Citation: Utrillas, M.P.; Marín, M.J.;

Estellés, V.; Marcos, C.; Freile, M.D.;

Gómez-Amo, J.L.; Martínez-Lozano,

J.A. Comparison of Cloud Amounts

Retrieved with Three Automatic

Methods and Visual Observations.

Atmosphere 2022, 13, 937. https://

doi.org/10.3390/atmos13060937

Academic Editor: Bryan C. Weare

Received: 3 May 2022

Accepted: 26 May 2022

Published: 9 June 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional affil-

iations.

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

atmosphere

Article

Comparison of Cloud Amounts Retrieved with ThreeAutomatic Methods and Visual ObservationsMaría Pilar Utrillas 1 , María José Marín 2,* , Víctor Estellés 1,3 , Carlos Marcos 1, María Dolores Freile 1,José Luis Gómez-Amo 1 and José Antonio Martínez-Lozano 1

1 Solar Radiation Group, Departament de Física de la Terra i Termodinàmica, Universitat de València,46100 Burjassot, Spain; maria.p.utrillas@uv.es (M.P.U.); victor.estelles@uv.es (V.E.); carlos.marcos@uv.es (C.M.);m.dolores.freile@uv.es (M.D.F.); jose.l.gomez-amo@uv.es (J.L.G.-A.); jmartine@uv.es (J.A.M.-L.)

2 Solar Radiation Group, Departament de Matemàtiques per a l’Economia i l’Empresa, Universitat de València,46022 Valencia, Spain

3 Consiglio Nazionale delle Ricerche, Istituto Scienze dell’Atmosfera e del Clima, 00133 Roma, Italy* Correspondence: marinfer@uv.es

Abstract: Four methods have been used for the estimation of the total cloud amount and cloudamount for low clouds: visual observations, the Long method applied on pyranometer measure-ments, the Automatic Partial Cloud Amount Detection Algorithm (APCADA) method applied onpyrgeometers measurements, and ceilometer measurements of the cloud base height. Records frommeteorological observers indicate that clear days (0–1 octa) represent the most frequent cloud amountfor low clouds. In contrast, the total cloud amount is more aleatory. Results obtained from the Longmethod show maximum frequency in the extreme cloud amount values. The APCADA method alsoindicates the predominance of cloudless skies. The ceilometer method shows a predominance of com-pletely clear skies, but the completely cloudy (8 octas) is the second most frequent case. Automaticmethods report more cloudless and overcast skies than the observer. Automatic methods agree withthe visual method or differ in ±1 octa for 60–76% cases for low cloud amount and for 56–63% casesfor total cloud amount. In general, low cloud amount agrees more with observer measurements thantotal cloud amount and the automatic methods underestimated total cloud amount observer valuespossibly due to the difficulty in monitoring high clouds.

Keywords: cloud cover; visual cloud observations; automatic cloud observations

1. Introduction

Clouds are the most critical factor in modulating the terrestrial radiative balance anddetermining climate sensitivity [1]. They reflect solar radiation to space, absorb and re-emitthe longwave radiation emitted by the Earth surface and the low troposphere [2–4], andthey must be considered in numerical models.

Clouds present the highest uncertainties for estimating and interpretating of thechanging Earth radiation balance [5]. These uncertainties are caused by the scarcenessof surface based global measurements, inhomogeneity in the existent datasets, and lowprecision in the measurement of small changes in cloudiness that scale to a significantimpact on the terrestrial climate [6,7].

A particular case of interest is the estimation of cloud amount. It represents the fractionof the sky covered by clouds of a particular type or combination. Since clouds are the largestattenuating factors of solar radiation, cloud cover is a useful predictor of solar resource [8]and photovoltaic solar energy [9]. Experimental determination of the cloud amount can beperformed using remote sensing techniques based on satellite platforms [10–12], or surface-based [13]. Ground-based remote sensing methods could be hemispheric or columntechniques. Shortwave or longwave radiation from the sky is used to retrieve the cloudamount, e.g., using pyranometers or pyrgeometers in the former and a vertical portion

Atmosphere 2022, 13, 937. https://doi.org/10.3390/atmos13060937 https://www.mdpi.com/journal/atmosphere

Atmosphere 2022, 13, 937 2 of 12

of the sky to retrieve the nadir projected cloud fraction and later the cloud amount usingceilometers in the latter. Moreover, some instruments can continuously obtain the cloudamount during the day and at night, such as pyrgeometers, while others can only be usedduring the day, such as the total sky imager. Tapakis and Charalambides [14] presenteda quite complete review of the equipment and methodologies for cloud detection andclassification. Unfortunately, the temporal evolution of cloud amount on decadal andlonger scales is unclear, mainly because of uncertainties in both satellite [15,16] and surface-based observational records [17].

For quite a long time now, the determination of the cloud amount has been simplyperformed by visual observation (i.e., subjective) from surface level [18–20]. Three timesa day, meteorological observers routinely record total cloud amount, cloud amount atlow levels and type of clouds [18]. This method is inherently subjective, and it can beconditioned by factors, such as the extension of the sky view and the uneven illumination ofthe cloud deck at night from the surface [21,22]. Further, the cloud amount errors increasefor low elevation angles because the human observer cannot identify the spaces betweenclouds but the cloud wall itself, overestimating the apparent cloud amount [23].

