High resolution topside in situ data of electron densities and VHF/GHz scintillations in the equatorial region

1983 ◽  
Vol 88 (A1) ◽  
pp. 403 ◽  
Author(s):  
Sunanda Basu ◽  
Santimay Basu ◽  
J. P. McClure ◽  
W. B Hanson ◽  
H. E. Whitney
Keyword(s):  
High Resolution ◽  
Equatorial Region ◽  
Electron Densities ◽  
In Situ Data

scholarly journals Tuning and assessment of the HYCOM-NORWECOM V2.1 modeling system

2014 ◽  
Vol 7 (6) ◽  
pp. 8399-8432 ◽  
Author(s):  
A. Samuelsen ◽  
C. Hansen ◽  
H. Wehde
Keyword(s):  
High Resolution ◽  
Arctic Ocean ◽  
Basic Research ◽  
Grazing Rate ◽  
The Arctic ◽  
Atlantic Sector ◽  
In Situ Data ◽  
Resolution Model ◽  
High Resolution Model

Abstract. The HYCOM-NORWECOM modeling system is used both for basic research and as a part of the forecasting system for the Arctic Marine Forecasting Centre through the MyOcean project. Here we present a revised version of this model. The present model, as well as the sensitivity simulations leading up to this version, has been compared to a dataset of in-situ measurements of nutrient and chlorophyll from the Norwegian Sea and the Atlantic sector of the Arctic Ocean. The revisions having most impact included adding diatoms to the diet of micro-zooplankton, increasing micro-zooplankton grazing rate and decreased silicate-to-nitrate ratio in diatoms. Model runs are performed both with a coarse- (~50 km) and higher-resolution (~15 km) model configuration, both covering the North Atlantic and Arctic Ocean. While the new model formulation improves the results in both the coarse- and high-resolution model, the nutrient bias is smaller in the high-resolution model, probably as a result of the better resolution of the main processes and with that improved circulation. The final revised version delivers satisfactory results for all three nutrients as well as improved result for chlorophyll in terms of the annual cycle amplitude. However, for chlorophyll the correlation with in-situ data remains relatively low. Besides the large uncertainties associated with observational data this is possibly caused by the fact that constant C / N and Chl / N ratios are implemented in the model.

scholarly journals Adequacy of the In Situ Observing System in the Satellite Era for Climate SST

2006 ◽  
Vol 23 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Huai-Min Zhang ◽  
Richard W. Reynolds ◽  
Thomas M. Smith
Keyword(s):  
High Resolution ◽  
Satellite Observations ◽  
Sea Surface ◽  
Bias Error ◽  
Random Errors ◽  
In Situ Data ◽  
Satellite Sst ◽  
Maximum Bias ◽  
Very High

Abstract A method is presented to evaluate the adequacy of the recent in situ network for climate sea surface temperature (SST) analyses using both in situ and satellite observations. Satellite observations provide superior spatiotemporal coverage, but with biases; in situ data are needed to correct the satellite biases. Recent NOAA/U.S. Navy operational Advanced Very High Resolution Radiometer (AVHRR) satellite SST biases were analyzed to extract typical bias patterns and scales. Occasional biases of 2°C were found during large volcano eruptions and near the end of the satellite instruments’ lifetime. Because future biases could not be predicted, the in situ network was designed to reduce the large biases that have occurred to a required accuracy. Simulations with different buoy density were used to examine their ability to correct the satellite biases and to define the residual bias as a potential satellite bias error (PSBE). The PSBE and buoy density (BD) relationship was found to be nearly exponential, resulting in an optimal BD range of 2–3 per 10° × 10° box for efficient PSBE reduction. A BD of two buoys per 10° × 10° box reduces a 2°C maximum bias to below 0.5°C and reduces a 1°C maximum bias to about 0.3°C. The present in situ SST observing system was evaluated to define an equivalent buoy density (EBD), allowing ships to be used along with buoys according to their random errors. Seasonally averaged monthly EBD maps were computed to determine where additional buoys are needed for future deployments. Additionally, a PSBE was computed from the present EBD to assess the in situ system’s adequacy to remove potential future satellite biases.

