Short-term solar irradiance forecasting using convolutional neural networks and cloud imagery
Abstract

Authors:
; ;
Publication Date:
NSF-PAR ID:
10362276
Journal Name:
Environmental Research Letters
Volume:
16
Issue:
4
Page Range or eLocation-ID:
Article No. 044045
ISSN:
1748-9326
Publisher:
IOP Publishing
National Science Foundation
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1. Abstract

Numerical weather prediction models and high-performance computing have significantly improved our ability to model near-surface variables, but their uncertainty quantification still remains a challenging task. Ensembles are usually produced to depict a series of possible future states of the atmosphere, as a means to quantify the prediction uncertainty, but this requires multiple instantiation of the model, leading to an increased computational cost. Weather analogs, alternatively, can be used to generate ensembles without repeated model runs. The analog ensemble (AnEn) is a technique to identify similar weather patterns for near-surface variables and quantify forecast uncertainty. Analogs are chosen based on a similarity metric that calculates the weighted multivariate Euclidean distance. However, identifying optimal weights for similarity metric becomes a bottleneck because it involves performing a constrained exhaustive search. As a result, only a few predictors were selected and optimized in previous AnEn studies. A new machine learning similarity metric is proposed to improve the theoretical framework on how weather analogs are identified. First, a deep learning network is trained to generate latent features using all the temporal multivariate input predictors. Analogs are then selected in this latent space, rather than the original predictor space. The proposed method does not requiremore »

2. Abstract

Spectral lines formed at lower atmospheric layers show peculiar profiles at the “leading edge” of ribbons during solar flares. In particular, increased absorption of the BBSO/GST Heiλ10830 line, as well as broad and centrally reversed profiles in the spectra of the Mgiiand Ciilines observed by the IRIS satellite, has been reported. In this work, we aim to understand the physical origin of such peculiar IRIS profiles, which seem to be common of many, if not all, flares. To achieve this, we quantify the spectral properties of the IRIS Mgiiprofiles at the ribbon leading edge during four large flares and perform a detailed comparison with a grid of radiative hydrodynamic models using theRADYN+FPcode. We also studied their transition region (TR) counterparts, finding that these ribbon front locations are regions where TR emission and chromospheric evaporation are considerably weaker compared to other parts of the ribbons. Based on our comparison between the IRIS observations and modeling, our interpretation is that there are different heating regimes at play in the leading edge and the main bright part of the ribbons. More specifically, we suggest that bombardment of the chromosphere by more gradual and modest nonthermal electron energy fluxes can qualitatively explain themore »

3. Abstract

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