Mialhe Pauline

Pohl Benjamin

Morel Béatrice

Jörg Trentmann

Jumaux Guillaume

Bonnardot Franc ¸ois Bessafi Miloud

Chabriat Jean-Pierre

On the determination of coherent solar climates over a tropical island with a complex topography

Keywords: Surface solar radiation, La Réunion island, Complex meteorological context, Seasonal/diurnal cycles, Cluster analysis, SARAH-

Many tropical islands are aiming at a greener self-sufficient energy production based on renewable energy, notably solar-generated electricity. This work explores the mean solar cycles over La Réunion (southwest Indian Ocean: 21 • S, 55.5 • E) and their spatial behavior using the Solar surfAce RAdiation Heliosat -East (SARAH-E) satellite data at high spatial and time resolutions (0.05 • , hourly data over 1999-2016). Comparisons of SARAH-E satellite-derived data with ground-based measurements show differences of 15 % for diurnal-seasonal variations. The solar resource over the island has strong spatial variability, with differences larger than 100 W.m -2 between coastal and mountainous zones.

The mean solar resource is more than 20 % lower on the island than on the nearby sea. The strongest interaction between diurnal and annual cycles are found at the windward mid-slopes and at the active volcano, related to wellknown cloud processes. A clustering of solar zones, based on diurnal-seasonal means, structures the island into a West/East dipole balancing the solar energy in a day through the year.

Introduction 1

Climate changes are leading to a major adaptation of the energy system and strategies (IPCC-WGII, 2014). This 2 emulation has led the smart-and micro-grid concepts to appear. They are the next upgrade of our current power grid 3 model [START_REF] Hirsch | Microgrids: A review of technologies, key drivers, and outstanding issues[END_REF][START_REF] Clastres | Smart grids: Another step towards competition, energy security and climate change objectives[END_REF]. They aim at increasing the efficiency of the power grid while ensuring 4 greener energy production for countries, cities and even smaller districts [START_REF] Unamuno | Hybrid ac/dc microgrids-Part I: Review and classification of topologies[END_REF].

5

This challenge is more a need for isolated areas, notably for those dependent on fossil fuels (e.g. Timilsina and 6 Shah, 2016). This is especially the case for small islands (e.g. [START_REF] Mendoza-Vizcaino | Integral approach to energy planning and electric grid assessment in a renewable energy technology integration for a 50/50 target applied to a small island[END_REF][START_REF] Praene | Renewable energy: Progressing towards a net zero energy island, the case of Reunion Island[END_REF],

7
Email address: pauline.mialhe@univ-reunion.fr (Mialhe Pauline)

Preprint submitted to Solar Energy

November 21, 2019 climate of La Réunion still limits the integration of renewable energy in the electric mix [START_REF] Bénard-Sora | Sustainable urban planning for a successful energy transition on Reunion Island: From policy intentions to practical achievement[END_REF][START_REF] Diagne | Review of solar irradiance forecasting methods and a proposition for small-scale insular grids[END_REF]. The island wants to build a self-sufficiency only based on its renewable resources and become a "zero-net energy" island [START_REF] Praene | Renewable energy: Progressing towards a net zero energy island, the case of Reunion Island[END_REF]Selosse et al., 2018a). The local research aims at completing and deepening the knowledge required to better exploit renewable energies, notably Surface Solar Radiation (SSR).

The solar resource strongly varies in both space and time over La Réunion island [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF][START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Badosa | Scales of spatial and temporal variation of solar irradiance on Reunion tropical island[END_REF][START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF]. Both of these scales of variation need to be described to improve the solar energy use. Few studies exist focusing on either the diurnal or the annual cycle of SSR. In addition, some of them are based on ground-based measurements essentially at low and mid altitudes performed by the national weather service (Météo-France), though more than 80 % of the island lies above 200 meters. [START_REF] Badosa | Scales of spatial and temporal variation of solar irradiance on Reunion tropical island[END_REF] classified the diurnal cycle was into 5 daily regimes of SSR using data over 2 years (2007)(2008) from 7 coastal and low altitude Météo-France stations (9-186 meters). Météo-France published two climate atlases using data from their measuring network [START_REF] Soler | Atlas Climatique de La Réunion[END_REF][START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF]. [START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF] reconstructed monthly SSR maps using data over 10 years (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) from 32 stations mainly at low and mid altitudes. Recently, monthly SSR maps were derived from the Solar surfAce RAdiation Heliosat -East (SARAH-E; [START_REF] Huld | Surface Solar Radiation Data Set -Heliosat, Meteosat-East (SARAH-E) -Edition 1. Satellite Application Facility on Climate Monitoring[END_REF] satellite dataset to describe the SSR patterns at higher elevations over the island [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF]. The present paper complements the aforementioned studies by describing the spatial features of the diurnal cycle. It also specifies the intersection of the two orbital components (namely, the diurnal and annual cycles), which have spatial particularities and can separate the island into coherent zones of SSR. To that end, we use the Solar surfAce RAdiation Heliosat -East (SARAH-E) dataset which provides SSR data over a long time period (from 1999 to 2016) at a high spatial resolution (0.05 • ×0.05 • ). The purpose of the present study is to:

1. describe the SSR diurnal cycle which was previously defined only at coastal and mid-elevated weather stations, 2. underline the intersection of the two orbital components of solar irradiance and its related spatial spread over the island, 3. identify, for the first time, favorable / complementary SSR zones for solar energy applications.

Section 2 describes La Réunion island case study, SARAH-E satellite dataset and the methods used. Section 3 presents the local solar context of the island with its related spatial SSR structures and section 4 the solar potential zones as obtained from clustering. Finally, a discussion and some concluding remarks are developed in section 5.

