A Neural Network Approach to Real-Time Rainfall Estimation for Africa Using Satellite Data

D. I. F. Grimes TAMSAT, Department of Meteorology, University of Reading, Reading, Berkshire, United Kingdom

Search for other papers by D. I. F. Grimes in
Current site
Google Scholar
PubMed
Close
,
E. Coppola TAMSAT, Department of Meteorology, University of Reading, Reading, Berkshire, United Kingdom, and Physics Department, University of L'Aquila, CETEMPS Coppito I'Aquila, Italy

Search for other papers by E. Coppola in
Current site
Google Scholar
PubMed
Close
,
M. Verdecchia Physics Department, University of L'Aquila, CETEMPS, Coppito l'Aquila, Italy

Search for other papers by M. Verdecchia in
Current site
Google Scholar
PubMed
Close
, and
G. Visconti Physics Department, University of L'Aquila, CETEMPS, Coppito l'Aquila, Italy

Search for other papers by G. Visconti in
Current site
Google Scholar
PubMed
Close
Restricted access

We are aware of a technical issue preventing figures and tables from showing in some newly published articles in the full-text HTML view.
While we are resolving the problem, please use the online PDF version of these articles to view figures and tables.

Abstract

Operational, real-time rainfall estimation on a daily timescale is potentially of great benefit for hydrological forecasting in African river basins. Sparseness of ground-based observations often means that only methodologies based predominantly on satellite data are feasible. An approach is presented here in which Cold Cloud Duration (CCD) imagery derived from Meteosat thermal infrared imagery is used in conjunction with numerical weather model analysis data as the input to an artificial neural network. Novel features of this approach are the use of principal component analysis to reduce the data requirements for the weather model analyses and the use of a pruning technique to identify redundant input data. The methodology has been tested using 4 yr of daily rain gauge data from Zambia in central Africa. Calibration and validation were carried out using pixel area rainfall estimates derived from daily rain gauge data. When compared with a standard CCD approach using the same dataset, the neural network shows a small but consistent improvement over the standard method. The improvement is greatest for higher rainfalls, which is important for hydological applications.

Corresponding author address: Dr. D. I. F. Grimes, Department of Meteorology, University of Reading, Earley Gate, Reading, Berkshire, RG6 6BB, United Kingdom. Email: d.i.f.grimes@reading.ac.uk

Abstract

Operational, real-time rainfall estimation on a daily timescale is potentially of great benefit for hydrological forecasting in African river basins. Sparseness of ground-based observations often means that only methodologies based predominantly on satellite data are feasible. An approach is presented here in which Cold Cloud Duration (CCD) imagery derived from Meteosat thermal infrared imagery is used in conjunction with numerical weather model analysis data as the input to an artificial neural network. Novel features of this approach are the use of principal component analysis to reduce the data requirements for the weather model analyses and the use of a pruning technique to identify redundant input data. The methodology has been tested using 4 yr of daily rain gauge data from Zambia in central Africa. Calibration and validation were carried out using pixel area rainfall estimates derived from daily rain gauge data. When compared with a standard CCD approach using the same dataset, the neural network shows a small but consistent improvement over the standard method. The improvement is greatest for higher rainfalls, which is important for hydological applications.

Corresponding author address: Dr. D. I. F. Grimes, Department of Meteorology, University of Reading, Earley Gate, Reading, Berkshire, RG6 6BB, United Kingdom. Email: d.i.f.grimes@reading.ac.uk

Save
  • Aires, F., Prigent C. , Rossow W. B. , and Rothstein M. , 2001: A new neural network approach including first guess for retrieval of atmospheric water vapour, cloud liquid water path, surface temperatures and emissivities over land from satellite microwave observations. J. Geophys. Res., 106 , (D14),. 1488714907.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arkin, P. A., 1979: The relationship between the fractional coverage of high cloud and rainfall accumulations during GATE over the B-scale array. Mon. Wea. Rev., 107 , 13821387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Arkin, P. A., Joyce R. , and Janowiak J. E. , 1994: The estimation of global monthly mean rainfall using infrared satellite data: The GOES Precipitation Index. Remote Sens. Rev., 11 , 107124.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barrett, E. C., 1970: The estimation of monthly rainfall from satellite data. Mon. Wea. Rev., 98 , 322327.

  • Barrett, E. C., and Martin D. W. , 1981: The Use of Satellite Data in Rainfall Monitoring. Academic Press, 340 pp.

