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| Titel |
Precipitation estimates from L-Band Radiometer Sea Surface Salinity |
| VerfasserIn |
Alexandre Supply, Jacqueline Boutin, Jean-Luc Vergely, Gilles Reverdin, Audrey Hasson, Nicolas Viltard, Cécile Mallet |
| Konferenz |
EGU General Assembly 2017
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| Medientyp |
Artikel
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| Sprache |
en
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 19 (2017) |
| Datensatznummer |
250145157
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| Publikation (Nr.) |
 EGU/EGU2017-9068.pdf |
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| Zusammenfassung |
| The Soil Moisture and Ocean Salinity (SMOS) satellite mission measures sea surface salinity
(SSS) since 2010 with a spatial resolution of about 50 km. Since 2015, Soil Moisture Active
and Passive (SMAP) mission also provides SSS with a similar resolution. In rainy regions, at
local and short time scales, the spatio-temporal variability of SSS is dominated by
rainfall. The relationship between sea surface freshening and rain rate (RR) has
been highlighted in the Pacific intertropical convergence zone (Boutin et al., JGR,
2014). This study investigates the rainfall characteristics that may be inferred from
SMOS and SMAP SSS based on a statistical approach, and to which extent this
information is complementary to IMERG (Integrated Multi-satellite Retrievals
for Global Precipitation Measurement mission) interpolated product. The IMERG
algorithm intercalibrates, merges and interpolates “all” satellite passive microwave
precipitation estimates (RPMW), together with microwave-calibrated infrared (IR)
satellite estimates (RIR) (Huffman et al., 2015) . The product contains the merged
RR (RmPMW) as well as the RPMWand RIR individual estimates used by the
algorithm.
Salinity anomalies (ΔS) associated with rainfall events are first estimated. A reference
salinity (i.e. an estimate of the salinity preceding the rainfall event) is inferred from the SSS
statistical distribution within 3˚ x3˚ region. It is derived from a Gaussian distribution fitted
onto the highest part of the distribution (quantile>0.8) taking advantage on the fact
that rainfall creates an asymmetrical SSS distribution towards low values. A RR
retrieval algorithm is then developed that combines SMOS ΔS and IR information. In
case of IR detects rain, SMOS rain rate, RSMOS is derived from SMOS ΔS. We
infer the relationship between RSMOS and SMOS ΔS using colocations within
30mn between SMOS ΔS and RPMW contained in IMERG product during the
2015 year. Correlation coefficient (r) between RSMOS and RPMW is equal to 0.75
(0.78 when the colocation radii is decreased to 3mn). In case there is no RPMW
at less than 1h20mn from RSMOS, r is decreased to 0.62. We then compare the
RSMOS with the IMERG merged product (RmPMW). In case there are RPMW at
less than 30mn (3mn) from SMOS pass, correlation coefficients remain about the
same as previously. In case there is no RPMW at less than 1h20mn from RSMOS, r
between RSMOS and RmPMW becomes equal to 0.72. This demonstrates that the
merging of RPMW with IR information by IMERG improves the rain detection
with respect to taking into account only RPMW but remains poorer than RPMW
measurements. This is confirmed by triple collocations between RSMOS, RIR and
RPMW.
We then evaluate the quality of the retrieval at monthly time scales from August 2014 to
July 2016. Hovmöller diagrams show a very good consistency between IMERG and SMOS
monthly rain estimates during this period (correlation of 0.92).
The SMOS RR retrieval algorithm is also applied to SMAP SSS measurements from
January 2016 to July 2016. SMAP rain estimates (RSMAP) are compared with
RSMOS. At monthly time scales, correlation between RSMAPand RSMOSis 0.96. |
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