Context, data and methods

The study case of La Réunion island

Figure 1(a) describes the topography of the island and locates its two highest peaks. The first one is the dormant Piton des Neiges volcano at 3, 071 m above sea level (asl) and the other one is the active Piton de La Fournaise volcano at 2, 560 m asl. They are separated by a 1, 500 m-high plateau making the island an isolated and especially prominent obstacle to the atmospheric circulations in the SWIO (Figure 2). The island is mainly under an east-southeast trade wind (e.g. [START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Baldy | Field observations of the vertical distribution of tropospheric ozone at the island of Reunion (southern tropics)[END_REF], which changes in direction according to the season: easterly in summer and more southeasterly in winter (Figure 2; [START_REF] Lesouëf | Numerical simulations of local circulations and pollution transport over Reunion Island[END_REF]. This main flow is more a "flow around" than a "flow over" regime. The latter particularly occurs during violent wind conditions, such as tropical cyclones (TC). The main cloud regimes over La Reunion are (a) orographic clouds generated by the local topography and (b) clouds caught on the island driven by synoptic systems [START_REF] André | Preliminary assessment of two spatio-temporal forecasting technics for hourly satellite-derived irradiance in a complex meteorological context[END_REF][START_REF] Cécé | Microscale anthropogenic pollution modelling in a small tropical island during weak trade winds: Lagrangian particle dispersion simulations using real nested LES meteorological fields[END_REF][START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Garrett | Orographic Cloud over the Eastern Slopes of Mauna Loa Volcano, Hawaii, Related to Insolation and Wind[END_REF]. The vertical development of the clouds in these two regimes is limited by the thermal inversion whose height over La Réunion ranges from 1,700 to 3,100 meters, with an average value of ∼2,000 m [START_REF] Bhugwant | Impact of traffic on black carbon aerosol concentration at la Reunion Island (Southern Indian Ocean)[END_REF][START_REF] Guilpart | The isotopic composition of near-surface water vapor at the Maïdo observatory (Reunion Island, southwestern Indian Ocean) documents the controls of the humidity of the subtropical troposphere: Water Vapor Isotopes in Reunion Island[END_REF][START_REF] Réchou | Spatio-temporal variability of rainfall in a high tropical island: Patterns and large-scale drivers in Réunion Island[END_REF]. Recently, [START_REF] Vérèmes | Spatial and seasonal variability of clouds over the South-West Indian Ocean based on the DARDAR mask product[END_REF] found two peaks of cloud occurrence in the SWIO, the first one related to deep convection in summer and the other one to predominant liquid clouds at low altitudes in winter.

Associated precipitations vary according to elevation, with maximum amounts at 650 m asl along the windward slopes [START_REF] Réchou | Spatio-temporal variability of rainfall in a high tropical island: Patterns and large-scale drivers in Réunion Island[END_REF][START_REF] Sisavath | Morphology and sedimentary architecture of a modern volcaniclastic turbidite system: The Cilaos fan, offshore La Réunion Island[END_REF]. In addition, the island experiences extreme events such as heavy rainfalls and storms as well as TC whose effects on rainfall are enhanced by the marked topography [START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF][START_REF] Morel | Regionalizing Rainfall at Very High Resolution over La Réunion Island Using a Regional Climate Model[END_REF][START_REF] Pohl | Regionalizing Rainfall at Very High Resolution over La Réunion Island: A Case Study for Tropical Cyclone Ando[END_REF][START_REF] Leroux | A Climatology of Southwest Indian Ocean Tropical Systems: Their Number, Tracks, Impacts, Sizes, Empirical Maximum Potential Intensity, and Intensity Changes[END_REF]. During TC Gamede, extreme rainfalls reached two world records of 3, 930 mm over 72 hours and 4, 936 mm over 4 days [START_REF] Quetelard | Extreme Weather: World-Record Rainfalls During Tropical Cyclone Gamede[END_REF].

Factors modulating SSR variability include clouds and their intrinsic characteristics (for instance: extension, shape, depth) but also aerosols and water vapor in the air column, especially under clear sky conditions, and site x(e-07)

-5

Figure 2: Seasonal characteristics of the wind ( U, in m.s -1 ) and the moisture flux (q sat . U, in m.s -1 ), for June (winter) and December (summer).

Vectors refers to the eastward and northward components of the two variables (i.e. U and q sat . U). Blue-to-red colors are the mass (humidity) flux.

Data comes from ERA5 (1979ERA5 ( -2018)): wind ( U) is the surface level U and V components of winds at 10 meters; near-surface specific humidity (q sat ) is computed from 2 meter dew-point temperature and surface pressure.

orography (e.g. [START_REF] André | Preliminary assessment of two spatio-temporal forecasting technics for hourly satellite-derived irradiance in a complex meteorological context[END_REF][START_REF] Mazorra Aguiar | Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty[END_REF]Bessafi et al., 2018a;[START_REF] Wild | The cloud-free global energy balance and inferred 25 cloud radiative effects: An assessment based on direct observations and climate models[END_REF][START_REF] Obregón | Impact of aerosol and water vapour on SW radiation at the surface: Sensitivity study and applications[END_REF][START_REF] Polo | Analysis of solar tower plant performance influenced by atmospheric attenuation at different temporal resolutions related to aerosol optical depth[END_REF][START_REF] Lauret | Characterization of the intraday variability regime of solar irradiation of climatically distinct locations[END_REF][START_REF] Gueymard | Impact of on-site atmospheric water vapor estimation methods on the accuracy of local solar irradiance predictions[END_REF][START_REF] Soubdhan | Classification of daily solar radiation distributions using a mixture of Dirichlet distributions[END_REF]. Their respective influences are discussed below.

Satellite dataset and pre-processing

Satellite Application Facility on Climate Monitoring (CMSAF) produces and provides satellite-derived datasets of the global energy and water cycle. Several product families of particular interest for the study of SSR are available such as the Solar surfAce RAdiation Heliosat -East (SARAH-E) regional climate data record which is based on km at this location, and with 89 pixels covering the island (Figure 1).