Despite its obvious utility, the meteorological observations are associated to a lackof homogeneity in the methodology and observation times. The method is rudimentary(limited to octas of the sky dome) and performed by a subjective human being, thereforemaking the method less reliable. Nowadays, the reduction of costs has pushed for theimplementation of more automatic methods with a reduced human role [13]. The instru-ments and algorithms devised for cloud identification and discrimination of clear/cloudboundaries improved largely, reducing the cloud amount and distribution determinationuncertainties [24]. The development of automatic detection systems is now priority asvisual observations must be substituted by automatic measurements [3,25,26]. For instance,differences in cloud characteristics introduced by the automation at the Royal NetherlandsMeteorological Institute (KNMI) are described by Wauben [27] and Wauben et al. [28]. Thistransition resulted in discontinuities in time series of cloud observations that cannot berectified posteriori.

This study’s first automatic cloud detection method is based on shortwave downwardradiation measurements performed by pyranometers. Long et al. [29] estimated the cloudfraction using high resolution measurements of global and diffuse solar radiation, based onthe methodology previously proposed by Long and Ackerman [30] for the separation ofclear and cloudy skies. Besides, Durr and Philipona [31] developed the Automatic PartialCloud Amount Detection Algorithm (APCADA) for estimating the cloud amount directlyfrom measurements of longwave downward radiation measured by pyrgeometers.

One of the most prominent instruments available for the determination of cloud amountis the ceilometer. The ceilometer is a specific type of LiDAR (light detection and ranging),based on the principle of elastic backscattering: it emits light pulses vertically into the atmo-sphere and measure the light backscattered by clouds at different altitudes [13,32,33]. Theseobservations can be used to calculate the cloud amount by averaging the binary cloud stateover a particular period of time.

In our study, we compared the cloud amounts determined with the three automaticmethods with the values recorded from a meteorological observer for the five years2013–2017. For example, a few studies have already compared pairs of the three meth-ods [13]. However, to our best knowledge, no previous study has dealt with a com-parison of the three automatic methods proposed in this article. We have not includedsatellite-based data, although the reader can find such studies, e.g., by Ackerman et al. [34];Werkmeister et al. [35]; An and Wang [36]; Calbó et al. [37]. The ultimate objective has beento find quantitative relationships between the automatic and visual cloud amount estima-tions. These relationships would help to understand how to continue the meteorologicalseries of visual observations, already discontinued in many observatories worldwide.

Atmosphere 2022, 13, 937 3 of 12

This article has been organized as follows: Section 2 describes the experimental setupand the methodology used. The results are presented in Section 3. Main conclusions havebeen summarized in Section 4.

2. Materials and Methods

This study takes as a reference the cloud amount values registered by a meteorologicalobserver at the airport of Manises (Valencia), located 5 km SW from the Burjassot campus,at the synoptic hours 7, 13, and 18. These cloud amount observations were recorded bythe State Agency of Meteorology (AEMET). The records are coded as an integer numberbetween 0 and 8. The value of 0 octa corresponds to clear skies (0% cloud fraction) and8 octa to overcast skies (100% cloud fraction). According to the guidelines of the WMO [21],a single cloud in a clear sky should be registered as 1 octa, while a tiny gap in an overcastsky is registered as 7 octa. This means that 1 and 7 octa represent a larger range of fractionalcloudiness conditions (18.75%) than from 2 to 6 octa (12.5%). Table 1 shows the conversionof the cloud fraction expressed in percentage to octa [13].

Table 1. Conversion table from cloud amount expressed in percentage to cloud amount expressedin octas [13].

% Octas

0 00 < % < 18.75 1

18.75 ≤ % < 31.25 231.25 ≤ % < 43.75 343.75 ≤ % < 56.25 456.25 ≤ % < 68.75 568.75 ≤ % < 81.25 681.25 ≤ % < 100 7

100 8

The automatic measurements were recorded on the roof of the Faculty of Physics,located on the Burjassot campus of the University of Valencia (39◦30′ N; 0◦25′ W; 60 m a.s.l).The automatic instruments used to determine the cloud amount were: a pyranometer, apyrgeometer, and a ceilometer. All methods used in this work calculate the cloud amountin percentages, except for APCADA that provides it in octa. The transformation of thecloud amount from percentages to octas was done by assuming the conversion from Table 1.Although the sites where the meteorological observer and the automatic methods operateare different, we assume that the sky condition is the same, given that the distance is shorterthan 5 km, and the vertical difference is only 18 m (i.e., the area in which both sites arelocated, is flat).

The specific instrumentation and the method used for the estimation of the cloudamount are described below:

2.1. Pyranometer (Long et al. 2016 Method)

The downwelling solar radiation measurements were registered with two Kipp-ZonenCMP21 pyranometers mounted on a Kipp-Zonen Solys-2 sun-tracker, equipped with GPSantenna and active solar tracker. One of them measures the global hemispherical downwardradiation, and the other uses a shadow ball to measure the diffuse component. The CMP21is a secondary standard which measures shortwave solar radiation in the spectral rangefrom 285 to 2800 nm. The temperature of the pyranometer is monitored with a Pt-100thermal sensor. The temperature dependence of the sensitivity is 0.5%. Its cosine responseis 1% for solar zenith angles up to 80◦ according to the manufacturer’s specifications [38].Both pyranometers are also installed on two CVF1 ventilation units to minimize occasionaldew built up on the domes and reduce thermal offset. Pyranometer measurements areperformed every 5 s and the average is registered every 1 min.

