The ARM Data Quality Program

Peppler, Randy A.; Kehoe, Kenneth; Monroe, Justin; Theisen, Adam; Moore, Sean T.

doi:10.1175/amsmonographs-d-15-0039.1

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…From ICARUS onward, ARM staff have monitored the successful collection of data during flights and continue to work alongside the ARM Data Management Facility (DMF) to implement and monitor the flow of UAS and TBS data to the DMF. After raw data are delivered, the Data Quality Office (Peppler et al 2016) and instrument mentors process the data to assess instrument performance, data quality, and uncertainty. In parallel with the above activities, DMF developers work with mentors to assess the quality control and processing level of the data and its readiness for release.…”

Section: Looking Ahead Advancing Capabilitiesmentioning

confidence: 99%

A Bird’s-Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska

Boer

Ivey

Schmid

et al. 2018

Bulletin of the American Meteorological Society

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Thorough understanding of aerosols, clouds, boundary layer structure, and radiation is required to improve the representation of the Arctic atmosphere in weather forecasting and climate models. To develop such understanding, new perspectives are needed to provide details on the vertical structure and spatial variability of key atmospheric properties, along with information over difficult-to-reach surfaces such as newly forming sea ice. Over the last three years, the U.S. Department of Energy (DOE) has supported various flight campaigns using unmanned aircraft systems [UASs, also known as unmanned aerial vehicles (UAVs) and drones] and tethered balloon systems (TBSs) at Oliktok Point, Alaska. These activities have featured in situ measurements of the thermodynamic state, turbulence, radiation, aerosol properties, cloud microphysics, and turbulent fluxes to provide a detailed characterization of the lower atmosphere. Alongside a suite of active and passive ground-based sensors and radiosondes deployed by the DOE Atmospheric Radiation Measurement (ARM) program through the third ARM Mobile Facility (AMF-3), these flight activities demonstrate the ability of such platforms to provide critically needed information. In addition to providing new and unique datasets, lessons learned during initial campaigns have assisted in the development of an exciting new community resource.

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Section: Looking Ahead Advancing Capabilitiesmentioning

confidence: 99%

A Bird’s-Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska

Boer

Ivey

Schmid

et al. 2018

Bulletin of the American Meteorological Society

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show abstract

“…The vertical resolution of the profile is 10 m, and the profiles usually end at a height of 20-25 km. ARM data are quality-controlled, as explained in Peppler et al [30,31], and quality control flags are applied to the data. Finally, a dry bias correction was applied to the RS data, as discussed in Section 3.3.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Integrated Water Vapor Measurements at High Latitudes from Co-Located and Near-Located Instruments

Fionda

Cadeddu

Mattioli³

et al. 2019

Remote Sensing

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Data from global positioning system (GPS) ground-based receivers, ground-based microwave radiometers (MWRs), and radiosondes (RS) at two high-latitude sites were compared. At one site, the North Slope of Alaska (NSA), Barrow, Alaska (USA), the instruments were co-located, while at the other site, the second ARM Mobile Facility (AMF2), Hyytiälä, Finland, the GPS receiver was located about 20 km away from the MWRs and RS. Differences between the GPS-derived integrated water vapor (IWV) and the other three instruments were analyzed in terms of mean differences and standard deviation. A comparison of co-located and near-located independently calibrated instruments allowed us to isolate issues that may be specific to a single system and, to some extent, to isolate the effects of the distance between the GPS receiver and the remaining instruments. The results showed that at these two high-latitude sites, when the IWV was less than 15 kg/m 2 , the GPS agreed with other instruments within 0.5-0.7 kg/m 2 . When the variability of water vapor was higher, mostly in the summer months, the GPS agreed with other instruments within 0.8-1 kg/m 2 . The total random uncertainty between the GPS and the other systems was of the order of 0.6-1 kg/m 2 and was the dominant effect when the IWV was higher than 15 kg/m 2 . a very-long-baseline interferometry (VLBI) radio telescope [5], and numerical weather prediction models [6][7][8]. From a statistical point of view, intercomparison analyses have highlighted discrepancies in terms of systematic errors (bias) and random errors (standard deviation, SD). These discrepancies can be attributed to climatic conditions, the length of observations, and characteristics of the instruments such as instrumental errors, the volume sampled, sensitivity, and the sampling time as well as to water vapor retrieval algorithms.The need to monitor and improve the quantification of water vapor is also essential considering incoming next-generation 5G wireless networks. In communications systems based on 5G technology, high data capacity and low latency are achieved by moving operational frequencies toward spectral regions such as the millimeter-wave band (30-300 GHz). From a communications point of view, for planning outdoor millimeter/submillimeter communication networks, water vapor is a source of signal degradations. To improve propagation models and experimental capability in the estimation of water vapor [9,10], accurate investigations of water vapor uncertainties in different climatic scenarios are desirable. However, even more importantly, since the critical 23.8-GHz water vapor frequency is so close to the 5G 24-GHz band, there is a high risk of interference, which impacts the ability to detect water vapor in the atmosphere accurately [11].In this work, we specifically focus on IWV high-latitude observations, as their retrieval remains an important challenge in this climatic regime: for example, satellite retrievals suffer from high surface reflectivity and low solar zenith angle [12]. IWV from GPSes ...

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Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation

et al. 2022

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The ARM Data Quality Program

Cited by 11 publications

References 27 publications

A Bird’s-Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska

A Bird’s-Eye View: Development of an Operational ARM Unmanned Aerial Capability for Atmospheric Research in Arctic Alaska

Intercomparison of Integrated Water Vapor Measurements at High Latitudes from Co-Located and Near-Located Instruments

Improving the prediction of DNI with physics-based representation of all-sky circumsolar radiation

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