"Can a single cloud spoil the view?" - RSPSoc Annual Student Meeting 2012

A presentation on my MSc project work entitled "Can a single cloud spoil the view?": Modelling the effect of an isolated cloud on surface solar irradiance

Transcript

1. Institute for Complex Systems Simulation “Can a single cloud spoil the view?” Modelling the effect of an isolated cloud on surface solar irradiance Robin Wilson, University of Southampton
2. Institute for Complex Systems Simulation The problem 2 We assume satellite images are always cloud free They aren’t! What effect does a cloud have on an image? • Underneath the cloud •  Simple! • Next to the cloud •  Harder! Shadows? • Further away from the cloud? •  Harder! How big is this effect?
3. Institute for Complex Systems Simulation Radiative Transfer Models (RTMs) •  Simulate the passage of light through the atmosphere •  Input: Atmospheric conditions, TOA solar intensity •  Simulate: Absorption & Scattering by Aerosols & Gases •  Output: Intensity at ground ( -> intensity at sensor) •  Examples: •  6S •  MODTRAN (-> ATCOR, MODO) 3
4. Institute for Complex Systems Simulation •  Transmittances – The proportion by which the light intensity is reduced •  Optical depth – Used to calculate the transmittances All dependent on wavelength All we need to do is calculate the optical depths! Fundamental equations of RTMs 4 T = e ⌧ Iground = IT OA T1 T2 T3 T4 . . . Tn
5. Institute for Complex Systems Simulation Current RTMs 5
   ⌧1 ⌧2 ⌧3 ⌧5 ⌧6 Vertically heterogeneous Horizontally homogeneous ⌧4
6. Institute for Complex Systems Simulation What we need 6 6
   ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ ⌧ Vertically heterogeneous Horizontally heterogeneous
7. Institute for Complex Systems Simulation RTWRTM •  Monte Carlo, Ray-tracing model –  Have to deal with each ray of light individually as generalised mathematics won’t work for horizontally heterogeneous atmospheres –  Run for 10,000’s of rays •  Based on a 2D atmospheric grid •  Exploratory model – for understanding, not accurate outputs •  Based on a very simple RTM – SPCTRAL2 (Bird and Riordan, 1984) 7
8. Institute for Complex Systems Simulation The Grid 8
9. Institute for Complex Systems Simulation The Grid SUN SENS 9
10. Institute for Complex Systems Simulation The Grid SUN SENS 10
11. Institute for Complex Systems Simulation Scattering •  Rayleigh: –  Gas molecules (small!) –  Probability: •  Mie –  Aerosols (large!) –  Probability: More constant with wavelength, depends on aerosol type and amount •  Each cell given a probability, and directions calculated from phase functions 11 / 1 4
12. Institute for Complex Systems Simulation Scattering phase functions 12 Maritime Aerosols Rayleigh 0.125 0.163 0.125 0.087 0.087 0.125 0.163 0.125 Data from OPAC database (Hess et al., 1998)
13. Institute for Complex Systems Simulation Absorption •  Two groups: –  General atmospheric gases (O, N, CO2 ) – constant –  Water vapour and Ozone – spatially variable •  Method –  Assign each cell an amount of water vapour and ozone –  Sum for each cell the ray passes through –  Add on other gases at the end (proportional to path length) 13
14. Institute for Complex Systems Simulation Grid parameterisation •  Each cell has –  Water vapour content –  Aerosol type (Maritime, Urban, Cloud etc) –  Aerosol amount (particles per cell) •  Can add a cloud (generated from a cellular-automaton model) anywhere in the grid – just change parameters for those cells •  Parameterisation developed from standard profiles, plus random element 14
15. Institute for Complex Systems Simulation Putting it all together SUN SENS 15 Water: 0cm 0.3cm 0.125 0.163 0.125 0.087 0.087 0.125 0.163 0.125 Choose scattering type: Rayleigh 0.58cm 0.9cm Choose scattering type: Mie ...etc 2.14cm
16. Institute for Complex Systems Simulation Real example paths 16
17. Institute for Complex Systems Simulation Results •  After all that model development…lets actually use it! •  Sanity check •  Effect of a cloud passing over a point on –  At-sensor Radiance –  NDVI (from idealised vegetation) 17
18. Institute for Complex Systems Simulation Results: Sanity check 18
19. Institute for Complex Systems Simulation Results: Cloud over a point 19 X: Location of cloud, Y: Percentage difference compared to no cloud
20. Institute for Complex Systems Simulation Increased scattering 20
    
21. Institute for Complex Systems Simulation Results: Cloud a long way away •  Used a large grid •  Moved cloud away from sensor •  Location defined by the Cloud Location Ratio: –  Designed to allow transfer to the real world •  Conversion to standard remote sensing output: NDVI 21 CLR = Horizontal distance from sensor to cloud Vertical height of cloud base
22. Institute for Complex Systems Simulation 22 Irradiance NDVI Scattering: Rays that would have gone off the grid are scattered towards sensor Wavelength dependence: Reduces NDVI >-5%
23. Institute for Complex Systems Simulation What does that NDVI change mean? •  Converted NDVIs to Net Primary Productivity (Ruimy et al., 1994) •  Maximum difference in NDVI •  Reduction of NPP by 2.8g/m2 –  Over a whole Landsat scene that is 89,000 tonnes! •  The CLR suggests this could happen even when the cloud is a long way away –  CLR of 16, with real-world height of 2000m, gives distance of 32km! 23
24. Institute for Complex Systems Simulation Conclusion We need to correct for this! Possible methods are under research…. 24 Cloud here Could affect irradiance here Causing a change of up to 5% in NDVI Nothing is done about this at the moment – clear skies are always assumed!