scholarly journals Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

Ocean Science ◽  
2012 ◽  
Vol 8 (5) ◽  
pp. 751-771 ◽  
Author(s):  
D. A. Ford ◽  
K. P. Edwards ◽  
D. Lea ◽  
R. M. Barciela ◽  
M. J. Martin ◽  
...  
Keyword(s):  
Real Time ◽  
Chlorophyll Concentration ◽  
Ocean Basin ◽  
Biogeochemical Model ◽  
State Variables ◽  
Ocean Colour ◽  
In Situ Observations ◽  
Time Basis ◽  
The Impact

Abstract. As part of the GlobColour project, daily chlorophyll a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and considerably improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However, the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. The other variables were not degraded by the assimilation, with annual mean surface fields of nutrients, alkalinity and carbon variables remaining of similar quality compared to climatology. There was evidence of improved representation of zooplankton concentration, and reduced errors were seen against in situ observations of nitrate and pCO2, but too few observations were available to conclude about global model skill. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

scholarly journals Assimilating GlobColour ocean colour data into a pre-operational physical-biogeochemical model

2012 ◽  
Vol 9 (2) ◽  
pp. 687-744 ◽  
Author(s):  
D. A. Ford ◽  
K. P. Edwards ◽  
D. Lea ◽  
R. M. Barciela ◽  
M. J. Martin ◽  
...  
Keyword(s):  
Real Time ◽  
Chlorophyll Concentration ◽  
Ocean Basin ◽  
Biogeochemical Model ◽  
State Variables ◽  
Ocean Colour ◽  
In Situ Observations ◽  
Time Basis ◽  
The Impact

Abstract. As part of the GlobColour project, daily chlorophyll-a observations, derived using remotely sensed ocean colour data from the MERIS, MODIS and SeaWiFS sensors, are produced. The ability of these products to be assimilated into a pre-operational global coupled physical-biogeochemical model has been tested, on both a hindcast and near-real-time basis, and the impact on the system assessed. The assimilation was found to immediately and significantly improve the bias, root mean square error and correlation of modelled surface chlorophyll concentration compared to the GlobColour observations, an improvement which was sustained throughout the year and in every ocean basin. Errors against independent in situ chlorophyll observations were also reduced, both at and beneath the ocean surface. However the model fit to in situ observations was not consistently better than that of climatology, due to errors in the underlying model. The assimilation scheme used is multivariate, updating all biogeochemical model state variables at all depths. Consistent changes were found in the other model variables, with reduced errors against in situ observations of nitrate and pCO2, and evidence of improved representation of zooplankton concentration. Annual mean surface fields of nutrients, alkalinity and carbon variables remained of similar quality compared to climatology. The near-real-time GlobColour products were found to be sufficiently reliable for operational purposes, and of benefit to both operational-style systems and reanalyses.

Assessment of the CMEMS global biogeochemical forecasting operational system, with assimilation of Ocean Colour data

2020 ◽  
Author(s):  
Julien Lamouroux ◽  
Alexandre Mignot ◽  
Coralie Perruche ◽  
Giovanni Ruggiero ◽  
Julien Paul ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Global Ocean ◽  
Effective Capacity ◽  
Biogeochemical Model ◽  
Ocean Colour ◽  
Operational System ◽  
Physical Data ◽  
Forecasting System ◽  
The Impact

<p>The operational production of data-assimilated biogeochemical state of the ocean is one of the challenging core projects of the Copernicus Marine Environment Monitoring Service (hereafter CMEMS). In that framework, Mercator Ocean International is in charge of improving the realism of its global 1⁄4° coupled physical-biogeochemical simulations, analyses and re‐analyses, and to develop an effective capacity to routinely estimate the biogeochemical state of the ocean, including, amongst others, the implementation of biogeochemical data assimilation. Primary objectives are to enhance the time representation of the seasonal cycle in the real time and reanalysis systems, and to provide a better control of the production in the equatorial regions.<br><br>In that framework, Mercator Ocean International has successfully updated its global biogeochemical analysis and forecasting system with an Ocean Color data assimilation capability. In this system, successfully commissioned in September 2019, the biogeochemical model (NEMO/PISCES) is offline coupled with the dynamical ocean (1/12° coarsened to 1/4° resolution) from the CMEMS global physical analysis and forecasting operational system (PSY4), at a daily frequency, and benefits from the assimilation of satellite (SSH-SST-SIC) and in situ physical data. Nevertheless, a biogeochemical climatological damping is activated in the biogeochemical model in order to mitigate the impact of some misconstrained processes (vertical velocities) from this physical data-assimilated forcing (under investigation). The dedicated assimilation of biogeochemical data relies on a simplified version of the SEEK filter, where the forecast error covariances are built from a fixed-basis - but seasonally variable - ensemble of anomalies computed from a multi-year numerical experiment (without biogeochemical data assimilation) with respect to a running mean. Regarding Ocean Colour observations, the system relies, as a first step, on the CMEMS Global Ocean surface chlorophyll concentration products, delivered in NRT.<br><br>The objective of this presentation is thus to provide (1) a short description of the implementation of the aforementioned data assimilation methodology in the forecasting system; (2) a synthesis of the assessment of this global biogeochemical forecasting system, by cross-comparing the assimilated solution with various datasets, both spatial (Ocean Colour) and in situ (BGC-Argo, GLODAP), and (3) a synthetic overview of the impact/benefit of the assimilation of the Ocean Colour data.</p>