  • 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
  • Carn, M., Lahuec J. P. , Dagorne D. , and Guillot B. , 1989: Rainfall estimation using TIR Meteosat imagery over the Western Sahel. Preprints, Fourth Conf. on Satellite Meteorology and Oceanography, San Diego, CA, Amer. Meteor. Soc., 126–129.

    • Search Google Scholar
    • Export Citation
  • Davolo, E., and Naim P. , 1991: Neural Networks. McMillan Education, 145 pp.

  • Flitcroft, I. D., Milford J. R. , and Dugdale G. , 1989: Relating point to area average rainfall in semi-arid West Africa and the implications for rainfall estimates derived from satellite data. J. Appl. Meteor., 28 , 252266.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimes, D. I. F., and Diop M. , 2003: Satellite-based rainfall estimation for river flow forecasting in Africa. Part I. Rainfall estimates and hydrological forecasts. Hydrol. Sci. J., 48 , 567584.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grimes, D. I. F., Pardo E. , and Bonifacio R. , 1999: Optimal areal rainfall estimation using raingauges and satellite date. J. Hydrol., 222 , 93108.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hecht-Nielsen, R., 1990: Neurocomputing. Addison-Wesley, 433 pp.

  • Herman, A., Kumar V. B. , Arkin P. A. , and Kousky J. V. , 1997: Objectively determined 10-day African rainfall estimates created for famine early warning. Int. J. Remote Sens., 18 , 21472160.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsieh, W. W., and Tang B. , 1998: Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull. Amer. Meteor. Soc., 79 , 18551870.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, K., 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
  • Journel, A. G., and Huijbregts C. J. , 1978: Mining Geostatistics. Academic Press, 600 pp.

  • Kidd, C., 1998: On rainfall retrieval using polarisation corrected temperatures. Int. J. Remote Sens., 19 , 981996.

  • Kidd, C., 2001: Satellite rainfall climatology: A review. Int. J. Climatol., 21 , 10411066.

  • Laurent, H., Jobard I. , and Toma A. , 1998: Validation of satellite data and ground-based estimates of precipitation over the Sahel. Atmos. Res., 47 , –48. 651670.

    • Search Google Scholar
    • Export Citation
  • Lonbladd, L., Peterson C. , and Rognvaldsson T. , 1991: Pattern recognition in high energy physics with artificial neural networks. Comput. Phys. Comm., 70 , 167.

    • Search Google Scholar
    • Export Citation
  • Milford, J. R., and Dugdale G. , 1990: Estimation of rainfall using geostationary satellite data. Applications of Remote Sensing in Agriculture, Proceedings of 48th Easter School in Agricultural Science, Butterworth.

    • Search Google Scholar
    • Export Citation
  • Morland, J. C., Grimes D. I. F. , and Hewison T. J. , 2001: Satellite observation of the microwave emissivity of a semi-arid land surface. Remote Sens. Environ., 77 , /2. 149164.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Snijders, F. L., 1991: Rainfall monitoring based on Meteosat data—A comparison of techniques applied to the Western Sahel. Int. J. Remote Sens., 12 , 13311347.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 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 rain. Bull. Amer. Meteor. Soc., 81 , 20352046.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thorne, V., Coakley P. , Grimes D. , and Dugdale G. , 2001: Comparison of TAMSAT and CPC rainfall estimates with rainfall, for southern Africa. Int. J. Remote Sens., 22 , 19511974.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, M. C., Barrett E. C. , and Beaumont M. J. , 1995: Satellite identification of raindays over the upper Nile River basin using an optimum infrared rain/no-rain threshold temperature model. J. Appl. Meteor., 34 , 26002611.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Todd, M. C., Kidd C. , Kniveton D. , and Bellerby T. , 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
  • Tsintikidis, D., Haferman J. L. , Anagnostou N. , Krajewski W. F. , and Smith T. F. , 1997: A neural network approach to estimating rainfall from spaceborne microwave data. IEEE Trans. Geosci. Remote Sens., 35 , 10791092.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weigend, A., Huberman B. , and Rumelhart D. , 1991: Predicting sunspots and exchange rates with connectionist networks. Non-linear Modelling and Forecasting, S. Eubank and M. Casdagli, Eds., Addison-Wesley, 1–36.

    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences. Academic Press, 467 pp.

  • Xie, P., and Arkin P. A. , 1996: Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. J. Climate, 9 , 840858.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1151 374 53
PDF Downloads 641 124 10