Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: David P. Ruth x
  • Refine by Access: All Content x
Clear All Modify Search
Harry R. Glahn and David P. Ruth

The National Weather Service (NWS) has entered a new era in the production and dissemination of weather information and service to the nation. No longer are textual forecasts the primary medium of dissemination, but a digital database of official forecasts will be available to partners and customers by the end of 2003.

The Interactive Forecast Preparation System (IFPS) is the means through which the forecasts are produced at Weather Forecast Offices (WFOs). Rather than typing forecasts, forecasters graphically prepare grids of sensible weather elements, such as surface temperature and probability of precipitation. These grids from the WFOs are then mosaicked on a central server into a National Digital Forecast Database (NDFD), and this NDFD is provided to all who desire it.

In the new forecasting paradigm, the National Centers for Environmental Prediction (NCEP) are playing a much larger role. In particular, the Hydrometeorological Prediction Center (HPC) collaborates with WFOs, and together HPC and the WFOs agree on the atmosphere's most likely evolution for the next several days before the grids are prepared. The NDFD will open up unparalleled possibilities for commercial entities to distribute value-added products that are based on official NWS forecasts.

Full access
David P. Ruth, Bob Glahn, Valery Dagostaro, and Kathryn Gilbert


Model output statistics (MOS) guidance forecasts have been produced for over three decades. Until recently, MOS guidance was prepared for observing stations and formatted in text bulletins while official National Weather Service (NWS) forecasts for stations and zones were prepared by forecasters typing text. The flagship product of today’s NWS is the National Digital Forecast Database (NDFD). In support of NDFD, MOS is now also produced on grids.

This paper compares MOS and gridded MOS (GMOS) to the forecaster-produced NDFD at approximately 1200 station locations in the conterminous United States. Results indicate that GMOS should provide good guidance for preparing the NDFD. In those areas of the country where station observations well represent the grid, GMOS features accuracy comparable to that of NDFD. In areas of complex terrain not well represented by station observations, GMOS appears similar to NDFD in its depiction. A new score is introduced to measure convergence from a long-range forecast to the final short-range forecast. This shows good GMOS forecast continuity when compared to station MOS and NDFD.

Full access
Bob Glahn, Kathryn Gilbert, Rebecca Cosgrove, David P. Ruth, and Kari Sheets


Model output statistics (MOS) guidance forecasts have been produced at stations and provided to National Weather Service forecasters and private entities for over three decades. As the numerical weather prediction models became more accurate, MOS followed that trend. Up until a few years ago, the MOS produced at observation locations met the basic need for guidance. With the advent of the Interactive Forecast Preparation System and the National Digital Forecast Database, gridded MOS forecasts became needed as guidance for forecasters. One method of providing such grids is to objectively analyze the MOS forecasts for points.

A basic successive correction method has been extended to analyze MOS forecasts and surface weather variables. This method is being applied to MOS forecasts to provide guidance for producing grids of sensible weather elements such as temperature, clouds, and snow amount. Guidance forecasts have been implemented for the conterminous United States for most weather elements contained in routine weather forecasts. This paper describes the method applied to daytime maximum temperature over the conterminous United States and gives example results.

Full access
David C. Fritts, Ronald B. Smith, Michael J. Taylor, James D. Doyle, Stephen D. Eckermann, Andreas Dörnbrack, Markus Rapp, Bifford P. Williams, P.-Dominique Pautet, Katrina Bossert, Neal R. Criddle, Carolyn A. Reynolds, P. Alex Reinecke, Michael Uddstrom, Michael J. Revell, Richard Turner, Bernd Kaifler, Johannes S. Wagner, Tyler Mixa, Christopher G. Kruse, Alison D. Nugent, Campbell D. Watson, Sonja Gisinger, Steven M. Smith, Ruth S. Lieberman, Brian Laughman, James J. Moore, William O. Brown, Julie A. Haggerty, Alison Rockwell, Gregory J. Stossmeister, Steven F. Williams, Gonzalo Hernandez, Damian J. Murphy, Andrew R. Klekociuk, Iain M. Reid, and Jun Ma


