The present work aims at contributing to the proper design of networks of measuring moorings with regard to the downwelling solar irradiance at surface (DSIS). Considering the yearly profiles of the monthly DSIS, this article studies whether the 17 moorings within the existing PIRATA network are well geographically distributed so that they capture the spatial variations of the yearly profiles in the tropical Atlantic Ocean. A clustering technique is performed onto the HelioClim-3v5 satellite-derived gridded dataset of DSIS spanning 12 years, thus yielding 17 zones of similar yearly profiles. It was found that the mean yearly profiles of several zones are similar to the measured ones at the three moorings having enough measurements to perform the comparison. Zones have clear connections with the climatology of the region and circulation regimes. The geographical distribution of the moorings has been analysed with regard to the zoning. In the northern region of the tropical basin, each zone contains at least one PIRATA mooring. Hence, the configuration of the PIRATA network is appropriate to reflect the spatial variations of the yearly profiles in this northern part. In the southern basin, the coverage is less complete; several zones do not contain any PIRATA mooring. Thus, the network does not capture the spatial variations of the yearly profiles and this may induce misinterpretation of the PIRATA measurements at synoptic scales. It was also found that the computation of latitudinal or longitudinal averages of irradiance may be wrong if based solely on measurements from moorings without taking into account the spatial representativeness of a mooring.
Getting high quality measurements at fixed measuring stations is very challenging, especially when the maintenance of the measuring instruments is difficult, such as at sea. The oceanic stations are in most cases moorings checked at each passage of scientific vessels. The maintenance of these isolated stations represents a huge investment to guarantee high quality measurements. When designing a measuring network, attention should be brought to the number and locations of the moorings among many other aspects (Augustine et al., 2000; Stokes and Schwartz, 1994; Wolfe and Gutman, 2000). The number of stations is a trade-off between the need to put out a large enough array for a long enough period of time to gain new insights into the studied phenomena and several constraints including limited funding, costs of maintenance, number of instruments, personnel..., which is especially crucial at sea (Servain et al., 1998). Too many stations may result in an over-investment with a negligible scientific gain; too few stations may lead to a misrepresentation of the spatial variability of the variables of interest, with possible biases in analyses and interpretation of the measurements. Techniques have been proposed for the optimal design of networks of measuring stations at ground (Bogárdi et al., 1985; Bras and Rodríguez-Iturbe, 1976; Stauffer et al., 2000; Vinnikov et al., 1999). They are often based on the analysis of a priori knowledge of the spatial correlation and more generally of the geostatistical properties of the variables of interest and the space and timescales to study. Such knowledge is hardly known over the ocean due to lack of measurements. Hence, there is no agreed protocol for the optimal design of a network at large scale over the ocean.
The present work aims at contributing to the proper design of networks of measuring moorings. No solution is proposed; the focus is on the study of the existing PIRATA network and the assessment of its capability to depict the spatial variations of the solar radiation at synoptic scales.
The PIRATA (Prediction and Research Moored Array in the Tropical Atlantic) network is made up of 17 moorings. It was established to improve knowledge and understanding of ocean–atmosphere variability in the tropical Atlantic Ocean (Bourlès et al., 2008). Each mooring measures several variables, including the downwelling solar irradiance at surface and hereafter abbreviated as DSIS. The DSIS is the downwelling solar radiation passing through and scattered by the atmosphere and received at the ocean surface. At a given location, the DSIS is varying noticeably during a year due to changes in the solar radiation received at the top of the atmosphere and in the cloud presence and their varying optical properties. The yearly cycle, i.e. the seasonal variability, is by far the most energetic cycle compared to the other timescales, irrespective of the geographical location or of the local climate (Bengulescu et al., 2016, 2017, 2018). As a consequence, this study focuses on the monthly DSIS.
