Abstract

During the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held in the summer of 2019 in northern Wisconsin, U.S.A., active and passive ground-based remote sensing instruments were deployed to understand the response of the planetary boundary layer to heterogeneous land surface forcing. These instruments include Radar Wind Profilers, Microwave Radiometers, Atmospheric Emitted Radiance Interferometers, Ceilometers, High Spectral Resolution Lidars, Doppler Lidars, and Collaborative Lower Atmospheric Modelling Profiling Systems that combine several of these instruments. In this study, these ground-based remote sensing instruments are used to estimate the height of the daytime planetary boundary layer, and their performance is compared against independent boundary-layer depth estimates obtained from radiosondes launched as part of the field campaign. The impact of clouds (in particular boundary layer clouds) on boundary-layer depth is also investigated. We found that while overall all instruments are able to provide reasonable boundary-layer depth estimates, each of them shows strengths and weaknesses under certain conditions. For example, Radar Wind Profilers perform well during cloud free conditions, and Microwave Radiometers and Atmospheric Emitted Radiance Interferometers have a very good agreement during all conditions, but are limited by the smoothness of the retrieved thermodynamic profiles. The estimates from Ceilometers and High Spectral Resolution Lidars can be hindered by the presence of elevated aerosol layers or clouds, and the multi-instrument retrieval from the Collaborative Lower Atmospheric Modelling Profiling Systems can be constricted to a limited height range in low aerosol conditions.

Highlights

  • The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held between the mid-summer and fall of 2019, investigated the surface energy balance and atmospheric response over the heterogeneous forest region of northern Wisconsin, U.S.A. (Butterworth et al, 40 2021)

  • An extensive array of instrumentation was deployed by the National Science Foundation (NSF), the National Oceanic and Atmospheric Administration (NOAA), other agencies, and universities to examine the impacts of land-surface heterogeneities within the forested region on planetary boundary-layer (PBL) structure and evolution

  • This definition is similar to that used in some operational numerical weather prediction models (Coniglio et al, 2003), whose verification and validation is the goal of a CHEESEHEAD19 study in progress

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Summary

Introduction

The Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19) field campaign, held between the mid-summer and fall of 2019, investigated the surface energy balance and atmospheric response over the heterogeneous forest region of northern Wisconsin, U.S.A. (Butterworth et al, 40 2021). If remote sensing systems are to be used to provide high temporal resolution estimates of the PBLH, a comprehensive understanding of 100 how these ground-based remote-sensing instruments resolve the PBLH is needed in order to accurately interpret the retrieved PBLHs. In this study, 170 daytime radiosonde observations collected during the field campaign are used to validate the performance of the aforementioned instruments deployed for CHEESEHEAD19 in retrieving PBLHs. Since not all instruments were deployed for the entire duration of the campaign (see Section 2 for details), evaluation of these instruments in their ability to 105 resolve PBLH will be broken down into two components. 140 The methods used by these instruments to discern PBLH development are described in the subsequent subsections and vary from simple methods, such as the parcel method, to more sophisticated instrument-specific techniques

Validation Dataset - Radiosondes
Potential temperature gradient method
Elevated inversion method
Passive Remote Sensing Instruments
Active Remote Sensing Instruments
CLAMPS multi-instrument
CL51 ceilometer
RadSys Stations
405 3 Evaluation and Characterization of PBLHs
Parcel Method
Multi-Month Time Period Analysis of the RWP, CL51, and MWR PBLH Estimates
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