Empirical Verification of Satellite Data on Solar Radiation and Cloud Cover over the Baltic Sea

Marcin Paszkuta aDivision of Geophysics, University of Gdańsk, Gdańsk, Poland

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Maciej Markowski bGIS Laboratory, University of Gdańsk, Gdańsk, Poland

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Adam Krężel cMaritime Institute, Gdynia Maritime University, Gdańsk, Poland

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Abstract

Empirical verification of the reliability of estimating the amount of solar radiation entering the sea surface is a challenging topic due to the quantity and quality of data. The collected measurements of total and diffuse radiation from the Multifilter Rotating Shadowband Radiometer (MRF-7) commercial device over the Baltic Sea were compared with the satellite results of using modeling data. The obtained results, also divided into individual spectral bands, were analyzed for usefulness in satellite cloud and aerosol detection. The article presents a new approach to assessing radiation and cloud cover based on the use of models supported by satellite data. Measurement uncertainties were estimated for the obtained results. To reduce uncertainty, the results were averaged to the time constant of the device, day, and month. The effectiveness of the method was determined by comparison against the SM Hel measurement point. The empirical results obtained confirm the effectiveness of using satellite methods for estimating radiation along with cloud-cover detection over the sea with the adopted uncertainty values.

Significance Statement

The difference in the amount of solar energy reaching the sea surface between cloudless and cloudy areas reaches tens of percent. Empirical results confirm the effectiveness of using satellite methods to estimate solar radiation along with cloud-cover detection. Over the sea in comparison to land, the amount of empirical data is limited. This research uses new empirical results of radiation to determine the accuracy of satellite estimation results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of satellite image features.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maciej Markowski, maciej.markowski@ug.edu.pl

Abstract

Empirical verification of the reliability of estimating the amount of solar radiation entering the sea surface is a challenging topic due to the quantity and quality of data. The collected measurements of total and diffuse radiation from the Multifilter Rotating Shadowband Radiometer (MRF-7) commercial device over the Baltic Sea were compared with the satellite results of using modeling data. The obtained results, also divided into individual spectral bands, were analyzed for usefulness in satellite cloud and aerosol detection. The article presents a new approach to assessing radiation and cloud cover based on the use of models supported by satellite data. Measurement uncertainties were estimated for the obtained results. To reduce uncertainty, the results were averaged to the time constant of the device, day, and month. The effectiveness of the method was determined by comparison against the SM Hel measurement point. The empirical results obtained confirm the effectiveness of using satellite methods for estimating radiation along with cloud-cover detection over the sea with the adopted uncertainty values.

Significance Statement

The difference in the amount of solar energy reaching the sea surface between cloudless and cloudy areas reaches tens of percent. Empirical results confirm the effectiveness of using satellite methods to estimate solar radiation along with cloud-cover detection. Over the sea in comparison to land, the amount of empirical data is limited. This research uses new empirical results of radiation to determine the accuracy of satellite estimation results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of satellite image features.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Maciej Markowski, maciej.markowski@ug.edu.pl

1. Introduction

The development of satellite technology is opening up new opportunities, but it also poses new challenges for researchers and scientists. One of the basic pieces of information that is acquired using satellite remote sensing techniques is cloud cover (cc). At the same time, cloud cover limits access to data from Earth in the visible and infrared ranges and is a source of uncertainty in the measurements used in environmental studies. Operationally, most of the solutions related to global cloud detection provided routinely by “meteo” services/tools are not satisfactory in terms of quality with respect to even the regional and even more so with respect to the local scale (Arola et al. 2022), whereas for studies with a global scope, or when looking for general climate trends, this is of little importance (Pincus et al. 2023), in the case of specialized regional or local estimations (including climate change at this level), it can have significance. In addition, still no clear interpretation of cloud cover exists in the literature (Spänkuch et al. 2022), which makes it much more difficult to interpret the results obtained. Therefore, in the development of local operational oceanography, it is important to seek, select, and develop proprietary techniques that can actively complement conventional solutions.

The main research objective is an empirical approach to the estimation of surface radiation using satellite cloud products. The research question is therefore: What is the right way for long-term verification of cloud cover over the sea? From an operational point of view, these can be supported by appropriate detection techniques. To this end, a satellite data comparison system has been developed based on ground-based measurements and original remote sensing techniques.

The research problem is the validation of satellite data at the local level with point and instant ground-based information. There is a fair number of objective difficulties in this type of analysis, one of the most important of which is the correlation between satellite projection and ground-based measurements (Young et al. 1998). Regular Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI) and in situ measurements are analyzed with a 15 min interval. Spatial resolution for the aforementioned devices can be expressed in a unit value on the order of a few square kilometers, thus generating standard but significant differences with respect to point measurements (Deneke et al. 2021). This results in the need to identify the level of uncertainty for the measurement method (Paszkuta et al. 2019).

The Baltic Sea area is characterized by a very high variability of cloud cover in the temporal as well as the spatial dimensions. As a result, in order to detail the information on cloud cover, it is necessary to continuously fill in the gaps that exist with regard to conventional and local detection. For marine areas, the availability of long-time data for validation is much lower than for land. Alternative measurements are made using buoys, offshore platforms (e.g., Baltic Beta), or ships (e.g., Research Vessel Oceanograf). Nevertheless, they are usually conducted quite irregularly and are subject to additional uncertainties that arise, for instance, from the drift motion of the ship, the installation of the measuring device in the shadow of the ship’s deck equipment, and the burners of engineering industrial installations. As an alternative to the aforementioned surface measurements, information is obtained from stationary devices located at appropriately selected measurement points on land. For the Baltic Sea, these conditions are met by the Marine Station of the Institute of Oceanography of the University of Gdańsk in Hel (SM Hel), which, from a methodological point of view (location at the end of the Hel Peninsula), is the only place of its kind in the territory of Poland used to conduct land-based marine research. The Hel Peninsula is surrounded by the sea on three sides and is connected to the mainland only by a narrow strip (approximately 100 m). Therefore, it can largely reflect the nature of marine conditions, while maintaining the continuity of measurements typical of those made on land.

