Mapping surface solar irradiance over Japan and Australia using MTSAT-2 and MODIS

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1 Mapping surface solar irradiance over Japan and Australia using MTSAT-2 and MODIS (9mm) By Sylvain Cros 1), Mathieu Turpin 1), Quentin Verspieren 1), Caroline Lallemand 1), Nicolas Schmutz 1) and Guy Pignolet 2) (5mm) 1) Reuniwatt SAS, Reunion Island, France 2) Reunion Island Space Initiative, Reunion Island, France (Received June 21 st, 2015) This paper presents a new version of a model converting radiance of geostationary meteorological satellite data into incident downwelling solar radiation at ground level. Images from visible channels of MTSAT-2 are presently used for the mapping of solar surface radiation over Japan and Australia. The present version uses climatological ground albedo maps to model MTSAT-2 pixel reflectance under clear sky conditions. This process permits to characterize ground albedo without using a long-term time series of MTSAT-2, avoiding high costs of archive purchase and large dataset management. Implementation takes benefit of a geographic web server permitting a convenient and interoperable use of different geolocated data respecting the standards of the Open Geospatial Consortium (OGC). Accuracy of GHI values has been quantified by comparing them with pyranometer measurements from the Baseline Surface Radiation Network (BSRN) stations located in Japan. Results are close to the ones obtained with the equivalent model used with Meteosat Second Generation (MSG) data over Europe. The paper concludes on the generic aspect of this model. Key Words: solar irradiance, geostationary satellite, atmosphere, radiative transfer, Earth Observation Nomenclature GHI : Global Horizontal Irradiance GHI cs : GHI under clear sky conditions K c : Clear sly index n : Cloud index ρ* : Albedo without atmospheric effects ρ g : Ground albedo ρ c : Albedo of the brightest clouds 1. Introduction The impact of the downwelling solar irradiance observed at ground level, called global horizontal irradiance (GHI) is preeminent in many fields of human activity. GHI mapping is necessary to achieve a successful energy transition, which includes PV production forecasts and monitoring. It also provides useful information for climatic studies and meteorological forecasts. In terms of health, GHI monitoring plays a great role in the prevention of UV-related cancers and eye diseases. For agriculture and agronomy, improved practices can be implemented to ensure optimal yields. It also has a great importance in the field of civil engineering to support the development of low-energy consumption buildings or for the evaluation of ageing materials. GHI assessments can also be used in commercial applications such as tourism and outdoor activities (concerts, sporting events ) with conditional weather insurance. Thus, the monitoring and collection of GHI data under the form of time-series using space-based technologies represent a significant benefit for our society. Images from geostationary meteorological satellites broadband visible channel are routinely used to retrieve solar irradiance at a world scale with a high spatial resolution going up to 1 km at nadir, with a sample time of 15 min 1). The general principle of such algorithms consists in comparing the reflectance observed at a given pixel with the reflectance from the same pixel under clear sky (cloud-free) conditions. A cloud index is computed from this difference. This cloud index is used as a linear attenuation factor of the simulated irradiance under clear sky 2). The main issue of such a method is the simulation of the pixel reflectance simulated under the clear sky. This reflectance is essentially driven by the ground albedo. A method to retrieve ground albedo consists in processing long term time-series of a given pixel and in gathering the lowest reflectance values during day time, assuming that the lowest reflectance occurs under clear sky conditions 2). However, time-series must be lengthy enough to avoid frequent cloudy situations but not too long because ground albedo is not constant with seasonal vegetation changes. Moreover, pixels including mountain shadows often show lower reflectance than actual ground albedo. To overcome this issue a ground albedo computation method using land surface data from the MODIS (Moderate Resolution Imaging Spectro-radiometer) instrument has been proposed 3). Monthly worldwide ground albedo maps are then available and can be used to model a consistent clear sky reflectance for methods converting satellite images into solar irradiance maps. These maps are operationally used within the satellite-to-irradiance model Heliosat-4 4). This model 1

