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Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Cloud detection methodologies: variants and development—a review

inferredcites →https://doi.org/10.1016/j.asr.2018.04.030openalex-snapshot

Introducing two Random Forest based methods for cloud detection in remote sensing images

inferredcites →https://doi.org/10.1016/j.pecs.2013.06.002openalex-snapshot

Solar forecasting methods for renewable energy integration

inferredcites →https://doi.org/10.1016/j.rser.2013.06.042openalex-snapshot

Review of solar irradiance forecasting methods and a proposition for small-scale insular grids

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Random Forests

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Gene Selection for Cancer Classification using Support Vector Machines

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Touring the atmosphere aboard the A-Train

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A Review of the Use of Geostationary Satellite Observations in Regional-Scale Models for Short-term Cloud Forecasting

inferredcites →https://doi.org/10.1175/1520-0493(2001)129<3911:nciugs>2.0.co;2openalex-snapshot

NWP Cloud Initialization Using GOES Sounder Data and Improved Modeling of Nonprecipitating Clouds

inferredcites →https://doi.org/10.1175/2009jtecha1281.1openalex-snapshot

Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms

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INTRODUCING THE NEXT-GENERATION ADVANCED BASELINE IMAGER ON GOES-R

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WRF-Solar: Description and Clear-Sky Assessment of an Augmented NWP Model for Solar Power Prediction

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Solar Irradiance Nowcasting Case Studies near Sacramento

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Evaluating Himawari-8 Cloud Products Using Shipborne and CALIPSO Observations: Cloud-Top Height and Cloud-Top Temperature

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Improving NWP Model Cloud Forecasts Using Meteosat Second-Generation Imagery

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A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh

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The Role of Unresolved Clouds on Short-Range Global Horizontal Irradiance Predictability

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Cloud Initialization in the Rapid Update Cycle of HIRLAM

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Multilevel Cloud Detection for High-Resolution Remote Sensing Imagery Using Multiple Convolutional Neural Networks

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Scikit-learn: Machine Learning in Python

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Multi-sensor Advection Diffusion nowCast (MADCast) for cloud analysis and short-term prediction

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A Description of the Advanced Research WRF Version 3

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CLOUD DETECTION BASED ON DECISION TREE OVER TIBETAN PLATEAU WITH MODIS DATA

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The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

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Scikit-learn: Machine Learning in Python

What points at this work

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Combining FY‐4B Infrared Window Channel's Cloud Analysis With Water Vapor Radiance Assimilation for the Prediction of Solar Irradiance

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Low Cloud Detection in Multilayer Scenes Using Satellite Imagery with Machine Learning Methods

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Experimental Parametric Forecast of Solar Energy over Time: Sample Data Descriptor

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Prediction of Solar Power Using Near-Real Time Satellite Data

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Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

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Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review

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Assessment of the GOES-16 Clear Sky Mask Product over the Contiguous USA Using CALIPSO Retrievals

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Exploring the Potential of Statistical Modeling to Retrieve the Cloud Base Height from Geostationary Satellites: Applications to the ABI Sensor on Board of the GOES-R Satellite Series

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Numerical Forecast Correction of Temperature and Wind Using a Single-Station Single-Time Spatial LightGBM Method

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Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

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inferred← citeshttps://doi.org/10.5194/egusphere-2023-1834-ac2openalex-snapshot

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inferred← citeshttps://doi.org/10.5194/egusphere-2023-1834-ac3openalex-snapshot

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inferred← citeshttps://doi.org/10.5194/egusphere-2023-1834-rc1openalex-snapshot

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inferred← citeshttps://doi.org/10.5194/egusphere-2023-1834-rc2openalex-snapshot

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