Northern Hemisphere Cyclone Locations and Characteristics
from NCEP/NCAR Reanalysis Data

Summary

This data set comprises a 50-year record of daily extratropical cyclone statistics computed for the Northern Hemisphere. Cyclone locations and characteristics were obtained by applying the updated Serreze (1997) algorithm to daily Sea Level Pressure (SLP) data at six-hour intervals (Serreze and Barrett 2008). The SLP source data are part of the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) Reanalysis data set, an assimilation of various atmospheric data collected by a wide variety of sensors within a global weather model. The parameters included in this data set are the position and central pressure of each cyclone, the distance the center of the cyclone traveled, whether the observation represents a cyclogenesis or cyclolysis event, and the local Laplacian of SLP and Sea Level Pressure Tendency (SLPT) at each cyclone center. The total volume of this data set is approximately 150 megabytes. Data are provided in tab-delimited ASCII text format and are available via FTP.

Citing These Data

The following example shows how to cite the use of this data set in a publication. For more information, see our Use and Copyright Web page.

Serreze, M. C. 2009. Northern Hemisphere Cyclone Locations and Characteristics from NCEP/NCAR Reanalysis Data. Boulder, Colorado USA: National Snow and Ice Data Center. Digital media.

Overview Table

Category	Description
Data format	ASCII text
Spatial coverage and resolution	Northern Hemisphere coverage with 250 km resolution Southernmost Latitude: 0° N Northernmost Latitude: 90° N Westernmost Longitude: 180° W Easternmost Longitude: 180° E
Temporal coverage and resolution	01 January 1958 - 31 December 2008
Tools for accessing data	Any Web browser or text editor
File naming convention	ncepstorms_1958_YYYY.txt
File size	~150 MB
Parameters	Cyclone Position Cyclone Central Pressure Distance the Cyclone Center Traveled Cyclogenesis/Cyclolysis Local Laplacian of Sea Level Pressure (SLP) at the Cyclone Center Sea Level Pressure Tendency (SLPT) at the Cyclone Center
Procedures for obtaining data	Data are available via FTP.

Table of Contents

1. Contacts and Acknowledgments
2. Detailed Data Description
3. Data Access and Tools
4. Data Acquisition and Processing
5. References and Related Publications
6. Document Information

1. Contacts and Acknowledgments

Investigator(s)

Mark C. Serreze
National Snow and Ice Data Center (NSIDC)
Cooperative Institute for Research in Environmental Sciences (CIRES)
449 UCB, University of Colorado at Boulder
Boulder, Colorado 80309-0449 USA

Technical Contact

Acknowledgements

This research was supported by National Science Foundation (NSF) grant ARC-0531302. The NCEP/NCAR Reanalysis data used as a basis for this data set were provided by the Physical Sciences Division (PSD) of the Earth System Research Laboratory (ESRL), a part of the National Oceanic and Atmospheric Administration (NOAA) in Boulder, Colorado USA.

2. Detailed Data Description

Format

Data are provided in a tab-delimited ASCII text file that contains cyclone locations and characteristics. Table 1 lists the column headings and data field descriptions for the file.

Table 1. Column Headings and Descriptions

Column Heading	Units (where applicable)	Description
dday	--	Decimal day of year (1.00, 1.25, 1.75, 2.00...365.75...)
rec_num	--	Cyclone system record number
year	--	2-digit year
month	--	1- or 2-digit month
day	--	1- or 2-digit day
hour	--	1- or 2-digit hour (0, 6, 12, 18 = 0000, 0600, 1200, 1800 UTC)
num_daily_sys	--	Total number of daily cyclone systems
num_grid_pts	--	Number of grid points defining central pressure of a given system
prev_day_skip_flag	--	Flag to indicate if previous day(s) were skipped (1 = yes, 2 = no)
cent_pressure	hPa	Cyclone central pressure
laplacian	mPa/km-2	Local laplacian of pressure system (a measure of cyclone system intensity)
distance	m	Distance traveled from last observation (-999 indicates the previous day was skipped or this is a cyclogenesis event)
press_tend	hPa/6h	Pressure tendency from last observation (-999 indicates the previous day was skipped or this is a cyclogenesis event)
lat	degrees North	Latitude of system center
lon	degrees East	Longitude of system center
EASE_grid_row	--	EASE-Grid row
EASE_grid_col	--	EASE-Grid column
cyclogen_flag	--	Flag indicating cyclogenesis event (1 = yes, 2 = no)
cyclol_flag	--	Flag indicating cyclolysis event (1 = yes, 2 = no)
sys_nu	--	System number during given year

