Search Results

You are looking at 1 - 10 of 14 items for :

  • Author or Editor: Michael E. Baldwin x
  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All Modify Search
Benjamin R. J. Schwedler and Michael E. Baldwin

Abstract

While the use of binary distance measures has a substantial history in the field of image processing, these techniques have only recently been applied in the area of forecast verification. Designed to quantify the distance between two images, these measures can easily be extended for use with paired forecast and observation fields. The behavior of traditional forecast verification metrics based on the dichotomous contingency table continues to be an area of active study, but the sensitivity of image metrics has not yet been analyzed within the framework of forecast verification. Four binary distance measures are presented and the response of each to changes in event frequency, bias, and displacement error is documented. The Hausdorff distance and its derivatives, the modified and partial Hausdorff distances, are shown only to be sensitive to changes in base rate, bias, and displacement between the forecast and observation. In addition to its sensitivity to these three parameters, the Baddeley image metric is also sensitive to additional aspects of the forecast situation. It is shown that the Baddeley metric is dependent not only on the spatial relationship between a forecast and observation but also the location of the events within the domain. This behavior may have considerable impact on the results obtained when using this measure for forecast verification. For ease of comparison, a hypothetical forecast event is presented to quantitatively analyze the various sensitivities of these distance measures.

Full access
Michael E. Baldwin and John S. Kain

Abstract

The sensitivity of various accuracy measures to displacement error, bias, and event frequency is analyzed for a simple hypothetical forecasting situation. Each measure is found to be sensitive to displacement error and bias, but probability of detection and threat score do not change as a function of event frequency. On the other hand, equitable threat score, true skill statistic, and odds ratio skill score behaved differently with changing event frequency. A newly devised measure, here called the bias-adjusted threat score, does not change with varying event frequency and is relatively insensitive to bias. Numerous plots are presented to allow users of these accuracy measures to make quantitative estimates of sensitivities that are relevant to their particular application.

Full access
Michael E. Baldwin, John S. Kain, and Michael P. Kay

Abstract

The impact of parameterized convection on Eta Model forecast soundings is examined. The Betts–Miller–Janjić parameterization used in the National Centers for Environmental Prediction Eta Model introduces characteristic profiles of temperature and moisture in model soundings. These specified profiles can provide misleading representations of various vertical structures and can strongly affect model predictions of parameters that are used to forecast deep convection, such as convective available potential energy and convective inhibition. The specific procedures and tendencies of this parameterization are discussed, and guidelines for interpreting Eta Model soundings are presented.

Full access
Nicole P. Kurkowski, David J. Stensrud, and Michael E. Baldwin

Abstract

One of the challenges in land surface modeling involves specifying accurately the initial state of the land surface. Most efforts have focused upon using a multiyear climatology to specify the fractional coverage of vegetation. For example, the National Centers for Environmental Prediction (NCEP) Eta Model uses a 5-yr satellite climatology of monthly normalized difference vegetation index (NDVI) values to define the fractional vegetation coverage, or greenness, at 1/8° (approximately 14 km) resolution. These data are valid on the 15th of every month and are interpolated temporally for daily runs. Yet vegetation characteristics change from year to year and are influenced by short-lived events such as fires, crop harvesting, droughts, floods, and hailstorms that are missed using a climatological database. To explore the importance of the initial state vegetation characteristics on operational numerical weather forecasts, the response of the Eta Model to initializing fractional vegetation coverage directly from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) data is investigated. Numerical forecasts of the Eta Model, using both climatological and near-real-time values of fractional vegetation coverage, are compared with observations to examine the potential importance of variations in vegetation to forecasts of 2-m temperatures and dewpoint temperatures from 0 to 48 h for selected days during the 2001 growing season. Results show that use of the near-real-time vegetation fraction data improves the forecasts of both the 2-m temperature and dewpoint temperature for much of the growing season, highlighting the need for this type of information to be included in operational forecast models.

Full access
William A. Gallus Jr., Michael E. Baldwin, and Kimberly L. Elmore

Abstract

This note examines the connection between the probability of precipitation and forecasted amounts from the NCEP Eta (now known as the North American Mesoscale model) and Aviation (AVN; now known as the Global Forecast System) models run over a 2-yr period on a contiguous U.S. domain. Specifically, the quantitative precipitation forecast (QPF)–probability relationship found recently by Gallus and Segal in 10-km grid spacing model runs for 20 warm season mesoscale convective systems is tested over this much larger temporal and spatial dataset. A 1-yr period was used to investigate the QPF–probability relationship, and the predictive capability of this relationship was then tested on an independent 1-yr sample of data. The same relationship of a substantial increase in the likelihood of observed rainfall exceeding a specified threshold in areas where model runs forecasted higher rainfall amounts is found to hold over all seasons. Rainfall is less likely to occur in those areas where the models indicate none than it is elsewhere in the domain; it is more likely to occur in those regions where rainfall is predicted, especially where the predicted rainfall amounts are largest. The probability of rainfall forecasts based on this relationship are found to possess skill as measured by relative operating characteristic curves, reliability diagrams, and Brier skill scores. Skillful forecasts from the technique exist throughout the 48-h periods for which Eta and AVN output were available. The results suggest that this forecasting tool might assist forecasters throughout the year in a wide variety of weather events and not only in areas of difficult-to-forecast convective systems.

