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Hydrometeorology. Dr. Andrew S. Jones* Prof. Pierre Julien Assoc. Prof. Jorge Ramirez. Hydrometeorology. Hydrometeorology. Spatial Data Filters. Satellite. Objective: Measure and Predict the Hydrologic Environment. 1DVAR Water Vapor Profiling. Data Assimilation and Modeling

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Hydrometeorology l.jpg

Hydrometeorology

Dr. Andrew S. Jones*

Prof. Pierre Julien

Assoc. Prof. Jorge Ramirez



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Hydrometeorology

Spatial

Data

Filters

Satellite

  • Objective:Measure and Predict the Hydrologic Environment

1DVAR Water Vapor Profiling

DataAssimilationandModeling

Physically links

the system

together in

space and time

Physical Hydrological Models

Energy and Mass Flux

Channel Flow / Erosion / Deposition

Emissivity

Soil Moisture


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Hydrometeorology

  • Dr. Andrew S. Jones, Mr. Phil Stephens1

    • Satellite Soil Moisture data assimilation, Soil Moisture retrieval, advanced spatial filter analysis (~5 to 50 km)

    • Trafficability, Land surface-weather feedbacks

  • Prof. Pierre Julian, Dr. Rosalia Rojas 2

    • Erosion Simulations: CASC2D-SED (30 to 300 m)

    • Channel flow, sediment transport, chemical dispersion

  • Assoc. Prof. Jorge Ramirez, Dr. David Raff 2

    • Hillslope Hydrology Model: HYDROR (> 1 cm)

    • Water/erosion process studies, interactive infiltration

      1 PhD Student at Cambridge

      2 Recent PhD Graduates


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HydrometeorologyCG/AR Theme Interconnections

NDDA

4DVAR

Soil Moisture

HYDROR

Clouds

Hydromet

Future: CDFS II

Spatial Filters / Backus-Gilbert

CASC2D-SED

1DVAR WV

Emissivities

TechTransition

DPEAS

Battlespace


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Hydrometeorology

Objectives:

Develop “deep” soil moisture remote sensing capabilities (therefore must include spatial and temporal soil moisture data assimilation capabilities)

Create spatial data filter for improved data assimilation / data fusion

Cross-sensor technology transitionData Processing and Error Analysis System (DPEAS) work


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Hydrometeorology

Principal Results:

The Microwave Land Surface Model (MWLSM) Observational Operator has been created and has passed preliminary tests. Publication of work in progress…

Current 4DDA work involves case study preparation efforts (physical insertion of IR and MW satellite data sets into the data assimilation system)

More details during the NDDA brief…


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Hydrometeorology

More Principal Results:

  • The 1D Discrete Backus-Gilbert (DBG) spatial filter has been published (IEEE TGRS), and full technical report delivered to DoD contacts

  • Extension of DBG to 2D and addition of noise minimization capabilities in the presence of RFI is underway…

  • The Data Processing and Error Analysis System (DPEAS) has been published (JTECH)

    Took NOAA opportunity to run DPEAS in an operational test environment at NOAA/NESDIS/OSDPD through related NOAA / High Performance Computing and Communications (HPCC) work


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Cross-sensor Technology TransitionConvergence of Research and Operations


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Hydrometeorology

DoD Contacts:

  • Pat Phoebus, many other NRL technical contacts,NRL, Monterey:(primarily data assimilation and satellite meteorology groups)formal collaboration proposals submitted(visit to NRL, July 2002)

  • Chuck Downer, ACE, ERDC, Vicksburg: (visit to CSU, Aug. 2002)

  • Gary McWilliams, ARL, Adelphi:NPOESS-related work / discussions


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Hydrometeorology

Future Plans and Deliverables:

“Deep” soil moisture experiments using 4D data assimilation of infrared/microwave satellite data

MWLSM observational operator publication

Complete 2D Discrete Backus-Gilbertspatial filter implementation and testing, extend to new MW sensors as they become available

Incorporation of 1DVAR water vapor method into DPEAS for global land emissivity studies (collaborations with Joint Center for Satellite Data Assimilation (JCSDA) participants)

Use AMSU to prepare for SSMIS data


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Backup Slides

Hydrometeorology


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HydrometeorologyPublications (2002)

Stephens, P. J., and A. S. Jones, 2002: A computationally efficient discrete Backus-Gilbert footprint-matching algorithm. IEEE Trans. Geosci. Remote Sens.,40, 1865-1878.

Jones, A. S., and T. H. Vonder Haar, 2002: A dynamic parallel data-computing environment for cross-sensor satellite data merger and scientific analysis. J. Atmos. and Oceanic Technol.,19, 1307-1317.

Jones, A. S., 2002: Variational data assimilation of soil moisture using a satellite observational operator and its adjoint. 4th USWRP Symposium, April 22-24,Boulder, CO. (talk).

Jones, A. S., and T. H. Vonder Haar, 2002: A cross-sensor Data Processing and Error Analysis System (DPEAS). CSU Atmospheric Science 40th Anniversary Symposium, July 9 2002, Fort Collins, CO. (poster).

Jones, A. S., S. Q. Kidder, K. E. Eis, and T. H. Vonder Haar, 2002: The use of an HDF-EOS-based parallel data-computing environment for cross-sensor satellite data merger and technology transition. Preprints, 18th International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology (Joint with the 6th Symposium on Integrated Observing Systems), January 13-17, Orlando, FL, Amer. Meteor. Soc.