The research presented in this article is local in nature; however, it can relate to processes occurring on a global scale—changes confirmed locally due to the nature of the modeled data indicate the direction of changes relating to the entire Baltic Sea. Krężel (1985) developed a semiempirical model for calculating monthly solar radiation totals over the Baltic Sea surface. This model was used by Dera and Woźniak (2010) to represent typical ranges of variation in natural marine irradiance on time scales of various characteristics found in the Baltic basins. However, the averages obtained are not fully representative, as in the absence of access to classic in situ measurement methods from the decks of research vessels operating in the Baltic Sea, it was not possible to collect the minimum amount of empirical data that would adequately reflect the variability of the optical characteristics of all basins. More extensive comparative analyses are presented in the publication of Darecki et al. (2008), where the effects of practical application and at the same time validation of the satellite algorithm are presented. With that said, the validation process was carried out on the basis of satellite maps of selected Baltic ecosystem parameters: radiation near the sea surface, chlorophyll concentrations, and primary organic matter production. Particular emphasis was placed on analyzing the precision of the estimations of these and other Baltic ecosystem parameters determined by remote sensing methods. Estimation uncertainties proved to be relatively low. In their further research, Krężel et al. (2008) extended the spectral model of solar energy input to the sea surface with data from space. The extension involved developing a method for determining the optical thickness of aerosols (based on AVHRR data) and the effect of cloud cover (based on Meteosat data) on solar flux. The satellite data assimilation algorithm involved analyzing satellite images for cloud identification and light transmission. Solar energy values measured near Earth’s surface by traditional methods were used to calibrate and validate the model. Ohlmann and Siegel (2000) estimated the amount of solar radiation incident on the sea surface as well as its divergence, or transmission. Lumb (1964) found empirical dependencies of instant shortwave radiation for an ocean weather station (52°30′N, 20°00′W), with respect to several types of clouds. On this basis, it was shown that the relationships derived can be applied to estimate the total solar radiation reaching the sea surface. Dobson and Smith (1988) analyzed various models that estimate solar radiation based on the sun’s altitude as well as the hourly cloud cover and type of clouds, using empirical data and simple physical formulas. Rozwadowska et al. (2013) present the results of measurements using the MRF-7 device of solar radiation reaching Earth’s surface conducted in the summer of 2011 in Sopot, Poland. Three cloudless days, characterized by different and typical directions of incoming air currents for the measurement site, were used to estimate the radiative forcing due to the presence of aerosols in each air mass (Lewandowska et al. 2017, 2018). However, remote cloud detection based on satellite radiation data is not straightforward (Krężel and Paszkuta 2011). Qin et al. (2015) analyzed empirical relationships 141 to improve the efficiency of satellite data acquisition but also its accuracy at the local and regional level. Mefti et al. (2008) proposed a model for Meteosat images and ground-based solar flux measurements collected at various locations in France during 1994/95, which were then converted to respective cloud-cover indices. A semiempirical satellite method was used in the estimation of global radiation by Chen et al. (2022), where cloud-cover clear-sky indices were analyzed. The main objective of the Prabha and Hoogenboom (2010) study was to evaluate predictions of solar irradiance for clear-sky conditions as well as cloud cover. A scheme for creating daily solar radiation maps from empirical data for verifying the components of the radiation budget on the Baltic Sea surface was described by Zapadka et al. (2015).

This research presents an empirical approach to estimating surface irradiance using satellite cloud products, which has been verified with surface measurements in subbands of the irradiance spectrum. The research covers two related areas, related to the effects of cloud cover and aerosols on irradiance. The main objective of the research is one and the individual operational objectives are closely related and dependent (as described in the cited literature) and therefore should not be considered separately.

The study presents a new approach to multiyear assessment of cloudiness over the sea. The originality of the study lies in the method, period, and location of data measurement for comparisons with different types and ranges of radiation. The proposed method of satellite-based cloud detection combined with modeling is new. However, it has been reviewed previously.

The paper is divided into five parts. The next section characterizes the database: in situ with details of the intercalibration process and satellite data with details of the relationship between radiation and cloud cover. The results section is divided into four subsections: instantaneous measurements with an explanation of the uncertainties; due to the significant uncertainties the next subsections present the averaged results for radiation, cloud cover, and aerosols, respectively. Finally, the results are discussed in the discussion section and summarized in the conclusions section.

2. Materials and methods

The device that was installed on the roof of SM Hel was the MRF-7 (Fig. 1c) equipped with a moving shadowband (Harrison et al. 1999). The geographic positioning of the device was 54.606 66°N, 18.800 88°E; in order to standardize the marine data, the area on the coastline was shifted toward the Gulf of Gdańsk to 54.609 52°N, 18.789 08°E (SM point; Fig. 1a). MRF-7 is a commercial product produced and calibrated by Yankee Environmental Systems (YES) manufactured and serviced in the United States. The MRF-7 contains one unfiltered, broadband silicon-cell pyranometer used to measure total and diffuse solar radiation, also known as illumination or radiation flux density (Dera 2003; Mlawer et al. 2000) for spectral bands (Table 1, set A.1) in the visible and near-infrared ranges. This device performs measurements taking into account the current state of the atmosphere, where clouds are the primary factor affecting radiation values. In 2010–18, the spectrometer was installed in an exposed location, at a height of approximately 10 m above the ground surface, 800 m from the open sea, and 25 m from the shore of the Bay of Puck. The horizon observed by the spectrometer was free of tall vegetation, buildings, or roofing installations that could interfere with the solar flux (Fig. 1b). Thus, the horizon is open in all directions in a radius of approximately 100 m in every season. Besides that, additionally one of the permanent piece of equipment at SM Hel is a Kipp and Zonen CMP3 pyranometer (Fig. 1d), which was used to conduct periodic intercalibration of the MRF-7 (Fig. 1c).

Fig. 1.
Fig. 1.

Marine Station Hel (SM Hel): (a) standard displacement of the measurement point described on the coastline (SM Hel) into the sea (SM point) (www.satbaltyk.pl), (b) the station building on which the sensor was installed with the coastline visible, (c) the MRF-7 device with the shadowband visible, and (d) in the background, the meteo station with the Kipp and Zonen CMP3 pyranometer.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

Table 1.

Spectral characteristics of the sources used in the analysis.

Table 1.

The total solar radiation reaching Earth’s surface is the sum of direct and diffuse radiation in the atmosphere. The magnitude of this radiation is often defined as the sum of the radiation flux reaching a horizontal surface unit directly from the sun and from all directions from the atmosphere. In marine optics, it is referred to as top-down vector illumination or illumination (W m−2) for short (Dera 2003). Radiation intensity (W sr−1) is the angular density of the radiation flux that determines the amount of radiation flux per unit solid angle around its direction of propagation. It depends primarily on the angle of direct sunlight and the state of the atmosphere, in which cloud cover is one of the most important factors. Cloud cover, depending on its size, density, transparency, etc., almost completely reduces direct solar radiation by reflecting it into space and scattering it in all directions. The type or height of cloud cover can also play an important role, e.g., higher parts transmit more shortwave radiation than lower ones and, on the other hand, absorb and emit more longwave radiation. The daily amount of energy from the total solar radiation that reaches Earth’s surface depends on the length of the day as well as the cloud cover, and therefore the season. Diffuse radiation, on the other hand, is defined as solar radiation reaching the sea surface, excluding direct radiation (Mlawer et al. 2000). Its spectral composition depends on the cloud cover and the state of the atmosphere. Direct radiation means radiation that is subject to little or no change in the atmosphere, such as changes in the quantities of aerosols, air molecules, ozone, water vapor, and trace gases (Lewandowska et al. 2017, 2018). It involves a small interaction of radiation with particles suspended in the air. This is due to the dependence of the scattering or absorption of different wavelengths on the size and chemical composition of atmospheric particles. The reverse effect is related to diffuse radiation. Suspended particles in the air act as condensation nuclei that are necessary for the formation of cloud droplets, and which increase the efficiency of radiation scattering. The described mechanisms of radiation transport in the atmosphere involve particles large enough to form condensation nuclei that absorb and scatter significant amounts of energy. Diffuse radiation is measured with instruments with a screen that eliminates direct radiation, such as the MRF-7. This radiation is produced by the scattering of direct radiation in Earth’s atmosphere on the particles that make up the atmosphere and on aerosols, water droplets, snow, etc. (Mlawer et al. 2000). For example, during the day, with the sky completely covered by thick (optically) clouds, visible light at Earth’s surface is almost entirely diffuse radiation.