2 computes the GHI and its components by combining cloudy data originating from Meteosat Second Generation and clear sky irradiance from the model McClear 5). Reuniwatt s algorithm, called Soleksat, used these albedo maps in a modified version of Heliosat-2 over MSG data to proceed to their forecasts 6). Heliosat-2 has the advantage of processing calibrated radiance from any meteorological geostationary satellites broadband visible channel. The purpose of this paper is to implement and assess the Soleksat model on the images of MTSAT-2 (Multi-Functional Transport Satellite-2) of the Japanese Meteorological Agency (JMA) (figure 1). This enables to validate Soleksat s capacity to deliver accurate GHI data on various parts of the world with minimal operational constraints. The second section describes the Soleksat model and its implementation. The third section presents the comparisons between GHI derived from Soleksat and ground measurements over Japan. Finally, the fourth section concludes and underlines the perspectives. as follows: n = (ρ* - ρ g ) / (ρ c - ρ g ) (1) where ρ g is the ground albedo and ρ c is the albedo of the brightest cloud. When ρ c is a modeled value, the determination of ρ g is performed by assuming that ground albedo is the lowest reflectance of ρ* during two weeks at daytime when the pixel is sufficiently illuminated. This cloud index is converted in a clear sky index K c which represents the part of the clear sky global radiation GHI cs that will actually reach the ground (GHI) and this latter permits to compute global irradiance as follows: GHI = K c * GHI cs (2) where GHI cs is the global irradiance simulated under a clear sky by the model McClear 5). 2.2 Ground albedo issues The computation of the albedo is a famous issue of the Heliosat-2 method. The time-series of ρ* should be long enough to avoid persistent cloud coverage but short enough to take into account the soil changes mainly due to vegetation. Moreover, the computation of the albedo assumes that ground reflectance is lambertian, which is not always the case. Then its dependence with θ s cannot be neglected. Finally, snow cover produces a very high albedo and can be seen as bright clouds. As shown in eq. (1), the ground albedo has a strong impact on GHI retrieval. The use of external information for surface reflectance characteristics is necessary. Fig. 1. MTSAT-2 visible channel image (source: Japan Meteorological Agency). 2. Surface solar radiation mapping using satellite data 2.1 General principles of the method The method presented in this article is called Soleksat. It is based on the principles of Heliosat-2 briefly described here. Details of this algorithm can be found in. 2) Numerical counts of pixels from broadband visible channel images are first converted into radiance. The various spatial agencies provide calibration coefficients of the affine function converting numerical counts into radiance. Once the channel s total solar irradiance is known, radiance is then converted into reflectance ρ sat. This value represents the part of solar irradiance reflected by the atmosphere and the ground toward free space. Effect of atmospheric gas and aerosol are modeled in order to convert ρ sat in ρ* which represents the reflectance only due to cloud and ground. Then a cloud index is computed 2.3 The use of MODIS data for ground albedo computation The Moderate Resolution Imaging Spectro-radiometer (MODIS) is onboard the satellites Aqua and Terra. Thanks to this instrument, the NASA has published worldwide maps of three ground albedo parameters: fiso, fvol and fgeo 7). fiso represents the isotropic part of the bidirectional reflectance distribution function (BRDF). The two other parameters represent the anisotropic part. These parameters are available in the data products MCD43C1 and MCD43C2 under the form of 16-day composites provided as a level-3 product, projected to a 0.05 grid in latitude/longitude, approximately 5.6 km at Equator. Only continental surfaces are available. Both products exhibit other irregular data gaps in time and space. Thus the operational use of these products is difficult. For these reasons, 12 monthly climatological maps of these parameters have been built using MODIS data from 2004 to Having these values, ground albedo could be computed using the BRDF model. Details are described in 3). 2.4 Implementation using a geographic web server One of the most important parts of the process consists in having a good managing system for geographical data. For this purpose, all the data are processed and distributed via a 2

