Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images

Domain: earth_science
Published: 2024-01-13
Editorial outcome: accept

Abstract
Accurate nowcasting for cloud fraction is still intractable challenge for stable solar photovoltaic electricity generation. By combining continuous radiance images measured by geostationary satellite and an advanced recurrent neural network, we develop a nowcasting algorithm for predicting cloud fraction at the leading time of 0–4 h at photovoltaic plants. Based on this algorithm, a cyclically updated prediction system is also established and tested at five photovoltaic plants and several stations with cloud fraction observations in China. The results demonstrate that the cloud fraction nowcasting is efficient, high quality and adaptable. Particularly, it shows an excellent forecast performance within the first 2-hour leading time, with an average correlation coefficient close to 0.8 between the predicted clear sky ratio and actual power generation at photovoltaic plants. Our findings highlight the benefits and potential of this technique to improve the competitiveness of solar photovoltaic energy in electricity market.

Introduction
Reducing fossil fuel use and global climate change requires a fast energy transition, and nations across the globe have successively set out their own targets and pathways to carbon neutrality1. Since 2009, as the fastest-growing renewable power source, the generating capacity of solar photovoltaic (PV) energy has grown globally by 41% per year2. It has put forward higher requirements for the conversion efficiency and capital cost reduction of PV energy generation3, which is always impacted by cloud cover, aerosol and panel soiling4–9. Yet, in a stark contrast to aerosol and panel soiling, cloud cover or advection can dramatically and intermittently affect incident solar radiation, resulting in unbalance between the load demand and PV energy generation, which poses a considerable risk to the stability of power grids10–12. Therefore, reliable and powerful PV energy generation or global tilted irradiance (GTI, the radiation captured by solar photovoltaic panels) forecast technique, particularly short-term forecasts of the intra-day GTI or PV power generation (at the leading time of 0–4 h), is also highly beneficial to power smoothing processes and other load-following applications9,13. In addition, currently, in most European countries, short-term prices for the physical delivery of electricity are formed by spot markets, such as the European Power Exchange SPOT (https://www.europex.org/members/epex-spot/). Although ~80% of trade volume is controlled by the day-ahead trading market, the intra-day auctions from hourly to 15-min intervals determine real-time electricity prices14. Thus, sophisticated solar PV power generation nowcasting technique not only can improve the stability of power generation, but also facilitates the developments of more commercially viable PV systems, the current electricity market and price transactions, and increases the competitiveness of the solar PV energy source15,16.

In recent years, rapid advances in artificial intelligence have promoted the application of data-driven machine learning-based approaches in Earth system science17,18. Particularly, some recent studies12,13,19–21 on the prediction of solar radiation also explicitly indicate that advanced prediction approaches based on machine learning perform better compared with empirical models, time series, and hybrid algorithms, such as artificial neural networks and support vector machines. Nevertheless, it is still a great challenge to predict cloud motion, formation, deformation and dissipation under complex atmospheric dynamics, geography, and climatic conditions9,22,23. Thus, there is still no solar radiation forecast model that can work well in every region and at every time21.

Cloud cover nowcasting remains a field of interest for forecasting the electricity production of PV plants24. We are committed to developing a daytime hourly intra-day cloud fraction (CF) prediction algorithm for small areas over PV plants. Based on the recurrent-neural-networks-based (RNNs) long short-term memory (LSTM) algorithm framework, the newly developed PredRNN and PredRNN++ (an extended and latest version of PredRNN)25,26 can well learn to predict long-term future imageries in various spatio-temporal tasks by modeling their spatial and temporal dependencies, including video frame prediction, human motion prediction, etc. Therefore, our primary objective is to develop an innovative and easy-to-promote algorithm or system based on the key framework of the PredRNN++ model. Through this algorithm, the 0–4 h CF at solar PV plants under all weather conditions can be predicted by using sequential Himawari-8/9 geostationary satellite images with high spatio-temporal resolutions27. Compared with the previous study28, it only used a single visible channel of geostationary satellite and a constant model to predict cloudiness. Some former studies directly used surface solar global horizontal irradiance (GHI) as model input to predict GHI values in the next few hours29, achieving the purpose of estimating the power generation of PV plants. Nevertheless, the presence of clouds is still identified as the primary uncertainty in current surface solar GHI forecasts30. In contrast, our investigation only predicts geostationary satellite Level 1B (L1B) radiance data. With the prediction results of satellite L1B radiance data and accurate cloud detection algorithm, this approach is expected to provide reliable and variable CF information for further improving the predictability of current GTI or PV power generation.

Results
Cloud fraction nowcasting and validations
In order to better simulate real application scenarios, a quasi-operational and near real-time (NRT) and cyclically updated prediction system is newly developed for 0–4 h CF nowcasting at solar PV plants (hereafter referred to as the NCP_CF). The predicted CF from this NCP_CF nowcasting system is also compared with the real PV power generation and the GTI to verify its feasibility, reliability and adaptability. The results from the NCP_CF system are examined and validated by using the observed CF values from twelve manual meteorological observation stations of the China Meteorological Administration (only the observations at 14:00 and 17:00 are used) and three all-sky imager stations (Fig. 1). Figure 2 shows the root mean square errors (RMSEs) and mean bias errors (MBEs) of the CF predictions at targeted stations with the forecast horizon, local time (diurnal cycle) and time series. In terms of forecast horizon (Figs. 2a–2f, Supplementary Table 1), the RMSE increases from 0.18 to 0.35 at all stations for 0–4 h forecast periods, and the MBE fluctuates around −0.1. Notably, the RMSE is less than 0.25 within the 2-h leading period, but the forecast accuracy decreases faster when the forecast leading period exceeds 2 h, indicating that the forecast performance threshold of this system is ~2-h leading time. Considering the continuity and coverage of observation time, Fig. 2g only shows the diurnal cycle of CF forecast accuracy at three all-sky imager stations. Within a one-day forecast window, the most and least accurate predictions occur during 12:00–17:00 and 08:00–09:30, respectively, whereas the relatively moderate decrease in the forecast accuracy before 09:30 is mainly attributed to the invalid satellite visible images before 08:00.

Regarding the predicted performance for different months, the monthly mean RMSE and MBE values in time series at three all-sky imager stations do not vary considerably, indicating the weak monthly dependence and stability of this cyclically updated CF nowcasting algorithm and system.

Furthermore, the correlation coefficients (R) between the 1–4 h predicted clear sky ratios (CSRs; CSR = 1 − CF) from the NCP_CF system and the actual power generation (GTI) are calculated. Figure 3 displays the comparisons among the 1–4 h predicted CSRs, the actual power generation and the GTI at Sangge, Leling, Xiaochengzi, Lijiamen, and Shiziyan PV plants from 09:00 to 17:00 in November 2022. The mean R values between the 1–4 h predicted CSRs and the actual PV power generation (the GTI) in November 2022 at five PV plants are 0.81, 0.73, 0.65, and 0.55 (0.81, 0.72, 0.64, and 0.55). This result highlights the good consistency of the predicted CSRs with the actual PV power generation and the GTI, especially for the first 2-h leading time. Overall, the EPM-model-based NCP_CF system developed in this research is applicable to provide high-quality CF estimations at PV plants in advance, which can be used to predict the GTI and power generation at the forecast leading time of 0–4 h. The results at Sangge and Leling PV plants from December 2022 to March 2023 are displayed in Supplementary Figs. 3–6.

Near real-time and cyclically updated prediction system
The satellite-based NRT and cyclically updated prediction system (Fig. 4) for 0–4 h CF nowcasting, operating at five real PV plants (Sangge, Leling, Xiaochengzi, Lijiamen and Shiziyan PV plants) belonged to China General Nuclear Power Group Wind Energy Co. Ltd., mainly consists of three subsystems, i.e., preprocessing, prediction and retrieval modules. Specifically, the preprocessing module regularly adjusts the real-time down-sampling Himawari-8/9 Advanced Himawari Imager (AHI) data received from the direct broadcast receiving system. The AHI is an advanced imager with 16 spectral bands ranging from 0.47 μm to 13.3 μm, which has spatio-temporal resolutions of 4 km and 10 min31. The full-disk Level-1B radiance data at 0.65 μm, 0.86 μm, 3.9 μm, 7.0 μm, 11.2 μm and 12.3 μm with high-quality geolocation and radiometric calibration is grided into a 32 × 32 pixel box (~128 km × 128 km) centered around the targeted PV plant. Then, these sequential and resized AHI images at six bands are converted into a tensor [tile size = 6 (band number) × 24 (4-h time sequence) × 32 × 32] of the prediction model for forecasting the following 0–4 h satellite images (6 × 24 × 32 × 32).

As a key function of the prediction module, an enhanced PredRNN++ model (EPM; more details in “Model” section) is developed in this study to predict 0–4 h sequential geostationary satellite images. In order to better track the fast and stochastic changes in cloud images, the neural network of the EPM is always cyclically generated by the scheduled training process of the model, which has a 1-h update frequency. The cumbersome cyclic process should take 40–50 min when using the latest sequential satellite images (6 × 24 × 32 × 32) between −5 h and −1 h as training samples and one graphics processing unit processor (NVIDIA Tesla-V100). For instance, a cyclic training process of the model starts at the scheduled local time of 12:08 and ends at ~12:53, the imported training samples are from 08:00 to 12:00, and the latest updated EPM is timely activated for CF nowcasting at ~13:08 (nowcasting from 13:00 to 17:00) and 13:38 (nowcasting from 13:30 to 17:30), respectively, with an update frequency of a frequency of half an hour. During the same period, the network of the next EPM is also trained simultaneously by using the sequential satellite images from 09:00 to 13:00. This cyclical procedure will continually update and replace the existing EPM every hour, ensuring that we always have the most up-to-date nowcasting model. Considering the use of satellite visible images, the NCP_CF system only operates from 07:20 to 17:20, which still meets the requirement of CF nowcasting at PV plants.

In the retrieval module, the 0–4 h predicted and resized cloud images mentioned above are used by a fast cloud mask algorithm32, which is able to calculate the number of cloudy pixels and the CF or cloud cover in the observation field of the targeted PV plant. Note that compared with the operational cloud mask algorithm, the fast cloud mask algorithm can fast retrieve the CF without any ancillary data, which is crucial for CF nowcasting. Supplementary Figure 1 presents the comparisons between the predicted and actual satellite images and cloud mask results at Zhuhai station on 17 November 2022, illustrating the good agreement between them.

Methods
The newly developed EPM and fast cloud mask algorithm in the NCP_CF system are applied to predict CF (or CSR) at the leading time of 0–4 h at two test PV plants. In this system, the −4–0 h geostationary satellite radiance data is used as input to dynamically provide 0–4 h satellite cloud images and fractions. To verify the reliability of the EPM, we first use the CF observations from twelve widely distributed ground-based manual stations and three all-sky imager stations for the period from 2019 to 2022 as true values to compare with the predictions in the corresponding period. Moreover, correlations of the predicted CSRs with the actual PV power generation and surface solar radiation at five test PV plants from October 2022 to March 2023 are analyzed. The benefits of the geostationary satellite data with high spatio-temporal resolutions and the advanced EPM to improve the PV power generation efficiency are investigated, as well as the wide applicability and generalizable value of the EPM system.

