Dear all,
I know that the significant topographic relief or the steep terrain would cause the stratified atmospheric effect. But could this topography condition be quantified? Such as how steep (related to the slope angle) or how large of the elevation difference would cause the vertical-stratified delay? From previous studies, I found that this kind of delay seems happen in area where the altitude difference is more than 1000 m (Parker et al., 2015; Dong et al., 2019; Kang et al., 2021). And from previous discussion in this topic, in the small volcano area (300 m peak elevation / 4 km2 area) the stratified atmospheric effects are likely to be small to negligible.
But I have applied the PSInSAR technique to monitoring the activities of slow-moving landslides. One of my study area exist obvious seasonal fluctuation in the middle and top of the unstable slope. The topography information are as follow:
According to previous studies and discussions, I think my study area wouldn’t be affected by the stratified atmospheric? Is that right?
For better understand the source of seasonal variation, I also tried the linear method in TRAIN to calculate the stratified atmospheric effect. I followed these steps:
1 : stamps(1,6)
2 : aps_linear
3 : stamps(7,7)
4 : stamps(6,6)
5 : setparm(‘subtr_tropo’, ‘y’)
6 : stamps(7,7)
And potting the time series ‘u-dmo’, ‘u-dao’ and ‘a’. I chose two points in this slow-moving landslide to plot the time series. The elevation difference and distance between these two points are about 188 m and 685 m, respectively. Point A is at the top of this slow-moving landslide, while point B is at the bottom of this slow-moving landslide. There seems exist obvious stratified atmospheric effect at point A. But the elevation difference between point A and B is just 188 m and the slope angle less than 20 degree. I’m not sure this kind of topography would cause such stratified atmospheric effect or not ? Unfortunately, I don’t have other continuous monitoring data could check it.
I also found a strange situation when check the time series in another slow-moving landslide area. The atmospheric delay shows seasonal fluctuation, but the time series of ‘u-dmo’ and ‘u-dao’ almost are the same as followed:
I don’t know how it would happen. I export the time series data by following command. Is there anything I did wrong?
ps_plot(‘u-dmo’,-1) % change ‘u-dmo’ to ‘u-dao’ and ‘a’
load(‘ps_plot_u-dmo.mat’,‘ph_disp’)
load(‘ps2.mat’,‘lonlat’)
load(‘parms.mat’,‘lambda’)
ts = -ph_disp * lambda * 1000 / (4 * pi);
ps_ts_u_dmo = [lonlat ts];
dlmwrite(‘ps_u-dmo_ts.txt’,ps_ts_u_dmo,‘precision’,’%7f’)
Could @mdelgado @ABraun @thho @mengdahl @falahfakhri help me or give me some suggestions?
Thank you for reading these long questions.
