized as Subhumid Megathermal climate with moderate winter deficit (C2wA’a’); the stations of Bohicon and Savè presented similar climate classification with Subhumid Dry Megathermal climate with low or without water surplus (C1dA’a’); Natitingou with Subhumid Dry climate Megathermal with large summer surplus (C1s2A’a’); Parakou-Airport with Subhumid climate Dry Megathermic with moderate summer surplus (C1sA’a’) and, Kandi-Airport presented Semi-arid Megathermal climate with moderate summer surplus (DsA’a’). In Benin, subsistence and rainfed farming showed greater risk in the north of the country due to the decrease in the rainy season and the water surplus from the coast (south) to the north of the country, with the increase in aridity.

BALANÇO HÍDRICO CLIMATOLÓGICO E CLASSIFICAÇÃO CLIMÁTICA PARA O MUNICÍPIO DE PARANAVAÍ, PARANÁ

The study of climatic conditions of Paranavaí region is necessary due to its importance in the national agricultural scenario. The study aimed to calculate the climatological water balance (CWB) as well as performing the climate classification by the method of Thornthwaite e Mather for the municipality of Paranavaí, Paraná. Data from a historical series from 1975 to 2018 were used. For the calculation of the CWB was adopted the value of 100 mm for the available water capacity (AWC). The municipality studied presentes na annual average of 1523,8 mm precipitation and 1090,62 evapotranspiration. The municipality presented a trend climate o fone month of water deficiency (August) and eleven months of water excess (Setember to July). Regarding climate classification, was found C1dA’a’ climate, characterized as a mesothermic climate, with little or no water deficiency.

Principal operators' farm risk attitudes in hot and cold climates

PurposeThe study investigates whether there is an association between climate types and farm risk attitudes of principal operators.Design/methodology/approachThe study exploits temperature variation in the diverse climate types across the US and defines hot- and cold-climate states. Ordered logit and generalized ordered logit models are used to model principal operators' farm risk attitudes, which are measured on a Likert scale. The study uses two datasets. The first dataset is a 2017 survey of US large commercial producers (LCPs). The second dataset provides a Köppen-Geiger climate classification of the US at a spatial resolution of 5 arcmin for a 25-year period (1986–2010).FindingsThe study finds that principal operators in hot-climate states are 4–5% more likely to have a higher willingness to take farm risk compared to principal operators in cold-climate states.Research limitations/implicationsIt is likely that farm risk mitigation decisions differ between hot- and cold-climate states. For instance, the authors show that corn acres' enrollment in federal crop insurance and computers' usage for farm business are pursued more intensely in cold-climate states than in hot-climate states. A differentiation of farm risk attitude by hot- and cold-climate states may help agribusiness, the government and economists in their farm product offerings, farm risk management programs and agricultural finance models, respectively.Originality/valueBased on Köppen-Geiger climate classification, the study introduces hot- and cold-climate concepts to understand the relationship between climate types and principal operators' farm risk attitudes.