Jones NDDA

Information about Jones NDDA

Published on January 20, 2008

Author: Susann

Source: authorstream.com

Content

NDDA - Hydrometeorology Soil Moisture / Cloud Assimilation Experiments:  NDDA - Hydrometeorology Soil Moisture / Cloud Assimilation Experiments Dr. Andrew S. Jones* Dr. Tomislava Vukicevic Current Status:  Current Status The passive microwave observational operator (including the adjoint) is complete Microwave Land Surface Model (MWLSM) Based on 6 and 10 GHz passive microwave data After Njoku (1999) (AMSR land algorithms) Applicable to a new generation of passive microwave imagers: AQUA’s AMSR-E (launched May 4, 2002; data > Feb. 2003) ADEOS-II’s AMSR (launched Dec. 14, 2002) WindSat (launch > Jan. 6, 2003) NPOESS “C1” CMIS (~ 2009) Current Status (continued):  Current Status (continued) The MWLSM observational operator is the link that connects the microwave remote sensing land surface physics to the atmospheric/land surface prognostic model during the data assimilation minimization process A much simpler IR land surface observational operator has also been constructed Related sensitivity studies are underway using the recently completed RAMDAS (the CSU/CIRA 4D data assimilation system) WRF data interfaces in RAMDAS are used to bring in conventional data Several experiments are in progress Microwave Land Surface Model (MWLSM) Observational Operator Components:  Microwave Land Surface Model (MWLSM) Observational Operator Components What We Learned:  What We Learned Satellite observational operator sensitivities can be a strong function of their base states This work creates an improved analysis of the multivariate physical interactions It required derivation of the adjoint in complex number space (publication is in progress) Complex numbers are not handled by current automated adjoint compiler technologies Has practical implications for all future satellite observational operators involving radiative scattering processes Cross-sensor data sets should improve results in particularly difficult base state environments (i.e., sensitivity transitions and/or sensitivity inflection points) 4DDA Soil Moisture / Cloud case study (May 2, 1996):  4DDA Soil Moisture / Cloud case study (May 2, 1996) GOES-9 Visible (Satellite Projection) Time-dependent IR data (after Jones et al., 1998a,b) Future… MW data IR / MW data IR / MW / VIS data For Improved Clouds Soil Moisture with minimal cloud effects Simplest method 4DDA case study (May 2, 1996):  4DDA case study (May 2, 1996) GOES-9 Visible RAMDAS Projection (via DPEAS) 4DDA case study (May 2, 1996):  4DDA case study (May 2, 1996) GOES-9 Visible RAMDAS Projection 25 km grid for testing purposes 4DDA case study (May 2, 1996):  4DDA case study (May 2, 1996) GOES-9 Chan 4 (IR) RAMDAS Projection 25 km grid for testing purposes Low clouds High clouds Clear 4DDA Soil Moisture Future Work:  4DDA Soil Moisture Future Work Finish RAMDAS / DPEAS satellite data interface Complete initial RAMDAS observational tests at 25 km, then go to finer model grid Obtain microwave (AMSR/WindSat) data sets as they become available Verification data sets (some preliminary candidates on hand, however much will depend on the final case study selections) – Upcoming field campaign info? e.g., SMEX03 DoD input is desired… Comparison to traditional soil moisture retrievals (AMSR-like methods) Backup Slides:  Backup Slides Microwave Land Surface Model (MWLSM) Observational Operator Land Surface Data Assimilation Process:  Land Surface Data Assimilation Process Passive Microwave and IR satellite data are complimentary surface data sources IR data has a unique high temporal diurnal temperature signature useful for surface flux retrievals MW data has a physical connection to the soil moisture via the dielectric constant and to key vegetation properties Together, MW and IR cross-sensor combinations can explore temporal data requirements, and mixed pixel issues for better use of satellite observations within the NWP context 14 input variables/model parameters 5 primary control variables for optimization Soil Moisture, Surface Roughness, Land Surface Temp., Veg. Canopy Temperature, and Veg. Water Content Slide14:  Forward Model Results (bare soil) Slide15:  Relative Response (bare soil) Slide16:  (bare soil) Analysis in higher dimensional space:  Analysis in higher dimensional space When only perturbations along the soil moisture base state are allowed, soil moisture is the only contributing variable to the cost function minimization What happens when all control variables in the 5-dimensional space are adjusted simultaneously with a positive bias, x’, along the red vector, and projected back-onto the soil moisture basis vector? SM Slide18:  Forward Model Results (vegetated soil) Slide19:  Relative Response (vegetated soil) We now have multiple “cross-over” conditions Slide20:  Large Veg./Roughness Effects Small Veg./Roughness Effects DRY WET large sensitivity to soil moisture reduced sensitivity to soil moisture no sensitivity to soil moisture Examples of Experiments Planned:  Examples of Experiments Planned Experiment Sequence: Simulation tests / verifications IR MW IR + MW 2 week mostly clear case study 2 week heavy precip event case study Cycling experiments to emulate 3DVAR Various data denial experiments (IR without MW, or in situ observations, etc.) Theoretical simulations to clarify physical cause and effect (i.e., how long does the remote sensing data impact the 4DDA system, and through what predominant physical mechanisms?) For more technical info, references…:  For more technical info, references… http://lamar.colostate.edu/~asjones/Jones/default.htm [email protected]

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