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- Author or Editor: Jacob Berg x
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Abstract
Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.
Significance Statement
This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.
Abstract
Recent advances in artificial intelligence (AI) and explainable AI (XAI) have created opportunities to better predict and understand drought processes. This study uses a machine learning approach for understanding the drivers of drought severity and extent in the Canadian Prairies from 2005 to 2019 using climate and satellite data. The model is trained on the Canadian Drought Monitor (CDM), an extensive dataset produced by expert analysis of drought impacts across various sectors that enables a more comprehensive understanding of drought. Shapley additive explanation (SHAP) is used to understand model predictions during emerging or worsening drought conditions, providing insight into the key determinants of drought. The results demonstrate the importance of capturing spatiotemporal autocorrelation structures for accurate drought characterization and elucidates the drought time scales and thresholds that optimally separate each CDM severity category. In general, there is a positive relationship between the severity of drought and the time scale of the anomalies. However, high-severity droughts are also more complex and driven by a multitude of factors. It was found that satellite-based evaporative stress index (ESI), soil moisture, and groundwater were effective predictors of drought onset and intensification. Similarly, anomalous phases of large-scale atmosphere–ocean dynamics exhibit teleconnections with Prairie drought. Overall, this investigation provides a better understanding of the physical mechanisms responsible for drought in the Prairies, provides data-driven thresholds for estimating drought severity that could improve future drought assessments, and offers a set of early warning indicators that may be useful for drought adaptation and mitigation.
Significance Statement
This work is significant because it identifies drivers of drought onset and intensification in an agriculturally and economically important region of Canada. This information can be used in the future to improve early warning for adaptation and mitigation. It also uses state-of-the-art machine learning techniques to understand drought, including a novel approach called SHAP probability values to improve interpretability. This provides evidence that machine learning models are not black boxes and should be more widely considered for understanding drought and other hydrometeorological phenomena.
Abstract
This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.
Abstract
This study demonstrates that a pulsed wind lidar is a reliable instrument for measuring angles between horizontal vectors of significance in the atmospheric boundary layer. Three different angles are considered: the wind turning, the angle between the stress vector and the mean wind direction, and the angle between the stress vector and the vertical gradient of the mean velocity vector. The latter is assumed to be zero by the often applied turbulent-viscosity hypothesis, so that the stress vector can be described through the vertical gradient of velocity. In the atmospheric surface layer, where the Coriolis force is negligible, this is supposedly a good approximation. High-resolution large-eddy simulation data show that this is indeed the case even beyond the surface layer. In contrast, through analysis of WindCube lidar measurements supported by sonic measurements, the study shows that it is only valid very close to the surface. The deviation may be significant even at 100 m. This behavior is attributed to mesoscale effects.
Abstract
This paper presents an analysis of mean wind measurements from a coordinated system of long-range WindScanners. From individual scan patterns the mean wind field was reconstructed over a large area, and hence it highlights the spatial variability. From comparison with sonic anemometers, the quality of the WindScanner data is high, although the fidelity of the estimated vertical velocity component is significantly limited by the elevation angles of the scanner heads. The system of long-range WindScanners presented in this paper is close to being fully operational, with the pilot study herein serving not only as a proof of concept but also verifying expectations of reliable wind measurements over arbitrary three-dimensional volumes, in future sustained meteorological campaigns.
Abstract
This paper presents an analysis of mean wind measurements from a coordinated system of long-range WindScanners. From individual scan patterns the mean wind field was reconstructed over a large area, and hence it highlights the spatial variability. From comparison with sonic anemometers, the quality of the WindScanner data is high, although the fidelity of the estimated vertical velocity component is significantly limited by the elevation angles of the scanner heads. The system of long-range WindScanners presented in this paper is close to being fully operational, with the pilot study herein serving not only as a proof of concept but also verifying expectations of reliable wind measurements over arbitrary three-dimensional volumes, in future sustained meteorological campaigns.