In Fig. 2d an example of the daily distribution of radiation recorded by the MRF-7 device on SM Hel over the entire spectral range is presented. In addition, the device records spectra in selected intervals of 415, 500, 615, 673, 870, and 940 nm, each 10 nm full width at half maximum (FWHM) (Table 1, set A.1). The device recorded total radiation and diffuse radiation with an interval of 15 s, from which direct and reflected radiation were determined. The presented distribution of the recorded radiation demonstrates the highly volatile nature of cloud cover in marine conditions (SM point). The variability of atmospheric conditions over the measurement point is presented in graphic form on the Satellite Monitoring of the Baltic Sea Environment (“SatBaltic”) portal (Figs. 2a–c). The atmospheric situation recorded by satellite systems is reflected in all the characteristics of the radiation analyzed. The greatest variability is in the midday period with semitransparent cloud cover. To some extent, this is a result of the highest values of recorded radiation. In the case of full cloud cover (before noon) and no cloud cover (afternoon), the variability is lower, while the nature of diffuse radiation is different. In the morning (full cloud cover) the amount of diffuse radiation is comparable to total radiation, and in the afternoon (no cloud cover) the value of diffuse radiation is lower. As in Figs. 2a–c, maps downloaded from the database were used for further research as a base to determine, i.e., solar radiation input to marine waters in different spectral ranges, cloud cover, etc.

Fig. 2.
Fig. 2.

Example of cloud changes over the Baltic Sea and SM Hel (red circle) by satellite system www.satbaltyk.pl: (a) 0700 UTC, (b) 1400 UTC, (c) 1730 UTC, and (d) corresponding radiation at Hel station according to MRF-7, SolRad model, and cc on 14 Apr 2011.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

The reported period of conducted research using the MRF-7 device covers 8 years (2010–18) with a break for servicing the device in 2013, resulting from a failure caused by bird damage. Radiation values were recorded only during the daytime. As a result, several hundred measurements per day were collected, sufficient to establish a correct temporal correlation (Paszkuta et al. 2019). Due to the location of the measurement site, a relatively high latitude, the daily sessions in the winter season (November–February) were limited to several hours. A summary of the number of ground measurements can be found in Table 2. In total, measurements from 2654 days were included in the analysis.

Table 2.

Summary of MRF-7 measurement series.

Table 2.

a. MRF-7 intercalibration

Absolute calibration of the MRF-7 requires frequent repetitions. To obtain properly calibrated data, a so-called between service intervals intercalibration of the measurement device was necessary. In the case of the MRF-7, this consisted of developing constant values for linear functions against the raw data to reproduce the values obtained by the commercial software supplied with the device as presented in Fig. 3a. The MRF-7 system works in tandem with the YESDAS Manager commercial data analysis software. During its operation, the MRF-7 was calibrated once at the manufacturer due to a seagull attack, recalibrated twice at the distributor in Poland, and systematically (every 6 months at most) intercalibrated based on lighting values provided by Kipp and Zonen (Fig. 3a). For this purpose, as an alternative off-service, the results provided by the pyranometer located at SM Hel next to the MRF-7 were used (see the online supplemental material). The Kipp and Zonen CMP3 device determines radiation in the full range of the visible radiation spectrum. The device, calibrated according to the manufacturer’s requirements, served as an ad hoc intercalibration of MRF-7 total radiation, excluding spectral ranges. Calibration of a nonspectral channel against a pyranometer is acceptable in the absence of access to better sources, such as spectral, where each channel has its own sensor and its own filter. Figure 3a shows a representative example of the intercalibration on 7 April 2010 without clouds. In reality, the intercalibrated times do not always match as well. Even if the times were accurate, the MRF-7 global horizontal irradiance measurement is based on a photodiode-based pyranometer and the calibration measurement is based on thermopiles. There are systematic differences between these measurements based on the spectral distribution of the incident radiation. The MRF-7 corrections take some of this bias into account, but there is always a small systematic bias in the broadband measurements.

Fig. 3.
Fig. 3.

(a) Example of rav radiation intercalibration on the day 7 Apr 2010 without clouds (Krężel et al. 2008) (YESDAS is commercial MRF-7 data analysis software) and (b) flow diagram of the preparation of the experimental procedure for radiation and cc data analysis.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

Analysis of the data revealed difficulties in recording the time of measurement and the value of direct radiation readings. According to the estimates made, they did not correspond to the actual values, especially for multiyear data. Hence, it was decided to reconstruct the timing of the measurements based on the length of the day and the radiation maximum for cloudless periods. Consequently, it was possible to reproduce the measurement time with a maximum difference of 75 min. At the same time, this difference varied depending on the period in which a given measurement series was carried out. The adjustment of the MRF-7 time to 75 min seems extreme, although this is the maximum value, in general the differences were not that large (around 15 min). A precise determination of the “key” to existing time differences was not possible. The problem was most likely related to a reduction in battery efficiency, as the series of measurements took a very long time in an unfavorable environment. Hence, a new time variable was eventually established for the entire series of data collected in addition to the system variable. Typically, the SEVIRI instrument on board the MSG satellites scans Earth every 15 min. The nominal repeat cycle for the full Earth scan service is 15 min, divided into ∼12.5 min for imaging and ∼2.5 min for calibration, retrace, and stabilization. The imaging process is a series of horizontal scan repetitions and it is difficult to determine the timing of the SM Hel point scan, even by empirical methods. It seems safer to assume that if the satellite survey was taken every 15 min, then the MRF-7 data were measured once every 15 min (although we have a series with a step of 5 min). Direct radiation was estimated as the difference in total and diffuse radiation. In addition, once the albedo value is determined, the value of reflected radiation can be estimated in several ways: using radiation models and sea surface albedo for a cloudless atmosphere, using radiation recorded by radiometers on board satellite platforms, including a cc estimate for a cloudy atmosphere (Paszkuta et al. 2019).

