GOES-14 will be in Super Rapid Scan Operations with imagery over the Carolina’s and Virginia’s Today (5/21)

GOES-14 Super Rapid Scan visible satellite imagery from 1336 UTC on 21 May viewed via the CIRA web site.

GOES-14 Super Rapid Scan visible satellite imagery from 1336 UTC on 21 May viewed via the CIRA web site.

GOES-14 Super Rapid Scan Operations for GOES-R (SRSOR) began on 14 May and will continue for through 12 June, 2015. Super Rapid Scan Operations (SRSO) will provide 1-minute imagery to support multiple research and GOES-R/S user readiness activities. The SRSO domain is usually selected a day or two in advance. The domain schedule along with selected imagery from prior days is available at: http://cimss.ssec.wisc.edu/goes/srsor2015/GOES-14_SRSOR.html#sched_and_movies  Additional background information including training and links to online imagery is available at: http://cimss.ssec.wisc.edu/goes/srsor2015/GOES-14_SRSOR.html

This will be a great opportunity to view the data over our region. NWS forecasters will be able to view some of this data in real-time in AWIPS.

Imagery including visible, infrared, and water vapor is available on the web at the links below…

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NWA Journal of Meteorology article examines utility of total lightning data in weak shear Appalachian storms

A collaborative effort between VA Tech and the NWS Blacksburg office, funded by the GOES-R program, studied the potential utility of total lightning data (from Earth Networks Inc) in weak shear storms over the Central Appalachian region.   Recently, a summary of this work was published in the NWA Journal of Operational Meteorology.  VT graduate student Paul Miller, now a PhD candidate at the University of Georgia, is the lead author. The title and abstract are below, and you can find the article here:

http://www.nwas.org/jom/abstracts/2015/2015-JOM8/abstract.php

Essentially, while there is certainly promise in using total flash rates and trends in diagnosing the severe potential for these weak shear (often known as “single cell” or “pulse” storms), a 2-sigma lightning “jump” (as defined in previous research) proved not to be a practical algorithm due mainly to a very high false alarm.  Differences in how storms are defined (i.e., radar vs. lightning clusters), the actual detection network, geographic region, as well as environment and storm mode, may all determine the effectiveness of any future algorithm designed to use flash counts and trends to better anticipate severe potential.

Fig_5

Article title and abstract:

Single-cell Thunderstorm Severity: Examples from the Central Appalachians Region

Paul W. Miller1, Andrew W. Ellis2, and Stephen J. Keighton3

1University of Georgia, Athens, Georgia
2Virginia Tech, Blacksburg, Virginia
3NOAA/NWS, Blacksburg, Virginia

Abstract

The performance of a total lightning jump algorithm for guiding severe thunderstorm warnings within a weakly sheared environment was investigated using data from the Earth Networks Total Lightning Network. Total lightning observations from two summers for a study domain within the central Appalachian Mountains region were clustered into likely thunderstorms using single-linkage clustering. The spatial and temporal characteristics of each flash cluster were evaluated and used to assign a “storm index” (SI) score to each cluster. Small, short-lived, slow-moving, circular clusters—consistent with single-cell thunderstorms—were given large SI scores, and large, long-lived, fast-moving, linear clusters—inconsistent with the single-cell mode—received smaller SI scores. Statistical testing revealed that days with a simple majority of lightning-defined (LD) single-cell storms possessed significantly weaker 0–6-km wind shear than days with a majority of non-single-cell storms. After classifying 470 clusters as either LD single-cell or multicell/supercell, the 2σ lightning jump algorithm was applied to the flashes associated with each cluster. Total lightning jumps identified by the algorithm were aligned with severe weather report data to evaluate the accuracy of the algorithm. Although probability of detection values for both categories compared well to previous studies, false alarm rates were significantly larger than previously documented. The algorithm performed unsatisfactorily among the LD single-cell and multicell/supercell storms studied, and its performance deteriorated further when applied to a subset of storms most clearly defined as single-cell. However, severe LD storms demonstrated greater flash rates, a promising characteristic for future lightning-based warning tools.

 

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Examining Gust Factors at the Land-Water Interface More Closely

A recent CSTAR project with NC State and over a half dozen WFOs in the Southeast, examined ways to improve inland wind and wind gust forecasts associated with tropical cyclones. The CSTAR project produced three primary improvements including a bias correction of the TCM wind vortex, using collaborated wind reductions over land, and using collaborated wind gust factors for wind gust grids across the domain.

Fig. 1. Example wind gust forecast as viewed in GFE showing wind gusts at land locations in southeast VA are greater than the wind gusts at adjacent marine areas.

