Article
Engineering, Civil
Yeditha Pavan Kumar, Rathinasamy Maheswaran, Ankit Agarwal, Bellie Sivakumar
Summary: The study introduces wavelet-based neural network models for downscaling daily precipitation in the Krishna River basin in India. These models, incorporating various climatic variables, demonstrate strong performance in capturing regional precipitation patterns and extreme events compared to traditional and recent downscaling methods. The improvement in the wavelet-based models is attributed to their ability to uncover the hidden relationship between predictors and precipitation, enhancing overall model performance.
JOURNAL OF HYDROLOGY
(2021)
Article
Water Resources
Na Zhao, Yimeng Jiao, Lili Zhang
Summary: This study investigates future precipitation variations in the Poyang Lake basin using a scale-based downscaling method. Results show that the basin will become wetter in the next 80 years, with increased precipitation in spring and autumn, and drying trends in some areas during summer and winter.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2022)
Article
Environmental Sciences
Xin Yan, Hua Chen, Bingru Tian, Sheng Sheng, Jinxing Wang, Jong-Suk Kim
Summary: This study proposes a downscaling-merging scheme based on random forest and cokriging, which efficiently generates high-resolution and high-quality daily precipitation data in a large area. The random forest model can accurately spatially downscale GPM daily precipitation data, retaining the accuracy of the original data and greatly improving their spatial details; moreover, the cokriging method significantly enhances the accuracy of the downscaled GPM daily precipitation data.
Article
Environmental Sciences
Fang Wang, Di Tian, Lisa Lowe, Latif Kalin, John Lehrter
Summary: Downscaling is a critical step in bridging the gap between large-scale climate information and local-scale impact assessment. The study introduces a novel deep learning approach, SRDRN, for downscaling daily precipitation and temperature data, showing remarkable performance in capturing spatial and temporal patterns as well as reproducing precipitation and temperature extremes.
WATER RESOURCES RESEARCH
(2021)
Article
Meteorology & Atmospheric Sciences
Rongsheng Jiang, Lei Sun, Chao Sun, Xin-Zhong Liang
Summary: The CWRF downscaling improved the CCSM4 in capturing observed precipitation characteristics and reduced model structural uncertainties for future projections, highlighting the reliability of regional precipitation changes.
Article
Environmental Sciences
Xiaohu Zhao, Guohe Huang, Yongping Li, Qianguo Lin, Junliang Jin, Chen Lu, Junhong Guo
Summary: Future changes in meteorological droughts in Henan Province, China show increased duration and intensity while decreased frequency. This study also finds differences in drought changes among different emission scenarios, with the SSP2-4.5 scenario showing lower magnitudes of changes in duration and intensity relative to other scenarios.
JOURNAL OF CONTAMINANT HYDROLOGY
(2021)
Article
Geosciences, Multidisciplinary
Xianyu Yang, Douwang Li, Zhou Yang, Kai Wu, Luyong Ji, Ziqiang Zhou, Yaqiong Lu
Summary: The warming climate driven by global change has the potential to alter regional and global hydrologic cycles, leading to significant changes in the spatial and temporal patterns of precipitation. This study examines the historical variations of precipitation in Northwest China (NW) from 1951 to 2020 and uses a regional climate model to investigate future precipitation patterns in this region. The findings suggest a significant decrease in precipitation, especially in summer, across the southern and eastern parts of NW in the 2050s under the SSP585 climate scenario. The study provides valuable information for regional mitigation and adaption strategies to potential impacts of future climate change on NW.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Multidisciplinary Sciences
Andrew D. Jones, Deeksha Rastogi, Pouya Vahmani, Alyssa M. Stansfield, Kevin A. Reed, Travis Thurber, Paul A. Ullrich, Jennie S. Rice
Summary: Regional climate models are used to simulate analogue versions of past weather events under different climate conditions. This study downscaled a 40-year sequence of past weather using a range of time-evolving thermodynamic warming signals based on future warming trajectories. The resulting dataset provides insights into the possible range of future climate conditions and their effects on historical extreme events.
Article
Environmental Sciences
Na Zhao, Kainan Chen
Summary: In this study, a coupled merging and downscaling method (CMD) was proposed to obtain multiple high-resolution and high-accuracy daily precipitation datasets. The CMD method showed significantly better performance compared to the original datasets and the widely used MSWEP dataset.
Article
Water Resources
Jungho Kim, Mike Amodeo, Edward J. Kearns
Summary: This study compares the National Oceanic and Atmospheric Administration's Precipitation-Frequency Atlas of the United States (NOAA Atlas 14) with a new atlas of precipitation frequency estimates (PFE) developed in this study to assess the changes in extreme precipitation properties. The NOAA Atlas 14 diverges quantitatively from the recent 20-year records due to precipitation non-stationarity, leading to underestimation of local flood risk. The majority of the United States faces three times more occurrences of extreme storms compared to the 20th century's 1-in-100-year return period.
