What can we learn about rainfall from SMOS/SMAP Sea Surface Salinity ?

Context and objectives

Data and methods

Results (learning & validation)

Conclusion & perspectives

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Figure 4 : Seasurface salinityanomaly andradiometer rain rate(04/10/2012) .

VALIDATION & other studies :Salinity : SMOS/ESA L2 v622 (2015-2016) & SMAP JPL/CAP L2 SSS (Summer 2016).Rain rate : REMSS, CMORPH, IMERG (µwave RR, IR RR, interpolated product (IMERG ATBD, 2015)).Salinity anomaly computation :Reference salinity is a salinity mean in a 3°x3° box estimated from pixels with a low probability of rain.

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A. Supply1, J. Boutin1, N. Martin1, J.L. Vergely2, G. Reverdin1, N. Viltard3 , A. Hasson1 , S.Marchand1 , H. Bellenger4

Mail : alexandre.supply@locean.upmc.fr

1 LOCEAN, Paris, France 2ACRI-st, Guyancourt, France 3LATMOS, Guyancourt, France 4JAMSTEC, Yokosuka, Japan

06/01/2016

mm/h

mm/h

mm/h

mm/h

mm/h

SMOSsalinity SMAPsalinity

RRSMOS RRSMAP

RRIMERGccSMOS RRIMERGccSMAP

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mm/h

RRSMOS RRIMERGccSMOS

RRSMAP RRIMERGccSMAP

RRSMAP–RRSMOS RRIM.ccSMAP– RRIM.ccSMOS

Figure 9 : Study case, 06/01/2015: SMOS, infrared RR andinterpolated IMERG within 15mn but time lag between SMOSand nearest radiometer RR : 2h10; top, left) RR SMOS; top,right) Radiometer RR; bottom, left) RR IMERG (interpolated) ;bottom, right) RR IR.

TimeRAD– TIMESMOS/SMAP

• Rainfall has a clear impact on SeaSurface Salinity SSS observed by L-Bandradiometry (Boutin et al. 2013, 2014 ;M.E. McCulloch et al, 2012) (Figure 1).

Figure 5: Determination of the relationshipbetween SSS anomaly and Rain Rate: cumulativedistribution of SSS anomaly (red) and Rain rate(black) are put in correspondence in order to get ridof time lags between SSS and RR measurements.

Figure 8 : Study case,11/08/2016: SMOS,SMAP & radiometerRR less than 15mnapart.Top: satellite SSS left)SMOS; right) SMAP;Middle: satellite RRleft) SMOS; right)SMAP;Bottom: RR IMERG(Interpolatedproduct at less than15mn from satelliteSSS) collocated withleft) SMOS; right)SMAP.

Figure 10 : Study case,15/07/2016 ; SMOS,SMAP & radiometerRR at more than15mn apart.Left: RR from satelliteSSS top) SMOS;middle) SMAP;Bottom) RRdisplacementRight: RR from IMERG(interpolated product)collocated with top)SMOS; middle) SMAP;Bottom) RRdisplacement

ITCZ Pacific OceanTRAINING SET : 2012 ; 110°W-180°W ; 0°N-15°N - Salinity : SMOS/ESA L2 SSS (at 1cm depth) (v622), ascending orbits - Rain rate : REMSS (Hilburn and Wentz, 2007).

• SMOS and SMAP satellite missionsprovide measurements at more than30mn from rain radiometer in ~40 % oftime (Figure 2). Given the strongintermittency of rain, we investigatewhich additional information on rainthey can bring.

Figure 2 : Cumulative distribution for Dt(TIME RAD – TIME SMOS/SMAP (inhours)) computed during June-July 2016.