Fig. 1. Example wind gust forecast as viewed in GFE showing wind gusts at land locations in southeast VA are greater than the wind gusts at adjacent marine areas.

Feedback from project participants noted that at times, the use of gust factors near the land-water interface can produce undesirable wind gusts in some scenarios. In an example described in this previous blog post (Examples of Gust Factor at Water-Land Interface), wind gusts at land locations in southeast VA are greater than the adjacent marine areas.  It is worth noting that the processes governing wind gusts at the land-water interface can be quite complicated and occur on very small spatial scales, including those smaller than current forecast grid lengths in GFE. Still, project participants desired a more seamless transition between the land and marine wind gust values.

Fig. 2. The location of the 9 oceanfront locations examined and a table noting the approximate location of the observational platform with the closest surf zone.

Fig. 2. The location of the 9 oceanfront locations examined and a table noting the approximate location of the observational platform with the closest surf zone.

compare.table

Fig. 3. A table comparing data from the 5 locations examined including all locations, non-oceanfront (inland) locations, oceanfront locations, Hatteras, NC (KHSE), and all marine locations.

In order to examine this, NC State student Victoria Oliva, examined the sustained winds, wind gusts, and GFs for 15 tropical cyclones that impacted the Carolinas, Virginia and Maryland. Routine hourly METAR observations with sustained wind speeds of 10 kts or more were used to calculate the hourly GF for each location.  For land locations, the METAR locations varied for each storm and were selected to capture the variations in the wind field with a total of 13,121 GFs computed. In order to examine the land-water interface more closely, we examined gust factors at 9 locations in which the METAR is located in close proximity to the coast, specifically within at least 2 miles from the surf zone. We labeled these locations as “oceanfront.” The METARs included in the oceanfront data set include KCRE, KFFA, KHSE, KMQI, KMRH, KMYR, KNBT, KNJM, and KSUT and their locations are shown in Figure 2. A total of 2,289 GFs from the 9 locations for the 15 storms were examined. In addition, the Hatteras, NC observation (KHSE) was singularly examined as it is located on an island just 0.2 miles from the surf zone. A total of 488 GFs were computed from 15 storms that impacted KHSE. Finally, GF were computed for more than two dozen buoys with an anemometer height of 5 meters that were impacted by the 15 storms. Hourly marine observations with wind speeds of 10 knots or more and wave heights less than 5 meters were used to compute 3,026 gust factors.

Fig. 4. Regression curves for the various locations examined including all locations, non-oceanfront (inland) locations, oceanfront locations, Hatteras, NC (KHSE), and all marine locations.

Fig. 4. Regression curves for the various locations examined including all locations, non-oceanfront (inland) locations, oceanfront locations, Hatteras, NC (KHSE), and all marine locations.

A table comparing the gust factors from the various locations is shown above in Figure 3. The locations include: all gust factors, non-oceanfront (inland) locations, oceanfront locations, Hatteras, NC (KHSE), and all marine locations. The results were surprising with the oceanfront gust factors having a slightly larger average and larger mean value than the non-oceanfront land locations. We had expected the oceanfront gust factors to be considerably smaller than the non-oceanfront or inland gust factors.  The KHSE average and mean gust factors were slightly lower than the average and mean oceanfront gust factor. Still, we expected the KHSE gust factor would be much lower.  The KHSE average gust factor of 1.50 was still significantly higher than the average marine gust factor average of 1.23. A plot of regression curves from the various locations is shown in Figure 4. Note the similarity in the curves for all of the land locations, whether inland, oceanfront, or KHSE which is located on an island. The land locations differ considerably with the gusts factors for the marine locations shown in the blue curve.

It was hoped that the gust factor values for the oceanfront locations would show a transition from the non-oceanfront or inland values where gust factors average 1.53 to the much lower marine values that average 1.23. Since the oceanfront observations were unable to capture a gradual change between the land and marine gust factors, some sort of blended approach could provide the transition that forecaster’s desire.
Additional investigation of oceanfront gust factor based on wind direction would be instructive. In addition, examining observations right at the shore and near the dunes might provide the expected transition. It is worth noting however, that KHSE is located on an island and is well offshore from the mainland, and that wind from just about any direction would provide a long fetch marine exposure.

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Another Early Season Tropical Cyclone on which to Utilize CSTAR Based TC Wind Tools

Fig. 1. NDFD wind gust forecast valid 8PM EDT Sun 10 May, 2015.

Fig. 1. NDFD wind gust forecast valid 8PM EDT Sun 10 May, 2015.