Ground-based observation data
The total cloud cover or CF (~20 km × 20 km square area) used in this study is obtained through manual observation at twelve ground-based meteorological stations (Fig. 1) in January, April, July, and October of 2019. Note that due to the relatively large errors in low-visibility conditions, the CF data with the matched and automatically measured visibility < 2 km is removed. Besides, the view zenith angles of ground-based stations from H8/AHI field of view used here, as stated in Supplementary Table 2, are smaller than 60°. Therefore, the parallax effect is negligible (error < 1 km) in the collocation between satellite pixels and ground-based stations for this study (For more explanations and details, please refer to Supplementary Note and Supplementary Figs. 7 and 8). Firstly, considering the daytime nowcasting applications and sunshine conditions at PV plants, only the manual observations at 11:00, 14:00 and 17:00 are collected for validation in this research. Secondly, three ground-based all-sky imager stations (equipped with a Japan EKO ASI-16 all-sky imager, https://www.eko-instruments.com/us/categories/products/all-sky-imagers/asi-16-all-sky-imager) can provide the high temporal resolution (5 min) and valuable CF and cloud cover data during the daytime, which are retrieved by the standard EKO ASI-16 cloud detection algorithm (https://www.eko-instruments.com/media/z2aalysq/asi-16-software-manual-find-clouds.pdf)36. ASI, equipped with a digital camera coupled with an upward looking fisheye lens, could provide field of view (FOV) of ~180°, but pixels at a FOV > 140° are excluded due to distortion. Digital images of the sky obtained by ASI are classified pixel by pixel into clear sky, optically thin and optically thick clouds, respectively. The cloud detection and opacity classification (CDOC) algorithm developed by Ghonima et al., 201236 could provide 96%, 60%, and 96.3% accuracy in the validation for clear, thin, and thick cloud, respectively. Finer CF data allow more accurate validation of the NCP_CF system, thereby demonstrating the specific prediction effect at the forecast leading time of 0–4 h. The study periods of the local CF data from three all-sky imagers located in Zhuhai, Nanjing and Beijing (Fig. 1) are from September 2022 to February 2023, April 2021 to September 2021, and September 2022 to December 2022, respectively. In addition, the actual power generation (MW, temporal resolution of 15 min) and GTI (W·m−2) measured at Sangge and Leling PV plants from November 2022 to March 2023 and at Xiaochengzi, Lijiamen and Shiziyan PV plants in November 2022 (Fig. 1) are also used to analyze the agreement with the predicted CF. The actual power generation and GTI data are obtained from the China General Nuclear Power Group Wind Energy Co. Ltd.

Geostationary satellite data and calculating the cloud fraction
The NRT 16-band full-disk AHI level-1B radiance data from the Himawari-8/9 satellite (the new-generation Japanese geostationary meteorological satellite) with spatio-temporal resolutions of 1–4 km at the original resolution (0.5–2 km) are also available for free download from the JAXA (Japan Aerospace Exploration Agency) Himawari satellite data FTP (File Transfer Protocol) site (ftp.ptree.jaxa.jp) from July 7, 2015 (http://www.jma-net.go.jp/msc/en/). The nadir point of the Himawari-8/9 satellite is located at 140.7° E, and the coverage of this satellite includes the Japan island and the eastern parts of China.

Based on the real-time Himawari-8/9 AHI full-disk observation data, each site would be centered around its precise location and matched with a 32 × 32 pixels box as the experimental area. The special fast cloud mask algorithm32 combines five inherited and improved cloudy/clear pixel tests in visible and infrared bands to determine the final confidence value (c) of every pixel of the satellite imager, i.e., c > 0.99 = clear, 0.95 < c ≤ 0.99 = probably clear, 0.66 < c ≤ 0.95 = probably cloudy, and c ≤ 0.66 = cloudy. As the real viewing field at a ground-based station approximates a 20 km × 20 km square area, a 5 × 5 pixels box of cloud mask centered around a targeted PV plant is used in this study to calculate the CF predictions, which is expressed as Eq. (1).

CF = (Num cloudy + Num prob-cloudy) / 25 (1)

where Num cloudy and Num prob-cloudy indicate the total numbers of the cloudy and probably cloudy pixels in the 5 × 5 pixel box37, respectively. The complementary CSR is equal to 1-CF.

Model
The PredRNN++ model24 (an improved prediction RNN), dedicated to the short-term prediction for 0–4 h CF nowcasting, is a deep neural network. This advanced model successfully overcomes the spatio-temporal predictive learning dilemma between deep-in-time structure and vanishing gradient. Previous research has demonstrated that the PredRNN++ consistently outperforms the ConvLSTM, TrajGRU, Discrete Fracture Network, MCnet and PredRNN at every future timestep for both peak signal-to-noise ratio and structural similarity index measure24. To achieve greater spatio-temporal modeling capability, in this investigation, we re-design and develop the EPM with five convolutional layers, whose elaborated structure is shown in Fig. 5. The details of the casual LSTM and the Gradient Highway Unit (GHU)24 in the EPM structure are also illustrated in Supplementary Fig. 2.

The causal LSTM, an upgraded version of the LSTM, increases the recurrence depth from one time step to the next and derives a more powerful modeling capability for stronger spatial correlations and short-term dynamics. As shown in Supplementary Fig. 2a, a causal LSTM unit contains two memories, namely a temporal memory Ck t and a spatial memory Mk t, where the superscripts k and t denote the kth hidden layer in the stacked causal LSTM network and the tth time step, respectively. The temporal memory Ck t depends on its preceding state Ck t−1 and is controlled by an input gate i t, a forget gate f t and an input modulation gate g t. The spatial memory Mk t relies on Mk t−1 which is in the deep transition route. Notably, the topmost spatial memory M5 t−1 is assigned to the bottom spatial memory M0 t. For the kth layer, the updated equations of the causal LSTM can be expressed as Eqs. (2–7).

[i t, f t, g t] = σ(W 1 ⊚ [X t, Hk t−1, Ck t−1]) (2)

Ck t = g t ⨂ i t + f t ⨂ Ck t−1 (3)

[i t, f t, g t] = σ(W 2 ⊚ [X t, Ck t, Mk t−1]) (4)

Mk t = g t ⨂ i t + f t ⨂ tanh(W 3 ⊚ Mk t−1) (5)

O t = tanh(W 4 ⊚ [X t, Ck t, Mk t]) (6)

Hk t = O t ⨂ tanh(W 5 ⊚ [Ck t, Mk t]) (7)

where “⊚" denotes the convolution, "⨂" the element-wise multiplication, tanh the element-wise hyperbolic tangent function, σ the element-wise sigmoid function, and “[]” a concatenation of tensors. W 1...5 the convolutional filters. All the equations in the causal LSTM can be briefly expressed as Eq. (8).

Hk t, Ck t, Mk t = CauLSTM k (Hk t−1, Hk t−1, Ck t−1, Mk t−1) (8)

where Hk−1 t is replaced by X and Z between the first and second layers.

The gradient highway, a shorter route from future outputs back to distant inputs, can alleviate the vanishing gradient problem. As shown in Supplementary Fig. 2b, in a GHU, W represents the convolutional filters, S t is a switch gate and enables adaptive learning between the transformed input P t and the hidden state Z t−1. The equation of the GHU can be written as Eqs. (9–11).

P t = tanh(W pz ⊚ Z t−1 + W ph ⊚ H1 t) (9)

S t = σ(W sz ⊚ Z t−1 + W sh ⊚ H1 t) (10)

Z t = S t ⨂ P t + (1 − S t) ⨂ Z t−1 (11)

The equations of the GHU can be briefly expressed as:

Z t = GHU(H1 t, Z t−1) (12)

As presented in Fig. 5a, combined with Eqs. (8) and (12), the key equations of the entire EPM framework can be written as Eqs. (13–18).

H1 t, C1 t, M1 t = CauLSTM 1 (X t, H1 t−1, C1 t−1, M5 t−1) (13)

Z t = GHU(H1 t, Z t−1) (14)

H2 t, C2 t, M2 t = CauLSTM 2 (Z t, H1 t−1, C1 t−1, M1 t) (15)

H3 t, C3 t, M3 t = CauLSTM 3 (H2 t, H3 t−1, C3 t−1, M2 t) (16)

H4 t, C4 t, M4 t = CauLSTM 4 (H3 t, H4 t−1, C4 t−1, M3 t) (17)

H5 t, C5 t, M5 t = CauLSTM 5 (H4 t, H5 t−1, C5 t−1, M4 t) (18)

In the EPM framework, the GHU is injected between the first and second causal LSTMs. The causal LSTM and GUH respectively capture short-term and long-term data or image dependencies. The gradient highway (blue line in Fig. 5a) supplies a quick path from the first to the last time step by quickly updating hidden state Z t. It is worth noting that, unlike temporal skip connections, the GHU controls the proportions of Z t−1 and the deep transition feature H1 t through S t, which allows the EPM to adaptively learn both short-term and long-term frame relations. In the causal LSTM of the EPM, the spatial memory is a function of the temporal memory through another set of gate structures. As the recurrence depth along the spatio-temporal transition paths grows considerably, each pixel in the final generated frame has a bigger receptive field of the input sequence at each time step, which is why the EPM has a better ability to model short-term video dynamics and sudden changes. Figure 5b displays the data flow process in the spatial memory of the EPM. The dimension of the input sequential satellite data (4 h and a time interval of 10 min) is 24 × 32 × 32. At the first convolutional layer, the input terms M5 t−1 and C1 t are concatenated to form a large tensor, and then H1 t is generated by the convolution calculation. The EPM performs a total of five convolution calculations, with dimensions of hidden state from 10 × 32 × 32 to 1 × 32 × 32.

The EPM training process involves the use of the Adam optimizer with a learning rate of 0.001 and the mean square error as the loss metric. The input is a four-dimensional tensor of size [c, t, h, w] (6, 24, 32, 32), where c represents the number of input channels, t represents time (spanning 4 h), h represents height, and w represents width. Our research area focuses on PV plants, which provides us with images measuring 32 × 32 pixels (~128 km × 128 km). To train our model, we created a dataset consisting of sequences of 48 images for each channel, spanning 4 h. During the initial training process of the optimal network structure, data were obtained from resized AHI images at six bands over twelve manual observation stations and three all-sky imager stations. The training set utilized data from January to October in 2018, while the remaining months of 2018 were used as the validation set. In the application scenario of PV plants, the training method is described in the section of NRT and cyclically updated prediction system.

Validation and assessment. The primary metrics used to evaluate the accuracy of the NCP_CF system for forecasting the CF are the RMSE, MBE and R, defined as shown in Eqs. (19)-(21).

RMSE = sqrt(1/N * Σ(P i − T i)^2) (19)

MBE = 1/N * Σ(P i − T i) (20)

