An Overview of the HYSPLIT Modeling 

System for Trajectory and Dispersion 

Applications

http://www.arl.noaa.gov/ready/hysplit4.html

Roland.Draxler@noaa.gov

• Trajectory computation method• Simulating plume dispersion• Air concentrations / deposition• Example calculations• Verification

Transport Modeling and Assessment Group
Integration Methods

• Eulerian

– Local derivative– Solve over the entire domain– Ideal for multiple sources– Easily handles complex chem

– Problems with artificial diffus

• Lagrangian - HYSPLIT

– Total derivative– Solve only along the trajecto– Ideal for single point sources– Implicit linearity for chemistry– Non-linear solutions availabl– Not as efficient for multiple s

Transport Modeling and Assessment Group
HYSPLIT Model Features

• Predictor-corrector advection scheme; forward or backward integra• Linear spatial & temporal interpolation of meteorology (external off-

• Converters available ARW, ECMWF, RAMS, MM5, NMM, GFS, ... • Vertical mixing based upon SL similarity, BL Ri, or TKE• Horizontal mixing based upon velocity deformation, SL similarity, o• Mixing coefficients converted to velocity variances for dispersion• Dispersion computed using 3D particles, puffs, or both simultaneou• Modelled particle distributions (puffs) can be either Top-Hat or Gau• Air concentration from particles-in-cell or at a point from puffs• Multiple simultaneous meteorology and concentration grids• Latitude-Longitude or Conformal projections supported for meteoro• Nested meteorology grids use most recent and finest spatial resolu• Non-linear chemistry modules using a hybrid Lagrangian-Eulerian e• Standard graphical output in Postscript, Shapefiles, or Google Eart• Distribution: PC and Mac executables, and UNIX (LINUX) source

Transport Modeling and Assessment Group
HYSPLIT Model Development History

• 1.0 – 1982: rawinsonde data with day/night (on/off) mixing

• 2.0 – 1988: rawinsonde data with continuous vertical diffusivity• 3.0 – 1992: meteorological model fields with surface layer modu

• 4.0 – 1997: multiple meteorological fields, combined particle-pu• 4.0 – 1998: switch from NCAR to Postscript graphics• 4.1 – 1999: isotropic turbulence for short-range simulations• 4.2 – 1999: terrain compression of sigma & use of polynomial• 4.3 – 2000: revised vertical auto-correlation for dispersion• 4.4 – 2001: dynamic array allocation and support lat-lon grids • 4.5 – 2002: ensemble, matrix, and source attribution options

• 4.6 – 2003: non-homogeneous turbulence correction, dust storm• 4.7 – 2004: velocity variance, TKE, new short-range equations• 4.8 – 2006: staggered WRF grids, turbulence ensemble, urban • 4.9 – 2009: incorporated global Eulerian model (grid-in-plume) 

Transport Modeling and Assessment Group
Computation of a Single Particle Trajectory

• Position computed from average velocity at the initial position (P) and 

position (P'):

P(t+dt) = P(t) + 0.5 [ V(P{t}) + V(P‘{t+dt}) ] dtP'(t+dt) = P(t) + V(P{t}) dt

• The integration time step is variable: Vmax dt < 0.75 

• The meteorological data remain on its native horizontal coordinate sy• Meteorological data are interpolated to an internal terrain-following s

system:

s = (Ztop - Zmsl) / (Ztop - Zgl

)

Transport Modeling and Assessment Group
Representation of a Plume using Trajectorie

• A single trajectory cannot 

properly represent the growth of a pollutant cloud when the wind field varies in space and height

• The simulation must be 

conducted using many pollutant particles

• In the illustration on the right, 

new trajectories are started every 4-h at 10, 100, and 200 m 

AGL to represent the boundary 

layer transport

• It looks like a plume because 

wind speed and direction varies with height in the boundary layer

Transport Modeling and Assessment Group
Trajectory based Plume Simulation Option

• Particle: a point mass of 

contaminant. A fixed number is released with mean and random motion.

• Puff: a 3-D cylinder with a 

growing concentration distribution in the vertical and horizontal. Puffs may split if they become too large.

• Hybrid: a circular 2-D object 

(planar mass, having zero depth), in which the horizontal contaminant has a “puff”distribution and in the vertical functions as a particle.

Transport Modeling and Assessment Group
Central Position of Particles and Puffs

3D-Particles (5000) 3D-Puffs (50

Position from mean wind +turblence Position from mea

Transport Modeling and Assessment Group
Horizontal Distribution for a Single Puff

Top Hat Gaussian 

• Top-Hat Distribution• Uniform over 1.54 sigma

• Gaussian Distribution• Shown over 3 sigma

Transport Modeling and Assessment Group
Computational Approach

3D-Particles Puffs

3d-particle positions are adjustedby the component turbulent velocities:X(t+dt) = Xmean(t+dt) + U'(t+dt) dt

U'(t+dt) = R(dt) U'(t) + U"(1-R(dt)

2

)0.5

R(dt) = exp(-dt/TLx

)

U“ = (s

u) (Gaussian Random Number)

The growth of 3d-puf

upon the turbulence:ds

h/dt = 20.5

s

u

s

u = (K

x / T

L

)0.5

Transport Modeling and Assessment Group 

The second moment of the 3D–particles gives the puff distr

s

h

2 = (Xi-X

m

)

2
Air Concentration

5000 Particles 500 Puffs

Transport Modeling and Assessment Group 
Summary of Air Concentration Equations

• Each particle is assigned a pollutant mass• Concentration is simply the mass sum / volume• Volume may be defined as the ...