scholarly journals A comparison between polynya area and associated ice production with mooring-based measurements of temperature, salinity and currents in the southwestern Ross Sea, Antarctica

2011 ◽  
Vol 52 (57) ◽  
pp. 291-300 ◽  
Author(s):  
Stefan Kern ◽  
Stefano Aliani
Keyword(s):  
Ross Sea ◽  
Time Lag ◽  
Ice Formation ◽  
Terra Nova Bay ◽  
In Situ Observations ◽  
Satellite Microwave ◽  
Terra Nova ◽  
Ice Production ◽  
The Impact

AbstractWintertime (April–September) area estimates of the Terra Nova Bay polynya (TNBP), Antarctica, based on satellite microwave radiometry are compared with in situ observations of water salinity, temperature and currents at a mooring in Terra Nova Bay in 1996 and 1997. In 1996, polynya area anomalies and associated anomalies in polynya ice production are significantly correlated with salinity anomalies at the mooring. Salinity anomalies lag area and/or ice production anomalies by about 3 days. Up to 50% of the variability in the salinity at the mooring position can be explained by area and/or ice production anomalies in the TNBP for April–September 1996. This value increases to about 70% when considering shorter periods like April–June or May–July, but reduces to 30% later, for example July–September, together with a slight increase in time lag. In 1997, correlations are smaller, less significant and occur at a different time lag. Analysis of ocean currents at the mooring suggests that in 1996 conditions were more favourable than in 1997 for observing the impact of descending plumes of salt-enriched water formed in the polynya during ice formation on the water masses at the mooring depth.

scholarly journals SEVIRI Hyper-Fast Forward Model with Application to Emissivity Retrieval

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1532 ◽  
Author(s):  
Guido Masiello ◽  
Carmine Serio ◽  
Sara Venafra ◽  
Laurent Poutier ◽  
Frank-M. Göttsche
Keyword(s):  
Radiative Transfer ◽  
Real Time ◽  
Forward Model ◽  
Radiative Transfer Model ◽  
Computational Time ◽  
Transfer Model ◽  
Real Time Processing ◽  
In Situ Observations ◽  
Infrared Radiances

Timely processing of observations from multi-spectral imagers, such as SEVIRI (Spinning Enhanced Visible and Infrared Imager), largely depends on fast radiative transfer calculations. This paper mostly concerns the development and implementation of a new forward model for SEVIRI to be applied to real time processing of infrared radiances. The new radiative transfer model improves computational time by a factor of ≈7 compared to the previous versions and makes it possible to process SEVIRI data at nearly real time. The new forward model has been applied for the retrieval of surface parameters. Although the scheme can be applied for the simultaneous retrieval of temperature and emissivity, the paper mostly focuses on emissivity. The inverse scheme relies on a Kalman filter approach, which allows us to exploit a sequential processing of SEVIRI observations. Based on the new forward model, the paper also presents a validation retrieval performed with in situ observations acquired during a field experiment carried out in 2017 at Gobabeb (Namib desert) validation station. Furthermore, a comparison with IASI (Infrared Atmospheric Sounder Interferometer) emissivity retrievals has been performed as well. It has been found that the retrieved emissivities are in good agreement with each other and with in situ observations, i.e., average differences are generally well below 0.01.

scholarly journals Tracking Metamorphic Dehydration Reactions in Real Time with Transmission Small- and Wide-Angle Synchrotron X-ray Scattering: the Case of Gypsum Dehydration

