September 29, 2000



# M ETEOROLOGICAL 

# M ODELING OF 

# 1996 FOR THE 

# U NITED 

# S TATES WITH 

# MM5 EPA T ASK 

## O RDER 

## N UM BER 

## CAA689805

Prepared for Norman Possiel, Client Representative U.S. EPA/OAQPS Prepared by Dona ld Ole r ud (MCNC) Kiran Ala p a ty (MCNC) Neil Wheeler (Sonoma Technology, Inc.)



### MCNC–Environmental Programs P.O. Box 12889 Research Triangle Park, NC 27709-2889Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## Contents
1. Introductio n ........................................................................................................................... 1 2. Model Description ................................................................................................................. 3 3. Model Configuration ............................................................................................................. 6 3.1 Do m a ins
.......................................................................................................................... 6 3.2 Physical Options
............................................................................................................. 8 4. Model Inputs and Preprocessing ........................................................................................ 11 4.1 Preproces sin g System
................................................................................................... 11 4.1.1 Terra in
............................................................................................................. 11 4.1.2 Datagrid
........................................................................................................... 12 4.1.3 Rawins
............................................................................................................. 12 4.1.4 Interp
............................................................................................................... 12 4.2 Lateral Boundary Conditions
....................................................................................... 12 4.3 Data Assim ilation
......................................................................................................... 13 5. Model Performance Evaluation Methodolog y .................................................................. 14 5.1 Data
.............................................................................................................................. 1 4 5.2 Procedures
.................................................................................................................... 14 6. Results ............................................................................................................................... .... 17 6.1 General Observations
................................................................................................... 17 6.2 Spatia l Analyses
........................................................................................................... 17 6.2.1 Upper-Level W i nds
......................................................................................... 17 6.2.2 Surface W i nds
................................................................................................. 19 6.2.3 Surface-Lev e l Tem p eratures ........................................................................... 20 6.2.4 Surface-Lev e l W a ter Vapor Mixing Ratio
...................................................... 24 6.2.5 Mixed-Layer Depths
....................................................................................... 26 6.2.6 Rainf a ll
............................................................................................................ 27 6.3 Point-spe c if ic Analyses
................................................................................................ 29 6.3.1 Analyses of Meteoro l ogical Variab les at Surface Observation a l Data Sites
................................................................................................................. 29 6.3.2 Statis tical A n alyses of Meteoro l ogical Variables
........................................... 31 7. Closing Ob servations .......................................................................................................... 39 References ............................................................................................................................... ..... 40 Appendices ............................................................................................................................... .... 41 Appendix A – Model Configuration File
.............................................................................. 41 Appendix B – Supplem e n tal Plots
......................................................................................... 42 Appendix C – Supplem e n tal Color Graphic P l ots ................................................................. 57

iiiMeteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 1. INTRO DUCTION The Air Quality Mod e lin g Group (AQMG) of the U.S. Environm ental Protection Agency (EPA) is responsible for providi ng air quality m o deling assessm e nt s and databases to support the developm ent of guidance, policies, and regulations on ozone, partic ulate m a tter, toxics, and other pollutants. As part of this m i ssion, AQMG is perform i ng a nationwide assessm ent of the beneficial effects of proposed em issions reductions from cars and trucks on ozone concentrations, particulate con centrations, visibility, and ac id deposition. This assessm ent requires developing m e teorological and other related inputs to air quality sim u lation models, perform i ng sim u lations using a regional ozone model for the western U n ited States, and perform i ng annual sim u lations of particulate m a tter. The work docum ented in this report is referred to as “Task 2” in the original task order. The purpose of this task was to run the Pennsylva nia Sta t e U n ivers ity (PSU)/Nationa l Center f o r At m o spheric Research (NCAR) Me soscale Model version 5, or MM5 1, 2 , at 108-km and 36-km resolutions f o r the entirety of 1996 over the entire continental Un ited States. This provides data necessary to drive air pollution m ode ls such as the variable-grid Urban Airshed Model (UAM-V 3 ), the Com p rehensive Air Quality Mo del with ex tensions (CA M x 4 ), the Regulatory Modeling S y stem for Aerosols and Deposition (REMSAD 5 ), the Commu nity Multi-scale Air Quality (CMAQ 6 ) m odeling system , or the Multiscale Air Q u ality Sim u lation Platform (MAQSIP 7 ). This report docum ents the m odel inputs, m odel configuration, output form ats, and results of these MM5 simulations. The work to be perform e d under Task 2 included the following activities: 1. Developing and quality assuring all input s to the required MM5 runs for 1996; 2. Running MM5 and quality a ssuring the results; 3. Delivering the outputs in electronic for m ; and 4. Providing docum e ntation of the inputs, assum p tions, and results. These tasks were further broke n down into eight subtasks: 1. Acquire Data —Purchase data from the National Center for Atm o spheric Research (NCAR) and the National Clim atic Data Center (NCDC) suitable for initializing, providing b oundary con d itions, p e rf orm i ng four -dim ensional data assim ilation (FDDA), and evaluating MM5 sim u lations for the year 1996. 2. Set up Domains —W orking with th e Client Repr e s enta tive, f i naliz e the m e teorological m odeling dom a ins and set up MM5 to run on them
. 3. Configure Model —Working with the Client Re presen tativ e , finalize th e configuration of MM5, including selecti on of boundary layer schem e and other physics options. Make modifications to MM5 to output additional variables if needed. 4. Preprocess i ng —Configure and run MM5 preprocessors.

1Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 5. Quality Assure Inputs —Review preprocessor logs and output files as needed to confirm that the MM5 inputs are reasonable. 6. Run MM5 —Prepare scripts to run MM5 and ar chive results. Provide staff and dedicated computing resour ces to run MM5. Monitor MM5 execution and take corrective actions should problem s arise. 7. Quality Assure/Evaluate MM5 Output —Review MM5 simulation logs. Visualize MM5 output and review visualizations. Ca lculate model perform a nce m e trics and generate graphics depicting m odel perfor m a nce. Metr ics will include b i as, error, correlation coefficient, and index of agreem ent for wind speed and direction, tem p erature, cloud cover, and m o istu re. Graphical analy s es will includ e com p arisons of observed and m odeled param e ters as tim e-series at specific locations and contour plots of m odeled va lues with observed values overplotted. [Note: Precipitation plots have been subs tituted f o r cloud cover, since total cloud cover is not an MM5 output variable. Gi ven the amount of data involved, the tim e series requirem ent was deem ed not to be a pplicable for this portion of the task. ] 8. Deliver out puts —Copy MM5 output files to m a gnetic tapes in a suitable for m at and ship th e tapes to a lo cation in the Un ited States, to be specified by the Client Representative. 9. Documentation —Prepare a report describing the i nputs, configuration, and results of this task. The report will be p r ovided in both hard and soft copy. Three appendices are included in this report. Appendix A contains the m o del configuration file. Appendix B contains supplem ental analysis/statistical plots. Appendix C describes the supplem ental color graphic files.

2Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 2. MODEL DESCRIPTION MM5 is one of the leading three-dim e nsiona l prognostic meteorological m odels available for air quality studies. It uses an ef f i c i ent sp lit se m i -im p licit tem poral integration schem e and has a nested -gr i d capability that c a n use up to ten d i f f

e rent dom ains of arbitr a r y horizon ta l resolution. T h is allows MM5 to sim u late local deta ils with high resolution (as fine as ~1 km ), while accou n ting for influences from great d i stan ces, with ho rizon t al reso lutions rang ing to about 200 km. MM5 uses a terrain-following nondim e nsionaliz ed pressure, or sigm a-P, vertical coordinate sim ilar to that used in m a ny operati onal and research m odels. In the nonhydrostatic MM5, the sigm a levels are defined according to an initia l hy drosta tic ally balanced re f e rence state so that these levels are also tim e-invarian t. The vertical sigm a-coordinate is determ ined in term s of pressure: σ p = (p – p t )/(p s – p t ), where p s and p t are th e s u rface and top pressures of the m odel, p t being a constant. The m e teorological fields can also be used in other air qu ality models with dif f

e re nt coordinate system s by perform i ng a vertical interpolation f o llowe d by a m a ss-consistency reconciliation step. The m odel c ontains several types of planet ary boundary layer (PBL) param e terizations suitable for air quality applica tions, which represent subgrid-sca le turbulent fluxes of heat, moisture, and m o m e ntum. A modified Blackadar PBL 8 schem e uses a first-order eddy diffusivity for m ulation for stable and neut ral environm ents and a nonlocal -closure schem e for unstable regim e s. The Gayno-Seam an PBL 9 schem e uses a prognostic equation for the second-order turbulent kinetic energy, while di agnosing the other key boundary layer term s. This is referred to as a 1.5-order PBL, or level-2.5, schem e 10 . W h ile not spec if ically developed for air quality application s , the m odel contains th e Hong and Pan Mid-Ran g e Forecast (MRF 11 ) schem e , which has the adv a ntage of calculating the v e rtical exchange coefficient, K v, that is used as input to several common air quality m odels. Initial and lateral bounda ry conditions are specified for real-data cases from m e soscale 3-D analyses perform e d at 12-h intervals on th e outerm ost grid m e sh selected by the user. Surface fields are an aly zed at 3

- h in tervals. A Cressm an-based techn i que is used to analyze standard surface and rad i osonde observations, us ing the Euro pean Center for Medium-range Weather Forecasting (E CM W F ) Tro p ical Ocean a nd Global At m o sphere (TOGA) analysis as a first guess. The lateral boundary data are introdu ced using a relaxation technique applied in the outerm o st five rows and colum n s of t h e coarsest grid dom ain. For m o st tradition a l (1 -h standard ) high-ozone ep isodes, pr ecipita tion is n o t the dom inant factor. On the other hand, preci pitation events m a y have a grea ter im pact on 8-h average ozone episodes. The MM5 contains five convective param e terization schem e s (Kuo, Betts-Miller, Fritsch -Cha ppell, Kain

- F ritsch, and Grell). It al s o has an exp lic it r e solve d -scale pre c ipita tion schem e that solves prognostic equations for cloud water/ice (q c ) and larger liquid or frozen hydrom eteors (q r ). In addition, the m o del contains a short- and long-wave radiation param e teriz a tion 12

.
3Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 The horizon tal g r id has an Arakawa-Lam b B- staggering of the velo c ity variab les with respect to th e scalars (Fi gure 2-1). It can be seen that the sc alars (T, q, etc.) are defined at the center of the grid square, while the eastward (u) and northward (v) velocity com ponents are collocated at the corn ers. The center points of the grid squares are referred to as cross points, and the corner points are dot points. Hence, horizontal velocity is defined at dot points, for exam ple, and when data are input to the m odel the prep roce ssors do th e necessary in terpo l ations to assure consistency with the grid.

Figure 2-1. Schematic representation sho w i n g the horizontal Arakaw a B-grid staggering of the dot ( O ) and cross (x) grid points. The smaller inner box is a representative mesh staggering for a 3:1 ratio of coarse-grid distance to fine-grid distance. (Grell et al., 1994) The nonhydrostatic m odel coordinate uses a re ference-state pressure to define the coordinate rather than the actual pressure that is used in the hydrostatic m odel. It can be seen from Figure 2-2 that σ is zero at the top and one at the su rface, and each model lev e l is defined by a value of σ . The m o del vertical resolution is defined by a list of values between zero and one that do not necessarily have to be ev enly spaced. Commonly the re solution in the boundary layer is m u ch fine r than above, and the number of le vels m a y vary from 10 to 40, although in principle there is no lim it.

4Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 2-2. Schematic representation of the vertical structure of the model. The example is for 15 vertical la y e rs . Dashed lines denote half-sigma levels, solid lines denote fullsigma levels. (Grell et al., 1994)

5Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 3. MODEL CONFIGURATION For this project we used MM5 Version 2.12 with m odifications to allow the output of the vertical exchange coefficient (K v ) for use in the UAM-V air quali ty m o del. W e also modified MM5 to use the Meteorology-Coupler (MCPL) ( http://envpro.ncsc.org/ projects/ppar/mcpl.htm l ) module to o u tput selected variab les in the Inpu t/Output Applications Pro g ramm ing I n terface (I/O API) ( http://envpro.ncsc. o rg/products /ioap i/ind e x. htm l ) for m at. This allowed for rapid review of model results using MC NC’s Package for Analys is and Visualization of Environm ental data (PAVE) ( http://envpro.ncsc.org/EDS S /pave_doc/Pave.htm l ) . The m odel was run for the period from 0000 UTC January 1 through 1200 UTC Decem ber 30, 1996. 

### 3.1 DOM A INS The dom ain for MM5 covers the entire United S t ates at 108-km and 36-km resolutions, and the western half of the United States at 12- km resolution (Figure 3-1). The horizontal grid sizes are shown in Table 3-1.

Figure 3-1. MM5 domai ns for the 1996 simulations.

6Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 3-1. Horiz o ntal structure o f the MM5 modeling sy stem Do m a in Resolution E-W Cells N-S Cells E-W Startin g Cell N-S Starting Cell D 0 1 1 0 8

- k m 6 6 8 8 1 1 D 0 2 3 6

- k m 1 1 4 1 6 8 1 5 1 8 D 0 3 1 2

- k m 1 8 9 1 6 5 3 5 2 1
The vertical dom a in extends fr om the surface to the100-m b pr essure surface in 23 layers. The first layer has a thickness of approxim a tely 38 m . The vertical structure is described in further detail in Table 3-2, where the height s are calcu lated u s ing stand a rd atm o spheric conditions. Table 3-2. Vertica l structure of th e MM5 mo deling system Level Sigm a Height (m ) Pressure (mb) Thickness (m ) 0 1.000 0.0 1000.0 0.0 1 0.995 38.0 995.5 38.0 2 0.988 91.5 989.2 53.5 3 0.980 152.9 982.0 61.4 4 0.970 230.3 973.0 77.3 5 0.956 339.5 960.4 109.2 6 0.938 481.6 944.2 142.1 7 0.916 658.1 924.4 176.4 8 0.893 845.8 903.7 187.8 9 0.868 1053.9 881.2 208.1 10 0.839 1300.7 855.1 246.8 11 0.808 1571.4 827.2 270.7 12 0.777 1849.6 799.3 278.2 13 0.744 2154.5 769.6 304.9 14 0.702 2556.6 731.8 402.1 15 0.648 3099.0 683.2 542.4 16 0.582 3805.8 623.8 706.8 17 0.500 4763.7 550.0 957.9 18 0.400 6082.5 460.0 1318.8 19 0.300 7627.9 370.0 1545.5 20 0.200 9510.5 280.0 1882.6 21 0.120 11465.1 208.0 1954.6 22 0.052 13750.2 146.0 2285.1 23 0.000 16262.4 100.0 2512.1

7Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

### 3.2 PHYSICAL OPTI ONS The physical options selected for this c onfiguration of MM5 include the following: 1. One-way nested grids 2. Nonhydrostatic dynam i c s 3. Four-dim ensional da ta as sim ilation ( F DDA): • Analysis nudging of wind, tem p erature, and m i xing ratios • Nudging coefficients range from 1.0 × 10 -5 s -1 to 3.0 × 10 -4 s -1 4. Explicit m o isture tre a tment: • 3-D predictions of cloud and precipitation fields • Sim p le ice m i crophysics • Cloud effects on surface radiation • Moist vertical diffusion in clouds • Nor m al evaporative coo ling 5. Boundary conditions : • Tim e and inflow/outflow relaxation 6. Cum u lus cloud param e terization schem e s: • Anthes-Kuo (108-km grid) • Kain-Fritsch (36-km grid) 7. No shallow convection 8. Full 3-dim e nsional Coriolis force 9. Drag coef f i cients v a ry with stability 10. Vertical m i xing of m o m e ntum in m i xed layer 11. Virtual tem p eratu r e effects 12. PBL process param e terization: MRF schem e 13. Surface layer param e terization : • Fluxes of mom e ntum , se nsib le and latent heat • Ground temperature prediction us ing energy balance equation • 24 land use categories 14. At m o spheric radiation schem e s: • Sim p le cooling • Long- and short-wave radiation schem e 15. Sea ice treatm ent: • Forced Great Lakes/Hudson Bay to perm anent ice under very cold conditions • 36-km treatm e nt keyed by observations of sea ice over the Great Lakes 16. Snow cover: • Assum e d no snow cover for July and August • National Center for Environm ental Predic tion (NCEP) snow cover for January to June, and for Septem ber to Decem ber A few comm ents on the above list: The physical param e ters and options listed above were chosen for their suitability for U.S. modeling duri ng all four seasons. Howeve r, it is important to point out that the param e terization schem e used to represent atm o spheric turbulent m i xing in this study (MRF ) was a local-closure schem e . In this si m p le schem e the coeffici ents of vertical eddy diffusivity are estim a ted (not calculated) using formulations suggested by Hong and Pan 11 . The
8Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 depth of the boundary layer is a f unction of the bulk Richardson num b er. The prim ary benefits of using the MRF sche m e are its speed of execution, its robustness, and its ab ility to rea d ily outpu t K v ’s. A concern is that its sim p lic ity m a y not allow it to perform adequately. Also, the am ount of longwave co oling of the land surf ace will not b e restricted, a s itu ation th at m a y lead to strong negative turbulent sensible heat fluxes over som e regions at night, ther eby excessively cooling the surface. In tes t sim u lations we no ticed spikes in the turbulent sens ible heat fluxes as a result of oscillations in the ground/skin te m p erature calculations. Som e tim es this error actually caused the model to f a il to execu te. W e identif ie d the sourc e of error re sp onsible fo r these oscillations and im ple m ented a process tim e -step m e thodology (m ite ring) for tim e integration of ground/skin tem p erature calcu lations. This new procedure was found to be successful in elim inatin g spurious oscillations in boundary layer fields. Below is the code before m odification, followed by the modified code.

IF(ISOIL.EQ.0)THEN SLAB.88 DTHGDT(I)=(RNET(I)-QS(I))/CAPG(I,J)-HM(I) SLAB.89 ELSEIF(ISLDIM.EQ.1.AND.ISOIL.EQ.1)THEN 24JUN97.355 C DETERMINE SOIL TIMESTEPS PER DELTSM FOR I-SLICE, NSOIL SLAB.91 NSOILI=1+IFIX(4*DIFSL/(ZS(2)-ZS(1))*DELTSM/DZS(1)) 25MAY98.774 NSOIL=MAX0(NSOIL,NSOILI) SLAB.93

C...Portions Copyright 1999, MCNC--North Carolina Supercomputing Center C... Kiran Alapaty has modified slab.F for use with MRF scheme. C IF(ISOIL.EQ.0)THEN SLAB.88 DTHGDT(I)=(RNET(I)-QS(I))/CAPG(I,J)-HM(I) SLAB.89 ELSEIF(ISLDIM.EQ.1.AND.ISOIL.EQ.1)THEN 24JUN97.355 C DETERMINE SOIL TIMESTEPS PER DELTSM FOR I-SLICE, NSOIL SLAB.91 NSOILI=1+IFIX(4*DIFSL/(ZS(2)-ZS(1))*DELTSM/DZS(1)) 25MAY98.774 NSOIL=MAX0(NSOIL,NSOILI) SLAB.93 CAQM: Kiran: Ensure stability if large diffusion exists in soil layers AQMSOIL.01 CAQM: particularly coarse horizontal resolution domain(s). AQMSOIL.01 IF(RNET(I).GT.0.0 .and. DELTSM.GE.29.) then AQMSOIL.02 DTKAY = AMIN1(30.,DELTSM) AQMSOIL.02 NSOIL = NINT(DELTSM/DTKAY) AQMSOIL.02 NSOIL = MAX(1,NSOIL) AQMSOIL.02 END IF AQMSOIL.02 CAQM: end of stable time step AQMSOIL.02

The wintertim e si m u lations exposed a coupl e of shortcom ings in MM5V 2.12. The snow cover fields are set at m odel initialization, and the snow is not “allowed” to m e lt or to accum u late. This som e tim es results in anom alously cold (or warm ) areas, depending on the synoptic situation. Of m o re concern is the m eans by which MM5 treats w a ter bodies that are subject to freezing over in the win t er. The defa ult m ode is to keep all water bodies liq uid regardless of the m e teorological situ ation. Our testing revealed that this deficiency leads to water tem p eratures never falling below 0 ° C. At tim es this resu lts in an artif i cia l “heat” low over Hudson Bay. To com b at this artificial feature, we im ple m ented a code change in MM5. If the
9Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 NMC analysis suggested snow cove r over a particular water cell, a nd if the initia l ground (skin) tem p erature was -5 ° C or less, we changed the land use category from water to snow/ice. For the 36-km grid, this m e thodology was only applied if th e National Ice Center (NIC) charts indicated significant ice coverage over the Great Lakes for the episode in question. (See http://www.natice.noaa.gov/greatlakes.htm .) The code that perform s this modification is included below.

