Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery
Abstract
:1. Introduction
2. Ground Measurements, Quality Control, and Validation Protocol
3. Methods to Derive PAR from Satellite Imagery
3.1. Three Surface Solar Irradiance Resources: HC3, CAMS-Rad, and SARAH-3
3.2. Two Groups of Methods
3.3. Towards an Optimal Cloud Extinction Method Dedicated to PAR
4. Results
5. Interpretation of Results
6. Lessons Learned and Recommendations
- We statistically assessed the performance of the current model as a function of each APOLLO cloud type to highlight where the largest errors lay. Furthermore, due to the increase in the signal-to-noise ratio in the early morning and in the afternoon, the performance should also be evaluated as a function of the elevation angle.
- The higher the cloud thickness, the less precise APOLLO. The expression that derived the PAR CMF as a function of the BB CMF, COD, and cloud type also combined the uncertainties of the subjacent models. Therefore, another alternative should be to explore an expression that avoids dependence on the COD but pays more attention to the type of weather, such as PAR CMF = a × BB CMF + b, where a and b would depend on the type of cloud (water/ice) and the type of weather: overcast skies, broken cloud conditions, and close to clear-sky conditions.
7. Conclusions and Policy Recommendations
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Acronyms
Acronym | Meaning |
APOLLO NG | AVHRR Processing Scheme over Clouds, Land, and Oceans—New Generation |
BB | Broadband |
BB CMF | BB cloud modification factor |
CAMS | Copernicus Atmosphere Monitoring Service |
CAMS-Rad | CAMS Radiation Service |
CC | Correlation coefficient |
CIEMAT | Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas |
CM SAF | Satellite Application Facility on Climate Monitoring |
COD | Cloud optical depth |
DAL | Daylight |
DLR | German Aerospace Center |
DWD | Deutscher WetterDienst |
EFDC | European Fluxes Database Cluster |
EUMETSAT | European Organization for the Exploitation of Meteorological Satellites |
FMI | Finnish Meteorological Institute |
GHI | Global horizontal irradiation |
HC3 | HelioClim-3 |
Cloud extinction in the broadband range ( = GHI/GHI in cloud-free conditions) | |
Cloud extinction in the PAR range = PAR/PAR in cloud-free conditions) | |
MBE | Mean bias error |
PAR | Photosynthetically active radiation |
PAR CMF | PAR cloud modification factor |
PPFD | Photosynthetic photon flux density |
RMSE | Root mean square error |
RTM | Radiative transfer model |
SRTM | Shuttle Radar Topography Mission |
SSI | Surface solar irradiance (or irradiation) |
ToA | Top of atmosphere |
STD | Standard deviation |
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33 Stations | Station Details and Country | Contacts and Projects | Climate from Köppen-Geiger Classification | Latitude (°) | Longitude (°) | Height (m) | Start Date | End Date | Time Step (min) | Time Reference (UT+hours) | Begin/Middle/End of Interval Hypothesis (NR: Not Relevant) |