3 Geographic Information System (GIS) respecting the standard of the Open Geospatial Consortium. MTSAT-2 images are received in real-time by a FTP access set up between Reuniwatt and the Japanese Meteorological Agency. Raw images are converted and stored in our database in geotiff format. The Soleksat algorithm allows the use of machine-to-machine processes to communicate indifferently with our web server and external sources of georeferenced data in the standard of the OGC. Concretely, georeferenced input data of the algorithm do not need to be formatted or resampled for an operational use. In situ BSRN measurements have been recorded in our GIS to perform an automatic accuracy computation. Considering that, we built a GHI database including Japan and Australia from June 2014 to January The accuracy of GHI values has been quantified by comparing them with pyranometer measurements from the BSRN (Baseline Surface Radiation Network) stations located in Japan (Fukuoka, Ishigakijima, Sapporo and Tateno) as shown in figure 2. 8) Table 1. around the MTSAT-2 pixel acquisition time. The differences between measurements and satellite assessments are computed by subtracting measurements for each instant of satellite assessment. We summarized these differences by the root mean square error, the bias and the correlation coefficient. Table 2 shows the results for each station. Table 2. Station Latitude Longitude Sapporo Fukuoka Tateno RMSE Corr. Number observed Coef. of samples Sapporo Fukuoka Ishigakijima Tateno Results are satisfactory. The correlation coefficient is high in all cases and the bias is low. The relative RMSE shows values between 25 and 30 %, which is slightly high compared to previous works done with MSG data over Europe. Figures 3 to 7 show the scatter plots for each station. Altitude (m) Ishigakijima Bias value BSRN stations characteristics Station Comparison results Mean Fig. 3. Scatter plot between BSRN measurements (GHImeas) and satellite assessment (GHIsat) in W.m-2. Station: Sapporo. Fig. 2. Location of BSRN measurement stations (SAP: Sapporo, FUA: Fukuoka, ISH: Ishigakijima, TAT: Tateno). 3. Results The quality assessment of Soleksat on MTSAT-2 is made by comparing its GHI values with those measured by BSRN pyranometers. BSRN measurements are available every minute. We computed a 10-minute average of measurements Fig. 4. Scatter plot between BSRN measurements (GHImeas) and satellite assessment (GHIsat) in W.m-2. Station: Fukuoka. 3

4 Fig. 5. Scatter plot between BSRN measurements (GHImeas) and satellite Fig. 8. GHI profiles of BSRN measurements (GHI-meas), satellite assessment (GHIsat) in W.m-2. Station: Ishigakijima assessment (GHI-sat) and McClear clear sky model outputs (GHI-cs) in W.m-2. Station: Ishigakijima. Date: June 21st, If figure 7 shows a good matching between satellite assessment and measurement in Sapporo, we can detect in figure 8 (Ishigakijima) a time shift between measured and assessed data. A cloud passing has been viewed by Soleksat with a slight advance between 12:00 and 13:00 (UTC+9). This phenomenon can be due to local cloud variability which is higher than the spatiotemporal characteristics of MTSAT-2 sensor. 4. Conclusions Fig. 6. Scatter plot between BSRN measurements (GHImeas) and satellite In this paper, we implemented the Soleksat model on a 4-month time-series of MTSAT-2 visible images. We described the new features of our model by highlighting the albedo computation. GHI assessments were compared with pyranometric measurements. Our Soleksat algorithm has shown very encouraging results. However, further studies are necessary to reduce the RMSE. Moreover, a complete study will include comparisons with sites in Australia. The forthcoming Himawari 8 satellite will permit to implement our model with a similar temporal resolution available with MSG satellites. assessment (GHIsat) in W.m-2. Station: Tateno. These scatter plots highlight the particularly good results with the measurements of Tateno. The dispersion of the difference appears smaller than in the others site. The station of Ishigakijima presents the largest deviations between satellite assessment and ground measurement. It is important to note that this site is located in an insular environment, where clouds often occur at a smaller spatial scale than MTSAT-2 spatial resolution. For all plots, man can remark the smaller dispersion for high values of GHI, meaning obviously that cloud free cases are better represented by Soleksat algorithm. Acknowledgments This work is part of the project Soleka RI, cofinanced by the European Union and Regional Council La Réunion. Europe invests in La Réunion with the European Regional Development Fund (ERDF). Figures 7 and 8 show GHI profiles for particular days in Sapporo and Ishigakijima. References 1) 2) 3) Fig. 7. GHI profiles of BSRN measurements (GHI-meas), satellite assessment (GHI-sat) and McClear clear sky model outputs (GHI-cs) in W.m-2. Station: Sapporo. Date: June 21st, Cros S., Sébastien N., Schmutz N., Photovoltaic production forecast: the significant role of the meteorological satellites, 13th EMS Annual Meeting & 11th European Conference on Applications of Meteorology (ECAM), September 2013 Reading, United Kingdom. Rigollier, C., Lefevre, M., & Wald, L. (2004). The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77(2), ). Blanc, P., Gschwind, B., Lefevre, M., & Wald, L. (2014, July). Twelve monthly maps of ground albedo parameters derived from MODIS data sets. In Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International (pp ).

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