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Julie Demargne
,
Mary Mullusky
,
Kevin Werner
,
Thomas Adams
,
Scott Lindsey
,
Noreen Schwein
,
William Marosi
, and
Edwin Welles

Forecast verification in operational hydrology has been very limited to date, mainly due to the complexity of verifying both forcing input forecasts and hydrologic forecasts on multiple space-time scales. However, forecast verification needs to be the driver in both hydrologic research and operations to help advance the understanding of predictability and help the diverse users better utilize the river forecasts. Therefore, in NOAA's National Weather Service, the Hydrologic Services Program is developing a comprehensive river forecast verification service to routinely and systematically verify all hydrometeorological and hydrologic forecasts. This verification service will include capabilities for archiving forecast and observed data, evaluating logistical properties of the forecast services, computing a variety of verification metrics to evaluate the different aspects of forecast quality, displaying and disseminating verification data and metrics, and analyzing the sources of forecast skill and uncertainty through the use of multiple forecast and hindcast scenarios. This paper describes ongoing and planned verification activities for enhancing the collaboration between the meteorological and hydrologic research and operational communities to quantify forecast improvements based on rigorous forecast verification.

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Lewis Grasso
,
Daniel T. Lindsey
,
Kyo-Sun Sunny Lim
,
Adam Clark
,
Dan Bikos
, and
Scott R. Dembek

Abstract

Synthetic satellite imagery can be employed to evaluate simulated cloud fields. Past studies have revealed that the Weather Research and Forecasting (WRF) single-moment 6-class (WSM6) microphysics scheme in the Advanced Research WRF (WRF-ARW) produces less upper-level ice clouds within synthetic images compared to observations. Synthetic Geostationary Operational Environmental Satellite-13 (GOES-13) imagery at 10.7 μm of simulated cloud fields from the 4-km National Severe Storms Laboratory (NSSL) WRF-ARW is compared to observed GOES-13 imagery. Histograms suggest that too few points contain upper-level simulated ice clouds. In particular, side-by-side examples are shown of synthetic and observed anvils. Such images illustrate the lack of anvil cloud associated with convection produced by the 4-km NSSL WRF-ARW. A vertical profile of simulated hydrometeors suggests that too much cloud water mass may be converted into graupel mass, effectively reducing the main source of ice mass in a simulated anvil. Further, excessive accretion of ice by snow removes ice from an anvil by precipitation settling. Idealized sensitivity tests reveal that a 50% reduction of the accretion rate of ice by snow results in a significant increase in anvil ice of a simulated storm. Such results provide guidance as to which conversions could be reformulated, in a more physical manner, to increase simulated ice mass in the upper troposphere.

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Peter A. Bieniek
,
Uma S. Bhatt
,
Larry A. Rundquist
,
Scott D. Lindsey
,
Xiangdong Zhang
, and
Richard L. Thoman

Abstract

Frozen rivers in the Arctic serve as critical highways because of the lack of roads; therefore, it is important to understand the key mechanisms that control the timing of river ice breakup. The relationships between springtime Interior Alaska river ice breakup date and the large-scale climate are investigated for the Yukon, Tanana, Kuskokwim, and Chena Rivers for the 1949–2008 period. The most important climate factor that determines breakup is April–May surface air temperatures (SATs). Breakup tends to occur earlier when Alaska April–May SATs and river flow are above normal. Spring SATs are influenced by storms approaching the state from the Gulf of Alaska, which are part of large-scale climate anomalies that compare favorably with ENSO. During the warm phase of ENSO fewer storms travel into the Gulf of Alaska during the spring, resulting in a decrease of cloud cover over Alaska, which increases surface solar insolation. This results in warmer-than-average springtime SATs and an earlier breakup date. The opposite holds true for the cold phase of ENSO. Increased wintertime precipitation over Alaska has a secondary impact on earlier breakup by increasing spring river discharge. Improved springtime Alaska temperature predictions would enhance the ability to forecast the timing of river ice breakup.

