VALIDATION OF SATELLITE PRECIPITATION ESTIMATES OVER

SOUTH AMERICA WITH A NETWORK OF HIGH SPATIAL

RESOLUTION OBSERVATIONS

María Paula Hobouchian (Department of Research and Development - NMS of Argentina)

Paola Salio

Daniel Vila

Yanina García Skabar

6th IPWG. São José dos Campos. October 2012

2

This work aims to communicate the performance of the

available products to users in different areas of interest in

the spatial and temporal distribution of precipitation.

Precipitation highly variable in space and time

Limitations:

Few surface measurements.

Not homogeneous distribution of surface stations.

Information from meteorological satellites is a vital tool.

MOTIVATION

3

Evaluate the performance of different satellite precipitation

estimates:

3B42 V7, V6 and Real-Time

CMORPH

HYDRO

CoSch

over South America.

Characterize errors considering different climatic regions and

seasons focusing on region south of 20° S.

OBJECTIVES

4

Estimate Resolution Type Reference Available period

3B42_RT (NASA)

0.25° - 3 h IR-PMW Huffman et al. (2003) 02/10/2008 - 31/12/2010

CMORPH

(NOAA/CPC) 0.25° - 3 h IR-PMW Joyce et al. (2004) 01/01/2003 - 31/12/2010

3B42_V6 (NASA)

0.25° - 3 h IR-PMW-OBS Huffman et al. (2007) 01/01/1999 - 31/12/2010

CoSch (CPTEC)

0.25° - 3 h IR-PMW-OBS Vila et al. (2009) 02/10/2008 - 31/12/2010

HYDRO (CPTEC)

4 Km - 15 min IR Scofield and Kuligowski (2003) 01/01/2003 - 31/12/2010

24 h accumulated rainfall (12 UTC).

Common period: October 2008 - December 2010.

Satellite precipitation estimates

DATA

Surface stations available over South America

5

Stations with more than the

70% of the days with

available data

Source of the data:

SMN - APA - SSRH - U de La Punta -

AIC - INTA - Bolsa de Cereales -

DNM Uruguay - DNAC Paraguay -

CTMSG - SAGyP - CPTEC – NOAA

DATA

Not all the dataset is included in GTS.

This is a large effort of collection and

consistency.

Average value was assigned to the

central grid of 25 Km resolution.

VALIDATION

Different Seasons

Different climatic regions

6 METHODOLOGY

DJF

JJA

Precipitation rate

Validation

7

Normalized RMSE and BIAS%

Graphics based on rainfall thresholds:

BIASS, ETS, POD and FAR

Probability distribution of rain

volume: Volumetric PDFs

Boxplots

CLIMATIC REGIONS

Complete Area (AC)

Northeastern Argentina (NE)

Southern Brazilian Coast (BS)

Central Argentina (CE)

Northwestern Argentina (MN)

Northwestern Patagonia (MS)

Eastern Brazil (BE)

Western Brazil (BO)

METHODOLOGY

8

Normalized RMSE 02/10/2008-31/12/2010 DJF

RESULTS

Values related to the precipitation rate.

More reliable values over northeastern

Argentina, Uruguay, Paraguay,

southern and northwestern Brazil.

9

Normalized RMSE 02/10/2008-31/12/2010 JJA

RESULTS

10

BIAS% 02/10/2008-31/12/2010 DJF

RESULTS

11

BIAS% 02/10/2008-31/12/2010 JJA

RESULTS

12

Graphics based on rainfall thresholds

Region south of 20° S

RESULTS

Best performance for CoSch.

Close Real-time estimates best result for 3B42 RT.

3B42 V7 shows improvement.

13

Boxplots - Region south of 20° S

Complete period and different seasons (2008-2010)

RESULTS

14

Volumetric pdfs – climatic regions

Complete period

RESULTS

Daily precipitation rate intervals

relative contribution to the total rain volume in the box

less sensitive to light precipitation events

15 RESULTS

Volumetric pdfs – climatic regions

Complete period

Less reliable results (few available stations)

16 RESULTS

Validation for a longer period

01/01/2006-31/12/2010

3B42 version 7 and 6

CMORPH

HYDRO

Stations with more than the

70% of the days with

available data

17

Boxplots - Region south of 20° S

Complete period and different seasons (2006-2010)

RESULTS

18 CONCLUSIONS

The inclusion of surface observations, as in the case of CoSch and 3B42 V6

and V7, improves performance over studied regions.

Extreme precipitation values are overestimated over SESA, except HYDRO

that underestimates observed precipitation in most of the thresholds.

Results show an error dependence with seasons and less performance

associated with not-convective precipitation events.

In the region extending south of 20° S, from the comparison between 3B42

RT, CMORPH and HYDRO (products closer to real time), 3B42 RT presents

a better result mainly in summer and in the NE region, while CMORPH

improves performance in winter and in the CE region.

In the region extending south of 20° S, from the comparison between 3B42

RT , V6 and V7, 3B42 V7 shows better result reducing the overestimation.

19 CONCLUSIONS

Future work:

It’s necessary further study of these products in relation to the topography,

areas where precipitation is solid and more frequently than every 24 hours

(3 hours).

Determine the atmospheric conditions that favor a better performance of the

precipitation estimates, and study the extreme cases related to a peak in the

distribution of errors.

21

Amitai, E., W. Petersen, X. Llort, and S. Vasiloff, 2011: Multi-Platform Comparisons of Rain Intensity for

Extreme Precipitation Events. IEEE Trans. Geosciences and Remote Sensing, 50, 675 – 686.

Ebert, E. E., J. Janowiak and C. Kidd, 2007: Comparison of near real time precipitation estimates from

satellite observations and numerical models. Bull. Amer. Met. Soc., 88, 47-64.

Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-

Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J. Hydrometeor., 8, 38–55.

Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie, 2004: CMORPH: A method that produces global

precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J.

Hydrometeor., 5, 487–503.

Sapiano, M. R. P., and P. A. Arkin, 2009: An Intercomparison and Validation of High-Resolution Satellite

Precipitation Estimates with 3-Hourly Gauge Data. J. Hydrometeor., 10, 149–166.

Scofield, R. A., and R. J. Kuligowski, 2003: Status and outlook of operational satellite precipitation

algorithms for extreme-precipitation events. Mon. Wea. Rev., 18, 1037-1051.

Vila, D. A., L. G. G. De Goncalves, D. L. Toll, and J. R. Rozante, 2009: Statistical evaluation of combined

daily gauge observations and rainfall satellite estimates over continental South America. J. Hydrometeor., 10,

533-543.

REFERENCES