R = Σ(P i − P_)(T i − T_) / sqrt(Σ(P i − P_)^2 * Σ(T i − T_)^2) (21)

where P i denotes the predicted CF, T i denotes the actual CF obtained from ground-based observations mentioned above, and N is the total number of the matched samples.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The paper in general has many good features and the method looks promising. The state of the article, as it stands now, needs revision (potentially major.)\nMinor issues include geometry concerns for both the ground-based observation data (beginning lines 247) and for the Himawari cloud fraction data.\nRegarding the ground observations, authors detail the EKO ASI-16 cloud fraction product, but do not specify how the cloud fraction at long limb angles from the 180º sensor are area-corrected. The optical geometry from the ASI-16 has significant compression at low angles and significant magnification at zenith angles - the accuracy of the derived cloud fraction will depend in great deal how this optical compression is corrected. More details on this process are needed.\nRegarding the satellite observations, there are no details provided on parallax correction or terrain correction. Orthorectification of surface and cloud location is critical for computing accurate cloud fractions - failing to correct, or incorrectly performing the parallax or terrain correction will result in incorrectly-located clouds and inaccurate cloud fractions for a specific location (such as the three locations detailed in the paper.) More details on the methods of parallax and terrain correction, if performed, are needed (or a justification as to why they were not.)\nA larger issue is the general layout of the paper - upon first read, the manuscript is confusing to put together. I would suggest a reorganization of the paper layout, thusly:\n1.) Move section 4.) Methods (beginning line 234) to immediately after the introduction (starting line 95.) Move section 2.) Results (currently line 95) to after the end of the new Methods section, and then move 3.) Discussion to after the end of the new Results section. Going from Introduction -> Methods -> Results -> Discussion is a more natural way to read the paper - currently, the paper reads as Introduction -> Results -> Discussion -> Methods and is very confusing. This was perhaps a simple clerical error that could be addressed with minor updating of text and figure references.\nThe section detailing the PredRNN++ model is excellent, and if located earlier in the paper as suggested, helps bring the research together, and as stated before, the research presented is compelling and interesting. Minor details on ground and satellite geometry, as well as a reordering of the paper to flow better, are the recommendations here.", "reviewer_2": "1. A numeric tabular account of the RMSEs and R for the all the studied sites is needed to be able to observe and compare the effectiveness of the algorithm.\n2. Line 155 – 157: “In order to better track the fast and stochastic changes in 156 cloud images, the neural network of the EPM is always cyclically generated by the 157 scheduled training process of the model, which has a one-hour update frequency.” Does the “cyclically generated” mean “finetuned based on the new set of images”? It is confusing. Would be it fair to assume that authors’ meant that the existing neural network is not deleted/removed instead the weight are updated based on the new data (images)? Is the finetuning being done online or offline? The “cyclically generated” point should be further explained.\n3. Figure 4 doesn’t show the term ‘fast cloud mask’. It should be added to show where exactly is this masking algorithm being applied in the process.\n4. It will be better to call out and address (as much as possible) the following caveats of using the proposed approach and algorithm:\n• Vanishing gradient problem: when the forecast leading time exceeds 2 hours. The results show a reduction in the accuracy and stability of the predictions. This problem could be partly alleviated by the Gradient Highway architecture, which provides shorter routes for gradient flows, but it is still a challenge to capture complex and long-term variations in cloud images.\n• Reliance on the quality of satellite data at visible and infrared bands: this data may be affected by low-visibility conditions, noise, or missing values. For example, according to Line 115 – 117, the invalid satellite visible images did contribute to moderate decrease in the forecast accuracy.\n• Fast Cloud Mask Algorithm: The proposed system uses a fast cloud mask algorithm to determine the confidence value of each pixel, but this algorithm may or may not be able to handle all kinds of cloud types and situations. Either its robustness with respect to all cloud types, weather conditions (including wind, humidity and other atmospheric variables) need to test or this caveat needs to be called out.\n• Generalizability claims: Need to bolster / support generalizability claims by including information about different regions as well as climates that have different cloud patterns and dynamics. Also, the article tests the model on two PV plants and several stations in China, but it does not compare its performance with other existing methods or datasets.", "reviewer_3": "This is a very interesting article on deterministic intraday solar satellite forecasting up to 4h ahead using advanced neural networks. Overall, I think the article has scientific merit, it should better frame its novelty in comparison to recent work in the field and can improve its evaluation framework. Also, there is some missing information and the article's structure is rather non-conventional. I provide the following feedback to the authors.\nMAJORS\n1. The algorithm's novelty or differences with similar work in the field is not clear. As far as I can tell, new features are the utilization of spectral satellite images (other than the visible channel) and the utilization of the past 4h images to forecast the next 4h (as usually only 2 to 4 previous images are used). However, from the algorithm itself, it is not clear to me if the proposal includes ad-hoc modifications to the neural network architecture to address the specific problem, or if it is a plain widely-used architecture adapted to the input-output problem (both cases are acceptable in my opinion, but should be clarified). In any case, differences, novelty, and/or similarities with respect to the current state-of-the-art architectures should be clearly given (Berthomier et al., 2020; Perez et al., 2021; Nielsen et al., 2021; Marchesoni-Acland et al., 2023; Garniwa et al., 2023).\nhttps://ieeexplore.ieee.org/document/9286606\nhttps://www.sciencedirect.com/science/article/pii/S0038092X21001353\nhttps://www.sciencedirect.com/science/article/pii/S0038092X21008306\nhttps://www.sciencedirect.com/science/article/pii/S0038092X23004450\nhttps://www.sciencedirect.com/science/article/pii/S0038092X23000439\n2. Training stage should be clarified. I understand the algorithm inputs the multi-spectral satellite radiances and outputs Cloud Fraction (CF). CF can be estimated by satellite or by ground cameras, and their comparison is non-trivial, say, there can be differences between both forms of estimation. So, is the algorithm trained to emulate the ground-measured CF or the satellite CF? Which is the ground truth for training? Then, the data set span is not given (6 months? 1 year?) and the train/test/validations splits are also not given. Or there is an online training? I recommend commenting or highlighting all these issues.\n3. Evaluation framework. Most of the evaluation is done by the correlation between the predicted CF with ground-measured CF, solar irradiance, and PV generation. Then, the RMSD for the CF comparison is also presented. I lacked the following:\n- Scatter plots for predicted CF and ground-measured CF. Maybe a comparison between satellite and ground-measured CFs can provide some context as to which portion of the forecast uncertainty is fairly attributable to the forecast, and not the eventual mismatch between satellite and ground-measured CF. This is related to the previous point, which I maybe understood wrong. So, I recommend clarifying this issue, maybe authors can provide insights on these.\n- In general, mean bias deviation, root mean squared error, and forecasting skills are required for comparison. Authors may check the recommended evaluation framework for the solar deterministic forecst(https://www.sciencedirect.com/science/article/pii/S0038092X20303947).\n- Authors are very close to providing a solar radiation comparison. They just need a clear-sky model to convert the CSR = (1-CF) to solar irradiance, by means of SR = CSR * Clear-Sky SR. This would allow them to compute metrics for the SR forecast, making it easier to compare with other works. Scatter plots are also recommended if authors decide to provide this comparison, which I think would add value to the article.\n- I think the article is missing a comparison with the other works' metrics, to provide context to the performance findings.\nMINORS\n- It would be informative to include the word \"satellite\" in the title or keywords.\n- Authors claim the performance downgrade after 2h ahead can be caused by the vanishing gradient problem. Just as a comment on this, in my opinion, this downgrade may be explained by the limited spatial domain that is considered (128 x 128 km). The size of the region limits the reachable forecast horizon. A cloud velocity of 30-50 km/h is roughly consistent with a 2h ahead forecast limitation at the center of a 128 km side square.\n- Please indicate if a different neural network is trained for each forecast horizon or if there is another scheme for this.\n- Line 258: It may be useful to provide an outline of the EKO cloud detection algorithm. This is related to point (2).\n- Lines 297-299: There is a claim about a comparison between different architectures, but it does not say for which specific problem were the findings. Also, I would like to note that these metrics are not used in the solar forecasting field, as they typically are uninformative for this task (or, at least, there is no work demonstrating that are useful for the solar forecasting objectives).\n- I wonder if the explanations of Lines 304-367 are useful to the article, as they maybe can be found in textbooks or original articles. On the other hand, I could not find the information regarding point (1) (differences with previous works or architectures).\n- The article's structure is unusual, as metrics and models are given at last, and there is no conclusion section. Authors, of course, can choose how to present the article. I just point it out as a comment."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe paper in general has many good features and the method looks promising.\nThe state of the article, as it stands now, needs revision (potentially major.)\nResponse: Thank you very much for taking the time to review our manuscript. We\ngreatly appreciate your constructive and kind comments. In response to your\nsuggestions, we have provided a point-by-point response below:\nMinor issues include geometry concerns for both the ground-based observation\ndata (beginning lines 247) and for the Himawari cloud fraction data.\nRegarding the ground observations, authors detail the EKO ASI-16 cloud\nfraction product, but do not specify how the cloud fraction at long limb angles\nfrom the 180º sensor are area-corrected. The optical geometry from the ASI-16\nhas significant compression at low angles and significant magnification at zenith\nangles - the accuracy of the derived cloud fraction will depend in great deal how\nthis optical compression is corrected. More details on this process are needed.\nResponse: Thanks for your suggestion. Actually, we have added some sentences and\nfurther illustrated the cloud detection algorithm of EKO ASI-16 at Line 372 to 381\n“ ASI, equipped with a digital camera coupled with an upward looking fisheye-lens,\ncould provide field of view of about 180º, but pixels at a FOV >140 º are excluded due\nto distortion. Digital images of the sky obtained by ASI are classified pixel by pixel into\nclear sky, optically thin and optically thick clouds, respectively. The cloud detection\nand opacity classification (CDOC) algorithm developed by Ghonima et al., 2012 32\ncould provide 96%, 60%, and 96.3% accuracy in the validation for clear, thin, and\nthick cloud, respectively.”. We believe the issue of optical compression you mentioned\nhere is very important for large angle pixel. Please see our following answer to your\nquestion from the observation principles of the All-Sky Imager (ASI-16) and cloud\nfraction evaluation processes.\nFirstly, all-sky imagers usually have a fisheye lens, which maps a hemispherical\nview field conformal to an image plane and produces a circular image. In the Ghonima’s\ncloud detection and opacity classification (CDOC) algorithm (Ghonima et al., 2012),\npixels at a Field of view (FOV) > 140° are excluded due to distortion. ((cid:2030) > 70° are\nexcluded in above Fig 1). Hence, there is no area-correction for the cloud fraction at\nlong limb angles from the 180º sensor.\nIn the CDOC algorithm, pixels in the images collected by the total/all sky imager\nare classified into three classes (clear, thin or thick) based on the difference between a\npixel’s actual red-blue ratio (RBR) and the corresponding expected RBR if the pixel is\nclear. A haze correction factor (HCF) is added to account for the effects of variations\nin aerosol optical depth (AOD) on RBR. Compared to fixed thresholding technique, in\nthe validation for clear, thin, and thick cloud, respectively, the CDOC algorithm\n(Ghonima et al., 2012) provided 96.0 %, 60.0 %, and 96.3 % accuracy as compared\nwith 89.3 %, 56.1 %, and 91.5 %.