Mapping Yearly Fine Resolution Global Surface Ozone through the Bayesian Maximum Entropy Data Fusion of Observations and Model Output for 1990–2017

2021 ◽  
Author(s):  
Marissa N. DeLang ◽  
Jacob S. Becker ◽  
Kai-Lan Chang ◽  
Marc L. Serre ◽  
Owen R. Cooper ◽  
...  
Keyword(s):  
Data Fusion ◽  
Maximum Entropy ◽  
Surface Ozone ◽  
Bayesian Maximum Entropy ◽  
Model Output ◽  
Fine Resolution ◽  
Global Surface

scholarly journals A new method (M<sup>3</sup>Fusion-v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

2018 ◽  
Author(s):  
Kai-Lan Chang ◽  
Owen R. Cooper ◽  
J. Jason West ◽  
Marc L. Serre ◽  
Martin G. Schultz ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Ozone ◽  
Multiple Model ◽  
World Region ◽  
Squared Error ◽  
Ozone Distribution ◽  
Fine Resolution ◽  
Global Surface

Abstract. We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8-hour average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly-spaced observations onto a fine resolution grid by using integrated nested Laplace approximations, and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of 8 world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias-corrected within two degrees of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean.

Spatialization of physical variables in soils by geophysical, geotechnical and geostatistic methods: the Bayesian maximum entropy data fusion approach

2021 ◽  
Author(s):  
Sara Rabouli ◽  
Vivien Dubois ◽  
Marc Serre ◽  
Julien Gance ◽  
Hocine Henine ◽  
...  
Keyword(s):  
Hydraulic Conductivity ◽  
Data Fusion ◽  
Maximum Entropy ◽  
Test Data ◽  
Saturated Hydraulic Conductivity ◽  
Geophysical Data ◽  
Real Field ◽  
Bayesian Maximum Entropy ◽  
Near Surface ◽  
Infiltration Test

&lt;p&gt;The soil is considered as a biological reactor or an outlet for treated domestic wastewater, respectively to reduce pollutant concentrations in the flows or because the surface hydraulic medium is too remote. In these cases, the saturated hydraulic conductivity of the soil is a key is a quantitative measure to assess whether the necessary infiltration capacity is available. To our knowledge, there is no satisfactory technique for evaluating the saturated hydraulic conductivity Ks of a heterogeneous soil (and its variability) at the scale of a parcel of soil. The aim of this study is to introduce a methodology that associates geophysical measurements and geotechnical in order to better described the near-surface saturated hydraulic conductivity Ks. Here we demonstrate here the interest of using a geostatistical approach, the BME &quot;Bayesian Maximum Entropy&quot;, to obtain a 2D spatialization of Ks in heterogeneous soils. This tool opens up prospects for optimizing the sizing infiltration structures that receive treated wastewater. In our case, we have Electrical Resistivity Tomography (ERT) data (dense but with high uncertainty) and infiltration test data (reliable but sparse). The BME approach provides a flexible methodological framework to process these data. The advantage of BME is that it reduces to kriging as its linear limiting cases when only Gaussian data is used, but can also integrate data of other types as might be considered in future works. Here we use hard and Gaussian soft data to rigorously integrate the different data at hand (ERT, and Ks measurement) and their associated uncertainties. Based on statistical analysis, we compared the estimation performances of 3 methods: kriging interpolation of infiltration test data, the transformation of ERT data, and BME data fusion of geotechnical and geophysical data. We evaluated the 3 methods of estimation on simulated datasets and we then do a validation analysis using real field data. We find that BME data fusion of geotechnical and geophysical data provides better estimates of hydraulic conductivity than using geotechnical or geophysical data alone.&lt;/p&gt;

scholarly journals A new method (M<sup>3</sup>Fusion v1) for combining observations and multiple model output for an improved estimate of the global surface ozone distribution

2019 ◽  
Vol 12 (3) ◽  
pp. 955-978 ◽  
Author(s):  
Kai-Lan Chang ◽  
Owen R. Cooper ◽  
J. Jason West ◽  
Marc L. Serre ◽  
Martin G. Schultz ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Ozone ◽  
Multiple Model ◽  
World Region ◽  
Ozone Distribution ◽  
The World ◽  
Fine Resolution ◽  
Global Surface

Abstract. We have developed a new statistical approach (M3Fusion) for combining surface ozone observations from thousands of monitoring sites around the world with the output from multiple atmospheric chemistry models to produce a global surface ozone distribution with greater accuracy than can be provided by any individual model. The ozone observations from 4766 monitoring sites were provided by the Tropospheric Ozone Assessment Report (TOAR) surface ozone database, which contains the world's largest collection of surface ozone metrics. Output from six models was provided by the participants of the Chemistry-Climate Model Initiative (CCMI) and NASA's Global Modeling and Assimilation Office (GMAO). We analyze the 6-month maximum of the maximum daily 8 h average ozone value (DMA8) for relevance to ozone health impacts. We interpolate the irregularly spaced observations onto a fine-resolution grid by using integrated nested Laplace approximations and compare the ozone field to each model in each world region. This method allows us to produce a global surface ozone field based on TOAR observations, which we then use to select the combination of global models with the greatest skill in each of eight world regions; models with greater skill in a particular region are given higher weight. This blended model product is bias corrected within 2∘ of observation locations to produce the final fused surface ozone product. We show that our fused product has an improved mean squared error compared to the simple multi-model ensemble mean, which is biased high in most regions of the world.

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