This paper describes a methodology developed by Karen Cady-Pereira of AER and collaborators at Environment and Climate Change Canada (ECCC) to explicitly identify and account for satellite measurements below a sensor’s measurement detection level in cloud-free scenes. These low signals are common in satellite observations of minor atmospheric species with weak spectral signals (e.g., ammonia (NH3)). Not accounting for these non-detects can bias high averages in locations where frequent conditions lead to signals below the detection limit of the sensor. The approach taken here is to utilize the information content from the satellite signal to explicitly identify non-detects and then determine if the signal is low due to clouds or to low ammonia amounts. If the former, the observation is rejected; if the latter, a consistent approach is used to insert a reasonable background value, determined by the local temperature. The methodology is applied to the CrIS Fast Physical Retrieval (CFPR) ammonia product and results in more realistic averages under conditions where there are a significant number of non-detects. These results show that in high emission source regions (i.e., surface values > 7.5 ppbv) the non-detects occur less than 5% of the time and have a relatively small impact (decreases by less than 5%) on the gridded averaged values. However, in regions that have low ammonia concentration amounts (i.e., surface values < 1 ppbv) the fraction of non-detects can be greater than 70%, and accounting for these values can decrease annual gridded averaged values by over 50% and bring the distributions closer to what is expected based on surface station observations.
Figure 1: CrIS averaged surface ammonia observations for the May 2012 to May 2021 period: excluding non-detects (upper left), including non-detects (upper right), normalize relative difference between the two maps (lower left), percentage of non-detects added (lower right).
Citation: Accounting for Non-Detects: Application to Satellite Ammonia Observations
E. White, M. W. Shepard, K. E. Cady-Pereira, S. K. Kharol, S. Ford, E. Dammers, E. Chow, N. Thiessen, D. Tobin, G. Quinn, J. O'Brien, J. Bash
Remote Sensing, 15, 2610, 2023