The population counts (PDFs) for select window channels are shown in three figures (1,2 and 3) Fig.1 Fig.2 Fig.3 for various subsets. For the window channels, the main differences from day to night are the absence of the hot scenes > 301 K and the diminution of the 299 K peak, some increase in the cold tail, more espcially in the SW channel. The tropical subset is mostly of warm oceans and some cold clouds but DCCs are not particularly evident. There is some variability in total SNO samples between the years, 2012 and 2018 are partial years, due to availabilty of the CrIS CCAST NSR L1C granules.

The variation of the AIRS:CRIS bias with scene brightness temperature for year averages for the three window channels are shown in three figures (4,5,6 and 7). Figure 4 Fig.4 illustrates the detail for one year, with three subsets of the SNOs, to help interpret the following figures. The curve exhibits the characteristic shape I have shown on previous occasions with a similar analysis.

In each of the next figures, the mean bias is shown in the top pane as a colored line, and the standard error of the sample is shown as the black dotted. The lower panel shows the population in each scene bin for each year average.

The LW window Fig.5 has the characteristic down-turn smile shape - AIRS colder than CrIS in the hot scenes. There is little significant change between day and night.

The MW window Fig.6 channel is similar to the LW window channel with some indication of increasing bias at cold scenes.

The SW window Fig.7 channel has a much greater down-turn smile with AIRS colder than CrIS for hot and for cold scenes. This is investigated further (see below).

To determine how stable is the bias between AIRS and CrIS for cold scenes, a reasonably well populated cold scene bin is selected for the given channel and the bias plotted for each year average in the sequence. These are shown in the next three figures 8,9 and 10.

The LW window channel at 899 cm-1 bias versus time is plotted in figure 8 Fig.8 with the three subsets for comparison. An estimate fo the standard error of the mean can be read directly from the previous series of plots, which for this channel is approximately 0.03 K. There is therefore no statistically significant trend of the bias at 899 cm-1.

The MW and SW window channel stability is shown in the next two figures. Fig.9 Fig.10 There is some indication of a variation over time outside the margins of the standard error. It is interesting to note that the result is consistent between the night and day subsets, even in the SW channel.

The IASI spectral radiance measurements are very noisy in the SW region, with an effective NEDT near 2400 to 2600 cm-1 of about 2 K to 6 K respectively for a 250 K black body. This has important consequences for finding the lowest temperature scenes as any individual observation could have a negative radiance - which is meaningless. In other words when searching for scenes that are at 215 K for example, IASI may actually be viewing a scene anywhere from 209 to 221 K. The lowest detectable scene temperature varies with wavenumber in accordance with the NEDT for that channel. This makes comparison with AIRS invalid for those low temperatures.

An example of the IASI radiance observations for the channel at 2456.50 cm-1 for all SNOs in 2017 is shown in figures 11 and 12. In figure 11 Fig.11 the raw radiance samples are shown (left panel), the empirical CDF (center panel) and their histogram (right panel) showing negative radiances and sampling discretization. Computation of statistics must be done with radiance and not brightness temperature. In figure 12 Fig.12 , the empirical CDF for the 2017 SNOs for AIRS, IASI and the translated IASItoAIRS (I2A) are shown together. For 2456.5 cm-1 channel radiance of 0.05 (0.01) W.cm-2.sr-1.cm corresponds to BT of 234 (212) K, resp. This demonstrates that inter-comparison is meaningless at BT cooler than about 205 K, because of the IASI signal.

Another illustration of the problem of differencing the two sensors with different signal characteristics near the zero radiance point is shown in figure 13. Fig.13

The population counts (PDFs) for each selected window channels are shown in three figures (14, 15 and 16) Fig.14 Fig.15 Fig.16 for various subsets. These distributions are different from the AIRS:CRIS SNOs mainly due to the absence of any hot scenes and are biased cooler. There is a notable peak near the melting point of ice. The nighttime subset are cooler than the day as expected. Also there tends to be more warm scenes in the north hemisphere than the south and more cold scenes inthe south hemisphere than the north, as expected. Notice that for the 2456 cm-1 channel IASI PDFs are highly impacted by the -ve going signal.

Figures 17, 18, 19 and 20 show the bias between AIRS and IASI as a function of scene BT for varisou subsets for the three window channels. Figure 17 Fig.17 is the average bias for a single year for three subsets provided for simplicity and aid to interpreting the following three figures. You can easily see the separation of day from night within the full sample set.

Figure 18 Fig.18 for 899 cm-1 window channel, shows a quite closely similar annual bias sets without much variation with scene BT, within the bounds of the sampling range.

Figure 19 Fig.19 for 1231 channel, shows quite closely similar annual bias sets, with the day subset not varying much with scene BT the the night subset exhibiting slight positive trend with BT. The day versus night subset should not be directly affected by short wave radiation and solar input, however there may be an indirect affect or artifact of sampling over different scene types. This could be investigated further.

Figure 20 Fig.20 for 2456 cm-1, shows quite closely similar annual bias sets, with every subset showing a large divergence (AIRS warmer than IASI) at temperatures cooler than about 230 K. The earlier analysis indicated that intercomparison is not possible for scenes at 205 K, so given some margin to 230 K it is possible that there is an increasing bias between the sensors with cooler scenes, as there is some idication of this from 270 K down to 240 K as shown in these figures.

An investigation into whether there are any other sampling artifacts that could significantly contribute to these SW bias differences has been undertaken. Any dependencies with respect to delay time between samples in each SNO pair, dependencies on scene type (Greenland versus Antartica, for example) were not discovered. Therefore most of the large offset at very low temperatures is an artifact of the hight IASI noise and -ve going radiance.

Figures 21, 22 and 23 Fig.21 Fig.22 Fig.23 show the year average bias for the three channels and the three subsets. There is no significant trend outside of the uncertainty over the period 2007 to end 2018. Mean bias (AIRS minus IASI) for 899, 1231 and 2456 cm-1 remain close to 0.1 K 0.05 K, 0.15 K respecitvely for this temeprature bin.

Subsets:
sub.un = ':'; % no subsetting
sub.nh = find(s.aLat(s.ig) >= 40);
sub.sh = find(s.aLat(s.ig) <= -40);
sub.day = find(s.asolz(s.ig) < 90);
sub.nit = find(s.asolz(s.ig) > 90);
sub.pdt = find(s.tdiff(s.ig) >= 0);
sub.ndt = find(s.tdiff(s.ig) < 0);
sub.trn = find(s.aLat(s.ig) < 40 & s.aLat(s.ig) > -40 & s.asolz(s.ig) > 90);

ac_sno_2012-18_trend_subset_year_lwb_v5.mat
CrIS NSR channels: [358:713]
ac_sno_2012-18_trend_subset_year_mwa_v5.mat
CrIS NSR channels: [714:933]
ac_sno_2012-18_trend_subset_year_sw_v5.mat
CrIS NSR channels: [1147:1305]

Subsets:
sub.un = ':'; % no subsetting
sub.nh = find(s.aLat(s.ig) >= 40);
sub.sh = find(s.aLat(s.ig) <= -40);
sub.day = find(s.asolz(s.ig) < 90);
sub.nit = find(s.asolz(s.ig) > 90);
sub.pdt = find(s.tdiff(s.ig) >= 0);
sub.ndt = find(s.tdiff(s.ig) < 0);

ai_sno_2007-18_trend_subset_year_lwa.mat
AIRS channels: [512:1269]
ai_sno_2007_18_trend_subset_mwa.mat
AIRS channels: [1270:1870]
ai_sno_2007_18_trend_subset_sw.mat
AIRS channels: [2163:2645]