Atmosphere 2022, 13, 937 4 of 12

The cloud amount is determined by the method of Long et al. [29]. The first step inthis method is to identify the clear-sky periods for a 160◦ field of view, using only 1-minmeasurements of the downwelling total and diffuse shortwave irradiance [30]. Then, thedataset is screened for overcast and clear-sky cases, and afterwards the cloud amount isestimated for the remaining data. The fractional sky cover is estimated using the normalizeddiffuse cloud effect, which includes both the measured and clear fit diffuse irradiances, andthe clear fit total shortwave irradiance. These values are all tailored to the characteristics ofeach instrument system. The cloud amount obtained by this methodology shows a highdegree of repeatability for well-maintained radiometers [29].

2.2. Pyrgeometer (APCADA Method)

Downwelling longwave radiation measurements were taken with a Kipp & ZonenCGR4 pyrgeometer, mounted on the same Kipp & Zonen Solys-2 sun-tracker and equippedwith a second shadow ball. The CGR4 pyrgeometer uses a dome that provides a 180◦ fieldof view with negligible directional response error. This instrument was calibrated in 2011at the WMO premises in Davos, and subsequently calibrated by intercomparison in twomeasurement campaigns in 2013 and 2015 in Lampedusa, Italy. On all these occasions, thepyrgeometer showed absolute stability, with differences between the calibration coefficientsless than 1%.

Dürr and Philipona [31] developed the Automatic Partial Cloud Amount DetectionAlgorithm (APCADA) for estimating the cloud amount without high clouds directly fromlongwave downward radiation (LDR), air temperature, and humidity.

The determination of partial cloud amount according to APCADA is based on twoparameters: the cloud-free index (CFI) and the variability of longwave downward radiation(STD LDR). The CFI is calculated as:

CFI =LDR∈AC σT4

L(1)

where σ is the Stefan–Boltzmann constant, TL the air temperature in Kelvin, εAC theemissivity of a cloud-free sky.

Usually, clear skies are represented by CFI ≤ 1, whereas CFI > 1 result in cloudy skies,so this parameter is used to distinguish between clear or cloudy conditions. Furthermore,variability of the longwave downward radiation during the previous 60 min is calculated,as it allows the distinction between cloud fraction types: broken clouds strongly influencethe variability signal, while overcast and cloudless skies lead to a low variability. This way,cloud amount is obtained combining the information given by the CFI and the longwaveradiation variability, according to the intervals defined by Durr and Philipona [31]. As adrawback, APCADA is able to detect only those clouds that have a measurable effect onlongwave downward radiation. Hence, the APCADA algorithm has a limited sensitivityfor high (i.e., cold) clouds.

2.3. Ceilometer

The ceilometer used in this work is a CL51 system from Vaisala. The CL51 uses a diodelaser that emits short light pulses (~110 ns) to the atmosphere at 910 nm with a repetitionrate of 6.5 kHz. The light backscattered by the atmosphere constituents between 0 and 15 kmaltitude is detected by the system with a vertical resolution of 10 m. The CL51 deployed atthe Burjassot station measures the backscattered light with an off-nadir angle of 12◦ andit is configured to retrieve vertical profiles of the atmosphere every minute. The system’ssoftware is equipped with the Sky Condition Algorithm provided by Vaisala [39]. Thisalgorithm uses measurements from the last 30 min to estimate the cloud cover fraction(CCF) at different altitudes. The altitudes corresponding to the CCF estimations are not

Atmosphere 2022, 13, 937 5 of 12

fixed and depend on the spatial distribution of clouds in the atmosphere. Total cloud cover,C, of an altitude interval limited by h0 and hf, is estimated by Equation (2):

C = max(CCF h) + (1−max(CCFh))hf

∏h0

max(CCF h) (2)

where max (CCFh) is the maximum cloud cover value at height h. Equation (2) is used forfour different height intervals (0–3 km), (0–7 km), (0–10 km), and (0–15 km). Layers 0–3and 0–15 km can represent low clouds and total cloud amount respectively.

The CL51 ceilometer method provides cloud amount data every minute, 24 h a day.Nevertheless, for the purposes of this study, only cloud amount obtained during day timehas been considered.

Table 2 summarizes the characteristics of the previous methods as well as the numberof registered data in the study and the number of simultaneous data with the automaticmethods and the meteorological observer.

Table 2. Summary of the main characteristics of the methods used in this study to derive thecloud amount.

