Validation and strategies to improve the Hydro-Estimator and NEXRAD over Puerto Rico

  • Authors:

  • Nazario D. Ramirez-Beltran;Robert J. Kuligowski;Eric W. Harmsen;Joan M. Castro;Sandra Cruz-Pol;Melvin J. Cardona-Soto

  • Affiliations:

  • Department of Industrial Engineering, University of Puerto Rico, Mayagüez, PR;NOAA, NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD;Department of Agricultural and Biosystems Engineering, University of Puerto Rico, Mayagüez, PR;Department of Computer and Electrical Engineering, University of Puerto Rico, Mayagüez, PR;Department of Computer and Electrical Engineering, University of Puerto Rico, Mayagüez, PR;Department of Computer and Electrical Engineering, University of Puerto Rico, Mayagüez, PR

  • Venue:
  • ICS'08 Proceedings of the 12th WSEAS international conference on Systems
  • Year:
  • 2008

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Abstract

Validation of the Hydro-Estimator (HE) and the Next Generation Radar (NEXRAD) during heavy storms over Puerto Rico (PR) is reported. The HE is a high resolution rainfall retrieval algorithm based on satellite and numerical whether prediction model data. The accuracy of the HE and the NEXRAD rainfall estimates can be measured by decomposing the rainfall process in sequences of discrete (rain/no rain) and continuous (rainfall rate) random variables. Validation results are based on five heavy storms that seriously impacted human life and the economy of PR during the period 2003 to 2005. The average discrete validation results indicate acceptable hit rate values for both the HE and NEXRAD (0.76 vs. 0.87) and reasonable discrete bias ratios (1.04 vs. 0.73) but a very low of probability of detection of rain for both the HE and NEXRAD (0.36 vs. 0.52). The HE shows an average overestimation whereas the NEXRAD exhibits underestimation in the continuous validation results (continuous bias ratio of 1.14 vs 0.70 for NEXRAD), which contributes to moderate overall errors for the HE and NEXRAD in terms of root mean squared error (2.14 mm vs. 1.66 mm) and mean absolute error (0.96 mm vs. 0.77 mm). The HE algorithm was designed to operate over US continental areas and satisfactory results have been reported. However, over tropical regions it was determined that warm clouds can generate substantial rainfall amounts that are not detected by the HE algorithm. Infrared band differencing techniques are using to explore the possibility of improving the detection of warm-cloud rain events over PR. We are also classifying clouds based on Geostationary Operational Environmental Satellite (GOES) Imager data in a manner that will lead to improved relationships between infrared brightness temperatures and rainfall rates.