scholarly journals On the Long-Wavelength Validation of the SWOT KaRIn Measurement

2019 ◽  
Vol 36 (5) ◽  
pp. 843-848 ◽  
Author(s):  
Jinbo Wang ◽  
Lee-Lueng Fu
Keyword(s):  
Wavelength Range ◽  
Spatial Scales ◽  
Global Ocean ◽  
Short Wavelength Range ◽  
Long Wavelength ◽  
Spatial Coverage ◽  
In Situ Observations ◽  
Wavenumber Spectrum ◽  
Ocean Topography

AbstractThe Surface Water and Ocean Topography (SWOT) mission will measure the sea surface height (SSH) using a Ka-band radar interferometer (KaRIn) over a swath off the nadir of the satellite tracks. The mission requires calibration and validation (CalVal) of the SSH wavenumber spectrum at wavelengths between 15 and 1000 km. The CalVal in the short-wavelength range (15–150 km) requires in situ observations. In the long-wavelength range (150–1000 km), the CalVal will use the onboard Jason-class nadir altimeter. Using a high-resolution global ocean simulation, this study identifies the spatial scales beyond which the nadir and off-nadir observations can be considered comparable. Our results suggest that the ocean signals at nadir can represent off-nadir ocean signals at wavelengths longer than 120 and 70 km along the midswath and the inner edge of the KaRIn grid, respectively, indicating that the nadir altimeter is able to fulfill its goal to validate the long-wavelength KaRIn measurement. The wavelength along the inner edge is limited around 70 km because the onboard nadir altimeter cannot resolve spatial scales longer than ~70 km. These wavelengths provide a reference point for the required spatial coverage of the SWOT SSH in situ CalVal.

scholarly journals An Observing System Simulation Experiment for the Calibration and Validation of the Surface Water Ocean Topography Sea Surface Height Measurement Using In Situ Platforms

2018 ◽  
Vol 35 (2) ◽  
pp. 281-297 ◽  
Author(s):  
Jinbo Wang ◽  
Lee-Lueng Fu ◽  
Bo Qiu ◽  
Dimitris Menemenlis ◽  
J. Thomas Farrar ◽  
...  
Keyword(s):  
Surface Water ◽  
Simulation Experiment ◽  
System Simulation ◽  
Sea Surface ◽  
Station Keeping ◽  
Calibration And Validation ◽  
Wavenumber Spectrum ◽  
Ocean Topography ◽  
Observing System Simulation Experiment

AbstractThe wavenumber spectrum of sea surface height (SSH) is an important indicator of the dynamics of the ocean interior. While the SSH wavenumber spectrum has been well studied at mesoscale wavelengths and longer, using both in situ oceanographic measurements and satellite altimetry, it remains largely unknown for wavelengths less than ~70 km. The Surface Water Ocean Topography (SWOT) satellite mission aims to resolve the SSH wavenumber spectrum at 15–150-km wavelengths, which is specified as one of the mission requirements. The mission calibration and validation (CalVal) requires the ground truth of a synoptic SSH field to resolve the targeted wavelengths, but no existing observational network is able to fulfill the task. A high-resolution global ocean simulation is used to conduct an observing system simulation experiment (OSSE) to identify the suitable oceanographic in situ measurements for SWOT SSH CalVal. After fixing 20 measuring locations (the minimum number for resolving 15–150-km wavelengths) along the SWOT swath, four instrument platforms were tested: pressure-sensor-equipped inverted echo sounders (PIES), underway conductivity–temperature–depth (UCTD) sensors, instrumented moorings, and underwater gliders. In the context of the OSSE, PIES was found to be an unsuitable tool for the target region and for SSH scales 15–70 km; the slowness of a single UCTD leads to significant aliasing by high-frequency motions at short wavelengths below ~30 km; an array of station-keeping gliders may meet the requirement; and an array of moorings is the most effective system among the four tested instruments for meeting the mission’s requirement. The results shown here warrant a prelaunch field campaign to further test the performance of station-keeping gliders.

scholarly journals How essential are Argo observations to constrain a global ocean data assimilation system?

