1. 4th Soil Moisture Validation and Application Workshop – Vienna 18-20 Sept 2017
New opportunities for integrating global precipitation products based on Triple Collocation Analysis
Christian Massari (1), Luca Brocca(1), Wade Crow(2), Thierry Pellarin(3), Carlos Román-Cascón(3), Yann Kerr(4), Diego Fernandez(5)
1IRPI-CNR, Perugia, Italy,
2USDA - Hydrology and Remote Sensing Laboratory, Beltsville, MD, USA
3Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, Grenoble F-38000, France
4Centre d’Etudes Spatiales de la BIOsphère (CESBIO), Université Toulouse 3 CNES CNRS IRD, Toulouse, France
5European Space Agency (ESA), Frascati, Italy

2. How much of the Earth surface is covered by rain gauges?
Equivalent areas of common sports pitches and courts compared with the total areas of orifices of all GTS and GPCP gauges
Global precipitation climatology center (GPCC)
Drop from to stations after 2010

3. Why do we need to improve satellite rainfall data?
An example: flood modelling
Median NSE satellite rainfall data <0.2
RED: GAUGE-BASED
CYAN: REANALYSIS
YELLOW: SATELLITE-BASED
BLUE: MSWEP

4. Semi-arid to temperate
SMOS+Rainfall project
Nb rain gauges Nb SM
Stations
Climate
Annul rainfall (mm)
Topography
South-West Niger 41 6
Semi-arid
600
flat
Central Benin 29 8
Sub-humid
1250
South-East France
187
Mediter-ranean
750
mountainous
Umbria, Italy 90 15
950
rolling
Walnut Gulch, AZ 14
320
Little Washita, OK
Little River, GA
Humid
1200
Reynolds Creek, ID
500
Yanco, Australia 13
Semi-arid to temperate
450
Provide a local assessment of soil moisture derived rainfall product over 10 selected sites using 3 different algorithms
Provide a 0.25° assessment of SMOS derived rainfall product over 9 selected sites
Provide a global assessment of SMOS derived rainfall product (50°N-50°S)

5. How we can improve satellite rainfall estimates with soil moisture observations? (1)
Crow et al
we can correct satellite rainfall estimates by assimilating soil moisture observations into land surface models…
Pellarin et al. (2008)

6. How to assign the weights?
How we can improve satellite rainfall estimates with soil moisture observations? (2)
Or…
we can estimate rainfall directly from soil moisture and integrate these estimates with satellite rainfall observations
PSM2RAIN
How to assign the weights?
Integrated rainfall
SM2RAIN derived rainfall
weights
Satellite rainfall product
(e.g., 3B42RT)

7. How to integrate SM2RAIN rainfall with satellite rainfall?
Given a REFERENCE (i.e., a high-quality ground-based rainfall dataset Pobs ) we can maximize the correlation between Pint and the reference Pobs
But we do not have such a good reference everywhere …
N.B. with some simplifications

8. TC correlations: ERA-Interim, SM2RAIN, 3B42RT
Triple collocation applied to global rainfall products
TC correlations: ERA-Interim, SM2RAIN, 3B42RT
We published a study where we demonstrated that
We can obtain correlations of three different rainfall products by using ETC provided that these products satisfy the underlying assumptions of TC
The main innovation is that we do not need ground bservations anymore to obtain correlations

9. How to use Triple Collocation for integrating SM2RAIN with other satellite rainfall estimates?
Weights calculation
Now correlations are given by TC and we do not need the ground reference anymore

10. Satellite-only rainfall
Objective:
Using SMOS (via SM2RAIN) for improving satellite-only rainfall products with TC-based weights
Which products?
Product
Reference
Type
Spatial resolution
SMOS level-3 RE04
Al Bitar et al. 2017
Soil moisture
0.25°
3B42RT v7.0
Huffman et al. 2007
Satellite-only rainfall
CMORPH raw 1.0
Joyce et al. 2004
ERA-Interim
Dee et al. 2011
Reanalysis rainfall
≃0.7°
GPCC
Schneider et al. 2014
Ground rainfall 1°
Other gauge based networks

