Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements
Stefan Metzger,Stefan Metzger,David Durden,Sreenath Paleri,Matthias Sühring,Brian J. Butterworth,Christopher Florian,Matthias Mauder,David M. Plummer,Luise Wanner,Ke Xu,Ankur R. Desai +11 more
TLDR
In this article, a numerical simulation-environmental response function (NS-ERF) approach is presented to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of mesoscale and microscale meteorology.Abstract:
. The observing system design of multidisciplinary field
measurements involves a variety of considerations on logistics, safety, and
science objectives. Typically, this is done based on investigator intuition
and designs of prior field measurements. However, there is potential for
considerable increases in efficiency, safety, and scientific success by
integrating numerical simulations in the design process. Here, we present a
novel numerical simulation–environmental response function (NS–ERF)
approach to observing system simulation experiments that aids
surface–atmosphere synthesis at the interface of mesoscale and microscale
meteorology. In a case study we demonstrate application of the NS–ERF
approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance
Study Enabled by a High-density Extensive Array of Detectors 2019
(CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered the
placement of 20 eddy covariance flux towers, operations for 72 h of
low-altitude flux aircraft measurements, and integration of various remote
sensing data products. A 2 h high-resolution large eddy simulation
created a cloud-free virtual atmosphere for surface and meteorological
conditions characteristic of the field campaign domain and period. To
explore two specific design hypotheses we super-sampled this virtual
atmosphere as observed by 13 different yet simultaneous observing system
designs consisting of virtual ground, airborne, and satellite observations.
We then analyzed these virtual observations through ERFs to yield an optimal
aircraft flight strategy for augmenting a stratified random flux tower
network in combination with satellite retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19's
potential to explore energy balance closure and spatial patterning science
objectives while substantially simplifying logistics. Owing to its modular
extensibility, NS–ERF lends itself to optimizing observing system designs also
for natural climate solutions, emission inventory validation, urban air
quality, industry leak detection, and multi-species applications, among other
use cases.read more
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