– size of the concentration grid cell for particles– the volumetric distribution of the puff

3D particle: dC = q (dx dy dz)-1

Hybrid Top-Hat: dC = q (pi r

2 dz)-1

Hybrid Gaussian: dC = q (2 pi s

2 dz)–1 exp(-x

2 / 2s

2

Puff Top Hat: dC = q (pi r

2 dzp)-1

Puff Gaussian: dC = q (2 pi s

2 dzp)–1 exp(-x

2 / 2s

Transport Modeling and Assessment Group 
Sensitivity to Particle Number - Why Puff Dispe

500 3D-particles

• A puff simulation models the growth of 

the particle distribution, the particle standard deviation

• Requires fewer puffs than particles to 

represent distribution

• Puff growth uses the same turbulence 

parameters as particle method

• The Puff-Particle Hybrid method

– Fewer puffs required for horizontal 

distribution

– Vertical shears captured more 

accurately by particles

Transport Modeling and Assessment Group 
HYSPLIT Default Deposition Configuration

• Dwet+dry = M [ 1 - exp (-∆t { βdry + βgas + βinc + βbel } ) ]

• Dry Deposition

– βdry = Vd / ∆Zp– Vd user defined; Vd = Vg; Resistance method– Vg gravitational settling (Stokes equation)

• Cloud Layer Definition

– Cloud bottom: 80% Rh– Cloud top: 60% Rh

• Particle Wet Deposition

– Within cloud: βinc = Vinc / ∆Zp; Vinc = S P; S=3.2 x 10

5

– Below cloud: βbel = 5x10-5

s-1

• Gaseous Wet Deposition

– βgas = Vgas / ∆Z; Vgas = H R T P 10

3

Transport Modeling and Assessment Group 
China April 2001

Particle Distribution and TOMS Aerosol Ind

April 7th 0600 UTC April 14th 0600 UTC

Transport Modeling and Assessment Group 
US PM10 Measurements from China Even

• First arrival in BL over the US 

around April 16th

• Model indicated spotty spatial 

distribution

• Arrival over eastern US 

between 19th and 22nd

• Predicted concentrations too 

high (in part because deposition was turned off)

Transport Modeling and Assessment Group 
Wild Fire Smoke Verification

http://www.arl.noaa.gov/smoke

Transport Modeling and Assessment Group 
Local Scale Verification

Washington D.C. - Metropolitan Tracer Experiment

• Tracer releases

– Rockville, Mt. Vernon, Lorton– every 36-h at 2 locations

• Sampling

– 3 locations at 8-h– 93 locations monthly

• Duration all 1984• Meteorology

– ECMWF ERA-40

Transport Modeling and Assessment Group 
Objective Verification for Sensitivity Testin

• The final model performance ranking is defined as the sum:

R

2 + {1-|FB/2| } + FMS/100 + {1-KS/100}

• where 

– the correlation (R) represents the scatter– the fractional bias (FB) is the mean difference between pai

predictions and measurements and yields a normalized methe prediction bias in concentration units 

– the Figure-of-Merit-in-Space (FMS) is defined as the perce

overlap between measured and predicted areas and is comas the intersection over the union of predicted and measureconcentrations 

– the Kolomogorov-Smirnov (KS) parameter is the maximum 

difference between the unpaired measured and calculated cumulative distributions

• The best model ranking result would be 4.0

Transport Modeling and Assessment Group 
Transport Modeling and Assessment Group

Data Archive of Tracer Experiments and Meteo

http://www.arl.noaa.gov/datem/results.html

EXPERIMENT Average Paired

ACURATE 3.25 1.77

ANATEX GGW 3.48 1.84ANATEX STC 2.66 1.63

CAPTEX 3.24 1.631ETEX 2.37 1.551INEL74 1.71 1.37METREX (t1) 2.81 1.77METREX (t2) 2.27 1.58OKC80 2.50 1.73

North American Regional Reanalysis: http://nomads.ncdc.no1NCAR/NCEP 2.5 degree reanalysis
Verification Example for ANATEX

76 Data points after temporal averaging

0.00 Percentile input for zero measured0.00 Zero measured concentration value0.97 Correlation coefficient (P=99%)

33.16 T-value (|Slope|/Standard Error)16.34 Average measured concentration22.43 Average calculated concentration

1.37 Ratio of calculated/measured

19.17 Normalized mean square error

76 Number of pairs analyzed

6.09 Average bias [(C-M)/N]

-19.58 Lo 99 % confidence interval

31.76 Hi 99 % confidence interval

0.31 Fractional bias [2B/(C+M)]

100.00 Fig of merit in space (%)

Transport Modeling and Assessment Group

-30.26 Factor exceeding [N

55.26 Percent C/M ± 285.53 Percent C/M ± 5

100.00 Percent M>0 and C>0

0.00 Percent M>0 and C=00.00 Percent M=0 and C>0

40.61 Measured 95-th pe34.92 Measured 90-th pe11.30 Measured 75-th pe

6.82 Measured 50-th pe

38.38 Calculated 95-th pe20.72 Calculated 90-th pe

8.38 Calculated 75-th pe4.16 Calculated 50-th pe

30.00 Kolmogorov-Smirnov 

3.48 Final rank (C,FB,FM
What’s in the pipeline for version 4.9 ...

• Web interactive verification linked to DATEM• Integrated global model for background contribution

s

• Chemical (CAMEO) and radiological effects database (we• GIS-like map background layers for graphical display (pc)• Model physics ensemble (pc/unix)

– meteorology and turbulence already in existing version

• Completely revised user’s guide with examples 

Transport Modeling and Assessment Group