scholarly journals Testing the relationship between the solar radiation dose and surface DMS concentrations using high resolution in situ data

2009 ◽  
Vol 6 (2) ◽  
pp. 3063-3085
Author(s):  
C. J. Miles ◽  
T. G. Bell ◽  
T. M. Lenton
Keyword(s):  
Radiation Dose ◽  
Solar Radiation ◽  
Mixed Layer Depth ◽  
Light Attenuation ◽  
Climate Feedback ◽  
Solar Ultraviolet Radiation ◽  
In Situ Data ◽  
Linear Relationships ◽  
The Relationship

Abstract. We tested the recently proposed, strong positive relationship between dimethylsulphide (DMS) concentrations and the solar radiation dose (SRD) received into the surface ocean. We utilised in situ daily data sampled concurrently with DMS concentrations from the Atlantic Meridional Transect (AMT) programme for the component variables of the SRD; mixed layer depth (MLD), surface insolation (I0) and a light attenuation coefficient (k), to calculate SRDin situ. We find a significant correlation (ρ=0.53) but the slope of the relationship is approximately half that previously proposed. The correlation is improved (ρ=0.76) by replacing the in situ data with an estimated I0 (which assumes a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following a previously described methodology. Equally significant, but non-linear relationships are also found between DMS and both in situ MLD (ρ=0.73) and the estimated I0 (ρ=0.76) alone. The DMS data shows an interesting relationship to an approximated UV attenuation depth profile. Using a cloud adjusted, satellite climatology of surface UVA irradiance to calculate a UV radiation dose (UVRD) provides an equivalent correlation (ρ=0.73) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied relationship between incident solar/ultraviolet radiation dose and sea surface DMS concentrations (modulated by MLD) is critical for closing a climate feedback loop.

scholarly journals Testing the relationship between the solar radiation dose and surface DMS concentrations using in situ data

2009 ◽  
Vol 6 (9) ◽  
pp. 1927-1934 ◽  
Author(s):  
C. J. Miles ◽  
T. G. Bell ◽  
T. M. Lenton
Keyword(s):  
Radiation Dose ◽  
Solar Radiation ◽  
Spatial Scales ◽  
Mixed Layer Depth ◽  
Light Attenuation ◽  
Climate Feedback ◽  
Solar Ultraviolet Radiation ◽  
In Situ Data ◽  
Linear Relationships

Abstract. The proposed strong positive relationship between dimethylsulphide (DMS) concentration and the solar radiation dose (SRD) received into the surface ocean is tested using data from the Atlantic Meridional Transect (AMT) programme. In situ, daily data sampled concurrently with DMS concentrations is used for the component variables of the SRD (mixed layer depth, MLD, surface insolation, I0, and a light attenuation coefficient, k) to calculate SRDinsitu. This is the first time in situ data for all of the components, including k, has been used to test the SRD-DMS relationship over large spatial scales. We find a significant correlation (ρ=0.55 n=65 p<0.01) but the slope of this relationship (0.006 nM/W m−2) is less than previously found at the global (0.019 nM/W m−2) and regional scales (Blanes Bay, Mediterranean, 0.028 nM/W m−2; Sargasso Sea 0.017 nM/W m−2). The correlation is improved (ρ=0.74 n=65 p<0.01) by replacing the in situ data with an estimated I0 (which assumes a constant 50% removal of the top of atmosphere value; 0.5×TOA), a MLD climatology and a fixed value for k following previous work. Equally strong, but non-linear relationships are also found between DMS and both in situ MLD (ρ=0.61 n=65 p<0.01) and the estimated I0 (ρ=0.73 n=65 p<0.01) alone. Using a satellite-retrieved, cloud-adjusted surface UVA irradiance to calculate a UV radiation dose (UVRD) with a climatological MLD also provides an equivalent correlation (ρ=0.67 n=54 p<0.01) to DMS. With this data, MLD appears the dominant control upon DMS concentrations and remains a useful shorthand to prediction without fully resolving the biological processes involved. However, the implied relationship between the incident solar/ultraviolet radiation (modulated by MLD), and sea surface DMS concentrations, is critical for closing a climate feedback loop.