---|---|---|---|---|---|---|---|---|---|---|---|
Aberystwyth University | United Kingdom (UK) | Jon Paul McCalmont, IBERS | Cfb | 52.422 | −4.070 | 110 | 1 January 2012 | 31 December 2017 | 30 | 0 | middle |
Abbotts Hall | UK | Tim Hill and Melanie Chocholek, CBESS | Cfb | 51.7858 | 0.8669 | 2 | 15 December 2012 | 27 January 2015 | 30 | 0 | middle |
Albacete | Spain (SP) | Rita Valenzuela, CIEMAT | BSk | 39.04 | −2.08 | 698 | 1 June 2019 | 31 December 2021 | 1 | 0 | NR |
Cordoba | SP | Rita Valenzuela, CIEMAT | Csa | 37.86 | −4.80 | 91 | 1 June 2019 | 31 December 2020 | 1 | 0 | NR |
Czech_BKF_SF | Bily Kriz Forest, spruce forest after thinning, Czech Republic (CR) | Milan Fischer, Global Change Research Institute GCAS | Dfb | 49.5021 | 18.5369 | 884 | 23 April 2008 | 31 December 2020 | 10 | 1 | end |
Czech_BKF_ST | Bily Kriz Forest, meteorological station (CR) | Milan Fischer, GCAS | Dfb | 49.5026 | 18.5386 | 890 | 1 January 2008 | 31 December 2020 | 10 | 1 | end |
Czech_BKG | Bily Kriz Grassland (CR) | Milan Fischer, GCAS | Dfb | 49.4944 | 18.5429 | 866 | 1 January 2008 | 31 December 2020 | 10 | 1 | end |
Czech_KRP | Kresin agroecosystem (CR) | Milan Fischer, GCAS | Dfb | 49.5732 | 15.0787 | 540 | 1 January 2013 | 31 December 2020 | 10 | 1 | end |
Czech_LNZ | Landzhot, wetland forest (CR) | Milan Fischer, GCAS | Dfb | 48.6815 | 16.9463 | 172 | 29 August 2014 | 31 December 2020 | 10 | 1 | end |
Czech_RAJ | Rajec, spure forest (CR) | Milan Fischer, GCAS | Dfb | 49.4437 | 16.6965 | 651 | 1 June 2011 | 31 December 2020 | 10 | 1 | end |
Czech_STI | Stitna, Beech forest (CR) | Milan Fischer, GCAS | Dfb | 49.036 | 17.9699 | 551 | 11 March 2009 | 31 December 2020 | 10 | 1 | end |
Czech_TRE | Trebon, Wetland (CR) | Milan Fischer, GCAS | Dfb | 49.0247 | 14.7703 | 425 | 29 April 2006 | 31 December 2020 | 30 | 1 | end |
EFDC_DE-Hai | Hainich, Germany (DE) | European Fluxes Database Cluster (EFDC) interface, project CarboExtreme, ICOS | Dfb | 51.0794 | 10.4521 | 460 | 1 February 2004 | 31 December 2020 | 30 | 1 | end |
EDFC_FR-Aur | Aurade, France (FR) | EFDC, CarboEuropeIP, GHG-Europe, Integrated Carbon Observation System (ICOS) station—Tiphaine Tallec and Aurore Brut, CESBIO | Cfb | 43.5496 | 1.1061 | 242 | 31 January 2004 | 30 August 2021 | 30 | 1 | end |
EFDC_FR-Pue | Puechabon (FR) | EFDC, CarboEuropeIP, CarboEuroFlux, Medeflu, IMECC GHG-Europe, ICOS station, CarboExtreme | Csb | 43.7413 | 3.5957 | 276 | 1 February 2004 | 31 December 2018 | 30 | 1 | end |
EFDC_GF-Guy | Guyaflux (FR) | EFDC, ICOS station | Am | 5.27878 | −52.9249 | 37 | 1 February 2004 | 1 January 2016 | 30 | −3 | end |
EFDC_IE-Dri | Dripsey, Ireland (IE) | EFDC, CarboEuropeIP | Cfb | 51.9867 | −8.7518 | 188 | 1 February 2004 | 31 December 2013 | 30 | 0 | end |