The early arrival of Subtropical Storm Ana will provide Southeast WFOs with another opportunity to use and evaluate a new technique to forecast tropical cyclone winds and wind gusts. A Collaborative Science, Technology, and Applied Research (CSTAR) project with North Carolina State University and over a half dozen WFOs in the Southeast, examined ways to add science and improve inland wind and wind gust forecasts associated with tropical cyclones. The CSTAR TC wind technique consists of three primary improvements including a bias correction of the TCM wind vortex, using collaborated wind reductions over land, and using collaborated wind gust factors for wind gust grids across the domain. This summer, the foot print of the project will expand to WFOs Wakefield and Sterling ,VA and Columbia, SC.  An example of the Wind Reduction Factor and Wind Gust Factor grids for 8PM EDT Sun 10 May, 2015 are shown below in Figure 2.

Fig. 2. CSTAR TC wind technique Wind Reduction Factor (left) and Wind Gust Factor (right) grids, valid 8PM EDT Sun 10 May, 2015 as viewed in GFE.

Fig. 2. CSTAR TC wind technique Wind Reduction Factor (left) and Wind Gust Factor (right) grids, valid 8PM EDT Sun 10 May, 2015 as viewed in GFE.

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Two New Convection Allowing WRF Ensembles Provide Unique and Useful Guidance

Forecasters have recently gained access to two new convection allowing (CAM) WRF ensemble model forecasts. One ensemble is provide by NSSL and is a nine-member, WRF-ARW ensemble initialized daily at 00 UTC  while the other is a ten-member, WRF-ARW ensemble initialized daily at 00 UTC and is provided by NCAR/MMM. These two modeling systems appear to be one of the first, regular and widely available convection allowing (CAM) ensembles.

CAMs have proven to be a great utility for forecasters as they can provide information about features smaller than those resolved by coarser resolution models, and in addition, they can predict convective mode, convective system propagation, diurnal cycle, and other characteristics of convection far better than models run with a convective parametrization. More information on CAMs is available at this reference.

In addition, the web sites in which these two modeling systems are available contain an easy to use graphical interface, the ability to zoom over regions, and access to several unique fields. Some of the fields include hourly max and updraft helicity probabilities, composite convective parameters, simulated satellite imagery, and ensemble max fields.

While these modeling systems were not likely created to provide guidance for tropical or sub-tropical weather systems, the developing storm system near the Bahamas provides an interesting example to view one set of output, the 1-km or composite reflectivity products in figures 1 and 2.

Fig 1. 0000 UTC 06 May 2015 NCAR/MMM WRF-ARW ensemble 36-hour 1-km reflectivity forecast valid 12 UTC 07 May 2015.

Fig 1. 0000 UTC 06 May 2015 NCAR/MMM WRF-ARW ensemble 36-hour 1-km reflectivity forecast valid 12 UTC 07 May 2015.

Fig 2. 0000 UTC 06 May 2015 NSSL WRF-ARW ensemble 36-hour composite reflectivity forecast valid 12 UTC 07 May 2015.

Fig 2. 0000 UTC 06 May 2015 NSSL WRF-ARW ensemble 36-hour composite reflectivity forecast valid 12 UTC 07 May 2015.


Nine-member NSSL WRF-ARW Ensemble initialized daily at 0000 UTC
http://www.nssl.noaa.gov/wrf/newsite/

The 9 ensemble members utilize the WRF-ARW V3.4.1 at 4-km grid length with similar configurations but varied initial conditions. The members are comprised of the regular NSSL-WRF, which uses the 0000 UTC initialized NAM for ICs and LBCs, one member that uses the 0000 UTC initialized GFS for ICs and LBCs, and 7 members that use different members of NCEP’s 2100 UTC initialized SREF system for ICs and LBCs. The SREF system ICs/LBCs include 3 WRF-ARW members (the control member and two perturbed members), 2 NMM members (the control and one perturbed member), and 3 NMMB members (the control and two perturbed members). The domain and physics parametrizations for each NSSL-WRF ensemble member are identical to the regular NSSL-WRF and include MYJ BL/turbulence parametrization, WSM6 microphysics, RRTM longwave radiation, Dudhia shortwave radiation, and
Noah land-surface model.

Ten-member NCAR/MMM WRF-ARW Ensemble initialized daily at 0000 UTC
http://ensemble.ucar.edu

The 10 ensemble members utilize the WRF-ARW V3.6.1 at 3-km grid length with similar configurations but varied initial conditions. Initial conditions provided by down scaled members of 0000 UTC WRF/DART EAKF analyses with perturbed lateral boundary conditions originating from GFS forecasts. The domain and physics parametrizations for each NCAR/MMM WRF ensemble member are the same and include MYJ BL/turbulence parametrization, Thompson microphysics, RRTMG longwave radiation, Dudhia shortwave radiation, and Noah land-surface model.