Sensor Detection CloudAmount Method Total Data Common

Data

Human Visual Low andtotal

Observer(octa) 6426 6426

Pyranometer Automatic Total Long (%) 4936

Pyrgeometer Automatic Low-medium

APCADA(octa) 4955

Ceilometer Automatic

Low,medium,high and

total

This article(%) 3544

3. Results3.1. Analysis of the Cloud Amount Obtained by the Different Methods

The cloud amount has been obtained by a meteorological observer and with the threeautomatic methods. The first one was estimated with the pyranometers by applying theLong method. The second automatic method analysed was the APCADA method appliedon the pyrgeometer data. In the case of the ceilometer, the maximum altitude sensed is15 km. We have calculated the cloud amount at four different altitude intervals: 0–3 km,0–7 km, 0–10 km, and 0–15 km (the last one is expected to be more representative of thetotal cloud amount).

Figure 1 represents the frequency of the low cloud amount values estimated by ameteorological observer considered all together for the three synoptic hours, low-mediumcloud amount by APCADA, low cloud amount for ceilometer (0–3 km), and low-mediumcloud amount for ceilometer (0–7 km).

Most of the observations (approximately 67%) correspond to clear skies (i.e., between0 and 1 octa) for low clouds by observer. The APCADA method also found a decreasingfrequency with the number of octas: it is maximum, about 23–28%, for 0 and 1 octa,respectively, and decreases to around 5% for the intermediate cloud amounts. Very cloudyskies are also more frequent than intermediate cases, with a frequency of 9% and 7% for7 and 8 octas respectively. The maximum frequency with the ceilometer appears at 0 octas(completely clear) for both the intervals, with a frequency higher than 69% for clouds inthe lowest interval (0–3 km). The second most frequent cloud amount is 8 octas (overcast)with a frequency of 16% (0–7 km). Any of the intermediate amount values are less frequent.It is apparent that the cloud ceilometer overestimates 0 and 8 octa occurrences and itunderestimates intermediate cloud coverages in agreement with other authors results [8].

Atmosphere 2022, 13, 937 6 of 12

Figure 1. Frequency of cloud amount at low-medium levels as registered by meteorological observers,APCADA and ceilometer methods.

Figure 2 represents the frequency of total cloud amount values estimated by a meteo-rological observer considered altogether for the three synoptic hours, following the Longmethod and by the ceilometer (0–10 km and 0–15 km).

Figure 2. Frequency of total cloud amount as registered by meteorological observers and derivedfrom the Long and ceilometer methods.

In contrast with low cloud observations, the frequency of the meteorologist total cloudamount is more homogeneously spread, with no clear dominance for any given numberof octas. Regarding the Long results, the frequency strongly decreases with the number ofoctas, with the absolute maximum found for 1 octa (~26%) and minimum for 7 octas (~4%).However, the method estimates that the 8 octas case (overcast) is also well represented (~22%).

Regarding ceilometer results, Figures 1 and 2 also indicate that when the consideredlayer top gradually increases (3 km to 15 km), the frequencies for non-clear skies casesincrease at the expense of the frequency for the completely clear case. For example, formeasurements up to 3 km, about a 69% of the cases correspond to 0 octas and 10% approxi-mately correspond to 8 octas. If the 0–15 km interval is considered, then 36% of the casescorrespond to 0 octas and 26% to 8 octas. This means that considering the 0–3 km layer,the completely clear cases increase by 33% in terms of frequency, and the overcast skiesdecrease in frequency by 16%, in comparison to the 0–15 km layer. Intermediate layers offerintermediate results: extending the upper limit implies that more clouds will be includedin the estimation of the cloud amount. These results agree with Wagner and Kleiss. Theyused a ceilometer (CEIL) (with cloud-base height up to 3660 m) and a micropulse lidar(MPL) (with cloud-base height up to 20 km); see Figure 2 in [40].

Atmosphere 2022, 13, 937 7 of 12

To sum up, automatic methods report more cloudless (0–1 octa) and overcast skies(8 octas) than the observer and it agrees with the results of Boers et al. [13]. Only theAPCADA method reports less cloudless (51%) than the observer for low clouds (67%).

We have calculated the annual statistics for cloud amount expressed in octas. The meancloud amount for low clouds is 1.6 (observer and ceilometer 0–3 km) and for low-mediumclouds is 2.7 (APCADA and ceilometer 0–7 km). Layer 0–3 km considers low clouds only.However, layer 0–7 km considers low and medium cloudiness as the APCADA methoddoes (see Table 2). For this reason, the mean value by observer agrees with ceilometer0–3 km and APCADA method agrees with ceilometer (0–7 km). The medians are 1 (observerand APCADA) and 0 (ceilometer 0–3 km and 0–7 km) with standard deviations 2, 2.7,2.8, and 3.3 respectively, which represents a similarly high deviation by APCADA andceilometer methods and higher than the observer method.

The mean cloud amount for total cloudiness is 3.5, 2.9, 3.1, and 3.4 for the observer,Long method, ceilometer (0–10 km), and ceilometer (0–15 km), respectively. The mediansare 3 octas for observer, 1.4 octas for Long method and ceilometer 0–10 km, and 2 octas forceilometer 0–15 km, with standard deviations between 2.5 and 3.4, which is a very highdeviation due to the great variety of values that exist. Again, automatic methods havehigher standard deviations than the observer method.

3.2. Comparison between Automatic and Visual Methods

In order to evaluate the performance of the automatic methods as a mean for thecontinuation of the meteorological series of visual observations, we first need to comparethe series for the period analysed in this study.

