Spatial variability in autumnal equatorial upwelling intensity within the Gulf of Guinea as inferred from in situ measurements

Abstract. This study introduces and evaluates a comprehensive, model-generated dataset of Northern Hemisphere permafrost conditions. Surface meteorological forcing fields from the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis data were used to drive an improved version of the land component of MERRA-2 in middle-to-high northern latitudes from 1980 to 2017. The resulting simulated permafrost distribution across the Northern Hemisphere captures well the observed extent of continuous permafrost except in Western Siberia, which is permafrost-free in the simulation. Noticeable discrepancies also appear along the southern edge of the permafrost region where sporadic and isolated permafrost types dominate. The evaluation of the simulated active layer thickness (ALT) climatology against in-situ measurements demonstrates reasonable skill except in Mongolia. In northern Alaska, both ALT retrievals from airborne remote sensing for 2015 and the corresponding simulated ALT exhibit reasonable accuracy vs. in-situ measurements. However, the remotely sensed ALT retrievals generally demonstrate lower levels of spatial variability than both the observed and simulated ALT. Controls on the spatial variability of ALT are examined with idealized numerical experiments focusing on northern Alaska; meteorological forcing and soil type are found to have dominant impacts on the spatial variability of ALT, with vegetation also playing a role through its modulation of snow accumulation. A correlation analysis further reveals that accumulated air temperature and maximum snow water equivalent (SWE) explain most of the year-to-year variability of ALT nearly everywhere over the model-simulated permafrost regions. Simulated ALT trends from 1980 to 2017 indicate that some permafrost areas are experiencing significant degradation, with ALT increasing up to 0.5 cm/year.

Download Full-text

Estimation of phytoplankton pigments from ocean-color satellite observations in the Sénégalo-Mauritanian region by using an advanced neural classifier

10.5194/os-2019-11 ◽

2019 ◽

Cited By ~ 1

Author(s):

Khalil Yala ◽

Ndeye Niang ◽

Julien Brajard ◽

Carlos Mejia ◽

Mory Ouattara ◽

...

Keyword(s):

Ocean Color ◽

In Situ Measurements ◽

Satellite Observations ◽

Pigment Concentration ◽

Global Ocean ◽

Color Parameters ◽

Validation Procedure ◽

Pigment Concentrations ◽

Upwelling Intensity

Abstract. We processed daily ocean-color satellite observations to construct a monthly climatology of phytoplankton pigment concentrations in the Senegalo-Mauritanian region. Thanks to the difficulty of the problem, we proposed a new method. It primarily consists in associating, in well-identified clusters, similar pixels in terms of ocean-color parameters and in situ pigment concentrations taken from a global ocean database. The association is carried using a new Self Organized Map (2S-SOM). Its major advantage is to allow taking into account the specificity of the optical properties of the water by adding specific weights to the different ocean color parameters and the in situ measurements. In the retrieval phase, the pigment concentration of a pixel is estimated by taking the pigment concentration values associated with the 2S-SOM cluster presenting the ocean-color satellite spectral measurements, which are the closest to those of the pixel under study according to some distance. The method was validated by using a cross-validation procedure. We focused our study on the fucoxanthin concentration, which is related to the abundance of diatoms. We showed that the fucoxanthin starts to develop in December, presents its maximum intensity in March when the upwelling intensity is maximum, extends up to the coast of Guinea in April and begins to decrease in May. The results are in agreement with previous observations and recent in situ measurements. The method is very general and can be applied in every oceanic region.

Download Full-text

Description and applications of a mobile system performing on-road aerosol remote sensing and in situ measurements

10.5194/amt-2018-103 ◽

2018 ◽

Author(s):

Ioana Elisabeta Popovici ◽

Philippe Goloub ◽

Thierry Podvin ◽

Luc Blarel ◽

Rodrigue Loisil ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Mass Concentration ◽