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NC State CSTAR HSLC SHERB Parameter Examined at the NWS Northern Indiana Office

The April 2015 high-shear, low-CAPE (HSLC) CSTAR conference call featured a guest presentation from Amos Dodson, forecaster from the NWS Northern Indiana (IWX) office. Amos recently presented work at the Central Iowa NWA’s Severe Storms and Doppler Radar Conference investigating HSLC events across the IWX CWA, including an evaluation of SHERBS3 performance therein, and he was gracious enough to share his work with the CSTAR group this past Tuesday. We are excited to share some highlights from his presentation as it shows the SHERBS3 performing well and it provides a great example of the CSTAR HSLC research being evaluated and utilized away from our Mid-Atlantic cluster.

amos_fig1Figure 1. List of recent HSLC tornado outbreaks within IWX and pie chart showing the percentage of IWX tornadoes associated with a given storm type over the 30-year period 1980-2010.

Within the IWX CWA, HSLC environments have accounted for half of the total tornadoes since 2010, including three recent notable outbreaks (11/17/13, 4/19/11, and 10/26/10; see Figure 1). To address this considerable forecasting concern, Amos examined the performance of the SHERBS3 parameter developed through recent CSTAR initiatives at discriminating between hits (i.e., days in which more than one severe report occurred in IWX) and nulls (days when zero or one severe reports occurred in IWX or SVR and TOR warning FAR in IWX was 100%) from 2005-2014. In addition, Amos created composite charts of various fields to determine differences between the synoptic setups associated with events and nulls.

amos_fig2Figure 2. Composite charts showing the mean 300 hPa wind speeds and vectors (m/s) for IWX HSLC hits and nulls.
amos_fig3Figure 3. Composite charts showing the mean 700 hPa wind speeds and vectors (m/s) for IWX HSLC hits and nulls.

Synoptically, hits were associated with a coupled jet feature aloft (Figure 2), a deeper 500-hPa trough, a stronger 700-hPa and 850-hPa jet (Figure 3), and slightly more negative LIs. This corroborates recent work within the CSTAR group suggesting that significantly severe HSLC events are associated with stronger forcing than those limited to non-severe convection. Parameter-wise, the SHERBS3 performed well in discriminating between hits and nulls within both datasets, as shown in Figures 4 and 5, though there were one or two puzzling misses and false alarms. Despite these exceptions and the limited dataset, the SHERBS3 showed promise as a guidance tool across IWX during HSLC environments, particularly when used in tandem with other techniques.

amos_fig4Figure 4. SHERBS3 distribution for hits (red) and nulls in the first null dataset (blue). The y-axis shows the number of events in a given SHERBS3 bin.
amos_fig5Figure 5. As in Figure 4, but for the 2nd null set (based on 100% warning FAR).

We would like to thank Amos again for sharing his work with the CSTAR group, and we look forward to additional dialogue in the future focused on similar projects across our collaborating CWAs!

The entire set of powerpoint slides can be viewed here: Dodson High Shear Low CAPE

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Update on HSLC event-relative composites

Note: For ease of viewing, figures referenced herein are provided in the attached powerpoint (at bottom).

As discussed during the February 2015 CSTAR conference call, Dr. Parker and I are in the process of developing event-relative composite maps for HSLC environments associated with significant severe reports (i.e., EF2+ tornadoes, 65+ kt wind gusts, and 2”+ hail) for comparison to those associated with unverified warnings (i.e., nulls, as defined in previous work). These composites are created with North American Regional Reanalysis (NARR) data, which are available every 3 h at a 32-km horizontal resolution with 29 vertical levels. Data are temporally averaged over a 20° latitude by 20° longitude domain centered on each respective report or null. The 3D nature of the NARR offers the opportunity to calculate nearly any variable desired and represents continuity throughout the time period of our report and null datasets. Archived SPC mesoanalysis data were used in this work only to determine which reports were HSLC using our previous criteria of SBCAPE ≤ 500 J kg-1, MUCAPE ≤ 1000 J kg-1, and 0-6 km bulk wind difference ≥ 18 m s-1.

The distribution of HSLC significant severe reports (2006-2011) and nulls (October 2006-April 2011) used in this study is shown in Figure 1. Nearly every state had at least one significant severe report occur within an HSLC environment over the eight-year period of record. However, the significant severe report distribution is notably skewed towards the Ohio, Tennessee, and Mississippi Valleys (Figure 2), particularly when considering only significant tornado reports (Figure 3). Nulls, despite having a distinct maximum in the Lower Mississippi Valley, are much more evenly distributed across the CONUS (Figure 4).

