Robin Wilson
March 26, 2012

# "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

March 26, 2012

## 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

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

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

19. ### Institute for Complex Systems Simulation Results: Cloud over a point

19 X: Location of cloud, Y: Percentage difference compared to no cloud

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!