Wind gusts, and in particular intense gusts, are societally relevant but extremely challenging to forecast. This study systematically assesses the skill enhancement that can be achieved using ...artificial neural networks (ANNs) for forecasting of wind gust occurrence and magnitude. Geophysical predictors from the ERA5 reanalysis are used in conjunction with an autoregressive term in regression and ANN models with different predictors, and varying model complexity. Models are derived and assessed for the warm (April–September) and cold (October–March) seasons for three high passenger volume airports in the United States. Model uncertainty is assessed by deriving models for 1000 different randomly selected training (70%) and testing (30%) subsets. Gust prediction fidelity in independent test samples is critically dependent on inclusion of an autoregressive term. Gust occurrence probabilities derived using five-layer ANNs exhibit consistently higher fidelity than those from regression models and shallower ANNs. Inclusion of the autoregressive term and increasing the number of hidden layers in ANNs from 1 to 5 also improve the model performance for gust magnitudes (lower RMSE, increased correlation, and model standard deviations that more closely approximate observed values). Deeper ANNs (e.g., 20 hidden layers) exhibit higher skill in forecasting strong (17–25.7 m s-1) and damaging (≥25.7 m s-1) wind gusts. However, such deep networks exhibit evidence of overfitting and still substantially underestimate (by 50%) the frequency of strong and damaging wind gusts at the three airports considered herein.
Significance StatementImproved short-term forecasting of wind gusts will enhance aviation safety and logistics and may offer other societal benefits. Here we present a rigorous investigation of the relative skill of models of wind gust occurrence and magnitude that employ different statistical methods. It is shown that artificial neural networks (ANNs) offer considerable skill enhancement over regression methods, particularly for strong and damaging wind gusts. For wind gust magnitudes in particular, application of deeper learning networks (e.g., five or more hidden layers) offers tangible improvements in forecast accuracy. However, deeper networks are vulnerable to overfitting and exhibit substantial variability with the specific training and testing data subset used. Also, even deep ANNs reproduce only half of strong and damaging wind gusts. These results indicate the need for future work to elucidate the dynamical mechanisms of intense wind gusts and advance solutions to their prediction.
New particle formation leads to cloud dimming Sullivan, Ryan C.; Crippa, Paola; Matsui, Hitoshi ...
NPJ climate and atmospheric science,
05/2018, Volume:
1, Issue:
1
Journal Article
Peer reviewed
Open access
Abstract
New particle formation (NPF), nucleation of condensable vapors to the solid or liquid phase, contributes significantly to atmospheric aerosol particle number concentrations. With sufficient ...growth, these nucleated particles may be a significant source of cloud condensation nuclei (CCN), thus altering cloud albedo, structure, and lifetimes, and insolation reaching the Earth’s surface. Herein we present one of the first numerical experiments conducted at sufficiently high resolution and fidelity to quantify the impact of NPF on cloud radiative properties. Consistent with observations in spring over the Midwestern USA, NPF occurs frequently and on regional scales. However, NPF is not associated with enhancement of regional cloud albedo. These simulations indicate that NPF reduces ambient sulfuric acid concentrations sufficiently to inhibit growth of preexisting particles to CCN sizes, reduces CCN-sized particle concentrations, and reduces cloud albedo. The reduction in cloud albedo on NPF days results in a domain average positive top of atmosphere cloud radiative forcing, and thus warming, of 10 W m
−2
and up to ~50 W m
−2
in individual grid cells relative to a simulation in which NPF is excluded.
Thank You to Our 2022 Reviewers Caprarelli, Graziella; Altintas, Ilkay; Baratoux, David ...
Earth and space science (Hoboken, N.J.),
April 2023, 2023-04-00, 20230401, 2023-04-01, Volume:
10, Issue:
4
Journal Article
Peer reviewed
Open access
Plain Language Summary
The Editors and Staff of Earth and Space Science acknowledge the importance of hundreds of peer reviewers who contributed to the scientific rigor of the papers published in the ...journal. The Editors wish to publicly recognize the 839 reviewers who gave selflessly of their time and expertise in 2022.