Considering the yearly profiles of the monthly DSIS, this article addresses the following question: can the spatial variations of the monthly DSIS be captured by the current configuration of the PIRATA moorings? The current configuration is determined by the location of the 17 moorings. The question may be reformulated as: are the 17 moorings geographically distributed in such a way that they capture the various yearly profiles? To answer this question, 17 zones, or classes, are created by a clustering technique applied to a satellite-derived gridded dataset of DSIS spanning 12 years. Geographical points in a given zone exhibit similar variations of the monthly DSIS. Then, it will be checked that there is one or more PIRATA mooring in each zone. If yes, and assuming that each PIRATA mooring exhibits yearly cycles of the DSIS similar to those of the zone to which it belongs, it will be concluded that the configuration of the PIRATA network is appropriate to reflect the spatial variations of the monthly DSIS.
The PIRATA and satellite-derived gridded datasets are presented in Sect. 2 as well as the clustering method. In Sect. 3, the resulting zoning is partly validated by comparing the yearly profiles of the monthly DSIS of the PIRATA moorings to those of the corresponding zones for the moorings with enough data. A further validation is made in this Section where the classes are analysed in light of known physical processes at synoptic scales. The Sect. 4 discusses the ability of the configuration of the PIRATA network to reflect the spatial variability of the monthly DSIS. A brief discussion on the method and its possible applications are given in Sect. 5. Section 6 concludes the paper.
The PIRATA network results from a joint project between Brazil, France and
the United States of America (Bourlès et al., 2008; see the Web site
Map showing the location of the PIRATA buoys.
The measurements can be downloaded as 2 min averages from the web site of the Global Tropical Moored Buoy Array (GTMBA) of the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA) in the USA. Measurements undergo quality checking at NOAA and suspicious measurements are flagged. The number of measurements and their quality depend on the mooring and year. Many moorings exhibit missing data or suspicious measurements in the early years of the network and the situation tends to improve. The analysis of the data and their flags indicates that all moorings seem to work properly. As a consequence, it is assumed that each mooring exhibits the same overall quality as the others and has its place within the network.
A subset of moorings has been selected in order to validate the results of
the clustering technique and their measurements will be considered as
reference for the comparison of yearly profiles. These moorings offer high
quality data spanning at least two years with a limited number of missing
data. A first selection was made by using the quality flags of the NOAA. As
the data must serve as reference and considering the different sources of
uncertainty which the PIRATA measurements are subject to (incorrect sensor
levelling, shading caused by close structures, accumulation of dust, dew,
water-droplets, bird droppings, miscalibration of sensors, electronic
failures, time shifts in data loggers, maintenance mishandling, buoy
motions, etc. see e.g. Muneer and Fairooz, 2002; Foltz et al., 2014), an
additional quality control was performed at MINES ParisTech. The quality
control comprises several tests of the 2 min DSIS against extremely rare
limits and physically possible limits (Korany et al., 2016). Values falling
outside the limits were excluded from the time-series. Eventually, a visual
analysis was performed to remove suspicious values. Only measurements that
passed all tests successfully were kept. The hourly mean of DSIS was
computed by averaging the 30 measurements within this hour if all
measurements were declared valid. Otherwise, the hourly mean was declared
invalid. Following the guidelines of the World Radiation Data Center
( mooring at 0 and 10 mooring at 6 mooring at 19
Meteorological re-analyses and images acquired by satellites observing the ocean surface provide means of getting a synoptic view of the DSIS. Boilley and Wald (2015) and Trolliet et al. (2018) compared the DSIS output of ERA-Interim, ERA5, MERRA, and MERRA-2 re-analyses to PIRATA measurements. They observed that the re-analyses often report cloud-free conditions while actual conditions are cloudy, yielding an underestimation of surface irradiance by these data sets, and reciprocally, cloudless conditions as cloudy, yielding an overestimation. The standard deviation of the errors was large as a whole and the biases varied considerably in time and space. Such changes in bias denote a spatial distortion of the DSIS field and will induce errors in the description of the variability. The authors do not recommend the use of such re-analysis datasets but instead suggest exploiting satellite-derived datasets of DSIS. Three of these, the HelioClim-3 version 5 (HC3v5), Surface Radiation Dataset – Heliosat (SARAH-2) and Copernicus Atmosphere Monitoring Service (CAMS) Radiation Service v2 datasets, have been compared to the PIRATA measurements in Trolliet et al. (2018). The authors concluded that the quality of the three satellite-derived datasets is fairly similar. They also concluded that each of these is appropriate for studying the variability of the downward solar irradiance at the surface of the tropical Atlantic Ocean.