Mechanically Aspirated Solar Radiation Shields: A CFD and Neural Network Design Analysis

2001 ◽  
Author(s):  
B. M. Fichera ◽  
R. L. Mahajan ◽  
T. W. Horst
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Temperature Sensor ◽  
Temperature Measurements ◽  
Analysis And Design ◽  
Computational Fluid Dynamics Analysis ◽  
Radiation Shields ◽  
Input Variables ◽  
The Relationship

Abstract Accurate air temperature measurements made by surface meteorological stations are demanded by climate research programs for various uses. Heating of the temperature sensor due to inadequate coupling with the environment can lead to significant errors. Therefore, accurate in-situ temperature measurements require shielding the sensor from exposure to direct and reflected solar radiation, while also allowing the sensor to be brought into contact with atmospheric air at the ambient temperature. The difficulty in designing a radiation shield for such a temperature sensor lies in satisfying these two conditions simultaneously. In this paper, we perform a computational fluid dynamics analysis of mechanically aspirated radiation shields (MARS) to study the effect of geometry, wind speed, and interplay of multiple heat transfer processes. Finally, an artificial neural network model is developed to learn the relationship between the temperature error and specified input variables. The model is then used to perform a sensitivity analysis and design optimization.

scholarly journals Estimation of Transmittance of Solar Radiation in the Visible Domain Based on Remote Sensing: Evaluation of Models Using In Situ Data

2017 ◽  
Vol 122 (11) ◽  
pp. 9176-9188 ◽  
Author(s):  
M. Laura Zoffoli ◽  
Zhongping Lee ◽  
Michael Ondrusek ◽  
Junfang Lin ◽  
Charles Kovach ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solar Radiation ◽  
In Situ Data

scholarly journals Towards Monitoring of Nutrient Pollution in Coastal Lake Using Remote Sensing and Regression Analysis

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1954
Author(s):  
Maruf Mortula ◽  
Tarig Ali ◽  
Abdallah Bachir ◽  
Ahmed Elaksher ◽  
Mohamed Abouleish
Keyword(s):  
Chlorophyll A ◽  
Total Nitrogen ◽  
Algal Blooms ◽  
Image Acquisition ◽  
Time Lag ◽  
Algal Growth ◽  
Coastal Lake ◽  
In Situ Data ◽  
The Relationship

The last few decades have witnessed a tremendous increase in nutrient levels (phosphorus and nitrogen) in coastal water leading to excessive algal growth (Eutrophication). The presence of large amounts of algae turns the water’s color into green or red, in the case of algal blooms. Chlorophyll-a is often used as an indicator of algal biomass. Due to increased human activities surrounding Dubai creek, there have been eutrophication concerns given the levels of nutrients in that creek. This study aims to map chlorophyll-a in Dubai Creek from WorldView-2 imagery and explore the relationship between chlorophyll-a and other eutrophication indicators. A geometrically- and atmospherically-corrected WorldView-2 image and in-situ data have been utilized to map chlorophyll-a in the creek. A spectral model, developed from the WorldView-2 multispectral image to monitor Chlorophyll-a concentration, yielded 0.82 R2 with interpolated in-situ chlorophyll-a data. To address the time lag between the in-situ data and the image, Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were used to demonstrate the accuracy of the WorldView-2 model. The images, acquired on 20 May and 23 July 2012, were processed to extract chlorophyll-a band ratios (Band 4/Band 3) following the standard approach. Based on the availability, the 20 May image acquisition date is the closest to the middle of Quarter 2 (Q2) of the in-situ data (15 May). The 23 July 2012 image acquisition date is the closest to the WorldView-2 image date (24 July). Another model developed to highlight the relationship between spectral chlorophyll-a levels, and total nitrogen and orthophosphate levels, yielded 0.97 R2, which indicates high agreement. Furthermore, the generated models were found to be useful in mapping chlorophyll-a, total nitrogen, and orthophosphate, without the need for costly in-situ data acquisition efforts.

scholarly journals A Kalman Filter-Based Method for Reconstructing GMS-5 Global Solar Radiation by Introduction of In Situ Data

