Scitools/Cartopy: V0.17.0

Elson, Phil; Andrade, Elliott Sales de; Hattersley, Richard; Campbell, E.; Dawson, Andrew; May, Ryan; Scmc,; Little, Bill; Pelley, Carwyn; Blay, Byron; Donkers, Kevin; Killick, Peter; marqh,; Lbdreyer,; Peglar, Patrick; Wilson, Nat; Kirkham, Daniel; Bosley, Corinne; Signell, J.; Filipe,; Krischer, Lion; Eriksson, D.; Smith, Andrea; Carlos,; McDougall, Damon; Crosby, Alex E.; herzmann, daryl; scaine,; Greg,; munslowa,

doi:10.5281/zenodo.1490296

Cited by 4 publications

(2 citation statements)

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“…SPI and SPEI calculations were performed using the SCI package in R (https://cran.r-project.org/package=SCI, last access: 9 January 2023; Gudmundsson and Stagge, 2016). The remaining calculations and visualisations were performed using Python: NumPy (https://github.com/numpy/numpy, last access: 5 January 2023; NumPy project, 2021; Harris et al, 2020) and pandas (https://doi.org/10.5281/zenodo.5203279, Reback et al, 2021) for data handling; scikit-learn (https://doi.org/10.5281/zenodo.4725836, Grisel et al, 2021;Pedregosa et al, 2011) for the construction, training and evaluation of the data-driven models; SciPy (https://doi.org/10.5281/zenodo.4635380, Virtanen et al, 2021Virtanen et al, , 2020 for computation of the correlations between predictors; xskillscore (https://doi.org/10.5281/zenodo.5173153, Bell et al, 2021) for calculating the correlation effective p value between the data-driven models and FWI metrics; and Matplotlib (https://doi.org/10.5281/zenodo.5194481, Caswell et al, 2021;Hunter, 2007), cartopy (https://doi.org/10.5281/zenodo.1490296, Elson et al, 2018;Met Office, 2010-2015 and seaborn (https://doi.org/10.5281/zenodo.5205191, Waskom et al, 2021;Waskom, 2021) for the visualisations of the results.…”

Section: Discussionmentioning

confidence: 99%

A data-driven model for Fennoscandian wildfire danger

Bakke

Wanders

Wiel

et al. 2023

Nat. Hazards Earth Syst. Sci.

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Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

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Section: Discussionmentioning

confidence: 99%

A data-driven model for Fennoscandian wildfire danger

Bakke

Wanders

Wiel

et al. 2023

Nat. Hazards Earth Syst. Sci.

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“…The figures in this paper were produced using MATLAB (versions R2018a and R2020a-The MathWorks Inc., 2018, 2020) and Python (version 3.7.11-Python Software Foundation, 2021). The main packages used for plotting in Python were Basemap (version 1.2.0-Whitaker, 2011), cartopy (version 0.17.0- Elson et al, 2018), Matplotlib (version 3.2.2- Caswell et al, 2020), MetPy (version 1.0.0rc1- May et al, 2020), andwrf-python (version 1.3.2-Ladwig, 2017). The data used to create the figures in this paper are available from Alter et al (2023).…”

Section: Data Availability Statementmentioning

confidence: 99%

The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

2023

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Infrasound – i.e., acoustic waves with frequencies below the threshold of human hearing – has historically been used to detect and locate distant explosive events over global ranges (≥ 1000 km). Simulations over these ranges have traditionally relied on large‐scale, synoptic meteorological information. However, infrasound propagation over shorter, local ranges (0‐100 km) may be affected by smaller, mesoscale meteorological features. To identify the effects of these mesoscale meteorological features on local infrasound propagation, simulations were conducted using the Weather Research and Forecasting (WRF) meteorological model to approximate the meteorological conditions associated with a series of historical, small‐scale explosive test events that occurred at the Big Black Test Site in Bovina, Mississippi. These meteorological conditions were then incorporated into a full‐wave acoustic model to generate meteorology‐informed predictions of infrasound propagation. A series of WRF simulations was conducted with varying degrees of horizontal resolution – 1, 3, and 15 km – to investigate the spatial sensitivity of these infrasound predictions. The results illustrate that convective precipitation events demonstrate potentially observable effects on local infrasound propagation due to strong, heterogeneous gradients in temperature and wind associated with the convective events themselves. Therefore, to accurately predict infrasound propagation on local scales, it may be necessary to use convection‐permitting meteorological models with a horizontal resolution ≤ 4 km at locations and times that support mesoscale convective activity.

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Meteorologically‐Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability

et al. 2022

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Renewable generation variability over multiple days is a key challenge in decarbonizing the European power system. Weather regimes are one way to quantify this variability, but so far, their applications to energy research have focused on wind power generation in winter. However, the projected growth of solar photovoltaic (PV) capacity implies that its absolute variability across the continent will grow substantially. Here we combine weather regimes based on ERA5 reanalysis data with country‐specific capacity factors to investigate multiday PV generation variability in Europe. With current installed capacity (131 GW), total PV production in Europe (52.3 GW) varies by 0.9 GW on average, with a maximum change of 3.0 GW, upon transition from one weather regime to another. Using projected PV capacity for 2050 (1.94 TW), variability would rise to 13.9 and 43.8 GW. We present optimized spatial distributions of capacity additions in three scenarios that substantially reduce variability by up to 40%. One of them ascertains a large local PV production, thereby minimizing the need for long‐range power transmission while still reducing variability by about 30%, highlighting that optimized siting and local generation can be reconciled. Our results emphasize the value of leveraging climate information in decarbonizing power systems.

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Scitools/Cartopy: V0.17.0

Cited by 4 publications

References 0 publications

A data-driven model for Fennoscandian wildfire danger

A data-driven model for Fennoscandian wildfire danger

The Influence of Mesoscale Atmospheric Convection on Local Infrasound Propagation

Meteorologically‐Informed Spatial Planning of European PV Deployment to Reduce Multiday Generation Variability

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