Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy

Abstract

Abstract. Daytime clear-sky total column water vapour (TCWV) is commonly retrieved from visible and shortwave infrared reflectance (VSWIR) measurements, and modern missions such as the upcoming Earth Surface Mineral Dust Source Investigation (EMIT) offer unprecedented horizontal resolution of order 30–80 m. We provide evidence that for convective planetary boundary layers (PBLs), spatial variability in TCWV corresponds to variability in PBL water vapour. Using an observing system simulation experiment (OSSE) applied to large eddy simulation (LES) output, we show that EMIT can retrieve horizontal variability in PBL water vapour, provided that the domain surface is uniformly composed of either vegetated surfaces or mineral surfaces. Random retrieval errors are easily quantified and removed, but biases from −7 % to +34 % remain in retrieved spatial standard deviation and are primarily related to the retrieval's assumed atmospheric profiles. Future retrieval development could greatly mitigate these errors. Finally, we account for changing solar zenith angle (SZA) from 15 to 60∘ and show that the non-vertical solar path destroys the correspondence between footprint-retrieved TCWV and the true TCWV directly above that footprint. Even at the 250 m horizontal resolution regularly obtained by current sensors, the derived maps correspond poorly to true TCWV at the pixel scale, with r2<0.6 at SZA=30∘. However, the derived histograms of TCWV in an area are closely related to the true histograms of TCWV at the nominal footprint resolution. Upcoming VSWIR instruments, primarily targeting surface properties, can therefore offer new information on PBL water vapour spatial statistics to the atmospheric community.

New sampling strategy mitigates a solar-geometry-induced bias in sub-kilometre vapour scaling statistics derived from imaging spectroscopy

“…We use q and cloud water (q c ) output from the five LES runs of shallow convection as in Richardson et al (2021a), with four cloudy cases (ARM, ARM_lsconv, BOMEX, RICO) and one case in which clouds do not form (DRY). Simulations use two models, EULAG for the ARM cases (Prusa et al, 2008) and JPL-UCONN LES for the others (Matheou and Chung, 2014).…”

“…Simulations use two models, EULAG for the ARM cases (Prusa et al, 2008) and JPL-UCONN LES for the others (Matheou and Chung, 2014). Simulation set-ups are described in Richardson et al (2021a) and the associated references (Brown et al, 2002;Kurowski et al, 2020;Matheou and Chung, 2014;Siebesma et al, 2003;vanZanten et al, 2011), and while some simulations represent oceanic boundary layers, we simply assume a land surface for the retrievals. Static reanalysis profiles from MERRA-2 (Gelaro et al, 2017) are appended above the LES domain, but the LES domains were shown to capture horizontal variability in q from analysis of LES output and airborne lidar profiles over the Pacific (Bedka et al, 2021).…”

“…The methodology here applies the results of Richardson et al (2021a), which developed a TCWV retrieval emulator to rapidly generate TCWV ret fields given TCWV eff derived from 3-D LES q fields. This emulator was developed from results of an observing system simulation experiment (OSSE).…”

“…This study focusses on the estimation of horizontal scaling of clear-sky TCWV at sub-kilometre scales and specifically on the integrated PBL water vapour which we refer to as the partial column water vapour (PCWV PBL ). Recent work using airborne data (Thompson et al, 2021) and large eddy simulation (LES) output (Richardson et al, 2021a) has provided evidence that sub-kilometre horizontal variability in total column water vapour (TCWV) is almost perfectly correlated with PCWV PBL variability, such that high-spatialresolution retrievals of TCWV from visible and shortwave infrared (VSWIR) imaging spectrometers can provide unique information about PBL q variability. This is not a statement that all water vapour is inside the PBL but rather that on sub-kilometre spatial scales, the variability in low-altitude water vapour dominates the horizontal column variability.…”

“…However, this severely limits viable data, so here we suggest a new method that exploits the directionality of the solar-smearing effect in order to perform such calculations across a wider range of conditions. This paper uses the solarpath-traced outputs of Richardson et al (2021a) to demonstrate that by calculating the structure function in a direction perpendicular to the solar azimuth, the bias in ζ 2 is removed within the 23 LES snapshots considered. To our knowledge, this is the first quantification of such a technique to mitigate solar-geometry-induced errors in spatial statistics of retrieved atmospheric properties.…”