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Jared K. Entin1,2 |
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Paul R. Houser2 |
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Jeffrey P. Walker1,2 |
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Richard De Jeu1 |
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Eleanor Burke3 |
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Background |
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Land Surface Modeling (LDAS) |
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Oklahoma Mesonet |
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Satellite Observations (TRMM) |
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Soil Moisture for El Reno |
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Data Assimilation |
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Experimental Plan |
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Results |
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Future Directions |
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Conclusions |
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Latent and Sensible Heat Fluxes |
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Runoff |
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Water Storage |
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Launched Dec. 1997 -> currently in space |
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10 GHz (h & v) |
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Footprint size
~ 45 km diameter |
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Data from 38S to 38N |
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Repeat time for S. USA ~ 1/day |
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Ever changing footprint positions |
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Background |
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Land Surface Modeling (LDAS) |
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Oklahoma Mesonet |
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Satellite Observations (TRMM) |
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Soil Moisture for El Reno |
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Data Assimilation (Kalman Filter) |
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Experimental Plan |
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Results |
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Future Directions |
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Conclusions |
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Correct land surface conditions are necessary
for accurate weather and climate predictions. |
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Soil moisture is a state variable in LSMs tied
to both the energy and water balances. |
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Errors in modeled soil moisture may result from: |
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Incorrect LSM soil moisture initialization. |
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Errors in atmospheric forcing data. |
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Inexact or inappropriate LSM physics. |
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Inconsistent land surface parameters values. |
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Finding the model representation, at all layers,
that is most consistent with the observations. |
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Control Run |
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Mosaic LSM w/one tile/grid box |
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LDAS forcing |
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April 1st – Dec 31st, 1998 |
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Dry Run |
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¼ the amount of precipitation |
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All else the same as Control (incl. initial
conditions) |
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Dry Run w/Data Assimilation |
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Kalman Filter w/TRMM soil moisture |
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Daily assimilation (if obs. available) |
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We are able to incorporate information from TRMM
into a Land Surface Model. |
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Data assimilation of TRMM soil moisture will be
more advantageous in areas where the forcing data is known to be poor. |
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Using TRMM data to help understand the data
assimilation procedure will improve the utility of future satellite data. |
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Notes