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NASA for case 4 onwards

Will concentrate on soil moisture but touch on soil temperature and snow

Situation applies equally to soil temperature – surface measurement propagated to depth

Snow is a little different – measure entire swe but not depth or temperature

NOAA’s GDAS forcing used for both land surface models.

Areas needing soil moisture are areas with the greatest difference in LSM predictions in slide 5 This slide is for Northern Hemisphere summer! Australia shows greater need during Southern Hemisphere Summer.

Passive Microwave -> all weather capability, less affected by roughness, topography and vegetation

Brightness Temp – soil moisture and temperature

SMMR -> AMSR (6.6GHz) and TRMM (10.7GHz)

L-band ideal

C-band affected in Europe

Highly simplistic view – normally not assimilating what you are observing; observation a function of many states Fancy calibration procedure – minimise an objective function over an observation window Requires a tangent linear model and an adjoint – gives sensitivity of objective function to initial conditions

Can use a brute force approach but a lot more computational

No covariances required or provided (ie. no estimate of uncertainty in final solution) Typically lot of iterations required but operational applications usually limit to a few

Requires covariance forecasts

Prohibitive for large systems without simplifications

Requires tangent linear model

May be unstable for highly non-linear problems

Basis for most sequential methods ie OI, nudging, PSAS etc

We will concentrate on the Kalman filter in this presentation

Interpolate:

Vertically -> overcome observation depth limitation

and

horizontally -> overcome vegetation problems? Relies on large scale correlation through soil properties and atmospheric forcing

Use typical advection/diffusion soil moisture and temperature equation ie. Richards equation

Applied:

Quasi updates

Moisture transformation

•Systematic error in model due to no root water uptake term

Soil type 2

•Start in dry period when correlation mismatch worst and lowest

•Surface layer on track after first update - not shown

•Retrieval for open loop as drying period - moisture controlled ET

•ie. open loop on track for very dry and very wet!

•Deep layer not retrieved during dry period as surface layer on track, ie. Kalman filter thinks everything is OK and because low correlation.

•Decoupling of near-surface from deep layer

Note bias in soil moisture due to bias in precip (east coast) and effective wet bias in obs due to dry soil (west coast)