The sum of direct, diffuse, and reflected radiation is the total radiation. Empirically, the reflected radiation reaching the SM Hel point was calculated as the difference between total, direct, and diffuse radiation. In general, direct radiation is the largest component of total radiation and diffuse radiation is the second largest component. Reflected radiation is generally a small fraction of total radiation. Therefore, most solar radiation tools do not include reflected radiation in the calculation.

The preparation of the experimental procedure consisted of several steps (Fig. 3b). The raw data stream of both satellite and in situ measurement data was downloaded from the sources to the computer’s internal drive. In the next step, the radiometrically and geographically calibrated data were fitted together according to the “SatBaltic” format of Woźniak et al. (2011a,b) and a uniform time format. According to the above instructions, correlating series were developed for comparative analysis including statistical analysis and averaging, accounting for uncertainty. In the last step, graphs and comparative relationships were determined, and conclusions were drawn.

b. Relationship of radiation to cloud cover over the Baltic Sea

In the “SatBaltic” project, the originally created SEVIRI geographic grid was normalized using the nearest-neighbor method to a spatial resolution of 1 km. To take full advantage of the potential of the source data, all available SEVIRI bands (Table 1, set B) were included in the cloud map compilation. For daytime, a split-window operation was applied to pairs of adjacent channels VIS0.6 and VIS0.8 and IR10.8 and IR12.0. Both long- and short-wavelength band combinations and the high-resolution visible (HRV) panchromatic channel were used. After transformation and normalization, map pixel intensities and HRV values belong to the (0–1) from 0 to 1 range. At night, only channels 3 and 4 are used. In the article, all cloud-cover values were estimated based on radiation. In the satellite image, the atmospheric transfer function h(x, y) summarizes, a priori, the emission of radiation from the sea and clouds. Its model equivalent refers only to radiation from the sea in cloudless weather. The value of h can be a normalized function that determines the path of radiation reaching the satellite. It can be adapted for further research with shortwave band data as follows:
h1(x,y)=VIS0.8VIS0.8MVIS0.6M×VIS0.8VIS0.8M×VIS0.6VIS0.8VIS0.6MVIS0.6+VIS0.8MVIS0.8M,
where h1—dimensionless parameter determining the cloud-cover factor based on the shortwave band; VIS0.6 and VIS0.8 (Table 1, set B)—radiation in adjacent SEVIRI channels; VIS0.6M and VIS0.8M (Table 1, set C.1)—radiation from the sea in a cloudless atmosphere modeled for SEVIRI channels 1 and 2, respectively (Table 1).
The radiation reaching the satellite in a cloudless atmosphere was determined using the SolRad model (Krężel et al. 2008) with respect to the solar zenith angle (SZA). An exhaustive analysis of the method is presented in the study by Paszkuta et al. (2019, 2022b). The longwave part is described according to the Saunders and Kriebel (1988) formula:
h2(x,y)=1IR12.0IR12.0MIR10.8M×IR12.0IR12.0M×IR10.8IR12.0IR10.8MIR10.8+IR12.0MIR12.0M,
where h2 is the dimensionless parameter determining the cloud-cover factor based on longwave radiation information; IR10.8 and IR12.0 (Table 1, set B) represent the SEVIRI temperature (K) (Table 1); and IR10.8M and IR12.0M (Table 1, set C.2) represent the cloudless atmosphere values (K) determined from the model.

The solution evaluates the radiation temperature based on the sea surface temperature, according to the M3D model (Kowalewski 1997). To estimate normalized values between 0 and 1 for shortwave and longwave radiation, the data are sequentially checked against Eqs. (1) and (2) (Paszkuta et al. 2019, 2022b, respectively). Finally, the value of h is selected as the maximum value of the pixel in the rasters (h1 or h2 or HRV), where h1 and h2 are dimensionless and normalized from 0 to 1 (at night h = h2). The higher value is used to generate the final cloud map according to the algorithm described by Paszkuta et al. (2019). The empirical description showed an underestimation of the satellite procedure for semitransparent centers.

The spectral composition of diffuse radiation is mainly related to cloud cover and the aerosols. With regard to direct radiation as a function of total radiation with a cloudless atmosphere, the contribution of diffuse radiation is typically less than with an overcast atmosphere. However, in the case of MRF-7, diffuse radiation can be an indicator of cloudiness. The relationship of diffuse radiation to the satellite-determined cloud cover is described later in this article.

3. Results

a. Instant measurements

Figure 2d shows the potential of point instant measurements without any averaging—using data from 14 April 2011 as an example. During the first half of the day, a high value of the cc coefficient is noted (Figs. 2a,d), which results in decreased yet homogeneous, values of radiation. At noon (Fig. 2b) cc values decrease, which is a result of the presence of translucent clouds causing an increase in the variation of radiation at the same time as the highest daily values in the last part of the day (Fig. 2c), when cloud cover dropped to zero and radiation values became uniform, showing relatively small changes. It is important to note here the significant uncertainty values for the data. However, they show expected and synchronized responses to changes caused by external factors. The relative error is the difference between the accurate value and the measured value, according to the formula Δx = |xx0|, where x is the accurate value and x0 is the measured value. The standard deviation of the collected datasets mentioned later in the article refers to a measure of how widely the values of the evaluated set deviate from the mean value—according to the rule (xx¯)/(n1), where x¯ is the mean value for sample x and n represents its size.

During analysis of Fig. 2d, it is also important to pay attention to the reactions caused by changes in diffuse radiation. Its value increases with the amount of cloud cover and correspondingly decreases when the cc coefficient is reduced. Using the selected example, the uncertainties are 55.59 W m−2 for the relative error and 19.2 W m−2 for the standard deviation; for averaged (15 min) values, these uncertainties are 54.25 and 18.78 W m−2, respectively. In addition, it is important to emphasize the high instant measurement potential of the MRF-7 device, which can be significant with such a highly volatile phenomenon as cloud cover. Due to the lack of access to rapidly changing instant satellite data, MRF-7 results were compared with data taken with a 15 min interval. Source data acquired from satellite measurements for the Baltic Sea have a spatial resolution of approximately 5 km × 7 km, and have been extrapolated to a pixel size of 1 km × 1 km. Extrapolation was done using the nearest-neighbor method.