\nSecondly, about image compression, the explanation is as follows: In the case of\nan ideal fisheye lens, the image radius is directly proportional to the view angle, and\nthe entire FOV is mapped onto concentric circles. However, in reality, a fisheye lens\ncan introduce distortions, particularly for larger angles. This results in deviations from\nthe linear relationship between the view angle and image radius, known as aberrations.\nNonetheless, for the purpose of this discussion, we assume a linear and undistorted\nmapping. When using a fisheye lens for cloud monitoring, a cloud cover, which appears\nas a two-dimensional plane at a specific height in the sky, will be mapped onto a\n2\nhemispherical view field in the captured image, rather than maintaining its original\nplanar shape (Sabburg et al., 2004; Kreuter et al., 2009).\nFig2 Fig3\nHere is the imaging principle of a fisheye lens.\nFig2 shows that for mapping of a plane in constant height h to an angular dependent\nfisheye lens image, the distance d depends on the tangent of viewing angle. For zenith\nangles approaching 90° this distance will approach infinity. So the angular dependent\nconcentric circles of the fisheye lens image for small angles φ maps the correspondent\ncircle of the sky plane almost 1:1. For larger angles the mapped area will rise fast, until\nit even correspondents to infinity when approaching 90°.\nFig3 shows, how an existing fisheye lens image can be used to create a\nreproduction of the original sky plane by rear projection. Now the path of rays starts\nfrom the fisheye lens image and crosses an image plane in height a. The image plane\ncovers the viewing angle ω. Dependent on ω and a, an image radius of Rω results. The\ncrossing point of image plane and angle φ results in height a and distance b and fulfils\nequations h / d = a / b = tan(α). It is obvious to see, that path of rays produces a\nprojected image (\"Projection\") that shows original sky plane true to scale.\nIn practice a constant viewing angle will be assigned to a constant image radius R ,\nω\nwhereby a constant distance a will result for all angles φ:\na / b = tan(α); α = 90° - φ → a = b * tan(90° - φ)\nBy use of the specified values for ω and Rω the constant a can be calculated:\na = R * tan(90° - ω)\nω\nUsing this it can be calculated, in which distance b results out of a projection angle\nφ. So every radius of the fisheye lens image results in a radius of the plane image. Rω\n3\nsets the unit of measurement and it is most simple to use the unit \"pixel\". This way a\npixel of the fisheye lens image can be moved to correspondent position on sky plane:\nb = a / tan(90° - φ) = a / tan(90° - 90° * r/R)\nwith r equal to the current radius and R equal to whole radius of fisheye lens image.\nBy using tan(α) = cot(90° - α), cot(α) = 1/tan(α) and keeping periodicity of tangent\nin mind it is possible to simply the equation:\nf(r) = a * tan(90° * r/R)\n(\"forward calculation\": Fisheye lens pixel to image plane)\nThis \"forward calculation\" doesn't include, that size of pixel raises in dependency from\nthe angle, so projecting pixel one to one causes rising gaps between the pixels. A simple\nsolution to avoid this problem is to calculate the pixel of the image plane backward to\nfisheye lens image and taking over one pixel for several times, when necessary:\nf(r)-1 = R / 90° * atan(b / a)\n(\"backward calculation\": plane image pixel with distance b to fisheye lens pixel\nwith radius r)\nThis way a complete image will be created, but because of aliasing for large zenith\nangles there will result an aberration from the original sky plane. The pixel is not only\nenlarged, but also a jitter appears, because the fisheye lens pixel won't match to an even\nnumber of plane image pixel. For plane pixel located on borderline of two fisheye lens\npixel an interpolation should be used to reduce aberration.\nLimits of useful graphical representation:\nTo appraise the effect of rising zenith angle and size of aberration it makes sense\nto look at the underlying tangent function. Especially the derivation of the tangent\n4\nshows directly the difference between original fisheye lens pixel and resulting plane\nimage pixel.\nf(r) = a * tan(90° * r/R) results in an angle range from 0° to 90°, because the ratio r/R\ncovers the range from 0 to 1.\nThis plot shows the dependency of the plane image radius (y-axis) on zenith angle\nin degrees (x-axis). For angles less than 50° it is plain to see, that the radius rises almost\nlinear with the angle, so the middle part of the image will project the original sky plane\nalmost unaltered. In range from 50° to about 80° the projection will get more and more\nstretched until for angles larger than 80° the radius steeply rises to infinity. Also the\nderivation shows accordant factor of projection for range from 0° to 50° by 1:1 or 1:2\nand fast rising for larger angles.\nReference:\nJ.M. Sabburg, C.N. Long: Improved sky imaging for studies of enhanced UV irradiance.\nAtmos. Chem. Phys. Discuss., 4, 6213-6238, 2004.\nA. Kreuter, M. Zangerl, M. Schwarzmann, M. Blumthaler: All-sky imaging: a simple,\nversatile system for atmospheric research. Applied Optics, Vol. 48, No. 7, 1.\nMarch 2009.\nM.S. Ghonima, B. Urquart, C.W. Chow, J.E. Shields, A. Cazorla, J. Kleissl: A method\nfor cloud detection and opacity classification based on ground based sky\nimagery. Atmos. Meas. Tech., 5, 2881-2892, 2012.\nRegarding the satellite observations, there are no details provided on parallax\ncorrection or terrain correction. Orthorectification of surface and cloud location\nis critical for computing accurate cloud fractions - failing to correct, or incorrectly\nperforming the parallax or terrain correction will result in incorrectly-located\nclouds and inaccurate cloud fractions for a specific location (such as the three\nlocations detailed in the paper.) More details on the methods of parallax and\nterrain correction, if performed, are needed (or a justification as to why they\nwere not.)\nencounter parallax issues when contrasted with ground-based information, potentially\nexerting a notable impact on the computation of precipitation stemming from low\ncloud-top temperature cloud clusters. Consequently, when investigating intense\nprecipitation events or typhoons, it becomes imperative to take into account the parallax\nconcerns associated with satellite products.\nNonetheless, prior research on the correction of cloud positions in satellite cloud\nimages (Li et al., 2012) has underscored that the precision of parallax correction is\nintricately linked to the accuracy of cloud-top height. This challenge constitutes one\nof the principal explanations for the current scarcity of parallax correction in satellite\nfull-disk image cloud parameter products. Regrettably, as of now, there is no precise\nand real-time Himawari-8 cloud-top height product with a spatial resolution of 4 km at\nour disposal for this investigation.\nAnother important reason for refraining from applying parallax correction to full-\ndisk satellite products is that when a cloud envelops a considerably vast area and\nmaintains relatively consistent features within a range akin to parallax correction (e.g.,\nstratiform clouds), the influence of parallax correction tends to be relatively negligible\n(Wang et al., 2011). Certainly, this becomes particularly crucial when dealing with\nsmall clouds or when cloud characteristics exhibit rapid variation over short distances,\nor when both conditions coexist. Consequently, as previously stated, parallax correction\nis generally utilized for research endeavors pertaining to convection and typhoons,\nrather than for rectifying parallax in full-disk cloud products. Hence, we firmly believe\nthat any research focused on deep convection or typhoons necessitates the\nutilization of our product, and we think that conducting localized parallax\ncorrection is necessary.\nFurthermore, some previous studies have conducted numerous studies employing\nHimawari-8 data for cloud parameter inversion tasks, encompassing the retrieval of\ncloud optical thickness (COT), effective particle radius (CER), surface radiation (Letu\net al., 2019; 2020). Nevertheless, it's worth noting that only a limited number of studies\nhave incorporated parallax correction into satellite data preprocessing procedures. This\nmight be due to the absence of high-precision cloud top height products. Cloud top\nheight products still manifest a notable degree of error, particularly in the case of\nrelatively high cloud top samples with relatively larger retrieval errors (exceeding\n6\n4 km when cloud top height exceeds 10 km, as illustrated in Figure 4 sourced from\nMin et al., 2020, see below Figure 1). Besides, the matched and nadir rectangle\narea for calculating cloud fraction a is 5×5 pixel box (more than 20×20 km), which\nis larger than the extreme parallax error of about 7~8 km (cloud top height must\nbe >15 km, such as typhoon samples) (Li et al., 2012). Therefore, hastily\nimplementing parallax corrections is likely to introduce some additional uncontrollable\nerrors.\nFortunately, we find and refer to a most-related Chinese paper for the parallax\ncorrection of the future geostationary microwave sounder (Wei and Sun 2022, we have\nattached this paper in Data Availability). We think the theoretical studies in this paper\ncan help us to further explain this issue. Figure 2 (see below) shows the sensitivity of\nposition deviation (or parallax correction) to satellite imaging pixel spatial resolution,\ncloud top height (CTH), and view zenith angle using parallax correction algorithm (Wei\nand Sun 2022). First, it clearly proves that the position deviation is not sensitive to\nsatellite imaging pixel spatial resolution. Second, the specific view zenith angles of\nground-based stations used in this study from Supplementary Table 2 are not larger\nthan 60 degrees (the maximum value is about 56 degrees). This figure also indicates\nthat the bias caused by parallax is unlikely to exceed 1km under the condition of view\nzenith angle<60 degrees. Parallax correction should only be applied to exceptionally\nhigh clouds (>10km) resulting from typhoons or DCC with a view zenith angle greater\nthan 70 degrees in the GEO satellite field of view.\nHowever, we have added two sentences to illustrate this issue at line 360 “Besides,\nthe view zenith angles of ground-based stations from H8/AHI field of view used here,\nas stated in Supplementary Table 2, are smaller than 60°. Therefore, the parallax effect\nis negligible (error<1km) in the collocation between satellite pixels and ground-based\nstations for this study.”\n(Figure 4 cited from Min et al., 2020)\nFigure 2. Sensitivity of position deviation (or parallax correction) to satellite imaging\npixel spatial resolution, cloud top height (CTH), and view zenith angle using parallax\ncorrection algorithm. (Figure 9 from Wei and Sun 2022).\nReferences:\nMa, R., Wang, T., Shang, H., Khatri, P., Chen, L., Shi, C., & Shi, J. (2020). High-\nresolution retrieval of cloud microphysical properties and surface solar radiation\nusing Himawari-8/AHI next-generation geostationary satellite. Remote Sensing\nof Environment, 239, 111583.\nhttps://doi.org/https://doi.org/10.1016/j.rse.2019.111583\nLetu. Husi, T. M. Nagao, T. Y. Nakajima, J. Riedi, H. Ishimoto, A. J. Baran, H. Shang,\nM. Sekiguchi, and M. Kikuchi (2019): Ice cloud properties from Himawari-\n8/AHI next-generation geostationary satellite: Capability of the AHI to monitor\nthe DC cloud generation process, IEEE Transactions on Geoscience and Remote\nSensing, 57, 3229-3239, doi:10.1109/TGRS.2018.2882803.\nLi, W., Wang, H., Wu, Q., Yu, W., & Wang, Y. (2012). The Position Deviation of\nGeostationary Satellite Image and the Geometric Correction. Acta Scientiarum\nNaturalium Universitatis Pekinensis, 48(5).\nWang, C., Luo, Z. J., & Huang, X. (2011). Parallax correction in collocating CloudSat\nand Moderate Resolution Imaging Spectroradiometer (MODIS) observations:\nMethod and application to convection study. Journal of Geophysical Research:\nAtmospheres, 116(D17). https://doi.org/https://doi.org/10.1029/2011JD016097\nMin Min, Jun Li, Fu Wang, Zijing Liu, W. Paul Menzel, 2020. Retrieval of cloud top\nproperties from advanced geostationary satellite imager measurements based on\nmachine learning algorithms [J]. Remote Sensing of Environment, 239: 111616,\ndoi: 10.1016/j.rse.2019.111616\nXiaocheng Wei and Fenglin Sun, ANALYSIS OF THE PARALLAX\nCHARACTERISTICS OF GEOSTATIONARY- ORBITING MICROWAVE\nSOUNDER, JOURNAL OF TROPICAL METEOROLOGY, 2022，38(6):901-\n914. (In Chinese)\nA larger issue is the general layout of the paper - upon first read, the manuscript\nis confusing to put together. I would suggest a reorganization of the paper\nlayout, thusly:\n1.) Move section 4.) Methods (beginning line 234) to immediately after the\nintroduction (starting line 95.) Move section 2.) Results (currently line 95) to\n9\nafter the end of the new Methods section, and then move 3.) Discussion to after\nthe end of the new Results section. Going from Introduction -> Methods ->\nResults -> Discussion is a more natural way to read the paper - currently, the\npaper reads as Introduction -> Results -> Discussion -> Methods and is very\nconfusing. This was perhaps a simple clerical error that could be addressed\nwith minor updating of text and figure references.