Neural networks in satellite rainfall estimation

Neural networks (NNs) have been successfully used in the environmental sciences over the last two decades. However, only a few review papers have been published, most of which cover image processing, classification, prediction and geophysical retrieval in general, while neglecting rainfall estimation issues. This paper reviews, without aiming to be exhaustive, NN approaches to satellite rainfall estimation (SRE) by providing an overview of some of the methodologies proposed. A basic

[1]  A. Linden,et al.  Inversion of multilayer nets , 1989, International 1989 Joint Conference on Neural Networks.

[2]  Warren S. Sarle,et al.  Neural Networks and Statistical Models , 1994 .

[3]  Frank S. Marzano,et al.  Inversion techniques for ground-based microwave radiometric retrieval of precipitation columnar contents and path attenuation , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[4]  Paul F. Fougère,et al.  Maximum Entropy and Bayesian Methods , 1990 .

[5]  Dominic Kniveton,et al.  The advantages and disadvantages of statistically -derived/empirically-calibrated passive microwave algorithms , 1998 .

[6]  F. Marzano,et al.  Results of WetNet PIP2 Project , 1998 .

[7]  Alexander Linden,et al.  Inversion of neural networks by gradient descent , 1990, Parallel Comput..

[8]  Holger R. Maier,et al.  Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..

[9]  Yeon Hong.  Cloud Classification and Rainfall Estimation using GOES Infrared Imagery , 2002 .

[10]  P. Atkinson,et al.  Introduction Neural networks in remote sensing , 1997 .

[11]  William W. Hsieh,et al.  Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography. , 1998 .

[12]  A. R. Jameson,et al.  What is a Raindrop Size Distribution , 2001 .

[13]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[14]  F. Göttsche,et al.  Evolution of neural networks for radiative transfer calculations in the terrestrial infrared , 2002 .

[15]  F. Wentz.  A well‐calibrated ocean algorithm for special sensor microwave / imager , 1997 .

[16]  Vladimir M. Krasnopolsky,et al.  A Neural Network Multiparameter Algorithm for SSM/I Ocean Retrievals , 2000 .

[17]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[18]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[19]  Phillip A. Arkin,et al.  The Relationship between Fractional Coverage of High Cloud and Rainfall Accumulations during GATE over the B-Scale Array , 1979 .

[20]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[21]  Frank S. Marzano,et al.  Statistical integration of satellite passive microwave and infrared data for high-temporal sampling retrieval of rainfall , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[22]  Vladimir M. Krasnopolsky,et al.  Some neural network applications in environmental sciences. Part II: advancing computational efficiency of environmental numerical models , 2003, Neural Networks.

[23]  Frank S. Marzano,et al.  A Neural Networks–Based Fusion Technique to Estimate Half-Hourly Rainfall Estimates at 0.1° Resolution from Satellite Passive Microwave and Infrared Data , 2004 .

[24]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[25]  Thierry Faure,et al.  Neural network retrieval of cloud parameters of inhomogeneous and fractional clouds , 2001 .

[26]  S. M. Amin,et al.  Maneuver prediction in air combat via artificial neural networks , 1992 .

[27]  F. Joseph Turk,et al.  Validation and applications of a realtime global precipitation analysis , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[28]  Gail A. Carpenter,et al.  ART neural networks for remote sensing: vegetation classification from Landsat TM and terrain data , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[29]  V. Tikhomirov.  On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition , 1991 .

[30]  Christian Kummerow,et al.  A simplified scheme for obtaining precipitation and vertical hydrometeor profiles from passive microwave sensors , 1996, IEEE Trans. Geosci. Remote. Sens..

[31]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[32]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[33]  E. T. Jaynes,et al.  Probability Theory as Logic , 1990 .

[34]  John J. Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities , 1999 .

[35]  Robert J. Marks,et al.  Inversion of feedforward neural networks: algorithms and applications , 1999, Proc. IEEE.

[36]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[37]  S. Sorooshian,et al.  Evaluation of PERSIANN system satellite-based estimates of tropical rainfall , 2000 .

[38]  Chris Kidd,et al.  Rainfall Estimation from a Combination of TRMM Precipitation Radar and GOES Multispectral Satellite Imagery through the Use of an Artificial Neural Network , 2000 .

[39]  James P. Hollinger,et al.  SSM/I instrument evaluation , 1990 .

[40]  Kurt Hornik,et al.  Some new results on neural network approximation , 1993, Neural Networks.

[41]  Frank S. Marzano,et al.  Results of WetNet PIP-2 Project , 1998 .

[42]  R. Parker.  Geophysical Inverse Theory , 1994 .

[43]  Li Li,et al.  Microwave radiometric technique to retrieve vapor, liquid and ice. II. Joint studies of radiometer and radar in winter clouds , 1997, IEEE Trans. Geosci. Remote. Sens..

[44]  Teuvo Kohonen,et al.  Self-Organizing Maps, Second Edition , 1997, Springer Series in Information Sciences.

[45]  Helmut Schiller,et al.  Some neural network applications in environmental sciences. Part I: forward and inverse problems in geophysical remote measurements , 2003, Neural Networks.

[46]  C. Mallet,et al.  Determination of integrated cloud liquid water path and total precipitable water from SSM/I data using a neural network algorithm , 2002 .

[47]  Alan F. Murray,et al.  Enhanced MLP performance and fault tolerance resulting from synaptic weight noise during training , 1994, IEEE Trans. Neural Networks.

[48]  Johannes Schmetz,et al.  Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation , 2001 .

[49]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[50]  Li Li,et al.  Microwave radiometric technique to retrieve vapor, liquid and ice. I. Development of a neural network-based inversion method , 1997, IEEE Trans. Geosci. Remote. Sens..

[51]  Rodney W. Johnson,et al.  Axiomatic derivation of the principle of maximum entropy and the principle of minimum cross-entropy , 1980, IEEE Trans. Inf. Theory.

[52]  J. G. Taylor,et al.  Neural Networks and the Brain , 1974 .

[53]  S. Grossberg.  Some Networks That Can Learn, Remember, and Reproduce any Number of Complicated Space-Time Patterns, I , 1969 .

[54]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .