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Forward models

Forward models. What is a forward model? . Used to relate an remotely sensed quantities covered previously with geophysical parameters. Generally requires some knowledge ( often assumed ) about the physical processes occurring to produce the signal, for example:

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Forward models

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  1. Forward models

  2. What is a forward model? • Used to relate an remotely sensed quantities covered previously with geophysical parameters. • Generally requires some knowledge (often assumed) about the physical processes occurring to produce the signal, for example: • Rayleigh scattering of EM waves off some medium. • Emission/absorption of radiation by stratified layers of atmospheric constituents (ieradiative transfer). • Various levels of detail, various input output methods. • Some knowledge of uncertainty/covariance can be determined by transferring the instrument error (eg Monte-Carlo) but as important is the knowledge of the uncertainty generated by physical assumptions… • Often these assumptions are used to reduce the number of free parameters… 35th American Meteorological Society Radar Conference, Talk 9A.1

  3. KDP, Ze and ZDR as a function of D0 and NW • One thing that the modeling community desires is quantitative estimates of microphysical parameters. • In liquid T-Matrix can be used as a forward model for this! Hooray! • Since T-Matrix (or any scattering code!) is an average over a distribution of scatterers it has many free parameters… 35th American Meteorological Society Radar Conference, Talk 9A.1

  4. http://bit.ly/12rQfKE

  5. 35th American Meteorological Society Radar Conference, Talk 9A.1

  6. Passive Remote sensing forward model • General forward model expression: • Y=F(x)+E • F: forward model may be linear, moderately linear, highly non linear • E: measurement error • For linear to moderate linear problems we can linearize (for example microwave) • For non linear problem we can’t linearize so we have to use the full non-linear model (infrared)

  7. The (post) covariance of x can be expressed as: S = B - BKT(KBKT+E+F)-1KB • F = NxN matrix; N=number of observations • Easy assumption: F=0 - Perfect forward model • Possible sources of forward model errors in passive retrievals: • Modeling of the gaseous resonant absorption (water vapor, oxygen, nitrogen) • Modeling of the water vapor continuum • Modeling of the dielectric properties of water • Assumptions of non-scattering atmosphere • Assumption of droplet size?? • Identify more…

  8. Possible topics for discussion • Identify forward model uncertainties • We probably use different forward models. Should we have a common framework to quantify uncertainty? • Do we need to come up with additional measurements/data to fully characterize the forward model uncertainties? • Diagonal or non-diagonal? • Do we setup a common place where we upload our forward models and covariances? • Move to common forward models with scattering - • Once we have characterized the forward model uncertainty how do we introduce them in the retrievals (in the Bayesian and OE framework there are prescribed ways of doing it, but in other algorithms, such as neural network or statistical retrievals, we may need to be creative).

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