∆ s-i ∆ s/i-1 ∆ s-i ∆ s/i-1 ∆ s-i ∆ s/i-1 ∆ s-i ∆ s/i-
A thorough overview of the basic retrieval principle of the Solar surfAce RAdiation Heliosat (SARAH) climate data records can be found in [START_REF] Müller | Digging the METEOSAT Treasure-3 Decades of Solar Surface Radiation[END_REF] and more details for SARAH [START_REF] Amillo | A New Database of Global and Direct Solar Radiation Using the Eastern Meteosat Satellite, Models and Validation[END_REF]. Information on aerosols comes from a monthly climatology derived from the Monitoring Atmospheric Composition and Climate (MACC) reanalysis [START_REF] Inness | The MACC reanalysis: An 8 yr data set of atmospheric composition[END_REF][START_REF] Benedetti | Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation[END_REF][START_REF] Morcrette | Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling[END_REF][START_REF] Müller | Digging the METEOSAT Treasure-3 Decades of Solar Surface Radiation[END_REF] over the period 2003-2010, at the spatial resolution of 120 km. Water vapor data are monthly means from the ERA-Interim reanalysis at the spatial resolution of 79 km [START_REF] Dee | The ERA-Interim reanalysis: Configuration and performance of the data assimilation system[END_REF]. A topographical correction to the water vapor to better account for the effects of topography has been applied in the second version of SARAH (SARAH-2; [START_REF] Pfeifroth | Trends and Variability of Surface Solar Radiation in Europe Based On Surface-and Satellite-Based Data Records: TRENDS AND VARIABILITY OF SURFACE SOLAR RADIATION[END_REF], but not in SARAH-E. The complex topography of La Réunion island should then be smoothed, probably leading to different SSR values at elevated areas where the path to the surface is overestimated. Yet, the use of SARAH-E still appears better than SARAH-2, in which the position of the island is too close to the picture limits.

The reliability of the SARAH-E against in-situ measurements has been assessed over India [START_REF] Riihelä | Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India[END_REF], China [START_REF] Wang | Validation of CM SAF CLARA-A2 and SARAH-E Surface Solar Radiation Datasets over China[END_REF] and La Réunion [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF]. [START_REF] Riihelä | Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India[END_REF] SARAH-E against in situ SSR measurements at 17 stations (and 120 station-year) over India (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and showed that, before 2009, SARAH-E estimates are 10-20 % higher than observations. [START_REF] Wang | Validation of CM SAF CLARA-A2 and SARAH-E Surface Solar Radiation Datasets over China[END_REF] using 59 stations for China (1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) also concluded on overestimations (by 7.5 W.m -2 on average) with the same positive trend in the differences as that found by [START_REF] Riihelä | Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India[END_REF]. Results of these studies are consistent with increasing levels of Aerosol Optical Depth (AOD) over India and China [START_REF] Li | India Is Overtaking China as the World's Largest Emitter of Anthropogenic Sulfur Dioxide[END_REF][START_REF] Allen | Evaluation of multidecadal variability in CMIP5 surface solar radiation and inferred underestimation of aerosol direct effects over Europe, China, Japan, and India[END_REF]. For example, Indian aerosol daily means are defined with a minima of 0.25 for the AOD at 440 nm and of 0.8 for the AOD at 675 nm during the years from 2005 to 2016 [START_REF] Riihelä | Validation of the SARAH-E Satellite-Based Surface Solar Radiation Estimates over India[END_REF]. In contrast, lower levels of aerosols (mainly biomass and marine origin) are found over La Réunion [START_REF] Duflot | Preliminary results from the FARCE 2015 campaign: Multidisciplinary study of the forest-gas-aerosol-cloud system on the tropical island of La Réunion[END_REF][START_REF] Lamy | Ultraviolet radiation modelling from ground-based and satellite measurements on Reunion Island, southern tropics[END_REF][START_REF] Horowitz | Evaluation of climate model aerosol seasonal and spatial variability over Africa using AERONET[END_REF][START_REF] Ruiz-Arias | Worldwide impact of aerosol's time scale on the predicted long-term concentrating solar power potential[END_REF][START_REF] Gueymard | Temporal variability in direct and global irradiance at various time scales as affected by aerosols[END_REF]. Recently, [START_REF] Mallet | Marine aerosol distribution and variability over the pristine Southern Indian Ocean[END_REF] compared the aerosol climatology used in SARAH-E to the AERONET in situ measurements over La Réunion (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) and found well-reproduced variations of the monthly mean AOD, with a fitting equation slope close to 1. SARAH-E SSR could underestimate La Réunion SSR due to an overestimated AOD climatology, but aerosol levels there are low (0.148 for MACC, 0.068 for AERONET), with a quadratic error of 0.084 [START_REF] Mallet | Marine aerosol distribution and variability over the pristine Southern Indian Ocean[END_REF]. Hence, SARAH-E should well capture SSR for La Réunion with no major estimation error due to aerosol levels, even though the size and complexity of the terrain make the evaluation step there necessary.

D N O S A J J M A M F J W/m2 % -100 -50 0 50 100 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H 6H 12H 18H D N O S A J J M A M F J (a) (b)
The accuracy of SARAH-E is assessed over La Réunion through comparisons with in-situ measurements performed by Météo-France. The WMO evaluates each station sensor and site by a classification [START_REF] Wmo | Guide to Meteorological Instruments and Methods of Observation WMO[END_REF]. Site classification evaluates shades projected by surrounding obstacles while sensor classification evaluates sensor sensitivity measurements. Figure 1 shows the spatial distribution of both classifications for all the SSR Météo-France stations. These are equipped with sensors classified B (ISO 9060:2018) except for two stations with sensors classified A, and sites are classified from 1 to 3. More characteristics of the Météo-France dataset (instrumentation, sites, etc.) are given in [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF]. [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF] compared monthly means of SARAH-E estimates to Météo-France SSR. This first comparison showed a relative difference of 10 % between the two datasets with higher divergence locally. Our study further compares the diurnal scales of SSR variations. The comparison here is performed over the period 2011-2015 with regard to the data at the same Météo-France stations as inBessafi et al.