EFDC_IL-Yat | EFDC, Yatir, Israel (IL) | EFDC, CarboEuropeIP, CarboEuroFlux | Csa | 31.345 | 35.052 | 654 | 1 February 2004 | 31 December 2018 | 30 | 2 | end |
EFDC_IT-BCi | Borgo Cioffi, Italy (IT) | EFDC, CarboEuropeIP, CarboItaly, ICOS station | Csa | 40.5238 | 14.9574 | 7 | 1 February 2004 | 31 December 2019 | 30 | 1 | end |
EFDC_IT-Noe | Arca di Noe, Le Prigionette (IT) | EFDC, CarboEuropeIP, CarboItaly, Medeflu, CarboExtreme, ICOS station | Csa | 40.6062 | 8.1517 | 26 | 1 February 2004 | 31 December 2008 | 30 | 1 | end |
EFDC_RU-Fyo | Fyodorovskoye, Russia (RU) | EFDC, GHG-Europe, InGOS, TCOS-Siberia | Dfb | 56.4615 | 32.9221 | 273 | 1 February 2004 | 31 December 2020 | 30 | 3 | end |
EFDC_SN-Dhr | Dahra, Senegal (SN) | EFDC, CarboAfrica, GHG-Europe | Bsh | 15.4028 | −15.4322 | 43 | 1 January 2010 | 31 December 2013 | 30 | 0 | end |
EFDC_UK_AMo | Auchencorth Moss (UK) | EFDC, CarboEuropeIP, CarboExtreme, ICOS station | Cfb | 55.7925 | −3.24362 | 264 | 1 February 2004 | 31 December 2016 | 30 | 0 | end |
EFDC_ZA-Kru | Skukuza, South Africa (ZA) | EFDC, CarboAfrica | Cwa | −25.0197 | 31.4969 | 365 | 31 December 2008 | 31 December 2010 | 30 | 2 | end |
Kishinev | Moldova | Alexandr Aculinin, Institute of Applied Physics (IAP) | Dfb | 47.0014 | 28.8156 | 205 | 1 January 2004 | 31 May 2021 | 1 | 0 | NR |
Lugo | SP | Rita Valenzuela, CIEMAT | Csb | 43.00 | −7.54 | 447 | 1 June 2019 | 31 December 2020 | 1 | 0 | NR |
Peronne Saint-Quentin | FR | Frédéric Bornet, INRA | Cfb | 49.8721 | 3.0207 | 84 | 21 November 2013 | 06 August 2021 | 30 | 0 | end |
Pokola | Congo | N. Philippon-Blanc and A. Mariscal, CNRS | Am | 1.4036 | 16.3167 | 332 | 2 January 2019 | 28 November 2021 | 15 | 0 | begin |
Uruguay | Uruguay | Agustin Laguarda, Univ. de la República | Cfa | −31.282 | −57.918 | 56 | 1 January 2017 | 31 December 2020 | 1 | 0 | end |
Valenciennes | Rooftop of the Valenciennes football stadium, FR | Didier Combes, INRAE | Cfb | 50.3487 | 3.5315 | 37 | 22 February 2019 | 31 December 2019 | 1 | 0 | NR |
Villaviciosa (Asturias) | SP | Rita Valenzuela, CIEMAT | Cfb | 43.48 | −5.44 | 6 | 1 June 2019 | 31 December 2020 | 1 | 0 | NR |
Vitoria-Gasteiz (Alava) | SP | Rita Valenzuela, CIEMAT | Cfb | 42.85 | −2.62 | 520 | 1 June 2019 | 31 December 2020 | 1 | 0 | NR |
Zaragoza | SP | Rita Valenzuela, CIEMAT | BSk | 41.73 | −0.81 | 226 | 1 June 2019 | 31 December 2020 | 1 | 0 | NR |
Stations | Nb Total Slots (at the Original Time Step of the Station) | Total Percentage of Discarded Values | Final Number of Slots |
---|---|---|---|
Aberystwyth University | 49,882 | 1% | 49,614 |
Abbotts Hall | 15,345 | 0% | 15,345 |
Albacete | 407,034 | 6% | 382,173 |
Cordoba | 407,647 | 1% | 403,138 |
Czech_BKF_SF | 312,640 | 8% | 287,747 |
Czech_BKF_ST | 329,115 | 3% | 318,431 |