Figure 3 represents the frequency distribution of absolute differences between thesimultaneous low or low-medium cloud amount obtained with the observer and the twoautomatic methods (APCADA and ceilometer 0–3 and 0–7 km). The frequency is expressedin percentage and the cloud amount differences are expressed in octas. The two automaticmethods agree or differ in one octa with the human observer among 60% (ceilometer,0–7 km), 69% (APCADA), and 76% (ceilometer, 0–3 km). Besides, zero octas is the mostfrequent value. If an octa is considered the measurement uncertainty, the results canbe appropriate to reproduce the visual observations with automatic methods. In thetwo methods we found an overestimation of the low cloud amount, i.e., there are morenegative differences except for the ceilometer in the 0–3 km layer.

Figure 3. Frequency distribution (%) of absolute difference, in octas, of the cloud amount at low-medium levels derived by the visual and automatic methods.

Suppose we calculate the coincidences defined by cases with a cloud amount differencewithin ±1 octa, for different ceilometer layers whose upper limit is varied between 2 and15 km at steps of 1 km. In that case, the layer that gives the maximum number of coinci-dences is 0–3 km [41]. To some extent, we could consider that this would be the effectivealtitude of the low clouds registered by the observer, instead of the expected 0–2 km.

Atmosphere 2022, 13, 937 8 of 12

The frequency distributions of total cloud amount differences are presented in Figure 4.The maximum number of coincidences or discrepancies in one octa (63%) happens for theceilometer, layer 0–15 km (56% for the Long method). The percentages are worse than forlow clouds (Figure 3), probably due to the difficulty in detecting high clouds. In the twocases, the automatic methods derived cloud amount values are underestimated due to thedifferent measurement principle adopted by these instruments. It is not possible to find acomplete agreement between observations and any automatic method [13]. Regarding theAPCADA method, results agree with Schade et al. [42], who found a 60% ± 1 octa for allclouds and 73% ± 1 octa at no cirrus conditions.

Figure 4. Frequency distribution (%) of absolute difference, in octas, of the total cloud amount derivedby the visual and automatic methods.

3.3. Relationships between Automatic and Visual Methods

Once we have analysed the absolute differences between the different automatic andvisual methods, it is interesting to derive numerical relationships that allow us to estimatecorrected values of cloud amount that are more consistent with the reference series registeredby the meteorological observer. For simplicity, linear regressions have been performed only,as shown in Table 3 (low-medium cloud amount) and Table 4 (total cloud amount).

Instead of using instantaneous data as in the previous sections, for the establishmentof the relationships, we first calculated the average of the cloud amount values given byeach of the automatic methods, for a given cloud amount registered by the observer. Thisway, for each automatic method, we get nine data points corresponding to 0, 1, . . . , 8 octasregistered by the observer. The relationships have been obtained separately for seasons.

The ceilometer (0–3 km) correlates better than the other two methods (APCADA andceilometer (0–7 km) with a slope near 1, as shown in Table 3, because until 3 km highonly low cloud amount is detected. It is possible that the observer method does not detectmedium clouds as the APCADA and ceilometer (0–7 km) do.

Atmosphere 2022, 13, 937 9 of 12

Table 3. Relationships between automatic and visual methods: linear regressions (low-mediumcloud amount).

APCADACOBSERVER =a CMODEL + b

a b r2

Spring 1.17 ± 0.09 −1.7 ± 0.5 0.96

Summer 1.18 ± 0.08 −1.2 ± 0.4 0.97

Autumn 1.18 ± 0.11 −1.8± 0.6 0.95

Winter 1.22 ± 0.09 −2.2 ± 0.5 0.96

Ceilometer 0–3 kmCOBSERVER =a CMODEL + b

a b r2

Spring 0.91 ± 0.05 0.5 ± 0.2 0.98

Summer 0.88 ± 0.06 0.7 ± 0.3 0.97

Autumn 0.95 ± 0.11 0.05± 0.30 0.97

Winter 0.93 ± 0.09 0.03 ± 0.30 0.96

Ceilometer 0–7 kmCOBSERVER =a CMODEL + b

a b r2

Spring 1.09 ± 0.09 −1.4 ± 0.5 0.97

Summer 0.99 ± 0.08 −0.7 ± 0.4 0.95

Autumn 1.18 ± 0.11 −2.4± 0.6 0.96

Winter 1.39 ± 0.09 −3.9 ± 0.5 0.96

Table 4. Relationships between automatic and visual methods: linear regressions (totalcloud amount).

Long COBSERVER =a CMODEL + b

a b r2

Spring 1.01 ± 0.05 0.2 ± 0.2 0.95

Summer 1.07 ± 0.03 −0.14 ± 0.13 0.97

Autumn 0.95 ± 0.06 0.6 ± 0.2 0.94

Winter 1.02 ± 0.07 0.6± 0.3 0.96

Ceilometer 0–15 kmCOBSERVER =a CMODEL + b

a b r2

Spring 1.06 ± 0.08 0.8 ± 0.3 0.96

Summer 1.05 ± 0.07 0.7 ± 0.3 0.97

Autumn 1.01 ± 0.04 0.16 ± 0.18 0.99

Winter 0.97 ± 0.08 −0.10± 0.4 0.96

A good agreement between the observer method and the two automatic methods isshown for total cloud amount, per Table 4, even better than for low-medium cloud amount,shown in Table 3. There is not any significant change in formulae with season and thecorrelation coefficient is always higher than 0.94.

