What can we learn about LSM’s from the ELDAS increments? Bart vd Hurk, Janneke Ettema & Pedro Viterbo.

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Presentation transcript:

What can we learn about LSM’s from the ELDAS increments? Bart vd Hurk, Janneke Ettema & Pedro Viterbo

A bit about ELDAS Soil moisture assimilation ‘the European way’ (corrections using atm RH & T) Observed precipitation and radiation 15 month pilot study –Oct 1999 – Dec 2000

Design of assimilation system precipitation radiation evaporation Soil moisture correction scheme Soil moisture content (sub)surface runoff Observations driving soil moisture correction Synops data METEOSAT or MSG Land surface parameterization scheme Boundary layer scheme

Validation sites

20yr ‘PILPS’-climatology (driven by ERA40) 15mnth CTL with obs P & R 15mnth DA with obs P & R

Effect of data assimilation One  of dW Accumulated increment Change of evaporation Change of runoff Change of soil water

A few examples

Ratio increment /  (WS) Apr – Oct 2000

Partitioning of increments Evaporation Runoff Storage Apr – Oct 2000

Partitioning of increments Evaporation Runoff Storage Apr – Jul 2000

Ranking of  E/I Nrs in order of rank  E/I First 10 are found in Mediterranean and Estonia

Limited response of E Precipitation Runoff Storage Evaporation

Evaporation 1 Apr – 1 Oct

Conclusions For many locations increments exceed interannual variability of WS For many non-Mediterranean points only a small portion of increments is returned into atmosphere Limited response of E in offline runs DA gives better evaporation for a few points, but generally only small effect compared to mean bias

Partners SMHI CEH Wallingford ECMWF Vienna Univ INM Madrid KNMI Alterra Wageningen Univ Bonn DWD/GPCC CNRM Plus User Advisory Board

Observations: precipitation Raw radar data

Observations: precipitation Corrected radar data

Observations: precipitation Interpolation to ELDAS grid

Observations: precipitation Blend with gauge data

ELDORADO LW & SW Radiation Simple data assimilation of METEOSAT TOA products in NWP RT model Verified against BSRN & local SW networks

Impact of forcings T Td Loobos, NL

Trial assimilation of surface heating rates (Murex) Soil moisture Days when SHR is available (50% data missing, 25% cloudy) + observations -- control -- only SHR assimilated -- only T & RH assimilated -- all assimilated

Assimilation of  -wave data (SGP97) T & RH All data Tb only