(2018b) but at an hourly timescale. The Météo-France SSR measurements have to be converted into hourly means in W.m -2 because they are hourly cumulative values in J.cm -2 integrated on the previous hour, sampled every 6 minutes, while SARAH-E estimates of SSR are instantaneous values. For the comparison with observations, we approximated hourly averaged data from the satellite by, first, temporal interpolation of the hourly clearness index (i.e., the ratio of the SSR to the radiation at the top of the atmosphere) to generate a 1-min clearness index time series. In a second step, this time series is multiplied by the top of the atmosphere radiation to result in a 1-min time series of SSR, which, in a third step, is used to calculate the hourly-averaged SSR data from SARAH-E (see also [START_REF] Trolliet | Downwelling surface solar irradiance in the tropical Atlantic Ocean: A comparison of re-analyses and satellite-derived data sets to PIRATA measurements[END_REF]. Hereafter, satellite will refer to SARAH-E hourly means and in situ will stand for Météo-France surface measurements. For a given in situ station, differences are given by:

∆ s-i = SSR s -SSR i , (1)
∆ s/i-1 = ∆ s-i / SSR i , (2)
where SSR i : the in situ SSR at x longitude, y latitude and z elevation, SSR i 0, and SSR s : the satellite SSR, extracted by nearest neighbor and converted into hourly means. W.m -2 and satellite at 343 W.m -2 at a station lying at an altitude of 595 m (Figure 1: "B class" sensor and class 3 site).

During this day, the island was also affected by TC Bejisa [START_REF] Pianezze | A New Coupled Ocean-Waves-Atmosphere Model Designed for Tropical Storm Studies: Example of Tropical Cyclone Bejisa (2013-2014) in the South-West Indian Ocean[END_REF]. Moreover, differences during the three days of TC Bejisa are higher than in stable meteorological conditions (Table 1), at 40 ± 95 W.m -2 and 190 ± 528 %. The differences between SARAH-E and in-situ SSR should be analyzed with care, especially since large percent differences can also result from shaded measures in the early and late hours and from sensor disturbances possibly caused by violent meteorological conditions.

As the present study uses hourly monthly means, Figure 3 displays for each hour and month the averaged differences of ∆ s-i and ∆ s/i-1 for all the in situ stations. ∆ s-i tends to be positive at all times except in the early afternoon during the winter-to-summer transition months (SON). At this time, La Réunion is under the influence of biomass burning in Madagascar and eastern Africa [START_REF] Randriambelo | Effect of biomass burning, convective venting, and transport on tropospheric ozone over the Indian Ocean: Reunion Island field observations[END_REF][START_REF] Queface | Climatology of aerosol optical properties in Southern Africa[END_REF][START_REF] Lamy | Ultraviolet radiation modelling from ground-based and satellite measurements on Reunion Island, southern tropics[END_REF]. The overestimation of aerosols in SARAH-E should lead to a different behavior, with a global underestimation persisting during the whole biomass burning period. Low ∆ s/i-1 are found at the same period, at ±10 % (Figure 3), consistently with [START_REF] Bessafi | Downscaling solar irradiance using DEM-based model in young volcanic islands with rugged topography[END_REF]. The largest ∆ s/i-1 differences mostly occur in the early morning, during the first full-hour of daylight (Figure 3). Early and late hours are the most challenging to estimate by SARAH-E, and this is when in situ measurements are the most probably shaded, which can lead to strong biases. Overall, selecting hours from 0800

RET to 1700 RET centers analysis on daylight (5 hours before to 5 hours after solar noon) and removes most of the potentially-biased data. These hours are also defined as the photovoltaic use hours by two scenarios with low and high renewable energy integration [START_REF] Drouineau | Increasing shares of intermittent sources in Reunion Island: Impacts on the future reliability of power supply[END_REF]. Hence, this study only retains and interprets the grids between 0800 RET through 1700 RET.

We also compared SARAH-E to the latest version of SARAH (namely SARAH-2; See in Supplementary Materials). Differences are, in general, very small and both data sets perform comparably with slight advantages for SARAH-E.

Hierarchical clustering analysis and Ward's clustering

Clustering-based approaches help assessing SSR variability by regrouping a large amount of n elements into a smaller group number. Among these approaches, agglomerative hierarchical clustering (AHC) is used here to compute all the clustering cases between 1 and n groups. Each of the n clustering cases iterates to optimize an agglomerative criterion. Here we choose Ward's criterion [START_REF] Ward | Hierarchical grouping to optimize an objective function[END_REF] which minimizes the within-group variance to produce groups as coherent as possible.

This clustering-based approach was successfully used to group non-Gaussian time series of SSR and other climate parameters such as rainfall: for instance, [START_REF] Willmott | Solar climates of the conterminous united states: A preliminary investigation[END_REF] defined 10 solar climates over the United

States from averaged global radiations as well as the stations day-to-day perturbations. This method was also used on 62 stations defined by their 12 monthly means of clearness index over Africa [START_REF] Diabaté | Solar radiation climate in Africa[END_REF]. Lima et al.

(2016) pointed out four homogeneous zones from data of global solar radiation and air temperature at 2 meters high, using 110 meteorological stations in the tropical North-eastern region of Brazil. Weather regimes were successfully extracted from rainfall data for La Réunion and South Africa [START_REF] Crétat | Recurrent daily rainfall patterns over South Africa and associated dynamics during the core of the austral summer[END_REF][START_REF] Crétat | The Angola Low: Relationship with southern African rainfall and ENSO[END_REF][START_REF] Morel | Regionalizing Rainfall at Very High Resolution over La Réunion Island Using a Regional Climate Model[END_REF].

In this work, this clustering approach is used to identify SSR zones from the pixels of the SARAH-E grid. Each pixel is characterized by a description of its two orbital components (OC) defined as following :

OC(h, m) = 1 n y n y i y =1 1 n d n d i d =1 SSR(h, m, i d , i y ) (3)
with (h, m, y, d) for hour, month, year and day and (n y , n d ) for number of years and days. Considering hours between 0800 RET to 1700 RET, each pixel OC regroups 120 combined averages, ie. 12 months × 10 hours. A first clustering on all the 3, 355-pixel grid is used to delimit the shape where the island topography changes the global SSR behavior.

SSR zones of solar mean behavior are grouped together considering only the 147 pixels inside this area (Figure 1).

