Novel approach to observing system simulation experiments improves information gain of surface–atmosphere field measurements

Abstract

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.

Space‐Scale Resolved Surface Fluxes Across a Heterogeneous, Mid‐Latitude Forested Landscape

“…This presents impactful opportunities to parsimoniously combine aircraft and tower data, when available as is the case for the CHEESEHEAD19 experiment, to arrive at a space‐time aligned view of surface fluxes. The airborne campaign numerical experiment design involved calculating space and time resolved flux maps across the domain from simulated tower and aircraft data (from candidate flight patterns) using a machine learning approach with the land‐surface properties as drivers (Figure 12 in Metzger et al., 2021).…”

“…Metzger et al. (2021) provide details about the numerical simulations, analysis framework, and design strategy used to come up with the final flight patterns for the airborne measurements. Figure 2 shows these different patterns and their respective waypoints.…”

“…Flying the numbered waypoints either in ascending order (SE1, SW1, WE1) or descending order (SE2, SW2, WE2) results in six distinct flight sequences that maximize data coverage under different wind conditions. Map credit: James Mineau, University of Wisconsin‐Madison (Metzger et al., 2021: Figure 14, published by Atmospheric Measurement Techniques, reproduced with permissions under https://creativecommons.org/licenses/by/4.0/).…”

“…The airborne experiment was designed with the help of numerical experiments to maximize spatial coverage over the domain, ensure adequate sampling of larger scale eddies and ensure crew safety. Metzger et al (2021) provide details about the numerical simulations, analysis framework, and design strategy used to come up with the final flight patterns for the airborne measurements. Figure 2 shows these different patterns and their respective waypoints.…”

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

“…It is also exceedingly rare for process models to inform the siting and measurement strategies of observational networks during their design phase, although a small number of examples do exist (e.g., Lahoz and Schneider 2014;Metzger et al 2021). Because of this, it is highly likely that resources are misallocated, and critical insights are not identified during the lifetime of the campaign.…”