\nResponse: Thanks for your suggestion which is very helpful for improving our\nmanuscript. We sincerely apologize for any inconvenience caused to your review due\nto the format of our manuscript. Our format is as similar as possible to the official\nformat of the journal that the manuscript will be published in. We will rearrange format\naccording to the official requirements of the journal.\nThe section detailing the PredRNN++ model is excellent, and if located earlier\nin the paper as suggested, helps bring the research together, and as stated\nbefore, the research presented is compelling and interesting. Minor details on\nground and satellite geometry, as well as a reordering of the paper to flow better,\nare the recommendations here.\nResponse: It is an excellent suggestion to include the details of the PredRNN++ model\nearlier in the paper. This will not only help to attract readers but also better showcase\nour research. Our format is as similar as possible to the official format of the journal\nthat the manuscript will be published in. We will rearrange format according to the\nofficial requirements of the journal. Thank you once again for providing us with your\nvaluable suggestions.\nBesides, we also added more details on ground and satellite geometries at Line 402\n“about 20 km×20 km square area” and at Line 364 “each site would be centered around\nits precise location and matched with a 32×32 pixels box as the experimental area”\nResponse: Thank you very much for taking the time to review our manuscript. We\ngreatly appreciate your constructive and kind comments. In response to your\nsuggestions, we have provided a point-by-point response below:\n1. A numeric tabular account of the RMSEs and R for the all the studied sites\nis needed to be able to observe and compare the effectiveness of the algorithm.\nResponse: Thanks for your suggestion. We have supplemented numeric tabular\naccounts of the metrics in our manuscript. It should be emphasized that we have\nchanged some metrics used to evaluate the effectiveness of the algorithm in the new\nversion of the manuscript. You can see the updated Figure 2 and Table S1 in the\nsupplementary documentation.\nsky imagers. Subfigures (a), (c), (e) and (b), (d), (f) are the RMSEs and MBEs for\nBeijing, Nanjing and Zhuhai all-sky imager stations, respectively. The different colored\nlines represent the results for different months, and the dashed black line represents the\nmean of all the lines. Subfigure (g) depicts the mean RMSE and MBE of three all-sky\nimager sites at different local time of one day.\nresults and manual observations at different forecast horizon.\n2. Line 155 – 157: “In order to better track the fast and stochastic changes in\n156 cloud images, the neural network of the EPM is always cyclically generated\nby the 157 scheduled training process of the model, which has a one-hour\nupdate frequency.” Does the “cyclically generated” mean “fine tuned based on\nthe new set of images”? It is confusing. Would be it fair to assume that authors’\nmeant that the existing neural network is not deleted/removed instead the\nweight are updated based on the new data (images)? Is the fine tuning being\ndone online or offline? The “cyclically generated” point should be further\nexplained.\nResponse: Thanks for your suggestion. Actually, we have illustrated this issue at Line\n161 “For instance, a cyclic training process of the model starts at the scheduled local\ntime of 12:08 and ends at about 12:53, the imported training samples are from 08:00\nto 12:00, and the latest updated EPM is timely activated for CF nowcasting at about\n13\n13:08 (nowcasting from 13:00 to 17:00) and 13:38 (nowcasting from 13:30 to 17:30),\nrespectively, with an update frequency of half an hour. During the same period, the\nnetwork of the next EPM is also trained simultaneously by using the sequential satellite\n sequential satellite\nimages from 09:00 to 13:00. ” Indeed, the “cyclically generated” model is a fine tuned\nnowcasting model for one solar photovoltaic plant based on two GPU cards. It will\nupdate and replace the existing nowcasting model every hour, ensuring that we always\nhave the most up-to-date nowcasting model. So the latest training dataset or satellite\nobservation data will be used to train the new model, ensuring that it incorporates the\nmost recent data available. This helps improve the accuracy of the predictions made by\nthe model. This is a large and significant innovation of our work, combining practical\napplication at PV plants and machine learning features. Note that, to make this\nparagraph more clear. We have added a sentence at the end of this paragraph to further\nexplain this issue. “This cyclical procedure will continually update and replace the\nexisting EPM every hour, ensuring that we always have the most up-to-date nowcasting\nmodel. ”\nAs satellite data is continually updated, it is indeed important to update the training\ndata of the model and also update the model itself. To address the need for rapid updates\nof real-time satellite observation data in CF nowcasting, we have developed a\ngroundbreaking cyclically updated training method. This approach allows for timely\nincorporation of new data into the training process, ensuring that the model stays up-\nto-date with the most recent information available.\nIn the real application scenarios described in this manuscript (as we introduced\nhere, we have used this novel method in 5 real solar PV plants belong to the CGN Wind\nEnergy Co., LTD.), it is crucial to continuously update the local CF nowcasting model\nat every solar photovoltaic plant in order to maintain its prediction effectiveness.\nAdditionally, we followed traditional methods and used a large amount of data for\ntraining, validation, and testing purposes when adjusting the parameters of the neural\nnetworks. These parameters here mainly refer to the Hyperparameter, such as learning\nrate, number of epochs et al.\nThe changes in clouds are very rapid and easily influenced by various\nmeteorological factors, and they also have obvious regional and climatic characteristics.\nThe prediction results of a unified model trained with a large amount of data and time\nare often affected by various sudden weather or climate conditions, resulting in\nunsatisfactory prediction results. Our method is to train and validate a set of optimal\n14\nhyperparameters through a certain amount of data in the early stage, and then train the\nmodel with the latest 4h satellite data in practical application scenarios. This model will\nbe used for prediction in the next hour and will be used every 10 minutes (based on the\nupdate frequency of satellite data). As the data continues to update, the new model will\nbe continuously trained, and each site’s model will be trained based on its own\ndata. This method can capture various mutation features in the data, ensuring maximum\nprediction performance for the next 4 hours.\n3. Figure 4 doesn’t show the term ‘fast cloud mask’. It should be added to show\nwhere exactly is this masking algorithm being applied in the process.\nResponse: Thanks for your suggestion. ‘Fast cloud mask algorithm (FCMA)’ should\nindeed be indicated in Figure 4, and we have made changes to Figure 4 (see below\nfigure) as required.\nHimawari/AHI Full Disk L1B Data\nSpatial and temporal resolutions: 4km & 10mins\nCollocating & Resizing\nPrediction Model\nR0.64um R0.86um BT3.9um BT7.0um BT11.2um BT12.3um\n32\nEnhanced PredRNN++ Model\nCyclic training Update per hour\nPrediction Input: -4 ~ 0h [Size: 6×24×32×32 ]\nRetrieval model\nPrediction Output: 0~4h [Size: 6×24×32×32 ]\nCloud retrieve (FCMA) Cloud Fraction/mask\n4. It will be better to call out and address (as much as possible) the following\ncaveats of using the proposed approach and algorithm:\n• Vanishing gradient problem: when the forecast leading time exceeds 2 hours.\nThe results show a reduction in the accuracy and stability of the predictions.\nThis problem could be partly alleviated by the Gradient Highway architecture,\nwhich provides shorter routes for gradient flows, but it is still a challenge to\ncapture complex and long-term variations in cloud images.\nability of the model is indeed limited, and its predictive stability can fluctuate in\ndifferent application scenarios; We realize that the predictive performance of the model\nwill decrease with the increase of prediction time, so we have developed a cyclic\nprediction system and continuously updated it (see Q#2). This can indeed provide better\npredictive factors for photovoltaic power generation prediction in practical application\nscenarios of photovoltaic plants (we have tested it at five different PV plants in this\nstudy).\nIn addition, we predict that the time resolution of the product is 10 minutes (same\nas H8/AHI observation frequency), which not only includes discrete point data but also\nenables the display of more detailed information. However, it should be noted that this\napproach sacrifices some accuracy in exchange for a longer time prediction span.\nWhen sudden changes happen (such as local convective cloud generation), future\nimages should be generated upon nearby frames rather than distant frames, which\nrequires that the predictive model learns short-term dynamics. If the model can better\nsolve the problem of short-term mutation and long-term gradient disappearance, then\nthe predictive ability will definitely further improve.\n• Reliance on the quality of satellite data at visible and infrared bands: this data\nmay be affected by low-visibility conditions, noise, or missing values. For\nexample, according to Line 115 – 117, the invalid satellite visible images did\ncontribute to moderate decrease in the forecast accuracy.\nResponse: Thanks for your suggestion. Regarding the quality of satellite data (H8/AHI),\nit is presented on the JMA official website\nhttps://www.data.jma.go.jp/mscweb/data/monitoring/gsics/ir/monit_geoleoir.html. It\nshows the very high quality of H8 GEO satellite multiband observation data. The H8 is\nthe first operational satellite of the new-generation of GEO series in the world, which\nwas launched in 2014, now it is replaced by H9 for operational application, H9 has the\nsame imager and same orbital location of H8.\nThe quality of H8 GEO satellite data can be guaranteed in low-visibility conditions.\nRegarding missing values, we can provide full-disk data every 10 minutes for all 16\nbands of H8/AHI. However, during the morning when the sun's altitude angle is low,\n17\nthere may be some incomplete visible data. This is depicted in the figures below:\nIn this case, the predictive performance of the model will be affected by the visible\nband data before 07:00. But fortunately, the power generation of the solar photovoltaic\nstation is basically close to zero in the morning, and the demand for the predictive\nability of the model is not high during this period.\n• Fast Cloud Mask Algorithm: The proposed system uses a fast cloud mask\nalgorithm to determine the confidence value of each pixel, but this algorithm\nmay or may not be able to handle all kinds of cloud types and situations. Either\nits robustness with respect to all cloud types, weather conditions (including wind,\nhumidity and other atmospheric variables) need to test or this caveat needs to\nbe called out.\nResponse: Thanks for your suggestion. Firstly, we would like to explain here why we\nneed to develop the fast cloud mask algorithm. Operational cloud mask algorithm\n(OCMA) does consider various cloud types, surface conditions, and weather conditions,\nand is combined with a fast radiative transfer model and many other real-time ancillary\ndata, such as numerical weather prediction data (see Wang et al., 2019). However, the\ncloud mask product of OCMA covers the entire satellite observation area (full-disk)\nand the specific process requires a relatively longer time and more financial\nresources. For 0-4 hour photovoltaic forecasting, timeliness is crucial. The cloud mask\nproducts only need to cover a small area directly above the photovoltaic plant, rather\nthan the entire satellite observation area. Therefore, we have developed a new algorithm\nthat can quickly and efficiently export local cloud mask products. Secondly, the FCMA\n18\nalso considers various types of clouds and underlying surfaces, and it has been tested\nunder various weather conditions. The following figure shows the climate and surface\ntypes of various sites during the FCMA testing process (Xia et al., 2023).\nConsidering the cost and efficiency, we have developed the FCMA for scattered\nPV plants, which only works during daytime using six bands of H8/AHI (at 0.64, 0.86,\n3.9, 7.0, 11.2, and 12.3 µm) and five inherited and improved cloudy/clear pixel tests\nfrom the MODIS official cloud mask algorithm (Frey et al., 2008). These five tests\nconsider various types of clouds, which are very effective (that is also proved by our\nvalidation results compared to the ground-based CF observations).\nIn addition, several different sites utilize varying thresholds have already been test\nbefore. The threshold is adjusted to achieve optimal detection effectiveness. More\ndetails about FCMA can be found. in “Xia P, Min M, Yu Y, Wang Y, Zhang L.\nDeveloping a near real-time cloud cover retrieval algorithm using geostationary\nsatellite observations for photovoltaic plants. Remote Sensing 15, 1141 (2023).”\nBesides, we also present all the cloud detection thresholds corresponding to all sites in\nthis manuscript in the Code Availability section. In Figure 1, we show aerial photos\nof each photovoltaic plant, which allows readers to see the surface type of each\nphotovoltaic station more clearly.\nReference:\nXi Wang, Min Min, Fu Wang, Jianping Guo, Bo Li, Shihao Tang, 2019.