# Presentation: 490073

## Hybrid Neural Network Based Rainfall Map for Global Precipitation Mapping as well as Ground Validation during TRMM

- V. Chandrasekar, Amin Alqudah, and Yanting Wang
- Colorado State University

## Neural Network based Rainfall Estimation

- Neural network (NN) is a non-linear mapping from the input space to a targeted estimate.
- NN consists of inter-connecting neurons, each characterizing a simple function.
- As a system, the network is adjusted, or trained, so that a particular input leads to a specific target output.
- NN is good to fit any practical functions and has been used for rainfall estimation.
- Xiao and Chandrasekar 1997, Liu et al 2001, Stefano Orlandini and Isabella Morlini, 2000.

## NN Training / Testing for Rainfall Estimation

- Choose a subset of rain gauge data
- Use the corresponding radar observations as input
- Train the network to update inter-layer weights
- The rest subset of rain gauge data are used for validation / evaluation

## Problem Statement

- Key challenge in radar rainfall estimation is the variability in precipitation microphysics, such as DSD and practical issues such as space time variability
- Empirical *Z*-*R* relation is not sufficient to capture the variability and has large uncertainty.
- It needs to be adaptively adjusted based on validation.
- TRMM Precipitation Radar (PR) is an unique instrument, capable of providing high-resolution vertical profile of precipitation.

**Challenges in TRMM validation:**
-  Horizontal resolution is coarse, so TRMM PR mismatches rain gauges in spatial scales.
-  Comparison pairs are scarce with limited overpasses in a single weather event.
-  Impractical to deploy a dense gauge network for TRMM PR validation.

## Objectives

- Build an adaptive relation for TRMM PR rainfall estimation
- Framework: neural network
- Ground truth: rain gauge
- Use ground validation (GV) radars to bridge the scale gap between TRMM PR and rain gauges
- Develop an accurate rainfall estimation for GV radars
- Transfer the GV rainfall to TRMM PR rainfall estimation
- Create a product for precipitation mapping using TRMM PR measurements

## The  Process

- Construct a neural network (NNG) to estimate GV radar based rainfall
- Train: GV radar reflectivity ~ rain gauge network
- Large population of sample pairs
- Smaller scale mismatch
- Construct another neural network (NNS) to estimate TRMM PR based rainfall
- Train: PR radar reflectivity ~ GV radar rainfall
- Use the vertical profile of reflectivity

## NN - Radial Basis Function Networks

- One input layer, one hidden layer, one output layer
- Nonlinear neuron transfer function
- Radial basis function: measure the Euclidean distance between input and a set of centers
- Linear output layer
- Optimized combination of neuron responses, solved by
- 	efficient LSE technique
- Advantages
- Fast convergence
- Suitable for adaptive and incremental training

## NN – RBF Adaptive Training

- Update the model on daily basis
- Adaptively train the system each day
- Operate the system based on the model trained using previous day observations [Gang and Chandrasekar, 2005]

*System**Architecture*

*Updating*

*Operating*

## 1. Training for Ground Radar

- Input
- Grid GV radar data: 1km x 1km x 1km (vertical)
- Vertical profile of reflectivity up to 4 km
- Target
- 5 min mean of rain gauge measurements
- Daily update

## Gauge Networks

- Ground validation gauge networks
- Melbourne, FL

- Within 100km: 116

## 2. Radar Data Alignment

**Geometry between TRMM-PR and GR-based measurements**

**TRMM-PR**

**GV Radar (GR)**

- GR
- Resolution Volume

- TRMM-PR
- Resolution Volume

**Common**
**Resolution Volume**

## A Matched volume between PR and GR at about 60 km from GR.

- 1 km.

- 1 km.

- 4 km.

- 4 km.

- 0.25 km.

- 0.3 km.

