Observations of Extratropical Cyclones — Databases

Using the MCMS algorithm of Bauer (Bauer and Del Genio, 2006; Bauer et al., 2016), we tracked extratropical cyclones using 6-hourly sea level pressure fields from ERA-Interim (for period 2006-2017; Dee et al., 2011) and more recently ERA5 (2018-present; Herbash et al., 2020). For all 6-hourly cyclone identifications, we applied published methods of automated cold and warm front detections (Hewson, 1998; Simmonds et al., 2012) on Modern Era Retrospective Analysis for Research and Applications (ver. 2, MERRA-2) potential temperature and wind (Gelaro et al., 2017). The resulting database of cyclones with cold and warm front locations is detailed below.

With funding from the Cloudsat recompete NASA program, we developed a novel method of automated occluded cyclone identification (Naud et al., 2023). Information on this subset of 6-hourly cyclones is now available.

As part of a PMM grant (2016-2018), we created separate databases for all cyclones with near coincident and collocated precipitation observations from Global Precipitation Measurement (GPM) Combined (CMB) (2014-2017), Integrated Multi-satellitE Retrievals for GPM (IMERG) (2014-2016), and CloudSat (2006-2016).


Database of Occluded Cyclones

As detailed in Naud et al. (2023), we proposed, implemented and tested an automated method that identifies occluded thermal ridges using a gridded 1000hPa:500hPa thickness product derived from Modern Era Retrospective Analysis for Research and Applications (ver. 2, MERRA-2) in conjunction with storm positions obtained from the MCMS cyclone tracker. This method has been applied to 11 years of MERRA-2 thickness fields, from 2006 to 2017, and the database of this subset of cyclones that are identified as being occluded is now available. The cyclone files are arranged into monthly TGZ folders and each file contains information on the cyclones, the fields that are used to determine the occlusion process is occurring, and the location of the thermal ridge as identified from the thickness field.


Database of Cyclones with Warm and Cold Front Locations

Example plot of MERRA-2 sea level pressure contours

Figure 1: MERRA-2 sea level pressure contours (solid) for North Pacific storm of 2006-11-04, 00UT, located at 47.3N and 146.47E (green star) with cold and warm front locations (blue and red + respectively).

The frontal boundaries are obtained using the second version of the MERRA-2 (Gelaro et al., 2017) temperature, wind, and geopotential height output.

The period covered is September 2006 to December 2016, and each year of data files is contained in one TGZ archive. Each file corresponds to one 6-hourly cyclone occurrence, and the file names contain information on the cyclone, as described in the README.

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Database of Cyclones with GPM-CMB Ka+Ku+GMI Observations

Example plot of a Southern Ocean cyclone

Figure 2: Example of a Southern Ocean cyclone with multiple GPM Ku+GMI orbits (in grey) with precipitation rates (in color) extracted within 3 hours of cyclone detection. The cyclone occurred on 2015-01-01, 6UT, with a center at latitude -49.5 and longitude 112.4.

The Global Precipitation Mission (GPM; Hou et al., 2003) core observatory platform was launched in 2014 and provides multiple precipitation related products.

Here we use the combined "CMB" product (Grecu et al., 2016) that utilizes the microwave imager (GMI), Ku-band radar (KuPR) and Ka-Band radar (KaPR) information to retrieve precipitation characteristics. For each 6-hourly cyclone detection, we selected segments of GPM-CMB Ka+Ku+GMI version 5 product orbits that were found within 3 hours and in a 2500 km radius region centered on the low pressure center. The product provides precipitation rates, type (large scale vs. convective) and fraction of liquid precipitation information in 120 km wide swath at 5 km resolution. The files provided here include information on the cyclone and its track, along with the GPM-CMB file names, and the aforementioned precipitation products for each segment. The database contains all files acquired from March 2014 to December 2016.

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Database of Cyclones with IMERG Observations

As part of the GPM project, a global, gridded, 30 minutes, 1°resolution precipitation dataset was created and is called Integrated Multi-Satellite Retrievals for GPM (IMERG; Huffman et al., 2017). Precipitation rates from this gridded product are extracted for a region of ±1500 km centered on the extratropical cyclones that occurred from March 2014 to January 2017 in the latitude bands 20°-60° North and South. There are between 6 and 12 IMERG files collected for each cyclone. The resulting information includes gridded precipitation rates, storm location and properties, and IMERG file names. Each monthly archive is about 50 Gb once uncompressed.