The relationship between the total radiation determined empirically and by satellite (determined based on SEVIRI data for an offshore reference point at the distance of some 50 m) for the 14 April 2011 scenario is presented in Fig. 4. For both the instant values with a time interval of 15 min (Fig. 4a) and the values averaged over a 15 min period (Fig. 4b), an upward trend was found with varying uncertainty. There are many ways to obtain instantaneous values from 15-min data and vice versa. Due to synchronization issues, the simple averaging method was chosen (Paszkuta et al. 2019). This demonstrates the correctness of the radiation measurements taken, and indicates that averaged results yield correct. The improved relationship for averaged values is due to the methodology for comparing instant and point measurements made in situ and from satellite. Figure 4 shows that averaging compared to instantaneous data from satellites has a greater uncertainty than instantaneous measured data compared to instantaneous data from satellites. It also shows that the results described are subject to inherent uncertainties. Therefore, obtaining the correct relationships is possible by including correct uncertainties analysis procedures and introducing averaging statistics. This is of great importance in climate change research, especially when analyzing large datasets from several years. In contrast, when it comes to proving environmental change, datasets for 30–40 years are the minimum to enable conservative climatological conclusions to be drawn. This involves the determination of averaged parameters, e.g., solar radiation (W m−2), daily solar radiation dose (MJ m−2 day−1), and monthly average solar radiation dose (MJ m−2 day−1) (Pashiardis et al. 2022). The corresponding weighted parameters also apply to cloud-cover products calculated according to Eq. (1).

Fig. 4.
Fig. 4.

The relationship between the total radiation from the MRF-7 device (SM Hel) and from satellite (SM point) for 14 Apr 2011: (a) instant values with a time interval of 15 min and (b) averaged values with a 15-min intervals.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

b. Averaged radiation values

Knowing the limitations of using instant values of measured radiation, in order to verify the results obtained in situ, it was decided to compile averaged radiation values. The instant values were averaged to daily values and to monthly values. Consequently, Fig. 5 shows the relationship between the daily average (Fig. 5a) and monthly average (Fig. 5b) values of solar radiation estimated by satellite methods based on the SolRad model (Krężel et al. 2008) for a reference point located at sea (SM point) and the averaged (daily and monthly, respectively) values of total radiation measured with the MRF-7 device for the SM Hel point. Based on results obtained, it can be assumed that despite the different data sources, the values correlate to a significant degree. Monthly averaged values significantly reduce the level of uncertainty in the aforementioned relationship. Uncertainties are reduced by the effects of systematic measurement errors as they tend to cancel each other out to some extent. The answer to the question of whether instantaneous or averaged values should be used depends on the application. Analyses of rapidly changing phenomena, such as cloud cover, often suggest the shortest possible analysis period, despite the increase in uncertainty. Long-term analyses, such as climate change, may focus on averaged values. An interesting effect is that two branches of the data in Fig. 5a should be associated with cyclical measurements in different seasons: spring–autumn.

Fig. 5.
Fig. 5.

Relation between in situ data and satellite model data of radiation dose for (a) daily and (b) monthly average.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

The relationship of the monthly average radiation doses measured by the MRF-7 device and by satellite is presented in Fig. 6a. The comparable values correspond to the same incidence situations of solar rays with respect to the information collected in the measurement series (Table 2). In the following analyses, the averaged temporal characteristics of total radiation data from MRF-7 (Table 1, set A.1) and from satellite data (Table 1, set C.1) are presented with fluctuation curves as a function of time (Fig. 6b). The results obtained show a high correlation between the two datasets, R2 = 0.922 and R2 = 0.985, respectively. With regard to the scale of the phenomenon, the differences in the amount of radiation between the datasets are small and mainly concern the maximum values. To determine the trend of long-term changes taking into account the seasonal cycle, it is proposed to indicate the trend equation per unit cycle—in this case, the annual cycle. For the designated measurement period for satellite data, the trend takes on positive values, while for empirical data the trend is negative. At the stage of submission of the manuscript, the multiyear trend (30-yr period) for radiation extracted from the satellite collection is clearly increasing. The result is a marked increase in the amount of solar radiation reaching the sea surface.

Fig. 6.
Fig. 6.

Mean monthly solar radiation dose for satellite and MRF-7 instruments: (a) for different solar angle situation, and (b) time graph included cc (SM Hel).

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

During the analyzed period, the maximum monthly peak deviation was 28.7 MJ m−2 day−1 for empirical data and 25.9 MJ m−2 day−1 for satellite data. The minimum deviations were 13.82% and 15.61%, respectively. The obtained multiyear trends of the analyzed datasets show a dependent relationship—as solar radiation increases, the amount of cloud cover decreases. For the in situ data, the radiation trend was not determined due to the nature of the empirical measurements and the associated service period of the MRF-7 device. Therefore, the multiyear trend obtained from SolRad data seems more reliable. However, given the averaged nature of the information, the resulting correlation of data measured by different methods and instruments is still high.

Figure 7 shows the ratio of solar radiation determined using satellite data in relation to the radiation measured in situ by the MRF-7 device for different absorption bands in ranges according to set A.1. The results show that there are spectral ranges in which this ratio is close to zero. Deviations of the measured values from straight lines indicate a change in the spectral composition of total (Fig. 7a), diffuse (Fig. 7b), and direct (Fig. 7c) radiation. The deviations are not large, but vary for different absorption bands. Analysis of the individual spectral components in the spectral range of radiation showed that the second variant of 495.5 nm describes more than 98.6% of the spectral variation, while the last one of 937.6 nm describes only 0.01%. The graph (Fig. 7d) shows the variation of the amount of incoming radiation (averaged value of the entire measurement period) due to wavelength, using MRF-7 data. It shows a relative dependence on Planck’s radiation law with an assumed sea emission factor of 0.08. The changes in the shape of the spectrum, once scattering is taken into account, may be due to variations in the composition of the atmosphere, i.e., its components that absorb radiation in the so-called absorption bands (such as ozone or water vapor). This fact, combined with the deviation values of the measured curves for different spectral intervals, allow the satellite model to be used to approximate the integrated and satellite model of solar radiation over a fairly wide spectral range.

Fig. 7.
Fig. 7.

Mean profiles of the solar radiation estimated by the satellite are shown for the six radiation bands MRF-7: (a) total, (b) diffuse, (c) direct, and (d) average, with reference to Planck’s law for sea emissivity.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

The monthly averaged difference between empirically measured total, diffuse, and direct radiation, and determined direct and reflected radiation as a function of satellite estimates using the SolRad model for selected spectral bands, are shown in Fig. 8. The model values were fitted to the MRF-7 bandwidth using the integral nature of the SolRad model. Unfortunately, the integral data in addition to the total radiation results were not calibrated due to the lack of alternative sources for comparison—containing fractional-discrete data. In contrast, the total radiation results (Fig. 8) were calibrated with set A.2 measurements (Table 1). Most of the spectral values reflect the 495.5 nm band, and the remaining shifts in average spectral values, e.g., between 612.8 and 672.9 nm, are small and may be due to scattering of radiation in the atmosphere (Figs. 8a,b). However, the 612.8 and 672.9 nm bands are also very close to each other in the diffuse set. Nevertheless, the differences between total and diffuse radiation alone for comparable bands remain at different and variable levels. This illustrates the nonlinear nature of Planck’s radiation law for situations with and without cloud cover. This relationship was used in the cloud detection method (Paszkuta et al. 2022b).

Fig. 8.
Fig. 8.

Example MRF-7 monthly Hel station solar radiation dose compared to the amount estimated by the satellite in the integrated manner: (a) 612.8 and (b) 672.9 nm.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

An example of the difference between adjacent spectral bands (612.8–672.9 nm) for the averaged characteristics of the collected total, diffuse, and direct radiation data of MRF-7 (set A.1) is shown in Fig. 9. Direct radiation values are lower (negative) and at similar levels. For situations involving clouds, with respect to diffuse and total radiation, the trend value is increasing, with a higher value for diffuse than total radiation. The trend lines for the set A.1 spectral band are marked with the corresponding colors (Fig. 9). However, it should be noted that the question of the linearity of the trend is not fully resolved. The average trend values differ in the shape and irradiance level of the individual components of the spectral difference. Changes in spectral composition have been shown to be small. This can be important for estimating the amount, extent, and type of cloud cover based on empirically measured radiation. In an overt sense, the difference in shape between the corresponding curves (Fig. 9) describing cloudless and cloudy situations captures the sense of the satellite cloud detection method using models for a cloudless atmosphere (Paszkuta et al. 2019).

Fig. 9.
Fig. 9.

Example MRF-7 monthly Hel station solar radiation difference dose compared to the amount estimated by the satellite in the integrated manner 612.8–672.9 nm.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

The primary measure of the amount of light that can reach a satellite radiometer from a scattering sea surface or from the atmosphere is its spectrum. The function of matching it to the blackbody radiation spectrum is nonlinear. Radiation can be approximated using Planck’s law. The proposed satellite-based cloud detection method uses a very simple ratio technique in which the ratio of radiation from two adjacent channels is compared with its simulated value under clear-sky conditions for a predetermined temperature, water vapor, or ice profile (Paszkuta et al. 2019, 2022b). In this case, a linear and nonlinear relationship is assumed between the radiation in two adjacent bands for cloudless and overcast seas, respectively. Such simplification has its advantages, as it makes the result more general, but provokes inevitable uncertainties, which due to the nature of the work, e.g., global (the difference between two wave bands estimated with comparable uncertainty) may be acceptable. This is one of the reasons why there are noticeable differences in determining the cloud factor from VIS and IR due to the nature of the radiation itself. If the values of emissivity were swapped, the radiation in the channel would be—depending on the temperature—higher or lower at the same temperature values of the emitting body.

A priori, this relationship is assumed to be linear, which affects the difference between daytime and nighttime cloud-cover detection. This makes it possible to perform linear fitting to linearized radiation spectra. In addition to its utility advantages, this operation provides a mapping of deviations through blackbody curves. Therefore, the matching can be done in a more universal form.

To solve the nonlinearity of Planck’s law, it is proposed to use two different radiative transfer equations for the same channels. Nonlinear procedures require fitting the parameters of Planck’s formula, which are less sensitive than the parameters of the linear equation, and if they differ from the true values (Fig. 9), the fit will not converge. For example, the radiation temperature determined from satellite sources is related to Planck’s law; however, the SST (determined by M3D), used to determine the radiation temperature in cloudless conditions—not necessarily so. The temperature coefficient proposed in this study assumes that SST with identical values within channels 9 and 10 are multiplied by a linear factor. Unfortunately, in reality, the radiation temperatures in different spectral ranges differ, although the difference may be small. In general, the source of the temperature may not matter if you multiply it by the fourth element of the emissivity coefficient. The results, shown in Fig. 10, suggest that the changes occur as a direct result of the difference between the bands, and that the profile of the atmosphere (including the cloud base) changes throughout the day. In this context, the described linear formulas should be modified to a form better suited to regional conditions by a nonlinear combination of Planck function and spectral wavelength. The relationship between radiation in two adjacent spectral bands is assumed to be nonlinear. The proposed solutions characteristically treat cloud (diffuse radiation) and clear sea (total radiation) data. The differences between the channels are most apparent for situations corresponding to cloudy pixels.

Fig. 10.
Fig. 10.

Example of instant dependence of cc on normalized (relative to SZA) solar radiation on 31 Mar 2011 for SM Hel + SM point.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

c. Cloud-cover values and uncertainties

Estimates of cloud cover based on radiation pose considerable difficulties, not least because this parameter is determined indirectly. So, in addition to the uncertainties in radiation estimation, there are additional ones related to the method of cloud detection. The very parameter of cloud cover over the Baltic Sea is a phenomenon that varies rapidly in time and space, and the measurements refer to a ground point as well as values collected from a statistical surface that is 1 pixel—1 km × 1 km (in reality, approximately 5 km × 3 km). In addition, instant and point relationships between radiation and cloud cover are inherently not easy. The essence of the problem is how and where the measurements are taken, together with the methodology used to process the source data. Point ground measurements collect information about the radiation of the visible cone/sphere, while satellite measurements are collected within the analyzed pixel. In addition, the interpretation of atmospheric parameters (including cloud cover) can be completely different and depend, for example, on the angle of the sun, the amount of water vapor, and the type and nature of cloud cover.

In Fig. 10, an attempt is made to establish a radiation relationship with respect to the SZA function. The dependence of the instant (measured every 15 min) cloud-cover coefficient (cc) as a function of radiation where the instant values of total radiation, estimated in two ways, by satellite and empirically, are depicted together. At the same time, the values of diffuse radiation measured on SM Hel are presented. When analyzing the results obtained, it should be borne in mind that the location of measurement (a difference of approximately 50 m between land and sea) may cause additional measurement uncertainty. However, direct comparisons of instant values did not show anything other than insignificant systematic differences, which are standard for studies related to cloud-cover analysis.

Conducted analysis (Fig. 10) illustrate the difficulty of remote-satellite estimation of cloud cover based on radiation determined by in situ as well as satellite sources. Uncertainties are significant even for the averaged values presented. For the reference/measurement period with cloud-cover results, three instant radiation measurement series were juxtaposed: MRF-7 total and diffusion and satellite estimated from the SolRad model (Fig. 10). Noteworthy is the very high measurement uncertainty between satellite-estimated and empirically measured radiation values. With respect to cloud cover, the two series of total radiation have a downward trend, i.e., as cloud cover increases, the amount of radiation reaching Earth’s surface decreases. In addition, there is a trend correlation between the two series. A different trend was established for empirical diffuse radiation measured by the MRF-7 device. In this case, a nonlinear trend was determined. The results shown (Fig. 10) confirm the complex nature of radiation-based cloud-cover measurements.

To compare empirical and satellite methods of radiation data acquisition, it is necessary to come up with values accounting for uncertainty. On this basis, the reliability of the proposed solutions can be determined. When comparing instant point measurements with estimated values in time and space, it was additionally decided to present averaged characteristics: daily and monthly. This allowed a statistical representation of the nature of the phenomenon described. Table 3 presents the sum of measured and calculated radiation uncertainties with the MRF-7 device in relation to model estimation and cloud cover based on satellite data.

