Search Results

You are looking at 1 - 10 of 19 items for :

  • Author or Editor: A. Jones x
  • Weather and Forecasting x
  • All content x
Clear All Modify Search
Thomas A. Jones and Daniel J. Cecil

Abstract

Three hurricanes, Claudette (2003), Isabel (2003), and Dora (1999), were selected to examine the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI) forecast accuracy for three particular storm types. This research was conducted using model analyses and tropical cyclone best-track data, with forecasts generated from a dependent sample. The model analyses and best-track data are assumed to be a “perfect” representation of the actual event (e.g., perfect prog assumption). Analysis of intensity change forecasts indicated that SHIPS-MI performed best, compared to operational SHIPS output, for tropical cyclones that were intensifying from tropical storm to hurricane intensity. Passive microwave imagery, which is sensitive to the intensity and coverage of precipitation, improved intensity forecasts during these periods with a positive intensity change contribution resulting from above normal inner-core precipitation. Forecast improvement was greatest for 12–36-h forecasts, where the microwave contribution to SHIPS-MI was greatest. Once a storm reached an intensity close to its maximum potential intensity, as in the case of Isabel and Dora, both SHIPS and SHIPS-MI incorrectly forecast substantial weakening despite the positive contribution from microwave data. At least in Dora’s case, SHIPS-MI forecasts were slightly stronger than those of SHIPS. Other important contributions to SHIPS-MI forecasts were examined to determine their importance relative to the microwave inputs. Inputs related to sea surface temperature (SST) and persistence–climatology proved to be very important to intensity change forecasts, as expected. These predictors were the primary factor leading to the persistent weakening forecasts made by both models for Isabel and Dora. For Atlantic storms (Claudette and Isabel), the contribution from shear also proved important at characterizing the conduciveness of the environment toward intensification. However, the shear contribution was often small as a result of multiple offsetting shear-related predictors. Finally, it was observed that atmospheric parameters not included in SHIPS, such as eddy momentum flux, could substantially affect the intensity, leading to large forecast errors. This was especially true for the Claudette intensity change forecasts throughout its life cycle.

Full access
Thomas A. Jones and David J. Stensrud

Abstract

One satellite data product that has received great interest in the numerical weather prediction community is the temperature and mixing ratio profiles derived from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite. This research assesses the impact of assimilating AIRS profiles on high-resolution ensemble forecasts of southern plains severe weather events occurring on 26 May 2009 and 10 May 2010 by comparing two ensemble forecasts. In one ensemble, the 1830 and 2000 UTC level 2 AIRS temperature and dewpoint profiles are assimilated with all other routine observations into a 36-member, 15-km Weather and Research Forecast Model (WRF) ensemble using a Kalman filter approach. The other ensemble is identical, except that only routine observations are assimilated. In addition, 3-km one-way nested-grid ensemble forecasts are produced during the periods of convection. Results indicate that over the contiguous United States, the AIRS profiles do not measurably improve the ensemble mean forecasts of midtropospheric temperature and dewpoint. However, the ensemble mean dewpoint profiles in the region of severe convective development are improved by the AIRS assimilation. Comparisons of the forecast ensemble radar reflectivity probabilities between the 1- and 4-h forecast times with nearby Weather Surveillance Radar-1988 Doppler (WSR-88D) observations show that AIRS-enhanced ensembles consistently generate more skillful forecasts of the convective features at these times.

Full access
Thomas A. Jones, Daniel Cecil, and Mark DeMaria

Abstract

The formulation and testing of an enhanced Statistical Hurricane Intensity Prediction Scheme (SHIPS) using new predictors derived from passive microwave imagery is presented. Passive microwave imagery is acquired for tropical cyclones in the Atlantic and eastern North Pacific basins between 1995 and 2003. Predictors relating to the inner-core (within 100 km of center) precipitation and convective characteristics of tropical cyclones are derived. These predictors are combined with the climatological and environmental predictors used by SHIPS in a simple linear regression model with change in tropical cyclone intensity as the predictand. Separate linear regression models are produced for forecast intervals of 12, 24, 36, 48, 60, and 72 h from the time of a microwave sensor overpass. Analysis of the resulting models indicates that microwave predictors, which provide an intensification signal to the model when above-average precipitation and convective signatures are present, have comparable importance to vertical wind shear and SST-related predictors. The addition of the microwave predictors produces a 2%–8% improvement in performance for the Atlantic and eastern North Pacific tropical cyclone intensity forecasts out to 72 h when compared with an environmental-only model trained from the same sample. Improvement is also observed when compared against the current version of SHIPS. The improvement in both basins is greatest for substantially intensifying or weakening tropical cyclones. Improvement statistics are based on calculating the forecast error for each tropical cyclone while it is held out of the training sample to approximate the use of independent data.

Full access
Christopher J. Nowotarski and Erin A. Jones

Abstract

Self-organizing maps (SOMs) have been shown to be a useful tool in classifying meteorological data. This paper builds on earlier work employing SOMs to classify model analysis proximity soundings from the near-storm environments of tornadic and nontornadic supercell thunderstorms. A series of multivariate SOMs is produced wherein the input variables, height, dimensions, and number of SOM nodes are varied. SOMs including information regarding the near-storm wind profile are more effective in discriminating between tornadic and nontornadic storms than those limited to thermodynamic information. For the best-performing SOMs, probabilistic forecasts derived from matching near-storm environments to a SOM node may provide modest improvements in forecast skill relative to existing methods for probabilistic forecasts.