2020 ◽  
Vol 61 (6) ◽  
Author(s):  
C E Schrank ◽  
K Gioseffi ◽  
T Blach ◽  
O Gaede ◽  
A Hawley ◽  
...  
Keyword(s):  
Real Time ◽  
Wide Angle ◽  
Length Scales ◽  
X Ray ◽  
Rock Samples ◽  
X Ray Scattering ◽  
Dehydration Reactions ◽  
The Impact ◽  
Ray Scattering

Abstract We present a review of a unique non-destructive method for the real-time monitoring of phase transformations and nano-pore evolution in dehydrating rocks: transmission small- and wide-angle synchrotron X-ray scattering (SAXS/WAXS). It is shown how SAXS/WAXS can be applied to investigating rock samples dehydrated in a purpose-built loading cell that allows the coeval application of high temperature, axial confinement, and fluid pressure or flow to the specimen. Because synchrotron sources deliver extremely bright monochromatic X-rays across a wide energy spectrum, they enable the in situ examination of confined rock samples with thicknesses of ≤ 1 mm at a time resolution of order seconds. Hence, fast kinetics with reaction completion times of about hundreds of seconds can be tracked. With beam sizes of order tens to hundreds of micrometres, it is possible to monitor multiple interrogation points in a sample with a lateral extent of a few centimetres, thus resolving potential lateral spatial effects during dehydration and enlarging sample statistics significantly. Therefore, the SAXS/WAXS method offers the opportunity to acquire data on a striking range of length scales: for rock samples with thicknesses of ≤ 10-3 m and widths of 10-2 m, a lateral interrogation-point spacing of ≥ 10-5 m can be achieved. Within each irradiated interrogation-point volume, information concerning pores with sizes between 10-9 and 10-7 m and the crystal lattice on the scale of 10-10 m is acquired in real time. This article presents a summary of the physical principles underpinning transmission X-ray scattering with the aim of providing a guide for the design and interpretation of time-resolved SAXS/WAXS experiments. It is elucidated (1) when and how SAXS data can be used to analyse total porosity, internal surface area, and pore-size distributions in rocks on length scales from ∼1 to 300 nm; (2) how WAXS can be employed to track lattice transformations in situ; and (3) which limitations and complicating factors should be considered during experimental design, data analysis, and interpretation. To illustrate the key capabilities of the SAXS/WAXS method, we present a series of dehydration experiments on a well-studied natural gypsum rock: Volterra alabaster. Our results demonstrate that SAXS/WAXS is excellently suited for the in situ tracking of dehydration kinetics and the associated evolution of nano-pores. The phase transformation from gypsum to bassanite is correlated directly with nano-void growth on length scales between 1 and 11 nm for the first time. A comparison of the SAXS/WAXS kinetic results with literature data emphasises the need for future dehydration experiments on rock specimens because of the impact of rock fabric and the generally heterogeneous and transient nature of dehydration reactions in nature. It is anticipated that the SAXS/WAXS method combined with in situ loading cells will constitute an invaluable tool in the ongoing quest for understanding dehydration and other mineral replacement reactions in rocks quantitatively.

scholarly journals The impact of near-surface soil moisture assimilation at subseasonal, seasonal, and inter-annual timescales

2015 ◽  
Vol 19 (12) ◽  
pp. 4831-4844 ◽  
Author(s):  
C. Draper ◽  
R. Reichle
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
In Situ Observations ◽  
The Impact

Abstract. A 9 year record of Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) soil moisture retrievals are assimilated into the Catchment land surface model at four locations in the US. The assimilation is evaluated using the unbiased mean square error (ubMSE) relative to watershed-scale in situ observations, with the ubMSE separated into contributions from the subseasonal (SMshort), mean seasonal (SMseas), and inter-annual (SMlong) soil moisture dynamics. For near-surface soil moisture, the average ubMSE for Catchment without assimilation was (1.8 × 10−3 m3 m−3)2, of which 19 % was in SMlong, 26 % in SMseas, and 55 % in SMshort. The AMSR-E assimilation significantly reduced the total ubMSE at every site, with an average reduction of 33 %. Of this ubMSE reduction, 37 % occurred in SMlong, 24 % in SMseas, and 38 % in SMshort. For root-zone soil moisture, in situ observations were available at one site only, and the near-surface and root-zone results were very similar at this site. These results suggest that, in addition to the well-reported improvements in SMshort, assimilating a sufficiently long soil moisture data record can also improve the model representation of important long-term events, such as droughts. The improved agreement between the modeled and in situ SMseas is harder to interpret, given that mean seasonal cycle errors are systematic, and systematic errors are not typically targeted by (bias-blind) data assimilation. Finally, the use of 1-year subsets of the AMSR-E and Catchment soil moisture for estimating the observation-bias correction (rescaling) parameters is investigated. It is concluded that when only 1 year of data are available, the associated uncertainty in the rescaling parameters should not greatly reduce the average benefit gained from data assimilation, although locally and in extreme years there is a risk of increased errors.

