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Bore-ing into Nocturnal Convection

Kevin R. Haghi
,
Bart Geerts
,
Hristo G. Chipilski
,
Aaron Johnson
,
Samuel Degelia
,
David Imy
,
David B. Parsons
,
Rebecca D. Adams-Selin
,
David D. Turner
, and
Xuguang Wang

Abstract

There has been a recent wave of attention given to atmospheric bores in order to understand how they evolve and initiate and maintain convection during the night. This surge is attributable to data collected during the 2015 Plains Elevated Convection at Night (PECAN) field campaign. A salient aspect of the PECAN project is its focus on using multiple observational platforms to better understand convective outflow boundaries that intrude into the stable boundary layer and induce the development of atmospheric bores. The intent of this article is threefold: 1) to educate the reader on current and future foci of bore research, 2) to present how PECAN observations will facilitate aforementioned research, and 3) to stimulate multidisciplinary collaborative efforts across other closely related fields in an effort to push the limitations of prediction of nocturnal convection.

Full access
Junho Ho
,
Guifu Zhang
,
Petar Bukovcic
,
David B. Parsons
,
Feng Xu
,
Jidong Gao
,
Jacob T. Carlin
, and
Jeffrey C. Snyder

Abstract

Raindrop size distributions (DSD) and rain rate have been estimated from polarimetric radar data using different approaches with the accuracy depending on the errors both in the radar measurements and the estimation methods. Herein, a deep neural network (DNN) technique was utilized to improve the estimation of the DSD and rain rate by mitigating these errors. The performance of this approach was evaluated using measurements from a two-dimensional video disdrometer (2DVD) at the Kessler Atmospheric and Ecological Field Station in Oklahoma as ground truth with the results compared against conventional estimation methods for the period 2006–17. Physical parameters (mass-/volume-weighted diameter and liquid water content), rain rate, and polarimetric radar variables (including radar reflectivity and differential reflectivity) were obtained from the DSD data. Three methods—physics-based inversion, empirical formula, and DNN—were applied to two different temporal domains (instantaneous and rain-event average) with three diverse error assumptions (fitting, measurement, and model errors). The DSD retrievals and rain estimates from 18 cases were evaluated by calculating the bias and root-mean-squared error (RMSE). DNN produced the best performance for most cases, with up to a 5% reduction in RMSE when model errors existed. DSD and rain estimated from a nearby polarimetric radar using the empirical and DNN methods were well correlated with the disdrometer observations; the rain-rate estimate bias of the DNN was significantly reduced (3.3% in DNN vs 50.1% in empirical). These results suggest that DNN has advantages over the physics-based and empirical methods in retrieving rain microphysics from radar observations.

Restricted access
Tammy M. Weckwerth
,
David B. Parsons
,
Steven E. Koch
,
James A. Moore
,
Margaret A. LeMone
,
Belay B. Demoz
,
Cyrille Flamant
,
Bart Geerts
,
Junhong Wang
, and
Wayne F. Feltz

The International H2O Project (IHOP_2002) is one of the largest North American meteorological field experiments in history. From 13 May to 25 June 2002, over 250 researchers and technical staff from the United States, Germany, France, and Canada converged on the Southern Great Plains to measure water vapor and other atmospheric variables. The principal objective of IHOP_2002 is to obtain an improved characterization of the time-varying three-dimensional water vapor field and evaluate its utility in improving the understanding and prediction of convective processes. The motivation for this objective is the combination of extremely low forecast skill for warm-season rainfall and the relatively large loss of life and property from flash floods and other warm-season weather hazards. Many prior studies on convective storm forecasting have shown that water vapor is a key atmospheric variable that is insufficiently measured. Toward this goal, IHOP_2002 brought together many of the existing operational and new state-of-the-art research water vapor sensors and numerical models.

The IHOP_2002 experiment comprised numerous unique aspects. These included several instruments fielded for the first time (e.g., reference radiosonde); numerous upgraded instruments (e.g., Wyoming Cloud Radar); the first ever horizontal-pointing water vapor differential absorption lidar (DIAL; i.e., Leandre II on the Naval Research Laboratory P-3), which required the first onboard aircraft avoidance radar; several unique combinations of sensors (e.g., multiple profiling instruments at one field site and the German water vapor DIAL and NOAA/Environmental Technology Laboratory Doppler lidar on board the German Falcon aircraft); and many logistical challenges. This article presents a summary of the motivation, goals, and experimental design of the project, illustrates some preliminary data collected, and includes discussion on some potential operational and research implications of the experiment.

