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Summary of HYSPLIT Modifications for
Short-Range Homeland Security Simulations

The dispersion equations used in HYSPLIT versions prior to 4.7 were more appropriate for longer-range simulations. For instance, the vertical mixing coefficient profile in the boundary layer was replaced by its average value to smooth some of the short-term fluctuations. Both horizontal and vertical mixing was computed from a diffusion coefficient, which required some assumptions about the turbulent length scale when converted to the turbulent velocity variance. Further, observational data could not be easily incorporated into the calculation, nor could turbulence data generated by some of the newest generation meteorological forecast models. All these deficiencies have been addressed with HYSPLIT version 4.7.

New Diffusion / Dispersion Parameterizations

The HYSPLIT code was restructured to be able to use the TKE (turbulent kinetic energy) fields from ETA (and other models) to compute dispersion. These changes are also required for the model to use measured turbulence data, regardless of whether TKE fields are available from the meteorological model. The dispersion subroutines and corresponding meteorological data arrays now contain the turbulent velocity variance (m2/s2) rather than a diffusion coefficient (m/s2). The equations used to compute the turbulent particle motion or puff growth did not change. The dispersion computation method can be selected from the model's “namelist” configuration file. Options include the use of the ETA or RAMS model's TKE field, measured velocity variances (if available in the meteorological data input file), or HYSPLIT's previous boundary layer (BL) parameterizations. An additional option was added to computed the mixed layer depth from the TKE profile. New relationships for the boundary layer velocity variances as a function of u*, w*, and zi, applicable for short-range dispersion simulations are incorporated into the code. These equations also permit the decomposition of the TKE into its vertical and horizontal components as a function of the BL properties. A more detailed discussion of the new equations added to HYSPLIT 4.7 can be found in the recently updated Technical Memorandum:

Information on how to configure the model's input files for these new options can be found in the updated User's Guide:

The Computation of Turbulent Velocity Variances

New equations were added to compute the turbulence directly from boundary layer stability functions (adapted from Kantha and Clayson, 2000, Small Scale Processes in Geophysical Fluid Flows). This method does not use the diffusivity and hence no assumptions are required about the turbulence scales. Either equation set (diffusivity or turbulence) can be selected for a simulation. However, simulations using the new turbulence equations require more CPU time than the diffusivity based calculation. For instance, in the boundary layer, the following equations would be used in stable and neutral conditions:

w'2 = 3.0 u*2 (1 - z/zi)3/2
u'2 = 4.0 u*2 (1 - z/zi)3/2
v'2 = 4.5 u*2 (1 - z/zi)3/2

and in unstable conditions:

w'2 = w*2 (z/zi)2/3 (1 - z/zi)2/3 (1 + 0.5 R2/3)
u'2 = v'2 = 0.36 w*2
R = 0.2 (Heat flux at inversion to surface)

An important aspect of introducing a new calculation scheme is to show that the model's performance has actually improved. High resolution meteorological data are not available for most of the historical tracer experiments currently available for analysis. It makes little sense trying to test a model's performance on the 10 km scale when the meteorological data's grid points are 250 km apart at six hour intervals. In this respect it is necessary to incorporate some of the local meteorological observations that have been archived with many of these tracer experiments.

Blending Observations for Higher Resolution HYSPLIT Simulations

A series of programs were developed to produce higher resolution meteorological data sets from the coarse resolution archives. These data can then be used for shorter range simulations. In particular, local observational data can be blended into existing gridded data files to permit simulations to easily transition from observed local data to the mesoscale, regional, or global domain. A future application (not yet incorporated) would be to customize relatively coarse grid data to be more representative of finer spatial scales by incorporating high resolution topography, land use data, and perhaps a mesoscale meteorological model in an assimilation mode. In the current blending procedure, the observational data processing consists of four steps:

1) The TKE field (only available from the ETA and RAMS models) is used to compute the 3D turbulent velocity variances (u'2, v'2, w'2). These new fields are then added as additional records to each level of the original data file, replacing the TKE field.