In Situ Measurements ◽

Quality Data ◽

The Sun ◽

Mobile System ◽

Modis Aod ◽

Sun Photometer

Abstract. The majority of ground-based aerosols observations are limited to fixed locations, narrowing the knowledge on their spatial variability. In order to overcome this issue, a compact Mobile Aerosol Monitoring System (MAMS) was developed to explore the aerosol vertical and spatial variability. This mobile laboratory is equipped with a micropulse lidar, a sun-photometer and an aerosol spectrometer. It is distinguished by other transportable platforms through its ability to perform on-road measurements and its unique feature lies in the sun-photometer capable to track the sun during motion. The system presents a great flexibility, being able to respond quickly in case of sudden aerosol events such as pollution episodes, dust, fire or volcano outbreaks. On-road mapping of aerosol physical parameters such as attenuated aerosol backscatter, aerosol optical depth, particle number and mass concentration and size distribution is achieved through the MAMS. The performance of remote sensing instruments on-board has been evaluated through intercomparison with instruments in reference networks (i.e. AERONET and EARLINET), showing that the system is capable of providing high quality data. This also illustrates the application of such system for instrument intercomparison field campaigns. Applications of the mobile system have been exemplified through two case studies in northern France. MODIS AOD data was compared to ground-based mobile sun-photometer data. A good correlation was observed with R2 of 0.76, showing the usefulness of the mobile system for validation of satellite-derived products. The performance of BSC-DREAM8b dust model has been tested by comparison of results from simulations to the lidar-sun-photometer derived extinction coefficient and mass concentration profiles. The comparison indicated that observations and model are in good agreement in describing the vertical variability of dust layers. Moreover, on-road measurements of PM10 were compared with modelled PM10 concentrations and with ATMO Hauts-de-France and AIRPARIF air quality in situ measurements, presenting an excellent agreement in horizontal spatial representativity of PM10. This proves a possible application of mobile platforms for evaluating the chemistry-models performances.

Download Full-text

Estimation of phytoplankton pigments from ocean-color satellite observations in the Senegalo–Mauritanian region by using an advanced neural classifier

Ocean Science ◽

10.5194/os-16-513-2020 ◽

2020 ◽

Vol 16 (2) ◽

pp. 513-533

Author(s):

Khalil Yala ◽

N'Dèye Niang ◽

Julien Brajard ◽

Carlos Mejia ◽

Mory Ouattara ◽

...

Keyword(s):

Ocean Color ◽

In Situ Measurements ◽

Satellite Observations ◽

Pigment Concentration ◽

Global Ocean ◽

Self Organizing Map ◽

Color Parameters ◽

Pigment Concentrations ◽

Upwelling Intensity

Abstract. We processed daily ocean-color satellite observations to construct a monthly climatology of phytoplankton pigment concentrations in the Senegalo–Mauritanian region. Our proposed new method primarily consists of associating, in well-identified clusters, similar pixels in terms of ocean-color parameters and in situ pigment concentrations taken from a global ocean database. The association is carried out using a new self-organizing map (2S-SOM). Its major advantage is allowing the specificity of the optical properties of the water to be taken into account by adding specific weights to the different ocean-color parameters and the in situ measurements. In the retrieval phase, the pigment concentration of a pixel is estimated by taking the pigment concentration values associated with the 2S-SOM cluster presenting the ocean-color satellite spectral measurements that are the closest to those of the pixel under study according to some distance. The method was validated by using a cross-validation procedure. We focused our study on the fucoxanthin concentration, which is related to the abundance of diatoms. We showed that the fucoxanthin starts to develop in December, presents its maximum intensity in March when the upwelling intensity is maximum, extends up to the coast of Guinea in April and begins to decrease in May. The results are in agreement with previous observations and recent in situ measurements. The method is very general and can be applied in every oceanic region.

Download Full-text

Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis

The Cryosphere ◽

10.5194/tc-13-2087-2019 ◽

2019 ◽

Vol 13 (8) ◽

pp. 2087-2110 ◽

Cited By ~ 8

Author(s):

Jing Tao ◽

Randal D. Koster ◽

Rolf H. Reichle ◽

Barton A. Forman ◽

Yuan Xue ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Northern Hemisphere ◽