The differences of typical synoptic and mesoscale features between events and nulls are striking, as shown in Figure 5. Events tend to be associated with deeper troughs and surface lows than nulls, leading to correspondingly stronger differential divergence, mid-level negative omega, low-level temperature advection, and a more pronounced upstream vorticity maximum. Buoyancy differences are relatively modest, with a mean difference in MLCAPE of 100-200 J kg-1 centered to the south of the composite report/null location.

Seasonal subsets reveal similarities to the entire dataset; however, the key feature appears to vary by season. For example, wintertime differences appear to be dominated by differential divergence near the composite center, likely influenced by a coupled-jet feature aloft and a stronger low-level front (Figure 6), while summertime differences are primarily found in low-level warm air advection and buoyancy southeast of the composite center (Figure 7). Variability in the importance of certain features is likely attributable to the annual cycle in report location (cf. Figures 8-11) and general climatology.

To assess regional variability in HSLC features, the dataset was split into three subsections (northeast, southeast, and west) based upon the latitude and longitude of each respective report and null (Figure 12). The southeast region composites, which encompass the majority of reports within our collaborating CWAs, reveal similar difference fields to the nationwide composites (Figure 13). The northeast composites, not shown, represent the weakest differences in upper-level and lower-level divergence fields and suggest reports tend to occur just south of a warm front, based upon positioning of the MSLP and MLCAPE differences. Composites in the western region (Figure 14) again suggest more of a warm front (or perhaps triple point) structure, though other fields depict differences of similar magnitude (or larger, such as low-level warm air advection) compared with the southeast composites.

As discussed during the conference call, nationwide SHERBS3 comparisons revealed that the 0-3 km shear vector magnitude was the primary constituent in discriminating between events and nulls, while the 700-500 hPa lapse rate was similar or even lower in the report composite. This is consistent with differences in the west, as shown in Figure 15 and, to a lesser extent, with those in the southeast (Figure 16). Similar differences are noted when using the 3-6 km lapse rate rather than the 700-500 hPa lapse rate (not shown). The effective shear term within the SHERBE is comparable in apparent importance to the 0-3 km shear vector magnitude in the SHERBS3. These findings suggest that the SHERBS3 and SHERBE can likely be improved upon in the future through modifications of existing terms and/or an inclusion of other terms.

Additional composites and variables continue to be investigated. For example, potential instability fields and 0-3 km CAPE do show some clear differences between events and nulls (Figure 17) in the vicinity of the composite center. Further, the 0-3 km CAPE differences line up quite well with the 1000-850 hPa theta-E differences, suggesting a conversion from potential instability to CAPE in the lowest 1-3 km. Finally, composite soundings are being generated at the composite report and null locations. These soundings have noted shear magnitude and CIN differences in all regions, the latter of which potentially for opposing reasons (e.g., lapse rates in southeast and low-level relative humidity in west; cf. Figures 18-19). Moreover, respectable differences in low-level relative humidity and hodograph shape were noted when comparing the significant tornado and significant wind composites (Figure 20), though given the spatial climatology of HSLC significant tornado and wind reports, regional analyses of these findings are necessary before any conclusions can be reached.

So far, we are confident in the following:

  • The composite environment associated with an HSLC significant severe report is characterized by considerably stronger forcing aloft and in low levels than the composite environment associated with an unverified warning.
  • On the mean, CAPE differences between events and nulls are modest, with primary dissimilarities noted to the south of the composite report/null location.
  • Similar findings were noted in seasonal and regional subsets, though the degree to which each variable is important fluctuates.
  • The SHERBS3 exhibits skill in all regions, but its signal may be swamped by the 0-3 km shear vector magnitude, particularly in the west. The 700-500 hPa lapse rate shows weak discrimination, suggesting an adjusted parameter may yield more skill in discriminating between events and nulls.

Since last month’s conference call, I have been in the process of re-running the composites to ensure accuracy and allow for the calculation of other variables. Initial steps are underway to develop these findings into a journal article to be submitted later in the year. Future work will assess additional composite fields and subsets along with the comparison of high-shear, high-CAPE environments to HSLC. Furthermore, skill tests utilizing the NARR composites will allow us to determine the utility of previously uninvestigated variables at discriminating between HSLC events and nulls, potentially improving existing forecasting parameters.

Figures

Posted in CIMMSE, Convection, CSTAR, High Shear Low Cape Severe Wx | 3 Comments