High-resolution sea surface salinity and temperature in coastal areas from Sentinel-2 and Copernicus Marine in situ data

2020 ◽  
Author(s):  
Encarni Medina-Lopez
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Sea Surface ◽  
Sea Surface Salinity ◽  
Global Ocean ◽  
Surface Salinity ◽  
In Situ Data ◽  
The Neural Network ◽  
Sentinel 2

<p>The aim of this work is to obtain high-resolution values of sea surface salinity (SSS) and temperature (SST) in the global ocean by using raw satellite data (i.e., without any band data pre-processing or atmospheric correction). Sentinel-2 Level 1-C Top of Atmosphere (TOA) reflectance data is used to obtain accurate SSS and SST information. A deep neural network is built to link the band information with in situ data from different buoys, vessels, drifters, and other platforms around the world. The neural network used in this paper includes shortcuts, providing an improved performance compared with the equivalent feed-forward architecture. The in situ information used as input for the network has been obtained from the Copernicus Marine In situ Service. Sentinel-2 platform-centred band data has been processed using Google Earth Engine in areas of 100 m x 100 m. Accurate salinity values are estimated for the first time independently of temperature. Salinity results rely only on direct satellite observations, although it presented a clear dependency on temperature ranges. Results show the neural network has good interpolation and extrapolation capabilities. Test results present correlation coefficients of 82% and 84% for salinity and temperature, respectively. The most common error for both SST and SSS is 0.4 C and 0.4 PSU. The sensitivity analysis shows that outliers are present in areas where the number of observations is very low. The network is finally applied over a complete Sentinel-2 tile, presenting sensible patterns for river-sea interaction, as well as seasonal variations. The methodology presented here is relevant for detailed coastal and oceanographic applications, reducing the time for data pre-processing, and it is applicable to a wide range of satellites, as the information is directly obtained from TOA data.</p>

scholarly journals New Downscaling Approach Using ESA CCI SM Products for Obtaining High Resolution Surface Soil Moisture

2020 ◽  
Vol 12 (7) ◽  
pp. 1119 ◽  
Author(s):  
Jovan Kovačević ◽  
Željko Cvijetinović ◽  
Nikola Stančić ◽  
Nenad Brodić ◽  
Dragan Mihajlović
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Initial Step ◽  
Surface Soil Moisture ◽  
In Situ Data ◽  
Random Forest Models

ESA CCI SM products have provided remotely-sensed surface soil moisture (SSM) content with the best spatial and temporal coverage thus far, although its output spatial resolution of 25 km is too coarse for many regional and local applications. The downscaling methodology presented in this paper improves ESA CCI SM spatial resolution to 1 km using two-step approach. The first step is used as a data engineering tool and its output is used as an input for the Random forest model in the second step. In addition to improvements in terms of spatial resolution, the approach also considers the problem of data gaps. The filling of these gaps is the initial step of the procedure, which in the end produces a continuous product in both temporal and spatial domains. The methodology uses combined active and passive ESA CCI SM products in addition to in situ soil moisture observations and the set of auxiliary downscaling predictors. The research tested several variants of Random forest models to determine the best combination of ESA CCI SM products. The conclusion is that synergic use of all ESA CCI SM products together with the auxiliary datasets in the downscaling procedure provides better results than using just one type of ESA CCI SM product alone. The methodology was applied for obtaining SSM maps for the area of California, USA during 2016. The accuracy of tested models was validated using five-fold cross-validation against in situ data and the best variation of model achieved RMSE, R2 and MAE of 0.0518 m3/m3, 0.7312 and 0.0374 m3/m3, respectively. The methodology proved to be useful for generating high-resolution SSM products, although additional improvements are necessary.

NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts

2005 ◽  
Author(s):  
Felix N. Kogan
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Thermal Conditions ◽  
Early Years ◽  
Data Availability ◽  
Sources Of Information ◽  
In Situ Data ◽  
Marginal Areas ◽  
The World

