Assessing the feasibility of using a neural network to filter Orbiting Carbon Observatory 2 (OCO-2) retrievals at northern high latitudes

Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter Orbiting Carbon Observatory 2 (OCO-2) B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (∼ 50 %), improves the precision by 0.18 ppm (∼ 12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest-latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

Assessing the Feasibility of Using a Neural Network to Filter OCO-2 Retrievals at Northern High Latitudes

Satellite retrievals of XCO2 at northern high latitudes currently have sparser coverage and lower data quality than most other regions of the world. We use a neural network (NN) to filter OCO-2 B10 bias-corrected XCO2 retrievals and compare the quality of the filtered data to the quality of the data filtered with the standard B10 quality control filter. To assess the performance of the NN filter, we use Total Carbon Column Observing Network (TCCON) data at selected northern high latitude sites as a truth proxy. We found that the NN filter decreases the overall bias by 0.25 ppm (~50 %), improves the precision by 0.18 ppm (~12 %), and increases the throughput by 16 % at these sites when compared to the standard B10 quality control filter. Most of the increased throughput was due to an increase in throughput during the spring, fall, and winter seasons. There was a decrease in throughput during the summer, but as a result the bias and precision were improved during the summer months. The main drawback of using the NN filter is that it lets through fewer retrievals at the highest latitude Arctic TCCON sites compared to the B10 quality control filter, but the lower throughput improves the bias and precision.

Peer Review : Critical Process of a Scholarly Publication

The quality of an article is a critical parameter for the success of any scholarly journal, and the Journal of Modern Materials (JMM) is no exception. Peer review process presents a barrier prior to publication which acts as a quality control filter in science. Typically, the journal editor assigns submitted paper to two or more qualified peers – recognized experts in the relevant field. The reviewers will then submit detailed criticism of the paper along with a recommendation to reject, accept with major revisions, accept with minor revisions, or accept as it is. The quality and consistency of peer review will be the key success for the Journal of Modern Materials.