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Scott Longmore
,
Steven Miller
,
Dan Bikos
,
Daniel Lindsey
,
Edward Szoke
,
Debra Molenar
,
Donald Hillger
,
Renate Brummer
, and
John Knaff

Abstract

The increasing use of mobile phones (MPs) equipped with digital cameras and the ability to post images and information to the Internet in real time has significantly improved the ability to report events almost instantaneously. From the perspective of weather forecasters responsible for issuing severe weather warnings, the old adage holds that a picture is indeed worth a thousand words; a single digital image conveys significantly more information than a simple web-submitted text or phone-relayed report. Timely, quality-controlled, and value-added photography allows the forecaster to ascertain the validity and quality of storm reports. The posting of geolocated, time-stamped storm report photographs utilizing an MP application to U.S. National Weather Service (NWS) Weather Forecast Office (WFO) social media pages has generated recent positive feedback from forecasters. This study establishes the conceptual framework, architectural design, and pathway toward implementation of a formalized photo report (PR) system composed of 1) an MP application, 2) a processing and distribution system, and 3) the Advanced Weather Interactive Processing System II (AWIPS II) data plug-in software. The requirements and anticipated appearance of such a PR system are presented, along with considerations for possible additional features and applications that extend the utility of the system beyond the realm of severe weather applications.

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Timothy J. Schmit
,
Steven J. Goodman
,
Mathew M. Gunshor
,
Justin Sieglaff
,
Andrew K. Heidinger
,
A. Scott Bachmeier
,
Scott S. Lindstrom
,
Amanda Terborg
,
Joleen Feltz
,
Kaba Bah
,
Scott Rudlosky
,
Daniel T. Lindsey
,
Robert M. Rabin
, and
Christopher C. Schmidt

Abstract

The Geostationary Operational Environmental Satellite-14 (GOES-14) imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of the summers of 2012 and 2013. This scan mode, known as the super rapid scan operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling of the mesoscale region scanning of the Advanced Baseline Imager (ABI) on the next-generation GOES-R series. This paper both introduces these unique datasets and highlights future satellite imager capabilities. Many phenomena were observed from GOES-14, including fog, clouds, severe storms, fires and smoke (including the California Rim Fire), and several tropical cyclones. In 2012 over 6 days of SRSOR data of Hurricane Sandy were acquired. In 2013, the first two days of SRSOR in June observed the propagation and evolution of a mid-Atlantic derecho. The data from August 2013 were unique in that the GOES imager operated in nearly continuous 1-min mode; prior to this time, the 1-min data were interrupted every 3 h for full disk scans. Used in a number of NOAA test beds and operational centers, including NOAA’s Storm Prediction Center (SPC), the Aviation Weather Center (AWC), the Ocean Prediction Center (OPC), and the National Hurricane Center (NHC), these experimental data prepare users for the next-generation imager, which will be able to routinely acquire mesoscale (1,000 km × 1,000 km) images every 30 s (or two separate locations every minute). Several animations are included, showcasing the rapid change of the many phenomena observed during SRSOR from the GOES-14 imager.

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Donald Hillger
,
Thomas Kopp
,
Thomas Lee
,
Daniel Lindsey
,
Curtis Seaman
,
Steven Miller
,
Jeremy Solbrig
,
Stanley Kidder
,
Scott Bachmeier
,
Tommy Jasmin
, and
Tom Rink

The Suomi National Polar-Orbiting Partnership (NPP) satellite was launched on 28 October 2011, heralding the next generation of operational U.S. polar-orbiting satellites. It carries the Visible– Infrared Imaging Radiometer Suite (VIIRS), a 22-band visible/infrared sensor that combines many of the best aspects of the NOAA Advanced Very High Resolution Radiometer (AVHRR), the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS), and the National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. VIIRS has nearly all the capabilities of MODIS, but offers a wider swath width (3,000 versus 2,330 km) and much higher spatial resolution at swath edge. VIIRS also has a day/night band (DNB) that is sensitive to very low levels of visible light at night such as those produced by moonlight reflecting off low clouds, fog, dust, ash plumes, and snow cover. In addition, VIIRS detects light emissions from cities, ships, oil flares, and lightning flashes.

NPP crosses the equator at about 0130 and 1330 local time, with VIIRS covering the entire Earth twice daily. Future members of the Joint Polar Satellite System (JPSS) constellation will also carry VIIRS. This paper presents dramatic early examples of multispectral VIIRS imagery capabilities and demonstrates basic applications of that imagery for a wide range of operational users, such as for fire detection, monitoring ice break up in rivers, and visualizing dust plumes over bright surfaces. VIIRS imagery, both single and multiband, as well as the day/night band, is shown to exceed both requirements and expectations.