4. Conclusions

In this study, we have performed an analysis of three different automatic methods forthe estimation of cloud amount at low levels and total cloud amount: The Long methodapplied to pyranometer measurements, the APCADA method applied to longwave mea-

Atmosphere 2022, 13, 937 10 of 12

surements of a pyrgeometer, and the analysis of ceilometer profiles at several atmosphericlayer depths. The results from the three automatic methods have been compared to visualestimations of a meteorological observer to understand the similarity between automaticand visual methods. After binning the datasets, we found linear relationships between theautomatic and visual methods for different altitudes, namely for low cloud amount andtotal cloud amount, and different atmospheric profile depths. The relationships proposedhere could be used to compare the automatic methods with the observations, but a specificalgorithm would be necessary, with data from various instruments (hemispheric and col-umn techniques and more information, such as the altitude of the cloud base) to be able tocomplete the cloud amount when the observer series is discontinued [13].

In particular, the results can be summarized as follows:

• Visual observations of the low cloud amount indicate that most of the time (approxi-mately 67% of the records) the sky can be considered clear (cloud amount between0 and 1 octa). In contrast, the total cloud amount is more variable, with no evidentdominance of any cloud cover class.

• The application of the APCADA method on the pyrgeometer data also show that clearskies are dominant (0–1 octas). The remaining cloud amount values are registeredwith a 5–12% frequency.

• The application of the Long method on the pyranometer measurements shows thatthe frequency, in relation to the number of octas, is higher for the extreme cloudamount values (0–1 and 8 octas). The remaining cloud amounts are more uniformlyrepresented with percentages between 4% and 9%.

• The ceilometer results are also consistent with the other two automatic methods,because the clear skies are also dominant. In particular, the maximum frequency corre-sponds to completely clear skies, with a frequency of 69% for low clouds(0–3 km). Overcast skies are also frequent (8 octas), specially for the 0–15 km layer,with a frequency of almost 26%. Any of the remaining values of cloud amount are lessfrequent than the extremes.

• The mean cloud amount ranges between 1.6 to 2.7 octas for low-medium clouds and2.9 to 3.5 octas for total cloud amount. Standard deviation ranges between 2 to 2.8 forlow clouds and 2.5 to 3.4 for total cloud amount.

To sum up, automatic methods report more cloudless (0–1 octas) and overcast (8 octas)skies than the observer, except the APCADA method for cloudless skies and low clouds.

If we consider one octa as the measurement uncertainty, automatic methods canreproduce the observer results, especially the ceilometer (0–3 km) and APCADA for partiallow cloud amount. These methods agree or differ in one octa in 76% and 69% respectively.Concerning the total cloud amount, the ceilometer (0–15 km) shows a better result (63%)than the Long method (56%). In general, low cloud amount agrees more with observermeasurements than total cloud amount and the automatic methods underestimated totalcloud amount observer values, possibly due to the difficulty in monitoring high clouds.

We have also compared the results from the automatic methods with the estimationsfrom the visual method and performed linear fittings to relate each of the automatic methodswith the reference observer method. Significant changes were not detected with season.

Author Contributions: Conceptualization, M.P.U. and J.A.M.-L.; methodology, M.J.M. and V.E.;validation, V.E., C.M., M.D.F. and J.L.G.-A.; formal analysis and investigation, C.M., M.D.F. and J.L.G.-A.; data curation, M.P.U. and V.E.; writing—original draft preparation, J.A.M.-L.; writing—reviewand editing, M.J.M. and M.P.U.; visualization and supervision, M.J.M.; project administration, M.P.U.;funding acquisition, M.P.U. and J.A.M.-L. All authors have read and agreed to the published versionof the manuscript.

Funding: This work was financed jointly by the Spanish Ministry of Economy and Competitiveness(MINECO) and the European Regional Development Fund (FEDER) through projects CGL2017-86966-R and RTI2018-096548-B-I00 and by the Valencia Autonomous Government through projectAICO/2021/341.

Atmosphere 2022, 13, 937 11 of 12

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: Not applicable.

Acknowledgments: We acknowledge the staff from the Spanish State Agency of Meteorology(AEMET) for the cloud amount data recorded at the Manises airport (Valencia).

Conflicts of Interest: The funders had no role in the design of the study; in the collection, analyses,or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References1. Free, M.; Sun, B.; Yoo, H.L. Comparison between Total Cloud Cover in Four Reanalysis Products and Cloud Measured by Visual

Observations at U.S. Weather Stations. J. Clim. 2016, 29, 2015–2021. [CrossRef]2. Mitchell, D.I.; Finnegan, W. Modification of cirrus clouds to reduce global warming. Environ. Res. Lett. 2009, 4, 045102. [CrossRef]3. Kazantzidis, A.; Tzoumanikas, P.; Bais, A.F.; Fotopoulos, S.; Economou, G. Cloud detection and classification with the use of

whole-sky ground-based images. Atmos. Res. 2012, 113, 80–88. [CrossRef]4. Wacker, S.; Gröbner, J.; Zysset, C.; Diener, L.; Tzoumanikas, P.; Kazantzidis, A.; Vuilleumier, L.; Stöckli, R.; Nyeki, S.; Kämpfer, N.