Abstract
Large-eddy simulation (LES) is used to model turbulent winds in a nominally neutral atmospheric boundary layer at varying mesh resolutions. The boundary layer is driven by wind shear with zero surface heat flux and is capped by a stable inversion. Because of entrainment the boundary layer is in a weakly stably stratified regime. The simulations use meshes varying from 1282 × 64 to 10242 × 512 grid points in a fixed computational domain of size (2560, 2560, 896) m. The subgrid-scale (SGS) parameterizations used in the LES vary with the mesh spacing. Low-order statistics, spectra, and structure functions are compared on the different meshes and are used to assess grid convergence in the simulations. As expected, grid convergence is primarily achieved in the middle of the boundary layer where there is scale separation between the energy-containing and dissipative eddies. Near the surface second-order statistics do not converge on the meshes studied. The analysis also highlights differences between one-dimensional and two-dimensional velocity spectra; differences are attributed to sampling errors associated with aligning the horizontal coordinates with the vertically veering mean wind direction. Higher-order structure functions reveal non-Gaussian statistics on all scales, but are highly dependent on the mesh resolution. A generalized logarithmic law and a k −1 spectral scaling regime are identified with mesh-dependent parameters in agreement with previously published results.
Abstract
Large-eddy simulation (LES) is used to model turbulent winds in a nominally neutral atmospheric boundary layer at varying mesh resolutions. The boundary layer is driven by wind shear with zero surface heat flux and is capped by a stable inversion. Because of entrainment the boundary layer is in a weakly stably stratified regime. The simulations use meshes varying from 1282 × 64 to 10242 × 512 grid points in a fixed computational domain of size (2560, 2560, 896) m. The subgrid-scale (SGS) parameterizations used in the LES vary with the mesh spacing. Low-order statistics, spectra, and structure functions are compared on the different meshes and are used to assess grid convergence in the simulations. As expected, grid convergence is primarily achieved in the middle of the boundary layer where there is scale separation between the energy-containing and dissipative eddies. Near the surface second-order statistics do not converge on the meshes studied. The analysis also highlights differences between one-dimensional and two-dimensional velocity spectra; differences are attributed to sampling errors associated with aligning the horizontal coordinates with the vertically veering mean wind direction. Higher-order structure functions reveal non-Gaussian statistics on all scales, but are highly dependent on the mesh resolution. A generalized logarithmic law and a k −1 spectral scaling regime are identified with mesh-dependent parameters in agreement with previously published results.
Abstract
The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.
Abstract
The Wind Forecast Improvement Project (WFIP) is a public–private research program, the goal of which is to improve the accuracy of short-term (0–6 h) wind power forecasts for the wind energy industry. WFIP was sponsored by the U.S. Department of Energy (DOE), with partners that included the National Oceanic and Atmospheric Administration (NOAA), private forecasting companies (WindLogics and AWS Truepower), DOE national laboratories, grid operators, and universities. WFIP employed two avenues for improving wind power forecasts: first, through the collection of special observations to be assimilated into forecast models and, second, by upgrading NWP forecast models and ensembles. The new observations were collected during concurrent year-long field campaigns in two high wind energy resource areas of the United States (the upper Great Plains and Texas) and included 12 wind profiling radars, 12 sodars, several lidars and surface flux stations, 184 instrumented tall towers, and over 400 nacelle anemometers. Results demonstrate that a substantial reduction (12%–5% for forecast hours 1–12) in power RMSE was achieved from the combination of improved numerical weather prediction models and assimilation of new observations, equivalent to the previous decade’s worth of improvements found for low-level winds in NOAA/National Weather Service (NWS) operational weather forecast models. Data-denial experiments run over select periods of time demonstrate that up to a 6% improvement came from the new observations. Ensemble forecasts developed by the private sector partners also produced significant improvements in power production and ramp prediction. Based on the success of WFIP, DOE is planning follow-on field programs.
