Search Results

You are looking at 1 - 5 of 5 items for

  • Author or Editor: Peter A. Stamus x
  • Refine by Access: All Content x
Clear All Modify Search
Ralph F. Milliff and Peter A. Stamus

Abstract

This study reports on the operational utility of ocean surface vector wind (SVW) data from Quick Scatterometer (QuikSCAT) observations in the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Weather Forecast Offices (WFOs) covering the coastal United States, including island states and territories. Thirty-three U.S. coastal WFOs were surveyed, and 16 WFO site visits were conducted, from late summer 2005 to the 2005/06 winter season, in order to quantify the impact of QuikSCAT SVW data on forecasts and warnings, with a particular focus on operations affecting marine users. Details of the survey design and site visit strategies are described. Survey results are quantified and site visit impressions are discussed. Key findings include (i) QuikSCAT data supplement primary datasets and numerical weather prediction fields, in the manual production of local public (weather) and marine forecasts and warnings; (ii) operational utility of satellite SVW data would be enhanced by SVW retrievals of finer temporal resolution, closer to the coasts; and (iii) rain flags in the SVW data have little impact on utility for WFO operations.

Full access
John A. McGinley, Steven C. Albers, and Peter A. Stamus

Abstract

Advances in remote sensing from earth- and spaceborne systems, expanded in situ observation networks, and increased low-cost computer capability will allow an unprecedented view of mesoscale weather systems from the local weather office. However, the volume of data from these new instruments, the nonconventional quantities measured, and the need for a frequent operational cycle require development of systems to translate this information into products aimed specifically at aiding the forecaster in 0- to 6-h prediction. In northeast Colorado an observing network now exists that is similar to those that a local weather office may see within 5–7 years. With GOES and TIROS satellites, Doppler radar, wind profilers, and surface mesonet stations, a unique opportunity exists to explore the use of such data in nowcasting weather phenomena. The scheme, called LAPS (the Local Analysis and Prediction System), objectively analyzes data on a high-resolution, three-dimensional grid. The analysed fields are used to generate mesoscale forecast products aimed at specific local forecast problems. An experiment conducted in the summer of 1989 sought to test the use of a preconvective index on the difficult problem of convective rain forecasting. The index was configured from surface-based lifted index and kinematically diagnosed vertical motion. The index involved a number of LAPS-derived meteorological fields and the results of the test measured in some sense the quality of those fields. Using radar reflectivity to verify the occurrence or nonoccurrence of convective precipitation, forecasts were issued for three time periods on each of 62 exercise days. The results indicated that the index was significantly better than persistence over a range of echo intensities. Skill scores computed from contingency tables indicated that the index had substantial skill in forecasting light convective precipitation with 1- to 3-h lead time. Less skill was shown for heavier convective showers. The skill of the index did not depend strongly on the density of surface data, but was negatively influenced by mountainous terrain.

Full access
Peter A. Stamus, Frederick H. Carr, and David P. Baumhefner

Abstract

A scale-separation technique based on two-dimensional Fourier decomposition is applied to the comparison and verification of analyses and forecasts produced by regional numerical weather prediction systems. A major emphasis of this study is the verification of secondary or derived parameters in addition to the evaluation of primary model variables. Two prediction models are used to illustrate the technique for a variety of forecast fields separated into three separate wavenumber bands. Three different sets of analyses, one from each model system and an independent set, are used for both analysis intercomparison and model verification. The comparison of the analyses is essential to establishing the level of uncertainty for each variable as a function of scale. The synoptic-scale database used to produce the analyses for this study does not allow the verification of scales 800 km or less, no matter how fine the resolution of the model.

Examining the spectra of difference fields with time allows one to study the evolution of model error (or differences between two models) as a function of wavenumber. In some instances where traditional statistical measures of skill indicated good agreement between two forecasts, spectral scale selection of the difference fields shows that the spatial distribution of the errors was quite different, pointing to different error-growth characteristics of the models. The technique allows one to partially separate phase and amplitude errors and, hence, barotropic-versus baroclinic-type error structure. It was found, as expected, that forecast skill decreases more rapidly with time for smaller scales, but this is not true for all parameters examined. The presence of lateral boundary conditions strongly influences the evaluation of skill in a regional model for the primary variables, but not as much for some secondary variables. Verification of secondary variables nearly always indicates significant errors in the forecast before serious problems in the primary variables are detected.

Full access
John S. Snook, Peter A. Stamus, James Edwards, Zaphiris Christidis, and John A. McGinley

Abstract

The National Weather Service (NWS) developed the Olympic Weather Support System (OWSS) to provide specialized operational weather support for the 1996 Centennial Olympic Games in Atlanta. Operational implementation of the National Oceanic and Atmospheric Administration Forecast Systems Laboratory’s Local Analysis and Prediction System (LAPS) was a key element of the OWSS. LAPS is a complete, three-dimensional data assimilation system that produced subhourly atmospheric analyses on an 8-km grid covering all the Olympic venues. The LAPS analyses also provided initial conditions to the Regional Atmospheric Modeling System (RAMS) mesoscale forecast model. RAMS forecasts were generated at least every 3 h using 8- or 2-km grids. For the first time, a comprehensive operational analysis and forecast system operated in a local NWS forecast office to support meso-β-scale forecasts and warnings. Numerous benefits of LAPS–RAMS to the local forecast office were demonstrated. The OWSS, with LAPS–RAMS included, provided a precursory view of the enhanced operational mesoscale forecast capabilities that can be available to the NWS and other forecast offices in the near future.

Full access
Stanley G. Benjamin, Keith A. Brewster, Renate Brümmer, Brian F. Jewett, Thomas W. Schlatter, Tracy L. Smith, and Peter A. Stamus

Abstract

A 3-h intermittent data assimilation system (Mesoscale Analysis and Prediction System—MAPS) configured in isentropic coordinates was developed and implemented in real-time operation. The major components of the system are data ingest, objective quality control of the observation, objective analysis, and a primitive equation forecast model, all using isentropic coordinates to take advantage of the improved resolution near frontal zones and greater spatial coherence of data that this coordinate system provides. Each 3-h forecast becomes the background for the subsequent analysis; in this manner, a four-dimensional set of observations can be assimilated.

The primary asynoptic data source used in current real-time operation of this system is air-craft data, most of it automated. Data from wind profilers, surface observations, and radiosondes are also included in MAPS.

Statistics were collected over the last half of 1989 and into 1990 to study the performance of MAPS and compare it with that of the Regional Analysis and Forecast System (RAFS), which is run operationally at the National Meteorological Center (NMC). Analyses generally fit mandatory-level observations more closely in MAPS than in RAFS. Three-hour forecasts from MAPS, incorporating asynoptic aircraft reports, improve on 12-h MAPS forecasts valid at the same time for all levels and variables, and also improve on 12-h RAFS forecasts of upper-level winds. This result is due to the quality and volume of the aircraft data as well as the effectiveness of the isentropic data assimilation used. Forecast fields at other levels are slightly poorer than those from RAFS. This may be largely due to the lack of diabatic and boundary-layer physics for the MAPS model used in this test period.

Full access