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Debbie Clifford
,
Raquel Alegre
,
Victoria Bennett
,
Jon Blower
,
Cecelia Deluca
,
Philip Kershaw
,
Christopher Lynnes
,
Chris Mattmann
,
Rhona Phipps
, and
Iryna Rozum

Abstract

For users of climate services, the ability to quickly determine the datasets that best fit one’s needs would be invaluable. The volume, variety, and complexity of climate data makes this judgment difficult. The ambition of CHARMe (Characterization of metadata to enable high-quality climate services) is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports, or feedback on previous applications of the data. The capture and discovery of this “commentary” information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search, and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator (“CHARMe Maps”), and a tool for correlating climate time series with external “significant events” (e.g., instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source and released under a liberal license, permitting future projects to reuse the source code as they wish.

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Mario Marcello Miglietta
and
Richard Rotunno

Abstract

The possibility offered by the Internet to share pictures of tornadoes, and the storm-report archiving in the European Storm Weather Database, have made it apparent that the occurrence of tornadoes over Europe has been underestimated. Together with weak waterspouts and tornadoes, large and intense vortices are occasionally observed. Among these, an EF3 multivortex tornado with a path width of some hundreds of meters affected southeastern Italy on 28 November 2012, causing one casualty and estimated damage of €60M to the largest steel plant in Europe. A tide gauge positioned near the location of tornado landfall and a vertical atmospheric profile available a few hours later near the affected region represent unique sources of information for these events in the Mediterranean. During its transit across the port of Taranto, a waterspout, which was to become the tornado, was observed to have induced a sea level rise of about 30 cm. The supercell responsible for the tornado developed from convective cells triggered by orographic uplift over the Apennines. The 0–1-km wind shear was exceptional in comparison with other Italian tornadoes, and was remarkable in comparison with U.S. events as well. Other indices for severe convection diagnosis also showed extremely high values. The occasional occurrence of events with similar or stronger intensities over Italy emphasizes the need for the Distributed National Weather Service—which will integrate Italian meteorological institutions under one agency and is currently under development—to devise a warning system dedicated to the monitoring and prediction of severe convective events.

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Robert J. Trapp
,
David J. Stensrud
,
Michael C. Coniglio
,
Russ S. Schumacher
,
Michael E. Baldwin
,
Sean Waugh
, and
Don T. Conlee

Abstract

The Mesoscale Predictability Experiment (MPEX) was a field campaign conducted 15 May through 15 June 2013 within the Great Plains region of the United States. One of the research foci of MPEX regarded the upscaling effects of deep convective storms on their environment, and how these feed back to the convective-scale dynamics and predictability. Balloon-borne GPS radiosondes, or “upsondes,” were used to sample such environmental feedbacks. Two of the upsonde teams employed dual-frequency sounding systems that allowed for upsonde observations at intervals as fast as 15 min. Because these dual-frequency systems also had the capacity for full mobility during sonde reception, highly adaptive and rapid storm-relative sampling of the convectively modified environment was possible. This article documents the mobile sounding capabilities and unique sampling strategies employed during MPEX.

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Sheng Chen
,
Jonathan J. Gourley
,
Yang Hong
,
Qing Cao
,
Nicholas Carr
,
Pierre-Emmanuel Kirstetter
,
Jian Zhang
, and
Zac Flamig

Abstract

In meteorological investigations, the reference variable or “ground truth” typically comes from an instrument. This study uses human observations of surface precipitation types to evaluate the same variables that are estimated from an automated algorithm. The NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS) system relies primarily on observations from the Next Generation Radar (NEXRAD) network and model analyses from the Earth System Research Laboratory’s Rapid Refresh (RAP) system. Each hour, MRMS yields quantitative precipitation estimates and surface precipitation types as rain or snow. To date, the surface precipitation type product has received little attention beyond case studies. This study uses precipitation type reports collected by citizen scientists who have contributed observations to the meteorological Phenomena Identification Near the Ground (mPING) project. Citizen scientist reports of rain and snow during the winter season from 19 December 2012 to 30 April 2013 across the United States are compared to the MRMS precipitation type products. Results show that while the mPING reports have a limited spatial distribution (they are concentrated in urban areas), they yield similar critical success indexes of MRMS precipitation types in different cities. The remaining disagreement is attributed to an MRMS algorithmic deficiency of yielding too much rain, as opposed to biases in the mPING reports. The study also shows reduced detectability of snow compared to rain, which is attributed to lack of sensitivity at S band and the shallow nature of winter storms. Some suggestions are provided for improving the MRMS precipitation type algorithm based on these findings.

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Kun Zhao
,
Qing Lin
,
Wen-Chau Lee
,
Y. Qiang Sun
, and
Fuqing Zhang

Abstract

Strong tropical cyclones often undergo eyewall replacement cycles that are accompanied by concentric eyewalls and/or rapid intensity changes while the secondary eyewall contracts radially inward and eventually replaces the inner eyewall. To the best of our knowledge, the only documented partial/incomplete tertiary eyewall has been mostly inferred from two-dimensional satellite images or one-dimensional aircraft flight-level measurements that can be regarded as indirect and tangential. This study presents the first high spatial and temporal resolution Doppler radar observations of a tertiary eyewall formation event in Typhoon Usagi (2013) over a 14-h time period before it makes landfall. The primary (tangential) and secondary (radial) circulations of Usagi deduced from the Ground-Based Velocity Track Display (GBVTD) methodology clearly portrayed three distinct axisymmetric maxima of radar reflectivity, tangential wind, vertical velocity, and vertical vorticity. Usagi’s central pressure steadily deepened during the contraction of the secondary and tertiary eyewalls until the tertiary eyewall hit the coast of southeast China, which erminated the intensification of the storm.