Others pixels are grouped together to be representative of the sea mean SSR but are excluded for the clustering of solar zones. To our knowledge, clustering of solar radiation have never been made in this way: using the two orbital components together to determine coherent "solar zones". These zones (obtained here as statistical classes resulting from the grouping of pixels having a similar behavior) are usually defined differently, for instance through monthly means, daily differences or variation metrics [START_REF] Philippon | Analysis of the diurnal cycles for a better understanding of the mean annual cycle of forests greenness in Central Africa[END_REF][START_REF] Philippon | The light-deficient climates of western Central African evergreen forests[END_REF][START_REF] Zagouras | Clustering the solar resource for grid management in island mode[END_REF][START_REF] Diabaté | Solar radiation climate in Africa[END_REF][START_REF] Badosa | Scales of spatial and temporal variation of solar irradiance on Reunion tropical island[END_REF][START_REF] De Lima | The seasonal variability and trends for the surface solar irradiation in northeastern region of Brazil[END_REF][START_REF] Watanabe | Evaluation of Variation in Surface Solar Irradiance and Clustering of Observation Stations in Japan[END_REF]. The locations of the extreme SSR are indicated in Figure 4(c). Largest SSR (> 520 W.m -2 ) is mostly found over coastal plains, while lowest SSR (< 420 W.m -2 ) is at higher elevations, on both the lee and windward sides of the island (Figure 4(b); Table 2).

At the eastern side of La Réunion, the lowest SSR values are found near the two wettest parts (Z - NE over the NE mid slopes and Z - V at the volcano site) where orographic clouds and associated rain develop on the windward slopes facing the dominant trade-wind flow (Figure 2; [START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF]. This is also where the main run-of-river hydropower plants of the island have been installed [START_REF] Selosse | Exploring sustainable energy future in Reunion Island[END_REF]. At the western side, low SSR is at mid-slopes (∼ 499-m elevation on average), due to thermally-driven orographic clouds leading to much less rain than further east [START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF][START_REF] Cécé | Microscale anthropogenic pollution modelling in a small tropical island during weak trade winds: Lagrangian particle dispersion simulations using real nested LES meteorological fields[END_REF].

Seasonal and diurnal cycles of SSR

In this section, we refine analyses at the seasonal and diurnal timescales. The annual mean SSR over the island is lower in winter (358 W.m -2 in June) and larger in summer (543 W.m -2 in December: Figure 5(a))). Spatial variability for each month and and (b) for each hour from 0800 to 1700 RET (boxplot formalism is the same as in Figure 4). (c), SSR means (in W.m -2 )

for each hour-month combination. (each mean is computed from all the 89 pixels of the island; red dashed lines locate the maximum from all the hours, from 0800 to 1700 RET, for each month; up (down) red triangles locate the two maxima (minima) on the dashed red line). Daily maximum SSR occurs at 1200 RET with a value of 635 W.m -2 on average over the island but with a maximum value of 779 W.m -2 at 1300 RET locally. Boxplots for the afternoon hours (1200 to 1500 RET) have a larger spread (up to 371 W.m -2 at 1300 RET) than those for the rest of the day. This denotes larger spatial variability in the middle of the day than in the early and late hours, consistent with the formation of orographic clouds [START_REF] Badosa | Scales of spatial and temporal variation of solar irradiance on Reunion tropical island[END_REF]. Figure 6 shows the spatial distribution of annual mean hourly SSR. Locations of the minimum and maximum values as displayed in Figure 4, are likely to vary during the day. On the eastern part of the island, minimum values of SSR occur in the morning (0800-1100 RET), notably between 0800 and 0900 RET at Z - V and at 1000 RET at Z -

NE

where SSR remains low during the whole day. On the west, minimum values occur later in the day (1100 RET) over a small area that increases in size during the afternoon, covering pixels at mid slopes and forming thus an elongated C-shaped pattern following the topography. This east/west differentiation in the diurnal cycle is consistent with the initiation and evolution of cloud cover [START_REF] Badosa | Scales of spatial and temporal variation of solar irradiance on Reunion tropical island[END_REF][START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF]. In the early morning, clouds advected from the sea by the dominant trade winds are often seen along the windward slopes, causing rainfall with intensities and peak locations depending on the season and the trade-wind inversion height. The leeward side, by contrast, experiences clear-sky conditions. During the day, orographic clouds with vertical development limited by the trade-wind inversion layer, form on the mountain slopes due to the combined effects of trade winds and thermal winds on the windward side and to thermal winds solely on the leeward side. Clouds on that side often spread towards the coast around or after noon. This spatial extension of clouds is common over islands with a mountainous topography which involves a return flow created by the combined blocking effect of the orography and the inversion layer above [START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Cécé | Microscale anthropogenic pollution modelling in a small tropical island during weak trade winds: Lagrangian particle dispersion simulations using real nested LES meteorological fields[END_REF][START_REF] Liu | Effects of the Hawaiian Islands on the vertical structure of low-level clouds from CALIPSO lidar: Hawaiian Clouds[END_REF][START_REF] Garrett | Orographic Cloud over the Eastern Slopes of Mauna Loa Volcano, Hawaii, Related to Insolation and Wind[END_REF].

As the synoptic wind direction changes over the year (Figure 2), so do the windward and the leeward zones, with major implication on cloud formation [START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF][START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF] and hence on SSR spatial distribution.

Figure 7 shows the particularities of summertime (December) and wintertime (June) diurnal cycles against the annual mean. It locates the pixels where the SSR means for individual months differ most from the annual mean (Figure 6). 6). (unit-less as it is a difference between two standardized maps (annual and month one); the black lines follow island contour (in bold) and ridge lines; red up (purple down) triangles locate the 10% maxima (minima) of the mean SSR of the month; each hour subtitle refers in bracket to the island mean and standard deviation (in W.m -2 ) of the differences between the mean SSR map of the month and the annual mean one; the process to plot the maps combine many steps whom description is done in supplementary materials).

mountainous island slopes, which are fed in moisture by the main trade-wind flow [START_REF] Chu | Extreme Rainfall Events in the Hawaiian Islands[END_REF][START_REF] Laing | Cases of Heavy Precipitation and Flash Floods in the Caribbean during El Niño Winters[END_REF].

Clustering of solar coherent zones

In this section, cluster analysis is used to regionalize spatial features of SSR variability over La Réunion island.

The AHC clustering method is tested for a number of groups ranging from 2 to 18 and on the 147 pixels with similar monthly mean diurnal cycles of SSR (details in Section 2.3). The number of clusters finally retained is 7 which allows to structure the solar resource of the island into zones that are fine enough to be linked to the local meteorology but not too small with a smallest cluster containing 7 pixels. More details of the clustering analysis for different numbers of groups along with the dendrogram and indices used to choose a cluster number are presented in Supplementary materials.