\nIntercomparisons of cloud mask product among Fengyun-4A, Himawari-8 and\nMODIS [J]. IEEE Transactions on Geoscience and Remote Sensing, 57(11):\n8827-8839, doi: 10.1109/TGRS.2019.2923247\nPan Xia, Min Min*, Yu Yu, Yun Wang, Lu Zhang, 2023. Developing a near real-time\ncloud cover retrieval algorithm using geostationary satellite observations for\nphotovoltaic plants [J]. Remote Sensing, 15(4), 1141, doi: 10.3390/rs15041141\nCloud detection with MODIS. Part I: Improvements in the MODIS cloud mask\nfor collection 5. J. Atmos. Ocean. Technol. 2008, 25, 1057–1072. [CrossRef]\n• Generalizability claims: Need to bolster / support generalizability claims by\nincluding information about different regions as well as climates that have\ndifferent cloud patterns and dynamics. Also, the article tests the model on two\nPV plants and several stations in China, but it does not compare its\nperformance with other existing methods or datasets.\nResponse: Thanks for your valuable suggestion. Agree with your suggestion.\nGeneralizability claims is indeed of great significance for the practicality of our\nmachine-learning-based algorithm. The manual observation stations, all-sky imagers\nstations, and photovoltaic plants in our research have a large latitude and longitude span,\nwhich could demonstrate the generalization of our algorithm. Sites distributed at\ndifferent latitudes could represent different climate types, cloud features.\nFor different PV plant sites, the EPM for CF nowcasting model will continually\nupdate and replace the existing EPM every hour (see Q#2), which can prove the\ngeneralization of our method. To further demonstrate the generalizability of our\ndeveloped method in this study, we have incorporated three additional PV plants\naffiliated with China General Nuclear Power Group (CGN) Wind Energy Co Ltd (It is\nvery difficult to obtain business data from business company). These new PV plants\ninclude Xiaochengzi, Lijiamen, and Shiziyan. We conducted tests using data from each\nplant for a duration of one month (same as the two PV plants mentioned before in the\nmanuscript). The updated testing outcomes are presented in the updated Figure 3, while\nthe locations of the new plants can be observed in the updated Figure 1.\nof the power (MW), GTI (W·m−2) and predicted clear sky ratio (CSR) at (a) Sangge,\n(b) Leling, (c) Xiaochengzi, (d) Lijiamen, and (e) Shiziyan PV plants from 09:00 to\n17:00 (local time or Beijing time) on each day in November of 2022. R1–R4 and R5–\nR8 indicate the Rs (correlation coefficient) of the predicted CSR with the power and\nthe GTI for forecast leading time of 1–4 hours, respectively. Note that the missing and\ninvalid data are not shown. The results for other months are shown in Figs. S3–S6 of\nthe supplementary materials.\nplants (dark green small solid circles), twelve manual observation stations (red solid\ncircles), three all-sky imager stations (blue solid circles) and five PV power test plants\n(yellow solid circles).\nReviewer #3 (Remarks to the Author):\ngreatly appreciate your constructive and kind comments. In response to your\nsuggestions, we have provided a point-by-point response below:\nThis is a very interesting article on deterministic intraday solar satellite\nforecasting up to 4h ahead using advanced neural networks. Overall, I think the\narticle has scientific merit, it should better frame its novelty in comparison to\nrecent work in the field and can improve its evaluation framework. Also, there\nis some missing information and the article's structure is rather non-\nconventional. I provide the following feedback to the authors.\nMAJORS\n1. The algorithm's novelty or differences with similar work in the field is not clear.\nAs far as I can tell, new features are the utilization of spectral satellite images\n(other than the visible channel) and the utilization of the past 4h images to\nforecast the next 4h (as usually only 2 to 4 previous images are used). However,\nfrom the algorithm itself, it is not clear to me if the proposal includes ad-hoc\nmodifications to the neural network architecture to address the specific problem,\nor if it is a plain widely-used architecture adapted to the input-output problem\n(both cases are acceptable in my opinion, but should be clarified). In any case,\ndifferences, novelty, and/or similarities with respect to the current state-of-the-\nart architectures should be clearly given (Berthomier et al., 2020; Perez et al.,\n2021; Nielsen et al., 2021; Marchesoni-Acland et al., 2023; Garniwa et al.,\n2023).\nhttps://ieeexplore.ieee.org/document/9286606\nhttps://www.sciencedirect.com/science/article/pii/S0038092X21001353\nhttps://www.sciencedirect.com/science/article/pii/S0038092X21008306\nhttps://www.sciencedirect.com/science/article/pii/S0038092X23004450\nhttps://www.sciencedirect.com/science/article/pii/S0038092X23000439\nResponse: Thanks for your suggestion and references. We have learned a lot from these\npapers. In this study, the novelty of our algorithm lies in its direct utilization of Level\n23\n1B (L1B) radiance data from H8/AHI. We use both visible and infrared channels as\ninput for the EPM model, which then predicts the Level-1B radiance data for the next\n4 hours. Additionally, we have developed a fast cloud mask algorithm that, when\ncombined with the predicted radiance data, allows us to obtain cloud masks and\nfractions specifically for solar photovoltaic plants.\nCompared with the work of Franco Marchesoni-Acland et al., (2023), they only\nuse a visible channel, and their cloud detection principle is that visible channel typically\nreflects more sunlight than the ground, appearing brighter in the images. This\nassumption does not hold for high albedo terrains, containing snow or salt flats. Our\nmethod used six channels (two visible and four infrared) of AHI and developed a\nrelatively comprehensive and adaptable cloud detection method that can detect different\ntypes of clouds under different and variable conditions (refer to U.S. NASA MODIS\nofficial cloud mask algorithm). It is practical and more complex in environmental\nconditions, and the detection effect is also more accurate. In practical application\nscenarios at PV plants, our new method provides timeliness and seamless integration,\nwhich is of great importance in the context of CF nowcasting at the next 0-4 hours (refer\nto Reviewer-2 Q#2 for a detailed flowchart depicting the specific process of this novel\nmethod).\nBesides, Predrnn++ is a plain widely-used architecture adapted to the input-output\nproblem. To achieve greater spatio-temporal modeling capability, we set the EPM with\na six-layer architecture (five convolutional layers and one GHU layer). Based on the\nspatial size and temporal resolution of the data, we carefully design the dimensions of\ntemporal memory ((cid:1829)(cid:3038)) and spatial memory ((cid:1839)(cid:3038)) in each convolutional layer. The\n(cid:3041) (cid:3041)\nadvantages of GHU and causal LSTM have explained at lines 489-498. “ The gradient\nhighway (blue line in Fig. 5a) supplies a quick path from the first to the last time step\nby quickly updating hidden state (cid:1852) . It is worth noting that, unlike temporal skip\n(cid:3047)\nconnections, the GHU controls the proportions of (cid:1852) and the deep transition feature\n(cid:3047)(cid:2879)(cid:2869)\n(cid:1834)(cid:2869) through (cid:1845) , which allows the EPM to adaptively learn both short-term and long-\n(cid:3047) (cid:3047)\nterm frame relations. In the causal LSTM of the EPM, the spatial memory is a function\nof the temporal memory through another set of gate structures. As the recurrence depth\nalong the spatio-temporal transition paths grows considerably, each pixel in the final\ngenerated frame has a bigger receptive field of the input sequence at each time step,\n24\nwhich is why the EPM has a better ability to model short-term video dynamics and\nsudden changes. ”\nDue to the distinct characteristics of data obtained from various ground-based sites,\nour model prioritizes both speed and accuracy. Consequently, we have refrained from\ntraining a single unified model for different sites. Instead, each PV (photovoltaic)\nplant undergoes individualized training using its own specific data, thereby creating a highly\ntailored model that exhibits superior predictive performance at its respective location.\nThis approach ensures optimal targeting and enhances the overall prediction\ncapabilities of the model. This cyclical updates in our prediction model represent a\nsubstantial and noteworthy innovation within our work. This approach combines the\npractical application of PV plants with advanced machine learning capabilities,\nresulting in a powerful synergy.\nReference:\nFranco Marchesoni-Acland, Andrés Herrera, Franco Mozo, Ignacio Camiruaga ,\nAlberto Castro , Rodrigo Alonso-Suárez; Deep learning methods for intra-day\ncloudiness prediction using geostationary satellite images in a solar forecasting\nframework, Solar Energy 262 (2023) 111820.\n2. Training stage should be clarified. I understand the algorithm inputs the multi-\nspectral satellite radiances and outputs Cloud Fraction (CF). CF can be\nestimated by satellite or by ground cameras, and their comparison is non-trivial,\nsay, there can be differences between both forms of estimation. So, is the\nalgorithm trained to emulate the ground-measured CF or the satellite CF?\nWhich is the ground truth for training? Then, the data set span is not given (6\nmonths? 1 year?) and the train/test/validations splits are also not given. Or\nthere is an online training? I recommend commenting or highlighting all these\nissues.\nResponse: Thanks for your suggestion. We have added a paragraph to clarify training\nstage. “The EPM training process involves the use of the Adam optimizer with a\nlearning rate of 0.001 and the mean square error as the loss metric. The input is a four-\ndimensional tensor of size [c, t, h, w] (6, 24, 32, 32), where c represents the number of\n25\ninput channels, t represents time (spanning 4 hours), h represents height, and w\nrepresents width. Our research area focuses on PV plants, which provides us with\nimages measuring 32×32 pixels (approximately 128km×128km). To train our model,\nwe created a dataset consisting of sequences of 48 images for each channel, spanning\n4 hours. During the initial training process of the optimal network structure, data were\nobtained from resized AHI images at six bands over 12 manual observation stations\nand 3 all-sky imager stations. The training set utilized data from January to October in\n2018, while the remaining months of 2018 were used as the validation set. In the\napplication scenario of PV plants, the training method is described in the section of\nNRT and cyclically updated prediction system.” Other questions are answered below.\n(1) Firstly, we have thoroughly illustrated and clarified the detailed training stage in our\nresponse to Reviewer-2 Q#2. We believe this is a crucial aspect of our study. Also, we\nhave emphasized this issue at Line 234 to highlight its significance.\n(2) Secondly, The cloud fraction (CF) obtained from ground-based stations is\nconsidered as the reference truth in our study. We have validated the retrieved results\nfrom GEO satellite imagers through manual observations and the ASI-16 method.\nBesides, the approach we utilized to derive cloud fraction (CF) from GEO satellites\ndoes not rely on CF measurements obtained through ground-based methods. Instead,\nwe employ a training process using continuous radiance images captured by GEO\nsatellites to extract cloud masks and calculate cloud fraction.\n(3) Thirdly, our algorithm is trained to emulate the satellite data, and the ground truth\nfor training is satellite data. The nowcasting CF is retrieved by FCMA based on the\npredicted satellite image data.\nDue to the differences between our training method and traditional methods, our\ninitial training was only to find the best network architecture parameters (the number\nof layers, epoch, and learning rate etc.) for the model. Satellite data from Jan. to Oct. in\n2018 at 12 manual observation stations and 3 all-sky imager stations were used as\ntraining set and the others in 2018 were used as validation set. In this process, we mainly\nsaved the optimal parameters of EPM in terms of structure. In the subsequent\napplication within the system depicted in Figure 4, we employed online training to\ncontinuously update the model, thereby ensuring the stability of its predictive\ncapabilities (also refer to Reviewer-2 Q#2 for a detailed flowchart depicting the\nspecific process of this novel method).\neach model is only used for prediction in the next 1 hour. As satellite data updates, new\nmodels will be continuously trained. This method aims to capture short-term changes\nin cloud fraction from 0 to 4 hours at each site and provide better prediction results.\nThe traditional method of using a large amount of data to train a unified and unchanging\nmodel is difficult to capture rapid cloud changes in small areas above photovoltaic\nstations, so this method is not adopted.\n3. Evaluation framework. Most of the evaluation is done by the correlation\nbetween the predicted CF with ground-measured CF, solar irradiance, and PV\ngeneration. Then, the RMSD for the CF comparison is also presented. I lacked\nthe following:\n- Scatter plots for predicted CF and ground-measured CF. Maybe a comparison\nbetween satellite and ground-measured CFs can provide some context as to\nwhich portion of the forecast uncertainty is fairly attributable to the forecast, and\nnot the eventual mismatch between satellite and ground-measured CF. This is\nrelated to the previous point, which I maybe understood wrong. So, I\nrecommend clarifying this issue, maybe authors can provide insights on these.