LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration

Tim Bellerby University of Hull, Hull, United Kingdom

Search for other papers by Tim Bellerby in
Current site
Google Scholar
PubMed
Close
,
Kuo-lin Hsu University of California, Irvine, Irvine, California

Search for other papers by Kuo-lin Hsu in
Current site
Google Scholar
PubMed
Close
, and
Soroosh Sorooshian University of California, Irvine, Irvine, California

Search for other papers by Soroosh Sorooshian in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (∼4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear.

Corresponding author address: T. J. Bellerby, Department of Geography, University of Hull, Hull HU6 7RX, United Kingdom. Email: t.j.bellerby@hull.ac.uk

Abstract

The Lagrangian Model (LMODEL) is a new multisensor satellite rainfall monitoring methodology based on the use of a conceptual cloud-development model that is driven by geostationary satellite imagery and is locally updated using microwave-based rainfall measurements from low earth-orbiting platforms. This paper describes the cloud development model and updating procedures; the companion paper presents model validation results. The model uses single-band thermal infrared geostationary satellite imagery to characterize cloud motion, growth, and dispersal at high spatial resolution (∼4 km). These inputs drive a simple, linear, semi-Lagrangian, conceptual cloud mass balance model, incorporating separate representations of convective and stratiform processes. The model is locally updated against microwave satellite data using a two-stage process that scales precipitable water fluxes into the model and then updates model states using a Kalman filter. Model calibration and updating employ an empirical rainfall collocation methodology designed to compensate for the effects of measurement time difference, geolocation error, cloud parallax, and rainfall shear.

Corresponding author address: T. J. Bellerby, Department of Geography, University of Hull, Hull HU6 7RX, United Kingdom. Email: t.j.bellerby@hull.ac.uk

Save
  • Adler, R. F., and Mack R. A. , 1986: Thunderstorm cloud top dynamics as inferred from satellite observations and a cloud top parcel model. J. Atmos. Sci., 43 , 19451960.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Adler, R. F., and Negri A. J. , 1988: A satellite infrared technique to estimate tropical convective and stratiform rainfall. J. Appl. Meteor., 27 , 3038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Anderson, J. L., 2001: An ensemble adjustment Kalman filter for data assimilation. Mon. Wea. Rev., 129 , 28842903.

  • Anderson, J. L., and Anderson S. L. , 1999: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev., 127 , 27412758.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Andrieu, C., Freitas N. D. , Doucet A. , and Jordan M. I. , 2003: Introduction to MCMC for machine learning. Mach. Learn., 50 , 543.

  • Arkin, P. A., and Meisner B. N. , 1987: The relationship between large-scale convective rainfall and cold cloud over the Western Hemisphere during 1982–84. Mon. Wea. Rev., 115 , 5174.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arulampalam, M. S., Maskell S. , Gordon N. , and Clapp T. , 2002: A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process., 50 , 174188.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Augustine, J. A., Griffith C. G. , Woodley W. L. , and Meitin J. G. , 1981: Insights into errors of SMS-inferred GATE convective rainfall. J. Appl. Meteor., 20 , 509520.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ba, M. B., and Gruber A. , 2001: GOES Multispectral Rainfall Algorithm (GMSRA). J. Appl. Meteor., 40 , 15001514.

  • Bellerby, T., 2004: A feature-based approach to satellite precipitation monitoring using geostationary IR imagery. J. Hydrometeor., 5 , 910921.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellerby, T., 2006: High-resolution 2-D cloud-top advection from geostationary satellite imagery. IEEE Trans. Geosci. Remote Sens., 44 , 36393648.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellerby, T., Todd M. , Kniveton D. , and Kidd C. , 2000: Rainfall estimation from a combination of TRMM precipitation radar and GOES multispectral satellite imagery through the use of an artificial neural network. J. Appl. Meteor., 39 , 21152128.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Capacci, D., and Conway B. J. , 2005: Delineation of precipitation areas from MODIS visible and infrared imagery with artificial neural networks. Meteor. Appl., 12 , 291305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Climate Prediction Center, cited. 2008a: NOAA CPC global merged full resolution IR data. [Available online at http://www.cpc.noaa.gov/products/global_precip/html/wpage.full_res.html].

