Evaluation of precipitation type determination from TRMM observations

Zafar, Basim; Mubarak, K.; Chandrasekar, V.

doi:10.1109/igarss.2003.1294720

Cited by 4 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rainfall estimation was introduced by the same authors (Xiao and Chandrasekar, 1995). An attempt to do rain type classification using self-organizing maps (SOM) was introduced by Zafar et al (2003).…”

Section: Introductionmentioning

confidence: 99%

Investigating rainfall estimation from radar measurements using neural networks

Alqudah

Chandrasekar

2013

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting

show abstract

Section: Introductionmentioning

confidence: 99%

Investigating rainfall estimation from radar measurements using neural networks

Alqudah

Chandrasekar

2013

Nat. Hazards Earth Syst. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The algorithm is an extension of an earlier method presented by Zafar et al (2003). Quantitative evaluations of the intercomparison for the thirty cases that have 61793 rain profiles are summarized in Tables I and II.…”

Section: Discussionmentioning

confidence: 99%

“…Wavelet analysis based on vertical profile has been successfully used to classify rain type [3]. The original TRMM algorithm uses both vertical and horizontal variability to deduce convective/stratiform structure.…”

Section: Introductionmentioning

confidence: 99%

Classification of precipitation type from space borne precipitation radar data and 2D wavelet analysis

Zafar

Chandrasekar

IEEE International IEEE International IEEE International Geoscience and Remote Sensing Symposium, 2004. IGARSS '04. Proceedings

Self Cite

View full text Add to dashboard Cite

Classification of precipitation into convective and stratiform type is an important product of Tropical RainfallMeasuring Mission (TRMM) data set. This paper presents the results of a new algorithm, used to classify precipitation into convective and stratiform types, developed using two dimensional wavelet transform. The methodology analyzes the three dimensional radar profile of precipitation in both horizontal and vertical directions (with respect to the surface of the earth). The rain type is determined by analyzing the wavelet coefficients of the horizontal and vertical details of each plane. The data structure used in each plane is rebuilt using the suitable wavelet coefficients to extract the proper signal components at the selected plane. Data from TRMM-PR are analyzed to produce precipitation type classification. The results are compared against Ground validation inferences.

show abstract