2015 ◽  
Vol 12 (3) ◽  
pp. 1145-1186 ◽  
Author(s):  
V. Turpin ◽  
E. Remy ◽  
P. Y. Le Traon
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Global Ocean ◽  
Altimeter Data ◽  
Western Boundary ◽  
Argo Data ◽  
Ocean Data Assimilation ◽  
In Situ Observations ◽  
The Impact

Abstract. Observing System Experiments (OSEs) are carried out over a one-year period to quantify the impact of Argo observations on the Mercator-Ocean 1/4° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of Argo data sets are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation-model forecast error is also significant from the surface down to a depth of 2000 m. Differences between independent observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. All Argo observations thus matter, even with a 1/4° model resolution. The main conclusion is that the performance of global data assimilation systems is heavily dependent on the availability of Argo data.

scholarly journals Application of machine learning techniques for regional bias correction of snow water equivalent estimates in Ontario, Canada

2020 ◽  
Vol 24 (10) ◽  
pp. 4887-4902
Author(s):  
Fraser King ◽  
Andre R. Erler ◽  
Steven K. Frey ◽  
Christopher G. Fletcher
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Water Resource Management ◽  
Snow Water Equivalent ◽  
Bias Reduction ◽  
Machine Learning Techniques ◽  
Spatial Coverage ◽  
Snow Water ◽  
In Situ Observations

Abstract. Snow is a critical contributor to Ontario's water-energy budget, with impacts on water resource management and flood forecasting. Snow water equivalent (SWE) describes the amount of water stored in a snowpack and is important in deriving estimates of snowmelt. However, only a limited number of sparsely distributed snow survey sites (n=383) exist throughout Ontario. The SNOw Data Assimilation System (SNODAS) is a daily, 1 km gridded SWE product that provides uniform spatial coverage across this region; however, we show here that SWE estimates from SNODAS display a strong positive mean bias of 50 % (16 mm SWE) when compared to in situ observations from 2011 to 2018. This study evaluates multiple statistical techniques of varying complexity, including simple subtraction, linear regression and machine learning methods to bias-correct SNODAS SWE estimates using absolute mean bias and RMSE as evaluation criteria. Results show that the random forest (RF) algorithm is most effective at reducing bias in SNODAS SWE, with an absolute mean bias of 0.2 mm and RMSE of 3.64 mm when compared with in situ observations. Other methods, such as mean bias subtraction and linear regression, are somewhat effective at bias reduction; however, only the RF method captures the nonlinearity in the bias and its interannual variability. Applying the RF model to the full spatio-temporal domain shows that the SWE bias is largest before 2015, during the spring melt period, north of 44.5∘ N and east (downwind) of the Great Lakes. As an independent validation, we also compare estimated snowmelt volumes with observed hydrographs and demonstrate that uncorrected SNODAS SWE is associated with unrealistically large volumes at the time of the spring freshet, while bias-corrected SWE values are highly consistent with observed discharge volumes.

scholarly journals Evaluation of TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) through a Soil Moisture Simulation

2018 ◽  
Vol 22 (15) ◽  
pp. 1-19 ◽  
Author(s):  
Xiaolei Fu ◽  
Lifeng Luo ◽  
Ming Pan ◽  
Zhongbo Yu ◽  
Ying Tang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Land Surface ◽  
Spatial Scales ◽  
Absolute Difference ◽  
Coarse Scale ◽  
Transfer Scheme ◽  
Surface Atmosphere ◽  
Spatial Coverage

Abstract Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.

Global In-Situ Observations of Essential Climate and Ocean Variables by the Global Drifter Program. Applications and Impacts

2020 ◽  
Author(s):  
Luca Centurioni ◽  
Verena Hormann
Keyword(s):  
North Atlantic Ocean ◽  
Spatial Scales ◽  
Data Access ◽  
Ocean Climate ◽  
The North ◽  
Free Data ◽  
Forecast Models ◽  
In Situ Observations ◽  
The Impact

<p>Accurate estimates and forecasts of physical and biogeochemical processes at the air-sea interface must rely on integrated in-situ and satellite surface observations of essential Ocean/Climate Variables (EOVs /ECVs). Such observations, when sustained over appropriate temporal and spatial scales, are particularly powerful in constraining and improving the skills, impact and value of weather, ocean and climate forecast models. The calibration and validation of satellite ocean products also rely on in-situ observations, thus creating further positive high-impact applications of observing systems designed for global sustained observations of EOV and ECVs.</p><p>The Global Drifter Program has operated uninterrupted for several decades and constitutes a particular successful example of a network of multiparametric platforms providing observations of climate, weather and oceanographic relevance (e.g. air-pressure, sea surface temperature, ocean currents). This presentation will review the requirements of sustainability of an observing system such as the GDP (i.e. cost effectiveness, peer-review of the observing methodology and of the technology, free data access and international cooperation), will present some key metrics recently used to quantify the impact of drifter observations, and will discuss two prominent examples of GDP regional observations and the transition to operations of novel platforms, such us wind and directional wave spectra drifters, in sparsely sampled regions of the Arabian Sea and of the North Atlantic Ocean.</p>

scholarly journals The 25-Day-Period Large-Scale Oscillations in the Argentine Basin Revisited

2005 ◽  
Vol 35 (8) ◽  
pp. 1473-1479 ◽  
Author(s):  
Chang-Kou Tai ◽  
Lee-Lueng Fu
Keyword(s):  
Simulation Study ◽  
Large Scale ◽  
Space Time ◽  
Sea Surface ◽  
Time Sampling ◽  
Numerical Studies ◽  
In Situ Observations ◽  
Ocean Topography ◽  
Argentine Basin

Abstract From sea surface height measurements made by the Ocean Topography Experiment (TOPEX)/Poseidon satellite, Fu et al. found and described large-scale oscillations at the period of 25 days in the Argentine Basin of the South Atlantic Ocean. These oscillations were previously hinted at by in situ observations. Only the extensive space–time sampling capability of TOPEX/Poseidon, however, was able to give a complete description of the phenomenon as a counterclockwise-rotating dipole centered at 45°S, 317°E over the Zapiola Rise. Fu et al. also undertook theoretical and numerical studies to suggest that the phenomenon is a resonantly excited barotropic normal mode of the locally closed f/H contour. In a simulation study, however, they also found that the space–time smoothing scheme employed would probably lower the amplitude of the estimated phenomenon by 30%–40%. By reprocessing the data using a different method and showing the amplitude to be almost 2 times as large, in this note it is confirmed that this is indeed the case. The original 5-yr study has also been extended to nearly 10 yr, demonstrating that the same phenomenon has persisted for almost 10 yr.

scholarly journals On the Spatial Scales to be Resolved by the Surface Water and Ocean Topography Ka-Band Radar Interferometer

2019 ◽  
Vol 36 (1) ◽  
pp. 87-99 ◽  
Author(s):  
Jinbo Wang ◽  
Lee-Lueng Fu ◽  
Hector S. Torres ◽  
Shuiming Chen ◽  
Bo Qiu ◽  
...  
Keyword(s):  
Surface Water ◽  
Spatial Resolution ◽  
Spatial Scales ◽  
Internal Gravity Waves ◽  
Global Ocean ◽  
Global Approximation ◽  
Ka Band ◽  
Ocean Eddies ◽  
Ocean Topography ◽  
Horizontal Grid

AbstractThe Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using a Ka-band radar interferometer (KaRIn). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean surface. This resolution is derived from the ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15-km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2-km horizontal grid spacing) tide-resolving global ocean simulation and map the SWOT scale as a function of location and season. The results show that the SWOT scale increases, in general, from about 15 km at low latitudes to ~30–45 km at mid- and high latitudes but with a large geographical dependence. A SWOT scale smaller than 30 km is expected in the high-latitude energetic regions. The SWOT scale varies seasonally as a result of the seasonality in both the noise and ocean signals. The seasonality also has a geographical dependence. Both eddies and internal gravity waves/tides contribute significantly to the SWOT scale variation. Our analysis provides model predictions for interpreting the anticipated observations from SWOT and guidance for the development of analysis methodologies.

scholarly journals From satellite altimetry to Argo and operational oceanography: three revolutions in oceanography

2013 ◽  
Vol 10 (4) ◽  
pp. 1127-1167 ◽  
Author(s):  
P. Y. Le Traon
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Sea Level ◽  
Ocean Circulation ◽  
Satellite Altimetry ◽  
Large Scale ◽  
Global Ocean ◽  
Operational Oceanography ◽  
In Situ Observations

Abstract. The launch of the US/French mission Topex/Poseidon (T/P) (CNES/NASA) in August 1992 was the start of a revolution in oceanography. For the first time, a very precise altimeter system optimized for large scale sea level and ocean circulation observations was flying. T/P alone could not observe the mesoscale circulation. In the 1990s, the ESA satellites ERS-1/2 were flying simultaneously with T/P. Together with my CLS colleagues, we demonstrated that we could use T/P as a reference mission for ERS-1/2 and bring the ERS-1/2 data to an accuracy level comparable to T/P. Near real time high resolution global sea level anomaly maps were then derived. These maps have been operationally produced as part of the SSALTO/DUACS system for the last 15 yr. They are now widely used by the oceanographic community and have contributed to a much better understanding and recognition of the role and importance of mesoscale dynamics. Altimetry needs to be complemented with global in situ observations. In the end of the 90s, a major international initiative was launched to develop Argo, the global array of profiling floats. This has been an outstanding success. Argo floats now provide the most important in situ observations to monitor and understand the role of the ocean on the earth climate and for operational oceanography. This is a second revolution in oceanography. The unique capability of satellite altimetry to observe the global ocean in near real time at high resolution and the development of Argo were essential to the development of global operational oceanography, the third revolution in oceanography. The Global Ocean Data Assimilation Experiment (GODAE) was instrumental in the development of the required capabilities. This paper provides an historical perspective on the development of these three revolutions in oceanography which are very much interlinked. This is not an exhaustive review and I will mainly focus on the contributions we made together with many colleagues and friends.

scholarly journals The MULTI OBSERVATIONS Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service

2021 ◽  
Author(s):  
Stephanie Guinehut ◽  
Bruno Buongiorno Nardelli ◽  
Trang Chau ◽  
Frederic Chevallier ◽  
Daniele Ciani ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Environment Monitoring ◽  
Surface Salinity ◽  
In Situ Observations ◽  
Marine Environment Monitoring ◽  
Monitoring Service

<p>Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is available through the MULTI OSERVATIONS (MULTIOBS) Thematic Assembly Center (TAC) of the European Copernicus Marine Environment Monitoring Service (CMEMS).</p><p>CMEMS MULTIOBS TAC proposes products based on satellite & in situ observations and state-of-the-art data fusion techniques. These products are fully qualified and documented and, are distributed through the CMEMS catalogue (http://marine.copernicus.eu/services-portfolio). They cover the global ocean for physical and biogeochemical (BGC) variables. They are available in Near-Real-Time (NRT) or as Multi-Year Products (MYP) for the past 28 to 36 years.</p><p>Satellite input observations include altimetry but also sea surface temperature, sea surface salinity as well as ocean color. In situ observations of physical and BGC variables are from autonomous platform such as Argo, moorings and ship-based measurements. Data fusion techniques are based on multiple linear regression method, multidimensional optimal interpolation method or neural networks.</p><p>MULTIOBS TAC provides the following products at global scale:</p><ul><li>3D temperature, salinity and geostrophic current fields, both in NRT and as MYP;</li> <li>2D sea surface salinity and sea surface density fields, both in NRT and as MYP;</li> <li>2D total surface and near-surface currents, both in NRT and as MYP;</li> <li>3D vertical current as MYP;</li> <li>2D surface carbon fields of CO<sub>2</sub> flux (fgCO<sub>2</sub>), pCO<sub>2</sub> and pH as MYP;</li> <li>Nutrient vertical distribution (including nitrate, phosphate and silicate) profiles as MYP;</li> <li>3D Particulate Organic Carbon (POC) and Chlorophyll-a (Chl-a) fields as MYP.</li> </ul><p>Furthermore, MULTIOBS TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor the global ocean 3D hydrographic variability patterns (water masses) and the global ocean carbon sink.</p>

Assessment and Intercomparison of NOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) version 2.1

2021 ◽  
pp. 1-57
Author(s):  
Boyin Huang ◽  
Chunying Liu ◽  
Eric Freeman ◽  
Garrett Graham ◽  
Tom Smith ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Optimum Interpolation ◽  
Climate Assessment ◽  
Spatial Coverage ◽  
In Situ Observations ◽  
Mean Square Difference ◽  
Global Oceans

AbstractNOAA Daily Optimum Interpolation Sea Surface Temperature (DOISST) has recently been updated to v2.1 (January 2016–present). Its accuracy may impact the climate assessment, monitoring and prediction, and environment-related applications. Its performance, together with those of seven other well-known sea surface temperature (SST) products, is assessed by comparison with buoy and Argo observations in the global oceans on daily 0.25°×0.25° resolution from January 2016 to June 2020. These seven SST products are NASA MUR25, GHRSST GMPE, BoM GAMSSA, UKMO OSTIA, NOAA GPB, ESA CCI, and CMC.Our assessments indicate that biases and root-mean-square-difference (RMSDs) in reference to all buoys and all Argo floats are low in DOISST. The bias in reference to the independent 10% of buoy SSTs remains low in DOISST, but the RMSD is slightly higher in DOISST than in OSTIA and CMC. The biases in reference to the independent 10% of Argo observations are low in CMC, DOISST, and GMPE; and RMSDs are low in GMPE and CMC. The biases are similar in GAMSSA, OSTIA, GPB, and CCI whether they are compared against all buoys, all Argo, or the 10% of buoy or 10% of Argo observations, while the RMSDs against Argo observations are slightly smaller than those against buoy observations. These features indicate a good performance of DOISST v2.1 among the eight products, which may benefit from ingesting the Argo observations by expanding global and regional spatial coverage of in situ observations for effective bias correction of satellite data.

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