Operational polar-orbiting environmental satellites launched in the early 1960s were designed for daily weather monitoring around the world. In the early years, they were mostly applied for cloud monitoring and for advancing skills in satellite data applications. The new era was opened with the series of TIROS-N launched in 1978, which has continued until present. These satellites have such instruments as the advanced very high resolution radiometer (AVHRR) and the TIROS operational vertical sounder (TOVS), which included a microwave sounding unit (MSU), a stratospheric sounding unit (SSU), and high-resolution infrared radiation sounder/2 (HIRS/2). These instruments helped weather forecasters improve their skills. AVHRR instruments were also useful for observing and monitoring earth surface. Specific advances were achieved in understanding vegetation distribution. Since the late 1980s, experience gained in interpreting vegetation conditions from satellite images has helped develop new applications for detecting phenomenon such as drought and its impacts on agriculture. The objective of this chapter is to introduce AVHRR indices that have been useful for detecting most unusual droughts in the world during 1990–2000, a decade identified by the United Nations as the International Decade for Natural Disasters Reduction. Radiances measured by the AVHRR instrument onboard National Oceanic Atmospheric Administration (NOAA) polar-orbiting satellites can be used to monitor drought conditions because of their sensitivity to changes in leaf chlorophyll, moisture content, and thermal conditions (Gates, 1970; Myers, 1970). Over the last 20 years, these radiances were converted into indices that were used as proxies for estimating various vegetation conditions (Kogan, 1997, 2001, 2002). The indices became indispensable sources of information in the absence of in situ data, whose measurements and delivery are affected by telecommunication problems, difficult access to environmentally marginal areas, economic disturbances, and political or military conflicts. In addition, indices have advantage over in situ data in terms of better spatial and temporal coverage and faster data availability. The AVHRR-based indices used for monitoring vegetation can be divided into two groups: two-channel indices, and three-channel indices.

scholarly journals Can We Use Satellite-Based Soil-Moisture Products at High Resolution to Investigate Land-Use Differences and Land–Atmosphere Interactions? A Case Study in the Savanna

2020 ◽  
Vol 12 (11) ◽  
pp. 1701
Author(s):  
Carlos Román-Cascón ◽  
Marie Lothon ◽  
Fabienne Lohou ◽  
Nitu Ojha ◽  
Olivier Merlin ◽  
...  
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
High Resolution ◽  
Satellite Data ◽  
Tree Cover ◽  
Optical Data ◽  
Pass Filter ◽  
Low Pass Filter ◽  
In Situ Data

The use of soil moisture (SM) measurements from satellites has grown in recent years, fostering the development of new products at high resolution. This opens the possibility of using them for certain applications that were normally carried out using in situ data. We investigated this hypothesis through two main analyses using two high-resolution satellite-based soil moisture (SBSM) products that combined microwave with thermal and optical data: (1) The Disaggregation based on Physical And Theoretical scale Change (DISPATCH) and, (2) The Soil Moisture Ocean Salinity-Barcelona Expert Center (SMOS-BEC Level 4). We used these products to analyse the SM differences among pixels with contrasting vegetation. This was done through the comparison of the SM measurements from satellites and the measurements simulated with a simple antecedent precipitation index (API) model, which did not account for the surface characteristics. Subsequently, the deviation of the SM from satellite with respect to the API model (bias) was analysed and compared for contrasting land use categories. We hypothesised that the differences in the biases of the varied categories could provide information regarding the water retention capacity associated with each type of vegetation. From the satellite measurements, we determined how the SM depended on the tree cover, i.e., the denser the tree cover, the higher the SM. However, in winter periods with light rain events, the tree canopy could dampen the moistening of the soil through interception and conducted higher SM in the open areas. This evolution of the SM differences that depended on the characteristics of each season was observed both from satellite and from in situ measurements taken beneath a tree and in grass on the savanna landscape. The agreement between both types of measurements highlighted the potential of the SBSM products to investigate the SM of each type of vegetation. We found that the results were clearer for DISPATCH, whose data was not smoothed spatially as it was in SMOS-BEC. We also tested whether the relationships between SM and evapotranspiration could be investigated using satellite data. The answer to this question was also positive but required removing the unrealistic high-frequency SM oscillations from the satellite data using a low pass filter. This improved the performance scores of the products and the agreement with the results from the in situ data. These results demonstrated the possibility of using SM data from satellites to substitute ground measurements for the study of land–atmosphere interactions, which encourages efforts to improve the quality and resolution of these measurements.

On Local Deflections of the Vertical Based on EGM2008 and In Situ Data

2012 ◽  
Vol 170-173 ◽  
pp. 2899-2903 ◽  
Author(s):  
Xin Liu ◽  
Li Guo Zhang ◽  
Jin Yun Guo ◽  
Xiao Lei Ju ◽  
Zhao Qiu Mai
Keyword(s):  
Gravitational Field ◽  
High Resolution ◽  
Field Model ◽  
Deflection Of The Vertical ◽  
Deflections Of The Vertical ◽  
In Situ Data ◽  
The Earth ◽  
Geophysical Study