Cloud observations in Switzerland using hemispherical sky cameras. J. Geophys. Res.-Atmos. 2015, 120, 695–707. [CrossRef]5. Boucher, O.; Randall, D.; Artaxo, P.; Bretherton, C.; Feingold, G.; Forster, P.; Kerminen, V.-M.; Kondo, Y.; Liao, H.;

Lohmann, U.; et al. Clouds and Aerosols. In Climate Change 2013: The Physical Science Basis; Contribution of Working GroupI to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Stocker, T.F., Quin, D., Plattner, G.,Tignor, M.M.B., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press:Cambridge, UK; New York, NY, USA, 2013; pp. 571–657. [CrossRef]

6. Trenberth, K.E.; Jones, P.D.; Ambenje, P.; Bojariu, R.; Easterling, D.; Klein Tank, A.; Parker, D.; Rahimzadeh, F.; Renwick, J.A.;Rusticucci, M.; et al. Observations: Surface and Atmospheric Climate Change. In Climate Change 2007: The Physical ScienceBasis; Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate, Change;Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K.B., Tignor, M., Miller, H.L., Eds.; Cambridge UniversityPress: Cambridge, UK; New York, NY, USA, 2007; pp. 235–336.

7. Norris, J.R.; Slingo, A. Trends in observed cloudiness and Earth’s radiation budget: What do we not know and what do we needto know? In Clouds in the Perturbed Climate System; Heintzenberg, J., Charlson, R.J., Eds.; MIT Press: Cambridge, MA, USA, 2009;pp. 17–36.

8. Smith, C.J.; Bright, J.M.; Crook, R. Cloud cover effect of clear-sky index distributions and differences between human andautomatic cloud observations. Sol. Energy 2017, 144, 10–21. [CrossRef]

9. Park, S.; Kim, Y.; Ferrier, N.J.; Collis, S.M.; Sankaran, R.; Beckman, P.H. Prediction of Solar Irradiance and Photovoltaic SolarEnergy Product Based on Cloud Coverage Estimation Using Machine Learning Methods. Atmosphere 2021, 12, 395. [CrossRef]

10. Badescu, V. Solar radiation estimation from cloudiness data. Satellite vs. groundbased observations. Int. J. Green Energy 2015, 12,852–864. [CrossRef]

11. Badescu, V.; Dumitrescu, A. Simple models to compute solar global irradiance from the CMSAF product Cloud FractionalCoverage. Renew. Energy 2014, 66, 118–131. [CrossRef]

12. Liu, Y.; Key, J.R. Assessment of Arctic Cloud Cover Anomalies in Atmospheric Reanalysis Products Using Satellite Data. J. Clim.2016, 29, 6065–6083. [CrossRef]

13. Boers, R.; de Haij, M.; Wauben, W.; Baltink, H.; van Ulft, L.; Savenije, M.; Long, C.N. Optimized fractional cloudiness determinationfrom five ground-based remote sensing techniques. J. Geophys. Res-Atmos. 2010, 115, D24116. [CrossRef]

14. Tapakis, R.; Charalambides, A.G. Equipment and methodologies for cloud detection and classification: A review. Sol. Energy2013, 95, 392–430. [CrossRef]

15. Norris, J.; Evan, A.T. Empirical removal of artifacts from the ISCCP and PATMOS-x satellite cloud records. J. Atmos. OceanicTechnol. 2015, 32, 691–702. [CrossRef]

16. Sun, B.; Free, M.; Yoo, H.; Foster, M.J.; Heidinger, A.; Karlsson, K. Variability and trends in U.S. cloud cover: ISCCP, PATMOS-x,and CLARA-A1 compared to homogeneity adjusted weather observations. J. Clim. 2015, 28, 4373–4389. [CrossRef]

17. Free, M.; Sun, B. Time-varying biases in U.S. total cloud cover data. J. Atmos. Oceanic Technol. 2013, 30, 2838–2849. [CrossRef]18. WMO. Guide to Instruments and Methods of Observation, 2018th ed.; WMO 8; World Meteorological Organization: Geneva,

Switzerland, 2018; pp. 490–492.19. Huo, J.; Lu, D. Comparison of Cloud Cover from All-Sky Imager and Meteorological Observer. J. Atmos. Oceanic Technol. 2012, 29,

1093–1101. [CrossRef]20. Min, Q.; Wang, T.; Long, C.N.; Duan, M. Estimating fractional sky cover from spectral measurements. J. Geophys. Res.-Atmos.

2008, 113, D20208. [CrossRef]21. WMO. Manual on the Observation of Clouds and Other Meteors, 2017th ed.; WMO-407; International Cloud Atlas: Geneva,

Switzerland, 2007; Volume 1. Available online: https://cloudatlas.wmo.int/en/home.html (accessed on 15 February 2022).

