pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH &amp; ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).</p

pvlib iotools—Open-source Python functions for seamless access to solar irradiance data

Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH &amp; ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).</p

Debt Maturity Choices, Multi-stage Investments and Financing Constraints

We develop a dynamic investment options framework with optimal capital structure and analyze the effect of debt maturity. We find that in the absence of financing constraints short-term debt maximizes firm value. In contrast with most literature results, in the absence of constraints, higher volatility may increase initial debt for firms with low initial revenues, issuing long term debt that expires after the investment option maturity. This effect, which is due to the option value of receiving the value of assets and remaining tax savings, does not hold for short term debt and firms with high profitability, where an increase in volatility reduces the firm value. The importance of short-term debt is reduced in the presence of non-negative equity net worth or debt financing constraints and firms behave more conservatively in the use of initial debt. With non-negative equity net worth, higher volatility has adverse effects on the firm value, while with debt financing constraints higher volatility may enhance firm value for firms with relatively low revenue that have out-of-the-money investment options

Seed availability and insect herbivory limit recruitment and adult density of native tall thistle

Understanding spatial and temporal variation in factors influencing plant regeneration is critical to predicting plant population growth. We experimentally evaluated seed limitation, insect herbivory, and their interaction in the regeneration and density of tall thistle (Cirsium altissimum) across a topographic ecosystem productivity gradient in tallgrass prairie over two years. On ridges and in valleys, we used a factorial experiment manipulating seed availability and insect herbivory to quantify effects of: seed input on seedling density, insect herbivory on juvenile density, and cumulative impacts of both seed input and herbivory on reproductive adult density. Seed addition increased seedling densities at three of five sites in 2006 and all five sites in 2007. Insect herbivory reduced seedling survival across all sites in both years, as well as rosette survival from the previous year’s seedlings. In both years, insecticide treatment of seed addition plots led to greater adult tall thistle densities in the following year, reflecting the increase in juvenile thistle densities in the experimental year. Seedling survival was not density dependent. Our analytical projection model predicts a significant long-term increase in adult densities from seed input, with a greater increase under experimentally reduced insect herbivory. While plant community biomass and water stress varied significantly between ridges and valleys, the effects of seed addition and insect herbivory did not vary with gradient position. These results support conceptual models that predict seedling and adult densities of short-lived monocarpic perennial plants should be seed limited. Further, the experiment demonstrates that even at high juvenile plant densities, at which density dependence potentially could have overridden herbivore effects on plant survival, insect herbivory strongly affected juvenile thistle performance and adult densities of this native prairie species

Magnetic order and fluctuations in quasi-two-dimensional planar magnet Sr(Co1−x_{1-x}Nix_x)2_2As2_2

We use neutron scattering to investigate spin excitations in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$, which has a $c$-axis incommensurate helical structure of the two-dimensional (2D) in-plane ferromagnetic (FM) ordered layers for $0.013\leq x \leq 0.25$. By comparing the wave vector and energy dependent spin excitations in helical ordered Sr(Co$_{0.9}$Ni$_{0.1}$)$_2$As$_2$ and paramagnetic SrCo$_2$As$_2$, we find that Ni-doping, while increasing lattice disorder in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$, enhances quasi-2D FM spin fluctuations. However, our band structure calculations within the combined density functional theory and dynamic mean field theory (DFT+DMFT) failed to generate a correct incommensurate wave vector for the observed helical order from nested Fermi surfaces. Since transport measurements reveal increased in-plane and $c$-axis electrical resistivity with increasing Ni-doping and associated lattice disorder, we conclude that the helical magnetic order in Sr(Co$_{1-x}$Ni$_{x})_2$As$_2$ may arise from a quantum order-by-disorder mechanism through the itinerant electron mediated Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions