The storm tracks are generated from the six-hourly NCEP reanalysis dataset and an algorithm developed by Mark Serreze and Fiona Lo. Detection of the cyclones is based on a local minimum threshold of sea level pressure. For the most recent 30 days, these cyclones are tracked through their lifecycle. The program used to generate this dataset identifies/tracks cyclones using as a basis gridded sea level pressure (SLP) analyses. Each cyclone identified is ascribed a unique number which is maintained thruought the life history of the system from cyclogenesis to cyclolysis. The output variables include the position of each cyclone, the cyclone number, the year, month day and hour of each observation, central pressure, the local Laplacian of SLP at the cyclone center (a measure of intensity), pressure tendency (as determined between subsequent central pressure values) and whether the system represents a cyclogenesis or cyclolysis event, based on the first and last observations. The output variables are listed again near the bottom of this file. All cyclone numbers are resest at 1 January 0000Z of each year. The program was originally developed by M. Serreze (Univ.Colorado, Boulder) for application to 12-hourly NMC fields in the Octagonal Grid format for the entire Northern Hemishere. The present version has been modified by F. Lo and M. Serreze for application to 6-hourly Northern Hemisphere fields from the NCEP/NCAR reanalysis. With only modest changes, it can also be applied to SLP fields in other formats (e.g., ECMWF, GCM or regional model outputs). The logic of the algorithm is discussed by Serreze [1995] and Serreze et al. [1997]. Potential users should contact M. Serreze (serreze@kryos.colorado.edu) for further details. The two major components of the algorithm are: 1) detection of cyclones from a series of search patterns, testing whether a grid-point SLP value is surrounded by grid point values higher than the central point being tested. The detection threshold determines how many systems are tracked (e.g., a threshold of 1 mb will identify more systems than if a 2 mb threshold is used); 2) system tracking, based on a "nearest neighbor" analysis of the positions of systems between time steps with a maximum distance threshold between candidate pairings, with further checks based on distance moved in the N/S and W directions and pressure tendency. These limits are also adjustable. The major modifications from the original version are as follows: 1) Prior to identification of cyclone centers, the input SLP arrays are interpolated to a 250x250 km version of the NSIDC EASE-grid [Armstrong and Brodzik, 1995]. This is a lower-resolution form of the same equal-area projection being used at NSIDC (Boulder, CO) for regridding of passive microwave satellite data. The interpolation (based on Cressman weights) is necessary for compatibility with the search logic for identifying system centers and also promotes flexibility when applying the algorithm to SLP fields other than the NCEP/NCAR reanalysis. Of course, the interpoation has the undesreable effect of smoothing the fields. The user has the option of adjusting the search radius/number of points in the interpolation. Future plans are to incorporate a better interpolation scheme. 2) Alteration of the distance and pressure tendency thresholds for cyclone tracking for use with 6-hourly as opposed to 12-hourly analyses. The parameter sets as supplied work well with the NCEP/NCAR analyses, but will need to be adjusted for use with other SLP fields. Before attempting to do so, contact M. Serreze. The most important threshold is set by variable "maxdist". For the NCEP/NCAR data used here, it is set to 800 km, meaning that the total allowable distance a cyclone can move between 6-hourly time steps is 800 km (133 km/hr). While seemingly "too fast" (a speed of 100 km is about the upper limit one could ever imagine for cyclone motion), it allows for "center jumps" to be tracked. It is also necessary as since one only has data at specific grid points, there are only a finite number of possible distances a cyclone can move (the distances are "quantized"). "Maxdist" and the other distance thresholds are adjusted to account for this. References: Armstrong, R.L. and M.J. Brodzik, 1995: An earth-gridded SSM/I data set for cryospheric studies and global change monitoring, Adv. Spave. Res., 16(10), 155-163. Serreze, M.C., 1995: Climatological aspects of cyclone development and decay in the Arctic, Atmos.-Ocean, 33, 1-23. Serreze, M.C., F. Carse, R.G. Barry and J.C. Rogers, 1997: Icelandic Low cyclone activity: Climatological Features, linkages with the NAO and relationships with recent changes in the Northern Hemisphere Circulation, J. Climate, 10(3), 453-464. ---------------------------------------------------------------------- ---------------------------------------------------------------------- ---------------------------------------------------------------------- Each data file contains one year worth of data. The files consist of 20 columns of data, namely: 1) julian day 2) record number (ignore this variable) 3) year (NOTE: up to 1999 it is 2-digit, starting 2000 it becomes 4-digit) 4) month 5) day of month 6) hour (0000 or 1200 UTC) 7) total number of systems for that day 8) number of grid points defining the central pressure 9) flag to indicate if previous day(s) were skipped (yes=1, no=0) 10) cyclone central pressure (mb) 11) local laplacian of pressure (system intensity) 12) distance traveled from last observations (-999 when iskip=1 or is cyclogenesis event) 13) pressure tendency from last observation (-999 when iskip=1 or is cyclogenesis event) 14) latitude of system center 15) longitude of system center (0 to 360 deg.) 16) NMC grid row 17) NMC grid column 18) flag for cyclogenesis event (yes=1,no=0) 19) flag for cyclolysis event (yes=1, no=0) 20) system number Please send questions and comments to maproom@cdc.noaa.gov