JOURNAL OF HYDROLOGY-REGIONAL STUDIES
(2023)
Article
Engineering, Civil
Hui Wang, Tirusew Asefa, Solomon Erkyihun
Summary: This study investigates how climate variabilities in summer and winter precipitation in the Southern United States are modulated by large scale atmospheric activities, including ENSO and ACE. Results show that summer precipitation ratios differ across Florida and the east coast, with some stations showing significant correlations with ACE. Extreme summer precipitation is modulated by geographic terrain and local atmospheric activities.
JOURNAL OF HYDROLOGY
(2021)
Article
Environmental Sciences
Li Xiang, Jie Xiang, Jiping Guan, Fuhan Zhang, Yanling Zhao, Lifeng Zhang
Summary: This article introduces a novel reference-based and gradient-guided deep learning model for improving the spatial resolution of precipitation prediction. Experimental results demonstrate that the proposed model outperforms other methods in downscaling, and a daily precipitation downscaling dataset is constructed based on relevant data.
Article
Geosciences, Multidisciplinary
Ryan D. Harp, Daniel E. Horton
Summary: The characterization of changes in precipitation intensities across the entire distribution is important for hazard assessments and water resource management. This study analyzed precipitation observations in 17 regions in the United States and found significant changes in wet day precipitation distributions, particularly a shift towards higher intensities.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Geosciences, Multidisciplinary
Hoa X. Pham, Asaad Y. Shamseldin, Bruce W. Melville
Summary: This study compared the performance of statistical and dynamic downscaling methods in simulating daily precipitation at different levels. Both methods performed well for station level precipitation simulations with return periods equal to or less than 100 years, but dynamic downscaling outperformed statistical downscaling at catchment level.
Article
Meteorology & Atmospheric Sciences
Iman Mallakpour, Mojtaba Sadeghi, Hamidreza Mosaffa, Ata Akbari Asanjan, Mojtaba Sadegh, Phu Nguyen, Soroosh Sorooshian, Amir AghaKouchak
Summary: Variability and spatiotemporal changes in precipitation characteristics can have profound impacts. A study using multiple precipitation datasets showed substantial discrepancies in the changes in extreme and non-extreme precipitation events. While there is relative agreement among datasets on changes in total annual precipitation, there are widespread discrepancies in other percentiles of the precipitation distribution.
WEATHER AND CLIMATE EXTREMES
(2022)
Article
Energy & Fuels
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, Sara C. Pryor
Summary: This study utilizes two years of high-resolution simulations with the WRF model to investigate the characteristics of low-level jets (LLJ) over the U.S. Atlantic coastal zone. The study finds that LLJs are most frequent in the southern lease areas during June, while they are less frequent further south and outside the summer season. LLJs frequently occur at heights that intersect with the wind turbine rotor plane and exhibit wind speeds suitable for wind turbine operation.
Review
Energy & Fuels
Sara C. Pryor, Rebecca J. Barthelmie, Jeremy Cadence, Ebba Dellwik, Charlotte B. Hasager, Stephan T. Kral, Joachim Reuder, Marianne Rodgers, Marijn Veraart
Summary: Leading edge erosion (LEE) of wind turbine blades can lead to decreased power production and increased costs. Understanding hydrometeor properties and joint probability distributions of precipitation and wind speeds is necessary. However, there is a lack of observational data for such locations.
Article
Meteorology & Atmospheric Sciences
S. C. Pryor, F. Letson, T. Shepherd, R. J. Barthelmie
Summary: The Southern Great Plains region experiences frequent heavy rainfall and hail events. This study used the WRF Model to simulate seven months of weather conditions, and evaluated the accuracy of the simulation. The results showed that the model performed well in simulating precipitation and wind speeds, but had some biases in simulating extremely high rainfall and hail. Greater accuracy is needed for joint probabilities of wind speed, rainfall, and hail for renewable energy applications.
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Jacob Coburn, Sara C. C. Pryor
Summary: CF projections for CMIP6 Earth System Models are made based on statistical downscaling models trained with CFs derived from operating wind farms in North America. The projections show declines in wind power production for most wind farms, except in parts of the southern Great Plains. The changes in CF are strongly dependent on the ESM and are linked to the relative intensity of future synoptic patterns.
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
S. C. Pryor, J. J. Coburn, R. J. Barthelmie, T. J. Shepherd
Summary: This study uses the Weather and Research Forecasting (WRF) Model nested in the MPI Earth System Model (ESM) to simulate the changes in wind power generation potential due to global warming. The simulations show that there may be changes in wind power generation, but the output from the model needs to be evaluated using observed data from operating wind farms. The projections indicate that the annual capacity factors (CF) and the probability of wind droughts and wind bonus periods will remain largely unchanged in most of North America.
