ARL Weekly News – April 24, 2020
Conference Presentations: ARL scientists Daniel Tong, Youhua Tang, Barry Baker, Patrick Campbell, Fantine Ngan, Hyuncheol Kim, Tianfeng Chai and Pius Lee contributed to four talks delivered at the 3rd International Smoke Symposium hosted by the International Association of Wildland Fire. These included Pius’ “Campaign-support forecast and its evaluation — testing of the Global Biomass Burning Emission Product Extension (GBBEPx) for NAQFC” and Daniel’s “How Well Can We Estimate Fire Emissions Using Satellites? Assessing Five Bottom-up and Top-down Fire Products during the 2018 Camp Fires in California.” The team also assisted with two talks by National Weather Service speakers: Ivanka Stajner’s “Air quality and aerosol predictions at NOAA/National Weather Service and their applications” and Jeff McQueen’s “Impact of horizontal resolution on wild fire smoke plume rise.” They were all intriguing talks well received.
A manuscript titled “Current and future uses of unmanned aircraft systems (UASs) for improved forecasts/warnings and scientific studies,” on which Bruce Baker and Temple Lee are coauthors, was accepted for publication in the Bulletin of the American Meteorological Society. This paper describes outcomes from a three-day workshop on how instrumented small unmanned aircraft systems, or drones, can be used to benefit research in atmospheric science.
Abstract: Unmanned aircraft systems (UASs) provide unique observations not readily available from piloted aircraft or ground- and satellite-based remote sensors. For example, they can reach difficult to observe areas in the Arctic (Reuder et al. 2011; de Boer et al. 2018a), in tropical cyclones (Cione et al. 2019), and within the atmospheric boundary layer (Jacob et al. 2018), and provide more routine measurements over a longer time range with repetitive vertical and horizontal profiles than piloted aircraft can. Furthermore, there are many scientific applications of UAS that go beyond weather research, which can aid weather applications and, in some instances, draw from weather applications. Although recent efforts have accelerated the development of UAS platforms and instruments (e.g., Wildmann et al. 2014; de Boer 2016a, Barbieri et al. 2019; Bell et al. 2019), there is still considerable uncertainty in how to best acquire and use these observations for improving forecasts, how to integrate them with other observations currently being obtained, and to enable process studies to improve conceptual and numerical modeling of the atmosphere and its constituent gases, aerosols and hydrometeors. In order to initiate a community effort for addressing such issues and to build upon the efforts of other community groups, such as the International Society for Atmospheric Research using with Remotely-piloted Aircraft (ISARRA, http://isarra.org), a workshop emphasizing the scientific applications of UAS was held at the National Weather Center (NWC) in Norman, Oklahoma from 29 to 31 October 2019.
A research article titled “The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations,” coauthored by John Kochendorfer, was just published in Atmospheric Science Letters.
Summary: Unless they are very well shielded, precipitation gauges typically underestimate snowfall measurements. In this paper, the effects of such underestimates on the validation of weather forecasts are evaluated using data collected at several international precipitation testbeds. The effects of applying corrections to the precipitation measurements were also quantified.
Abstract: Precipitation forecasts made by Numerical Weather Prediction (NWP) models are typically verified using precipitation gauge observations that are often prone to the wind-induced undercatch of solid precipitation. Therefore, apparent model biases in solid precipitation forecasts may be due in part to the measurements and not the model. To reduce solid precipitation measurement biases, adjustments in the form of transfer functions were derived within the framework of the World Meteorological Organization Solid Precipitation Inter-Comparison Experiment (WMO-SPICE). These transfer functions were applied to single-Alter shielded gauge measurements at selected SPICE sites during two winter seasons (2015-2016 and 2016-2017). Along with measurements from the WMO automated field reference configuration at each of these SPICE sites, the adjusted and unadjusted gauge observations were used to analyze the bias in a Global NWP model precipitation forecast. The verification of NWP winter precipitation using operational gauges may be subject to verification uncertainty, the magnitude and sign of which varies with the gauge-shield configuration and the relation between model and site-specific local climatologies. The application of a transfer function to Alter-shielded gauge measurements increases the amount of solid precipitation reported by the gauge and therefore reduces the NWP precipitation bias at sites where the model tends to overestimate precipitation, and increases the bias at sites where the model underestimates the precipitation. This complicates model verification when only operational (non-reference) gauge observations are available. Modelers, forecasters, and climatologists must consider this when comparing modeled and observed precipitation.
Citation: Buisán ST, Smith CD, Ross A, et al. The potential for uncertainty in Numerical Weather Prediction model verification when using solid precipitation observations. Atmos Sci Lett. 2020;e976. https://doi.org/10.1002/asl.976
A research article titled “Errors in top-down estimates of emissions using a known source,” on which Alice Crawford and Christopher Loughner are coauthors, is online for discussion and under review for the journal Atmospheric Chemistry and Physics.
Abstract: Air pollutant emissions estimates by top-down methods are subject to a variety of errors and uncertainties. This work uses a known source, a coal-fired power plant, to explore those errors. The known emissions amount and location remove two major types of error, facilitating understanding of other types. Biases and random errors are distinguished. A Lagrangian dispersion model (HYSPLIT) is run forward in time from the known source, and virtual measurements of the resulting tracer plume are compared to actual measurements from research aircraft. Four flights in different years are used to illustrate a variety of conditions. The measurements are analyzed by a mass-balance method, and the assumptions of that method are discussed. Some of those assumptions can be relaxed in analysis of the modeled plume, allowing testing of their validity. Meteorological fields to drive HYSPLIT are provided by the European Center for Medium Range Weather Forecasts Fifth Reanalysis (ERA5). A unique feature of this work is the use of an ensemble of meteorological fields intrinsic to ERA5. This analysis supports reasonably large (30–40 %) uncertainties on top-down analyses.
Citation: Angevine, W. M., Peischl, J., Crawford, A., Loughner, C. P., Pollack, I. B., and Thompson, C. R.: Errors in top-down estimates of emissions using a known source, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-169, in review, 2020.