The Earth gravitational field model EGM2008 with the high resolution has been applied in data processing and analysis in practical geodetic and geophysical study. We calculate the deflection of the vertical on testing areas in China from EGM2008 with the different grids based on the geopotential theory. What’s more, the precisions of the deflection of the vertical determined with EGM2008 up to different degrees and orders are compared and analyzed by calculating the actual astro-geodetic data and modelled data to evaluate the regional quality of EGM2008 with in situ data. Precisions of meridian and prime vertical components of vertical deflections estimated with EGM2008 up to 2190 degrees are 1.59″ and 1.74″, respectively.

scholarly journals Towards high resolution mapping of 3-D mesoscale dynamics from observations

Ocean Science ◽  
2012 ◽  
Vol 8 (5) ◽  
pp. 885-901 ◽  
Author(s):  
B. Buongiorno Nardelli ◽  
S. Guinehut ◽  
A. Pascual ◽  
Y. Drillet ◽  
S. Ruiz ◽  
...  
Keyword(s):  
High Resolution ◽  
Equation Model ◽  
Dynamical Analysis ◽  
Test Case ◽  
Mesoscale Dynamics ◽  
In Situ Data ◽  
Innovative Methods ◽  
High Resolution Mapping ◽  
Resolution Mapping

Abstract. The MyOcean R&D project MESCLA (MEsoSCaLe dynamical Analysis through combined model, satellite and in situ data) was devoted to the high resolution 3-D retrieval of tracer and velocity fields in the oceans, based on the combination of in situ and satellite observations and quasi-geostrophic dynamical models. The retrieval techniques were also tested and compared with the output of a primitive equation model, with particular attention to the accuracy of the vertical velocity field as estimated through the Q vector formulation of the omega equation. The project focused on a test case, covering the region where the Gulf Stream separates from the US East Coast. This work demonstrated that innovative methods for the high resolution mapping of 3-D mesoscale dynamics from observations can be used to build the next generations of operational observation-based products.

scholarly journals LUCAS Copernicus 2018: Earth-observation-relevant in situ data on land cover and use throughout the European Union

2021 ◽  
Vol 13 (3) ◽  
pp. 1119-1133
Author(s):  
Raphaël d'Andrimont ◽  
Astrid Verhegghen ◽  
Michele Meroni ◽  
Guido Lemoine ◽  
Peter Strobl ◽  
...  
Keyword(s):  
Land Use ◽  
European Union ◽  
High Resolution ◽  
Land Cover ◽  
Earth Observation ◽  
The European Union ◽  
In Situ Data ◽  
Cardinal Directions ◽  
High Resolution Satellite Imagery

Abstract. The Land Use/Cover Area frame Survey (LUCAS) is an evenly spaced in situ land cover and land use ground survey exercise that extends over the whole of the European Union. LUCAS was carried out in 2006, 2009, 2012, 2015, and 2018. A new LUCAS module specifically tailored to Earth observation (EO) was introduced in 2018: the LUCAS Copernicus module. The module surveys the land cover extent up to 51 m in four cardinal directions around a point of observation, offering in situ data compatible with the spatial resolution of high-resolution sensors. However, the use of the Copernicus module being marginal, the goal of the paper is to facilitate its uptake by the EO community. First, the paper summarizes the LUCAS Copernicus protocol to collect homogeneous land cover on a surface area of up to 0.52 ha. Secondly, it proposes a methodology to create a ready-to-use dataset for Earth observation land cover and land use applications with high-resolution satellite imagery. As a result, a total of 63 364 LUCAS points distributed over 26 level-2 land cover classes were surveyed on the ground. Using homogeneous extent information in the four cardinal directions, a polygon was delineated for each of these points. Through geospatial analysis and by semantically linking the LUCAS core and Copernicus module land cover observations, 58 426 polygons are provided with level-3 land cover (66 specific classes including crop type) and land use (38 classes) information as inherited from the LUCAS core observation. The open-access dataset supplied with this paper (https://doi.org/10.6084/m9.figshare.12382667.v4 d'Andrimont, 2020) provides a unique opportunity to train and validate decametric sensor-based products such as those obtained from the Copernicus Sentinel-1 and Sentinel-2 satellites. A follow-up of the LUCAS Copernicus module is already planned for 2022. In 2022, a simplified version of the LUCAS Copernicus module will be carried out on 150 000 LUCAS points for which in situ surveying is planned. This guarantees a continuity in the effort to find synergies between statistical in situ surveying and the need to collect in situ data relevant for Earth observation in the European Union.

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