This case takes an in-depth look at one of the tools which can be used to prepare, analyse and visualise CM SAF NetCDF formatted data for Mauritius.
30 October 2020
29 August 2017
By Steffen Kothe and Kumar Ram Dhurmea
The EUMETSAT UFA took place from 12 to 16 September 2016 in Kigali, Rwanda. At the UFA 2016 the Satellite Application Facility on Climate Monitoring (CM SAF) presented their Climate Data Records and Services to the African community.
The CM SAF provides several data records of essential climate variables (ECV) of the global energy and water cycle, which are suitable for multiple climatic applications.
To support and simplify the usage of CM SAF climate data, an R-based toolbox was developed. This R-toolbox can be used to prepare, analyse and visualise CM SAF NetCDF formatted data.
Kumar Ram Dhurmea, from the Mauritius Meteorological Service, took the advantage of this meeting to do some hands-on exercises with the R-toolbox and the CM SAF Climate Data Records.
The diversity of functions, the easy usage and the quality of the CM SAF data were the inspiration for this case study.
CM SAF Data for Mauritius
Mauritius is an island state in the Indian Ocean with a size of about 2000 km2. Given its small size, retrieving sufficient details and information from satellite-based climate data is a real challenge. The CM SAF SARAH Climate Data Record provides, among other variables, the solar incoming radiation at the surface (SIS). It has a horizontal resolution of 0.05°, which allows the retrieval of some details.
Mauritius is close to the outer border of the Meteosat field of view. The latest release of SARAH, i.e. SARAH-2, incorporates a viewing angle correction to reduce uncertainties in the surface radiation due to the observation angle. As such the sampling from SARAH-2 also allows a detailed view of Mauritius.
The relatively low irradiation at the inner island mountains, the windward side (east) with less radiation due to accumulation of clouds (resulting in a steep gradient) and leeward side (west) with a smoother transition between low and high insolation patterns, as well as the flat plains in the north and northeast are clearly distinguishable in SIS (Figure 1). SIS shows a ring pattern around the island, where clouds accumulate because of topographic effects.
The Mauritius Meteorological Service offers climate services for different fields of interest, such as general information, information on agriculture, or information on the sea state for fisherman.
Most of these services are based on a rather sparse network of climatological stations, except for rainfall stations (rain gauges) for which there is a good coverage over the island. The climatological stations deliver mainly measurements for 2 m temperature and precipitation.
Although this station network provides a reasonably good overview of the climatic situation on the island, satellite-based information supplements the station-based data, providing additional parameters and a spatial view, including the surrounding ocean areas.
The mountains of Mauritius are likely to influence climatic patterns many kilometers away from the island. This can be seen, for instance, in the sunshine duration (SDU) standard deviation (Figure 2), where island effects can be seen up to 100 km west of Mauritius.
The SIS seasonal means for December to February (DJF, Figure 3, top) and June to August (JJA, Figure 3, bottom) illustrate the shift in wind regimes, which results in a shift of clouds, and lower values of surface radiation occur on the west side of the island in DJF.
Tourism and energy are important economic sectors of Mauritius and reliable climate information can help to improve and to extend climate services.
SARAH SIS and SDU are homogeneous long-term climate data records, which can be very valuable in these sectors. For example, the usage of these high-quality data allows the analysis of anomalies.
An interesting example of a SDU anomaly in DJF 2014/15 is shown in Figure 4. In this season SDU partly dropped by more than 50 h in the southeast of the island, compared to the DJF long-term mean.
Another advantage of long-term spatial data records is the analysis of time series at every pixel. Figure 5 illustrates an analysis done with the R-toolbox for the SDU pixel of Port Louis. The good news for all tourists from this figure is that SDU has no pronounced annual cycle and has constantly high values throughout the year.
All analyses and figures in this case study were done with the CM SAF R-toolbox, which is freely available via http://www.cmsaf.eu/tools .
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