Czech_BKG | 311,520 | 12% | 275,339 |
Czech_KRP | 203,436 | 0% | 203,380 |
Czech_LNZ | 159,943 | 0% | 159,904 |
Czech_RAJ | 243,286 | 4% | 233,733 |
Czech_STI | 297,350 | 0% | 297,153 |
Czech_TRE | 123,954 | 1% | 123,266 |
EFDC_DE-Hai | 139,885 | 14% | 120,216 |
EDFC_FR-Aur | 134,567 | 1% | 133,589 |
EFDC_FR-Pue | 116,388 | 1% | 114,740 |
EFDC_GF-Guy | 66,738 | 1% | 66,309 |
EFDC_IE-Dri | 68,588 | 26% | 50,639 |
EFDC_IL-Yat | 113,752 | 0% | 113,364 |
EFDC_IT-BCi | 85,806 | 11% | 77,301 |
EFDC_IT-Noe | 39,211 | 0% | 39,045 |
EFDC_RU-Fyo | 135,580 | 1% | 134,367 |
EFDC_SN-Dhr | 26,320 | 0% | 26,261 |
EFDC_UK_AMo | 107,934 | 1% | 106,991 |
EFDC_ZA-Kru | 9401 | 48% | 4833 |
Kishinev | 4,356,335 | 0% | 4,348,111 |
Lugo | 407,178 | 12% | 356,332 |
Péronne Saint-Quentin | 60,482 | 0% | 60,397 |
Pokola | 48,999 | 7% | 45,633 |
Uruguay | 28,349 | 0% | 28,349 |
Valenciennes | 210,240 | 2% | 206,499 |
Villaviciosa | 405,930 | 2% | 396,216 |
Vitoria | 407,192 | 7% | 397,643 |
Zaragoza | 407,305 | 9% | 372,324 |
Method Index | Method Name | Group Number |
---|---|---|
M1 | Jacovides from HC3 | 1 |
M2 | Udo and Aro from HC3 | 1 |
M3 | Szeicz from HC3 | 1 |
M4 | Weighted_Kato with BB CMF from HC3 | 2 |
M5 | Weighted_Kato with PAR CMF from HC3 | 2 |
M6 | Jacovides from CAMS-Rad | 1 |
M7 | Udo and Aro from CAMS-Rad | 1 |
M8 | Szeicz from CAMS-Rad | 1 |
M9 | Weighted_Kato with BB CMF from CAMS-Rad | 2 |
M10 | Weighted_Kato with PAR CMF from CAMS-Rad | 2 |
M11 | SARAH-3 | 2 |
Variable | Value |
---|---|
SZA | Uniform between 0 and 89 (degrees) |
Ground albedo | Uniform between 0 and 0.9 |
Elevation of the ground above mean sea level | Equiprobable in the set {0, 1, 2, 3} (km) |
Total column ozone | Ozone content is 300 × β + 200 in Dobson units Beta distribution, with A parameter = 2, and B parameter = 2, to compute β |
Atmospheric profiles (Air Force Geophysics Laboratory standards) | Equiprobable in the set {“Midlatitude Summer”, “Midlatitude Winter”, “Subarctic Summer”, “Subarctic Winter”, “Tropical”, “US. Standard”} |
Aerosol optical depth at 550 nm | Gamma distribution, with shape parameter = 2 and scale parameter = 0.13 |
Angstrom exponent coefficient | Normal distribution, with mean = 1.3 and standard deviation = 0.5 |
Aerosol type | Equiprobable in the set {“urban”, “rural”, “maritime”, “tropospheric”, “desert”, “continental”, “Antarctic”} |
Cloud Optical Depth | Water Cloud (Cloud Base Height + Thickness, km) | Ice Cloud (Cloud Base Height + Thickness, km) |
---|---|---|
0.5, 1, 2, 3 (and 4 for ice cloud only) | Cu: 0.4 + 0.2, 1 + 1.6, 1.2 + 0.2, 2 + 0.5 Ac: 2 + 3, 3.5 + 1.5, 4.5 + 1 | Ci: 6 + 0.5, 8 + 0.3, 10 + 1 |
5, 7, 10, 20 (and 15 for ice cloud only) | Sc: 0.5 + 0.5, 1.5 + 0.6, 2 + 1, 2.5 + 2 As: 2 + 3, 3.5 + 2, 4.5 + 1 | Cs: 6 + 0.5, 8 + 2, 10 + 1 |
40, 70 | St: 0.2 + 0.5, 0.5 + 0.3, 1 + 0.5 Ns: 0.8 + 3, 1 + 1 Cb: 1 + 6, 2 + 8 | - |