File and Directory Structure

Data are available on the FTP site in the following directory:
ftp://sidads.colorado.edu/pub/DATASETS/atmosphere/nsidc0423_cyclone_ncep_ncar_reanalysis/

File Naming Convention

The ASCII data file is named according to the following convention and as described in Table 2:

ncepstorms_1958_YYYY.txt

Where:

Table 2. File Naming Convention

Variable	Description
ncep	National Centers for Environmental Prediction (NCEP)
storms	Cyclone storm systems
1958	First 4-digit year of time series
YYYY	Last 4-digit year of time series
.txt	Indicates this is an ASCII text file

File Size

The ASCII text file is approximately 150 megabytes.

Spatial Coverage

Spatial coverage is the Northern Hemisphere.

Southernmost Latitude: 0° N
Northernmost Latitude: 90° N
Westernmost Longitude: 180° W
Easternmost Longitude: 180° E

Spatial Resolution

The spatial resolution of this data set is 250 km.

Prior to identification of cyclone centers, the input SLP data were interpolated from the NCEP/NCAR 2.5° x 2.5° grid to a 250 x 250 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 for re-gridding passive microwave satellite data. The interpolation (based on Cressman weights) is necessary for compatibility with the algorithm search logic for identifying system centers and also promotes flexibility when applying the algorithm to SLP fields other than the NCEP/NCAR Reanalysis. However, the interpolation has the undesirable effect of smoothing the fields.

Temporal Coverage

Temporal coverage for this data set spans 01 January 1958 to 31 December 2008. Data were subsetted from the NCEP/NCAR Reanalysis time series which provides global coverage from 1948 to the present. Due to fewer upper-air data observations made during the first decade of the time series, only data from 1958 to 2008 have been used for this data set (Kistler et al. 2001).

Temporal Resolution

For the purposes of this data set, data originating from the NCEP/NCAR Reanalysis time series were analyzed four times per day at six-hour intervals.

Parameter or Variable

Parameters include the position and central pressure of each cyclone, the distance the center of the cyclone traveled, whether the observation represents a cyclogenesis or cyclolysis event, the local Laplacian of the SLP at each cyclone center, and the SLPT at each cyclone center.

Parameter Description

The cyclone position, or cyclone system center, is provided in latitude and longitude coordinates, and the cyclone central pressure is provided in hectopascals (hPa). The distance each cyclone center traveled is provided in kilometers. Cyclogenesis and cyclolysis represent the first and last appearance of a closed 1-hPa isobar, respectively.

The following text from Serreze et al. 1997 describes the local Laplacian and SLPT parameters:

The local Laplacian parameter is proportional to the geostrophic relative vorticity and, unlike cyclone central pressure, provides an index of cyclone intensity largely independent of changes in the background pressure field (Murray and Simmonds 1991). In turn, SLPT provides a useful index of synoptic development (Sanders and Gyakum 1980), provided that the decrease in cyclone central pressure is not embedded within a region of generally falling or rising pressure and the storm maintains an approximately constant size through the 6-hr analysis period (Roebber 1989). Following Roebber (1984), all SLPT values were adjusted by latitude using the relationship in Equation 1:

 

SLPTadj = SLPT sin ref/sin

Equation 1

where _ref is a reference latitude of 60° N and is the latitude of the SLPT observation. This accounts for the latitudinal variation in geostrophic wind for a unit pressure gradient. The choice of the reference latitude is arbitrary; 60° N was chosen here following Sanders and Gyakum (1980) and Serreze (1995). (Serreze et al. 1997)

Table 3 provides a brief description and the units of each parameter.

Table 3. Parameter Description and Units

Parameter Name	Description	Units
Cyclone Position	Latitude and longitude of cyclone system center	degrees
Cyclone Central Pressure	Pressure at the center of the cyclone system	hPa
Distance Cyclone Center Traveled	Distance the cyclone center traveled since it was initially tracked	km
Cyclogenesis/Cyclolysis	Cyclogenesis and cyclolysis represents the first and last appearance of a closed 1-hPa isobar, respectively	--
Local Laplacian of SLP	A measure of intensity of sea level pressure at the cyclone center	mPa/km-2
SLPT	Sea level pressure tendency at each cyclone center, as determined between subsequent central pressure values	hPa (6 h)−1

Sample Data Record

Figure 1 displays a partial sample of the ncepstorms_1958_2008.txt file.

Figure 1. Partial Sample Data Record

 

Error Sources

The NCEP/NCAR Reanalysis time series data were used as source data to derive this data set. For a list of errors with the NCEP/NCAR Reanalysis data, visit the NCEP/NCAR Reanalysis Problems List Web page.

Quality Assessment

Serreze and Barrett (2008) oultine several screening steps that users of this data set may wish to consider. These include discarding cyclones that remained stationary during their system life cycle, such as remained within the same grid cell, and discarding cyclones that lasted less than one day and thus comprised less than four 6-hr charts. As most spurious systems appear due to a reduction in surface pressure to sea level over high or complex topography, these screening steps eliminated those systems. These screening steps also tended to eliminate most mesoscale features, such as those in the range of 10 to 1000 kilometers.

Another potentially useful screening is to verify that a cyclone had deepened sometime during its life cycle. Serreze and Barrett (2008) retained only cyclones with a minimum total of 2 hPa SLP change over their life cycle. Though these steps helped limit analysis to most robust cyclone systems, some spurious systems may still remain, particularly over Greenland. (Serreze and Barrett 2008)

3. Data Access and Tools

Data Access

Data are available via FTP.

Volume

The total volume of this data set is approximately 150 megabytes.

Software and Tools

The ASCII text file may be viewed using any text editor or Web browser.

Related Data Collections

• Arctic Cyclone Track Data Set, 1966-1993
• ARCSS Data

4. Data Acquisition and Processing

Theory of Measurements

Prior to the early 1950s, prevailing views of the Arctic atmospheric circulation remained largely speculative. Interest in the Arctic atmospheric circulation stems from recognition of the importance of atmospheric variability in driving anomalies in the circulation, extent, concentration, and thickness of the sea ice cover (Walsh and Chapman 1990). Such anomalies, through atmospheric feedbacks, may have potentially significant impacts on regional and hemispheric climates. In light of this interest, this data set provides Arctic cyclone characteristics based on results from an automated cyclone detection and tracking algorithm applied to a 50-year record (1958-2008) of daily NCEP/NCAR reanalysis of sea level pressure.

Sensor or Instrument Description

The NCEP [formerly the National Meteorological Center (NMC)]/NCAR Reanalysis source data includes an assimilation of land surface, rawinsonde, ship, pibal, aircraft, satellite, and various other data within a global weather model. A partial list of some of the sensors and data sources used in the NCEP/NCAR Reanalysis is provided in Table 4. For complete documentation regarding the sensors used as a basis for this data set, refer to the NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation paper.

 

Table 4. Examples of Data Sources/Sensors used in NCEP/NCAR Reanalysis Data

Example Data Type	Example Data Source and/or Sensor(s)	Description
Rawinsonde	NCEP Global Telecommunication System (GTS) data	The main source for the rawinsonde data, a global collection of upper-air observation data. Also includes pibal and aircraft data.
Surface Marine Data	Comprehensive Ocean-Atmosphere Data Set (COADS) data	Among other data, includes data from ships, drifting buoys, fixed buoys, pack-ice buoys, and near-surface data from ocean station reports, such as Expendable Bathythermographs (XBTs).
Aircraft Data	NCEP Global Telecommunication System (GTS) data	The main source for the aircraft data, a global collection of upper-air observation data. Also includes pibal and rawinsonde data.
Surface Land Synoptic Data	Air Force Global Telecommunication System (GTS) data	The main source for the surface land synoptic data, a global collection of surface data.
Satellite Sounder Data	TIROS Operational Vertical Sounder (TOVS) sensors: High Resolution Infrared Radiation Sounder (HIRS) Microwave Sounding Unit (MSU) Stratospheric Sounding Unit (SSU)	The TOVS suite of sensors provides global measurements used in weather forecasting, such as the vertical distribution of temperature and moisture in the atmosphere.
Surface Wind Speed Data	Special Sensor Microwave Imager (SSM/I)	SSM/I data were used with the Krasnopolsky et al. (1995) algorithm which resulted in wind speeds closer to buoy data, and coverage under cloudy conditions. Measurements include SSM/I wind speed, total precipitable water, and other parameters. (Kalnay et al. 1996)
Satellite Cloud Drift Wind Data	Geostationary Meteorological Satellite (GMS) data	The GMS program is a series of satellites operated by the Japan Meteorological Agency (JMA). The Visible and Infrared Spin Scan Radiometer (VISSR), the primary instrument aboard GMS, collects visible and infrared images of Earth and its cloud cover.

 

Data Acquisition Methods

NCEP/NCAR Reanalysis source data were acquired from the Physical Sciences Division (PSD) of the Earth System Research Laboratory (ESRL), a part of the National Oceanic and Atmospheric Administration (NOAA) in Boulder, Colorado USA. For access to the original source data, visit the NOAA ESRL Reanalysis Datasets at PSD Web page.

Data Source

The NCEP/NCAR Reanalysis time series data were used as source data to derive this data set. The NCEP/NCAR Reanalysis source data set includes an assimilation of land surface, rawinsonde, ship, pibal, aircraft, satellite, and various other data within a global weather model. For complete documentation regarding the sensors, platforms, and methods used as a basis for this data set, refer to the NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation paper.

Derivation Techniques and Algorithms

The following is adapted from Serreze et al. 1997.

The current detection and tracking algorithm is largely identical to that of Serreze (1997). The two major components of the algorithm are:

1. A detection threshold to determine how many systems are identified in a given SLP chart
2. A nearest neighbor analysis of the positions of cyclone systems to track the movement of systems

Cyclones are identified using a series of search patterns, testing whether a grid point SLP value is surrounded by grid point values at least 1 hPa higher than the central point being tested. Starting on the first 6-hr chart (Chart 1), each cyclone is ascribed a number. A grid array is then centered over each system on the next 6-hr chart (Chart 2). If a cyclone on Chart 1 falls within a given array, the Chart 2 cyclone at the center of the array is taken to be a continuation of the Chart 1 system. This immediately tracks stationary or slow-moving cyclones. It is possible that two or more Chart 1 systems could fall within the same array, but this was rarely observed. The minimum distances from all remaining untracked systems on Chart 2 are then determined with respect to the remaining numbered systems on Chart 1. Typically, two or more untracked Chart 2 systems have their minimum distance with respect to the same Chart 1 system. The number of the Chart 1 system is carried over to the closest Chart 2 system, provided that the minimum distance between them is less than a specified limit, and several other screening steps involving the 6-hr SLPT at the cyclone centers for a candidate pairing and the direction of system motion are satisfied. Otherwise, the Chart 2 system is taken as new (cyclogenesis), and successively more distant Chart 2 systems are tested in the same manner, up to the distance limit. At the end of the search process, all remaining Chart 1 systems that could not be paired with a Chart 2 system are considered to have filled (cyclolysis). The process is then repeated for each subsequent pair of charts. If any chart is missing, all systems on the next available chart are taken as new and numbered accordingly. (Serreze et al. 1997)

Processing Steps

A Fortran program was used to apply an algorithm that identifies and tracks cyclones using gridded SLP analyses as a basis. Each cyclone identified is ascribed a unique number which is maintained throughout the life history of the system from cyclogenesis to cyclolysis. The output variables include the following: the position of each cyclone; the cyclone number, year, month, day, and hour of each observation; cyclone 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 observation. All cyclone numbers are reset at 01 January 0000Z of each year, where Z indicates Zulu Time Zone/UTC. The program was originally developed by Mark Serreze at the University of Colorado for application to 12-hr NMC fields for the entire Northern Hemisphere. The present version was subsequently modified for application to 6-hr Northern Hemisphere fields from the NCEP/NCAR Reanalysis data set. The logic of the algorithm is further explained in Serreze 1997.

Prior to identification of cyclone centers, the input NCEP/NCAR SLP arrays are interpolated to a 250 x 250 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 for re-gridding passive microwave satellite data. The interpolation (based on Cressman weights) is necessary for compatibility with the algorithm 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 interpolation has the undesirable effect of smoothing the fields, but it avoids the problem of strong convergence of meridians at high latitudes.

The distance and pressure tendency thresholds for cyclone tracking were altered for use with 6-hr as opposed to 12-hr analyses. The parameter sets work well with the NCEP/NCAR analyses, but would need to be adjusted for use with other SLP fields. The most important threshold is set by the 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 six-hour intervals 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), this allows for center jumps to be tracked. Further, since data are only at specific grid points, there are only a finite number of possible distances a cyclone can move (the distances are quantized). The maxdist and other distance thresholds were adjusted to account for this; and, as noted in the Quality Assessment section of this document, the user may wish to screen the data to eliminate short-lived and/or spurious cyclone systems.

5. References and Related Publications

Armstrong, R. L., and M. J. Brodzik. 1995. An Earth-Gridded SSM/I Data Set for Cryospheric Studies and Global Change Monitoring. Advances in Space Research 16: 155-63.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, S. Saha, G. White, J. Woollen, Y. Zhu, A. Leetmaa, R. Reynolds, M. Chelliah, W. Ebisuzaki, W. Higgins, J. Janowiak, K. Mo, C. Ropelewski, J. Wang, R. Jenne, and D. Joseph. 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77, 437–471.

Kistler, R., E. Kalnay, W. Collins, S. Saha, G. White, J. Woollen, M. Chelliah, W. Ebisuzaki, M. Kanamitsu, V. Kousky, H. van den Dool, R. Jenne, and M. Fiorino. 2001. The NCEP-NCAR 50-Year Reanalysis: Monthly Means CD-ROM and Documentation. Bulletin of the American Meteorological Society, 82, 247-268. doi: 10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2.

Krasnopolsky, V. M., L. C. Breaker, and W. H. Gemmill. 1995. A Neural Network as a Nonlinear Transfer Function Model for Retrieving Surface Wind Speeds from the Special Sensor Microwave Imager. Journal of Geophysical Research, 100(C6), 11,033–11,045. PDF

Serreze, M. C., and A. P. Barrett. 2008. The Summer Cyclone Maximum over the Central Arctic Ocean. Journal of Climate, 21, 1048–1065. doi: 10.1175/2007JCLI1810.1.

Serreze, M. C., A. H. Lynch, and M. P. Clark. 2001. The Arctic Frontal Zone as Seen in the NCEP–NCAR Reanalysis. Journal of Climate, 14, 1550–1567. doi: <1550:TAFZAS>2.0.CO;2.

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. Journal of Climate, 10, 453–464. doi: 10.1175/1520-0442(1997)010<0453:ILCACF>2.0.CO;2.

Serreze, M. C. 1995. Climatological Aspects of Cyclone Development and Decay in the Arctic. ATMOSPHERIC-OCEAN 33 (1), 1-23. PDF

Tsukernik, M., D. N. Kindig, and M. C. Serreze. 2007. Characteristics of Winter Cyclone Activity in the Northern North Atlantic: Insights from Observations and Regional Modeling. Journal of Geophysical Research 112(d3): D03101, doi:10.1029/2006JD007184.

6. Document Information

Acronyms

The acronyms used in this document are listed in Table 5.

Table 5. Acronyms and Abbreviations

Acronym	Description
ARC	Arctic Sciences Division
ASCII	American Standard Code for Information Interchange
FTP	File Transfer Protocol
CIRES	Cooperative Institute for Research in Environmental Sciences
COADS	Comprehensive Ocean-Atmosphere Data Set
DAAC	Distributed Active Archive Center
E	Evaporation
EASE-Grid	Equal Area Scalable Earth-Grid
ESRL	Earth System Research Laboratory
GMS	Geostationary Meteorological Satellite
GTS	Global Telecommunication System
HIRS	High Resolution Infrared Radiation Sounder
hPa	hectopascal
JMA	Japan Meteorological Agency
mPa	millipascal
MSU	Microwave Sounding Unit
NCAR	National Center for Atmospheric Research
NCEP	National Centers for Environmental Prediction
NMC	National Meteorological Center
NOAA	National Oceanic and Atmospheric Administration
NSF	National Science Foundation
NSIDC	National Snow and Ice Data Center
P	Precipitation
PSD	Physical Sciences Division
SLP	Sea Level Pressure
SLPT	Sea Level Pressure Tendency
SSM/I	Special Sensor Microwave Imager
SSU	Stratospheric Sounding Unit
TIROS	Television Infrared Observation Satellite
TOVS	TIROS Operational Vertical Sounder
UCAR	University Corporation for Atmospheric Research
URL	Uniform Resource Locator
UTC	Coordinated Universal Time
VISSR	Visible and Infrared Spin Scan Radiometer
XBT	Expendable Bathythermographs

Document Creation Date

September 2009

Document URL

http://nsidc.org/data/docs/daac/nsidc0423_cyclone/index.html