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Database of Cyclones with CloudSat 2C-PRECIP-COLUMN Observations

The CloudSat platform was launched in 2006 (Stephens et al. 2002) and hosts a W-band radar (DPR). Precipitation is retrieved and reported in the 2C-PRECIP-COLUMN product files (Haynes et al., 2009). For each 6-hourly cyclone detection, we selected segments of CloudSat orbits that were found within 3 hours and in a 2500 km radius region centered on the low pressure center. The swath is only one pixel wide of approximate resolution 1 km × 2km. The files provided here include information on the cyclone and its track, along with the 2C-PRECIP-COLUMN file names, and the surface precipitation rates and types (shallow, stratiform and convective) for each segment. The database uses the R04 version of the product, which can be extended to R05 using the filename information. It covers 2006-2016.

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Caveats

The code that tracks the cyclone is the same for all years, but from 2013 onward, some of the conditions to keep a cyclone were modified. So using this database to look at trends is highly discouraged. Studies susceptible to time changes should first thoroughly evaluate potential differences between pre and post 2013.

The frontal detection is entirely automated and as such no systematic individual verification has been performed (2006-11 was extensively tested, and some specific dates, but not more). Errors have been found, often times a cold front associated to a storm center is found to be in fact associated with another low. Such errors are unavoidable at this point. Suggestions to fix this or other problems are welcome.

Land cyclone locations should not be trusted in high elevation areas, and same for the front detections. Testing has been very sparse for frontal locations over land, so use at your own risk. We recommend the 1 km warm front instead of the 850 mb. Also, some frontal locations near coastlines might be erroneous.

Some cyclones do not have a cold and/or a warm front location.


Funding

These databases were assembled over multiple years and were funded by: NASA CloudSat-CALIPSO science and modeling and analysis program (PI Del Genio), the NASA CloudSat science team recompete (PI Posselt; NNX13AQ33G), NASA the science of Terra and Aqua (PI Naud, NNX11AH22G), NOAA MAPP program (PI Booth, NA15OAR4310094), GPM-ETC by NASA Precipitation Mission Measurements (PI Naud, NNX16AD82G), and NASA CloudSat-CALIPSO science team (PI Naud, 18-CCST18-0029).

References

Bauer, M. and A. D. Del Genio, 2006: Composite analysis of winter cyclones in a GCM: influence on climatological humidity. J. Climate, 19, 1652-1672, doi:10.1175/JCLI3690.1.

Bauer, M.P., G. Tselioudis, and W.B. Rossow, 2016: A new climatology for investigating storm influences in and on the extratropics. J. Appl. Meteorol. Climatol., 55, 1287-1303, doi:10.1175/JAMC-D-15-0245.1.

Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F. Vitart, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. R. Meteorol. Soc., 137, 553–597, doi:10.1002/qj.828.

Bauer, M.P., G. Tselioudis, and W.B. Rossow, 2016: A new climatology for investigating storm influences in and on the extratropics. J. Appl. Meteorol. Climatol., 55, 1287-1303, doi:10.1175/JAMC-D-15-0245.1.

Dee, D. P., S. M. Uppala, A. J. Simmons, P. Berrisford, P. Poli, S. Kobayashi, U. Andrae, M. A. Balmaseda, G. Balsamo, P. Bauer, P. Bechtold, A. C. M. Beljaars, L. van de Berg, J. Bidlot, N. Bormann, C. Delsol, R. Dragani, M. Fuentes, A. J. Geer, L. Haimberger, S. B. Healy, H. Hersbach, E. V. Hólm, L. Isaksen, P. Kållberg, M. Köhler, M. Matricardi, A. P. McNally, B. M. Monge-Sanz, J.-J. Morcrette, B.-K. Park, C. Peubey, P. de Rosnay, C. Tavolato, J.-N. Thépaut, and F. Vitart, 2011: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. R. Meteorol. Soc., 137, 553–597, doi:10.1002/qj.828.

