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Peter Knippertz
,
Hugh Coe
,
J. Christine Chiu
,
Mat J. Evans
,
Andreas H. Fink
,
Norbert Kalthoff
,
Catherine Liousse
,
Celine Mari
,
Richard P. Allan
,
Barbara Brooks
,
Sylvester Danour
,
Cyrille Flamant
,
Oluwagbemiga O. Jegede
,
Fabienne Lohou
, and
John H. Marsham

Abstract

Massive economic and population growth, and urbanization are expected to lead to a tripling of anthropogenic emissions in southern West Africa (SWA) between 2000 and 2030. However, the impacts of this on human health, ecosystems, food security, and the regional climate are largely unknown. An integrated assessment is challenging due to (a) a superposition of regional effects with global climate change; (b) a strong dependence on the variable West African monsoon; (c) incomplete scientific understanding of interactions between emissions, clouds, radiation, precipitation, and regional circulations; and (d) a lack of observations. This article provides an overview of the DACCIWA (Dynamics–Aerosol–Chemistry–Cloud Interactions in West Africa) project. DACCIWA will conduct extensive fieldwork in SWA to collect high-quality observations, spanning the entire process chain from surface-based natural and anthropogenic emissions to impacts on health, ecosystems, and climate. Combining the resulting benchmark dataset with a wide range of modeling activities will allow (a) assessment of relevant physical, chemical, and biological processes; (b) improvement of the monitoring of climate and atmospheric composition from space; and (c) development of the next generation of weather and climate models capable of representing coupled cloud–aerosol interactions. The latter will ultimately contribute to reduce uncertainties in climate predictions. DACCIWA collaborates closely with operational centers, international programs, policymakers, and users to actively guide sustainable future planning for West Africa. It is hoped that some of DACCIWA’s scientific findings and technical developments will be applicable to other monsoon regions.

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Jason A. Otkin
,
Mark Shafer
,
Mark Svoboda
,
Brian Wardlow
,
Martha C. Anderson
,
Christopher Hain
, and
Jeffrey Basara
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Jerôme Schalkwijk
,
Harmen J. J. Jonker
,
A. Pier Siebesma
, and
Erik Van Meijgaard

Abstract

Since the advent of computers midway through the twentieth century, computational resources have increased exponentially. It is likely they will continue to do so, especially when accounting for recent trends in multicore processors. History has shown that such an increase tends to directly lead to weather and climate models that readily exploit the extra resources, improving model quality and resolution. We show that Large-Eddy Simulation (LES) models that utilize modern, accelerated (e.g., by GPU or coprocessor), parallel hardware systems can now provide turbulence-resolving numerical weather forecasts over a region the size of the Netherlands at 100-m resolution. This approach has the potential to speed the development of turbulence-resolving numerical weather prediction models.

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Marina Timofeyeva-Livezey
,
Fiona Horsfall
,
Annette Hollingshead
,
Jenna Meyers
, and
Lesley-Ann Dupigny-Giroux

Abstract

NOAA’s NWS implemented the new Local Climate Analysis Tool (LCAT) on 1 July 2013. The tool supports the delivery of climate services by quickly providing information to help with climate-sensitive decisions and to facilitate the development of local climate studies and assessments. LCAT provides its users with the ability to conduct local climate variability and change analyses using scientific techniques and the most trusted data, identified through consultation and approval with NOAA subject matter experts. LCAT data include climate-relevant surface observations for individual stations, regional divisions, and gridded reanalysis output. LCAT methods include trend-fitting techniques to assess the local rate of climate change, frequency and conditional probability analyses, and correlation studies to identify existing relationships between local climate and modes of climate variability, such as El Niño Southern Oscillation (ENSO). The tool produces customized output for individual users through a web-interface. These include graphical and tabular numeric data that can be either saved in the LCAT online environment or exported in standard formats for further analysis. For each query, LCAT provides an explanation for all graphical output to help users interpret the scientific results. LCAT also offers training modules explaining usability, data, scientific methods, and potential applications, with emphasis on the tool’s appropriate and inappropriate uses. Examples of LCAT applications include guidance for planning, resources management, and assessment purposes. LCAT has the potential for expansion to include a wide variety of datasets for broader application in environmental and socioeconomic decision support.

