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
Green & Sustainable Science & Technology
Aida Hosseini Baghanam, Vahid Nourani, Ehsan Norouzi, Amirreza Tabataba Vakili, Hueseyin Gokcekus
Summary: This study focuses on bias correction of climate models using multi-resolution wavelet transform and assessing the correlation between climate signals. The results show that the bias correction method based on discrete wavelet transform outperforms the quantile mapping method. The combination of wavelet transform and artificial neural network can identify the most important predictors in climate models.
Article
Computer Science, Artificial Intelligence
Rilwan A. Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta
Summary: This study presents a Deep Learning approach, TRU-NET, for high-resolution precipitation prediction, which outperforms traditional models and achieves more accurate results. The model architecture and loss function proposed in the study contribute to the improvement in precipitation prediction accuracy across various data formulation strategies.
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
Mathematics
Haoxuan Yuan, Qiangyu Zeng, Jianxin He
Summary: The proposed nonlocal residual network (NLRN) based on CNN achieves superior performance in extracting detailed structure information of radar echo and super-resolution reconstruction.
JOURNAL OF MATHEMATICS
(2021)
Article
Meteorology & Atmospheric Sciences
Rocio Balmaceda-Huarte, Maria Laura Bettolli
Summary: This study performed empirical statistical downscaling (ESD) to simulate daily maximum and minimum temperatures in different climatic regions of Argentina. The results showed that different ESD models had varying levels of skill, and the predictor set and model configuration were key factors. The downscaling models were able to capture the general characteristics of temperature, with better performance in minimum temperature. However, regions with complex topography posed a challenge for capturing local variability. The extrapolation skill of the models in warm conditions was similar to that in the cross-validated period. The results of this study provide a reference for future ESD developments and comparisons in Argentina.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
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
Myeong-Ho Yeo, Hoang-Lam Nguyen, Van-Thanh-Van Nguyen
Summary: The study introduces a climate change assessment tool based on statistical downscaling (SD) to describe the relationship between large-scale climate predictors and daily rainfall characteristics at a local site. The tool incorporates logistic regression and nonlinear regression models to represent daily rainfall occurrences and amounts, with suggested scaling factors and correction coefficients to improve model accuracy. Results show that the proposed tool provides marked improvement in describing daily precipitation amounts and occurrences, outperforming the currently popular SDSM method.
JOURNAL OF WATER AND CLIMATE CHANGE
(2021)
Article
Engineering, Civil
Shadi Arfa, Mohsen Nasseri, Hassan Tavakol-Davani
Summary: This study assessed and compared the effects of different downscaling methods on an urban network in Tehran, Iran. The findings suggest that DMDM outperforms other techniques in daily downscaling, and the GEV distribution method is more effective in sub-daily disaggregation. Simulation results indicate a higher risk of urban flooding under the RCP 8.5 scenario compared to RCP 4.5 and RCP 2.6 scenarios.
WATER RESOURCES MANAGEMENT
(2021)
Article
Meteorology & Atmospheric Sciences
Taesam Lee, Jaephil Jo, Vijay P. Singh
Summary: This study enhanced the PNTD method to downscale daily precipitation to a finer temporal scale of 10 minutes, and successfully tested it in the Jinju area. Results showed that the method accurately reproduced the key statistics of precipitation data and the statistical characteristics of extreme events. Additionally, the future precipitation scenarios were downscaled satisfactorily using the method.
Article
Engineering, Environmental
Huidong Jin, Weifan Jiang, Minzhe Chen, Ming Li, K. Shuvo Bakar, Quanxi Shao
Summary: Skilful and localised daily weather forecasts are needed by climate-sensitive sectors, which can be provided by downscaling techniques applied to the ensemble forecasts from General circulation models. This study focuses on deep-learning-based downscaling method for ensemble rainfall forecasts and proposes a two-step procedure to enhance the accuracy and skill. The results demonstrate that the developed very deep statistical downscaling model outperforms other models and improves the raw forecasts, although further research efforts are required for skilful seasonal climate forecasts.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
Article
Meteorology & Atmospheric Sciences
Lucy Harris, Andrew T. T. McRae, Matthew Chantry, Peter D. Dueben, Tim N. Palmer
Summary: In this study, the authors utilize generative adversarial networks (GANs) to improve the accuracy and resolution of low-resolution weather forecasting model outputs, using high-resolution radar measurements as ground truth. By learning to add resolution and structure, GANs can generate high-resolution and spatially coherent precipitation maps, matching the statistical properties of state-of-the-art downscaling methods.
JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS
(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
Environmental Sciences
Maria J. Chinita, Mark Richardson, Joao Teixeira, Pedro M. A. Miranda
Summary: The study found that changes in heavy precipitation frequency have been rapidly increasing globally in recent years, especially for hourly and once-per-day heavy events. Relative frequency increases are mainly present in rare events, and the frequency increases are more pronounced in oceanic regions.
ENVIRONMENTAL RESEARCH LETTERS
(2021)
Article
Meteorology & Atmospheric Sciences
Christian Merkenschlager, Stephanie Koller, Christoph Beck, Elke Hertig
Summary: This study introduces two novel weather analog methods within urban climate modeling, allowing day-by-day comparison with observations and adaptability for future projections. These methods impact the first level of analogy by selecting circulation patterns and pre-processing time series data. While effective for local or small regional applications, they also offer low computational costs.
THEORETICAL AND APPLIED CLIMATOLOGY
(2021)
Article
Environmental Sciences
Flavia D. S. Moraes, Francisco E. Aquino, Thomas L. Mote, Joshua D. Durkee, Kyle S. Mattingly
Article
Meteorology & Atmospheric Sciences
Paul W. Miller, Craig A. Ramseyer
JOURNAL OF HYDROMETEOROLOGY
(2020)
Article
Geochemistry & Geophysics
Bradford S. Barrett, Gina R. Henderson, Erin McDonnell, Major Henry, Thomas Mote
ATMOSPHERIC SCIENCE LETTERS
(2020)
Article
Meteorology & Atmospheric Sciences
Edward Hanna, John Cappelen, Xavier Fettweis, Sebastian H. Mernild, Thomas L. Mote, Ruth Mottram, Konrad Steffen, Thomas J. Ballinger, Richard Hall
Summary: The analysis of Greenland temperature data reveals that despite significant warming trends in recent decades, overall temperature trends have been relatively stable and insignificant since 2001. Additionally, both coastal and inland stations in Greenland show similar trends in summer temperature changes.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Lori J. Wachowicz, Jonathon R. Preece, Thomas L. Mote, Bradford S. Barrett, Gina R. Henderson
Summary: As the Arctic warms, the hypothesis of increased Greenland blocking due to changes in jet stream and Rossby waves propagation is supported with observational data. However, trends in Greenland blocking are found to be sensitive to different blocking metrics, leading to inconsistencies in trends. Seasonal variations show significant increases in blocking for certain months, but overall trends are not statistically significant. Using multiple metrics can help provide a more comprehensive understanding of Greenland blocking trends.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
P. W. Miller, M. Williams, T. Mote
Summary: Long-range aerosol transport plays a crucial role in the Earth's ecological, biological, and hydrological elements. This study evaluates the accuracy of different dust emission settings in the WRF-Chem model and finds that the GOCART-AFWA scheme provides the best balance between AOD and GDI accuracy. Different dust emission configurations in the model lead to varying meteorological conditions and aerosol optical depth biases in certain regions, highlighting the covariability between SAL dust loadings and thermodynamic conditions.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2021)
Article
Meteorology & Atmospheric Sciences
Craig A. Ramseyer, Paul W. Miller
Summary: This study fills the gap in understanding the climatology and evolution of trade wind inversion (TWI) in the tropical North Atlantic (TNA) region using high-resolution ERA5 reanalysis data. The results show stronger and more frequent TWIs in the central TNA across all seasons. Analysis also reveals increasing trends in TWI frequency and strength, with a particularly strong signal from December to July. Regional forcing mechanisms responsible for these changes are discussed.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Article
Meteorology & Atmospheric Sciences
Zachary J. Suriano, Daniel J. Leathers, Thomas L. Mote, Gina R. Henderson, Thomas W. Estilow, Lori J. Wachowicz, David A. Robinson
Summary: At a continental scale, changes in snow ablation events inform regional hydroclimate, affecting streamflow, soil moisture, and groundwater supplies. The study shows a significant decrease in snow ablation frequency over time, with some regions experiencing up to a 75% decline in events, mainly due to reductions in snow cover.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2021)
Review
Geosciences, Multidisciplinary
Gina R. Henderson, Bradford S. Barrett, Lori J. Wachowicz, Kyle S. Mattingly, Jonathon R. Preece, Thomas L. Mote
Summary: Arctic amplification is a fundamental feature of Earth's climate system, but its specific causes are not fully understood. The future Arctic amplification may be influenced by multiple mechanisms, including both local processes and external forces. Climate change will have impacts on sea ice, ice sheet surface mass balance, snow cover, and other surface cryospheric variables in the Arctic region.
