A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data
Abstract
:1. Introduction
2. Study Area and Data Acquisition
2.1. Data Source
Function | Visble channels | Infrared channels |
---|---|---|
Number | 4 (+4 redundant ) | 3 (+3 redundant ) |
IFOV | 35 × 31 μrad | 140 × 140 μrad |
Band | 0.55–0.90 μm | 10.5–11.5 μm (IR1) |
11.5–12.5 μm (IR2) | ||
6.5–7.5 μm (IR3) | ||
Resolution | 1.25 km | 5 km |
Noise performance | S/N ≥ 84 (albedo = 100%) S/N ≤ 6.5 (albedo = 25%) | NE∆T (300 K) NE∆T (220 K) |
IR1 ≤ 0.35K ≤ 1.00K | ||
IR2 ≤ 0.35K ≤ 0.90K | ||
IR3 ≤ 0.22K ≤ 1.50K | ||
Digitization | 6-bit | 6-bit |
Calibration | Preflight | On board blackbody and space view |
2.2. Study Area
3. Method
- Time-series reconstruction based on a single observation site (point scale):
- √
- The in situ GSR data at meteorological stations in the research area were processed;
- √
- Daily GSR retrieved from GMS-5 (VISSR) were extracted corresponding to the location information of the meteorological stations ;
- √
- The time series of GSR retrieved from GMS-5(VISSR) were reconstructed using an ensemble Kalman filter.
- Verification of the time-series reconstruction based on single site-pixels.
- Application of the method to the whole research area: taking the climatic sub-regions as the basic unit, the measured radiation data at representative sites in the sub-regions were assimilated using the methods noted above for each pixel in the research area.
- Regional verification: using the measured radiation values at the sites of (3) as “true values”, the application results of our method were verified over the whole research area.
3.1. Kalman Filter-Based Reconstruction Algorithm
3.2. From Sites to the Regional Scale
4. Result and Analyses
4.1. Results and Analysis of Reconstruction on the Single Site-Pixel Scale
Meteorological stations | Mean of GMS-5 (VISSR) derived GSR (w/m2) | Mean of in situ GSR (w/m2) | Mean after reconstruction (w/m2) | Standard deviations of GMS-5 (VISSR) derived GSR | Standard deviations of in situ GSR | Standard deviations after reconstruction |
---|---|---|---|---|---|---|
Chaoyang | 88.13 | 84.52 | 84.50 | 44.89 | 39.17 | 42.77 |
Taiyuan | 87.65 | 86.09 | 86.16 | 44.37 | 41.20 | 42.00 |
Xi’an | 86.50 | 82.50 | 82.45 | 51.35 | 48.31 | 49.88 |
Shenyang | 84.20 | 78.56 | 78.57 | 45.73 | 40.70 | 44.94 |
Jinan | 93.37 | 93.17 | 93.14 | 44.08 | 47.44 | 47.99 |
4.2. Reconstruction on the Research-area Scale
Meteorological stations | Mean of GMS-5 (VISSR) derived GSR (W/m2) | Mean of in situ GSR (W/m2) | Mean after reconstruction (w/m2) | Standard deviations of GMS-5 (VISSR) derived GSR | Standard deviations of in situ GSR | Standard deviations after reconstruction |
---|---|---|---|---|---|---|
Yan’an | 86.17 | 84.16 | 84.64 | 47.22 | 42.15 | 47.70 |
Houma | 88.04 | 82.41 | 83.95 | 47.92 | 43.21 | 47.34 |
Beijing | 87.94 | 84.69 | 87.49 | 44.84 | 43.13 | 43.45 |
Tianjin | 90.09 | 87.28 | 89.42 | 45.84 | 43.20 | 44.42 |
Leting | 88.56 | 84.84 | 88.13 | 46.93 | 42.84 | 45.47 |
Zhengzhou | 88.90 | 88.00 | 88.48 | 49.18 | 46.96 | 47.65 |
Jvxian | 93.81 | 91.27 | 93.55 | 46.93 | 42.75 | 45.55 |
Fushan | 89.47 | 87.28 | 89.07 | 47.04 | 41.79 | 45.58 |
4.3. Error Analysis
- Errors from the satellite data source. The global solar radiation data for 2002 came from Japan’s GMS-5(VISRR), launched in 1995. The satellite had been running beyond its intended service life even by 2000, and the sensor performance has been declining. The parameters and models used during the calibration and atmospheric correction have the unavoidable problem of uncertainty, which affects the data accuracy. However, the products we used in this paper have been modified by two calibration coefficients.
- The retrieval algorithm is an indirect method. The radiation signals received by the sensor are the results of the entire process of atmospheric reflection, absorption, transmission to the ground and reflection back to the sensor, which is very complex. The satellite remote-sensing retrieval involves many unknown parameters, and empirical or semi-empirical formulas are often adopted. Therefore, a precise quantification is almost impossible.
- Errors may inevitably arise from the reconstruction method established in this study in the following two aspects: during the reconstruction process using the Kalman filter, the setting of the initial values is uncertain and requires experience. The accuracy of the initial value setting will impact the reconstruction results. We used the first in situ GSR as the initial estimated state in this paper, and the MSE of the first day of 2002 was the largest. Moreover, there are scale differences between the ground-observation sites and the satellite pixels, which is another source of error. The in situ GSR obtained at the point scale while the GMS-5(VISRR) derived GSR were retrieved at the resolution of 5 km. We used MSE in this study to quantify the difference between the reconstructed GSR and the in situ GSR. It is about 2.5 at the beginning of reconstruction of GSR by Kalman filter and decreased to less than 1.5 to the end.
5. Conclusions
- The spatial variation of the GMS-5 solar radiation data was relatively stable on scale of pixels of 25 km2 in this paper, and the temporal variation was significant. Such a variation pattern of the time series is obviously seasonal, which can be used as the basis for precision analyses and the reconstruction of time series of radiation data products retrieved from remote-sensing data.
- On the site-pixel scale, the accuracy and consistency of the entire time series was effectively improved by reconstruction using a Kalman filter. The MSE which can quantify the difference between the reconstructed GSR and the in situ GSR is about 2.5 at the beginning of reconstruction of GSR by Kalman filter and decreased to less than 1.5 later to the end. Some representative sites in the research area were selected, and the measured solar radiation values were constantly introduced as “true values” to reconstruct the retrieved data using a Kalman filter. The means and standard deviations of the solar radiation data after reconstruction were significantly improved which can be seen from Table 2; From the third column of Figure 3, most of the differences of the pairs were near zero: lower than 10 W/m2 in winter and lower than 20 W/m2 in other days. This finding indicates that the time-series reconstruction method established in this study for solar radiation data based on Kalman filtering is effective for applied research.
- Apart from the Kalman filter algorithm and the improved algorithm, other new data-assimilation methods can also be used in further studies. Through comparative analysis of the application effects of various methods, we can obtain the desired reconstruction results. With respect to the conversion method for data on the point scale to data on the regional scale, data-assimilation techniques using a land surface-process model as the model operator and a Kalman filter as the assimilation algorithm have been rapidly developed. However, there have been few reports on the application of data-assimilation techniques in time-series reconstruction for surface parameters. Therefore, this technology has great application potential and research value in this respect.
Acknowledgments
Conflict of Interest
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Fu, J.; Jiang, D.; Huang, Y.; Zhuang, D.; Wang, Y. A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data. Energies 2013, 6, 2804-2818. https://doi.org/10.3390/en6062804
Fu J, Jiang D, Huang Y, Zhuang D, Wang Y. A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data. Energies. 2013; 6(6):2804-2818. https://doi.org/10.3390/en6062804
Chicago/Turabian StyleFu, Jingying, Dong Jiang, Yaohuan Huang, Dafang Zhuang, and Yong Wang. 2013. "A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data" Energies 6, no. 6: 2804-2818. https://doi.org/10.3390/en6062804