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19 pages, 13363 KiB

Open AccessArticle

Quantification of Shortwave Surface Albedo Feedback Using a Neural Network Approach

by Diana Laura Diaz Garcia and Yi Huang

Atmosphere 2024, 15(2), 150; https://doi.org/10.3390/atmos15020150 - 24 Jan 2024

Viewed by 773

Radiative transfer is a nonlinear process. Despite this, most current methods to evaluate radiative feedback, such as the kernel method, rely on linear assumptions. Neural network (NN) models can emulate nonlinear radiative transfer due to their structure and activation functions. This study aims [...] Read more.

Radiative transfer is a nonlinear process. Despite this, most current methods to evaluate radiative feedback, such as the kernel method, rely on linear assumptions. Neural network (NN) models can emulate nonlinear radiative transfer due to their structure and activation functions. This study aims to test whether NNs can be used to evaluate shortwave radiative feedbacks and to assess their performance. This study focuses on the shortwave radiative feedback driven by surface albedo. An NN model is first trained using idealized cases, simulating truth values from a radiative transfer model via the partial radiative perturbation method. Two heuristic cases are analyzed: univariate feedback, perturbing the albedo; and bivariate feedback, perturbing the albedo and cloud cover concurrently. These test the NN’s ability to capture nonlinearity in the albedo–flux and albedo–cloud–flux relationships. We identify the minimal NN structure and predictor variables for accurate predictions. Then, an NN model is trained with realistic radiation flux and atmospheric variable data and is tested with respect to its predictions at different order levels: zero-order for the flux itself, first-order for radiative sensitivity (kernels), and second-order for kernel differences. This paper documents the test results and explains the NN’s ability to reproduce the complex nonlinear relationship between radiation flux and different atmospheric variables, such as surface albedo, cloud optical depth, and their coupling effects. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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19 pages, 5956 KiB

Open AccessArticle

Radiative Regime According to the New RAD-MSU(BSRN) Complex in Moscow: The Roles of Aerosol, Surface Albedo, and Sunshine Duration

by Daria Piskunova, Natalia Chubarova, Aleksei Poliukhov and Ekaterina Zhdanova

Atmosphere 2024, 15(2), 144; https://doi.org/10.3390/atmos15020144 - 23 Jan 2024

Viewed by 663

Abstract

The radiative budget is one of the key factors that influences climate change. The aim of this study was to analyze the radiative regime in Moscow using the RAD-MSU(BSRN) complex and to estimate the radiative effects of the main geophysical factors during the [...] Read more.

The radiative budget is one of the key factors that influences climate change. The aim of this study was to analyze the radiative regime in Moscow using the RAD-MSU(BSRN) complex and to estimate the radiative effects of the main geophysical factors during the 2021–2023 period. This complex is equipped and maintained according to the recommendations of the Baseline Surface Radiation Network; however, it is not a part of this network. In cloudless conditions, the decrease in global shortwave irradiance (Q) is about 18–22% due to the aerosol content with a pronounced change in the direct to diffuse ratio. In winter, the increase in Q is about 45 W/m² (or 9%) at h = 30° due to a high surface albedo and reduced aerosol and water vapor contents, while the net shortwave irradiance (Bsh) demonstrates a significant decrease due to the prevailing effects of snow albedo. In cloudy conditions, a nonlinear dependence of Q and Bsh cloud transmittance on the relative sunshine duration is observed. The mean changes in Q for the 2021–2023 against the 1955–2020 period are characterized by negative anomalies (−22%) in winter and positive anomalies in summer (+3%) due to the changes in cloudiness. This is in line with the global tendencies in the long-term changes in shortwave irradiance in moderate climates in Europe in recent years. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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25 pages, 15357 KiB

Open AccessArticle

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

by Abderrahmane Mendyl, Vahdettin Demir, Najiya Omar, Osman Orhan and Tamás Weidinger

Atmosphere 2024, 15(1), 103; https://doi.org/10.3390/atmos15010103 - 14 Jan 2024

Cited by 1 | Viewed by 1308

Abstract

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather [...] Read more.

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m². Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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29 pages, 24262 KiB

Open AccessArticle

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

by Xin Zhou, Yangang Liu, Yunpeng Shan, Satoshi Endo, Yu Xie and Manajit Sengupta

Atmosphere 2024, 15(1), 39; https://doi.org/10.3390/atmos15010039 - 28 Dec 2023

Cited by 1 | Viewed by 964

Abstract

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and [...] Read more.

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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17 pages, 6738 KiB

Open AccessArticle

Mapping Prediction of Surface Solar Radiation with Linear Regression Models: Case Study over Reunion Island

by Qi Li, Miloud Bessafi and Peng Li

Atmosphere 2023, 14(9), 1331; https://doi.org/10.3390/atmos14091331 - 23 Aug 2023

Viewed by 1475

Abstract

This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° [...] Read more.

This paper presents a novel mapping prediction method for surface solar radiation with linear regression models. The dataset for surface solar radiation prediction is the daily surface incoming shortwave radiation (SIS) product from CM SAF SARAH-E. The spatial resolution is 0.05° × 0.05° and the temporal coverage is from 2007 to 2016. The first five years (2007–2011) are used as training data, and the remaining five years (2012–2016) are used as test data in the prediction model. Datasets were detrended, de-seasonalized, and normalized before being applied to multiple linear regression (MLR), principal component regression (PCR), stepwise regression (SR), and partial least squares regression (PLSR), which are used to perform prediction mapping. The statistical analysis using MAE, MSE, and RMSE shows that the PCR model had the smallest MAE, MSE, and RMSE as compared to the other three models. The PCR model seems better for SSR mapping prediction over Reunion Island. Although the PCR model provides better prediction results, its MAE, MSE, and RMSE are quite large. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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28 pages, 2153 KiB

Open AccessArticle

Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery

by Claire Thomas, William Wandji Nyamsi, Antti Arola, Uwe Pfeifroth, Jörg Trentmann, Stephen Dorling, Agustín Laguarda, Milan Fischer and Alexandr Aculinin

Atmosphere 2023, 14(8), 1259; https://doi.org/10.3390/atmos14081259 - 8 Aug 2023

Viewed by 1431

Abstract

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon [...] Read more.

Photosynthetically active radiation (PAR) is the 400–700 nm portion of the solar radiation spectrum that photoautotrophic organisms including plants, algae, and cyanobacteria use for photosynthesis. PAR is a key variable in global ecosystem and Earth system modeling, playing a prominent role in carbon and water cycling. Alongside air temperature, water availability, and atmospheric CO₂ concentration, PAR controls photosynthesis and consequently biomass productivity in general. The management of agricultural and horticultural crops, forests, grasslands, and even grasses at sports venues is a non-exhaustive list of applications for which an accurate knowledge of the PAR resource is desirable. Modern agrivoltaic systems also require a good knowledge of PAR in conjunction with the variables needed to monitor the co-located photovoltaic system. In situ quality-controlled PAR sensors provide high-quality information for specific locations. However, due to associated installation and maintenance costs, such high-quality data are relatively scarce and generally extend over a restricted and sometimes non-continuous period. Numerous studies have already demonstrated the potential offered by surface radiation estimates based on satellite information as reliable alternatives to in situ measurements. The accuracy of these estimations is site-dependent and is related, for example, to the local climate, landscape, and viewing angle of the satellite. To assess the accuracy of PAR satellite models, we inter-compared 11 methods for estimating 30 min surface PAR based on satellite-derived estimations at 33 ground-based station locations over several climate regions in Europe, Africa, and South America. Averaged across stations, the results showed average relative biases (relative to the measurement mean) across methods of 1 to 20%, an average relative standard deviation of 25 to 30%, an average relative root mean square error of 25% to 35% and a correlation coefficient always above 0.95 for all methods. Improved performance was seen for all methods at relatively cloud-free sites, and quality degraded towards the edge of the Meteosat Second Generation viewing area. A good compromise between computational time, memory allocation, and performance was achieved for most locations using the Jacovides coefficient applied to the global horizontal irradiance from HelioClim-3 or the CAMS Radiation Service. In conclusion, satellite estimations can provide a reliable alternative estimation of ground-based PAR for most applications. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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37 pages, 24503 KiB

Open AccessArticle

An Assessment of Global Dimming and Brightening during 1984–2018 Using the FORTH Radiative Transfer Model and ISCCP Satellite and MERRA-2 Reanalysis Data

by Michael Stamatis, Nikolaos Hatzianastassiou, Marios-Bruno Korras-Carraca, Christos Matsoukas, Martin Wild and Ilias Vardavas

Atmosphere 2023, 14(8), 1258; https://doi.org/10.3390/atmos14081258 - 8 Aug 2023

Cited by 3 | Viewed by 1587

Abstract

In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR [...] Read more.