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Jacob Coburn, Sara C. Pryor
Summary: Climate modes are assessed in this study using Earth system model (ESM) projections under different radiative forcing scenarios. The study examines changes in mode characteristics and interactions and highlights the importance of choosing the right ESM for climate projections. The findings provide insights into internal variability and have implications for future climate projections.
JOURNAL OF CLIMATE
(2023)
Article
Astronomy & Astrophysics
Jacob Coburn, Julian Arnheim, Sara C. Pryor
Summary: This study discusses the importance of short-term forecasting of wind gusts and develops predictive models using wind gust observations from airports in the United States. The results show that artificial neural networks with 3-5 hidden layers generally outperform logistic regression models in terms of accuracy, but deeper networks may lead to increased false alarms and prediction errors. The inclusion of an autoregressive term is critical for model skill, while wind speeds and lapse rates also contribute significantly.
EARTH AND SPACE SCIENCE
(2022)
Article
Energy & Fuels
Jeanie A. Aird, Rebecca J. Barthelmie, Sara C. Pryor
Summary: Wind turbine blade leading edge erosion is a significant issue affecting power production. Two machine learning models were developed to automatically quantify the extent, morphology, and nature of damage from field images. Both models successfully identified approximately 65% of total damage area in independent images.
Article
Energy & Fuels
Rebecca J. Barthelmie, Gunner C. Larsen, Sara C. Pryor
Summary: Offshore wind energy development along the East Coast of the US is progressing quickly due to favorable wind conditions, shallow waters, and close proximity to large electricity markets. Using wind modeling and cost analysis, the study examined the potential energy production and cost implications of different wind turbine layouts in offshore lease areas. The results showed that deploying 15 MW wind turbines at a spacing of 1.85 km could meet 4 to 4.6% of national electricity demand and achieve competitive levelized cost of energy ranging from $68 to $102/MWh, depending on the selected layout and wake model.
Article
Energy & Fuels
Fred Letson, Sara C. C. Pryor
Summary: The study investigates the impact of different types of disdrometers on the characterization of wind turbine blade leading-edge erosion potential at a site in the US Southern Great Plains. The results show significant variations in the estimated kinetic energy of hydrometeor impacts and coating lifetime depending on the disdrometer type used. The damage potential is found to be concentrated in a specific time period, and rotor-speed curtailment during the most erosive periods leads to longer blade lifetimes and lower levelized cost of energy.
Article
Geosciences, Multidisciplinary
Jisesh Sethunadh, F. W. Letson, R. J. Barthelmie, S. C. Pryor
Summary: This study investigates the ability of the Weather Research and Forecasting model to simulate cold-season windstorms and examines their future changes using pseudo-global-warming simulations. The results suggest that under projected climate changes, there will be a decline in maximum wind speeds and precipitation.
Article
Meteorology & Atmospheric Sciences
Jacob Coburn, Sara C. Pryor
Summary: This study evaluates the capacity factors of wind farms in different regions using daily expected wind power production, and quantifies the time scales and seasonality of capacity factor variability. The research suggests that the current summertime wind power production is not well synchronized with electricity demand, and there is a potential decline in summertime capacity factors, exacerbating the offset between peak power production and load. These findings are significant for the efficient use of renewable energy and the redesign of power systems.
JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY
(2023)
Article
Meteorology & Atmospheric Sciences
Tristan Shepherd, Jacob J. Coburn, Rebecca J. Barthelmie, Sara C. Pryor
Summary: This study explores the projected changes of the El Nifio Southern Oscillation (ENSO) climate mode and its impacts on temperature and precipitation anomalies over eastern North America. Regional climate modeling (RCM) is employed, and the results show uncertainties in predicting the ENSO phase response under future climate scenarios.
Article
Meteorology & Atmospheric Sciences
Rebecca J. Barthelmie, Sara C. Pryor
Summary: Global wind resources are abundant and the cost of energy from wind turbines has decreased significantly, making wind energy an important renewable energy source for reducing greenhouse gas emissions. Countries and regions are planning to increase wind energy penetration to mitigate climate change.
Article
Green & Sustainable Science & Technology
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, Sara C. Pryor
Summary: Output from 6 months of high-resolution simulations with the Weather Research and Forecasting (WRF) model were analyzed to characterize local low-level jets (LLJs) over Iowa for winter and spring. The results indicate that LLJs are most frequently associated with nocturnal stable stratification and low turbulent kinetic energy, which makes them more common during the winter months. Sensitivity analyses showed that LLJ characteristics are highly variable with different definitions, but implementing polynomial interpolation reduced sensitivity of LLJ characteristics to down-sampling.
WIND ENERGY SCIENCE
(2021)