Abstract
The American WAKE experimeNt (AWAKEN) is a multi-institutional collaborative field campaign, starting in March 2022, that will gather an unprecedented data set including both atmospheric ...observations and wind plant operational data. This comprehensive data set will be used to characterize the wind plant performance and turbine loading in different operational and atmospheric conditions and validate the use of different wind plant control strategies and simulation frameworks. An extensive field campaign like AWAKEN requires proper coordination and long-term planning to be successful. In this paper, we review the major activities planned during AWAKEN to provide information for current and future project partners. Specifically, we provide information about the project sites, their planned instruments, and how these will relate to the scientific objectives of the overall AWAKEN project.
Doppler lidars are frequently operated in a mode referred to as arc scans, wherein the lidar beam scans across a sector with a fixed elevation angle and the resulting measurements are used to derive ...an estimate of the n minute horizontal mean wind velocity (speed and direction). Previous studies have shown that the uncertainty in the measured wind speed originates from turbulent wind fluctuations and depends on the scan geometry (the arc span and the arc orientation). This paper is designed to provide guidance on optimal scan geometries for two key applications in the wind energy industry: wind turbine power performance analysis and annual energy production prediction. We present a quantitative analysis of the retrieved wind speed uncertainty derived using a theoretical model with the assumption of isotropic and frozen turbulence, and observations from three sites that are onshore with flat terrain, onshore with complex terrain and offshore, respectively. The results from both the theoretical model and observations show that the uncertainty is scaled with the turbulence intensity such that the relative standard error on the 10 min mean wind speed is about 30 % of the turbulence intensity. The uncertainty in both retrieved wind speeds and derived wind energy production estimates can be reduced by aligning lidar beams with the dominant wind direction, increasing the arc span and lowering the number of beams per arc scan. Large arc spans should be used at sites with high turbulence intensity and/or large wind direction variation.
The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of ...FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data collected during 2005–2022 to derive hourly ice accumulation at 883 stations across the contiguous USA. These estimates are combined with wind gust observations to compute time series of hourly FZG hazard magnitude using the Sperry–Piltz Ice Accumulation (SPIA) index. Results are evaluated using Storm Reports of damage and economic disruption. The hourly SPIA estimates are also used to (i) derive a geospatial atlas of the hazard including the 50 yr return period event intensities for each US state derived using superstations, and (ii) describe storylines of significant events in terms of meteorological drivers and socioeconomic impacts. The highest values of SPIA during the 18 yr study period occur in a region extending from the Southern Great Plains over the Midwest into the densely populated Northeast. States in these regions also have high 50 yr return period maximum radial ice accumulation of 3–5 cm and co-occurring wind gusts >30 ms-1. These values are comparable to past estimates for the 500 yr event which may imply this hazard has been previously underestimated. This atlas can be used to inform optimal FZG hazard mitigation strategies for each state/region.
Thank You to Our 2021 Reviewers Caprarelli, Graziella; Altintas, Ilkay; Baratoux, David ...
Earth and space science (Hoboken, N.J.),
April 2022, Volume:
9, Issue:
4
Journal Article
Peer reviewed
Open access
On behalf of the Editorial Board and Staff of Earth and Space Science, I thank the reviewers whose selfless dedication to science has ensured, once again, that the papers published in our journal in ...2021 highlight the best Earth and space science in a manner that does justice to the authors and their work. All of us at Earth Peer reviewing is a demanding and often thankless job. It is however an essential component of the scientific process, ensuring the highest standards of integrity and rigor. Without the work of reviewers, who check data and procedures for possible bias and to ensure reproducibility, and who share their expertise to verify that the interpretations and conclusions of a paper are consistent with assumptions and existing knowledge, it would not be possible to trust in the scientific process. Our journal is particularly indebted to our reviewers: Earth and Space Science is a multidisciplinary journal that highlights methods, instruments, data and algorithms, and therefore we rely heavily on the direct expertise of our reviewers to verify and vouch for the quality of the papers we publish. We are indebted to all our reviewers, and we are delighted to acknowledge them publicly in this Editorial.
Plain Language Summary
The Editors and Staff of Earth and Space Science acknowledge the importance of hundreds of peer reviewers who contributed to the scientific rigor of the papers published in the journal. The Editors wish to publicly recognize the 881 reviewers who gave selflessly of their time and expertise in 2021.
Key Points
The editors thank the 2021 peer reviewers
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