Map of the 17 resulting classes of DSIS
For the sake of the simplicity, the HC3v5 database has been selected for this study. Images of the Meteosat Second Generation satellites are routinely processed by MINES ParisTech and a companion company Transvalor to yield the HC3v5 dataset. HC3v5 covers Europe, Africa, the Middle East, parts of South America and the Atlantic Ocean. It is available from 2004 up to the present with a 15 min time step. The spatial resolution depends on the pixel position and is approximately 3–4 km in the tropical Atlantic Ocean. The processing method is the Heliosat-2 method (Rigollier et al., 2004; Lefèvre et al., 2007), modified by Qu et al. (2014). The method itself, as well as the HC3v5 dataset, have been the subject of several comparisons against measurements of DSIS made at land-based sites, see e.g. Bengulescu et al. (2017), Eissa et al. (2015), Marchand et al. (2017, 2018), or Thomas et al. (2016a, b).
The DSIS from HC3-v5 has been downloaded from the SoDa Service
(
The clearness index, usually noted
Typical profiles of DSIS for each class. Colours are the same as
those in Fig. 3a. 4 northernmost classes
The spatial and temporal variations of the DSIS on the one hand and of
The zoning has been applied to all cells in the Atlantic Ocean between
53
Figure 2a shows the map of the 17 classes of irradiance with colours chosen
randomly in order to maximize the contrast between two adjacent classes. One
observes that the classes north of 10
Figure 2b shows a map of the same 17 classes but where the colour of each
class represents the yearly mean of the DSIS computed from each typical
profile. The mean irradiance varies between 120 W m
Yearly profiles of DSIS for the mooring at latitude 19
Map of the 17 classes of clearness index
The typical profile of each class is shown in Fig. 3a, b and c, with the same colour as its class in Fig. 2a. The profiles of the four northernmost classes are represented in Fig. 3a, those of the six southernmost classes in Fig. 3b, and the seven profiles for the tropical basin in Fig. 3c.
In the Northern Atlantic Ocean (Fig. 3a), the four profiles have fairly similar shapes. They exhibit a maximum of irradiance in June–July, corresponding to the boreal summer, and a minimum in December–January, i.e. the boreal winter. The maximum and minimum increase as the latitude decreases from the North to the tropics. As expected, opposite features are found in the Southern Atlantic Ocean (Fig. 3b). The six profiles offer fairly similar shapes and exhibit a maximum of irradiance in December–January, corresponding to the austral summer, and minimum in June–July, i.e. the austral winter. The maximum and the minimum increase as the latitude decreases from South to the tropics while their difference decreases. In the tropical region (Fig. 3c), the profiles are diverse and smooth without well marked peaks and troughs.
In order to partly validate the resulting zoning, a comparison was performed
between the yearly profiles measured at a moorings located at (0
It can be seen that the three individual PIRATA profiles exhibit the same shape as the typical profile as expected. The PIRATA profiles fit well within the envelope, except for May and June 2011. Also shown in blue in this graph is the typical profile of the class which is the closest to the mooring but not containing it. As expected, the typical profile is quite different from the three individual PIRATA profiles, especially during the boreal summer. The three individual profiles are outside the envelope thus demonstrating a contrario that the class to which the PIRATA mooring belongs is the most similar to the individual PIRATA profiles. In this example, the typical profile is in agreement with the measurements at the mooring. Similar results were found for the two other moorings (not shown). Since the comparison was limited to three moorings, it cannot be concluded that the typical profiles are in agreement with the measurements at all moorings. Nevertheless, this partial validation brings confidence in the quality of the resulting zoning.