\nResponse: Thanks for your advice. We plan to create scatter plots comparing predicted\nCF values with ground-measured CF values. However, the manual observed CF values\nonly have one decimal place (e.g., 0.1, 0.2, 0.3, .... etc.), which might result in strange-\nlooking scatter plots (see below figure).\nFor the comparation between predicted CF and All-sky imager observed CF, the\nscatter plot is as follow:\nHowever, this is indeed a shortcoming of our paper, so we have updated the\nevaluation parameters, redesigned Figure 2, and added Supplementary Table 1,\nhoping that those changes could better show the difference between the predicted CF\nand ground-measured CF.\nAs you said, there are indeed two parts of the error here. One comes from the\nprediction error of EPM, and the other comes from the error between the CF retrieved\nby satellite data and observed on the ground. We have made a detailed comparison\nbetween manual observed CF and satellite retrieved CF in our previous work and\nproven the error between them is small. “ Xia P, Min M, Yu Y, Wang Y, Zhang L.\nDeveloping a near real-time cloud cover retrieval algorithm using geostationary\nsatellite observations for photovoltaic plants. Remote Sensing 15, 1141 (2023)”. Here,\nour primary focus lies in determining whether the prediction outcomes of our model\ncan effectively aid in forecasting photovoltaic power for the future 0-4 hour timeframe\nat the PV plant. In this context, we place greater emphasis on evaluating the overall\nsystem error depicted in Figure 4, rather than dissecting it into the two aforementioned\ncomponents. By consolidating the evaluation of errors, we gain a comprehensive\nunderstanding of the model's performance and its potential to provide valuable\nassistance in accurate photovoltaic power forecasting within the specified time range.\n- In general, mean bias deviation, root mean squared error, and forecasting\n28\nskills are required for comparison. Authors may check the recommended\nevaluation framework for the solar deterministic forecast\n(https://www.sciencedirect.com/science/article/pii/S0038092X20303947).\nResponse: Thanks for your suggestion and reference. We have realized that the\ncorrelation coefficient (R) was not a good choice when evaluating the model prediction,\nso we added MBE, removed R and redesigned Figure 2 and added Supplementary Table\nS1(see below). Forecasting skill are often used to compare the forecasting effect\nbetween different forecasting models. Unfortunately, there is no standard CF\nforecasting model for comparison.\nsky imagers. Subfigures (a), (c), (e) and (b), (d), (f) are the RMSEs and MBEs for\nBeijing, Nanjing and Zhuhai all-sky imager stations, respectively. The different colored\nlines represent the results for different months, and the dashed black line represents the\nmean of all the lines. Subfigure (g) depicts the mean RMSE and MBE of three all-sky\nimager sites at different local time of one day.\nand manual observations at different forecast horizon.\n- Authors are very close to providing a solar radiation comparison. They just\nneed a clear-sky model to convert the CSR = (1-CF) to solar irradiance, by\nmeans of SR = CSR * Clear-Sky SR. This would allow them to compute metrics\nfor the SR forecast, making it easier to compare with other works. Scatter plots\nare also recommended if authors decide to provide this comparison, which I\nthink would add value to the article.\nResponse: Thanks for your suggestion. The solar radiation measured by photovoltaic\nplants is the radiation captured on solar photovoltaic panels, which is called global tilted\nirradiance (GTI). GTI can be estimated from global horizontal irradiance (GHI). With\nyour inspiration, we are in the process of utilizing radiation measurements under\ncloudless conditions along with our CSR (Cloud Sky Rate) model to estimate the\namount of radiation that reaches the surface at solar PV plants. We utilized the clear\nsky solar radiation product derived from the classical ERA5 reanalysis data\n(https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels) that\nhas been spatially aligned with our specific solar PV plant location. Mean surface\n31\ndownward short-wave radiation flux at clear sky (msdwswrfcs) of ERA5 is the\namount of solar radiation (also known as shortwave radiation) that reaches a horizontal\nplane at the surface of the Earth, assuming clear-sky (cloudless) conditions. We use\nmsdwswrfcs * CSR as the GHI, and analyze its correlation with GTI and PV power\ngeneration. The comparison of Sangge station is shown as follow:\nThe time for radiation data is from 09:00 to 17:00 every day. The pattern of GTI is\nrelatively stable, with the strongest radiation at noon and lower in the morning and\nevening. However, msdwswrfcs fluctuates greatly throughout the day, and the patterns\nof different dates vary. This may be due to the data from ERA5 is an averaged value in\na fixed region. And the spatial resolution of the ERA5 data is relatively low, making it\ndifficult to directly obtain data near the specific photovoltaic plant, and data can only\nbe obtained through deviation calculation. We use CSR* msdwswrfcs as the solar\nradiation reaching the surface of PV plant (GHI), and then compare its change trend\nwith the measured GTI of the power plant, it is found that the change trend of GHI was\ninfluenced by msdwswrfcs and fluctuated greatly in a day, which is quite different from\nthe change trend of GTI.\nThe comparison of Lelin station is shown as follow:\nit is difficult for us to combine this radiation data to estimate the GHI on the surface of\nphotovoltaic stations and compare it with GTI. It seems better to directly use our\npredicted CSR to compare with GTI.\n- I think the article is missing a comparison with the other works' metrics, to\nprovide context to the performance findings.\nResponse: Thanks for your suggestion. We know exactly what you mean, but it’s a\ntough thing for us to achieve it. Our research is conducted in conjunction with practical\napplication scenarios of PV plants, which makes our work very different from other\nworks. For the CF prediction of the next 4 hours, the data from the first 4 hours is often\nvery important. Our method is to directly use L1B data, which ensures the timeliness\nof the data. Our goal is to predict the precise changes in cloud fraction of the next 4\nhours over PV plants. Unlike many methods that use visible images (Franco\nMarchesoni-Acland et. al, 2023; Pranda M.P. Garniwa et. al, 2023;) to obtain cloud\nfractions, we have developed a fast cloud detection algorithm that uses a total of 6\n33\nchannels satellite data and considers different surface types. This method can quickly\nand accurately retrieve cloud fractions. In addition, many previous works have a\nrelatively large experimental scope (1024km ×1024km in Franco Marchesoni-Acland\net. al, 2023; squares of size 256 × 256 pixels showing France and the final resolution\nof the images was 4.5 kilometers in Lea Berthomier et. al, 2020;). In order to better\ncapture cloud changes near PV plants and save computational resources, our\nexperimental scope is relatively small (128km×128km), which makes it difficult to\ncompare with other works.\nRegarding the predictive ability of neural networks, we also hope to demonstrate\nthe feasibility of our method through Forecast Skill, but no one has adopted our method\nin previous work. The differences in model data input and cloud detection methods\nmake it difficult to compare. But when we first chose the neural network structure, we\nconsulted a lot of information and chose a model (PredRNN++, Yunbo Wang et. al,\n2018) with strong prediction ability that is suitable for our prediction task. Our EPM\nhas been running at five independent PV plants (see Figure 1) for about half a year\n(started at Nov. 2022), and it has been proven that its predicted CF can greatly improve\nthe accuracy of photovoltaic power generation prediction (see Figure 3).\nReference:\nFranco Marchesoni-Acland, Andrés Herrera, Franco Mozo, Ignacio Camiruaga ,\nAlberto Castro , Rodrigo Alonso-Suárez; Deep learning methods for intra-day\ncloudiness prediction using geostationary satellite images in a solar forecasting\nframework, Solar Energy 262 (2023) 111820.\nPranda M.P. Garniwa , Rial A. Rajagukguk , Raihan Kamil , HyunJin Lee; Intraday\nforecast of global horizontal irradiance using optical flow method and long short-\nterm memory model, Solar Energy 252 (2023) 234–251.\nLea Berthomier, Bruno Pradel,Lior Perez; Cloud Cover Nowcasting with Deep\nLearning, 978-1-7281-8750-1/20/$31.00 ©2020 IEEE.\ndeep-in-time dilemma in spatiotemporal predictive learning. In: Proceedings of\nthe 35th International Conference on Machine Learning). PMLR (2018).\nMINORS\n- It would be informative to include the word \"satellite\" in the title or keywords.\nResponse: Per your suggestion, we have changed the title of this paper to “Accurate\nnowcasting of cloud cover at solar photovoltaic plants using geostationary satellite\nimages”\n- Authors claim the performance downgrade after 2h ahead can be caused by\nthe vanishing gradient problem. Just as a comment on this, in my opinion, this\ndowngrade may be explained by the limited spatial domain that is considered\n(128 x 128 km). The size of the region limits the reachable forecast horizon. A\ncloud velocity of 30-50 km/h is roughly consistent with a 2h ahead forecast\nlimitation at the center of a 128 km side square.\nResponse: Thanks for your suggestion. In the specified range of 128km×128km (which\nis actually about 150km×150km or larger due to the nominal 4km spatial resolution\nbeing applicable only to the nadir point pixel). Cloud movement probably affects the\nforecasting performance of EPM after 2 hours. However, we believe that this impact is\nrelatively small. (1) Firstly, our EPM takes into account the spatial changes in cloud\nimage features when making predictions. This consideration reduces the influence of\ncloud movement on prediction accuracy. We also cyclically update our EPM every hour\nusing multiple continuous satellite images. This frequent updating can help compensate\nfor the effects of cloud movement and improve the overall forecasting performance. (2)\nSecondly, we have made multiple attempts to determine the final size of the prediction\nregion, considering factors such as available computer resources, model computation\ntime, and test results. After careful evaluation, we settled on a 32×32 pixel box. We\nexperimented with larger sizes (such as 64×64 pixel box (cost 4 hours for one training\n35\nsession) and 128×128 pixel box), but ultimately found that 128km×128km provided a\ngood balance between computational efficiency and accurate predictions.\nThe rapid movement of clouds may indeed worsen the prediction results, but we\nbelieve that this impact is relatively small here. Thank you again for your valuable\nsuggestions. In our revised manuscript, we add the potential reason of the decrease in\nprediction performance caused by cloud movement at line 275-276, “ By the limited\nspatial domain, the rapid movement of clouds may cause a small bias between the\npredicted CF and the actual CF.”\n- Please indicate if a different neural network is trained for each forecast horizon\nor if there is another scheme for this.\nResponse: Thanks for your suggestion. We employed a fixed neural network to train\nall prediction models for various forecast horizons. Additionally, the model used for\nmaking predictions is updated on an hourly basis. At Line 424, we have depicted the\nPredRNN++ model, which relies on a framework utilizing recurrent neural networks\n(RNNs) with long short-term memory (LSTM). This model has the capability to\nforecast future CF within the range of 0 to 4 hours.\n- Line 258: It may be useful to provide an outline of the EKO cloud detection\nalgorithm. This is related to point (2).\nResponse: Thanks for your suggestion. The EKO cloud detection algorithm is the cloud\ndetection and opacity classification (CDOC) algorithm. More details could be found in\n“Ghonima et al., 2012.” In the CDOC algorithm, pixels in the images collected by the\ntotal/all sky imager are classified into three classes (clear, thin or thick) based on the\ndifference between a pixel’s actual red-blue ratio (RBR) and the corresponding\nexpected RBR if the pixel were clear. A haze correction factor (HCF) is added to\naccount for the effects of variations in aerosol optical depth (AOD) on RBR. Compared\nto fixed thresholding technique, in the validation for clear, thin, and thick cloud,\nrespectively, the CDOC algorithm provided 96.0 %, 60.0 %, and 96.3 % accuracy as\ncompared with 89.3 %, 56.1 %, and 91.5 % (Ghonima et al., 2012).\nReference:\ncloud detection and opacity classification based on ground based sky imagery.\nAtmospheric Measurement Techniques 5, 2881–2892, 2012.\n- Lines 297-299: There is a claim about a comparison between different\narchitectures, but it does not say for which specific problem were the findings.\nAlso, I would like to note that these metrics are not used in the solar forecasting\nfield, as they typically are uninformative for this task (or, at least, there is no\nwork demonstrating that are useful for the solar forecasting objectives).\nResponse: Thanks for your suggestion. The comparison here mainly analyzes the\nadvantages of different structures in dealing with spatiotemporal sequence problems.\nThe comparison here does not involve specific problems for analysis, but mainly\ndemonstrates the structural advantages of PredRNN++ compared to other networks.