 SOLYS 2 - copyright Kipp&Zonen |
Calibration of Satellite time series with Ground Station Data |
A problem of units ? of terminology ? Consult the Education service. |
We remind that the HelioClim3 database were globally calibrated using the 29 stations listed on this page. As a consequence, important discrepancies might be observed between satellite estimations and ground stations measurements. The origins of these differences are exposed in this page (to be developed). The SoDa team proposes a method to calibrate the HelioClim3 data onto ground station data using a least square regression.
Framework
Financial challenge: Evaluate the profitability of a project. Need the most accurate sizing of the plant. A bad radiation estimation generates an error in the electrical productibility. If the report is an over estimation of the reality, the plant will not be big enough and there will a financial loss. But underestimating is also very dangerous since the system can be over dimensioned, and moreover the plant will be under sold since the investors will put less money if the product will make less profit. IDEAL: slightly underestimate the productibility while remaining the most attractive possible.
Limit of HC3 regarding this challenge and advantage of the calibration: The spatial resolution of our most recent database HelioClim-3 is not adapted to local radiation estimations. A solution is to settle a ground station to retrieve irradiation values during a short period, and then to calibrate the long-term time series of HC3 irradiation values.
For settling solar power plant purposes, it is interesting to use time series of irradiation values over the longer period of time available and localize precisely on your region of interest. This long period of time enables you to see if there is any global trend of irradiation along the years, as well as analysing the different scenarios, worst or best year under the form of Typical Mean Year, P50 or P90.
Size of a power plant:
| Area | Power | Price |
|---|---|---|
| 30 m2 | 1 kWc | 5 000-8 000 euro |
| 1 ha or 100*100 m2 | 300-400 kWc | 2-2.5 Mi euro |
| 100 ha or 1 km2 | 30-40 MWc | 200-250 Mi euro => 1/10 of a MSG pixel! |
The satellite estimations have the advantages to usually provide long-term time series, with a good spatial coverage and density. However, they integrate the radiation over the pixel, in our case, on a pixel of size 4 * 4 km2. The ground station has the advantage to be localized in space and thus to be more adapted to the local analysis of the solar resource for a solar power plant sizing and the computation of its electrical production. There is no project which has the size of a total pixel (cf table). Nevertheless, a ground station is very expensive and complex. You need time to settle the ground station and receive and process the measurements, and in the meantime the project can't be initiated and there is no return on investment yet. The maintenance of high tech station is intricate and costly, and the explotation of the data requires high-skilled staff. Also need good quality checks + good routines of agregation in the time: from 10 min for instance to the month. Only 6 months of data most of the time, not even a full year. As a consequence, the ground station time series does not provide information on all the seasons and even less on the interannual variability. How to extrapolate the short-time and good quality ground station measurements to a longer period covered by a satellite estimation?
The objective of the calibration is to correct the scale effect of the satellite estimations using the ground station data as well as reducing the error of the model HelioSat-2. The process of downscaling of HC3 (or calibration) will be explained in next section.
Objective: combining the advantages of the local and precise ground station measurements and the long term satellite data.
Process
The principle is to considere all the local effect that might intervene in the local radiation estimation. The HC3 time series over the whole period of time available is corrected => We take into account two effects:
- 1_Far shadow effect
- 2_Difference of optical thickness due to heigh.
 Copyright Philippe Blanc, MINES ParisTech |
The far horizon is estimated using the last version of the SRTM-v4 elevation database (90 m of spatial resolution). We remind that the spatial resolution of the MSG satellite is about 4 km, and approximately 5 km over Europe. The higher spatial resolution of the SRTM database can be used to modulate the irradiation inside the pixel, leading to a higher spatial resolution of the irradiation time series.
Two steps:
- 1_ Calibrated-fitting procedure of the HC3 time series on the ground station data on the common period of time.
- 2_ Applying this fit to the whole period of HC3.
Calibration with ground stations irradiation values
 Copyright Kipp&Zonen |
Taking the experience of the Solar Atlas PACA, the research center of MINES ParisTech has developed an expertise concerning the calibration of HelioClim irradiation time series using ground station data. The HelioClim3 databases are globally calibrated using the 29 ground stations. The protocol of this calibration is described here. As a consequence, discrepancies might be locally observed between ground station values and satellite estimations. The local calibration of HelioClim3 with ground stations values turns out to return more accurate irradiation values. The ground station irradiation values can come from: Let us admit that you order a calibrated HC3 time series over a site named B, and let us also admit that a ground station returns irradiation values on site A. In brief, the mathematical principle consists in two phases: |
Last update: 2/23/12 5:20 PM