Gelaro, R., W. McCarty, M.J. Suárez, R. Todling, A. Molod, L. Takacs, C.A. Randles, A. Darmenov, M.G. Bosilovich, R. Reichle, K. Wargan, L. Coy, R. Cullather, C. Draper, S. Akella, V. Buchard, A. Conaty, A.M. da Silva, W. Gu, G. Kim, R. Koster, R. Lucchesi, D. Merkova, J.E. Nielsen, G. Partyka, S. Pawson, W. Putman, M. Rienecker, S.D. Schubert, M. Sienkiewicz, and B. Zhao, 2017: The Modern-Era Retrospective Analysis for research and Applications, version 2 (MERRA-2). J. Climate, 30, 5419-5454, doi:10.1175/JCLI-D-16-0758.1.

Grecu M., W. S. Olson, S. J. Munchak, S. Ringerud, L. Liao, Z. Haddad, B. L. Kelley and S. F. McLaughlin, 2016: The GPM combined algorithm. J. Atmos. Ocean. Technol., 33, 2225-2245, doi:10.1175/JTECH-D-16-0019.1.

Haynes J. M., T. S. L’Ecuyer, G. L. Stephens, S. D. Miller, C. Mitrscu, N. B. Wood and S. Tanelli, 2009: Rainfall retrieval over the ocean with spaceborne W-band radar. J. Geophys. Res., 114, D00A22, doi:10.1029/2008JD009973.

Hewson, T. D., 1998: Objective fronts. Meteorol. Appl., 5, 37–65, doi:10.1017/S1350482798000553.

Hou, A. Y., R. K. Kakar, S. Neeck, A. A. Azarbarzin, C. D. Kummerow, M. Kojima, R. Oki, K. Nakamura and T. Iguchi, 2014: The Global Precipitation Measurement Mission, Bull. Amer. Meteorol. Soc., 95, 701-722, doi:10.1175/BAMS-D-13-00164.1.

Huffman, G. J., D. T. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, C. Kidd, E. J. Nelkin, S. Sorooshian, J. Tan, and P. Xie, 2017: NASA Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG), Algorithm Theoretical Basis Document (ATBD) Version 4.6. PDF document available for download from pmm.nasa.gov (link last accessed June 21, 2018).

Naud, C.M., A.D. Del Genio, M. Bauer, and W. Kovari, 2010: Cloud vertical distribution across warm and cold fronts in CloudSat-CALIPSO data and a general circulation model. J. Climate, 23, 3397-3415, doi:10.1175/2010JCLI3282.1.

Naud, C.M., D.J. Posselt, and S.C. van den Heever, 2015: A CloudSat-CALIPSO view of cloud and precipitation properties across cold fronts over the global oceans. J. Climate, 28, 6743-6762, doi:10.1175/JCLI-D-15-0052.1.

Naud, C.M., J.F. Booth, and A.D. Del Genio, 2016: The relationship between boundary layer stability and cloud cover in the post-cold frontal region. J. Climate, 29, 8129-8149, doi:10.1175/JCLI-D-15-0700.1.

Naud, C.M. , J.E. Martin, P. Ghosh, G.S. Elsaesser, and D.J. Posselt, 2023: Automated identification of occluded sectors in midlatitude cyclones: Method and some climatological applications. Q. J. Roy. Meteorol. Soc., 149, no. 754, 1990-2010, doi:10.1002/qj.4491.

Stephens G. L., D. G. Vane, R. J. Boain, G. G. Mace, K. Sassen, Z. Wang, A. J. Illingworth, E. J. O’Connor, W. B. Rossow, S. L. Durden, S. D. Miller, R. T. Austin, A. Benedetti, C. Mitrescu, and the CloudSat Science Team, 2002: The CloudSat mission and the A-TRAIN: A new dimension to space-based observations of clouds and precipitation. Bull. Amer. Meteorol. Soc., 83, 1771-1790, doi:10.1175/BAMS-83-12-1771.

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Contacts

For questions/suggestions and for any help with the datasets and the methods please contact Dr. Catherine Naud.