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Robert Drost
,
Jay Trobec
,
Christy Steffke
, and
Julie Libarkin

Abstract

Televised media is one of the most frequently accessed sources of weather information. The local weathercaster is the link between weather information and the public, and as such weathercaster characteristics, from vocal cadence to physical appearance, can impact viewer understanding. This study considers the role of weathercaster gesturing on viewer attention during weather forecasts. Two variations of a typical weather forecast were viewed by a total of 36 students during an eye tracking session. The first forecast variation contained physical gestures toward forecast text by the newscaster (Gesture condition) while the second variation contained minimal gesturing (No Gesture condition). Following each eye tracking session, students completed a retention survey related to the forecast. These data were used to identify areas of interest to which students attended during viewing and to ascertain how well the forecast was retained across the gesturing treatments. Study results suggest that the weathercaster’s gesturing during forecasts may have induced confusion among participants, but did not affect retention of the weather information investigated in the study. Gesturing diverted attention from other areas of interest within the forecast by encouraging participants to focus on the weathercaster’s hands. This study indicates that minor modifications to weathercaster behavior can produce significant changes in viewer behavior.

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Arthur T. DeGaetano
,
William Noon
, and
Keith L. Eggleston
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Fuqing Zhang
and
Yonghui Weng

Abstract

Performance in the prediction of hurricane intensity and associated hazards has been evaluated for a newly developed convection-permitting forecast system that uses ensemble data assimilation techniques to ingest high-resolution airborne radar observations from the inner core. This system performed well for three of the ten costliest Atlantic hurricanes: Ike (2008), Irene (2011), and Sandy (2012). Four to five days before these storms made landfall, the system produced good deterministic and probabilistic forecasts of not only track and intensity, but also of the spatial distributions of surface wind and rainfall. Averaged over all 102 applicable cases that have inner-core airborne Doppler radar observations during 2008–2012, the system reduced the day-2-to-day-4 intensity forecast errors by 25%–28% compared to the corresponding National Hurricane Center’s official forecasts (which have seen little or no decrease in intensity forecast errors over the past two decades). Empowered by sufficient computing resources, advances in both deterministic and probabilistic hurricane prediction will enable emergency management officials, the private sector, and the general public to make more informed decisions that minimize the losses of life and property.

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Catherine A. Smith
,
Gilbert P. Compo
, and
Don K. Hooper

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

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Sourabh S. Diwan
,
P. Prasanth
,
K. R. Sreenivas
,
S. M. Deshpande
, and
Roddam Narasimha

Cumulus clouds, which are among the largest sources of uncertainty in climate change science and tropical circulation, have to-date resisted the numerous attempts made during the last six decades to unravel their cloud-scale dynamics. One major reason has been the lack of a convincing fluid-dynamical model and the difficulty of making repeatable measurements in an inherently transient flow. This article summarizes recent work showing that cumulus-type f lows can be generated in the laboratory by releasing volumetric heat into a plume above a height analogous to cloud condensation level and in quantities dynamically similar to the release of latent heat in the natural cloud. Such a “transient diabatic plume” (TDP) seems to mimic cumulus clouds with adiabatic/pseudoadiabatic processes of latent heat release. With appropriate heating profile histories, the TDP simulates a variety of cumulus-cloud forms, from cumulus congestus to cumulus fractus, and permits tracking their evolution through a complete life cycle. Selected examples of such laboratory simulations are supported by preliminary results from direct numerical simulations based on the Navier-Stokes-Boussinesq equations. These simulations suggest that the baroclinic torque plays an important role in the dynamics of both large- and small-scale motions in cloud-type flows.

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Joao Teixeira
,
Duane Waliser
,
Robert Ferraro
,
Peter Gleckler
,
Tsengdar Lee
, and
Gerald Potter

The objective of the Observations for Model Intercomparison Projects (Obs4MIPs) is to provide observational data to the climate science community, which is analogous (in terms of variables, temporal and spatial frequency, and periods) to output from the 5th phase of the World Climate Research Programme's (WCRP) Coupled Model Intercomparison Project (CMIP5) climate model simulations. The essential aspect of the Obs4MIPs methodology is that it strictly follows the CMIP5 protocol document when selecting the observational datasets. Obs4MIPs also provides documentation that describes aspects of the observational data (e.g., data origin, instrument overview, uncertainty estimates) that are of particular relevance to scientists involved in climate model evaluation and analysis. In this paper, we focus on the activities related to the initial set of satellite observations, which are being carried out in close coordination with CMIP5 and directly engage NASA's observational (e.g., mission and instrument) science teams. Having launched Obs4MIPs with these datasets, a broader effort is also briefly discussed, striving to engage other agencies and experts who maintain datasets, including reanalysis, which can be directly used to evaluate climate models. Different strategies for using satellite observations to evaluate climate models are also briefly summarized.

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