Date of Completion

1-19-2018

Embargo Period

1-19-2020

Keywords

Precipitation, Satellite, Numerical Weather Model

Major Advisor

Emmanouil N. Anagnostou

Associate Advisor

Guiling Wang

Associate Advisor

Marina Astitha

Associate Advisor

Efthymios I. Nikolopoulos

Associate Advisor

Malaquias Peña

Field of Study

Environmental Engineering

Degree

Doctor of Philosophy

Open Access

Open Access

Abstract

The quantification of heavy precipitation events over mountainous regions has been a challenge for all types of satellite precipitation products. This research developed a numerical weather model-based adjustment technique to correct satellite precipitation estimates for HPEs. To successfully apply the technique, there are two prerequisites: i) the raw satellite data captures the relative spatial and temporal variabilities of precipitation (i.e. no significant surface contamination effects on satellite precipitation detection), and ii) the model provides relatively accurate precipitation outputs in terms of overall magnitude (not necessarily location). The technique was demonstrated over mountainous areas all over the world representing varying terrain complexity and climatic conditions. Results show that model-based adjustment outperforms, or at least is comparable to, the gauge-based adjustment for all high-resolution satellite products examined. In addition, the model-based adjustment requires no in situ observations and much less processing time. The results are promising for future satellite precipitation applications over mountainous areas lacking ground observations. Furthermore, the model-adjusted satellite products were used in a distributed hydrological model to evaluate the error propagation on flood simulations. Results showed that the basin outlet runoff derived from model-adjusted satellite precipitation was comparable to the one with gauge-adjusted satellite precipitation, and both of them outperformed the runoff derived from raw satellite.

Available for download on Sunday, January 19, 2020

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