    • Search Google Scholar
    • Export Citation
  • Climate Prediction Center, cited. 2008b: NOAA CPC merged microwave. [Available online at http://www.cpc.ncep.noaa.gov/products/janowiak/mwcomb_description.html].

    • Search Google Scholar
    • Export Citation
  • Doucet, A., Godsill S. , and Andrieu C. , 2000: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. Comput., 10 , 197208.

  • Ferraro, R. R., 1997: Special sensor microwave imager derived global rainfall estimates for climatological applications. J. Geophys. Res., 102 , 1671516736.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ferraro, R. R., Weng F. , Grody N. C. , and Zhao L. , 2000: Precipitation characteristics over land from the NOAA-15 AMSU sensor. Geophys. Res. Lett, 27 , 26692672.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Griffith, C. G., Augustine J. A. , and Woodley W. L. , 1981: Satellite rain estimation in the U.S. High Plains. J. Appl. Meteor., 20 , 5366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimes, D. I. F., Coppola E. , Verdecchia M. , and Visconti G. , 2003: A neural network approach to real-time rainfall estimation for Africa using satellite data. J. Hydrometeor., 4 , 11191133.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, Y., Hsu K. , Sorooshian S. , and Gao X. , 2004: Precipitation estimation from remotely sensed imagery using an artificial neural network cloud classification system. J. Appl. Meteor., 43 , 18341852.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Horsfield, N., 2006: Development of a mass balance approach to modelling cloud lifecycles and rainfall using satellite observations. Ph.D. thesis, University of Hull, 353 pp.

  • Hou, A. Y., Skofronick-Jackson G. , Kummerow C. D. , and Shepherd J. M. , 2008: Global precipitation measurement. Precipitation: Advances in Measurement, Estimation and Prediction, S. C. Michaelides, Ed., Springer, 131–170.

    • Search Google Scholar
    • Export Citation
  • Hsu, K-L., Gao X. , Sorooshian S. , and Gupta H. V. , 1997: Precipitation estimation from remotely sensed information using artificial neural networks. J. Appl. Meteor., 36 , 11761190.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K-L., Gupta H. , Gao X. , and Sorooshian S. , 1999: Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resour. Res., 35 , 16051618.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K-L., Bellerby T. , and Sorooshian S. , 2009: LMODEL: A satellite precipitation methodology using cloud development modeling. Part II: Validation. J. Hydrometeor., 10 , 10961108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8 , 3855.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Janowiak, J. E., Joyce R. J. , and Yarosh Y. , 2001: A real-time global half-hourly pixel-resolution infrared dataset and its applications. Bull. Amer. Meteor. Soc., 82 , 205217.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5 , 487503.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kidd, C., Kniveton D. R. , Todd M. C. , and Bellerby T. J. , 2003: Satellite rainfall estimation using combined passive microwave and infrared algorithms. J. Hydrometeor., 4 , 10881104.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kummerow, C., and Coauthors, 2001: Evolution of the Goddard Profiling Algorithm (GPROF) for rainfall estimation from passive microwave sensors. J. Appl. Meteor., 40 , 18011820.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuo, H. L., 1974: Further studies of the parameterization of the influence of cumulus convection on large-scale flow. J. Atmos. Sci., 31 , 12321240.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lin, Y., and Mitchell K. E. , 2005: The NCEP stage II/IV precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at http://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm].

    • Search Google Scholar
    • Export Citation
  • Liu, Y., and Gupta H. V. , 2007: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework. Water Resour. Res., 43 , W07401. doi:10.1029/2006WR005756.

    • Search Google Scholar
    • Export Citation
  • Machado, L. A. T., Rossow W. B. , Guedes R. L. , and Walker A. W. , 1998: Life cycle variations of mesoscale convective systems over the Americas. Mon. Wea. Rev., 126 , 16301654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Marzano, F. S., Palmacci M. , Cimini D. , Giuliani G. , and Turk F. J. , 2004: Multivariate statistical integration of satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale. IEEE Trans. Geosci. Remote Sens., 42 , 10181032.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moradkhani, H., Hsu K-L. , Gupta H. , and Sorooshian S. , 2005: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter. Water Resour. Res., 41 , W05012. doi:10.1029/2004WR003604.

    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., and Coauthors, 2003a: Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part I: Validation of GPCC rainfall product and pre-TRMM satellite and blended products. J. Appl. Meteor., 42 , 13371354.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nicholson, S. E., and Coauthors, 2003b: Validation of TRMM and other rainfall estimates with a high-density gauge dataset for West Africa. Part II: Validation of TRMM rainfall products. J. Appl. Meteor., 42 , 13551368.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Okamoto, K., Iguchi T. , Takahashi N. , Iwanami K. , and Ushio T. , 2005: The global satellite mapping of precipitation (GSMaP) project. Proc. 25th Int. Symp. on Geoscience and Remote Sensing, Seoul, South Korea, Institute of Electrical and Electronics Engineers, 3414–3416.

    • Search Google Scholar
    • Export Citation
  • Press, W. H., Teukolsky S. A. , Vetterling W. T. , and Flannery B. P. , 1992: Numerical Recipes in Fortran: The Art of Scientific Computing. 2nd ed. Cambridge University Press, 963 pp.

    • Search Google Scholar
    • Export Citation
  • Rendu, J-M. M., 1979: Normal and lognormal estimation. Math. Geol., 11 , 407422.

  • Slater, A. G., and Clark M. P. , 2006: Snow data assimilation via an ensemble Kalman filter. J. Hydrometeor., 7 , 478493.

  • Sorooshian, S., Hsu K-L. , Gao X. , Gupta H. V. , Imam B. , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite–based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81 , 20352046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Staniforth, A., and Côté J. , 1991: Semi-Lagrangian integration schemes for atmospheric models—A review. Mon. Wea. Rev., 119 , 22062223.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Stout, J. E., Martin D. W. , and Sikdar D. N. , 1979: Estimating GATE rainfall with geosynchronous satellite images. Mon. Wea. Rev., 107 , 585598.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tapiador, F. J., Kidd C. , Levizzani V. , and Marzano F. S. , 2004: A neural networks–based fusion technique to estimate half-hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infared data. J. Appl. Meteor., 43 , 576594.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, M., Kidd C. , Kniveton D. R. , and Bellerby T. J. , 2001: A combined satellite infrared and passive microwave technique for estimation of small-scale rainfall. J. Atmos. Oceanic Technol., 18 , 742755.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Turk, F. J., and Miller S. D. , 2005: Toward improved characterization of remotely sensed precipitation regimes with MODIS/AMSR-E blended data techniques. IEEE Trans. Geosci. Remote Sens., 43 , 10591069.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vicente, G., Scofield R. A. , and Mensel W. P. , 1998: The operational GOES infrared rainfall estimation technique. Bull. Amer. Meteor. Soc., 79 , 18811898.

    • Search Google Scholar
    • Export Citation
  • Vicente, G., Devenport J. C. , and Scofield R. A. , 2002: The role of orographic and parallax corrections on real time high resolution rainfall rate distribution. Int. J. Remote Sens., 23 , 221230.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walker, J. P., Willgoose G. R. , and Kalma J. D. , 2002: Three-dimensional soil moisture profile retrieval by assimilation of near-surface measurements: Simplified Kalman filter covariance forecasting and field application. Water Resour. Res., 38 , 1301. doi:10.1029/2002WR001545.

    • Search Google Scholar
    • Export Citation
  • Weng, F. W., Zhao L. , Ferraro R. , Pre G. , Li X. , and Grody N. C. , 2003: Advanced microwave sounding unit (AMSU) cloud and precipitation algorithm. Radio Sci., 38 , 80688079.

    • Search Google Scholar
    • Export Citation
  • Woodley, W. L., Griffith C. G. , Griffin J. S. , and Stromatt S. C. , 1980: The inference of GATE convective rainfall from SMS-1 imagery. J. Appl. Meteor., 19 , 388408.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wu, R., Weinmann J. , and Chin R. , 1985: Determination of rainfall rates from GOES satellite images by a pattern recognition technique. J. Atmos. Oceanic Technol., 2 , 314330.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, L., Gao X. , Sorooshian S. , and Arkin P. A. , 1999: A microwave infrared threshold technique to improve the GOES precipitation index. J. Appl. Meteor., 38 , 569579.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, M., and Scofield R. A. , 1994: Artificial neural network techniques for estimating heavy convective rainfall and recognizing cloud mergers from satellite data. Int. J. Remote Sens., 15 , 32413261.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 247 66 3
PDF Downloads 143 63 1