Soon 8 candles for SMOS!!!!! (5/8)

Category : CATDS, L2, L3, Model, Ocean

Another post from Jacqueline…and Jérôme

Water cycle in the Bay of Bengal

J. Vialard , S. Marchand et al. (LOCEAN)

The Bay of Bengal receives large amounts of freshwater from the Ganges-Brahmaputra river and monsoonal rainfall. The associated very low surface salinities induce a very stable stratification that inhibits vertical mixing of heat and nutrients. This has strong consequences for the climatological rainfall, intensification of tropical cyclones and ocean productivity in this region.

Available climatologies based on in situ data (e.g. World Ocean Atlas, top row) do not resolve the very strong horizontal gradients in this region. SMOS data (middle row) reveal that the narrow, coastal-trapped East-Indian Coastal Current transport the freshwater plume of Ganges-Brahmaputra along the Indian coast from October to December, resulting in large horizontal gradients (typically ~5 pss between coastal and offshore waters). The 8 years-long time series reveals a strong inter-annual variability of the freshwater plume southward extent, which can be related to Indian Ocean climate variability.


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Caption: World ocean atlas (derived from in situ data, top row) and SMOS (middle row) (SSS climatology (altimeter-derived surface current climatology are overlaid on both panels). (Bottom row) Latitude-time section of SMOS SSS along the east coast of India. The southward extent of the freshwater plume varies depending on Indian Ocean climate variability associated with the Indian Ocean Dipole (Akhil et al. in prep.). (SMOS CATDS CPDC L3Q SSS)

To know more about associated work:

Akhil, V.P., F. Durand, M. Lengaigne, J. Vialard, M.G. Keerthi, V.V. Gopalakrishna, C. Deltel, F. Papa and C. de Boyer Montégut, 2014: A modeling study of the processes of surface salinity seasonal cycle in the Bay of Bengal, J. Geophys. Res. Oceans, 119, doi:10.1002/2013JC009632.

Akhil, V. P., M. Lengaigne, J. Vialard, F. Durand, M. G. Keerthi, A. V. S. Chaitanya, F. Papa, V. V. Gopalakrishna, and C. de Boyer Montégut, 2016a: A modeling study of processes controlling the Bay of Bengal sea surface salinity interannual variability, J. Geophys. Res. Oceans, 121, 8471–8495, doi:10.1002/2016JC011662.

Akhil, V.P., M. Lengaigne, F. Durand, J. Vialard, V.V. Gopalakrishna, C. de Boyer Montégut and J. Boutin, 2016b: Validation of SMOS and Aquarius remotely-sensed surface salinity in the Bay of Bengal, IJRS, 37,  doi: 10.1080/01431161.2016.1145362

Boutin, J., J.L. Vergely, S. Marchand, F. D’Amico, A. Hasson, N. Kolodziejczyk, N. Reul, G. Reverdin (2017), Revised mitigation of systematic errors in SMOS sea surface salinity: a Bayesian approach, Remote Sensing of Environment, in revision.

Chaittanya, A.V.S., M. Lengaigne, J. Vialard, V.V. Gopalakrishna, F. Durand, Ch. Krantikumar, V. Suneel, F. Papa and M. Ravichandran, 2014: Fishermen-operated salinity measurements reveal a “river in the sea” flowing along the east coast of India, Bull. Am. Met. Soc., 95, 1897-1908.

Fournier, S., J. Vialard, M. Lengaigne, T. Lee, M.M. Gierach, A.V.S. Chaitanya, Unprecedented satellite synoptic views of the Bay of Bengal “river in the sea”, 2017: J. Geophys. Res., in (minor) revision.

Soon 8 candles for SMOS!! (4/8)

Category : CATDS, Cal/Val, Data, L2, Non classé, ground measurements

Today let’s have a look back on what was done over land… but remember: it is only a quick summary of part of the findings!!

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Of course all the emphasis at the beginning was on the soil moisture retrievals over what as called « nominal surfaces », which meant land surface with moderate vegetation cover (fallow, crop land, savannah etc..) with all the cal val efforts related to it. For this in particular, several sites were dedicated to Cal Val (VAS in Spain, UDB in Germany, AACES/COSMOS/NAFE in Australia, and later HOBE in Denmark, with also sites in France, Poland, Finland, Tibet, etc…). We also relied heavily on the USDA so called « Watershed sites » and various sparse networks. Actually it is for SMOS that ESA and NASA decided to start the International Soil moisture Network.

lewis-faugaAACES 6MELBEX-II EMIRAD Installation 004LEWIS_3IMG_9674ELBARA-Sodankyla

Various pictures SMOSREX, AACES, VAS, Crolles, Mysore, Sodankylä …

Surprisingly enough we obtained good results almost immediately. But this was only the beginning as, in parallel, both level 1 and level 2 made significant progresses, leading to always improved retrievals. Actually with such fast progresses, it has always been a bit of a frustration to see people use not up to date products, as publications looking at SMOS data tended – for obvious reasons – to be a couple of version old (but generally failed to stipulate which version they were looking at!).

The most striking features of these always improved retrievals was, to me, the fact that the range of validity tended to regularly increase. Low to medium topography did not seem to a be a limitation, we managed to make sense in case of flooded areas (see for instance Mississipi floods) and we could get information in case of dense vegetation. The Tor Vergata University for instance related very quickly the vegetation depth to tree height and performed soil moisture retrievals under rainforest. No so accurate of course, but the tendencies are well depicted.