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Alexander P. Trishchenko
,
Louis Garand
,
Larisa D. Trichtchenko
, and
Lidia V. Nikitina

Abstract

A novel type of multiple-apogee highly elliptical orbits termed as MAP HEO with a period of rotation between 14 h and 15 h is introduced. These orbits are designed to achieve continuous geostationary (GEO)-like imaging of the polar regions in an optimum way. The combination of GEO and HEO satellites would then offer continuous monitoring of weather from space at any point of the globe. This capacity would represent a breakthrough for short- and long-term weather forecasting and narrowing uncertainties in the knowledge of the Earth’s climate through better sampling and more accurate characterization of the diurnal cycle. MAP HEO systems can be launched at critical inclination and are characterized by a local minimum of ionizing radiation. These features simplify the process of orbit maintenance, reduce radiation shielding requirements, and favor a longer lifetime of the mission. Unlike previously considered HEO systems implemented for communications, such as 12-h Molniya and 24-h Sirius radio systems, a MAP HEO constellation achieves a uniform geometrical sampling, which reduces view angle dependent biases. These observational conditions with complete coverage of the diurnal cycle, diverse range of solar illumination, and viewing observational conditions are beneficial for high-latitude meteorological and climate applications, such as the retrieval of Essential Climate Variables (ECV).

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Faye E. Barthold
,
Thomas E. Workoff
,
Brian A. Cosgrove
,
Jonathan J. Gourley
,
David R. Novak
, and
Kelly M. Mahoney

Abstract

Despite advancements in numerical modeling and the increasing prevalence of convection-allowing guidance, flash flood forecasting remains a substantial challenge. Accurate flash flood forecasts depend not only on accurate quantitative precipitation forecasts (QPFs), but also on an understanding of the corresponding hydrologic response. To advance forecast skill, innovative guidance products that blend meteorology and hydrology are needed, as well as a comprehensive verification dataset to identify areas in need of improvement.

To address these challenges, in 2013 the Hydrometeorological Testbed at the Weather Prediction Center (HMT-WPC), partnering with the National Severe Storms Laboratory (NSSL) and the Earth System Research Laboratory (ESRL), developed and hosted the inaugural Flash Flood and Intense Rainfall (FFaIR) Experiment. In its first two years, the experiment has focused on ways to combine meteorological guidance with available hydrologic information. One example of this is the creation of neighborhood flash flood guidance (FFG) exceedance probabilities, which combine QPF information from convection-allowing ensembles with flash flood guidance; these were found to provide valuable information about the flash flood threat across the contiguous United States.

Additionally, WPC has begun to address the challenge of flash flood verification by developing a verification database that incorporates observations from a variety of disparate sources in an attempt to build a comprehensive picture of flash flooding across the nation. While the development of this database represents an important step forward in the verification of flash flood forecasts, many of the other challenges identified during the experiment will require a long-term community effort in order to make notable advancements.

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Imke Durre
and
Michael F. Squires

Abstract

Are we going to have a white Christmas? That is a question that scientists at the National Oceanic and Atmospheric Administration (NOAA) receive each autumn from members of the media and general public. NOAA personnel typically respond by way of a press release and map depicting the climatological probability of observing snow on the ground on 25 December at stations across the contiguous United States. This map has become one of the most popular applications of NOAA’s 1981–2010 U.S. Climate Normals.

The purpose of this paper is to expand upon the annual press release in two ways. First, the methodology for empirically calculating the probabilities of snow on the ground is documented. Second, additional maps describing the median snow depth on 25 December as well as the probability and amount of snowfall are presented.

The results are consistent with a climatologist’s intuitive expectations. In the Sierras, Cascades, the leeward side of the Great Lakes, and northern New England, snow cover is a near certainty. In these regions, most precipitation falls as snow, and the probability of snowfall can exceed 25%. At higher elevations of the Rocky Mountains and at many locations between the northern Rockies and New England, snowfall is considerably less frequent on Christmas Day, yet the probability of snow on the ground exceeds 50%. For those who would like to escape the snow, the best places to be in late December are in Southern California, the lower elevations of the Southwest, and Florida.

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M. Heistermann
,
S. Collis
,
M. J. Dixon
,
J. J. Helmus
,
A. Henja
,
D. B. Michelson
, and
Thomas Pfaff

Abstract

In a recent BAMS article, it is argued that community-based Open Source Software (OSS) could foster scientific progress in weather radar research, and make weather radar software more affordable, flexible, transparent, sustainable, and interoperable.

Nevertheless, it can be challenging for potential developers and users to realize these benefits: tools are often cumbersome to install; different operating systems may have particular issues, or may not be supported at all; and many tools have steep learning curves.

To overcome some of these barriers, we present an open, community-based virtual machine (VM). This VM can be run on any operating system, and guarantees reproducibility of results across platforms. It contains a suite of independent OSS weather radar tools (BALTRAD, Py-ART, wradlib, RSL, and Radx), and a scientific Python stack. Furthermore, it features a suite of recipes that work out of the box and provide guidance on how to use the different OSS tools alone and together. The code to build the VM from source is hosted on GitHub, which allows the VM to grow with its community.

We argue that the VM presents another step toward Open (Weather Radar) Science. It can be used as a quick way to get started, for teaching, or for benchmarking and combining different tools. It can foster the idea of reproducible research in scientific publishing. Being scalable and extendable, it might even allow for real-time data processing.

We expect the VM to catalyze progress toward interoperability, and to lower the barrier for new users and developers, thus extending the weather radar community and user base.

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