# Presentation: 064599

## Ground Validation of Satellite Precipitation Estimates over Spain

- Francisco J. Tapiador
- Institute of Environmental Sciences (ICAM)
- University of Castilla-La Mancha, UCLM
- Toledo, Spain
- francisco.tapiador@uclm.es
- With inputs from Antonio Rodriguez and Miguel A.Martínez, Spanish Nal. Meteorological Institute (INM), Madrid, Spain

## Introduction

**A. The UCLM’s Environmental Modeling Group **
- GCM and NWP– The PROMES model
- Remote Sensing – Satellite Precipitation
- Algorithm development
- Some Validation
**B. Some examples of our validation work over Spain**
- Andalusia case study (METEOSAT+SSM/I)
- IPWG satellite estimates over Spain (CICS, University of Maryland data)
- EUMETSAT Convective Rain Rate product (INM, Spain)
**C. Some notes on Spain as validation site**

**Intro**

**Val example 1**

**Val example 2**

**Conclusions**

**Algorithms**

**Val example 3**

## Merged Satellite Rainfall Algorithms

- 	 -   **EURAINSAT/A** algorithm (Tapiador et al. 2004, IJRS)
- 		-  PMW-calibrated IR
**Neural Networks** (Tapiador et al. 2004, Met App)
- PMW+IR IR spatial and temporal resolution + PMW directness
- 4km/30 minutes resolution
- Used by some farmers for irrigation planning – advised on shortcomings and limitations
**Cloud motion winds** PMW+IR estimate

**Intro**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

## Neural Networks Product

**Neural Networks Product**

- SSM/I only

- Neural Net (Meteosat+SSM/I)

- Histogram Matching (Meteosat+SSM/I)

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Cloud Motion Winds (CMW) Scheme

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Cloud Motion Winds (CMW) Scheme**
- Similar to CPC Morphing
- Difference: CMW are directly modeled using Navier-Stokes equations instead of spatial correlation windows: more physically-direct and more realistic fields
- Reference: Tapiador, 2004. 2nd IPWG meeting, Monterey, CA
- Used for data assimilation into GCM

**Intro**

## 02:30

- 03:00

- 03:30

**04:30**

- 05:00

**05:30**

- ACTUAL RAIN
- MEASUREMENT

- RAIN ESTIMATE

- ACTUAL RAIN
- MEASUREMENT

- RAIN ESTIMATE

**Independent**** **Validation

- CMW Diffusion

- CMW Diffusion

## Comparison between CMW estimate and (independent) reference rainfall for 02:30 TUC

**Comparison between CMW estimate and (independent) reference rainfall for 02:30 TUC**
**(2 hour step, forward propagation)**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## What if we use the 02:30 measure instead of the 04:30 CMW-scheme estimate when comparing @ 04:30?

**So, the CMW scheme is actually transporting rainfall**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Time degradation: 

**Average for 31/OCT/2003**
**Using the CMW, we can maintain correlations > 0.80 for up to 2.5 hours**
**The performances of the method when compared with ground rainfall at instantaneous scale will be linked with the performances of the rainfall to be transported: relevant perhaps for GPM**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## http://hermes.uclm.es

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Val example 1

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Validation activities

- Opportunity: we needed data for algorithm pre-calibration
- Validation has a geographical component: validation results are different in different places, and we need the algorithms tuned for Spain.
- Validation against gauge, GR; comparison with models

**Intro**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

## Andalusia case study

**Val example 2**

**Algorithms**

**Val example 3**

**Andalusia case study**

**Conclusions**

**Intro**

## Half-hourly raingauge data availability

- Neural network IR+PMW fusion
- Algorithm characteristics:
- High temporal resolution
- High spatial resolution
- High accuracy
- Tapiador, F.J., Kidd, C., Levizzani, V., Marzano, F.S., 2004. **A Neural Networks-Based Fusion Technique to Estimate Half Hourly Rainfall Estimates at 0.1o Resolution from Satellite Passive Microwave and Infrared Data**. *Journal of Applied Meteorology*, 43, 576-594.

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## 
Rain Gauges in Andalusia

- Rain Gauges in Andalusia

- Interpolation I – Kriging

- Interpolation II – Inverse distance

- SSM/I data

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Correlations

- at 0.1o (monthly)

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Correlations

- at 0.5o (monthly)

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Validation of

**Val example 2**

**Algorithms**

**Val example 3**

**Validation of**
**IPWG Products **
**on Spain**

**Conclusions**

**Intro**

## (CICS, University of Maryland archive) 

- NRL PMW

- CPC

- Geo

- NOGAPS

**Val example 1**

**Algorithms**

**Val example 3**

**Conclusions**

- (CICS, University of Maryland archive)
- 00Z-00Z products
-  NOGAPS
-  NRL GEO
-  NRL PWM
-  CPC Morphing
-  3B42RT

**Val example 2**

**Intro**

## Rain Gauges Location

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Val example 1

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Geolocation error surface analysis

**Algorithms**

**Val example 3**

**Geolocation error surface analysis**
** **
** ****Data from 01/JAN/2005 to 01/SEP/2005**
** ****Satellite vs gauge**
** ****Assuming 5km interval error in the nominal satellite data geolocation**

**Conclusions**

**Val example 2**

**Intro**

## 3B42RT

**Algorithms**

**Val example 3**

| Algorithm | Best r2 |
| --- | --- |
| 3BR42 | 0.30 |
| CPC | 0.33 |
| GEO | 0.09 |
| PMW | 0.09 |
| NOGAPS | 0.15 |

**Conclusions**

**Val example 2**

**3B42RT**

**CPC Morphing**

**NRL GEO**

**NRL PMW**

**NOGAPS**

**Intro**

## EUMETSAT’s 

**Val example 2**

**Algorithms**

**Val example 3**

**EUMETSAT’s **
**Convective Rain Rate Product (CRR)**
**Nowcasting Satellite Application Facility (SAF)**

**Conclusions**

**Intro**

## Val example 1

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Val example 1

**Val example 1**

**Val example 2**

**Algorithms**

**Conclusions**

**Val example 3**

**Intro**

## Validation – Comparison data sources

**Val example 1**

**Val example 2**

**Algorithms**

**Conclusions**

**Val example 3**

**Intro**

## GR Visual comparison

**Val example 1**

**Val example 2**

**Algorithms**

**Conclusions**

**Val example 3**

**Intro**

## Spain as validation site

**Spain as validation site**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Pros and Cons

-  Many examples of frontal, convective and orographic precipitation – and mixed cases.
-  Three rainfall regimes in 500,000 sq km (Texas= 696,000 sq Km)
-  High N-S gradient. Well-calibrated, reliable validation net
-  Rain gauges nets (INM, river authorities, etc.)
-  Ground Radar
-  TRMM coverage (South), MSG, SSM/I, AMSU, AVHRR, etc.
-  Limited area
-  Limited public availability of validation data – but this could be solved for GPM

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Available Validation Data

-  INM gauges network
-  GR
-  River authorities networks
-  Agrarian Meteo Nets
-  Specifically-tailored nets and instrumentation

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## 37N

**37N**

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Geography**

**Conclusions**

**Intro**

## Conclusions

-  Suitability for validation site in Catalonia (Daniel Sempere, GRAHI):
-  Experience in satellite rainfall estimates algorithms
-  Interface with NWP modelers (NWP+Sat+Merged algorithms)
-  Data availability and support from agencies
-  Geography of Spain: very different from other validation places

**Val example 1**

**Val example 2**

**Algorithms**

**Val example 3**

**Conclusions**

**Intro**

## Slide 34