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SMOS opacity vs tree height from ICESat for two season (Rahmoune et al)

The only trouble we had was that the vegetation optical depth was not as satisfactory as we would have expected. It remained noisy in spite of significant overall progresses. To address this problem and also to keep on improving our retrievals (parametrisations) INRA and CESBIO worked on a different approach, the so called SMOS-IC and, lo and behold, first results are rather amazing! We believe we have again struck gold. More about this in the near future!

To finish with the surface soil moisture and vegetation opacity retrievals, we were faced with the fact that the retrieval algorithm is not so fast and thus tests or re-processings are a lengthy and tedious. This was another motivation for SMOS-IC but we also wanted to go a step further and, as soon as enough data was acquired, we developed a global neural network retrieval scheme. It has since been implemented in ECMWF and delivers Soil moisture fields less than 3 hours of sensing, paving the way to many applications…. to be summarised soon: stay tuned!

Further reading

Fernandez-Moran, R.; Al-Yaari, A.; Mialon, A.; Mahmoodi, A.; Al Bitar, A.; De Lannoy, G.; Rodriguez-Fernandez, N.; Lopez-Baeza, E.; Kerr, Y.; Wigneron, J.-P. SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product. Remote Sens. 2017, 9, 457.

Kerr, Y. H., et al. (2012), The SMOS Soil Moisture Retrieval Algorithm, IEEE Transactions on Geoscience and Remote Sensing, 50(5), 1384-1403, doi:10.1109/tgrs.2012.2184548.

Rahmoune, R., Ferrazzoli, P., Singh, Y., Kerr, Y., Richaume, P., Al Bitar,  A. SMOS Retrieval Results Over Forests: Comparisons With Independent Measurements. J-STARS ,2014

Rodriguez-Fernandez, N.J., Aires, F., Richaume, P., Kerr, Y.H., Prigent, C., Kolassa, J., Cabot, F., Jimenez, C., Mahmoodi, A., & Drusch, M. (2015). Soil Moisture Retrieval Using Neural Networks: Application to SMOS. Ieee Transactions on Geoscience and Remote Sensing, 53, 5991-6007

Vittucci, C., Ferrazzoli, P., Kerr, Y., Richaume, P., Guerriero, L., Rahmoune, R., & Laurin, G.V. (2016). SMOS retrieval over forests: Exploitation of optical depth and tests of soil moisture estimates. Remote Sensing of Environment, 180, 115-127

SMOS helps discriminating water sources in cold seas

Category : L2, Ocean

Discriminating water sources from space in the Arctic Ocean: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data

A recent paper by Matsuoka et al. (2016), using SMOS ESA L2 SSS, found nice correlations between the interannual variability of SMOS SSS and ocean color CDOM (see fig 1 and 2 below) in the Mackenzie river mouth. Using this region as a case study, they derive an algorithm using this two sets of data for getting reasonable fractions of river water. As stated by the authors ‘Application of this algorithm may lead to the discrimination of water sources in the surface layer of the Arctic Ocean in various environments where seawater, ice melt water, and river water are intermingled,which might be useful to improve our understanding of physical and biogeochemical processes related to each water source’.

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Fig.1 : Satellite images of CDOM absorption coefficient at 443 nm [aCDOM(443),m−1] using MODIS ocean color data in July to August 2010 to 2012 (from Matsuoka et al. (2016))

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Fig. 2 : Same as Fig. 1 for SMOS SSS (from Matsuoka et al. (2016)).

Atsushi Matsuoka, Marcel Babin, Emmanuel C. Devred, A new algorithm for discriminating water sources from space: A case study for the southern Beaufort Sea using MODIS ocean color and SMOS salinity data, Remote Sensing of Environment, Volume 184, October 2016, Pages 124-138, ISSN 0034-4257, http://dx.doi.org/10.1016/j.rse.2016.05.006. (//www.sciencedirect.com/science/article/pii/S0034425716301997)

Workshop announcement: « measuring high-wind speeds over the ocean »

Category : L2, L3, Ocean

On behalf of the organizing committee, I would like to draw your attention on the SMOS+STORM ESA project workshop

International workshop on measuring high-wind speeds over the ocean

15-17 November 2016, Met Office, Exeter

Overview:

A three-day workshop on the science and applications of ocean surface winds, with a focus on measuring high or extreme wind speeds. The workshop will be held at the Met Office headquarters in the cathedral city of Exeter.

Topics will include:

  • Satellite measurement techniques including new capabilities from SMOS, SMAP and AMSR2 sensors
  • Ground and airborne measurements
  • Core/operational applications and challenges e.g. hurricane/extreme forecasting, storm surges, waves
  • Air-sea applications and challenges e.g. seasonal/climate, upper ocean dynamics, air-sea interaction, biogeochemistry
  • Numerical Weather Prediction (NWP) applications
  • Numerical Ocean Prediction (NOP) applications

Organisers: Pete Francis (Met Office), James Cotton (Met Office), Nicolas Reul (IFREMER), Craig Donlon (ESA)

Further information on how to register for the workshop and submit an abstract and preliminary program is available from the link below:

http://www.metoffice.gov.uk/conference/ocean-winds-workshop

Please find also attached the workshop flyer.

workshop_flyer_v1.0

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