Cloud detection methodologies: variants and development—a review

Mahajan, Seema; Fataniya, Bhavin

doi:10.1007/s40747-019-00128-0

Cited by 109 publications

(55 citation statements)

References 55 publications

(53 reference statements)

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“…A novel aspect in our approach is that we do this inside the NWP model, so the clouds are consistent with the model meteorological variables. A summary of previous research in cloud detection has been presented in [13] with many machine learning technologies used from convolution neural networks on images [14], Bayesian classifier [15], decision tree [16], boosting and RF [17] among others. However, these methods did not utilize GOES-R data in the training; they did not directly compare to the GOES Level 2 product for cloud detection, and they did not utilize a WRF specifically tuned to improve solar irradiance forecasting.…”

Section: Introductionmentioning

confidence: 99%

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

McCandless

Jiménez

2020

Energies

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In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predicting cloud cover. Further developments to enhance the cloud mask estimations for improved short-term solar irradiance and power forecasting with the MAD-WRF NWP model are discussed.

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Section: Introductionmentioning

confidence: 99%

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

McCandless

Jiménez

2020

Energies

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“…Machine learning, and in particular neural networks, are emerging in many remote sensing applications for clouds (Mahajan and Fataniya, 2020). Application of neural networks has led to more use of geostationary satellite data in cloudrelated products such as cloud type classification or rainfall rate estimation which has been challenging in the past (Bankert et al, 2009;Gorooh et al, 2020;Hayatbini et al, 2019;Hirose et al, 2019).…”

Section: Minutes Over Contiguous Unitedmentioning

confidence: 99%

Applying machine learning methods to detect convection using GOES-16 ABI data

Lee

Kummerow

Ebert‐Uphoff

2020

Preprint

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Abstract. An ability to accurately detect convective regions is essential for initializing models for short term precipitation forecasts. Radar data are commonly used to detect convection, but radars that provide high temporal resolution data are mostly available over land and the quality of the data tends to degrade over mountainous regions. On the other hand, geostationary satellite data are available nearly anywhere and in near-real time. Current operational geostationary satellites, the Geostationary Operational Environmental Satellite-16 (GOES-16) and -17 provide high spatial and temporal resolution data, but only of cloud top properties. One-minute data, however, allow us to observe convection from visible and infrared data even without vertical information of the convective system. Existing detection algorithms using visible and infrared data look for static features of convective clouds such as overshooting top or lumpy cloud top surface, or cloud growth that occurs over periods of 30 minutes to an hour. This study represents a proof-of-concept that Artificial Intelligence (AI) is able, when given high spatial and temporal resolution data from GOES-16, to learn physical properties of convective clouds and automate the detection process. A neural network model with convolutional layers is proposed to identify convection from the high-temporal resolution GOES-16 data. The model takes five temporal images from channel 2 (0.65 μm) and 14 (11.2 μm) as inputs and produces a map of convective regions. In order to provide products comparable to the radar products, it is trained against Multi-Radar Multi-Sensor (MRMS), which is a radar-based product that uses rather sophisticated method to classify precipitation types. Two channels from GOES-16, each related to cloud optical depth (channel 2) and cloud top height (channel 14), are expected to best represent features of convective clouds: high reflectance, lumpy cloud top surface, and low cloud top temperature. The model has correctly learned those features of convective clouds, and resulted reasonably low false alarm ratio (FAR) and high probability of detection (POD). However, FAR and POD can vary depending on the threshold, and a proper threshold needs to be chosen based on the purpose.

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“…This task is usually addressed as a cloud detection problem, where the image pixels are classified into classes such as ground, clouds, cirrus, snow, haze, etc. (Chandran, Jojy, 2015, Mahajan, Fataniya, 2019.…”

Section: Introductionmentioning

confidence: 99%

Temporal Repetition Detection for Ground Visibility Assessment

Gioi

Hessel

Dagobert

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Assessing ground visibility is a crucial step in automatic satellite image analysis. Nevertheless, several recent Earth observation satellite constellations lack specially designed spectral bands and use a frame camera, precluding spectrum-based and parallax-based cloud detection methods. An alternative approach is to detect the parts of each image where the ground is visible. This can be done by comparing locally pairs of registered images in a temporal series: matching regions are necessarily cloud free. Indeed, the ground has persistent patterns that can be observed repetitively in the time series while the appearance of clouds changes at each date. To detect reliably the “visible” ground, we propose here an a contrario local image matching method coupled with an efficient greedy algorithm.

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Cloud detection methodologies: variants and development—a review

Cited by 109 publications

References 55 publications

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

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

Applying machine learning methods to detect convection using GOES-16 ABI data

Temporal Repetition Detection for Ground Visibility Assessment

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