- A Matched volume between PR and GR at about **60** km from GR.
- GR: 0.95o beamwidth and 300 m. range resolution

- Matched volume dimension changes according to range from GR.
- At 30 km. from GR : Vertical extent of matched volume ~ 0.6 km.
-                                  Horizontal extent is equal to PR horizontal resolution
- At 100 km. from GR : Vertical extent of matched volume ~ 1.8 km.
-                                   Horizontal extent equal to PR horizontal resolution

## 3. Training for TRMM PR

- Input
- Aligned TRMM PR vertical profiles of reflectivity
- Up to 4 km
- Target
- Ground radar NN rainfall estimation
- Aligned at TRMM PR resolution
- Adaptive update based on previous overpasses (for each month)

- Alignment

## 4. Validation

- Evaluate both the neural networks against the rain gauge measurements
- The rain gauges are located within the overpass

## Evaluation Scores

*RFg*: gauge measured rainfall
*RFn*: neural network estimated (radar) rainfall

-  Results
- KMLB 2005

## 33 precipitation events with

- both GR and PR coincident data

- GR NN estimate vs Gauge

- TRMM product vs Gauge

- TRMM NN estimate vs Gauge

## Performance ScoresPoint-wise Instantaneous Rainfall

** ****8.05**

**0.52**

**0.52**

**-4.36**

**TRMM Product**
**vs.**
**Rain Gauge**

**5.45**

**0.41**

**0.72**

**0.32**

**TRMM NN**** ****Est. **
**vs. **
**Rain Gauge**

** ****3.72**

**0.29**

** ****0.88**

**0.10**

**KMLB NN Est. **
**vs. **
**Rain Gauge**

**RMSE**
**mm/hr**

**NSE**
**(Fraction)**

**Corr.**

**Bias**
**mm/hr**

## Snapshot of Rain Mapping

- Overpass on 2005-09-02

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

## Snapshot of Rain Mapping

- Overpass on 2005-09-05

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

## Snapshot of Rain Mapping

- Overpass on 2005-09-06

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

## Snapshot of Rain Mapping

- Overpass on 2005-09-06

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

- KMLB 2006

## Performance ScoresPoint-wise Instantaneous Rainfall

**7.61**

**0.52**

**0.59**

**-3.21**

**TRMM Product**
**vs.**
**Rain Gauge**

**5.98**

**0.46**

**0.74**

**1.25**

**TRMM NN**** ****Est. **
**vs. **
**Rain Gauge**

** ****5.25**

**0.35**

** ****0.87**

**1.11**

**KMLB NN Est. **
**vs. **
**Rain Gauge**

**RMSE**
**mm/hr**

**NSE**
**(fraction)**

**Corr.**

**Bias**
**mm/hr**

## Snapshot of Rain Mapping

- Overpass on 2006-09-16

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

## Snapshot of Rain Mapping

**NN Rainfall **

- Overpass on 2006-09-19

**TRMM Reflectivity **

**NN Rainfall **

**TRMM Product **

## Process Review

- Precipitation
- Mapping

**NNG – GR **
**Neural Network**

- GV Radar
- Reflectivity

- Rain Gauge

**NNS – TRMM PR**
**Neural Network**

- TRMM PR
- Reflectivity

- Validation
- and
- Evaluation

*Daily Update*

*Monthly Update*

- Training

- Operating

## Work moving forward

- Apply the full resolution TRMM measurement to the second neural network
- Input to the TRMM PR NN with the vertical profiles of reflectivity in native resolution
- Extend  evaluation to different ground validation regions
- To incorporate the adaptability in space

*Construct regional NNs for each ground validation site as representatives for various global rainfall mapping relations*

## Summary

- Neural networks are used to translate radar observations to rainfall estimates
- Time adaptability is achieved using adaptive training
- Scale mismatch between TRMM PR and ground gauge is mitigated through the use of ground validation radars as  intermediate transfer agents.
- Output
- Accurate precipitation map that is matched better to ground radar estimates and raingages gages
- A practical system that is  easy to implement in PPS

-    Thank You

## NN - Multilayer Back-propagation

- One input layer, two hidden layers,and one output layer
- Neuron transfer function (layer *k*): raising ramp
- Slow convergence in training
- Back-propagate the evaluation through multiple layers
- Small steps are preferred in back-propagation updating
- Lack adaptability
- Suitable for batch training

## 15 precipitation  events with 

- 15 precipitation  events with
- Common ground and TRMM data

- GR NN estimate vs Gauge

- TRMM product vs Gauge

- TRMM NN estimate vs Gauge