Atmosphere 2022, 13, 937 12 of 12

22. Hahn, C.J.; Warren, S.G.; London, J. The effect of moonlight on observation of cloud cover at night, and application to cloudclimatology. J. Clim. 1995, 8, 1429–1446. [CrossRef]

23. Kassianov, E.; Ackerman, T.; Marchand, R.; Ovtchinnikov, M. Satellite multiangle cumulus geometry retrieval: Case study. J.Geophys. Res.-Atmos. 2003, 108, 4117. [CrossRef]

24. Aebi, C.; Gröbner, J.; Kämpfer, N. Cloud fraction determined by thermal infrared and visible all-sky cameras. Atmos. Meas.Technol. 2018, 11, 5549–5563. [CrossRef]

25. Münkel, C.; Eresmaa, N.; Räsänen, J.; Karppinen, A. Retrieval of mixing height and dust concentration with lidar ceilometer.Bound-Lay. Meteorol. 2007, 124, 117–128. [CrossRef]

26. Emeis, S.; Schäfer, K.; Münkel, C. Long-term observations of the urban mixing-layer height with ceilometers. In Proceedings ofthe IOP Conference Series: Earth and Environmental Science, Roskilde, Denmark, 23–25 June 2008; p. 012027. [CrossRef]

27. Wauben, W.M.F. Evaluation of the NubiScope; Technical Report 291; KNMI: De Bilt, The Netherlands, 2006.28. Wauben, W.M.F.; Klein Baltink, H.; de Haij, M.; Maat, N.; Verkaik, J. Status, Evaluation and New Developments in the Automated

Cloud Observations in the Netherlands. In Proceedings of the 4th ICEAWS, Lisbon, Portugal, 24–26 May 2006.29. Long, C.N.; Ackerman, T.P.; Gaustad, K.L.; Cole, J.N.S. Estimation of fractional sky cover from broadband shortwave radiometer

measurements. J. Geophys. Res.-Atmos. 2006, 111, D17202-1. [CrossRef]30. Long, C.N.; Ackerman, T.P. Identification of clear skies from broadband pyranometer measurements and calculation of down-

welling shortwave cloud effects. J. Geophys. Res.-Atmos. 2000, 105, 15609–15626. [CrossRef]31. Durr, B.; Philipona, R. Automatic cloud amount detection by surface longwave downward radiation measurements. J. Geophys.

Res.-Atmos. 2004, 109, D05201. [CrossRef]32. Ahrens, C. Meteorology Today: An Introduction to Weather, Climate, and the Environment; Cengage Learning: Andover, UK, 2008.33. Martucci, G.; Milroy, C.; O’Dowd, C.D. Detection of Cloud-Base Height Using Jenoptik CHM15K and Vaisala CL31 Ceilometers. J.

Epidemiol. Community Health 2010, 27, 305–318. [CrossRef]34. Ackerman, S.A.; Holz, R.E.; Frey, R.; Eloranta, E.W.; Maddux, B.C.; McGill, M. Cloud Detection with MODIS. Part II: Validation. J.

Atmos. Ocean. Technol. 2008, 25, 1073–1086. [CrossRef]35. Werkmeister, A.; Lockhoff, M.; Schrempf, M.; Tohsing, K.; Liley, B.; Seckmeyer, G. Comparing satellite- to ground-based automated

and manual cloud coverage observations-a case study. Atmos. Meas. Techbol. 2015, 8, 2001–2015. [CrossRef]36. An, N.; Wang, K. A Comparison of MODIS-Derived Cloud Fraction with Surface Observations at Five SURFRAD sites. JAMC

2015, 44, 1009–1920. [CrossRef]37. Calbó, J.; Badosa, J.; González, J.A.; Dmitrieva, L.; Khan, V.; Enríquez-Alonso, A.; Sanchez-Lorenzo, A. Climatology and changes

in cloud cover in the area of the Black Caspian, and Aral seas (1991–2010): A comparison of surface observations with satelliteand reanalysis products. Int. J. Climatol. 2016, 36, 1428–1443. [CrossRef]

38. Kipp-Zonen. Available online: http://www.kippzonen.com/Product/14/CMP21-Pyranometer#.WNkWHfmLSUk (accessed on14 January 2022).

39. Vaisala. Available online: https://www.vaisala.com/en/products/weather-environmental-sensors/ceilometers-CL31-CL51(accessed on 14 January 2022).

40. Wagner, T.J.; Kleiss, J.M. Error Characteristics of Ceilometer-Based Observations of Cloud Amount. J. Atmos. Ocean. Technol. 2016,33, 1557–1567. [CrossRef]

41. Marín, M.J.; Estellés, V.; Gómez-Amo, J.L.; Camarasa, J.; Catalán, P.; Utrillas, M.P. Study of cloud cover at different heightsfrom measurements of a ceilometer. In Proceedings of the 8th International Meeting on Meteorology and Climatology of theMediterranean, Virtual Congress. 25–27 May 2021. Available online: https://www.metmed.eu/53568/section/27848/8th-international-conference-on-meteorology-and-climatology-of-the-mediterranean.html (accessed on 23 February 2022).

42. Schade, N.H.; Macke, A.; Sandmann, H.; Stick, C. Total and partial cloud amount detection during summer 2005 at Westerland(Sylt, Germany). Atmos. Chem. Phys. 2009, 9, 1143–1150. [CrossRef]