Abstract: Rainfall estimation based on satellite
measurements has proven to be very
useful for various applications. A
number of precipitation products at
multiple time and space scales have
been developed based upon satellite
observations. For example, the
National Oceanic and Atmospheric
Administration (NOAA) Climate
Prediction Center has developed a
morphing technique (i.e., CMORPH) to
produce global precipitation products
by combining existing space based
rainfall estimates. The CMORPH
products are essentially derived
based on Infrared (IR) brightness
temperature information observed by
geostationary satellites and passive
microwave (PMW) based retrievals
from low earth orbit satellite
measurements. Although the space-
based precipitation products provide
an excellent tool for regional and
global hydrologic and climate studies
as well as improved situational
awareness for operational forecasts,
its accuracy is restricted due to the
limitations of spatial and temporal
sampling as well as the parametric
retrieval algorithms, particularly for
extreme events such as heavy rain or
light rain.

On the other hand, ground-based
radar is more mature for quantitative
precipitation estimation (QPE),
especially after the implementation of
dual-polarization technique and
further enhanced by urban scale
radar networks. Therefore, ground
radars are often critical for providing
local scale rainfall estimation and a
“heads-up” for operational forecasters
to issue watches and warnings, as well
as validation of various space
measurements and products. Since
2012, the center for Collaborative
Adaptive Sensing of the Atmosphere
(CASA) has been operating a high-
resolution dense urban radar network
in Dallas-Fort Worth (DFW) Metroplex.
The CASA DFW QPE system, which is
based on a local S-band National
Weather Service (NWS) Weather
Surveillance Radar – 1988 Doppler
(WSR-88DP) and dual-polarization X-
band CASA radar network, has
demonstrated its excellent
performance during several years of
operation in a variety of precipitation
regimes. The real-time CASA DFW
QPE products are used extensively for
localized hydrometeorological
applications such as urban flash flood
forecasting. It also serves as a reliable
dataset for validation of global
precipitation measurement (GPM)
satellite precipitation products. In this
study, the real-time high-resolution
CASA DFW QPE system is presented.
The specific dual-polarization radar
rainfall algorithms at S- and X-band
frequencies, as well as the fusion
methodology combining radar
observations at different temporal
resolution are detailed. Cross-
comparison between radar rainfall
estimates and rainfall measurements
from rain gauges is conducted to
demonstrate the excellent
performance of this urban QPE
system.

In addition, a neural network based
data fusion system, termed as "Deep
Multi-Layer Perceptron" (DMLP), is
introduced to improve satellite-based
rainfall estimation using DFW radar
rainfall products. Particularly, the
CMORPH methodology is first applied
to derive combined PMW rainfall
estimates and combined IR data from
multiple satellites. The combined
PMW-based retrievals and IR data
then serve as input to the proposed
DMLP model. The high-quality DFW
rainfall products are used as target to
train the DMLP model. In this
dissertation, prototype architecture of
the DMLP model is detailed, and the
DMLP-based rainfall products are
evaluated using existing CMORPH
products and surface rainfall
measurements from gauge networks.

Adviser: V. Chandrasekar
Co-Adviser: N/A
Non-ECE Member: Prof. Paul Mielke, Statistics
Member 3: Prof. Steven C. Reising, ECE
Addional Members: Prof. Margaret Cheney, ECE

Publications:
Please contact Haonan.Chen@ColoState.Edu for details.

Program of Study:
ATS737
ATS693
ECE516
ECE742
ECE752
ECE799
MATH676
GSTR600