Table 3.

Estimating uncertainties in radiation and coefficient cc (Paszkuta et al. 2019).

Table 3.

d. Aerosol scattering

Aerosol scattering can have a significant effect on the intensity of solar radiation reaching the sea surface. However, satellite measurements of aerosol products are subject to significant uncertainties—it was decided to verify this information with ground-based observations. The aerosol optical depth (AOD) product is a measure of solar attenuation by aerosols. AOD results estimated empirically on the basis of MRF-7 and by satellite using the one-channel method proposed by Zawadzka and Markowicz (2014) were compared for the 615 and 630 nm band, respectively. Aerosols affect some narrow band wavelengths, so by comparing their ratios with clean atmosphere data one can attempt to characterize them. Ground-based aerosol measurements can be made directly using the automated Langley ratio analysis software provided with the MRF-7 (Cerqueira et al. 2014). Langley diagrams are used in the calibration of solar radiometers, primarily to measure the aerosol component of the atmosphere that scatters and absorbs incoming direct solar radiation. In essence, the calibration of a solar radiometer is a simple application of the Bouguer–Lambert–Beer law (Kiedron and Michalsky 2016).

The satellite sea surface reflectance was determined for a cloudless and aerosol free atmosphere to eliminate the influence of other atmospheric components on the reflectance. The SolRad radiative model was used for this purpose, which was possible due to the integral structure of the model. The first step in the analysis is the rejection of cloudy pixels. We use the previously proposed cloud mask for classification. Only pixels marked as cloud-free are taken into account. The next step is to minimize the difference function between the compared radiances, which allows the modeling results to be used.

Figure 11a shows the AOD results for SEVIRI and MRF-7 data obtained at SM Hel between 2011 and 2018, using the one-channel method. The statistical distribution shows a mean below 0.01 and standard deviations of 0.035 and 0.031, with unnatural horizontal clusters of points for the lowest values. The results show little change in the systematic uncertainty, suggesting that the uncertainty in AOD is not related to the particle size. A possible explanation could be the technical specifications of the data sources. Figure 11b shows the probability distribution of the difference density function AOD. The statistical distribution of the difference in AOD has a mean of less than 0.0015 and a standard deviation of 0.04. For the selected one-channel method, about 2% of the observations are greater than the absolute value of 0.1. In the analyzed region, there is also a high probability of convective cloud formation/development on aerosols with sizes smaller than the satellite pixel size. This leads to AOD uncertainties related to the phenomenon of clouds transmitting or reflecting solar radiation (also reflected from Earth/sea surface). It can also be related to the level of atmospheric pollution and the nature of the pollutants (some reflect, others absorb radiation). Some compounds (e.g., sulfates) and spores cause clouds to reflect radiation, while others (black carbon) absorb it. The method described can be recommended for rapid use at other latitudes for different satellite detectors.

Fig. 11.
Fig. 11.

AOD for one-channel method and cloudless conditions in SM Hel: (a) AOD between SEVIRI (635 nm) and MRF-7 (615 nm) and (b) AOD difference.

Citation: Journal of Atmospheric and Oceanic Technology 41, 2; 10.1175/JTECH-D-23-0061.1

4. Discussion

The article compares empirical radiation results obtained from the MRF-7 device for a measurement point located above the sea surface, with results estimated according to the SolRad solar radiation model using satellite data. The juxtaposition of two different methodologies for measuring solar radiation (ground based versus satellite based) with accompanying characteristics approximating the state of the atmosphere, i.e., diffuse radiation and the coefficient of cc is noteworthy. The results collected methodically for the Polish economic zone on the Hel Peninsula are the most favorable for conducting “land-based marine measurements.” As reported in the literature (Calbó et al. 2005; Belcher and DeGaetano 2007; Zhang et al. 2011), the comparison of satellite and empirical radiation measurements, in general, is associated with high uncertainties. This uncertainty is compounded by the complex nature of comparable measurements with respect to the time and location of the measurement. Therefore, it was decided in this study to introduce averaging statistics: instant 15 min (matched to the satellite series), for days, and for months. By averaging the results according to the adopted rules, it was possible to reduce uncertainties and draw conclusions about the course of radiation dependence trends and other derived characteristics. Consequently, the comparison of total radiation results provides good correlations with a low uncertainty factor. The determination of transition trends confirmed the consistency of results obtained in the literature (Gryning et al. 2001; Furlan and de Oliveira 2008; Zhou et al. 2020). The analyses carried out have established for point source and satellite source a decrease in the amount of cc with an increase in total radiation. At the same time, it was found that with the increase of cc the proportion of diffuse radiation increases, which indicates that this type of radiation can be characterized by the state of the atmosphere. In addition, the article analyzed the radiation dependence of the empirically recorded spectral bands. On this basis, the difference in adjacent bands was indicated as a result of the presence/description of cloud cover (total and diffuse radiation). The indicated difference is a confirmation of the nonlinear nature of Planck’s radiation law. At the same time, it confirms the validity of the selected detection method, at least for the visible range.

Changes in the amount of total radiation and, above all, diffuse radiation can be a consequence of changes in cloud cover. This is due to a change in the balance between the trends of empirical data caused by clouds, as well as the phenomenon of clouds transmitting or reflecting solar radiation (also reflected from the surface of Earth/sea).

The main objective of the study was to obtain a set of empirical radiation data that can be used to validate models using satellite data and derived parameters (i.e., cloud cover). The resulting dataset can be used for further comparisons, as well as for forecasting cloud-cover changes. In addition, the detection method can be used to complement satellite solutions. Significant uncertainties in cloud-cover detection may be of some concern. However, the cloud cover was estimated based on radiation, which was also estimated with uncertainty. To reduce the uncertainty of the results as described, it was decided to analyze the averaged radiation values. Relevant for further such studies are the results obtained from selected spectral bands. They can be used to develop satellite-based cloud detection methods (Paszkuta et al. 2019, 2022a,b; Paszkuta 2022).

The analyses carried out clearly indicate the importance of choosing the site for radiation measurements. In making this choice, it is important to take into account the specific differences in measurements made over the sea (e.g., albedo) and over land.

5. Conclusions

Improving the quality of radiation and atmospheric state analysis obtained using satellite data over the sea is of great importance in various scientific fields, from oceanography to climatology. To effectively complement cloud detection at the local scale, more attention should be paid to in situ radiation measurements conducted over the sea. One way to achieve this is to benchmark the parameters obtained at sea level with results from the use of satellite data. To obtain a homogeneous map of the sea for radiation, elimination of measurement uncertainties must be carried out. Unfortunately, conventional methods of such elimination are few, and this can lead to the loss of important features of the satellite image, thus reducing the accuracy of radiation measurements.

