- Subscribe via RSS
Word Cloud LinksAnimation (3) CSP (3) Data (22) Data Science (6) Distributions (1) Dust (7) Economics (15) Engineering (11) Equipment Vendors (3) Faster R (2) GDAL (5) Germany (1) ggplot2 (20) GIS (5) Irradiance (17) Kuwait (1) LaTeX (11) Linux (2) Meteorology (16) Misc Tricks (3) Modeling (9) Natural Gas (2) Nuclear (1) O&M (2) Projects (5) Project Valuations (6) Qatar RE (13) R Colors (9) R Data Import (6) R Data Objects (16) R Data Syntax (6) Renewable Energy (14) RE Policy (7) Resource Assessment (19) R Graphics (19) R Packages (3) R Programming (22) Saudi Arabia (3) Scientific Computing (1) Solar (39) Spatial Analysis (6) Storage (3) UAE (4) Ubuntu (1) Website (4) WECC (3) Wind (5)
Category Archives: Resource Assessment
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.
- 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;
The 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.
Dust aerosols are one of the greatest sources of uncertainty in atmospheric modeling and solar energy forecasting. Aerosols vary in time and space, and can lead to meso-scale variations in cloud cover, temperature, and ground-level radiation.
In the GCC countries, dust storms are most evident during the change in seasons when the Shamal wind is at its strongest. This was evidenced on the evening of October 29, when Doha experienced accelerated winds just prior to sunset, combined with limited visibility over a 2 hour period as shown in the picture below: