In Situ TAC Dashboard, an Advanced Tool for visualizing CMEMS In Situ products

2021 ◽  
Author(s):  
Paz Rotllán-García ◽  
Fernando Manzano ◽  
Maria Sotiropoulou ◽  
Keyword(s):  
Product Family ◽  
Spatial Coverage ◽  
Wide Range ◽  
Report Data ◽  
In Situ Observations ◽  
Rest Api ◽  
Marine Environment Monitoring ◽  
Monitoring Service ◽  
User Friendly

<p>The In Situ Thematic Assembly Center (In Situ TAC) for the Copernicus Marine Environment Monitoring Service (CMEMS) is the only data component in the system, out of a total of fifteen, in charge of delivering quality-checked in situ observations in both near real time (NRT products) and delay mode (REP products) for their use in the characterisation of ocean state and variability, assimilation and/or validation activities carried out by the metocean community. </p><p>These in situ observations are gathered by a wide range of platforms (tide gauges, buoys, vessels, CTDs, profilers, gliders, drifters, HF radars, saildrones etc) and include many different parameters (Temperature, Salinity, Sea Level, Currents, Waves, Oxygen, Chlorophyll, Nutrients, Carbon etc). They are made available through known networks and regional data providers to a set of Production Units (PUs) or dedicated Data Centers (Ifremer, PdE, HCMR, IMR, IO-BAS, BSH, SMHI, UiB, CNR, AZTI) where they are quality-checked and homogenized before delivery in terms of format, quality control conventions and standards.</p><p>Unlike most of the products available in the CMEMS catalog (90%), in situ  data products do not naturally provide a regular temporal and spatial coverage or resolution. Indeed, these in situ observations can be available at fixed locations, or on a trajectory, or in a gridded area, at fixed depths or on profiles and the transmitting equipment can be configured to report data in different time samplings. Such a  complexity has traditionally prevented 82% of the In Situ TAC products from fully taking advantage of  CMEMS centralized improvements  in terms of the visualization of datasets (WMS) and subsetting (Subsetter). </p><p>To overcome  this situation, a first version of the CMEMS In Situ TAC Dashboard was released in 2017. This tool provides a user-friendly interface which enables the discovery, subsetting, sharing and downloading of files containing in-situ observations from In Situ TAC multiparameter NRT products. The tool relies on a set of python scripts which process homogenized metadata on an hourly basis as well as complementary information submitted by Sea Data Net (provider overview). The resulting information is then accessible through  the interface with the aid of a json-server REST API, which allows users to make queries and filter the information according to their interest.</p><p>In 2020, the current release of the CMEMS In Situ Dashboard has been officially approved as an “Advanced Visualization Tool” by CMEMS and is now showcased as a complementary tool to the official viewer. Future developments will explore its extension to the whole In Situ product family (beyond the present In Situ multiparameter NRT datasets), the improvement of data visualization options (currently using EMODnet widget services) and the implementation of data discovery capabilities.</p>

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>

scholarly journals The Mediterranean Ocean Colour Level 3 Operational Multi-Sensor Processing

2018 ◽  
Author(s):  
Gianluca Volpe ◽  
Simone Colella ◽  
Vittorio Brando ◽  
Vega Forneris ◽  
Flavio La Padula ◽  
...  
Keyword(s):  
Fine Tuning ◽  
Ocean Colour ◽  
Basin Scale ◽  
Sensor Processing ◽  
Level 3 ◽  
The Mediterranean ◽  
Set Up ◽  
Marine Environment Monitoring ◽  
Monitoring Service

Abstract. This work describes the main processing steps operationally performed to enable single ocean colour sensors to enter the multi-sensor chain for the Mediterranean Sea of Ocean Colour Thematic Assembling Centre. Here, the multi-sensor chain takes care of reducing the inter-sensor bias before data from different sensors are merged together. The basin-scale in situ bio-optical dataset is used both to fine-tuning the algorithms for the retrieval of phytoplankton chlorophyll and attenuation coefficient of light, Kd, and to assess the uncertainty associated with them. The satellite multi-sensor remote sensing Reflectance spectra better agree with the in situ observations than that of the single sensors, and are comparable with the ESA-OC-CCI multi-sensor product, highlighting the importance of reducing the inter-sensor bias. The Mediterranean near-real-time multi-sensor processing chain has been set up and is operational in the framework of the Copernicus Marine Environment Monitoring Service.