In this study, an assessment of the FORTH radiative transfer model (RTM) surface solar radiation (SSR) as well as its interdecadal changes (Δ(SSR)), namely global dimming and brightening (GDB), is performed during the 35-year period of 1984–2018. Furthermore, a thorough evaluation of SSR and (Δ(SSR)) is conducted against high-quality reference surface measurements from 1193 Global Energy Balance Archive (GEBA) and 66 Baseline Surface Radiation Network (BSRN) stations. For the first time, the FORTH-RTM Δ(SSR) was evaluated over an extended period of 35 years and with a spatial resolution of 0.5° × 0.625°. The RTM uses state-of-the-art input products such as MERRA-2 and ISCCP-H and computes 35-year-long monthly SSR and GDB, which are compared to a comprehensive dataset of reference measurements from GEBA and BSRN. Overall, the FORTH-RTM deseasonalized SSR anomalies correlate satisfactorily with either GEBA (R equal to 0.72) or BSRN (R equal to 0.80). The percentage of agreement between the sign of computed GEBA and FORTH-RTM Δ(SSR) is equal to 63.5% and the corresponding percentage for FORTH-RTM and BSRN is 54.5%. The obtained results indicate that a considerable and statistically significant increase in SSR (Brightening) took place over Europe, Mexico, Brazil, Argentina, Central and NW African areas, and some parts of the tropical oceans from the early 1980s to the late 2010s. On the other hand, during the same 35-year period, a strong and statistically significant decrease in SSR (Dimming) occurred over the western Tropical Pacific, India, Australia, Southern East China, Northern South America, and some parts of oceans. A statistically significant dimming at the 95% confidence level, equal to −0.063 Wm⁻² year⁻¹ (or −2.22 Wm⁻²) from 1984 to 2018 is found over the entire globe, which was more prevalent over oceanic than over continental regions (−0.07 Wm⁻² year⁻¹ and −0.03 Wm⁻² year⁻¹, statistically significant dimming at the 95% confidence level, respectively) in both hemispheres. Yet, this overall 35-year dimming arose from alternating decadal-scale changes, consisting of dimming during 1984–1989, brightening in the 1990s, turning into dimming over 2000–2009, and brightening during 2010–2018. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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12 pages, 671 KiB

Open AccessArticle

Tuning Monte Carlo Models to Reproduce Cosmic Radiation Interacting with the Earth’s Atmosphere

by Muhammad Ajaz, Abd Haj Ismail, Muhammad Waqas, Ramoona Shehzadi, Ishrat Asghar, Hannan Younis, Mateen Ullah Mian, Atef AbdelKader, Muhammad Adil Khan and Kashif Safeen

Atmosphere 2023, 14(6), 1028; https://doi.org/10.3390/atmos14061028 - 15 Jun 2023

Viewed by 999

Abstract

In this work, we performed a comparative study between HIJING, Sibyll, and QGSJET model-based event generators. Such Monte Carlo (MC) models are used to simulate the interaction and propagation of high-energy cosmic radiation (e.g., coming from the sun) with the Earth’s atmosphere. The [...] Read more.

In this work, we performed a comparative study between HIJING, Sibyll, and QGSJET model-based event generators. Such Monte Carlo (MC) models are used to simulate the interaction and propagation of high-energy cosmic radiation (e.g., coming from the sun) with the Earth’s atmosphere. The global event observables selected for the study were the transverse momentum (

p_{T}

) spectra and rapidity density distributions of strange particles (

K_{S}^{0}

,

Λ

, and

Ξ^{-}

). This study was performed in the STAR and CMS fiducial phase spaces by simulating the strange particles in

p p

collisions at

\sqrt{s}

= 200 GeV, 900 GeV, and 7 TeV, and the simulations were then compared to the experimental measurements. It was observed that none of the discussed model-based event generators ultimately predicted the experimental results, except QGSJET, which generally agrees reasonably with the data. However, QGSJET does not produce

Ξ

particles; therefore, it does not provide any predictions for

Ξ

. The other two models reproduced the data only in a limited rapidity or transverse momentum region while mainly underpredicting the data in the rest of the areas. These cosmic radiation simulation models are capable of covering the mid-rapidity regions of density distributions. Utilizing model-based observations, some fundamental parameters can be re-tuned and extrapolations to the highest energies can be investigated. Furthermore, these observations can provide valuable insights that could potentially constrain and improve perturbative- and non-perturbative-based QCD event generators, thereby facilitating a better understanding of the underlying physics. Full article

(This article belongs to the Special Issue Solar Radiation: Measurements and Model Studies—Progress and Perspectives)

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