1 1 1 1 2 2 1 1 1 1 1 1 2 3 3 2 2 2 1 1 1 3 2 2 2 2 3 2 1 1 1 1 1 2 3 2 2 2 2 3 3 2 1 1 1 1 2 3 2 2 2 3 3 3 3 2 2 1 1 1 1 3 2 2 2 3 3 3 3 2 1 1 1 1 2 3 2 2 2 2 2 3 3 2 1 1 1 1 2 2 2 3 3 2 1 1 1 1

1 2 2 2 3 3 2 1 1 1 1 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Sea( 213) EW( 18) W1( 17 Figure 8 shows the spatial distribution of the 7 clusters. The pixels are separated graphically to an East and a West group, (corresponding respectively to clusters E1-3 and W1-3), and located at the eastern (windward) and western (leeward) sides of the island. The EW cluster can be found over both sides of the island, being mainly coastal on the leeward side and located at a higher elevation on the opposite side.

The eastern (E1-3) clusters are sorted out by increasing elevation and decreasing SSR. As shown in Table 3 and Figure 9, E1 stretches along the coastline and experiences the highest mean SSR among the eastern clusters, while E3 is located at higher elevations and displays the lowest SSR on this side of the island. The relationship between elevation and SSR value is not as straightforward for the western (W1-3) clusters. W3, which experiences the lowest mean SSR among all clusters and is coincident with Z - W zone, lies at lower altitudes than W2 (Figure 9).

The Sea and eastern clusters have sensibly larger diurnal than annual cycles of SSR, while western clusters show an opposite behavior. E1 and Sea clusters have the largest diurnal variations from all clusters and W2 has the largest SSR seasonality.

We next analyse the diurnal and seasonal cycles of SSR (ie. OC as defined eq. ( 3)) in each cluster through the difference between the cluster and the island average, and the standard deviation of the cluster. These two metrics are further normalized to the island average as follows:

∆OC(h, m) = OC Ci -OC island OC island (h, m) (4)
and:

σ(h, m) = σ(OC Ci ) OC island (h, m) , (5)
where (h, m) is for an hour and a month given, OC Ci is the OC average over all the pixels of the class i and, OC island over all the 89 pixels of the island (Figure 5 (c)) and σ is the standard-deviation operator. Figure 10 shows the time evolution of ∆OC and σ for each class. Sea SSR differs most from island SSR in the afternoon, especially at 1400-1500 RET. This relative difference is larger in summer than in winter, reaching +60 % (∼330 W.m -2 ) in November. The largest ∆OC occurs in November-December for all land clusters, except for W2

and W3 for which it is at its lowest value in October-November. Internal σ is larger in midday and early afternoon in summer, especially in SON, except for E1 and E2.

W3 has a distinctive diurnal cycle of ∆OC which is higher in the morning than the island average, and lower during the rest of the day. ∆OC reaches a minimum of -34 % (-185 W.m -2 ) at 1400 RET in October. This cluster is co-located with Z - W in Figure 4 where orographic clouds with specific diurnal and seasonal cycles usually develop (as described in Figures Figures 6 and7). These clouds form earlier in summer (with increased intensity, notably in October). W2 evolves in a similar way, though the relative differences to the island average are smaller (minimum of -10 %, -56 W.m -2 , at 1400 RET in November). Even if orographic clouds occur preferentially at the lower altitudes of W3, they are likely extended occasionally to W2, particularly in summertime. The seasonality of the ∆OC behavior of western groups can be related to the Mascarene anticyclone which is further southward and stronger in winter than in summer (e.g. [START_REF] Jumaux | Atlas climatique de la Réunion. Météo-France, Direction interrégionale de la Réunion[END_REF][START_REF] Baldy | Field observations of the vertical distribution of tropospheric ozone at the island of Reunion (southern tropics)[END_REF]. Cloud processes are then mainly governed by local flows thanks to higher sea-land differences of temperatures. Higher sea temperatures also provide more humidity to bring at higher elevations, notably due to the higher elevation of the inversion layer in summer. This induces that orographic clouds can form more easily and expend over the leeward of the island [START_REF] Badosa | Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions[END_REF]. This process especially occurs in the afternoon in October-November.

J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M 8
Eastern classes have lower ∆OC in the morning than during the rest of the day. For these classes, ∆OC is largest at 1400 RET, except for E3 (maximum at 1700 RET). This cluster displays negative ∆OC at all times, particularly in the mid-morning (1100 RET) where a minimum relative difference of -18 % (-103 W.m -2 ) is found in September.

The diurnal evolution of ∆OC for the two western classes, W2 and W3, is the reverse to that of the eastern classes, with positive values in the morning and negative ones during the afternoon. By contrast, western class W1 displays positive ∆OC at all times, with lower values in the early morning and higher values during the afternoon.

Solar energy production could greatly benefit from the complementary timing of the mean SSR between the eastern and western sides of the island [START_REF] Jurasz | A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions[END_REF], reducing its cost through a geographic smoothing [START_REF] Lave | High-frequency irradiance fluctuations and geographic smoothing[END_REF][START_REF] Perez | Achieving very high PV penetration -The need for an effective electricity remuneration framework and a central role for grid operators[END_REF][START_REF] Perez | Overbuilding & curtailment: The cost-effective enablers of firm PV generation[END_REF]. Solar energy systems are mainly installed at locations with high mean SSR (Figure 4; OER, 2019). Their mean variations of production can be considered according to the spatial and diurnal-seasonal mean variations described here (Figure 10). They are usually +30 % higher than the mean SSR of the island during summertime afternoon for sites located in Z + NE and Z + S (Figure 4; matching with some E1-W1 pixels, see Figure 10).