Figure 5a shows the map of the 17 classes of
Figure 5b shows a map of the same 17 classes but where the colour represents
the yearly mean of
This zoning has many similarities to the mean annual clouds amount from the
International Satellite Cloud Climatology Project (ISCCP) displayed in Fig. 6,
despite differences in spatial resolution. Though the cloud optical depth
is the most influential property of the clouds affecting the DSIS, the cloud
amount may be considered as a proxy of
Map of the mean annual cloud amount for the period 1983–2009 from
the International Satellite Cloud Climatology Project (ISCCP). Downloaded
from
Similarly to the DSIS, the typical profile of
The shapes of the typical profiles of
The profiles in the tropical region (Fig. 7c) are diverse and the amplitude
of change from month to month can be large. Knowing that the cloud regime is
the most influential variable on the seasonal variability, one may relate
the strong decrease in
Typical profiles of
As several reasons to trust the zoning have been highlighted, one may use the zoning to analyse the geographical distribution of the 17 moorings with a view to capture the various yearly profiles in the tropical basin. As mentioned in Sect. 1, if there is one or more PIRATA mooring in each class, it can be concluded than the configuration of the PIRATA network is appropriate to reflect the spatial variations of the monthly DSIS. Figure 8 shows the location of the 17 buoys superimposed on the map of the DSIS classes.
Location of the 17 buoys superimposed on the map of classes of DSIS. The colour indicates the mean yearly DSIS.
One observes that there are one or more moorings in each of the classes
between 0 and 30
The selected methodology may apply to other oceanic basins provided archives of gridded DSIS data are available. The selected methodology may apply to other variables than the DSIS, such as sea surface temperature which is routinely monitored by satellites. There are a number of arbitrary choices, e.g. clustering technique or input state vectors that merit a deeper discussion beyond the scope of this work which is a preliminary step in a climatic description of the changes in space and time of the irradiance in the tropical Atlantic Ocean.
Zoning may help in designing or evolving a network. The recent advances in the assessment of DSIS from satellite data may help in revising and completing the first studies, done several years ago, in the context of the deployment of the PIRATA network. Considering the important financial, scientific and material effort that occurs in the deployment of a buoy, the satellite estimates could aid decision-making on the number of moorings to deploy and the selection of appropriate geographical locations.
The present analysis only looked at DSIS. PIRATA moorings are equipped with other instruments than pyranometers, which measure more than 20 atmospheric and subsurface variables. All of these variables have to be taken into account for designing network. The zoning approach can be used on a bundle of variables, knowing that other observables may be subject to other considerations compare to DSIS. However, the analyses of the results would be much more complex with respect to decision making.
A map of similar zones with respect to the yearly profile of monthly means and standard deviations of the DSIS has been realized over the Atlantic Ocean thanks to a clustering technique. It was found that the resulting zones reproduce the measured DSIS at the three moorings in the tropical Atlantic Ocean having enough measurements to perform the comparison. Zones have clear connections with the climatology of the region and circulation regimes. The geographical repartition of the moorings has been analysed with regard to the zoning. The zones are well covered by the PIRATA network except a few. It was also found that the computation of latitudinal or longitudinal averages of irradiance may be wrong if based solely on measurements from moorings without taking into account the spatial representativeness of a mooring. Finally, this zoning highlights the high quality of the PIRATA network regarding the synoptic scales of the DSIS.
PIRATA measurements performed every 2 min were downloaded from
the web site (
All authors contributed equally to this work.
The authors declare that they have no conflict of interest.
This article is part of the special issue “17th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017”. It is a result of the EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2017, Dublin, Ireland, 4–8 September 2017.
The authors thank the anonymous reviewers and the Editor for the valuable
and encouraging comments greatly help in improving the readability of this
article. They acknowledge the key role of the PIRATA team in servicing the
network and freely providing the data. The authors state that the PIRATA
network is a unique and valuable means of studying and monitoring the
surface irradiance in the tropical Atlantic Ocean and hope it will receive
support for operations to further enrich the datasets. The authors
acknowledge the help of the GTMBA Project Office of NOAA/PMEL in acquiring
the data. Time-series of HelioClim-3v5 data were downloaded from the SoDa
Service web site (