scholarly journals Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

2021 ◽  
Author(s):  
Yaoping Wang ◽  
Jiafu Mao ◽  
Mingzhou Jin ◽  
Forrest M. Hoffman ◽  
Xiaoying Shi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Earth System Model ◽  
System Model ◽  
Surface Model ◽  
Earth System ◽  
Model Simulations ◽  
The Earth ◽  
Wide Range ◽  
In Situ Observations

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

scholarly journals In situ observations of greenhouse gases over Europe during the CoMet 1.0 campaign aboard the HALO aircraft

2021 ◽  
Vol 14 (2) ◽  
pp. 1525-1544
Author(s):  
Michał Gałkowski ◽  
Armin Jordan ◽  
Michael Rothe ◽  
Julia Marshall ◽  
Frank-Thomas Koch ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Isotopic Signature ◽  
Standard Uncertainty ◽  
The European Union ◽  
Air Samples ◽  
Regional Sources ◽  
Research Aircraft ◽  
In Situ Observations ◽  
Monitoring Service

Abstract. The intensive measurement campaign CoMet 1.0 (Carbon Dioxide and Methane Mission) took place during May and June 2018, with a focus on greenhouse gases over Europe. CoMet 1.0 aimed at characterising the distribution of CH4 and CO2 over significant regional sources with the use of a fleet of research aircraft as well as validating remote sensing measurements from state-of-the-art instrumentation installed on board against a set of independent in situ observations. Here we present the results of over 55 h of accurate and precise in situ measurements of CO2, CH4 and CO mole fractions made during CoMet 1.0 flights with a cavity ring-down spectrometer aboard the German research aircraft HALO (High Altitude and LOng Range Research Aircraft), together with results from analyses of 96 discrete air samples collected aboard the same platform. A careful in-flight calibration strategy together with post-flight quality assessment made it possible to determine both the single-measurement precision as well as biases against respective World Meteorological Organization (WMO) scales. We compare the result of greenhouse gas observations against two of the available global modelling systems, namely Jena CarboScope and CAMS (Copernicus Atmosphere Monitoring Service). We find overall good agreement between the global models and the observed mole fractions in the free tropospheric range, characterised by very low bias values for the CAMS CH4 and the CarboScope CO2 products, with a mean free tropospheric offset of 0 (14) nmol mol−1 and 0.8 (1.3) µmol mol−1 respectively, with the numbers in parentheses giving the standard uncertainty in the final digits for the numerical value. Higher bias is observed for CAMS CO2 (equal to 3.7 (1.5) µmol mol−1), and for CO the model–observation mismatch is variable with height (with offset equal to −1.0 (8.8) nmol mol−1). We also present laboratory analyses of air samples collected throughout the flights, which include information on the isotopic composition of CH4, and we demonstrate the potential of simultaneously measuring δ13C−CH4 and δ2H−CH4 from air to determine the sources of enhanced methane signals using even a limited number of discrete samples. Using flasks collected during two flights over the Upper Silesian Coal Basin (USCB, southern Poland), one of the strongest methane-emitting regions in the European Union, we were able to use the Miller–Tans approach to derive the isotopic signature of the measured source, with values of δ2H equal to −224.7 (6.6) ‰ and δ13C to −50.9 (1.1) ‰, giving significantly lower δ2H values compared to previous studies in the area.

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.

Copernicus Marine Service: achievements, future challenges and long-term evolution

2020 ◽  
Author(s):  
Pierre-Yves Le Traon
Keyword(s):  
Marine Environment ◽  
Global Ocean ◽  
Reference Information ◽  
Operational Capabilities ◽  
Service Evolution ◽  
Wide Range ◽  
Future Challenges ◽  
Marine Environment Monitoring ◽  
Monitoring Service

<p>The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical state, variability and dynamics of the ocean, ice and marine ecosystems for the global ocean and the European regional seas.  The Copernicus Marine Service has run a successful initial phase over the past five years.  Operational capabilities have been demonstrated, user uptake and user base have been steadily increasing and service evolution activities have allowed regular improvements of the products and services provided to users.  CMEMS now serves a wide range of users (more than 21,000 subscribers are registered to the service) and applications (maritime safety, marine resources, coastal and marine environment, weather, seasonal forecast and climate).  An overview of CMEMS achievements will be given and the presentation will highlight the essential role of R&D activities.  CMEMS priorities and scientific challenges for Copernicus 2 (2021-2027) will then be discussed.   </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.