FRONTIERS IN EARTH SCIENCE
(2021)
Article
Meteorology & Atmospheric Sciences
Flavia D. S. Moraes, Thomas L. Mote, Lynne Seymour
Summary: This study investigates the spatial distribution of seasonal drought in the insular Caribbean and its relationship with ENSO, NAO, and AMM. The results show a drying trend in both Greater Antilles and Lesser Antilles, with more intense and frequent drought events in the Lesser Antilles. The study aims to improve drought forecasts and help the region prepare for seasonal droughts.
INTERNATIONAL JOURNAL OF CLIMATOLOGY
(2022)
Article
Geography, Physical
Craig A. Ramseyer, Natalie Teale
Summary: The research on atmospheric rivers (ARs) has transformed from focusing on the U.S. West Coast to becoming a globally relevant driver of extreme hydrometeorological events. The literature now covers regions beyond the U.S. and Europe, including high latitudes, New Zealand, China, North Africa, and the Middle East. The impact of AR-driven events on land surface processes and water resources is rapidly developing, sparking new applied research frontiers.
PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT
(2021)
Article
Meteorology & Atmospheric Sciences
J. R. Preece, L. J. Wachowicz, T. L. Mote, M. Tedesco, X. Fettweis
Summary: The recent increase in summer Greenland blocking, driven by an increase in Omega patterns, has played a central role in the accelerating mass loss of the Greenland Ice Sheet. Different blocking patterns, such as summer ridge patterns, Omega blocks, and cyclonic wave breaking patterns, have varying impacts on the surface melt of the ice sheet.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Meteorology & Atmospheric Sciences
Craig A. Ramseyer, Tyler J. Stanfield, Zachary Van Tol, Tyler Gingrich, Parker Henry, Peter Forister, Bradley Lamkin, Shakira Stackhouse, Samrin Samaiya Sauda
Summary: Atmospheric rivers (ARs) are the main moisture transport force in the Western United States and are the main producers of extreme precipitation events in this region. Increasing evidence suggests similar impacts in the Central and Eastern US. This study uses machine learning to determine the most prominent types of ARs in the region. The results show that extratropical cyclones are the most common driver of ARs, and coastal and lee-side cyclones produce the strongest ARs. Trend analysis indicates increasing intensity and/or size of ARs associated with Nor'easters and those originating in the Gulf of Mexico.
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
(2022)
Article
Multidisciplinary Sciences
Kyle S. S. Mattingly, Jenny V. V. Turton, Jonathan D. D. Wille, Brice Noel, Xavier Fettweis, Asa K. Rennermalm, Thomas L. L. Mote
Summary: The Greenland Ice Sheet has been melting at an accelerated rate, particularly in northeast Greenland. The extreme melt events in this region are mainly caused by atmospheric rivers (ARs) from northwest Greenland, which induce foehn winds in the northeast. These events have become more frequent in the twenty-first century and are expected to continue increasing with climate warming.
NATURE COMMUNICATIONS
(2023)
Article
Multidisciplinary Sciences
Jonathon R. Preece, Thomas L. Mote, Judah Cohen, Lori J. Wachowicz, John A. Knox, Marco Tedesco, Gabriel J. Kooperman
Summary: A shift in summer atmospheric circulation has accelerated Greenland Ice Sheet melt. The authors provide evidence of two potentially synergistic mechanisms linking high-latitude warming to the observed increase in Greenland blocking. They show that a wavier summer atmospheric circulation over the North Atlantic, along with a direct stationary Rossby wave response to low spring North American snow cover, contribute to the prevalence of Greenland blocking.
NATURE COMMUNICATIONS
(2023)