[HTML][HTML] Using artificial intelligence to improve real-time decision-making for high-impact weather
Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather
in: Bulletin of the American Meteorological Society Volume 98 Issue 10 (2017) Jump to …
in: Bulletin of the American Meteorological Society Volume 98 Issue 10 (2017) Jump to …
MPF-Net: A computational multi-regional solar power forecasting framework
Short-term solar irradiance forecasting plays a pivotal role in the effective integration of
significantly fluctuating solar power into power grids. Existing computational approaches …
significantly fluctuating solar power into power grids. Existing computational approaches …
Outlook for exploiting artificial intelligence in the earth and environmental sciences
SA Boukabara, V Krasnopolsky… - Bulletin of the …, 2021 - journals.ametsoc.org
Promising new opportunities to apply artificial intelligence (AI) to the Earth and
environmental sciences are identified, informed by an overview of current efforts in the …
environmental sciences are identified, informed by an overview of current efforts in the …
A machine learning tutorial for operational meteorology. Part II: Neural networks and deep learning
Over the past decade the use of machine learning in meteorology has grown rapidly.
Specifically neural networks and deep learning have been used at an unprecedented rate …
Specifically neural networks and deep learning have been used at an unprecedented rate …
[HTML][HTML] Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction …
Accurate operational solar irradiance forecasts are crucial for better decision making by
solar energy system operators due to the variability of resource and energy demand …
solar energy system operators due to the variability of resource and energy demand …
[HTML][HTML] Building the Sun4Cast system: Improvements in solar power forecasting
As integration of solar power into the national electric grid rapidly increases, it becomes
imperative to improve forecasting of this highly variable renewable resource. Thus, a team of …
imperative to improve forecasting of this highly variable renewable resource. Thus, a team of …
Machine learning for applied weather prediction
SE Haupt, J Cowie, S Linden… - 2018 IEEE 14th …, 2018 - ieeexplore.ieee.org
The National Center for Atmospheric Research (NCAR) has a long history of applying
machine learning to weather forecasting challenges. The Dynamic Integrated foreCasting …
machine learning to weather forecasting challenges. The Dynamic Integrated foreCasting …
A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning
In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart
Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the …
Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the …
Variable generation power forecasting as a big data problem
To blend growing amounts of power from renewable resources into utility operations
requires accurate forecasts. For both day ahead planning and real-time operations, the …
requires accurate forecasts. For both day ahead planning and real-time operations, the …
On the estimation of boundary layer heights: a machine learning approach
R Krishnamurthy, RK Newsom, LK Berg… - Atmospheric …, 2021 - amt.copernicus.org
The planetary boundary layer height (zi) is a key parameter used in atmospheric models for
estimating the exchange of heat, momentum, and moisture between the surface and the free …
estimating the exchange of heat, momentum, and moisture between the surface and the free …