\nAbout metrics, we have realized that the correlation coefficient (R) was not a good\nchoice when evaluating the model prediction, so we added MBE, removed R and\nredesigned Figure 2 and added Table S1 in the supplementary documentation.\n- I wonder if the explanations of Lines 304-367 are useful to the article, as they\nmaybe can be found in textbooks or original articles. On the other hand, I could\nnot find the information regarding point (1) (differences with previous works or\narchitectures).\nResponse: Thanks for your suggestion. We place this explanation here mainly to\nfacilitate readers to have a clearer understanding of the PredRNN++ structure in Figure\n5, and to be able to clearly see the data transfer in each memory. The main structural\nchange is that we set the number of convolutional layers to 5, and the data dimensions\nof each layer's memory unit have been carefully designed, as shown in Figure 5B.\n- The article's structure is unusual, as metrics and models are given at last, and\nthere is no conclusion section. Authors, of course, can choose how to present\nthe article. I just point it out as a comment.\nResponse: Thanks for your suggestion. We sincerely apologize for any inconvenience\ncaused to your review due to the format of our manuscript. Our format is as similar as\n37\npossible to the official format of the journal that the manuscript will be published in.\nWe will rearrange format according to the official requirements of the journal.\nReviewer #1 (Remarks to the Author):\nThank you for your edits and additions to the paper - it appears that many of the issues regarding\nparallax and pixel-matching between the ASI imager and satellite imagery have been addressed. Much\nof the supporting documentation that was provided in the 'response to reviewer' document could be\nincluded in the manuscript itself to support the assertions - this reviewer is satisfied that parallax\nconcerns and imaging issues have been addressed, but readers without the benefit of the supporting\nmaterial may not be.\nI still find the layout of the paper confusing; with results first and discussion second, followed by\nmethods, I still find myself flipping back and forth between the paper to interpret the results. This is a\nchoice for the editor and the authors to consider, of course.\nReviewer #2 (Remarks to the Author):\nThanks for thoroughly addressing the comments and concerns from the previous round of review. No\nfurther suggestions from my end.\nReviewer #3 (Remarks to the Author):\nI think the authors have made a good revision work. The reviewers' response has cleared all my\nconcerns about the manuscript. I agree with the authors about not providing a tilted plane solar\nirradiance evaluation, which includes further considerations than the forecast itself. Most PV plants\nalso record the global horizontal irradiance (GHI), and my previous comment referred to this\nmagnitude. I also agree with not providing the discrete-values scatter plots, as they are\nuninformative. The non-inclusion of the forecasting skill metric, however, is debatable, as it can be\neasily constructed from CF persistence. Maybe I am losing something here, but the authors' reply on\nthis issue was not clear to me.\nMy only comment left is that the reviewers' response frames the article better in the field than the\ncurrent manuscript, in which there is no mention of previous satellite solar or cloud nowcasting\nmethods based on neural networks. By this, it may be interpreted that this is the only solar satellite\nnowcasting work based on satellite images and neural networks, which it is not. In my opinion, the\ncurrent manuscript's introduction reads as disconnected from the specific state-of-the-art.\nReviewer #1 (Remarks to the Author):\nThank you for your edits and additions to the paper - it appears that many of\nthe issues regarding parallax and pixel-matching between the ASI imager and\nsatellite imagery have been addressed. Much of the supporting documentation\nthat was provided in the 'response to reviewer' document could be included in\nthe manuscript itself to support the assertions - this reviewer is satisfied that\nparallax concerns and imaging issues have been addressed, but readers\nwithout the benefit of the supporting material may not be.\nResponse: Thanks for your valuable suggestion. We have added a sentence at line 333\n“For more explanations and details, please refer to Supplementary Note and\nSupplementary Figs. 7 and 8”. In supplementary file, we have added Supplementary\nNote and Supplementary Figs. 7 and 8 to explain this parallax issue, which is written\nas follows:\nSupplementary Note: We think the theoretical studies from Reference-1 (Wei and Sun\n2022) mentioned above can help us to explain the relatively small or negligible parallax\neffect. Supplementary Figure 7 shows the sensitivity of position deviation (or parallax\ncorrection) to satellite imaging pixel spatial resolution, cloud top height (CTH), and\nview zenith angle using parallax correction algorithm (Wei and Sun 2022). First, it\nclearly proves that the position deviation is not sensitive to satellite imaging pixel\nspatial resolution. Second, the specific view zenith angles of ground-based stations\nused in this study from Supplementary Table 2 are not larger than 60 degrees (the\nmaximum value is about 56 degrees). This figure also indicates that the bias caused by\nparallax is unlikely to exceed 1km under the condition of view zenith angle<60 degrees.\nParallax correction should only be applied to exceptionally high clouds (>10km)\nresulting from typhoons or Deep Convective Clouds with a view zenith angle greater\nthan 70 degrees in the GEO satellite field of view.\nIn addition, it is worth noting that the parallax correction for GEO satellite images\nneeds high-precision cloud top height products. Cloud top height products still manifest\na notable degree of error, particularly in the case of relatively high cloud top samples\nwith relatively larger retrieval errors (exceeding 4 km when cloud top height exceeds\n1\n10 km, as illustrated in Supplementary Figure 8). This may introduce more\nuncontrollable errors to the parallax correction algorithm.\nSupplementary Figure 7. Sensitivity of position deviation (or parallax correction) to\nsatellite imaging pixel spatial resolution, cloud top height (CTH), and view zenith\nangle using parallax correction algorithm. (Supplementary Figure 7 from Reference-\n2: Xiaocheng Wei and Fenglin Sun, Analysis of the parallax characteristics of\ngeostationray-orbiting microwave sounder [J]. Journal of Tropical Meteorology, 2022,\n38(6): 901-914, doi: 10.16032/j.issn.1004-4965.2022.067 (In Chinese), we have\nattached this paper in Data Availability)\n”\nerror; STD=standard deviation) of cloud top height (CTH) retrieved from Himawari-\n8 satellite using CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite\nObservation) product. (Supplementary Figure 8 cited from Reference-3: Min Min, Jun\nLi, Fu Wang, Zijing Liu, W. Paul Menzel, 2020. Retrieval of cloud top properties from\nadvanced geostationary satellite imager measurements based on machine learning\nalgorithms [J]. Remote Sensing of Environment, 239: 111616, doi:\n10.1016/j.rse.2019.111616)\nI still find the layout of the paper confusing; with results first and discussion\nsecond, followed by methods, I still find myself flipping back and forth between\nthe paper to interpret the results. This is a choice for the editor and the authors\nto consider, of course.\nResponse: Thanks for your valuable suggestion. We sincerely apologize for any\ninconvenience caused to your review due to the format of our manuscript. The current\nlayout of the manuscript may indeed bring many inconveniences to readers, but the\nlayout is determined by the editor. I believe that the editor will provide a reasonable\nlayout for our manuscript. Thank you again for your valuable suggestion.\nThanks for thoroughly addressing the comments and concerns from the\nprevious round of review. No further suggestions from my end.\nResponse: Thank you very much for your valuable suggestions. Your suggestions have\nmade our manuscript more reasonable and perfect, and we have also learned a lot during\nthe process of responding to your comments. Thank you again and best wishes.\nReviewer #3 (Remarks to the Author):\nI think the authors have made a good revision work. The reviewers' response\nhas cleared all my concerns about the manuscript. I agree with the authors\nabout not providing a tilted plane solar irradiance evaluation, which includes\nfurther considerations than the forecast itself. Most PV plants also record the\nglobal horizontal irradiance (GHI), and my previous comment referred to this\nmagnitude. I also agree with not providing the discrete-values scatter plots, as\nthey are uninformative. The non-inclusion of the forecasting skill metric,\nhowever, is debatable, as it can be easily constructed from CF persistence.\nMaybe I am losing something here, but the authors' reply on this issue was not\nclear to me.\nResponse: Thank you very much for your valuable suggestion. Forecasting skill metric\nis indeed a good parameter for measuring the accuracy of forecasting methods, but its\nuse is based on the premise that different methods predict the same thing under the same\nconditions. In this study, our predicted result is geostationary satellite L1B radiance\ndata, and then we use cloud mask algorithms to obtain Cloud Fraction over solar PV\nplants. There is no other study adopted our idea and method. Some previous study\n4\n(Marchesoni-Acland et al., 2023) only used a single visible channel of geostationary\nsatellite and a constant model to predict cloudiness (see Q2 of Reviewer-3). While we\nused predicted six channels (two visible and four infrared) of AHI/H8 to determine\ncloudy pixels over PV plants. More importantly, our prediction range is very small,\nonly covering an area of 128km×128km. In contrast, many other studies (Marchesoni-\nAcland et al., 2023, Nielsen AH et al. 2021.) have a large prediction range, even\ncovering the entire area of France.\nOf course, we understand that it is very difficult for different studies to have exactly\nthe same initial conditions. We try to find similar ones that can be compared with our\nresearch, but this is still difficult. We have not found a suitable former study to compare\nwith our method. With the development of neural networks, in our future research, we\nmay adopt more advanced neural network models, so that we can use forecast skills to\ncompare the predictive performance of different models.\nReference:\nMarchesoni-Acland F, Herrera A, Mozo F, Camiruaga I, Castro A, Alonso-Suárez R.\nDeep learning methods for intra-day cloudiness prediction using geostationary satellite\nimages in a solar forecasting framework. Solar Energy 262, (2023).\nNielsen AH, Iosifidis A, Karstoft H. IrradianceNet: Spatiotemporal deep learning\nmodel for satellite-derived solar irradiance short-term forecasting. Solar Energy 228,\n659-669 (2021).\nMy only comment left is that the reviewers' response frames the article better\nin the field than the current manuscript, in which there is no mention of previous\nsatellite solar or cloud nowcasting methods based on neural networks. By this,\nit may be interpreted that this is the only solar satellite nowcasting work based\non satellite images and neural networks, which it is not. In my opinion, the\ncurrent manuscript's introduction reads as disconnected from the specific state-\nof-the-art.\nResponse: Thank you very much for your valuable suggestions. We have updated the\nlast paragraph of the manuscript’s introduction a line 96-155 (see below). In this part,\nwe have added some similar previous papers here.\n“Cloud cover nowcasting remains a field of interest for forecasting the electricity\nproduction of PV plants24. We are committed to developing a daytime hourly intra-day\nCF prediction algorithm for small areas over PV plants. Based on the recurrent-neural-\nnetworks-based (RNNs) long short-term memory (LSTM) algorithm framework, the\nnewly developed PredRNN and PredRNN++ (an extended and latest version of\nPredRNN) 25, 26 can well learn to predict long-term future imageries in various spatio-\ntemporal tasks by modeling their spatial and temporal dependencies, including video\nframe prediction, human motion prediction, etc. Therefore, our primary objective is to\ndevelop an innovative and easy-to-promote algorithm or system based on the key\nframework of the PredRNN++ model. Through this algorithm, the 0 – 4 hour CF at\nsolar PV plants under all weather conditions can be predicted by using sequential\nHimawari-8/9 geostationary satellite images with high spatio-temporal resolutions 27.\nCompared with the previous study 28, it only used a single visible channel of\ngeostationary satellite and a constant model to predict cloudiness. Some former studies\ndirectly used surface solar global horizontal irradiance (GHI) as model input to predict\nGHI values in the next few hours 29, achieving the purpose of estimating the power\ngeneration of PV plants. Nevertheless, the presence of clouds is still identified as the\nprimary uncertainty in current surface solar GHI forecasts 30. In contrast, our\ninvestigation only predicts geostationary satellite Level 1B (L1B) radiance data. With\nthe prediction results of satellite L1B radiance data and accurate cloud detection\nalgorithm, this approach is expected to provide reliable and variable CF information\nfor further improving the predictability of current GTI or PV power generation.”\n6"}]

Decision letter