Energies ◽  
2013 ◽  
Vol 6 (6) ◽  
pp. 2804-2818 ◽  
Author(s):  
Jingying Fu ◽  
Dong Jiang ◽  
Yaohuan Huang ◽  
Dafang Zhuang ◽  
Yong Wang
Keyword(s):  
Kalman Filter ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
In Situ Data

Evaluating the relationship between SIF and GPP under climate extremes

2021 ◽  
Author(s):  
Sebastian Wieneke ◽  
Ana Bastos ◽  
Manuela Balzarolo ◽  
José Miguel Barrios ◽  
Ivan Janssens
Keyword(s):  
Large Scale ◽  
Gross Primary Production ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Vegetation Stress ◽  
Linear Relationships ◽  
Forest Sites ◽  
Good Proxy ◽  
The Relationship

&lt;p&gt;Sun Induced Chlorophyll Fluorescence (SIF) is considered as a good proxy for photosynthesis given its closer link to the photosynthetic light reactions compared to remote sensing vegetation indices typically used for ecosystem productivity modelling (eg. NDVI). Satellite-based SIF shows significant linear relationships with gross primary production (GPP) from in-situ measurements across sites, biomes and seasons. While SIF can be considered a good proxy for large scale spatial and seasonal variability in GPP, much of the SIF-GPP co-variance can be explained by their common dependence on the absorbed photosynthetically active radiation. Whether SIF can be an equally good proxy for interannual variability in GPP especially during periods of vegetation stress (drought/heat) is, so far, not clear.&lt;/p&gt;&lt;p&gt;In this study, we evaluate the relationship between satellite-based SIF and in-situ GPP measurements during vegetation stress periods (drought/heat), compared to non-stress periods. GPP is obtained from eddy-covariance measurements from a set of forest sites pre-filtered to ensure homonegeous footprints. SIF is obtained from GOME-2 covering the period 2007-2018. Because of scale mismatch between each site&amp;#8217;s footprint (in the order of hundred meters) and the spatial resolution of GOME-2 (ca. 50km), we additionally use SIF from the downscale product from Duveiller et al. 2020 (ca. 5km) and the more recent dataset from TROPOMI (ca. 7 x 3.5 km), covering only the last year of the study period.&lt;/p&gt;&lt;p&gt;We develop a classification of stress periods that is based on both the occurrence of drought/heat extreme events and the presence of photosynthetic downregulation. We then evaluate the relationship between SIF and GPP and their yields, for different plant functional types and at site-level. We evaluate how these relationships vary depending on environmental conditions and in particular for &amp;#8220;stress&amp;#8221; versus &amp;#8220;non-stress&amp;#8221; days.&lt;/p&gt;&lt;p&gt;Duveiller, G., Filipponi, F., Walther, S., K&amp;#246;hler, P., Frankenberg, C., Guanter, L., and Cescatti, A.: A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity, Earth Syst. Sci. Data, 12, 1101&amp;#8211;1116, https://doi.org/10.5194/essd-12-1101-2020, 2020.&lt;/p&gt;

Methods to estimate solar radiation dosimetry in coral reefs using remote sensed, modeled, and in situ data

2008 ◽  
Vol 151 (1-4) ◽  
pp. 445-455 ◽  
Author(s):  
Mace G. Barron ◽  
Deborah N. Vivian ◽  
Susan H. Yee ◽  
Deborah L. Santavy
Keyword(s):  
Solar Radiation ◽  
Coral Reefs ◽  
Radiation Dosimetry ◽  
In Situ Data

scholarly journals Estimating the monthly <i>p</i>CO<sub>2</sub> distribution in the North Atlantic using a self-organizing neural network

2009 ◽  
Vol 6 (8) ◽  
pp. 1405-1421 ◽  
Author(s):  
M. Telszewski ◽  
A. Chazottes ◽  
U. Schuster ◽  
A. J. Watson ◽  
C. Moulin ◽  
...  
Keyword(s):  
Neural Network ◽  
North Atlantic ◽  
Mixed Layer Depth ◽  
Statistical Modelling ◽  
Self Organizing Map ◽  
The North ◽  
Linear Relationships ◽  
The North Atlantic ◽  
Self Organizing

Abstract. Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a 1° latitude by 1° longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFS-MODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437 μatm. The root mean square error (RMSE) of the neural network fit to the data is 11.6 μatm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

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