2) The second step performs a bilinear spatial interpolation to create new grid points between those of the existing grid. The new grid spacing should be comparable to the spacing of any observational data that will be subsequently blended into the data file.

3) Normally the meteorological model output fields are usually of rather coarse temporal resolution. The third step is to linearly interpolate the data file to a finer temporal frequency to match the temporal frequency of the observations.

4) In the final step, the gridded meteorological data file is edited based upon observations within the domain such as wind direction, speed, and turbulence. A correction factor is computed to match the interpolated gridded data to the same location, height, and time as the observation. All the gridded data within the mixed layer are adjusted by the same correction factor.

This blending process is not intended to be a replacement for 4D data assimilation, but a quick way to adjust the initial transport direction to match observations near the pollutant release location. An example calculation is shown in the illustration below. The 12 km ETA fields and the DCNet tower data were interpolated into a 2-km 15 minute resolution data grid. A one-hour duration plume release using only the ETA fields is shown on the left and the same calculation using local tower data (DCNet) blended into the ETA grid is shown on the right. The calculation with the tower data shows a more easterly component to the transport direction. Concentrations are about the same.

12 km Eta HYSPLIT plume 12 km Eta with DCNet data HYSPLIT plume

METREX Data over Washington, D.C.

HYSPLIT Verification over Washington, D.C. using METREX data

The meteorological data blending procedure has been tested against tracer data collected during METREX. The Metropolitan Tracer Experiment (METREX) consisted of simultaneous 6-h duration perfluorocarbon releases from two locations every 36-h in the Washington D.C. suburbs for one year (1984). Sequential 8-h air concentrations were collected at three locations in the urban area. Monthly average tracer concentrations were collected at 93 locations. The experimental data archive is available on-line at

The experimental domain is shown on the adjacent map. The tracer release locations are shown as the blue squares; the sequential sampling locations are the red dots; meteorological towers are the green diamonds; and the monthly sampling locations are shown by the plus symbols. For reference, the approximate distance between the sequential sampling locations is 10 km.

Although meteorological data were collected during METREX, these files are not compatible with HYSPLIT. The 2.5 deg 6-h NCAR/NCEP reanalysis data were interpolated to a 5 km resolution 30-min interval grid covering a 1 deg square domain centered over Washington D.C. Meteorological tower observations at five locations, collected during METREX, were blended into the gridded data using the interpolation procedure discussed in the previous section. The model was run for 1984 for each release location. However, only the results from the monthly sampling network are discussed because they provide the greatest number of sampling locations within 10 km of each release location. The measured data and model results were converted to the DATEM compatible format ( so that existing statistical programs could be used for data analysis. The results, summarized in the Table shown below, have been averaged for the entire year so that the statistics represent the means and variations between the 93 monthly sampling locations. The PMCH was released from Rockville, MD, from January through May and from Lorton, VA, the rest of the year, while the PDCH was released from Mt. Vernon, VA, the entire year. Absolute PDCH numbers are higher because its release rate was six times that of the PMCH release rate. Concentration units are in pico grams (pg/m3). The diffusivity approach is the original Hysplit mixing scheme and the turbulent velocity method is the new approach for short-range dispersion discussed in the previous section.

Statistic Reanalysis Data Diffusivity Reanalysis Data Turbulent Velocity + Tower Data Diffusivity + Tower Data Turbulent Velocity
  pmch pdch pmch pdch pmch pdch pmch pdch
Mean Calculated
Mean Measured
Ratio C / M
NMSE (pg/m 3 )
Bias (pg/m 3 )
Percent x2
Percent x5

In general the use of the turbulent velocity variance method provides slightly better results than the diffusivity method only when the finer resolution tower data are introduced into the calculation. These results, shown in the last two columns, have the lowest NMSE and the greatest number of samples within a factor of two of the measurements. The introduction of tower data without corresponding improvements to the model result in a degradation of performance. The poor performance of the model when the older diffusivity method is used with the tower data is believed to be caused by too much vertical mixing. The addition of the tower temperatures to the reanalysis data temperature profiles resulted in larger changes to the vertical mixing in the diffusivity method than in the turbulent velocity method.

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