Full access
Shushi Zhang
,
David B. Parsons
,
Xin Xu
,
Jisong Sun
,
Tianjie Wu
,
Abuduwaili Abulikemu
,
Fen Xu
,
Gang Chen
,
Wenqiang Shen
,
Lihang Liu
,
Xidi Zhang
,
Kun Zhang
, and
Wei Zhang

Abstract

The bow-and-arrow Mesoscale Convective System (MCS) has a unique structure with two convective lines resembling the shape of an archer’s bow and arrow. These MCSs and their arrow convection (located behind the MCS leading line) can produce hazardous winds and flooding extending over hundreds of kilometers, which are often poorly predicted in operational forecasts. This study examines the dynamics of a bow-and-arrow MCS observed over the Yangtze–Huai Plains of China, with a focus on the arrow convection provided. The analysis utilized backward trajectories and Lagrangian vertical momentum budgets to simulations employing the WRF‐ARW and CM1 models. Cells within the arrow in the WRF-ARW simulations of the MCS were elevated, initially forming as convectively unstable air within the low-level jet (LLJ), which gently ascended over the cold pool and converged with the MCS’s mesoscale convective vortex (MCV) at higher altitudes. The subsequent ascent in these cells was enhanced by dynamic pressure forcing due to the updraft being within a layer where the vertical shear changed with height due to the superposition of the LLJ and the MCV. These dynamic forcings initially played a larger role in the ascent than the parcel’s buoyancy. These findings were bolstered by idealized simulations employing the CM1 model. These results illustrate a challenge for accurately forecasting bow-and-arrow MCSs as the updraft magnitude depends on dynamical forcing associated with the interaction between vertical shear associated with the environment and due to convectively generated circulations.

Free access
Gilbert Brunet
,
David B. Parsons
,
Dimitar Ivanov
,
Boram Lee
,
Peter Bauer
,
Natacha B. Bernier
,
Veronique Bouchet
,
Andy Brown
,
Antonio Busalacchi
,
Georgina Campbell Flatter
,
Rei Goffer
,
Paul Davies
,
Beth Ebert
,
Karl Gutbrod
,
Songyou Hong
,
P. K. Kenabatho
,
Hans-Joachim Koppert
,
David Lesolle
,
Amanda H. Lynch
,
Jean-François Mahfouf
,
Laban Ogallo
,
Tim Palmer
,
Kevin Petty
,
Dennis Schulze
,
Theodore G. Shepherd
,
Thomas F. Stocker
,
Alan Thorpe
, and
Rucong Yu

Abstract

Our world is rapidly changing. Societies are facing an increase in the frequency and intensity of high-impact and extreme weather and climate events. These extremes together with exponential population growth and demographic shifts (e.g., urbanization, increase in coastal populations) are increasing the detrimental societal and economic impact of hazardous weather and climate events. Urbanization and our changing global economy have also increased the need for accurate projections of climate change and improved predictions of disruptive and potentially beneficial weather events on kilometer scales. Technological innovations are also leading to an evolving and growing role of the private sector in the weather and climate enterprise. This article discusses the challenges faced in accelerating advances in weather and climate forecasting and proposes a vision for key actions needed across the private, public, and academic sectors. Actions span (i) utilizing the new observational and computing ecosystems; (ii) strategies to advance Earth system models; (iii) ways to benefit from the growing role of artificial intelligence; (iv) practices to improve the communication of forecast information and decision support in our age of internet and social media; and (v) addressing the need to reduce the relatively large, detrimental impacts of weather and climate on all nations and especially on low-income nations. These actions will be based on a model of improved cooperation between the public, private, and academic sectors. This article represents a concise summary of the white paper on the Future of Weather and Climate Forecasting (2021) put together by the World Meteorological Organizations’ Open Consultative Platform.

Open access
Bart Geerts
,
David Parsons
,
Conrad L. Ziegler
,
Tammy M. Weckwerth
,
Michael I. Biggerstaff
,
Richard D. Clark
,
Michael C. Coniglio
,
Belay B. Demoz
,
Richard A. Ferrare
,
William A. Gallus Jr.
,
Kevin Haghi
,
John M. Hanesiak
,
Petra M. Klein
,
Kevin R. Knupp
,
Karen Kosiba
,
Greg M. McFarquhar
,
James A. Moore
,
Amin R. Nehrir
,
Matthew D. Parker
,
James O. Pinto
,
Robert M. Rauber
,
Russ S. Schumacher
,
David D. Turner
,
Qing Wang
,
Xuguang Wang
,
Zhien Wang
, and
Joshua Wurman

Abstract

The central Great Plains region in North America has a nocturnal maximum in warm-season precipitation. Much of this precipitation comes from organized mesoscale convective systems (MCSs). This nocturnal maximum is counterintuitive in the sense that convective activity over the Great Plains is out of phase with the local generation of CAPE by solar heating of the surface. The lower troposphere in this nocturnal environment is typically characterized by a low-level jet (LLJ) just above a stable boundary layer (SBL), and convective available potential energy (CAPE) values that peak above the SBL, resulting in convection that may be elevated, with source air decoupled from the surface. Nocturnal MCS-induced cold pools often trigger undular bores and solitary waves within the SBL. A full understanding of the nocturnal precipitation maximum remains elusive, although it appears that bore-induced lifting and the LLJ may be instrumental to convection initiation and the maintenance of MCSs at night.

To gain insight into nocturnal MCSs, their essential ingredients, and paths toward improving the relatively poor predictive skill of nocturnal convection in weather and climate models, a large, multiagency field campaign called Plains Elevated Convection At Night (PECAN) was conducted in 2015. PECAN employed three research aircraft, an unprecedented coordinated array of nine mobile scanning radars, a fixed S-band radar, a unique mesoscale network of lower-tropospheric profiling systems called the PECAN Integrated Sounding Array (PISA), and numerous mobile-mesonet surface weather stations. The rich PECAN dataset is expected to improve our understanding and prediction of continental nocturnal warm-season precipitation. This article provides a summary of the PECAN field experiment and preliminary findings.

Full access