Assessing the Impacts of Two Averaging Methods on AIRS Level 3 Monthly Products and Multiyear Monthly Means

Abstract

The Atmospheric Infrared Sounder (AIRS) on board NASA's Aqua satellite provides more than 16 years of data. Its monthly gridded (Level 3) product has been widely used for climate research and applications. Since counts of successful soundings in a grid cell are used to derive monthly averages, this averaged by observations (ABO) approach effectively gives equal importance to all participating soundings within a month. It is conceivable then that days with more observations due to day-to-day orbit shift and regimes with better retrieval skills will contribute disproportionately to the monthly average within a cell. Alternatively, the AIRS Level 3 monthly product can be produced through an averaged by days (ABD) approach, where the monthly mean in a grid cell is a simple average of the daily means. The effects of these averaging methods on the AIRS version 6 monthly product are assessed quantitatively using temperature and water vapor at the surface and 500 hPa. The ABO method results in a warmer (slightly colder) global mean temperature at the surface (500 hPa) and a drier global mean water vapor than ABD method. The AIRS multiyear monthly mean temperature and water vapor from both methods are also compared with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product and evaluated with a simulation experiment, indicating the ABD method has lower error and is more closely correlated with MERRA-2. In summary, the ABD method is recommended for future versions of the AIRS Level 3 monthly product and more data services supporting Level 3 aggregation are needed.

Biophysical impacts of earth greening can substantially mitigate regional land surface temperature warming

“…This method generates the daily mean LST for each pixel using the weighted mean value of different MODIS observation combinations (at least one daytime and one nighttime observation for both Aqua and Terra observations). Second, the daily average temperature is temporally aggregated to the monthly value using averaged by day (ABD) approach 45 and spatially aggregated to 0.05° resolution. The flow chart for the monthly LST production can be found in Supplementary Fig.…”

PBL Height From AIRS, GPS RO, and MERRA‐2 Products in NASA GES DISC and Their 10‐Year Seasonal Mean Intercomparison

“…The AIRS L2 product contains retrievals from swath (satellite raster) observations with a spatial resolution about 50 km × 50 km at nadir. The AIRS L3 products are derived by gridding and averaging successfully retrieved L2 soundings (i.e., quality screened) into 1 o × 1 o latitude/longitude grid cells in a day and month (Tian et al, 2020;Ding et al, 2020). Data from the Ascending/Daytime and Descending/Nighttime orbits are gridded and averaged separately.…”

Satellite Remote Sensing of Global Land Surface Temperature: Definition, Methods, Products, and Applications

“…When temporal‐continuous measurements are not available, which is similar to the sparse observations of polar satellites, a common method is estimating daily mean air temperature by using a linear combination of data collected at several special moments (Nordli et al., 1997). In the context of DMLSTs, a widely used model is the simple average of two instantaneous LSTs (denoted as TSA) of afternoon satellites, for example, MODIS (Alkama & Cescatti, 2016; Compo et al., 2013; Duveiller et al., 2018; Y. Li et al., 2016; W. Shen et al., 2019) and Atmospheric Infrared Sounder (Ding et al., 2020; Susskind et al., 2019). The basis is that the overpass times of afternoon satellites (i.e., 13:30 + 01:30) can be considered the times of maximum and minimum LST (Beale et al., 2020).…”

“…In the context of DMLSTs, a widely used model is the simple average of two instantaneous LSTs (denoted as TSA) of afternoon satellites, for example, MODIS (Alkama & Cescatti, 2016;Compo et al, 2013;Duveiller et al, 2018;Y. Li et al, 2016; and Atmospheric Infrared Sounder (Ding et al, 2020;Susskind et al, 2019). The basis is that the overpass times of afternoon satellites (i.e., 13:30 + 01:30) can be considered the times of maximum and minimum LST (Beale et al, 2020).…”

“…The schematic for these two schemes is illustrated in Figure 22. Obviously, the main difference between these two schemes is that the ABO approach assigns equal importance to all participating instantaneous observations within a month, whereas ABD treats all DMLSTs equally (Ding et al, 2020). Figure 23a is the comparative result of six methods for estimating MMLST, which are the cross-combinations of the aforementioned two temporal aggregation schemes and three conversion models by using the same data as Xing et al (2021).…”