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, christian.massari@irpi.cnr.it 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

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 40000 to 10000 stations after 2010

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

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)

How we can improve satellite rainfall estimates with soil moisture observations? (1) Crow et al. 2009-2011 we can correct satellite rainfall estimates by assimilating soil moisture observations into land surface models… Pellarin et al. (2008)

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)

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

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

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

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

(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 2011-2013 Min(RMSE) Mask SM2RAINsmos 1 day rainfall ERA-Interim Benchmark

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) 2014-2015 N.B. Triplets 2 and 4 were adopted only for checking the consistency of the results 2014-2015 PSMOS+CMORPH 0.25°

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)

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

Regional assessment: Improvements obtained with respect to CMORPH

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

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

Thanks for your attention christian.massari@irpi.cnr.it

Triplets 3B42RT-SM2RAIN-ERA CMORPH-SM2RAIN-ERA CMORPH-3B42RT-ERA A B C Period 2007-2012 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.

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

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!!