Figure 3 : Example of rain induced surface coolingobserved on 14 December 2011 by a Surface SalinityProfiler (SSP; Asher et al. 2014) at -11 cm depth (blackline) in the northern Pacific Ocean and simulations (incolors) of (top) salinity in the skin layer (50µm depth)blue dashed) and at 11cm depth (blue solid); (bottom)observed rain rate (solid, mmh-1) and wind speed(dashed, ms-1). (Bellenger et al. 2016)

• Rain Rate derived from satellite SSS is in line with rain radiometer measurements but depends on the RR learning product.• SMOS and SMAP bring additional information on rain cells evolution that could be merged with rain radiometers data.• Validate these results using information independent from RR satellite products: e.g. in-situ rain radar data.• Take advantage of synergy between SMAP and SMOS for studying the temporal evolution of freshening after a rain event.

Conclusions

Perspectives

Figure 1 :DSSS from SMOS versus radiometer rain rate in2012 (1°x1° smoothing); Colorbar = Log10 of occurrences,red line = orthogonal regression, magenta points anderrorbar = mean and standard deviation in rain rateclasses (RR=0 and every 1 mm/h classes) ; theoreticalrelationship for skin layer (Schlüssel et al. 1996) .

• In-situ SSS observations and surface ocean models show strongfreshening after a rain event but supplementary studies are needed toimprove understanding of rain/SSS interactions (Figure 3, Bellenger etal. 2016)

• Asher, W. et al.(2014), Observations of rain-induced near-surface salinity anomalies, J. Geophys. Res. Oceans.• Bellenger, H. et al. (2016), Extension of the prognostic model of sea surface skin temperature to rain-induced cool and fresh lenses, submitted to JGR-Ocean.• Boutin, J. et al. (2014), Sea surface salinity under rain cells: SMOS satellite and in situ drifters observations, J. Geophys. Res. Oceans.• Boutin, J. et al. (2013), Sea surface freshening inferred from SMOS and ARGO salinity: Impact of rain, Ocean Science.• McCulloch, M. et al. (2012), Have mid-latitude ocean rain-lenses been seen by the SMOS satellite?, Ocean Model.

On-going study

SALINITYANOMALY

RR(mm/h)

• SSM/I, SSMIS,Windsat and TMI data areproduced by Remote Sensing Systems.Data are available atwww.remss.com/missions/

• SMAP data are distributed by JPLOurOcean Portal and available atwww.ourocean.jpl.nasa.gov

• IMERG data are distributed by GoddardSpace Flight Center and available atwww.pmm.nasa.gov

• CMORPH data are distributed by NOAAand available at http://www.cpc.noaa.gov

Acknowledgements:This work is supported by the ESA/STSE SMOS+Rainfall (coordinated byARGANS company) and by the CNES/TOSCA SMOS-OCEAN projects.We thank Cécile Mallet for her recommendations on statistical aspects.

Figure 7: RR distributions for SMOS 2012, REMSS2012, CMORPH 2012, REMSS 2015 (January toJune) and SMOS 2015 (January to June).

• SMOS RR, SMAP RR and radiometer RR are veryconsistent at <15mn lag (Figure 8).

• Satellite SSS give additional information on RRwhen it flies over the study area at >~30mnbefore/after the nearest rain radiometer (Figure 9).

• RR SMOS and RR SMAP allow to reconstruct thetrajectory of the rain cell (Figure 10).

• A DSSS-RR relationship is derivedfrom 2012 data (Figure 5)

• RR SMOS / RR RAD are in goodagreement when considering theREMSS RR products both for learningand validation, with two validationmethods: (Figure 6) colocations within[-15mn; 30mn] and (Figure 7)statistical distributions. A slightlydifferent statistical distribution isobtained from a different RR product(CMORPH ; Figure 7).

Figure 6: Rain rate from SMOS versus radiometer rain rate for left)2012 (learning year) and right) 2015 (validation) ; Colorbar = Log10of occurrences, red line = orthogonal regression.

2012

RMSE=0.29R2=0.40y=-0.19x-0.03N=685944

2015(JanuarytoJune)2012(JanuarytoJune)

RMSE=0.89Corr=0.73y=1.06x+0.01N=343737

RMSE=1.08Corr=0.60y=0.94x-0.01N=203483

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