Category Archives: Data

Aerosol Animation

Aerosol Optical Depth (AOD) defines the degree to which aerosols prevent the transmission of sunlight by absorption or scattering.  AOD is measured using an integrated extinction coefficient over a vertical column of air.  The extinction coefficient can be used to analyze solar extinction and the performance of solar power systems as a function of location and time.

GOCART

Posted in Animation, Data, Dust, Meteorology, Modeling, Qatar RE, R Data Import, R Graphics, Resource Assessment, Saudi Arabia, Solar, Spatial Analysis | Leave a comment

Solar Data Collection in Qatar

KippZonen_Doha_pyranometers_pyrgeometerSolar data collection, as defined by ISO and WMO standards, is a relatively new activity in Qatar.    This article summarizes initiatives underway.  In nearly all cases, public access to the solar radiation data is  limited since data collection remains immature and unvalidated, or the data is strongly aligned to private commercial interests.  Notwithstanding, data collection efforts are making significant progress and are likely to benefit renewable energy strategy development and policy design at the national level.

Posted in Data, Irradiance, Meteorology, Qatar RE, Resource Assessment, Solar | Leave a comment

Top 15 Wind Turbine Suppliers (2013)

WTGMAKE Consulting has released its list of the top 15 global wind turbine suppliers of 2013. According to the report, Vestas has maintained its No. 1 spot, GE has dropped from second to sixth place, and Chinese original equipment manufacturers (OEMs) have secured over half of the top positions.
RankSupplierMarket Share
1Vestas13.2%
2Goldwind10.3%
3Enercon10.1%
4Siemens8.0%
5Suzlon6.3%
6GE4.9%
Posted in Data, Equipment Vendors, Wind | Leave a comment

The Solar Constant

The solar constant represents the mean amount of total solar irradiance (TSI) at the top of the Earth’s atmosphere.  The following code downloads TSI measurements across a range of experiments and satellite data collection efforts to present a composite time series of TSI spanning the period 1976 to 2013.  The objective is to confirm the numeric value of the solar constant and to define the amount of variation in solar irradiance entering the Earth’s atmosphere.

Posted in Data, Irradiance, Resource Assessment, Solar | Leave a comment

Qatar: Meteosat Solar Data

This is the second article in a series on Meteosat solar data from EUMETSAT.  The intent is to define the basic parameters of meteorological data coverage for the State of Qatar.  Specifically:

  • Simple trigonometry is defined to assess the resolution of the satellite coverage area; 
  • A land surface analysis is conducted to visualize the geographic coordinates of the satellite pixels across the State of Qatar; 
Posted in Data, GDAL, GIS, Qatar RE, Resource Assessment, Solar, Spatial Analysis | Leave a comment

Meteosat Solar Data

Meteosat solar dataThe Meteosat series of satellites are equatorial geostationary satellites operated for meteorological data collection by EUMETSAT.  Meteosat solar data is obtained under the Meteosat First Generation (MFG) and the Meteosat Second Generation (MSG) programs.  EUMETSAT is responsible for the launch and operation of the satellites and for delivering satellite data to end-users.

Data for renewable energy resource assessment, particularly solar energy, can be accessed from the EUMETSAT Satellite Application Facility (SAF) for Land Surface Analysis (LSA) located here.  This article provides essential reference data for working with Meteosat solar data.

Posted in Data, Dust, Meteorology, Resource Assessment, Solar | Leave a comment

Binary Data In R

There are many reasons to work with binary data in R.  Solar resource data, solar PV performance data, and real-time grid monitoring data are typically stored and transmitted in binary data formats.  

In practice, the ability to access binary data in R is impossible in the absence of a vender or format specific “can opener” and a properly configured scientific programming environment.  As a result, many business applications often bypass binary data use altogether or, instead, rely on secondary sources and summary statistics with no ability to validate data integrity and accuracy.  

Posted in Data, Data Science, GDAL, R Data Import | Leave a comment