COD ≤ 100 | COD > 100 | |||
---|---|---|---|---|
Water Clouds | Ice Clouds | Water Clouds | Ice Clouds | |
a0 | 0.010595062 | 0.0159515048 | 0.1416678840 | 0.1286531944 |
a1 | 0.006268797 | 0.0073167730 | 0.0011951132 | 0.0015225904 |
a2 | −0.00007277 | −0.0000994434 | −0.000001971 | −0.000002432 |
a3 | 0.000000337 | 0.0000005107 | 0.0000000014 | 0.00000000167 |
Group of Stations | Number of Stations in the Group | Stations | Köppen–Geiger Climate Code |
---|---|---|---|
“Western Europe” Group | 7 | Aberystwyth University Abbotts Hall EFDC_FR-Aur EFDC_IE-Dri Péronne Saint-Quentin Valenciennes Villaviciosa | Cfb |
“Central Europe” Group | 6 | Czech_KRP Czech_LNZ Czech_RAJ Czech_STI Czech_TRE EFDC_DE-Hai | Dfb |
“Mediterranean” Group | 5 | Albacete | Bsk |
Cordoba EFDC_IT-Bci EFDC_IT-Noe | Csa | ||
EFDC_FR-Pue | Csb | ||
“Eastern Europe” Group | 4 | Czech_BKF_SF Czech_BKF_ST Czech_BKG EFDC_RU-Fyo | Dfb |
“Central Spain” Group | 3 | Lugo | Csb |
Vitoria | Cfb | ||
Zaragoza | Bsk | ||
“Congo” Group | 1 | Pokola | Am |
“Moldova” Group | 1 | Kishinev | Dfb (close to Dfa and BSk) |
“Israel” Group | 1 | EFDC_IL-Yat | Csa |
“French Guyana” Group | 1 | EFDC_GF-Guy | Am |
“Uruguay” Group | 1 | Uruguay | Cfa |
“South-Africa” Group | 1 | EFDC_ZA-Kru | Cwa |
“Senegal” Group | 1 | EFDC_SN-Dhr | Bsh |
Station | Spread of Values (Standard Deviation for Each Statistical Index) | Range (Maximum–Minimum) |
---|---|---|
All stations | MBE 5.3, STD 6.8, RMSE 6.9, CC 0.02 | MBE 11.7, STD 14.2, RMSE 14.4, CC 0.04 |
“Western Europe” Group | MBE 2.5, STD 4.6, RMSE 4.6, CC 0.01 | MBE 6.5, STD 13.6, RMSE 13.4, CC 0.04 |
“Central Europe” Group | MBE 1.9, STD 2.3, RMSE 2.4, CC 0.01 | MBE 6.0, STD 6.4, RMSE 6.6, CC 0.03 |
“Mediterranean” Group | MBE 1.8, STD 3.7, RMSE 3.7, CC 0.01 | MBE 4.4, STD 8.8, RMSE 8.9, CC 0.03 |
“Eastern Europe” Group | MBE 1.9, STD 1.6, RMSE 1.6, CC 0.01 | MBE 4.2, STD 3.3, RMSE 3.7, CC 0.02 |
“Central Spain” Group | MBE 2.3, STD 4.7, RMSE 5.0, CC 0.01 | MBE 4.4, STD 9.3, RMSE 9.9, CC 0.02 |
Index | M1 | M2 | M3 | M4 | M5 | M6 | M7 | M8 | M9 | M10 | M11 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
“Western Europe” | MBE | −15.0 (−2.7%) | 30.8 (5.5%) | 89.8 (15.9%) | 6.1 (1.1%) | 27.7 (4.9%) | −4.3 (−0.8%) | 42.4 (7.5%) | 102.6 (18.2%) | 17.2 (3.1%) | 40.2 (7.1%) | 38.2 (6.8%) |
NBDATA: 323478 | STD | 135.8 (24.1%) | 136.4 (24.2%) | 152.2 (27.0%) | 134.7 (23.9%) | 137.4 (24.3%) | 143.7 (25.4%) | 144.2 (25.5%) | 159.1 (28.2%) | 143.2 (25.4%) | 150.8 (26.7%) | 156.0 (27.6%) |
MEANREF: 564.5 | RMSE | 136.7 (24.2%) | 139.9 (24.8%) | 176.8 (31.3%) | 134.8 (23.9%) | 140.2 (24.8%) | 143.8 (25.5%) | 150.3 (26.6%) | 189.3 (33.5%) | 144.3 (25.6%) | 156.0 (27.6%) | 160.6 (28.4%) |
CC | 0.963 | 0.963 | 0.963 | 0.963 | 0.961 | 0.958 | 0.958 | 0.958 | 0.958 | 0.953 | 0.950 | |