Western production sites, if they can be installed at higher altitudes (W2-W3), could receive +5 % more (-5 % less) SSR in the morning (afternoon) than the island mean (Figure 5), with a peak at +11 % (-34 %) during summer. The diurnal variation of solar energy could also be stabilized by combining western sites with eastern ones, for instance in zone E2. There, the timing is opposite to the western side, with a mean SSR of -10 % (+25 %) than the mean SSR of the island during summer morning (afternoon) and a peak of -13 % (+32 %). Storage of solar production could also smooth these mean variations over seasons and during the day. A systematic overproduction occurring in the morning should then be automatically stored and redelivered during the afternoon, sized according to the mean amplitudes defined. Secondary sources of energy production could be set to deliver the recurrent loss of energy [START_REF] Kougias | A methodology for optimization of the complementarity between smallhydropower plants and solar PV systems[END_REF][START_REF] Jurasz | Solar-hydro hybrid power station as a way to smooth power output and increase water retention[END_REF][START_REF] Jurasz | A review on the complementarity of renewable energy sources: Concept, metrics, application and future research directions[END_REF][START_REF] Jerez | The impact of climate change on photovoltaic power generation in Europe[END_REF]. Yet, in addition to these smart grid approaches, temperature and wind velocity should also be considered as they influence the PV production [START_REF] Mattei | Calculation of the polycrystalline PV module temperature using a simple method of energy balance[END_REF][START_REF] Goossens | Effect of wind on temperature patterns, electrical characteristics, and performance of buildingintegrated and building-applied inclined photovoltaic modules[END_REF][START_REF] Kabeel | A comprehensive investigation of the optimization cooling technique for improving the performance of PV module with reflectors under Egyptian conditions[END_REF], paving the way for future research and adjustments to be done over La Réunion and nearby southwest Indian Ocean region.

Conclusion

This work focused on the spatial distribution and timing of the solar resource over the complex topography of the small tropical island of La Réunion in the SWIO. The SARAH-E satellite dataset was used here at an hourly timescale over a 0.05 • × 0.05 • grid of 89 pixels on the island covering the period 1999-2016. It allowed us to delimit the area where the mean solar behavior is influenced by the island topography. The intersection of the seasonal and diurnal scales at particular locations emphasized the necessity to consider jointly the two orbital components to describe the solar behavior. Coherent solar zones were defined by applying an ascending hierarchical clustering on the diurnal and seasonal mean SSR. A side opposition in the SSR patterns in the form of an east-west dipole was found over the island, in accordance with the differentiated windward and leeward cloud processes.

This dipole usefully complements the spatial knowledge of the solar potential by their particular timing through months and hours. Solar energy use can be improved considering this side opposition and the complementary timing of both sides of the island. The production and storage sizing should be done with regard to both the seasonal and diurnal amplitudes of SSR. Around these average behaviors, natural and anthropogenic-forced climate variability (from local to global scales, and from synoptic meteorology to climate change) can induce changes to the mean solar resource [START_REF] Tang | Numerical simulation of surface solar radiation over Southern Africa. Part 2: Projections of regional and global climate models[END_REF]. Forecasts can help anticipate drop of solar energy at different horizons [START_REF] De Felice | Scoping the potential usefulness of seasonal climate forecasts for solar power management[END_REF][START_REF] Antonanzas | Review of photovoltaic power forecasting[END_REF]. They can prevent the short-and long-term variations which can be compensated by specific storage (e.g. Li-ion batteries, fuel-cells; [START_REF] Akbari | Efficient energy storage technologies for photovoltaic systems[END_REF]. Robustness of smart grids energy systems, under a complex meteorological context such as La Réunion island, has to combine a steady knowledge of the essential cycles of its solar resource, that can strongly vary due to the abrupt topography, to efficient forecasts. The island solar resource can also vary due to large-scale dynamics. Regional-scale atmospheric configurations known to influence local-scale SSR, which first need to be assessed, could then be used to analyze and interpret coarse-resolution global forecasts (e.g. S2S, C3S respectively: [START_REF] Vitart | The Subseasonal to Seasonal (S2S) Prediction Project Database[END_REF][START_REF] Manzanas | Bias adjustment and ensemble recalibration methods for seasonal forecasting: A comprehensive intercomparison using the C3S dataset[END_REF] for PV applications in La Réunion.

Analysis of the predictability of these features at various lead-times, from a few hours to a few months in advance, could help adjust energy production or management.

Figure 1 :

 1 Figure 1: Topographical characteristics of La Réunion island : (a) elevation (in m) map based on SARAH-E grids (dotted gray lines). The pixels in grey locate where the island changes sea SSR. (b) the sky view factor map (unitless). Both maps are based on the Digital Elevation Model from NASA Jet Propulsion Laboratory (2009). The letters in blue stand for Cilaos (C), Mafate (M) and Salazie (S) Cirques; the triangles locate the volcanos : Le Piton de La Fournaise (F) and Le Piton des Neiges (N). Symbols refer to the WMO classification for site (1 to 3) and sensors (A to B). Sites classed 1 have no shade at ± 3 degrees above the local horizon, class 2 at ± 5 degrees and class 3 at ± 7 degrees. Sensors classified A are more precise than the B.

Figure 3 :

 3 Figure 3: Hourly-monthly means of (a) ∆ s-i (W.m -2 ) and (b) ∆ s/i-1 (%) for each year and for a mean over 2011-2015. The circles • locate the in situ averages 50 W.m -2 and the crosses + locate when ∆ s/i-1 100 %. All 32 Météo-France stations are used. Subtitles values in brackets are (mean;standard-deviation).

2. 4 .

 4 Parameters selected for the characterization of clustersClusters interpretation is done regarding associated solar and topographical parameters. SSR diurnal cycles are computed on monthly bases. Daily amplitudes are averaged over the monthly maxima of the mean SSR diurnal profile. Seasonal amplitudes are the highest differences between 12 monthly maxima of the mean SSR diurnal profile.Topographical parameters (slope, aspect and distance to the sea) are derived from the 30-meters resolution Digital Elevation Model (DEM) from NASA Jet Propulsion Laboratory(2009). Slope is the highest difference on the 4 neighbors of each DEM pixels. Aspect gives the direction of the slope. Distance to the sea is calculated as the minimum Euclidean 3-D distance from each 30-meters pixel the DEM to the coastline. These parameters are selected for their relevance for solar energy. The day amplitude reveals the mean solar potential meanly produced in a day while the season amplitude will design the energy systems such as solar farms. Daily mean SSR changes are important for short-term forecast and storage (such as Li-ion batteries). Monthly variation gives indication for the sizing of a solar farm or for hydrogen storage (fuel cells technologies). Topographical parameters underlines the terrain characteristics, useful for the energy systems installation. They can be seen as indicators of the easy installation and maintenance considering that the biggest cities of the island are concentrated on the coast.3. Local solar context3.1. Mean SSRMean SSR from 1999 to 2016 of the SARAH-E dataset is displayed in Figure4(b). It is significantly higher over the sea: the lowest 10 % sea-averaged SSR values are similar to the highest 50 % island-averaged SSR values. Only the sea pixels close to the island are impacted by the topography (Figures1 and 4(b)). Over the island, mean SSR is lower by ∼ 19.5 % on average, mostly due to clouds caused by orographic instability. Yet, SSR over the island is highly variable spatially, due to differential cloud cover, with values ranging from 380 to 550 W.m -2 (Figure4(b)).

