NASA - SatCORPS - Overshooting-Tops

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Overshooting Tops Home  →  Climatology Datasets and Description

Overshooting Tops Home  →  Climatology Datasets and Description

Overshooting tops (OTs) are the product of deep convective storm updrafts of sufficient strength to rise above the local equilibrium level and cirrus anvil outflow layer near the tropopause and penetrate into the lower stratosphere. Thunderstorms with OTs can produce a variety of hazards such as hail, damaging winds, tornadoes, heavy rain, lightning, aircraft engine icing, and turbulence, each of which represent a significant threat to life and property.

OTs continue to cool adiabatically as they ascend and exhibit infrared (IR) brightness temperatures (BTs) that are significantly colder than the surrounding anvil cloud. Turbulent motions within the OT region and the fact that the OT is higher than the surrounding anvil cause the cloud top to exhibit enhanced texture and to produce shadows in satellite visible channel imagery. These satellite-observed characteristics of OTs can be automatically identified by computer algorithms developed at NASA Langley Research Center, resulting in a map of OT regions and thus hazardous storm tracks at the ~3-5 km satellite pixel scale.

The OT detection product can be produced anywhere and at anytime a satellite image is available, which can be as often as 5-minute intervals for high impact severe weather events over the U.S. The detection methods are described in the peer-reviewed literature in Bedka et al. (2010) and Bedka and Khlopenkov (2016) listed below. Other references below describe algorithm validation and product applications, including relationships with severe weather hazards. An animation and further description of the OT detection products can be found here: https://youtu.be/SXZbMjb6aNw

The OT detection approach paired with immediate access to the entire geostationary and polar-orbiting satellite imager data record enables establishment of high temporal and spatial resolution climatologies of hazardous storm events. OT detection datasets of up to 18 years in duration over many regions such as the Eastern U.S., Europe and North Africa, the Lake Victoria Region over East Africa, and Australia have been generated using geostationary satellite imagery. A 35-year OT detection database has also recently been developed using Advanced Very High Resolution Radiometer (AVHRR) polar-orbiting data. These methods were developed using the Bedka et al. (2010) approach but could be regenerated for an established collaboration using the Bedka and Khlopenkov (2016) approach.

The links below provide access to long-term European and Eastern U.S. OT detection databases. The European database was generated using Meteosat Second Generation SEVIRI geostationary imagery from April-September 2004-2014. The Eastern U.S. databases were generated using a combination of GOES-8, GOES-12, and GOES-13 imagery throughout the entirety of 1995-2012. Geostationary satellite-derived databases over Northeast South America (centered on Cayenne, French Guyana), Puerto Rico, East Africa (centered on Lake Victoria), and Australia can be made available upon request. Gridded 0.25 degree resolution monthly-mean detections from the AVHRR database described above can also be made available.

The European and Eastern U.S. databases are formatted a bit differently. OTs can be up to 15 km in diameter and occupy several satellite pixels. The European database only provides coordinates (i.e. latitude and longitude) of the coldest pixel within an OT region. The Eastern U.S. database provides coordinates for all pixels that comprise each and every OT region. If matching with severe weather reports, users should consider the parallax-corrected OT coordinates. If matching with other satellite image features, the user should consider the satellite-relative OT coordinates (unavailable for the European database).

The European database is formatted as followed:
Column 1: YYYY-MM-DD-HHmm, i.e. 2013-07-19-1730 of the SEVIRI scan
Columns 2 and 3: Parallax-corrected Latitutde and Longitude of the coldest IR BT within a particular OT region (negative longitude=Western Hemisphere)
Column 4: Coldest IR BT of the OT region
Column 5: OT minimum - mean anvil IR BT, a metric of updraft intensity
Columns 6 and 7: Ancillary parameters, irrelevant to most users

The Eastern U.S. database is formatted as followed:
Column 1: YYYY-MM-DD-HHmm, i.e. 2013-07-19-1730 of the SEVIRI scan
Columns 2 and 3: Satellite-relative Latitude and Longitude of an OT pixel (negative longitude=Western Hemisphere)
Column 4: Coldest IR BT of the OT region. Pixels adjacent to each other with the same Column 4 and 5 values compose the same OT region.
Column 5: OT minimum - mean anvil IR BT of the coldest pixel within an OT.
Column 6 Ancillary parameter, irrelevant to most users
Columns 7 and 8: Parallax-corrected Latitude and Longitude of an OT pixel

Users of these products are kindly asked to contact Kristopher Bedka using the contact form at the following URL: http://science.larc.nasa.gov/profiles/Kristopher_M_Bedka to describe their intended application of this data. This also ensures that users can be periodically contacted if there are any updates or news regarding these products. Knowledge of intended applications can help the algorithm developers be better aware of user needs and tailor future developments accordingly. Also, please cite relevant papers below in any publications that use this data.