“Central Europe” | MBE | −2.9 (−0.5%) | 42.1 (7.8%) | 100.1 (18.4%) | 13.9 (2.6%) | 31.8 (5.9%) | 15.1 (2.8%) | 61.6 (11.3%) | 121.5 (22.4%) | 32.5 (6.0%) | 55.3 (10.2%) | 45.1 (8.3%) |
NBDATA: 540960 | STD | 147.2 (27.1%) | 156.9 (28.9%) | 181.6 (33.4%) | 150.6 (27.7%) | 149.3 (27.5%) | 139.7 (25.7%) | 145.0 (26.7%) | 165.1 (30.4%) | 141.7 (26.1%) | 147.7 (27.2%) | 151.0 (27.8%) |
MEANREF: 543.0 | RMSE | 147.3 (27.1%) | 162.5 (29.9%) | 207.3 (38.2%) | 151.3 (27.9%) | 152.6 (28.1%) | 140.5 (25.9%) | 157.6 (29.0%) | 205.0 (37.8%) | 145.4 (26.8%) | 157.7 (29.0%) | 157.6 (29.0%) |
CC | 0.952 | 0.952 | 0.952 | 0.952 | 0.953 | 0.956 | 0.956 | 0.956 | 0.956 | 0.953 | 0.950 | |
“Mediterranean” | MBE | 2.6 (0.3%) | 64.9 (8.7%) | 145.2 (19.5%) | 32.9 (4.4%) | 51.4 (6.9%) | 3.9 (0.5%) | 66.4 (8.9%) | 146.8 (19.7%) | 34.3 (4.6%) | 51.8 (6.9%) | 57.6 (7.7%) |
NBDATA: 265039 | STD | 141.1 (18.9%) | 146.7 (19.7%) | 171.5 (23.0%) | 143.6 (19.3%) | 149.3 (20.0%) | 139.6 (18.7%) | 147.0 (19.7%) | 174.1 (23.4%) | 143.6 (19.3%) | 149.4 (20.0%) | 147.4 (19.8%) |
MEANREF: 745.2 | RMSE | 141.1 (18.9%) | 160.4 (21.5%) | 224.7 (30.2%) | 147.3 (19.8%) | 157.9 (21.2%) | 139.6 (18.7%) | 161.3 (21.6%) | 227.7 (30.6%) | 147.6 (19.8%) | 158.1 (21.2%) | 158.3 (21.2%) |
CC | 0.967 | 0.967 | 0.967 | 0.967 | 0.965 | 0.968 | 0.968 | 0.968 | 0.968 | 0.965 | 0.966 | |
“Eastern Europe” | MBE | 46.6 (9.7%) | 90.4 (18.9%) | 146.8 (30.7%) | 61.0 (12.7%) | 82.1 (17.1%) | 38.0 (8.1%) | 80.5 (17.1%) | 135.2 (28.7%) | 51.7 (11.0%) | 75.3 (16.0%) | 52.5 (11.1%) |
NBDATA: 425945 | STD | 163.6 (34.2%) | 178.5 (37.3%) | 206.9 (43.2%) | 170.9 (35.7%) | 174.3 (36.4%) | 158.3 (33.6%) | 165.6 (35.1%) | 185.3 (39.3%) | 162.1 (34.4%) | 167.9 (35.6%) | 152.6 (32.4%) |
MEANREF: 478.9 | RMSE | 170.1 (35.5%) | 200.1 (41.8%) | 253.7 (53.0%) | 181.5 (37.9%) | 192.7 (40.2%) | 162.8 (34.5%) | 184.1 (39.0%) | 229.3 (48.6%) | 170.2 (36.1%) | 184.0 (39.0%) | 161.4 (34.2%) |
CC | 0.936 | 0.936 | 0.936 | 0.936 | 0.933 | 0.936 | 0.936 | 0.936 | 0.935 | 0.932 | 0.943 | |
“Central Spain” | MBE | 34.9 (5.4%) | 91.6 (14.2%) | 164.7 (25.5%) | 63.9 (9.9%) | 85.4 (13.2%) | 42.4 (6.6%) | 99.8 (15.4%) | 173.7 (26.9%) | 71.7 (11.1%) | 93.5 (14.5%) | 90.0 (13.9%) |
NBDATA: 39864 | STD | 173.8 (26.9%) | 180.9 (28.0%) | 203.6 (31.5%) | 176.4 (27.3%) | 181.4 (28.1%) | 178.8 (27.7%) | 186.7 (28.9%) | 210.1 (32.5%) | 182.3 (28.2%) | 190.4 (29.5%) | 190.4 (29.5%) |
MEANREF: 645.9 | RMSE | 177.3 (27.4%) | 202.7 (31.4%) | 261.9 (40.5%) | 187.6 (29.0%) | 200.5 (31.0%) | 183.8 (28.4%) | 211.6 (32.8%) | 272.6 (42.2%) | 195.9 (30.3%) | 212.1 (32.8%) | 210.7 (32.6%) |
CC | 0.947 | 0.947 | 0.947 | 0.948 | 0.945 | 0.944 | 0.944 | 0.944 | 0.944 | 0.940 | 0.939 | |
“Congo” | MBE | −27.9 (−3.5%) | 37.1 (4.6%) | 120.8 (15.0%) | 40.2 (5.0%) | 63.6 (7.9%) | 70.0 (8.7%) | 143.2 (17.7%) | 237.4 (29.4%) | 146.8 (18.2%) | 171.9 (21.3%) | 157.9 (19.6%) |
