Bayesian Cloud Detection over Land for Climate Data Records
Abstract
:1. Introduction
2. Bayesian Cloud Detection over Land
2.1. Radiative Transfer and Numerical Weather Prediction (NWP) Data
2.2. Uncertainty Specification
2.3. Gaussian Mixed Model
2.4. Surface Characterisation
2.5. Cloudy Probability Distribution Functions (PDFs)
2.6. Aerosol Characterisation
3. Results
4. Application to CDR Generation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Channels (μm) | Dimension | Range | No. of Bins | Time | |
---|---|---|---|---|---|
1 | 11.0, 12.0 | Biome | 1–8 | 7 | Day |
Solar zenith angle | 0–180° | 2 | |||
Atmospheric path length | 1–2.4 | 4 | |||
NWP surface temperature | 240–330 K | 90 | |||
11.0–12.0 μm channel difference | −1–6 K | 35 | |||
11.0 μm-NWP surface temperature | −15–15 K | 30 | |||
2 | 3.7, 11.0, 12.0 | Biome | 1–8 | 7 | Night |
Solar zenith angle | 90–180° | 1 | |||
Atmospheric path length | 1–2.4 | 4 | |||
NWP surface temperature | 240–330 K | 90 | |||
3.7–11.0 μm channel difference | −6–10 K | 80 | |||
11.0–12.0 μm channel difference | −1–6 K | 35 | |||
11.0 μm-NWP surface temperature | −15–15 K | 30 | |||
3 | 0.6, 0.8, 1.6 | Biome | 1–8 | 7 | Day |
Solar zenith angle | 0–95° | 38 | |||
Atmospheric path length | 1–2.4 | 4 | |||
1.6 μm channel | 0–1 | 50 | |||
0.8 μm channel | 0–1 | 50 | |||
0.6–0.8 μm channel difference | −0.5–0.2 | 35 |
Biome | Name | Land Cover CCI Classes | ALB2 Classes |
---|---|---|---|
1 | Cropland | 10, 11, 12, 20, 30, 40, 100, 110 | - |
2 | Forest | 50, 60, 61, 62, 70, 71, 72, 80, 81, 82, 90 | - |
3 | Shrubland | 120, 121, 122, 130, 140, 150, 151, 152, 153 | - |
4 | Flooded | 160, 170, 180 | - |
5 | Urban | 190 | - |
6 | Bare Soil | 200, 201, 202 | 20, 21, 22, 23, 24, 25 |
7 | Snow and Ice | 220 | - |
Scene | Location | Date | Orbit Number | Day/Night | Project |
---|---|---|---|---|---|
1 | China | 21/05/2007 | 27304 | Night | GlobT |
2 | Russia | 22/05/2007 | 27314 | Day | GlobT |
3 | Florida, USA | 23/05/2007 | 27333 | Day | GlobT |
4 | UK | 23/08/2007 | 28647 | Day | GlobT |
5 | Algeria | 11/06/2010 | 43290 | Day | GlobT |
6 | Ukraine | 05/08/2010 | 44083 | Night | GlobT |
7 | Antarctica | 08/08/2010 | 44121 | Night | GlobT |
8 | Mauritania | 08/08/2010 | 44128 | Night | GlobT |
9 | Canada | 07/06/2011 | 48474 | Day | GlobT |
10 | Uruguay | 11/11/2009 | 40250 | Night | GlobT |
11 | Brazil | 06/06/2004 | 11858 | Day | SYNERGY |
12 | Brazil | 26/05/2005 | 17369 | Day | SYNERGY |
13 | Oklahoma, USA | 09/08/2004 | 12776 | Day | SYNERGY |
14 | Oklahoma, USA | 16/05/2005 | 16784 | Day | SYNERGY |
15 | Oklahoma, USA | 25/07/2005 | 17786 | Day | SYNERGY |
16 | Oklahoma, USA | 05/06/2006 | 22295 | Day | SYNERGY |
17 | Oklahoma, USA | 08/10/2007 | 29309 | Day | SYNERGY |
18 | Burkina Faso | 28/11/2002 | 03897 | Day | SYNERGY |
19 | Burkina Faso | 01/12/2002 | 03941 | Day | SYNERGY |
20 | Burkina Faso | 02/01/2003 | 04398 | Day | SYNERGY |
21 | Burkina Faso | 21/01/2003 | 04670 | Day | SYNERGY |
22 | Burkina Faso | 15/07/2003 | 07176 | Day | SYNERGY |
23 | Zambia | 15/01/2003 | 04583 | Day | SYNERGY |
24 | Zambia | 09/07/2003 | 07088 | Day | SYNERGY |
25 | Zambia | 17/11/2003 | 08090 | Day | SYNERGY |
