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

Joseph Mendonca,Rigel Kivi,Justus Notholt,Kimberly Strong,Ray Nassar,Christof Petri,Debra Wunch,Christopher W O'Dell,Isamu Morino

doi:10.5194/amt-14-7511-2021

Abstract

Abstract. 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.

Highlights

Northern high-latitude regions are undergoing considerable changes related to climate change
We investigate the feasibility of using a simple neural network to filter the current Orbiting Carbon Observatory 2 (OCO-2) data version (B10) (Osterman et al, 2020) XCO2 retrievals at northern high latitudes
A neural network was used to filter the OCO2 bias-corrected XCO2 data collected near northern highlatitude Total Carbon Column Observing Network (TCCON) stations as described in Sect

Summary

Introduction

Northern high-latitude regions are undergoing considerable changes related to climate change. Mendonca et al.: Using a neural network to filter OCO-2 retrievals at northern high latitudes servatory 2 (OCO-2) (Crisp et al, 2004) record solar absorption spectra reflected off the Earth’s surface, which are used to retrieve column-averaged dry-air mole fractions of CO2 (XCO2), giving regional information on atmospheric CO2 These data can be used to learn about the carbon cycle but require low bias and high precision to be useful (Rayner and O’Brien, 2001). The study by Jacobs et al (2020) showed that when making modifications to the quality control filtering scheme and bias correction used by OCO-2, one can increase the throughput of OCO-2 retrievals (data version B9) (Kiel et al, 2019; O’Dell et al, 2018) in the boreal region This was done by changing limits on the features used in the quality control scheme created in O’Dell et al (2018). We discuss results of the study and future work to improve the NN filtering

Coincidence criteria

Neural network architecture and training

Validation

Findings

Discussion and conclusions