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Michael Peterson, Chuntao Liu, Douglas Mach, Wiebke Deierling, and Christina Kalb

Abstract

A unique dataset of coincident high-altitude passive microwave and electric field observations taken by the NASA ER-2 aircraft is used to assess the feasibility of estimating electric fields above electrified clouds using ubiquitous global and multidecadal satellite products. Once applied to a global dataset, such a product would provide a unique approach for diagnosing and monitoring the current sources of the global electric circuit (GEC).

In this study an algorithm has been developed that employs ice scattering signals from 37- and 85-GHz passive microwave observations to characterize the electric fields above clouds overflown by the ER-2 aircraft at nearly 20-km altitude. Electric field estimates produced by this passive microwave algorithm are then compared to electric field observations also taken by the aircraft to assess its potential future utility with satellite datasets. The algorithm is shown to estimate observed electric field strengths over intense convective clouds at least 71% (58%) of the time over land and 43% (40%) of the time over the ocean to within a factor of 2 from 85-GHz (37 GHz) passive microwave observations. Electric fields over weaker clouds can be estimated 58% (41%) of the time over land and 22% (8%) of the time over the ocean from 85-GHz (37 GHz) passive microwave observations. The accuracy of these estimates is limited by systematic errors in the observations along with other factors. Despite these sources of error, the algorithm can produce reasonable estimates of electric fields over carefully selected individual electrified clouds that differ from observations by less than 20 V m−1 for clouds that produce 200–400 V m−1 electric fields at 20 km.

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Michael Peterson, Wiebke Deierling, Chuntao Liu, Douglas Mach, and Christina Kalb

Abstract

High-altitude atmospheric electricity measurements have been used to calculate the conduction (Wilson) currents that are supplied to the global electric circuit (GEC) by individual electrified clouds. Quantifying the global average current and assessing its temporal variability is a challenge, however, because it requires measurements in every stormy region of the world. Thus, a retrieval algorithm has been developed to infer the electric fields and Wilson currents above electrified weather from NASA ER-2 passive microwave high-altitude aircraft observations that are also common satellite products.

This study documents the adaptation of the passive microwave electric field and the Wilson current retrieval algorithm for use with satellite platforms. Three distinct variants on the algorithm are produced to respond to specific use cases that differ in 1) whether swath or microwave feature data are available to describe the lateral extent of electrified clouds, 2) the availability of coincident radar data to characterize the vertical structure of electrified clouds, and 3) the prioritization of scientific accuracy or computational expense and product latency. The Wilson currents produced by the satellite retrievals are compared with each other and also with coincident lightning measurements and the Carnegie curve. The advantages, caveats, and limitations of each variant are discussed.

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Burkely T. Gallo, Christina P. Kalb, John Halley Gotway, Henry H. Fisher, Brett Roberts, Israel L. Jirak, Adam J. Clark, Curtis Alexander, and Tara L. Jensen

Abstract

Evaluation of numerical weather prediction (NWP) is critical for both forecasters and researchers. Through such evaluation, forecasters can understand the strengths and weaknesses of NWP guidance, and researchers can work to improve NWP models. However, evaluating high-resolution convection-allowing models (CAMs) requires unique verification metrics tailored to high-resolution output, particularly when considering extreme events. Metrics used and fields evaluated often differ between verification studies, hindering the effort to broadly compare CAMs. The purpose of this article is to summarize the development and initial testing of a CAM-based scorecard, which is intended for broad use across research and operational communities and is similar to scorecards currently available within the enhanced Model Evaluation Tools package (METplus) for evaluating coarser models. Scorecards visualize many verification metrics and attributes simultaneously, providing a broad overview of model performance. A preliminary CAM scorecard was developed and tested during the 2018 Spring Forecasting Experiment using METplus, focused on metrics and attributes relevant to severe convective forecasting. The scorecard compared attributes specific to convection-allowing scales such as reflectivity and surrogate severe fields, using metrics like the critical success index (CSI) and fractions skill score (FSS). While this preliminary scorecard focuses on attributes relevant to severe convective storms, the scorecard framework allows for the inclusion of further metrics relevant to other applications. Development of a CAM scorecard allows for evidence-based decision-making regarding future operational CAM systems as the National Weather Service transitions to a Unified Forecast system as part of the Next-Generation Global Prediction System initiative.

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Burkely T. Gallo, Christina P. Kalb, John Halley Gotway, Henry H. Fisher, Brett Roberts, Israel L. Jirak, Adam J. Clark, Curtis Alexander, and Tara L. Jensen
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Barbara Brown, Tara Jensen, John Halley Gotway, Randy Bullock, Eric Gilleland, Tressa Fowler, Kathryn Newman, Dan Adriaansen, Lindsay Blank, Tatiana Burek, Michelle Harrold, Tracy Hertneky, Christina Kalb, Paul Kucera, Louisa Nance, John Opatz, Jonathan Vigh, and Jamie Wolff

Capsule summary

MET is a community-based package of state-of-the-art tools to evaluate predictions of weather, climate, and other phenomena, with capabilities to display and analyze verification results via the METplus system.

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