26 | Russia | 03/06/2004 | 11810 | Day | SYNERGY |
27 | Russia | 12/08/2004 | 12812 | Day | SYNERGY |
28 | Russia | 23/06/2005 | 17321 | Day | SYNERGY |
29 | Russia | 06/10/2005 | 18824 | Day | SYNERGY |
30 | Russia | 21/09/2006 | 23834 | Day | SYNERGY |
31 | Russia | 02/08/2007 | 28343 | Day | SYNERGY |
Performance Metric | Bayesian Cloud Detection (%) | Operational Cloud Detection (%) |
---|---|---|
Percentage of Perfect Classification | 86.6 | 81.0 |
Hit Rate | 93.9 | 86.0 |
False Alarm Rate | 16.7 | 21.6 |
True Skill Score | 77.2 | 64.7 |
Confusion Matrix | Manual Mask Cloud | Manual Mask Clear |
---|---|---|
Bayesian Cloud | 1,437,732 | 570,659 |
Bayesian Clear | 93,099 | 2,856,615 |
Operational Cloud | 1,316,944 | 729,595 |
Operational Clear | 213,887 | 2,697,679 |
Station | Country | Latitude (Degrees) | Longitude (Degrees) | Frequency of Observations |
---|---|---|---|---|
Bingley | UK | 53.8 | −1.9 | 1 min |
Coleshill | UK | 52.5 | −1.7 | 1 min |
North Slope | Alaska | 71.3 | −156.6/−156.7 | 35 s |
Ny Alesund | Norway | 78.9 | 11.9 | 5 min |
Oliktok Point | Greenland | 70.5 | −149.9 | 30 s |
Paris | France | 48.7 | 2.2 | 3 s |
Sabana | Puerto Rico | 18.3 | −65.7 | 1 h |
Southern Great Plains | USA | 36.6 | −97.5 | 1 min |
Tropical West Pacific | Papua New Guinea | −2.1 | 147.4 | 1 min |
University of Reading | UK | 51.4 | 0.9 | 1 min |
Location | ATSR-2 | AATSR | AATSR | MODIS | MODIS | SLSTR |
---|---|---|---|---|---|---|
Bingley, UK | - | - | 94.4 | 92.8 | 93.3 | 96.1 |
Coleshill, UK | - | - | 89.7 | 90.9 | 92.4 | 92.7 |
North Slope Alaska | 77.5 | 83.3 | 78.8 | 95.2 | 95.0 | 91.4 |
Ny Alesund | 86.1 | 87.2 | 88.9 | 95.2 | 94.6 | 96.0 |
Oliktok Point Alaska | - | - | - | - | 95.7 | 94.5 |
Paris | - | - | - | - | 96.2 | 98.5 |
Puerto Rico | - | - | - | - | 100.0 | 100.0 |
Southern Great Plains | 91.9 | 96.3 | 90.1 | 87.4 | 89.9 | 97.2 |
Tropical West Pacific | 76.5 | 70.0 | 83.9 | 84.8 | - | - |
University of Reading, UK | - | - | - | - | 91.9 | 95.5 |
Location | ATSR-2 | AATSR | AATSR | MODIS | MODIS | SLSTR |
---|---|---|---|---|---|---|
Bingley, UK | - | - | 36 | 125 | 570 | 563 |
Coleshill, UK | - | - | 29 | 110 | 542 | 547 |
North Slope Alaska | 120 | 102 | 170 | 673 | 686 | 455 |
Ny Alesund | 532 | 501 | 973 | 2147 | 2156 | 2231 |
Oliktok Point Alaska | - | - | - | - | 515 | 327 |
Paris | - | - | - | - | 292 | 326 |
Puerto Rico | - | - | - | - | 10 | 11 |
Southern Great Plains | 62 | 54 | 81 | 174 | 189 | 290 |
Tropical West Pacific | 17 | 30 | 87 | 151 | - | - |
University of Reading, UK | - | - | - | - | 405 | 445 |
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Bulgin, C.E.; Embury, O.; Maidment, R.I.; Merchant, C.J. Bayesian Cloud Detection over Land for Climate Data Records. Remote Sens. 2022, 14, 2231. https://doi.org/10.3390/rs14092231
Bulgin CE, Embury O, Maidment RI, Merchant CJ. Bayesian Cloud Detection over Land for Climate Data Records. Remote Sensing. 2022; 14(9):2231. https://doi.org/10.3390/rs14092231
Chicago/Turabian StyleBulgin, Claire E., Owen Embury, Ross I. Maidment, and Christopher J. Merchant. 2022. "Bayesian Cloud Detection over Land for Climate Data Records" Remote Sensing 14, no. 9: 2231. https://doi.org/10.3390/rs14092231