Jones, A. S., P. J. Stephens, and T. H. Vonder Haar: 2003: An improved Backus-Gilbert spatial filter for satellite data processing. Preprints, 12th Conference on Satellite Meteorology and Oceanography, February 10-13, Long Beach, CA, Amer. Meteor. Soc.


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HydrometeorologyPublications (2002) (cont.)

Jones, A. S., T. Vukicevic, and T. H. Vonder Haar, 2003: Variational data assimilation of soil moisture using 6 and 10 GHz passive microwave data. Preprints, 7th Symposium on Integrated Observing Systems, February 10-13, Long Beach, CA, Amer. Meteor. Soc.

McKague, D. S., J. M. Forsythe, A. S. Jones, S. Q. Kidder, T. H. Vonder Haar, 2003: A passive microwave optimal-estimation algorithm for near real-time atmospheric profiling, Preprints, 12th Conference on Satellite Meteorology and Oceanography, February 10-13, Long Beach, CA, Amer. Meteor. Soc.

P. J. Stephens, and A. S. Jones, 2002: Derivation and analysis of a computationally efficient discrete Backus-Gilbert footprint-matching algorithm. CIRA Tech. Report, Colorado State University, Fort Collins, CO, 59 pp. [ISSN: 0737-5352-55].

Vukicevic, T., D. Zupanski, M. Zupanski, T. Greenwald, A. Jones, T. Vonder Haar, K. Eis, 2002: Data assimilation research in CIRA: Regional Atmospheric Modeling and Data Assimilation System (RAMDAS). CSU Atmospheric Science 40th Anniversary Symposium, July 9, Fort Collins, CO. (poster).

Vukicevic, T., M. Zupanski, D. Zupanski, T. Greenwald, A. Jones, T. Vonder Haar, D. Ojima, and R. Pielke, 2002: An overview of a mesoscale 4DVAR data assimilation research model: RAMDAS. Preprints, The Symposium on Observations, Data Assimilation, and Probabilistic Prediction, January 13-17, Orlando, FL, Amer. Meteor. Soc. (poster P3.1).

Zupanski, M., D. Zupanski, T. Vukicevic, T. Greenwald, A. Jones, K. Eis, T. Vonder Haar, 2002: Estimation theory and data assimilation: a challenge of mesoscale data assimilation. CSU Atmospheric Science 40th Anniversary Symposium, July 9, Fort Collins, CO. (poster).


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Backup Slides

Discrete Backus-GilbertSpatial Filter Results


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Adaptive Spatial Filter Work for SatelliteData Assimilation Use and RFI Avoidance

A discrete Backus-Gilbert algorithm for dynamic and more efficient sensor footprint matching (Stephens and Jones, 2002)

Discretization creates a new diagonal matrix form of the BG method with the Stogryn minimization constraints

Up to 250% more efficient

Similar RMS errors (~± 5 %)

(compare to the ref. line)

This allows for faster calc.of BG coefficients where noise is dynamic (e.g., in RFI contaminated environs)


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Data Fusion Aspects

  • Spectral/spatial/temporal/radiometric influences of instrument characteristicsvs.physical phenomenology

  • Q: How to extract known instrument-dependent effects (resolution behaviors, sampling issues, RFI problems?) AND objectively quantify this for an operational data assimilation system running in near real-time?


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Data Fusion aspects of 3D and 4DVAR

Why use a Backus-Gilbert spatial filter?

  • Cross-sensor observations have different spatial resolutions and noise

  • Quantitatively trade spatial resolution for signal/noise strength

  • Accurately manage data information while going from multi-source observational spaces to the model grid space

  • Focus on important spatial areas in high-gradient environments (e.g., littoral zones, rivers, mountainous terrain)


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Backus-Gilbert method: Pros and Cons

  • Advantages of Backus-Gilbert methods

    • Mathematically-consistent error propagation behaviors

    • Antenna gain patterns are explicitly used

    • Analysis function is highly tunable

    • Heritage within the remote sensing community

  • Disadvantagesof Backus-Gilbert methods that are solved by the Discrete BG method

    • Computationally expensive and rigid(not dynamic)

    • Difficult to use in RFI-contaminated environments


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Discrete Backus-Gilbert Method Performance

RMS

error(K)

CPU time(s)

> 2X faster

# Discrete Intervals


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An example of how the DBG Singular Value Decomposition (SVD) approximation works on a coastline cross-section

1%

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Only ~15%of the termsare needed

3%

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DBG Optimization Performance

Approximately 2X faster due to SVD optimizations,> 4X faster overall when compared to prior methods

SVD Optimization Performance Increases


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Improved computational method to integrate data with models

  • The new method is available and is 4X faster than existing Backus-Gilbert methods

  • Computationally flexible and efficient

  • Handles the spatial analysis and data uncertainties

  • Suitable for multi-source data

  • Conducive to additional optimizations

  • With additional work, the performance of the method could be dynamically adjustablee.g., requests like “I’d like 0.2 K precision in my temperature data for my NWP data assimilation grid” are now possible.


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Future DBG Work

Near-term

  • Construct an AMSR/Windsat-specific DBG spatial filter

  • Use within the 4DVAR DA system for deep soil moisture retrievals using 6 and 10 GHz MW data

    Long-term

  • Use the BG-derived spatial error characteristics within the 4DVAR DA system

  • Use within RFI-contaminated environments

  • Use as a quantitative data compression method using spatial adaptive grid techniques and specified S/N