scholarly journals What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

2019 ◽  
Vol 147 (7) ◽  
pp. 2433-2449
Author(s):  
Laura C. Slivinski ◽  
Gilbert P. Compo ◽  
Jeffrey S. Whitaker ◽  
Prashant D. Sardeshmukh ◽  
Jih-Wang A. Wang ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Mean Square ◽  
Observational Network ◽  
In Situ Observations ◽  
The Mean ◽  
The Impact ◽  
Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

scholarly journals An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4285 ◽  
Author(s):  
Shubha Sathyendranath ◽  
Robert Brewin ◽  
Carsten Brockmann ◽  
Vanda Brotas ◽  
Ben Calton ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Marine Ecosystem ◽  
Ocean Colour ◽  
Remote Sensing Reflectance ◽  
Resolution Imaging ◽  
In Situ Observations ◽  
Climate Studies ◽  
Reflectance Data

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

scholarly journals A perturbed biogeochemistry model ensemble evaluated against in situ and satellite observations

2018 ◽  
Vol 15 (21) ◽  
pp. 6685-6711 ◽  
Author(s):  
Prima Anugerahanti ◽  
Shovonlal Roy ◽  
Keith Haines
Keyword(s):  
Peak Height ◽  
Ecosystem Dynamics ◽  
Abyssal Plain ◽  
Biogeochemical Model ◽  
Structural Sensitivity ◽  
Statistical Measures ◽  
Biogeochemical Models ◽  
Nutrient Utilisation ◽  
The Impact

Abstract. The dynamics of biogeochemical models are determined by the mathematical equations used to describe the main biological processes. Earlier studies have shown that small changes in the model formulation may lead to major changes in system dynamics, a property known as structural sensitivity. We assessed the impact of structural sensitivity in a biogeochemical model of intermediate complexity by modelling the chlorophyll and dissolved inorganic nitrogen (DIN) concentrations. The model is run at five different oceanographic stations spanning three different regimes: oligotrophic, coastal, and the abyssal plain, over a 10-year timescale to observe the effect in different regions. A 1-D Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration, and Acidification (MEDUSA) ensemble was used with each ensemble member having a combination of tuned function parameterizations that describe some of the key biogeochemical processes, namely nutrient uptake, zooplankton grazing, and plankton mortalities. The impact is quantified using phytoplankton phenology (initiation, bloom time, peak height, duration, and termination of phytoplankton blooms) and statistical measures such as RMSE (root-mean-squared error), mean, and range for chlorophyll and nutrients. The spread of the ensemble as a measure of uncertainty is assessed against observations using the normalized RMSE ratio (NRR). We found that even small perturbations in model structure can produce large ensemble spreads. The range of 10-year mean surface chlorophyll concentration in the ensemble is between 0.14 and 3.69 mg m−3 at coastal stations, 0.43 and 1.11 mg m−3 on the abyssal plain, and 0.004 and 0.16 mg m−3 at the oligotrophic stations. Changing both phytoplankton and zooplankton mortalities and the grazing functions has the largest impact on chlorophyll concentrations. The in situ measurements of bloom timings, duration, and terminations lie mostly within the ensemble range. The RMSEs between in situ observations and the ensemble mean and median are mostly reduced compared to the default model output. The NRRs for monthly variability suggest that the ensemble spread is generally narrow (NRR 1.21–1.39 for DIN and 1.19–1.39 for chlorophyll profiles, 1.07–1.40 for surface chlorophyll, and 1.01–1.40 for depth-integrated chlorophyll). Among the five stations, the most reliable ensembles are obtained for the oligotrophic station ALOHA (for the surface and integrated chlorophyll and bloom peak height), for coastal station L4 (for inter-annual mean), and for the abyssal plain station PAP (for bloom peak height). Overall our study provides a novel way to generate a realistic ensemble of a biogeochemical model by perturbing the model equations and parameterizations, which will be helpful for the probabilistic predictions.

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