**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 1** **Integration Methods •** Eulerian**– Local derivative – Solve over the entire domain** **– Ideal for multiple sources – Easily handles complex chemistry** **– Problems with artificial diffusion •** Lagrangian -HYSPLIT**– Total derivative – Solve only along the trajectory** **– Ideal for single point sources – Implicit linearity for chemistry** **– Non-linear solutions available – Not as efficient for multiple sources** **Transport Modeling and Assessment Group 2** **HYSPLIT Model Features • Predictor-corrector advection scheme;** **forward or backward integration • Linear spatial & temporal interpolation of meteorolo** **gy ( external off-line** **) • Converters available ARW, ECMWF, RAMS, MM5, NMM, GFS,** **... • Vertical mixing based upon SL similarity, BL Ri, or** **TKE • Horizontal mixing based upon velocity deformation, S** **L similarity, or TKE • Mixing coefficients converted to velocity variances** **for dispersion • Dispersion computed using 3D particles, puffs, or b** **oth simultaneously • Modelled particle distributions (puffs) can be eith** **er Top-Hat or Gaussian • Air concentration from particles-in-cell or at a p** **oint from puffs • Multiple simultaneous meteorology and concentration g** **rids • Latitude-Longitude or Conformal projections support** **ed for meteorology • Nested meteorology grids use most recent and finest** **spatial resolution • Non-linear chemistry modules using a hybrid Lagrangi** **an-Eulerian exchange • Standard graphical output in Postscript, Shapefile** **s, or Google Earth (kml) • Distribution: PC and Mac executables, and UNIX (LIN** **UX) source Transport Modeling and Assessment Group** **3 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 module** **• 4.0 – 1997: multiple meteorological fields, combined p article-puff** **• 4.0 – 1998: switch from NCAR to Postscript graphics • 4.1 – 1999: isotropic turbulence for short-range si** **mulations • 4.2 – 1999: terrain compression of sigma & use of pol** **ynomial • 4.3 – 2000: revised vertical auto-correlation for d** **ispersion • 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, d** **ust storm • 4.7 – 2004: velocity variance, TKE, new short-range** **equations • 4.8 – 2006:** **staggered WRF grids, turbulence ensemble, urban TKE • 4.9 – 2009: incorporated global Eulerian model (grid-** **in-plume) Transport Modeling and Assessment Group** **4 Computation of a Single Particle Trajectory** **• Position computed from average velocity at the init ial position (P) and first-guess** **position (P'): P(t+dt) = P(t) + 0.5 [ V(P{t}) + V(P‘{t+dt}) ] dt** **P'(t+dt) = P(t) + V(P{t}) dt • The integration time step is variable: V** **max dt < 0.75** **• The meteorological data remain on its native horizon tal coordinate system** **• Meteorological data are interpolated to an internal terrain-following sigma coordinate** **system: s = (Z** **top - Z** **msl ) / (Z** **top - Z** **gl )** **Transport Modeling and Assessment Group 5** **Representation of a Plume using Trajectories • 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 6** **Trajectory based Plume Simulation Options •** 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 7** **Central Position of Particles and Puffs 3D-Particles (5000) 3D-Puffs (** **500) Position from mean wind +turblence Position from mea** **n wind Transport Modeling and Assessment Group** **8 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** **9 Computational Approach** **3D-Particles Puffs 3d-particle positions are adjusted** **by the component turbulent velocities: X(t+dt) = X** **mean (t+dt) + U'(t+dt) dt** **U'(t+dt) = R(dt) U'(t) + U"(1-R(dt) 2** **) 0.5** **R(dt) = exp(-dt/T Lx** **) U“ = (s** **u ) (Gaussian Random Number)** **The growth of 3d-puffs is based upon the turbulence:** **ds h** **/dt = 2 0.5** **s u** **s u** **= (K x** **/ T L** **) 0.5** **Transport Modeling and Assessment Group 10** **The second moment of the 3D–particles gives the puf f distribution:** **s h** **2 = (X** **i -X** **m )** **2 Air Concentration** **5000 Particles 500 Puffs Transport Modeling and Assessment Group** **11 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 2** **) Transport Modeling and Assessment Group** **12 Sensitivity to Particle Number - Why Puff Dispersion** **? 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 13** **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 14** **China April 2001 Particle Distribution and TOMS Aerosol Index** April 7th0600 UTC April 14th0600 UTC**Transport Modeling and Assessment Group 15** **US PM 10** **Measurements from China Event • First arrival in BL over the US** **around April 16 th** **• Model indicated spotty spatial distribution** **• Arrival over eastern US between 19** **th and 22** **nd • Predicted concentrations too** **high (in part because deposition was turned off)** **Transport Modeling and Assessment Group 16** **Wild Fire Smoke Verification http://www.arl.noaa.gov/smoke** **Transport Modeling and Assessment Group 17** **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** **18 Objective Verification for Sensitivity Testing** **• The final model performance ranking is defined as th e 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 bet** **ween paired predictions and measurements and yields a normalized** **measure of the prediction bias in concentration units** **– the Figure-of-Merit-in-Space (FMS) is defined as th e percentage of** **overlap between measured and predicted areas and is computed** **as the intersection over the union of predicted and measured** **concentrations – the Kolomogorov-Smirnov (KS) parameter is the maximum** **difference between the unpaired measured and calcula ted** **cumulative distributions • The best model ranking result would be 4.0** **Transport Modeling and Assessment Group 19** **Transport Modeling and Assessment Group Data Archive of Tracer Experiments and Meteorology** **http://www.arl.noaa.gov/datem/results.html EXPERIMENT Average Paired** **ACURATE 3.25** **1.77 ANATEX GGW** **3.48 1.84** **ANATEX STC 2.66** **1.63 CAPTEX** **3.24 1.63** **1 ETEX** **2.37 1.55** **1 INEL74** **1.71 1.37** **METREX (t1) 2.81** **1.77 METREX (t2)** **2.27 1.58** **OKC80 2.50** **1.73 North American Regional Reanalysis:** **http://nomads.ncdc.noaa.gov 1** **NCAR/NCEP 2.5 degree reanalysis 20** **Verification Example for ANATEX**76 Data points after temporal averaging0.00 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 concentration1.37 Ratio of calculated/measured19.17 Normalized mean square error76 Number of pairs analyzed6.09 Average bias [(C-M)/N]-19.58 Lo 99 % confidence interval31.76 Hi 99 % confidence interval0.31 Fractional bias [2B/(C+M)]100.00 Fig of merit in space (%)**Transport Modeling and Assessment Group**-30.26 Factor exceeding [N(C>M)/N-0.5]55.26 Percent C/M ± 285.53 Percent C/M ± 5100.00 Percent M>0 and C>00.00 Percent M>0 and C=00.00 Percent M=0 and C>040.61 Measured 95-th percentile34.92 Measured 90-th percentile11.30 Measured 75-th percentile6.82 Measured 50-th percentile38.38 Calculated 95-th percentile20.72 Calculated 90-th percentile8.38 Calculated 75-th percentile4.16 Calculated 50-th percentile30.00 Kolmogorov-Smirnov Parameter3.48 Final rank (C,FB,FMS,KSP)**21 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 (** **web) • GIS-like map background layers for graphical displa** **y (pc) • Model physics ensemble (pc/unix)** **– meteorology and turbulence already in existing ver sion** **• Completely revised user’s guide with examples Transport Modeling and Assessment Group** **22**