The comparative analyses presented in this article are based on in situ measurements of different types of radiation, broken down into spectral bands and satellite data with derived characteristics. This allows optimization of derived parameters derived from satellite radiation, such as cloud cover. The article uses empirical radiation results to determine the accuracy of satellite estimation. The corresponding in situ spot measurement range was assigned to the measurement for a cell (pixel) from the satellite image. Radiation results are studied as a multiconnection domain to describe the interaction between derivative sets determined from satellite radiation. This method involves comparing a set of satellite image data in the same time unit for averaged values, effectively reducing systematic biases. The measurement uncertainties of the instruments are small compared to the systematic biases and have been taken into account in the results. Experimental results show that the proposed method is effective and adequately parameterizes the detection of the features of satellite images. Further planned research involves using the source data and the results of the analysis conducted to classify radiation over the Baltic Sea to assess climate change.

Data availability statement.

The data from MRF-7 are available upon request; the calibration file is available in the online supplemental material. All used satellite based data can be accessed from http://www.satbaltyk.pl/?lng=eng#/and https://ecudo.pl/english.

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  • Woźniak, B., and Coauthors, 2011a: SatBałtyk—A Baltic environmental satellite remote sensing system—An ongoing project in Poland. Part 1: Assumptions, scope and operating range. Oceanologia, 53, 897924, https://doi.org/10.5697/oc.53-4.897.

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  • Woźniak, B., and Coauthors, 2011b: SatBałtyk—A Baltic environmental satellite remote sensing system—An ongoing project in Poland. Part 2: Practical applicability and preliminary results. Oceanologia, 53, 925958, https://doi.org/10.5697/oc.53-4.925.

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  • Zapadka, T., A. Krężel, M. Paszkuta, and M. Darecki, 2015: Daily radiation budget of the Baltic Sea surface from satellite data. Pol. Marit. Res., 22, 5056, https://doi.org/10.1515/pomr-2015-0056.

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  • Zawadzka, O., and K. Markowicz, 2014: Retrieval of aerosol optical depth from optimal interpolation approach applied to SEVIRI data. Remote Sens., 6, 71827211, https://doi.org/10.3390/rs6087182.

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  • Zhang, R.-H., D. Chen, and G. Wang, 2011: Using satellite ocean color data to derive an empirical model for the penetration depth of solar radiation (Hp) in the tropical Pacific Ocean. J. Atmos. Oceanic Technol., 28, 944965, https://doi.org/10.1175/2011JTECHO797.1.

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  • Zhou, Y., X. Wu, W. Ju, L. Zhang, Z. Chen, W. He, Y. Liu, and Y. Shen, 2020: Modeling the effects of global and diffuse radiation on terrestrial gross primary productivity in China based on a two-leaf light use efficiency model. Remote Sens., 12, 3355, https://doi.org/10.3390/rs12203355.

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Supplementary Materials

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    • Search Google Scholar
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  • Young, D. F., P. Minnis, D. Baumgardner, and H. Gerber, 1998: Comparison of in situ and satellite-derived cloud properties during SUCCESS. Geophys. Res. Lett., 25, 11251128, https://doi.org/10.1029/98GL00116.

    • Search Google Scholar
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  • Zapadka, T., A. Krężel, M. Paszkuta, and M. Darecki, 2015: Daily radiation budget of the Baltic Sea surface from satellite data. Pol. Marit. Res., 22, 5056, https://doi.org/10.1515/pomr-2015-0056.

    • Search Google Scholar
    • Export Citation
  • Zawadzka, O., and K. Markowicz, 2014: Retrieval of aerosol optical depth from optimal interpolation approach applied to SEVIRI data. Remote Sens., 6, 71827211, https://doi.org/10.3390/rs6087182.

    • Search Google Scholar
    • Export Citation
  • Zhang, R.-H., D. Chen, and G. Wang, 2011: Using satellite ocean color data to derive an empirical model for the penetration depth of solar radiation (Hp) in the tropical Pacific Ocean. J. Atmos. Oceanic Technol., 28, 944965, https://doi.org/10.1175/2011JTECHO797.1.

    • Search Google Scholar
    • Export Citation
  • Zhou, Y., X. Wu, W. Ju, L. Zhang, Z. Chen, W. He, Y. Liu, and Y. Shen, 2020: Modeling the effects of global and diffuse radiation on terrestrial gross primary productivity in China based on a two-leaf light use efficiency model. Remote Sens., 12, 3355, https://doi.org/10.3390/rs12203355.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Marine Station Hel (SM Hel): (a) standard displacement of the measurement point described on the coastline (SM Hel) into the sea (SM point) (www.satbaltyk.pl), (b) the station building on which the sensor was installed with the coastline visible, (c) the MRF-7 device with the shadowband visible, and (d) in the background, the meteo station with the Kipp and Zonen CMP3 pyranometer.

  • Fig. 2.

    Example of cloud changes over the Baltic Sea and SM Hel (red circle) by satellite system www.satbaltyk.pl: (a) 0700 UTC, (b) 1400 UTC, (c) 1730 UTC, and (d) corresponding radiation at Hel station according to MRF-7, SolRad model, and cc on 14 Apr 2011.

  • Fig. 3.

    (a) Example of rav radiation intercalibration on the day 7 Apr 2010 without clouds (Krężel et al. 2008) (YESDAS is commercial MRF-7 data analysis software) and (b) flow diagram of the preparation of the experimental procedure for radiation and cc data analysis.

  • Fig. 4.

    The relationship between the total radiation from the MRF-7 device (SM Hel) and from satellite (SM point) for 14 Apr 2011: (a) instant values with a time interval of 15 min and (b) averaged values with a 15-min intervals.

  • Fig. 5.

    Relation between in situ data and satellite model data of radiation dose for (a) daily and (b) monthly average.

  • Fig. 6.

    Mean monthly solar radiation dose for satellite and MRF-7 instruments: (a) for different solar angle situation, and (b) time graph included cc (SM Hel).

  • Fig. 7.

    Mean profiles of the solar radiation estimated by the satellite are shown for the six radiation bands MRF-7: (a) total, (b) diffuse, (c) direct, and (d) average, with reference to Planck’s law for sea emissivity.

  • Fig. 8.

    Example MRF-7 monthly Hel station solar radiation dose compared to the amount estimated by the satellite in the integrated manner: (a) 612.8 and (b) 672.9 nm.

  • Fig. 9.

    Example MRF-7 monthly Hel station solar radiation difference dose compared to the amount estimated by the satellite in the integrated manner 612.8–672.9 nm.

  • Fig. 10.

    Example of instant dependence of cc on normalized (relative to SZA) solar radiation on 31 Mar 2011 for SM Hel + SM point.

  • Fig. 11.

    AOD for one-channel method and cloudless conditions in SM Hel: (a) AOD between SEVIRI (635 nm) and MRF-7 (615 nm) and (b) AOD difference.

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