11. (areas with SMOS temporal coverage < 5% excluded)
Calibration strategy
SMOS
Flagging
RFI>0.35
DQX>0.05
RFI
Masking
(areas with SMOS temporal coverage < 5% excluded)
SM2RAIN
Calibration
Min(RMSE)
Mask
SM2RAINsmos
1 day rainfall
ERA-Interim
Benchmark

12. Extended Triple Collocation (McColl et al. 2014)
Triple Collocation and weight calculations
SM2RAINsmos
SRP (3B42RT, CMORPH)
ERA-Interim
GPCC
SM2RAINsmos
CMORPH
Extended Triple Collocation (McColl et al. 2014)
N.B. Triplets 2 and 4 were adopted only for checking the consistency of the results
PSMOS+CMORPH 0.25°

13. Validation strategy (2014-2015)
mask
validation
Eobs 14-15
CPC 14-15
IMD 14-15
AWAP 14-15
Regional scale in India, US, Europe and Australia
(daily, 0.25°)
Local scale, on 10 selected sites (pixel scale, daily and 5-daily)
FINAL MASKED AREAS= LOW SMOS TEMPORAL COVERAGE due to flagging + LOW DENSITY OF GPCC RAIN GAUGES
Global scale using GPCC 1° as a reference
(N.B. areas characterized by low rain gauges density of GPCC were masked out)

14. Local scale: Results on the 10 sites of the SMOS+Rainfall project
3B42RT+SM2Rsmos
CMORPH+SM2Rsmos
PERSIANN+SM2Rsmos
10 SMOS sites
Improvements with respect to 3B42RT, CMORPH, PERSIANN

15. Regional assessment: Improvements obtained with respect to CMORPH

16. Global assessment: results on a global scale vs. GPCC 1°
Improvements with respect to CMORPH
3B42RT
CMORPH

17. Conclusions
Triple collocation can be successfully used for merging different rainfall datasets
Results confirm that SMOS can provide improvements of satellite-only rainfall estimates where its quality is high and its temporal coverage is sufficiently dense
The integrated product can be implemented in real time with several advantages in some applications
The analysis has taken into account only continuous scores (i.e., correlation and RMSE). The effect of the integration on the categorical scores and on the BIAS has to be still thoroughly quantified
Other soil moisture sensors (ASCAT, SMAP) can be potentially used
Improvements

18. Thanks for your attention

19. Triplets 3B42RT-SM2RAIN-ERA CMORPH-SM2RAIN-ERA CMORPH-3B42RT-ERA A B C
Period
Study area Contiguos United States (CONUS)
Temporal resolution daily
Spatial resolution 1°
How to test the method?
Take a high quality gauge based rainfall dataset i.e., CPC 1 2
Calculate the
Pearson correlation coefficient of each product Xi against CPC 3
Compare Ri with the correlations obtained through TC (ρi)
CPC= Climate Prediction Center Unified Gauge-Based Analysis of Global Daily Precipitation.

20. Pearson correlation obtained with CPC
TC correlations: ERA-Interim, SM2RAIN, 3B42RT
TC correlations: ERA-Interim, SM2RAIN, CMORPH
TC correlations: ERA-Interim, 3B42RT, CMORPH

21. Which is the first (best), the second and the third (worst) product in terms of correlation according to Pearson and TC-based correlations?
Medium
Best
Worst
Satellite-only rainfall
We cannot use two satellite-only rainfall products within the same triplet… Hence, the only option for applying TC is to use SM2RAIN. It provides independent non-ground based rainfall estimates characterized by non-cross correlated errors that can be used for assessing the product performance in poorly gauged areas
Product complementary
N.B. Different results!!