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Dan Bikos
,
Daniel T. Lindsey
,
Jason Otkin
,
Justin Sieglaff
,
Louie Grasso
,
Chris Siewert
,
James Correia Jr.
,
Michael Coniglio
,
Robert Rabin
,
John S. Kain
, and
Scott Dembek

Abstract

Output from a real-time high-resolution numerical model is used to generate synthetic infrared satellite imagery. It is shown that this imagery helps to characterize model-simulated large-scale precursors to the formation of deep-convective storms as well as the subsequent development of storm systems. A strategy for using this imagery in the forecasting of severe convective weather is presented. This strategy involves comparing model-simulated precursors to their observed counterparts to help anticipate model errors in the timing and location of storm formation, while using the simulated storm evolution as guidance.

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Taneil Uttal
,
Judith A. Curry
,
Miles G. McPhee
,
Donald K. Perovich
,
Richard E. Moritz
,
James A. Maslanik
,
Peter S. Guest
,
Harry L. Stern
,
James A. Moore
,
Rene Turenne
,
Andreas Heiberg
,
Mark. C. Serreze
,
Donald P. Wylie
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Ola G. Persson
,
Clayton A. Paulson
,
Christopher Halle
,
James H. Morison
,
Patricia A. Wheeler
,
Alexander Makshtas
,
Harold Welch
,
Matthew D. Shupe
,
Janet M. Intrieri
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Knut Stamnes
,
Ronald W. Lindsey
,
Robert Pinkel
,
W. Scott Pegau
,
Timothy P. Stanton
, and
Thomas C. Grenfeld

A summary is presented of the Surface Heat Budget of the Arctic Ocean (SHEBA) project, with a focus on the field experiment that was conducted from October 1997 to October 1998. The primary objective of the field work was to collect ocean, ice, and atmospheric datasets over a full annual cycle that could be used to understand the processes controlling surface heat exchanges—in particular, the ice–albedo feedback and cloud–radiation feedback. This information is being used to improve formulations of arctic ice–ocean–atmosphere processes in climate models and thereby improve simulations of present and future arctic climate. The experiment was deployed from an ice breaker that was frozen into the ice pack and allowed to drift for the duration of the experiment. This research platform allowed the use of an extensive suite of instruments that directly measured ocean, atmosphere, and ice properties from both the ship and the ice pack in the immediate vicinity of the ship. This summary describes the project goals, experimental design, instrumentation, and the resulting datasets. Examples of various data products available from the SHEBA project are presented.

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Sid A. Boukabara
,
Tong Zhu
,
Hendrik L. Tolman
,
Steve Lord
,
Steven Goodman
,
Robert Atlas
,
Mitch Goldberg
,
Thomas Auligne
,
Bradley Pierce
,
Lidia Cucurull
,
Milija Zupanski
,
Man Zhang
,
Isaac Moradi
,
Jason Otkin
,
David Santek
,
Brett Hoover
,
Zhaoxia Pu
,
Xiwu Zhan
,
Christopher Hain
,
Eugenia Kalnay
,
Daisuke Hotta
,
Scott Nolin
,
Eric Bayler
,
Avichal Mehra
,
Sean P. F. Casey
,
Daniel Lindsey
,
Louie Grasso
,
V. Krishna Kumar
,
Alfred Powell
,
Jianjun Xu
,
Thomas Greenwald
,
Joe Zajic
,
Jun Li
,
Jinliong Li
,
Bin Li
,
Jicheng Liu
,
Li Fang
,
Pei Wang
, and
Tse-Chun Chen

Abstract

In 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies (S4). This O2R environment is critical for successful research-to-operations (R2O) transitions because it allows rigorous tracking, implementation, and merging of any changes necessary (to operational software codes, scripts, libraries, etc.) to achieve the scientific enhancement. So far, the S4 O2R environment, with close to 4,700 computing cores (60 TFLOPs) and 1,700-TB disk storage capacity, has been a great success and consequently was recently expanded to significantly increase its computing capacity. The objective of this article is to highlight some of the major achievements and benefits of this O2R approach and some lessons learned, with the ultimate goal of inspiring other O2R/R2O initiatives in other areas and for other applications.

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