NBDATA: 15392 | STD | 209.6 (26.0%) | 211.6 (26.2%) | 227.3 (28.1%) | 210.3 (26.0%) | 210.1 (26.0%) | 213.5 (26.4%) | 223.3 (27.6%) | 248.9 (30.8%) | 223.2 (27.6%) | 231.8 (28.7%) | 235.0 (29.1%) |
MEANREF: 807.7 | RMSE | 211.5 (26.2%) | 214.8 (26.6%) | 257.4 (31.9%) | 214.1 (26.5%) | 219.5 (27.2%) | 224.7 (27.8%) | 265.2 (32.8%) | 344.0 (42.6%) | 267.2 (33.1%) | 288.6 (35.7%) | 283.1 (35.1%) |
CC | 0.934 | 0.934 | 0.934 | 0.935 | 0.936 | 0.932 | 0.932 | 0.932 | 0.933 | 0.928 | 0.925 | |
“Moldova” | MBE | −45.7 (−7.0%) | 5.2 (0.8%) | 70.8 (10.8%) | −23.5 (−3.6%) | −3.5 (−0.5%) | −49.4 (−7.5%) | 1.2 (0.2%) | 66.4 (10.1%) | −27.4 (−4.2%) | −7.0 (−1.1%) | −5.5 (−0.8%) |
NBDATA: 140960 | STD | 145.6 (22.2%) | 145.3 (22.1%) | 162.0 (24.7%) | 143.6 (21.9%) | 144.2 (22.0%) | 142.5 (21.7%) | 139.1 (21.2%) | 152.3 (23.2%) | 138.9 (21.2%) | 141.2 (21.5%) | 137.1 (20.9%) |
MEANREF: 656.2 | RMSE | 152.6 (23.3%) | 145.4 (22.2%) | 176.8 (26.9%) | 145.5 (22.2%) | 144.2 (22.0%) | 150.8 (23.0%) | 139.1 (21.2%) | 166.1 (25.3%) | 141.6 (21.6%) | 141.4 (21.5%) | 137.2 (20.9%) |
CC | 0.965 | 0.965 | 0.965 | 0.965 | 0.965 | 0.967 | 0.967 | 0.967 | 0.967 | 0.966 | 0.968 | |
“Israel” | MBE | −37.2 (−3.9%) | 38.9 (4.1%) | 137.0 (14.4%) | −11.5 (−1.2%) | 6.9 (0.7%) | −44.2 (−4.6%) | 31.4 (3.3%) | 128.8 (13.5%) | −18.8 (−2.0%) | −0.7 (−0.1%) | 61.2 (6.4%) |
NBDATA: 112699 | STD | 116.6 (12.3%) | 126.6 (13.3%) | 162.2 (17.1%) | 118.1 (12.4%) | 122.2 (12.9%) | 127.1 (13.4%) | 132.4 (13.9%) | 161.7 (17.0%) | 126.4 (13.3%) | 125.3 (13.2%) | 141.5 (14.9%) |
MEANREF: 950.5 | RMSE | 122.4 (12.9%) | 132.4 (13.9%) | 212.3 (22.3%) | 118.6 (12.5%) | 122.4 (12.9%) | 134.5 (14.1%) | 136.1 (14.3%) | 206.7 (21.7%) | 127.8 (13.4%) | 125.3 (13.2%) | 154.1 (16.2%) |
CC | 0.981 | 0.981 | 0.981 | 0.981 | 0.980 | 0.978 | 0.978 | 0.978 | 0.978 | 0.978 | 0.973 | |
“French Guyana” | MBE | 1.5 (0.2%) | 70.1 (8.5%) | 158.3 (19.3%) | 85.8 (10.5%) | 114.5 (14.0%) | 63.7 (7.8%) | 137.5 (16.7%) | 232.5 (28.3%) | 154.6 (18.8%) | 187.1 (22.8%) | 170.5 (20.8%) |
NBDATA: 65742 | STD | 241.9 (29.5%) | 262.8 (32.0%) | 299.4 (36.5%) | 268.1 (32.7%) | 272.8 (33.2%) | 227.3 (27.7%) | 245.0 (29.8%) | 278.2 (33.9%) | 249.0 (30.3%) | 255.1 (31.1%) | 296.5 (36.1%) |
MEANREF: 820.6 | RMSE | 241.9 (29.5%) | 272.0 (33.1%) | 338.7 (41.3%) | 281.5 (34.3%) | 295.9 (36.1%) | 236.0 (28.7%) | 280.9 (34.2%) | 362.6 (44.1%) | 293.1 (35.7%) | 316.4 (38.5%) | 342.0 (41.6%) |
CC | 0.911 | 0.911 | 0.911 | 0.910 | 0.909 | 0.918 | 0.918 | 0.918 | 0.918 | 0.915 | 0.893 | |
“Uruguay” | MBE | −75.8 (−7.9%) | −2.0 (−0.2%) | 93.0 (9.7%) | −23.6 (−2.5%) | 8.7 (0.9%) | −81.3 (−8.5%) | −8.0 (−0.8%) | 86.5 (9.0%) | −29.2 (−3.0%) | 0.0 (0.0%) | −2.8 (−0.3%) |
NBDATA: 28349 | STD | 184.2 (19.2%) | 195.3 (20.3%) | 225.1 (23.4%) | 191.1 (19.9%) | 219.0 (22.8%) | 162.7 (16.9%) | 165.7 (17.2%) | 187.8 (19.5%) | 163.8 (17.0%) | 177.2 (18.4%) | 186.4 (19.4%) |