Full access
C. A. Doswell III, R. Davies-Jones, and D. L. Keller

Abstract

No abstract available

Full access
Charles A. Doswell III, Robert Davies-Jones, and David L. Keller

Abstract

The so-called True Skill Statistic (TSS) and the Heidke Skill Score (S), as used in the context of the contingency, table approach to forecast verification, are compared. It is shown that the TSS approaches the Probability of Detection (POD) whenever the forecasting is dominated by correct forecasts of non-occurrence, i.e., forecasting rare events like severe local storms. This means that the TSS is vulnerable to “hedging” in rare event forecasting. The S-statistic is shown to be superior to the TSS in this situation, accounting for correct forecasts of null events in a controlled fashion. It turns out that the TSS and S values are related in a subtle way, becoming identical when the expected values (due to chance in a k × k contingency table) remain unchanged when comparing the actual forecast table to that of a hypothetical perfect set of forecasts. Examples of the behavior of the TSS and S values in different situations are provided which support the recommendation that S be used in preference to TSS for rare event forecasting. A geometrical interpretation is also given for certain aspects of the 2 × 2 contingency table and this is generalized to the k × l case. Using this geometrical interpretation, it is shown to be possible to apply dichotomous verification techniques in polychotomous situations, thus allowing a direct comparison between dichotomous and polychotomous forecasting.

Full access
Matthew S. Jones, Brian A. Colle, and Jeffrey S. Tongue

Abstract

A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

Full access
Thomas A. Jones, Kevin M. McGrath, and John T. Snow

Abstract

Nearly 100 000 vortex detections produced by the Mesocyclone Detection Algorithm (MDA) are analyzed to gain insight into the effectiveness of the detection algorithm in identifying various types of tornado-producing events. Radar and algorithm limitations prevent raw vortex detections from being very useful without further discrimination. Filtering techniques are developed to remove spurious vortex detections and discriminate between vortices that are and are not related to mesocyclones.

To investigate whether various vortex detections (and their attributes) are associated with severe weather phenomena, they are compared with available tornado reports to determine if detections with certain types of attributes can be associated with tornadic events. Tornado reports are used since the ground truth tornado set is more reliable than other databases of severe weather phenomena. Basic skill scores and more advanced principal component methods are used to quantify the correlation between vortex detection attributes and tornadoes.

The results of this analysis reveal that only a very small percentage (<5%) of vortex detections, using the most basic definition, are associated with the occurrence of a tornado. Percentages increase to approximately 10% as the criteria for defining a vortex detection as a mesocyclone detection become more strict; however, many tornadic events are only associated with weaker detections and are “missed” when the detection threshold is increased. Several velocity-derived detection attributes are shown to have weak to moderate predictive ability when determining whether a detection is (or is not) tornadic.

Full access
Thomas A. Jones, Daniel J. Cecil, and Jason Dunion

Abstract

The evolution of Hurricane Erin (2001) is presented from the perspective of its environmental and inner-core conditions, particularly as they are characterized in the Statistical Hurricane Intensity Prediction Scheme with Microwave Imagery (SHIPS-MI). Erin can be described as having two very distinct periods. The first, which occurred between 1 and 6 September 2001, was characterized by a struggling tropical storm failing to intensify as the result of unfavorable environmental and inner-core conditions. The surrounding environment during this period was dominated by moderate shear and mid- to upper-level dry air, both caused in some part by the presence of a Saharan air layer (SAL). Further intensification was inhibited by the lack of sustained deep convection and latent heating near the low-level center. The authors attribute this in part to negative effects from the SAL. The thermodynamic conditions associated with the SAL were not well sampled by the SHIPS parameters, resulting in substantial overforecasting by both SHIPS and SHIPS-MI. Instead, the hostile conditions surrounding Erin caused its dissipation on 6 September. The second period began on 7 September when Erin re-formed north of the original center. Erin began to pull away from the SAL and moved over 29°C sea surface temperatures, beginning a rapid intensification phase and reaching 105 kt by 1800 UTC 9 September. SHIPS-MI forecasts called for substantial intensification as in the previous period, but this time the model underestimated the rate of intensification. The addition of inner-core characteristics from passive microwave data improved the skill somewhat compared to SHIPS, but still left much room for improvement. For this period, it appears that the increasingly favorable atmospheric conditions caused by Erin moving away from the SAL were not well sampled by SHIPS or SHIPS-MI. As a result, the intensity change forecasts were not able to take into account the more favorable environment.

Full access
D. Hudson, A. G. Marshall, O. Alves, G. Young, D. Jones, and A. Watkins

Abstract

There has been increasing demand in Australia for extended-range forecasts of extreme heat events. An assessment is made of the subseasonal experimental guidance provided by the Bureau of Meteorology’s seasonal prediction system, Predictive Ocean Atmosphere Model for Australia (POAMA, version 2), for the three most extreme heat events over Australia in 2013, which occurred in January, March, and September. The impacts of these events included devastating bushfires and damage to crops. The outlooks performed well for January and September, with forecasts indicating increased odds of top-decile maximum temperature over most affected areas at least one week in advance for the fortnightly averaged periods at the start of the heat waves and for forecasts of the months of January and September. The March event was more localized, affecting southern Australia. Although the anomalously high sea surface temperature around southern Australia in March (a potential source of predictability) was correctly forecast, the forecast of high temperatures over the mainland was restricted to the coastline. September was associated with strong forcing from some large-scale atmospheric climate drivers known to increase the chance of having more extreme temperatures over parts of Australia. POAMA-2 was able to forecast the sense of these drivers at least one week in advance, but their magnitude was weaker than observed. The reasonably good temperature forecasts for September are likely due to the model being able to forecast the important climate drivers and their teleconnection to Australian climate. This study adds to the growing evidence that there is significant potential to extend and augment traditional weather forecast guidance for extreme events to include longer-lead probabilistic information.

Full access