Chronological Development of Terrestrial Mean Precipitation

2017 ◽  
Vol 98 (11) ◽  
pp. 2411-2428 ◽  
Author(s):  
Kylie J. Park ◽  
Kei Yoshimura ◽  
Hyungjun Kim ◽  
Taikan Oki
Keyword(s):  
Data Exchange ◽  
Research Unit ◽  
Global Scale ◽  
Climatic Research Unit ◽  
Telecommunication System ◽  
Spatial Coverage ◽  
Global Telecommunication System ◽  
The World ◽  
In Situ Observations

Abstract Over 150 years of investigations into global terrestrial precipitation are revisited to reveal how researchers estimated annual means from in situ observations before the age of digitization. After introducing early regional efforts to measure precipitation, the pioneering estimates of terrestrial mean precipitation from the late nineteenth and early twentieth centuries are compared to successive estimates, including those using the latest gridded precipitation datasets available. The investigation reveals that the range of the early estimates is comparable to the interannual variation in terrestrial mean precipitation derived from the latest Climatic Research Unit (CRU) dataset. In-depth revisions of the estimates were infrequent up to the 1970s, due in part to difficulty obtaining and maintaining up-to-date datasets with global coverage. This point is illustrated in a “family tree” that identifies the key publications that subsequent authors referenced, sometimes decades after the original publication. Significant efforts to collate global observations facilitated new investigations and improved data exchange, for example, in the International Hydrological Decade (1965–74) and following the establishment of the Global Telecommunication System under the World Weather Watch Programme of the World Meteorological Organization. Also in the 1970s were the first attempts to adjust in situ observations on a global scale to account for gauge undercatch, and this had a noticeable impact on mean annual estimates. There remains no single satisfactory approach to gauge bias adjustment. Echoing the repeated message of past researchers, today’s authors cite poor spatial coverage, temporal inhomogeneity, and inadequate sharing of in situ observations as the key obstacles to obtaining more accurate estimates of terrestrial mean precipitation.

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 Enhancing Model Skill by Assimilating SMOPS Blended Soil Moisture Product into Noah Land Surface Model

2015 ◽  
Vol 16 (2) ◽  
pp. 917-931 ◽  
Author(s):  
Jifu Yin ◽  
Xiwu Zhan ◽  
Youfei Zheng ◽  
Jicheng Liu ◽  
Li Fang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Surface Model ◽  
Spatial Coverage ◽  
In Situ Observations ◽  
The Impact

Abstract Many studies that have assimilated remotely sensed soil moisture into land surface models have generally focused on retrievals from a single satellite sensor. However, few studies have evaluated the merits of assimilating ensemble products that are merged soil moisture retrievals from several different sensors. In this study, the assimilation of the Soil Moisture Operational Products System (SMOPS) blended soil moisture (SBSM) product, which is a combination of soil moisture products from WindSat, Advanced Scatterometer (ASCAT), and Soil Moisture and Ocean Salinity (SMOS) satellite sensors is examined. Using the ensemble Kalman filter (EnKF), a synthetic experiment is performed on the global domain at 25-km resolution to assess the impact of assimilating the SBSM product. The benefit of assimilating SBSM is assessed by comparing it with in situ observations from U.S. Department of Agriculture Soil Climate Analysis Network (SCAN) and the Surface Radiation Budget Network (SURFRAD). Time-averaged surface-layer soil moisture fields from SBSM have a higher spatial coverage and generally agree with model simulations in the global patterns of wet and dry regions. The impacts of assimilating SMOPS blended data on model soil moisture and soil temperature are evident in both sparsely and densely vegetated areas. Temporal correlations between in situ observations and net shortwave radiation and net longwave radiation are higher with assimilating SMOPS blended product than without the data assimilation.

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