REDUCTION OF SYSTEMATIC ERROR ON IMERG-E RAINFALL ESTIMATION USING BAYESIAN MODEL AVERAGING
The Integrated Multi-satellite Retrievals for GPM (IMERG) satellite rainfall estimate has two products: IMERG-E and IMERG-F. IMERG-E has the potential to be used for early warning operations because of its fast release time, but its accuracy is less than IMERG-F, which involves gauge calibration....
Saved in:
Main Author: | |
---|---|
Format: | Theses |
Language: | Indonesia |
Online Access: | https://digilib.itb.ac.id/gdl/view/68912 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Institut Teknologi Bandung |
Language: | Indonesia |
Summary: | The Integrated Multi-satellite Retrievals for GPM (IMERG) satellite rainfall
estimate has two products: IMERG-E and IMERG-F. IMERG-E has the potential
to be used for early warning operations because of its fast release time, but its
accuracy is less than IMERG-F, which involves gauge calibration. The Bayesian
Model Averaging (BMA) method offers a calibration solution that can be used in
IMERG-E rainfall estimation. In this study, the Bayesian Model Averaging (BMA)
method, generally used for ensemble prediction calibration, calibrates or reduces
the IMERG-E error. Because it only uses one type of satellite estimation product,
the ensemble used is the rain area around the reference station while
accommodating the uncertainty of spatial satellite estimation.
The results show that the BMA method can reduce the systematic error of
IMERG-E and produce a calibration in the form of probability for its uncertainty.
BMA reduces the bias and variance components of the Mean Square Error (MSE)
systematic error. The configuration of the BMA method greatly affects the
calibration results, especially the configuration of spatial sampling or regional
rainfall. The Conditional Training Window (CTW) scheme shows the advantages
of the Sequential Training Window (STW) scheme in the BMA method. This can
be seen in the deterministic evaluation with MSE and the ensemble evaluation
with the Verification Rank Histogram (VRH) and Continuous Rank Probability
Score (CRPS). In the probabilistic evaluation, the CTW scheme has an Area
Under Curve-Relative Operating Characteristic (AUC-ROC), which is larger
than the STW scheme, and the smallest resolution attribute is indicated by the
Brier Score (BS).
|
---|