MEANREF: 961.2 | RMSE | 199.2 (20.7%) | 195.3 (20.3%) | 243.6 (25.3%) | 192.5 (20.0%) | 219.2 (22.8%) | 181.9 (18.9%) | 165.9 (17.3%) | 206.8 (21.5%) | 166.4 (17.3%) | 177.2 (18.4%) | 186.4 (19.4%) |
CC | 0.952 | 0.952 | 0.952 | 0.952 | 0.936 | 0.963 | 0.963 | 0.963 | 0.963 | 0.956 | 0.954 | |
“South Africa” | MBE | −22.5 (−2.8%) | 43.5 (5.3%) | 128.6 (15.8%) | 25.0 (3.1%) | 56.6 (7.0%) | 16.4 (2.0%) | 85.7 (10.5%) | 174.8 (21.5%) | 66.5 (8.2%) | 99.0 (12.2%) | 82.1 (10.1%) |
NBDATA: 4833 | STD | 143.2 (17.6%) | 145.2 (17.8%) | 169.5 (20.8%) | 143.0 (17.6%) | 152.6 (18.7%) | 161.0 (19.8%) | 169.7 (20.8%) | 199.6 (24.5%) | 168.8 (20.7%) | 183.3 (22.5%) | 154.3 (18.9%) |
MEANREF: 814.4 | RMSE | 144.9 (17.8%) | 151.6 (18.6%) | 212.8 (26.1%) | 145.2 (17.8%) | 162.8 (20.0%) | 161.8 (19.9%) | 190.1 (23.3%) | 265.3 (32.6%) | 181.4 (22.3%) | 208.3 (25.6%) | 174.8 (21.5%) |
CC | 0.973 | 0.973 | 0.973 | 0.973 | 0.970 | 0.965 | 0.965 | 0.965 | 0.964 | 0.958 | 0.971 | |
“Senegal” | MBE | −113.8 (−11.7%) | −42.2 (−4.3%) | 50.0 (5.1%) | −64.2 (−6.6%) | −49.4 (−5.1%) | 7.8 (0.8%) | 89.5 (9.2%) | 194.7 (20.0%) | 65.2 (6.7%) | 78.4 (8.1%) | 144.2 (14.8%) |
NBDATA: 26234 | STD | 158.8 (16.3%) | 162.3 (16.7%) | 184.5 (19.0%) | 160.6 (16.5%) | 158.4 (16.3%) | 140.0 (14.4%) | 157.4 (16.2%) | 196.6 (20.2%) | 156.0 (16.0%) | 161.4 (16.6%) | 170.7 (17.6%) |
MEANREF: 972.5 | RMSE | 195.4 (20.1%) | 167.7 (17.2%) | 191.2 (19.7%) | 172.9 (17.8%) | 166.0 (17.1%) | 140.3 (14.4%) | 181.1 (18.6%) | 276.7 (28.5%) | 169.1 (17.4%) | 179.4 (18.4%) | 223.4 (23.0%) |
CC | 0.962 | 0.962 | 0.962 | 0.962 | 0.963 | 0.972 | 0.972 | 0.972 | 0.970 | 0.967 | 0.967 |
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Thomas, C.; Wandji Nyamsi, W.; Arola, A.; Pfeifroth, U.; Trentmann, J.; Dorling, S.; Laguarda, A.; Fischer, M.; Aculinin, A. Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery. Atmosphere 2023, 14, 1259. https://doi.org/10.3390/atmos14081259
Thomas C, Wandji Nyamsi W, Arola A, Pfeifroth U, Trentmann J, Dorling S, Laguarda A, Fischer M, Aculinin A. Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery. Atmosphere. 2023; 14(8):1259. https://doi.org/10.3390/atmos14081259
Chicago/Turabian StyleThomas, Claire, William Wandji Nyamsi, Antti Arola, Uwe Pfeifroth, Jörg Trentmann, Stephen Dorling, Agustín Laguarda, Milan Fischer, and Alexandr Aculinin. 2023. "Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery" Atmosphere 14, no. 8: 1259. https://doi.org/10.3390/atmos14081259
APA StyleThomas, C., Wandji Nyamsi, W., Arola, A., Pfeifroth, U., Trentmann, J., Dorling, S., Laguarda, A., Fischer, M., & Aculinin, A. (2023). Smart Approaches for Evaluating Photosynthetically Active Radiation at Various Stations Based on MSG Prime Satellite Imagery. Atmosphere, 14(8), 1259. https://doi.org/10.3390/atmos14081259