Full access
John S. Kain, Michael E. Baldwin, and Steven J. Weiss

Abstract

Parameterized updraft mass flux, available as a unique predictive field from the Kain–Fritsch (KF) convective parameterization, is presented as a potentially valuable predictor of convective intensity. The KF scheme is described in some detail, focusing on a version that is currently being run semioperationally in an experimental version of the Eta Model. It is shown that updraft mass flux computed by this scheme is a function of the specific algorithm that it utilizes and is very sensitive to the thermodynamic characteristics of input soundings. These same characteristics appear to be related to the severity of convection, suggesting that updraft mass flux predicted by the KF scheme has value for predicting severe weather. This argument is supported by anecdotal evidence and a case study.

Full access
John S. Kain, Stephen M. Goss, and Michael E. Baldwin

Abstract

The process of atmospheric cooling due to melting precipitation is examined to evaluate its contribution to determining precipitation type. The “melting effect” is typically of second-order importance compared to other processes that influence the lower-tropospheric air temperature and hence the type of precipitation that reaches the ground. In some cases, however, cooling due to melting snowflakes can emerge as the dominant agent of temperature change, occasionally surprising forecasters (and the public) by inducing an unexpected changeover from rain to heavy snow. One such case occurred on 3–4 February 1998 in east-central Tennessee and surrounding areas.

Commonly applied considerations for predicting precipitation type had convinced forecasters that significant snowfall was not likely with this event. However, real-time observations and a postevent analysis by forecasters at the Storm Prediction Center led to the hypothesis that the melting effect must have provided the cooling necessary to allow widespread heavy snowfall. To test this hypothesis, the Pennsylvania State University–NCAR Mesoscale Model was used to generate a mesoscale-resolution, four-dimensional dataset for this event. Diagnostic analysis of the model output confirmed that cooling due to melting snowflakes was of a sufficient magnitude to account for the disparity between observed and forecasted lower-tropospheric temperatures in this case.

A simple formula is derived to provide a “rule of thumb” for anticipating the potential impact of the melting effect. In addition, guidelines are provided for identifying meteorological patterns that favor a predominance of the melting effect.

Full access
Melissa S. Bukovsky, John S. Kain, and Michael E. Baldwin

Abstract

Bowing, propagating precipitation features that sometimes appear in NCEP's North American Mesoscale model (NAM; formerly called the Eta Model) forecasts are examined. These features are shown to be associated with an unusual convective heating profile generated by the Betts–Miller–Janjić convective parameterization in certain environments. A key component of this profile is a deep layer of cooling in the lower to middle troposphere. This strong cooling tendency induces circulations that favor expansion of parameterized convective activity into nearby grid columns, which can lead to growing, self-perpetuating mesoscale systems under certain conditions. The propagation characteristics of these systems are examined and three contributing mechanisms of propagation are identified. These include a mesoscale downdraft induced by the deep lower-to-middle tropospheric cooling, a convectively induced buoyancy bore, and a boundary layer cold pool that is indirectly produced by the convective scheme in this environment. Each of these mechanisms destabilizes the adjacent atmosphere and decreases convective inhibition in nearby grid columns, promoting new convective development, expansion, and propagation of the larger system. These systems appear to show a poor correspondence with observations of bow echoes on time and space scales that are relevant for regional weather prediction, but they may provide important clues about the propagation mechanisms of real convective systems.

Full access
Qingyun Zhao, Thomas L. Black, and Michael E. Baldwin

Abstract

An explicit cloud prediction scheme has been developed and incorporated into the Eta Model at the National Centers for Environmental Prediction (NCEP) to improve the cloud and precipitation forecasts. In this scheme, the cloud liquid water and cloud ice are explicitly predicted by adding only one prognostic equation of cloud mixing ratio to the model. Precipitation of rain and snow in this scheme is diagnostically calculated from the predicted cloud fields. The model-predicted clouds are also used in the model’s radiation calculations. Results from the parallel tests performed at NCEP show improvements in precipitation forecasts when prognostic cloud water is included. Compared with the diagnostic clouds, the model-predicted clouds are more accurate in both amount and position. Improvements in specific humidity forecasts have also been found, especially near the surface and above the freezing level.

Full access
Logan C. Dawson, Glen S. Romine, Robert J. Trapp, and Michael E. Baldwin

Abstract

The utility of radar-derived rotation track data for the verification of supercell thunderstorm forecasts was quantified through this study. The forecasts were generated using a convection-permitting model ensemble, and supercell occurrence was diagnosed via updraft helicity and low-level vertical vorticity. Forecasts of four severe convective weather events were considered. Probability fields were computed from the model data, and forecast skill was quantified using rotation track data, storm report data, and a neighborhood-based verification approach. The ability to adjust the rotation track threshold for verification purposes was shown to be an advantage of the rotation track data over the storms reports, because the reports are inherently binary observations whereas the rotation tracks are based on values of Doppler velocity shear. These results encourage further pursuit of incorporating observed rotation track data in the forecasting and verification of severe weather events.

Full access