Figure 5 :

 5 Figure 5: SSR diurnal and seasonal cycles characteristics : In (a) and (b), boxplots detail the SSR mean distribution of the 89 pixels on island (a)

Figure 5

 5 Figure 5(b) summarizes the spatial variability of the annual mean SSR over the island for each hour of the day.

Figure 5

 5 Figure 5(c) represents the time evolution as a function of both hour and month of the spatial mean SSR for the island (89 pixels). The shape and timing of the diurnal cycle vary throughout the year. The maximum SSR is generally at noon, except in late austral winter/early summer (OND) during which it occurs one hour earlier. This is because clouds develop earlier in the day in summer.

  SSR spatial distribution in summer (Figure 7(a)) does not differ significantly from that on the annual mean, except in the mid-morning when negative values occur at 1000 and 1100 RET in the Z - W zone due to earlier development of local clouds. In contrast in winter (Figure 7(b)), positive values occur in Z - W in line with the later development of orographic clouds. In addition, large negative differences to the annual mean are found in Z - V , associated with the minimum values of SSR throughout the day. This is likely due to orographic rainfall and clouds usually found on

Figure 7 :

 7 Figure 7: Diurnal standardized SSR maps for (a) December and (b) June after the removal of the annual cycle (in Figure 6). (unit-less as it is a

Figure 9 :

 9 Figure 9: Mean parameters group comparison (sorted by increasing SSR; each parameter is standardized; the topographical parameters : E for elevation, a for aspect, s for slope and D Sea for sea distance; the 3 solar parameters : SSR, A Day day and A Season season amplitudes).

Figure 10 :

 10 Figure 10: Seasonal and diurnal evolution of each class ∆OC and σ (both percentages are based on the island mean and are expressed in two color-bars (a) for ∆OC and (b) for σ (in Figure 4); up red (down purple) triangles locate the maximum (minimum) by class; for each class, values are (min; max) ∆OC (in %) and [number of pixels])

Table 1 :

 1 Comparison of statistics of ∆ s-i (W.m -2 ) and ∆

	s/-1 (%) for the cases : (1) all hours considered, (2) only 0800 to 1700 RET, (3) all case

Table 2 :

 2 SSR and elevation averaged by zones as defined in Figure4. also lower in winter (123 W.m -2 in June) and higher in summer (271 W.m -2 in November), illustrating that SSR does not behave similarly throughout the year.

		SSR	elevation
	Zone	(W.m -2 )	(m)
	Z + NE	534	114
	Z + S	533	105
	P + C	520	2,100
	P + V	510	1,894
	Z -W	402	499
	Z -NE	399	1,314
	Z -V	407	1,883

  Figure

	08h (223;16) 09h (411;32) 10h (542;46) 11h (622;57) 12h (635;74)	3
		2
		1
	13h (605;84) 14h (542;79) 15h (457;62) 16h (365;38) 17h (231;18)	0
		-1
		-2
		-3

6: Diurnal evolution of the mean SSR spatial distribution (unit-less: a spatial standardization is performed over the 89 islands pixels; the black lines show island contour (in bold) and ridge lines; red up (purple down) triangles locate the 10% maxima (minima) of the island; each hour subtitle refers to the island mean and standard deviation (in W.m -2 ) in bracket; the equivalent non-standardized maps (in W.m -2 ) are in supplementary materials).

  Figure 8: Solar potential micro-climates on La Réunion (each colour refers to one group; for group names, E (green) is for East and W (orange)for West and EW (grey) for East-West; numbers refers from lowest (1) to highest (3) SSR; blue dotted line underlines the east-west clusters limit; numbers in brackets are the number of pixels for each group; Sea group (white) was determined by a first clustering; details in section 2.2).

	20.8 (°S)				E2(11) E3(18)
	21.0				W3(7) E1(43)
	21.2				) W2(33)
	21.4
	55.2	55.4	55.6	55.8	56.0 (°E)

Table 3 :

 3 Mean parameters group description (topographical parameters : E for elevation (in meters), a for aspect, s for slope (in degrees) and D Sea for sea distance (in kilometers); 3 solar parameters (in W.m -2 ) : SSR, A Day day and A Season season amplitudes).

		SSR A Day A Season D Sea	E	a	s
	Sea	581	968	91	1	0	91	0
	EW 490	797	111	6	597 166 12
	E1	552	931	100	0	15	96	1
	E2	508	864	105	3	272 115	8
	E3	419	755	88	12 1,289 149 18
	W1	542	874	136	1	39 118	2
	W2	461	705	160	10 1,022 198 19
	W3	404	581	120	4	620 222 14

Acknowledgements

The authors acknowledge EUMETSAT for providing the satellite data through the Satellite Application Facility on Climate Monitoring (www.cmsaf.eu) and Météo-France for providing the surface solar irradiance measurements used in this study. Calculations were performed using HPC resources from DNUM CCuB (Centre de Calcul de l'université de Bourgogne). Parts of this work have been done thanks to a PhD fellowship funded by the European Regional Development Fund and Regional Council of La Réunion. Pauline Mialhe gratefully acknowledges Nathalie Philippon, Philippe Blanc, Télésphore Brou and Jordi Badosa for their discussion improving the analyses.

Appendices

Supplementary materials are downloadable on website version of the article at doi place holder.

SSR East Zones