The Deep Propagating Gravity Wave Experiment (DEEPWAVE) was designed to quantify gravity wave (GW) dynamics and effects from orographic and other sources to regions of dissipation at high altitudes. The core DEEPWAVE field phase took place from May through July 2014 using a comprehensive suite of airborne and ground-based instruments providing measurements from Earth’s surface to ∼100 km. Austral winter was chosen to observe deep GW propagation to high altitudes. DEEPWAVE was based on South Island, New Zealand, to provide access to the New Zealand and Tasmanian “hotspots” of GW activity and additional GW sources over the Southern Ocean and Tasman Sea. To observe GWs up to ∼100 km, DEEPWAVE utilized three new instruments built specifically for the National Science Foundation (NSF)/National Center for Atmospheric Research (NCAR) Gulfstream V (GV): a Rayleigh lidar, a sodium resonance lidar, and an advanced mesosphere temperature mapper. These measurements were supplemented by in situ probes, dropsondes, and a microwave temperature profiler on the GV and by in situ probes and a Doppler lidar aboard the German DLR Falcon. Extensive ground-based instrumentation and radiosondes were deployed on South Island, Tasmania, and Southern Ocean islands. Deep orographic GWs were a primary target but multiple flights also observed deep GWs arising from deep convection, jet streams, and frontal systems. Highlights include the following: 1) strong orographic GW forcing accompanying strong cross-mountain flows, 2) strong high-altitude responses even when orographic forcing was weak, 3) large-scale GWs at high altitudes arising from jet stream sources, and 4) significant flight-level energy fluxes and often very large momentum fluxes at high altitudes.

Full access
Jennifer A. MacKinnon, Zhongxiang Zhao, Caitlin B. Whalen, Amy F. Waterhouse, David S. Trossman, Oliver M. Sun, Louis C. St. Laurent, Harper L. Simmons, Kurt Polzin, Robert Pinkel, Andrew Pickering, Nancy J. Norton, Jonathan D. Nash, Ruth Musgrave, Lynne M. Merchant, Angelique V. Melet, Benjamin Mater, Sonya Legg, William G. Large, Eric Kunze, Jody M. Klymak, Markus Jochum, Steven R. Jayne, Robert W. Hallberg, Stephen M. Griffies, Steve Diggs, Gokhan Danabasoglu, Eric P. Chassignet, Maarten C. Buijsman, Frank O. Bryan, Bruce P. Briegleb, Andrew Barna, Brian K. Arbic, Joseph K. Ansong, and Matthew H. Alford


Diapycnal mixing plays a primary role in the thermodynamic balance of the ocean and, consequently, in oceanic heat and carbon uptake and storage. Though observed mixing rates are on average consistent with values required by inverse models, recent attention has focused on the dramatic spatial variability, spanning several orders of magnitude, of mixing rates in both the upper and deep ocean. Away from ocean boundaries, the spatiotemporal patterns of mixing are largely driven by the geography of generation, propagation, and dissipation of internal waves, which supply much of the power for turbulent mixing. Over the last 5 years and under the auspices of U.S. Climate Variability and Predictability Program (CLIVAR), a National Science Foundation (NSF)- and National Oceanic and Atmospheric Administration (NOAA)-supported Climate Process Team has been engaged in developing, implementing, and testing dynamics-based parameterizations for internal wave–driven turbulent mixing in global ocean models. The work has primarily focused on turbulence 1) near sites of internal tide generation, 2) in the upper ocean related to wind-generated near inertial motions, 3) due to internal lee waves generated by low-frequency mesoscale flows over topography, and 4) at ocean margins. Here, we review recent progress, describe the tools developed, and discuss future directions.

Open access