Snow Accumulation ◽

In Situ Measurements ◽

Active Layer Thickness ◽

Meteorological Forcing ◽

Northern Alaska ◽

Permafrost Regions

Abstract. This study introduces and evaluates a comprehensive, model-generated dataset of Northern Hemisphere permafrost conditions at 81 km2 resolution. Surface meteorological forcing fields from the Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2) reanalysis were used to drive an improved version of the land component of MERRA-2 in middle-to-high northern latitudes from 1980 to 2017. The resulting simulated permafrost distribution across the Northern Hemisphere mostly captures the observed extent of continuous and discontinuous permafrost but misses the ecosystem-protected permafrost zones in western Siberia. Noticeable discrepancies also appear along the southern edge of the permafrost regions where sporadic and isolated permafrost types dominate. The evaluation of the simulated active layer thickness (ALT) against remote sensing retrievals and in situ measurements demonstrates reasonable skill except in Mongolia. The RMSE (bias) of climatological ALT is 1.22 m (−0.48 m) across all sites and 0.33 m (−0.04 m) without the Mongolia sites. In northern Alaska, both ALT retrievals from airborne remote sensing for 2015 and the corresponding simulated ALT exhibit limited skill versus in situ measurements at the model scale. In addition, the simulated ALT has larger spatial variability than the remotely sensed ALT, although it agrees well with the retrievals when considering measurement uncertainty. Controls on the spatial variability of ALT are examined with idealized numerical experiments focusing on northern Alaska; meteorological forcing and soil types are found to have dominant impacts on the spatial variability of ALT, with vegetation also playing a role through its modulation of snow accumulation. A correlation analysis further reveals that accumulated above-freezing air temperature and maximum snow water equivalent explain most of the year-to-year variability of ALT nearly everywhere over the model-simulated permafrost regions.

Download Full-text

On the Nutrient distribution and phytoplankton biomass in the Gulf of Guinea equatorial band as inferred from In-situ measurements

Journal of Oceanography and Marine Science ◽

10.5897/joms2016.0124 ◽

2016 ◽

Vol 7 (1) ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

O A Nubi ◽

B Bourl egrave s ◽

C A Edokpayi ◽

M N Hounkonnou

Keyword(s):

Phytoplankton Biomass ◽

In Situ Measurements ◽

Gulf Of Guinea ◽

Nutrient Distribution ◽

Equatorial Band

Download Full-text

Description and applications of a mobile system performing on-road aerosol remote sensing and in situ measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4671-2018 ◽

2018 ◽

Vol 11 (8) ◽

pp. 4671-4691 ◽

Cited By ~ 4

Author(s):

Ioana Elisabeta Popovici ◽

Philippe Goloub ◽

Thierry Podvin ◽

Luc Blarel ◽

Rodrigue Loisil ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Mass Concentration ◽

In Situ Measurements ◽

Quality Data ◽

The Sun ◽

Mobile System ◽

Modis Aod ◽

Sun Photometer

Abstract. The majority of ground-based aerosols observations are limited to fixed locations, narrowing the knowledge on their spatial variability. In order to overcome this issue, a compact Mobile Aerosol Monitoring System (MAMS) was developed to explore the aerosol vertical and spatial variability. This mobile laboratory is equipped with a micropulse lidar, a sun photometer and an aerosol spectrometer. It is distinguished from other transportable platforms through its ability to perform on-road measurements and its unique feature lies in the sun photometer's capacity for tracking the sun during motion. The system presents a great flexibility, being able to respond quickly in case of sudden aerosol events such as pollution episodes, dust, fire or volcano outbreaks. On-road mapping of aerosol physical parameters such as attenuated aerosol backscatter, aerosol optical depth, particle number and mass concentration and size distribution is achieved through the MAMS. The performance of remote sensing instruments on-board has been evaluated through intercomparison with instruments in reference networks (i.e. AERONET and EARLINET), showing that the system is capable of providing high quality data. This also illustrates the application of such a system for instrument intercomparison field campaigns. Applications of the mobile system have been exemplified through two case studies in northern France. MODIS AOD data was compared to ground-based mobile sun photometer data. A good correlation was observed with R2 of 0.76, showing the usefulness of the mobile system for validation of satellite-derived products. The performance of BSC-DREAM8b dust model has been tested by comparison of results from simulations for the lidar–sun-photometer derived extinction coefficient and mass concentration profiles. The comparison indicated that observations and the model are in good agreement in describing the vertical variability of dust layers. Moreover, on-road measurements of PM10 were compared with modelled PM10 concentrations and with ATMO Hauts-de-France and AIRPARIF air quality in situ measurements, presenting an excellent agreement in horizontal spatial representativity of PM10. This proves a possible application of mobile platforms for evaluating the chemistry-models performances.

Download Full-text