References

Bedka, K. M., and K. Khlopenkov, 2016: A probabilistic multispectral pattern recognition method for detection of overshooting cloud tops using passive satellite imager observations. J. Appl. Meteor. And Climatol. In press.

Bedka, K. M., C. Wang, R. Rogers, L. Carey, W. Feltz, and J. Kanak, 2015: Examining deep convective cloud evolution using total lightning, WSR-88D, and GOES-14 Super Rapid Scan datasets. Wea. Forecasting. 30, 571-590

Bedka, K. M., R. Dworak, J. Brunner, and W. Feltz, 2012: Validation of satellite-based objective overshooting cloud top detection methods using CloudSat Cloud Profiling Radar observations. J. Appl. Meteor. And Climatol., 27, 684-699.

Dworak, R., K. M. Bedka, J. Brunner, and W. Feltz, 2012: Comparison between GOES-12 overshooting top detections, WSR-88D radar reflectivity, and severe storm reports. Wea. Forecasting. 10, 1811-1822.

Bedka, K. M., J. Brunner, and W. Feltz, 2011: Objective overshooting top and enhanced-V signature detection for the GOES-R Advanced Baseline Imager: Algorithm Theoretical Basis Document.

Bedka, K. M., 2011: Overshooting cloud top detections using MSG SEVIRI infrared brightness temperatures and their relationship to severe weather over Europe. Atmos. Res., 99, 175-189.

Bedka, K. M., J. Brunner, R. Dworak, W. Feltz, J. Otkin, and T. Greenwald, 2010: Objective satellite-based overshooting top detection using infrared window channel brightness temperature gradients. J. Appl. Meteor. And Climatol., 49, 181-202.

Gravelle, C. M., J. R. Mecikalski, W. E. Line, K. M. Bedka, R. A. Petersen, J. M. Sieglaff, G. T. Stano, and S. J. Goodman, 2016: Demonstration of a GOES-R Satellite Convective Toolkit to “Bridge the Gap” Between Severe Weather Watches and Warnings: An Example from the 20 May 2013 Moore, OK Tornado Outbreak. Bull. Amer. Meteor. Soc, 97, 69-84.

Griffin, S., K. M. Bedka, and C. S. Velden, 2016: A method for calculating the height of overshooting convective cloud tops using satellite-based IR imager and CloudSat Cloud Profiling Radar Observations. J. Appl. Meteor. and Climatol. 55, 479-491

Monette, S. A., C. S. Velden, K. S. Griffin, and C. M. Rozoff, 2012: Examining trends in satellite-detected tropical overshooting tops as a potential predictor of tropical cyclone rapid intensification. J. Appl. Meteor. Climatol., 51, 1917–1930.

Proud, S. R., 2014: Analysis of overshooting top detections by Meteosat Second Generation: a 5-year dataset. Q.J.R. Meteorol. Soc., 141: 909–915. doi: 10.1002/qj.2410

Punge, H. J., K. M. Bedka, M. Kunz, A. Werner, 2014: A new physically based stochastic event catalog for hail in Europe. Natural Hazards, 73, 1625-1645

Schmit, T. J., S. J. Goodman, D. T. Lindsey, R. M. Rabin, K. M. Bedka, J. L. Cinteneo, C. S. Velden, A. S. Bachmeier, S. S. Lindstrom, M. M. Gunshor, C. C. Schmidt, 2013: GOES-14 Super Rapid Scan Operations to Prepare for GOES-R. J. Appl. Remote. Sens., 7, doi:10.1117/1.JRS.7.073462

Setvak, M. K. M. Bedka, D. T. Lindsey, A. Sokol, Z. Charvat, J. Stastka, P. K. Wang, 2013: A-Train observations of deep convective storm tops. Atmos. Res., 123, 229-248

Thiery, W., E. L. Davin, S. I. Seneviratne, K. M. Bedka, S. Lhermitte, N. van Leipzig, 2016: Hazardous thunderstorm intensification over Lake Victoria. Submitted to Nature Communications


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