IF(IFSNOW(1).EQ.0)GOTO 190 INIT.314 DO 185 I=1,ILX INIT.315 DO 185 J=1,JLX INIT.316 IS=INT(SATBRT(I,J)+0.001) INIT.317 CAQM IF((XLAND(I,J)-1.5).GE.0)THEN INIT.318 IF((XLAND(I,J)-1.5).GE.0 .and. SNOWC(I,J).LE.0.0)THEN INIT.318 SNOWC(I,J)=-0.01 INIT.319 ELSE INIT.320 SNOWC(I,J)=SNOWC(I,J) INIT.321 ENDIF INIT.322 CAQM-Kiran: Begin Mods IF((XLAND(I,J)-1.5).GE.0 .and. SNOWC(I,J).GT.0.0 .and. INITAQM1 & TGB(I,J).LT.268.15)THEN INITAQM1 XLAND(I,J) = 1.0 INITAQM2 SATBRT(I,J) = 24 INITAQM3 END IF INITAQM4 CAQM-Kiran: End Mods

10Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 4. MODEL INPUTS AND PREPROCESSING Various typ e s of m easured and analy zed m e teorological fields are required to prepare model inputs: surface d a ta, such as temperature, winds, and mois ture; up per-air data (soundings); sea surface tem p erature; and global/regional analysis fields. The National Center for Environm ental Predictio n supplies d a ta for the surface fields for every 3 and 6 hours, and upperair data at 12-h resolution. The National Me teorological Center provi des global and regional analysis data along with th e sea surface tem p erature data . Under the TOGA program , the ECM W F pr ovides global analysis fields; analys is fields are usually available at 0000 and 1200 UTC at a resolution of 2.5 ° on a latitude-longitude grid. In this sec tio n, we brief l y de scribe preprocessors and thei r f unctiona lities in preparing inputs to MM5. For detailed information, the reader is referred to the MM5 system docum entation available from NCAR ( http://www. mmm.uca r .edu/mm 5/ mm5-ho m e. htm l ). 

### 4.1 PREPROCESSING SYSTEM There are four prepro ces sors that pro cess va rious types of input data for use in MM5: Terrain, Datagrid/REGRID, Rawins, and Interp. 4.1.1 Terrain The Terrain preprocessor allows th e u s er to choose m a p projections, horizontal grid resolutions, the num ber of horiz ontal grid cells to be used in MM5 (and thus the domain coverage), and the num ber of land us e catego r ies to use in rep r esen ting each of the grid cells in the dom ain. Inputs are available in a preprocesse d for m at. For exam ple, global terrain and land use data are available in th e fol l o wi ng fi ve for m at s:
• 1 o (~111 km) • 5’ (~9 km ) • 30’ (~56 km) • 30” (~0.9 km ) • 10’ (~19 km) In this study we used 108-, 36-, and 12-km horizontal grid resolutions to perform the model sim u lations. Thus, 56-, 19-, and 9-km input da ta were used. There ar e 24 ca teg o ries in the U.S. Geological Survey (USGS) data to repr esent land use. Table 4.1 lists these land use catego r ies. The dom inant land us e was used for each of the grid cells. S m oothing an d desm oothing techniqu es were used to f ilter no ise in the ter r ain data (su c h as 2- ∆ X m o des). W e projected the input data to La m b ert-Confor m a l coordinates.

11Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 4.1. USGS land use categories 1. Urban 2. Dry Land Crops Pasture 3. Irrigated Crops Pasture 4. Mixed Dry/Irrig. Crops Past. 5. Cr ops/Grassland Mosaic 6. Crops/Wood Mosaic 7. Grassland 8. Shrubland 9. Mix Shrubland/Grassland 10. Savanna 11. Deciduous Broadleaf 12. Deciduous Needleleaf 13. Evergreen Broadleaf 14. Evergreen Needleleaf 15. Mixed F o rest 16. W a ter Bodies 17. Herbaceous W e tland 18. W ooden W e tland 19. Barren S p arse Vegetation 20. Herbaceous Tundra 21. W ooden Tundra 22. Mixed T undra 23. Bare Ground Tundra 24. Snow or Ice
4.1.2 Datagrid The Datagrid preprocessor is used to genera te m e teorological data at standard pressure levels and at all grid cells al ong the m a pped coordinate s. Typically, all the basic m e teorological param e ters (such as winds, tem p erature, re lative hum i dity, and geopotential height) are inte rpola t ed f r om a latitu de-long itude grid to a La mbert-Conf orm a l grid using a two-d i m e nsional 16-grid overlapping parabolic fit m e thod 13

. 4.1.3 Ra wins Datagrid output is used to im prove the analys is fields, since the original analysis data (TOGA/NM C) are at a very coarse horizontal resolution. Using the upper-air data, Rawins perform s objective analysis, a nd removes erroneous observations. 4.1.4 Interp The Interp preprocessor handles the data tr ansform a tion required to go from pressure levels to m o del sigm a-based levels. Appropriate vertical interpolati on schem e s are used to facilitate the proper transfer of data from pressu re to sigm a coordinates. This preprocessor takes in data generated by Datagrid and Rawins to crea te final formats of ini tial and lateral boundary conditions for the outermost dom a in (108-km grid). For the inner 36-km dom a in, this preprocessor is used in “back-end” mode to ge nerate initial and lateral b oundary con d itions. 

### 4.2 LATERAL BOUNDARY CONDI TI ONS For the outerm o st dom a in (108-km ), the late ral b oundary con d itions are u s ually available at 0000 and 1200 UTC from the Inte rp preprocessor. For the i nner 36-km dom a in, lateral boundary co nditions are generated for every hou r vi a the “back-end” m o de of Interp, using the 108-km results as input. During the MM5 m odel integ r ation, these lateral boundary co nditions are further interpo l ated in tim e to every m odel tim e -step.

12Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

### 4.3 DATA AS SIMILATION We chose to use the continuous FDDA m e t hod, in which prognostic fields (winds, tem p erature, and water vapor m i xing ratio) are continuously nudged towards respective observations. Basically, tim e tendencies from the an alysis fields are m e rged with tim e tendencies from the m o del in a m a nner such th at the m odel solution exhibits a sm ooth behavior. For each of the prognostic variables, there is an associated weighting function used in the assim ilation technique. In this study, winds were nudged in the entire atmos pheric colum n, while tem p erature and m i xing ratio were nudged only above the PB L. The weighting coefficients for various dom a ins used are given below: 108-km dom a in: Tem p erature and winds: 3.0 × 10 -4 s -1 Mixing ratio: 1.0 × 10 -5 s -1 36-km dom a in: Tem p erature and winds: 2.5 × 10 -4 s -1 Mixing ratio: 1.0 × 10 -5 s -1

13Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5



## 5. MODEL PERFORMANCE EVALUATIO N METHODOLOGY
5.1 DATA For this p r oject we used the Techniq u es De velopm ent Laboratory (TDL) U.S. and Ca nada surface hourly observations ( http://dss.ucar.edu/datasets/ds472.0/ ). These data contain m o st of the first- and second-order National Weather Servi ce stations, as well as autom a ted surface sites. W h ile these data have b e en subjec ted to a m easure of quality contro l, exp e rien ce has s hown that a few spurious reports typically rem a in. Tim e a nd budget constraints did not allow us to perform additional quality control on thes e data, so the plots that follow m a y show evidence of these erroneous reports. Becau se the err oneous reports are lim ited, however , the essence of the plots is unaffected. The analyses that follow focus pr im a r ily on daily averages. We included only the reports that had nonm iss i ng data for at least 21 h ours in a day; our experi ence with the TDL data suggests that erroneous reports ar e less likely to occur at stations that repor t regularly. We also perform e d a n alysis based on daily m a xim u m an d m i nim u m temperatures. Again, we included only regularly reporting stati ons, but we also excluded ma x i mu m/ mi n i mu m t h a t w a s considerably higher/lower than th e second highest/lowest value. For the upper-air wind plots, we modified Rawins to allow upper-air observations to be extracted. These data likewise were not quality controlled, so anom alous reports m i ght appear on the upper-air pl ots that follow. It should be reiterated that MM5’s quality control procedures au tom a tically rem ove erroneous observations before using them in its FDDA routine. 

### 5.2 PROCEDURES We perfor med qualitative and quantitative an alyses of various pr ognostic and diagnostic param e ters obtained from the MM5 sim u lations. Fi rst, we qu alitatively v i sually analy zed the spatial distributions of boundary layer param e ters such as simulated winds, tem p erature, water vapor m i xing ratio, m e a n sea level pressure, skin te m p erature, rainfall, and turbulent heat fluxes. Second, we condensed the hourly data to daily-avera ged quantities, as specified above. The daily data were used to produce spatia l plots of m odel results with observations (or m odel-observation differences) overlaid. To reduce the num b er of plots, we pro duced spatial plots for o n e tim e each month, and the 15 th day of each m o nth was rando m l y selected . Aloft, we produced once-a-m onth “snap shots” of the wind field at three levels; we alternated between 1200 UTC and 0000 UTC, since the lower levels could show a diurnal variation. Tim e series of observations/m odel predictions were also m a de at a f e w selec t ed s ite s. Finally, we pr oduced statistical plots for daily (CST) averaged tem p erature, m i xing ratio, wi nd speed, and wind direction, as well as daily m a xi m u m a nd m i nim u m tem p eratures. These plots contain specified m o del-observation averages, mean bias, m ean absolute error, index of agreem ent, and coefficient of determ ination. The plots w e re created for the en tire dom a in (p eripheral 2-3 cells exclude d), and also for the 4 subdom ains shown in Figure 5.1.
14Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## NW NE

SW SE Figure 5.1. Domains for statistical plots. The full domain matches the MM5 full 36-km domain w i t h a 3-cell buffer to the w est and s outh, and a 2-cell buffer to the east and north. The colored cells indicate those cells w i th valid o bservations for a randoml y selected day . The com pass designations NW, NE, SW an d SE are us ed to differentiate the four subdomains.

The following m e thodologies were used to accomplish m o del perform a nce evaluation.
• PAVE—The Package for Analysis and Visualiza tion of Environm ental data is a graphical anim ation to ol with in ter activ e GUI capabil ity. It is designed to assist m o delers perform i ng tim e series analysis, horizontal a nd vertical spatial analysis, and anim ations. Detailed inform ation on using PAVE can be found at http://envpro.ncsc.org/E D SS/pave_doc/Pave.htm l We used PAVE to perform qualitativ e anal ys is of various d i agnostic and prognostic param e ters, as well as quality assurance.
• GrADS—The Grid Analysis and Display Syst em is used to visualize m odeled data overlaid with observations. W e select ed th ree m o del ver tic al levels ( L =6, 11, and 16),
15Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 which approxim ately correspond to 400, 1400, and 3400 m above ground level (AGL). Spatial plots of wind, temperature, m i xing ra tio, and precipitation were generated with observations (or m odel-observation differences ) overlaid. These plots are presented in Section 6. F u rther details on GrADS can be obtained at http://grads.iges.org/grads/
. • XMgr—The XMgr is a sim p le and elegant gra phical tool that we used to produce tim e series plots. More inform ati on about XMgr can be found at http://p lasmagate.weizmann.ac.il/X mgr/
. • Graph and NCAR graphics—Graph is an MM5 postprocessor that produces graphical im ages of modeled input and output data. It uses graphical softwa re developed by NCAR. We used this postprocessor to produce sea-leve l pressure and 500-m b height spatial plots, and m odel sounding plots for preselected stati ons. These plots were generated daily at 0000 and 1200 UTC.

16Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 6. RESULTS In this sec tio n we analyz e th e m odel perform a nce. First we give a qualitative overview; then we focus on spatial analyses of specified qu antities at th e surface an d aloft. Next we exam ine statistical m easures of surface variables for the entire analys is d o m a in and for the four subregions. We then exam ine point-sp ecific perform a nce using tim e series. 

### 6.1 GENERAL OBSERVATIONS During the q u alitative ex am ination of MM5 output variables, three concerns surfaced. First, the PBL heights seem ed to be capped at about 2000 m
. This problem is m o st noticeable in the summ er, since PBL heights seldom approa ch 2000 m at other tim es during the year. The second concern involves lake tem p eratures. A pr oblem in MM5 version 2.12 exists whenever the “24 land use” option is selected, a nd this problem m a nifests itself in the temperatures assigned to the lakes. T h e resulting lake tem p eratures are far too high, often by as much as 10 ° C. W e did not notice any anom alous flows resulting from this e rror, but we have seen high tem p eratures and m i xing ratio s in the areas near th ese lakes. The problem affec t s only the Great Salt L a ke within the United S t ates, while num e rous Canadian lakes (e.g., Lake W i nne peg) are affected. The Great Lakes are immune to the problem
. A third concern is the treatm ent of sn ow by MM5. Because MM5 version 2.12 does not have a land -surface m o dule, the initial surface co nditions rem a in th rougho ut each individual 5.5 - day sim u lation. These initial snowfields are base d on the snow cover fields in the NCEP global analyses, which them selves are updated about onc e a week. So it is conceivable that the snow coverage used by MM5 near the end of a sim u la tion is over ten days old from the “observed” snow coverage field. The fact th at the NCEP analysis is at a 2.5° resolution (~270 km ) suggests that snow could be artificially spread out over too m a ny 36-km cel ls, espec i ally in are a s with significant topographical gradients. 

### 6.2 SPATI A L ANALYS ES 6.2.1 Upper-Level Winds We focused on three aloft levels fo r our upper-air anal yses: 0.938, 0.808, and 0.582 sigm a, which approxim a tely correspond to 400, 1400, and 3400 m AGL. These levels were chosen so that the lowest level is clearly in the m i ddle of the b oundary layer, the m i ddle layer is towards the top of the boundary/r esidual layer, and the highest level is clearly above the boundary layer. Obviously there are too m a ny days in our simulations to plot them all, so we focus only on a sam p ling. For the odd m onths (Janua ry, March, May, etc.) the plots are m a de at 1200 UTC on the 15 th of each m onth. The even months are p l otted at 000 0 UTC on the 15 th of each m onth. Appendix B contain s all these sam p le plots. Figu res 6.1-6.3 show the plots for April 15, 1996 (note that the im ages in Appendix B are larger and much easier to view).

17Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.1. Modeled and observed horizontal w i n d vectors at 0000 UTC 15 April 1996 at ~400 m AGL. Bold vectors are observations. Every fou r th model vector is plotted.

Figure 6.2 Modeled and observed horizontal w i n d vectors at 0000 UTC 15 April 1996 at ~1400 m AGL. Bold ve ctors are observations. Every fo urth model vector is plotted. 18Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.3. Modeled and observed horizontal w i n d vectors at 0000 UTC 15 April 1996 at ~3400 m AGL. Bold ve ctors are observations. Every fo urth model vector is plotted. Since we nudge the m o del to the observed uppe r-level winds, we expect the m odel and observations to show m u ch better ag reem ent than what we m i ght expect to see at the s u rface. At each lev e l th e m odeled wind direction s and m a gnitudes agree with the obs ervations. As expected, th e perform a nce above th e boundary layer (~3400 m altitud e ) is best, thoug h at no level does one see poor model- observation agreem ent. As exp ected, MM5 fully captures the large-scale synoptic flow
. 6.2.2 Surface Winds Later in this report we co ncentr ate on a sta tis tic al analys is of surface variables, and that is the prim ary m eans of model evalua tion. Still, statistics alone are insufficient to paint a com p lete picture of model perform a nce. W e augm ent the statistica l in for m ation by showing a sam p le spatial plo t of each surface variable that we ha ve statistically analyze d, starting with s u rface winds. Following the pattern we used for the upper-level winds, we have included dailyaveraged winds for the 1 5 th of each month. Figure 6.4 shows the surface wind plot for April 15, 1996. Appendix B includes the plots for the 15 th of each m o nth in 1996. F i gure 6.4 sh ows a large low-pressure system over the Great Lakes regi on, which MM5 resolves quite well. The strong flow behind the trailing cold fr ont is also resolved, though it appears that MM5 has stronger winds than what were actually observed. W h ile model output and observations show excellent directional agreem ent in the east, the mountainous west proves to be a different story. The m odel resolution of 36 km is clearly too coarse to m a tc h the observations. The hope is that at least the
19Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 synoptic flows over the mountains can be m odeled, a nd that these flows can be hinted at in the observations. That appears to be the case for April 15.

Figure 6.4. Near-surfac e dail y -averaged modeled and observed horizontal w i nd vectors for April 15, 1996. Bold vectors are observations. Every third model vector is plotted.
6.2.3 Surface-Level Temperatures We used linear interpolation between the ground and the low e st m odel layer to calculate tem p eratures at the observational height of 1.5 m. These interpolat ed near-surface temperatures were then av eraged over each 24-hou r peri od (00 00-2300 CST). The daily m a xim u m and m i ni m u m for each cell w e re also sto r ed for later analysis. Attempting to produce m ode lobservation plots for scalar quanti ties such as temperature can be quite a challenge for a dom ain as large as the one we used for this modeli ng. Even the wind plots show n earlier becam e cluttered at tim e s, and we plotted only every th ird or fourth model vect or. For sm aller dom a ins one can plot the observed value on top of m odel c ontours/tiles, but these num b ers tend to cover each other u p (and possibly even th e m odel resu lts too). It is a diffi cult problem . What we decided to do was to plot m odel-observation di fferences. Clearly the problem of the num bers obscuring each other exists, but we attem p ted to minim i ze this effect by colorizing thes e differences. Under this schem e , only absolute diffe rences are plotted. The color scale is given in Table 6.1.

20Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.1. Difference color scale D i f f

e r e n c e C o l o r s c a l e # < -5 Deep Blue -5 < # < -3 Medium Blue -3 < # < -1 Light Blue -1 < # < 1 White 1 < # < 3 Yellow 3 < # < 5 Orange

# > 5 Red The difficulty with the approach given above is that it does not translate to black-andwhite disp la ys. Also the num b ers the m selves are not easy to read. Nevertheless it seems to be as good a way as any to plot scalar overlays. One sh ould also keep in m i nd that the num bers that appear on these plots are not the s a me as those u s ed in the statistical p l ots. For these ov erlay plots, all observations within th e domain are plotted. The m o del va lu es ar e inte rpola t ed to the observational points by the standa rd GrADS Cressm an-type schem e . The statistical plots tha t are shown later follow a slightly different m e t hodology. For those plots, the observations are accum u lated within m o del grid cells. Multip le ob serv ations w ithin a sing le cell are av eraged. All cells with observations are paired with the corresponding m o del cells to produce the raw statistical data. Eith er ap proach is acceptab l e, es pecially for quali tative assessm ents, which is our purpose in this section. Figure 6.5 shows the daily-averaged tem p erat ure plot for April 15. Figures 6.6-6.7 show correspond ing plots for m i ni m u m and m a xim u m tem p eratures. Appendix C is an accu mulation of gif files containing the com p lete suite of figures for the 15 th of each month in 1996 , at a 33% larger size to facilitate viewing. Notice in the temperature plots the dele terious effects of the snowfields, especially on the m a xi mum tem p eratur es in Figure 6.7. Consider a west-east transect starting over the state of Wash ington. Modeled m a xim u m te mperatures are in the range 10-15 o C, and these values are close to what the observa tions indicated. Eastern W a shington, Idaho, and western Montana show a clear snow signa ture, with daily maxim a just over 0 o C. These low values lead to a trem endous underpredi ction in the m odel of more than 10 o C. Continuing eastward, M M 5 places n o snow over eastern Mo ntana, so daily m a xi m u m tem p eratures are 15- 20 o C, agreeing within 1 o C to the observations. T h is patt ern o f significant tem p erature underpredictions over snow and m o re reasonable predictions in the absence of snow is a common oc currence in our m odeli ng. Part of the problem is due to the lim itations of MM5 regarding snow cover, as has al ready been discussed. But that doe s not explain everything we see in the m odeling. Part of the problem is a know n defect in MM5 whenever warm advec tion occurs over snow cover. The ai r quickly and sometim es anomalous ly cools to m a tch the frozen surface. An additional concern is the m e thodol og y em ployed in our analy s is. The linear interpolation technique we used m eans that th e M M 5 skin tem p eratu r e is the dom inant factor when we calculate the values we use to com p are with observations. In the absence of snow this procedure works quite well. Over snow, however, th ere is likely to be a d ecoupling, to an extent, between the surface and the atm o sphere. W e have noticed that the lay e r 1 air tem p eratures in these cases could be over 5 o C higher than the skin tem p er ature during the afternoon. The differences can be m u ch greater at the edge of the snow pack. Th ese snow effects should be kept in m i nd when exam ining the statistic a l plo t s tha t are pr esente d late r in this repor t.
21Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.5. Modeled dail y -average d temperatures ( o C) from MM5 at 1.5 m height for April 15, 1996. T he numbers are model-observation differences; blue colors represe nt model underpredictions, w h it e colors indicate near 0 differences, and y e llow / orange/red colors indicate model overpredictions.

22Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.6. Modeled dail y minimu m temperatures ( o C) from MM5 at 1.5 m height for April 15, 1996. T he numbers are model-observation differences; blue colors represe nt model underpredictions, w h it e colors indicate near 0 differences, and y e llow / orange/red colors indicate model overpredictions.

23Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.7. Modeled dail y maximu m temperatures ( o C) from MM5 at 1.5 m height for April 15, 1996. T he numbers are model-observation differences; blue colors represe nt model underpredictions, w h it e colors indicate near 0 differences, and y e llow / orange/red colors indicate model overpredictions.
6.2.4 Surface-Level Water Vapor Mixing Ratio Our analysis of “surface”-level water vapor m i xing ratio follo wed the same procedu r e we used for temperature, except we perf orm e d no ve rti cal interpolation; we sim p ly used the lowest model layer value. Figure 6.8 show s the daily-averaged plot for our sam p le day. As usual, plots for t h e 15 th of each m o nth can be fo und in Appendix C. No strong m o isture bias is ev ident, and the m odeled pattern seem s to agree reasonably well with th e observa tio ns. Overall the water vapor m i xing ratios are modeled very well throughout the year.

24Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Figure 6.8. Modeled near-surface dail y -avera ged w a ter vapor mixing ratios (g/kg) from MM5 fo r Ap ril 15, 1996. The numbers are model-observation differences; blue colors represent model underpredictions, w h ite c o lors indicate near 0 differences, and y e llow / orange/red colors indicate model overpredictions.

25Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 6.2.5 Mixed-Layer Depths We have already stated our concern about the boundary laye r heights output by MM5 during the sim u lation period (see Section 6.1). Unfortuna tely, routine observations of PBL heights are not directly m a de, so it is difficult to determ in e how well the MRF scheme is actually doing. To address this issue, we crudely estim ated obs erved m i xing heights from the 0000 UTC s oundings. Our sim p le m e thodology assum e d a potential te mperature lapse rate of no m o re than 1.0 ° C/km in the m i xed layer. Once th e potential tem p erature in creas ed at a higher rate, we assum e d that it did so because it had encountered th e m i xed-lay e r cap ping inversion. W e did try to in corp orate into this m e thodology inform ation from the dew point pr ofile, but that actually seem ed to cause the estim ated PBL heights to deviate further from wh at an experienced m e teorologist would estim a te based on a visual exam ination of an observ ed sounding. Due to the weaknesses of this m e thodology, one should not place a lot of cred ence in the so-called PBL “observations,” especially if the valu e is low. Any number of factors (showers, fronts, etc.) could cause the m e thodology to fail. This m e thodology has very lit tle chance of producing reasonable results in the winter half of the year. It is m o re dif f i cult to im agine a s cen ario th at wo uld cause th e “obs erved” PBL heights to exceed th e actual h e ights , rather than the rev e rse. Therefore, if MM5 predicts a m i xed-layer depth of 1800 m , while the “observed” depth is 5 00 m , this situation shou ld not be considered evidence that the m odel is flawed. However, if a couple of the “obs ervations” indicate P B L heights in excess of 3000 m , while the m odel predicts 1800 m in that area, there is ju stification in assum i ng a model low bias in m i xing-height prediction. Figure 6.9 shows the m a xim u m m i xing height s f o r April 14, 1996 (based on 0000 UTC April 15 soundings). As has already been noted, the PB L depths are capped in the 1750-2000 m range, which appears to be a problem even this early in the year, based on the “observations” in Texas and New Mexico. The snowfield dram atically inhibits the m odel m i xing heights. In the mountainous northwestern United States, where we previously noted significant temperature underpredictions stemm i ng from the underlying snowfield, we note observed m i xing heights well over 1000 m , m u ch higher than that out put from MM5. Otherwise the modeled m i xing heights seem to reasonably correlate with the “o bservations.” One should also note the paucity of plotted “observations” east of the Mississippi River. This occurs whenever the observational procedure fails to calculate a va lue at the sounding site, and this situation is the norm during the winter. The full set of once-a-m onth plots of m o deled versus “observed” PBL heights can be found in Appendix B.

26Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

500 1000 1500 1750 2000 Figure 6.9. Modeled and “observed” day time maximum depths (m) of the mixed la y e r for April 14, 1996.
6.2.6 Rainfall From an air quality m o deling point of view , it is important to corre ctly p r e d ict the geographical location of rainfall . In m e teorological m odeling, it is additionally im portant to accurately p r edict rainfall am ounts. Figure 6.10 shows the 2 4 -h (ending 1200 UTC April 15, 1996) accu mulated precipitation amounts outp u t by MM5 with the co rresponding surface m easure m ents. For this day the observati ons indicate precipitation along the extrem e northwestern U.S. coast, in New England, and es pecially in the Mississippi Valley. MM5 also produces precipitation over these areas, while no t predic ting p r ecip ita tion over m o st of the areas where no observed precipitation was reported. In ge nera l the predic ted pr ecipitation m a tches the observed both in area coverage and, to a le sser extent, in amount. Daily (once a m onth) precipitation plots can be found in Appendix B.

27Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

0. 01 0.1 0. 2 0. 5 1 2 4 Figure 6.10. Modeled and observed accumulated rainfall for 24 hours ending 1200 UTC 15 April 1996. The numbers indicate observed rainfall in tenths of a mm/da y . A “0 ” denotes no observed rainfall and “1” den otes trace amount of observed rainfall.

28Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

### 6.3 POINT-SPECIFIC ANALYSES 6.3.1 Analyses of Meteoro log i cal Variab les at Surface Observational Data Sites We selected six surface o b servational sites fo r co mparison with m odeled data (Tab le 6.2). The sites were chosen prim arily for geographical balance, while also providing a sam p ling of the various geographical features enco untered in the dom a in. For exam ple, the northern sites provide inform ation about m odel perform a nc e during the s now season and the tran sition from snow to no snow, and vice versa. The Los Angeles site gives a coastal perspective. Th e plots for Missoula, MT, show conditions in mountainou s terrain. Fo r each of these sites we p r oduced time series plots (m odel vs. observed) for daily-averaged te mperature, daily m a xim u m tem p erature, daily m i ni m u m temperature, daily-averaged water vapo r m i xing ratio, daily-averaged wind direction, and daily-averaged wind speed. Each plot is broken into three segm ents of four m onths apiece. Also note that these observational tim e series re present the average of all valid observations in each cell co ntain i ng a site from Tabl e 6.2. Appendix B contains the en tire suite of surf ace tim e series plots. A sa m p le plot of daily m a xi m u m temperature f o r Missoula is shown in Figure 6.11. MM5 alm o st always underpredicts the m a xi m u m tem p erature for this site, es pecially in the la te spring when the m odel keeps the temperatu r es lo w due to snow coverage. After the snow goes away on April 23, the perform a nce is m u ch im pr oved for a couple of m ont hs. By July and August the underpredictions becom e more significant, often being greater than 5 o C. Perform a nce im proves again for September and early Octobe r. This cell becom e s snow-covered again on October 20, and the m o del underpredictions increase to 5-10 o C for the next 15 days or so. Then the cell becom e s snow- f ree for 10 days before be com i ng snow-covered the rest of the year. The last m onth and a half show som e of the best m o del perform a nce of the en tire year, indicating that snow cover in and of itself does not nece ssarily portend poor m a xi m u m temperature perform a nce. The tim e series for the other variables and for other sites reveal m o re interesting inform ation. In general, the wind direction plots show excellent agreem ent for the sites that are relatively geographically hom ogeneous (ATL, DFW , SYR, MSP). Los Angeles and Missoula show m u ch worse perform a nce, presum ably because 36-km resolution cannot resolve the flow features adequately in coasta l or m o untainous environm ents. Wh en viewing the wind direction plots, one should keep in m i nd the discontinuity at 360 o /0 o that som e tim es causes larg e apparen t differences in these plots. The wind speed plots s how results sim ilar to w h at was seen in the wind direction plots, though with slightly m o re error. The daily-averaged m i xing ratio plots show excellent agreem e n t for all plotted site s except Los Angeles, which again suggests a resolution problem near a large water source. Many of these site s show a tendency to slightly underpredict water vapor m i xing ratio during the su mmer, while showing no bias the rest of the year. The snow cover effects already discussed reve al them selves in all of the tem p erature plots. In the absence of snow cover, the southern sites tend to underpredict tem p eratures m o re so than the northern sites, Missoul a excluded. This underprediction of tem p eratures is most evident in the daily m a xi m u m pl ots during snow cover in th e spring, but it shows itself in all the plots at one tim e or another. The transient synoptic features such as frontal passages appear to be picked up at approxim ately the right tim e s in this m odeling. Surprisingly, MM5 even tends to underpredict the daily m i nim u m te m p eratures. 29Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.2. Surface observational sites Station ID Site nam e LAX Los Angeles, CA DFW Dallas/Ft Worth, TX A T L A t l a n t a , G A M S O M i s s o u l a , M T MSP Minneapo lis /St Paul, M N S Y R S y r a c u s e , N Y

Figure 6.11. Temporal variation of modeled and observed dail y max i mum temperature for Missoula, MT, for the period January 2 to December 29, 1996.

30Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 6.3.2 Statis tica l Analyses of Meteoro log i cal Variab les To com p lete our m odel perform a nce eval uation, we calculated and plotted a few statistical measures for the va riables we have been tracking. The variables ar e daily-averaged wind direction and speed, daily-averaged tem p er ature and water vapor m i xing ratio, and daily m a xi m u m a nd m i nim u m tem p eratures. The m e thodol ogy is the sam e as what we used for the tim e series plots in that we are perform i ng a cell-to-cell com p arison. The observations are averaged into the cells d e m a rcated by the m odel, and in the case of winds, rotated to the m odel coordinates. The resultant wind di rections are therefore relativ e to the m odel grid, not to realearth coordinates. Figure 5.1 contained an ex ample of the density of cells with valid observations. It also shows the subdom ains over which the f o llowing s t a tistics we re calcu lated. The plots are broken in to four seaso n s, each la sting three m o nths, startin g with Janua ry. The statistics sho w n are (1) m ean of observations and m odel output, (2) m ean absolute error, (3) m ean bias, (4) ind e x of agreem ent (IA), and (5) coefficient of determ ination (r 2 ); the latter four statistics are defined below. 1. Mean Absolute Error = 

## ∑

− | ) ( | 1 O M N where N is the num ber of station pairs, M is the modeled value, and O is the observed value 2. Mean Bias = 

## ∑

− ) ( 1 O M N
3. Index of Agreem ent = 1- 

# ( ) ⎥ ⎥ ⎦ ⎤ ⎢ ⎢ ⎣ ⎡

− + − 2 2 | | | | ) ( O O O M RMSE N

where RMSE (root m ean square error) = 5
. 0 2 | | 1 ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− 

## ∑ O M N
4. Coefficient of Determ ination (r 2 ) = 

# [ ] ∑ ∑ ∑

− − − − 2 2 2 ) ( ) ( ) )( ( M M O O M M O O

Table 6.3 shows the statistical summary for wind direction. We calcu lated wind direction only whenever the observed wind speed exceed ed 0.5 m / s. W i nd direction statistics can be slightly m i sleading in th at we are d e aling with a discontinuous quantit y. Som e m easures, like index of agreem ent, depend on the actual error divided by the total possi ble error ran g e. The m a xi m u m error rang e is assum e d to be 360°, tho ugh in actu a lity the m a ximum error is only 180°. This num e rical artifact leads to higher than realistic index of agreement values. Generally speaking, m odel perform a nce tends to be better in the east than in the west, presum ably due to orographic effects. The summ er period also tends to show slightly poorer perform a nc e than is
31Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 seen for the other seasons. This is expected, si nce the synoptic forcing is weakest in the summ er. Overall, MM5 does a credible job in m odeling the daily-averaged wind direction.

32Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.3. Surface sta t istica l summary for w i nd direction J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Wind direction Obs m e a n 2 0 3
. 8 1 9 0
. 9 1 8 7
. 1 1 9 6
. 0

(full dom ain) MM5 m ean 202.2 190.0 186.9 194.7 B i a s 4
. 0 2
. 5 3
. 4 4
. 0 A b s o l u t e e r r o r 2 3
. 0 2 2
. 7 2 4
. 6 2 1
. 7 R 2 0
. 4 6 0
. 4 8 0
. 4 3 0
. 4 8 Index of agreem ent 0.97 0.96 0.96 0.97 R M S E 3 7
. 1 3 6
. 1 3 8
. 5 3 5
. 2 Wind direction Obs m e a n 1 9 8
. 1 1 9 6
. 1 1 7 9
. 6 1 9 3
. 8 (SW subdom ain) MM5 m ean 199.5 200.8 184.1 191.4 B i a s 6
. 8 6
. 5 6
. 7 7
. 3 A b s o l u t e e r r o r 3 0
. 9 2 7
. 4 3 0
. 3 3 0
. 5 R 2 0
. 3 4 0
. 4 4 0
. 4 0 0
. 3 0 Index of agreem ent 0.95 0.94 0.92 0.95 R M S E 4 7
. 8 4 3
. 2 4 6
. 6 4 7
. 1 Wind direction Obs m e a n 1 9 7
. 1 1 8 0
. 6 1 6 1
. 0 1 7 3
. 8 (SE subdomain) MM5 m ean 195.6 176.6 159.7 171.7 B i a s 0
. 7

- 0
. 3 0
. 4 0
. 1 A b s o l u t e e r r o r 1 7
. 4 1 7
. 9 2 0
. 3 1 6
. 9 R 2 0
. 5 8 0
. 5 9 0
. 5 2 0
. 5 9 Index of agreem ent 0.98 0.97 0.97 0.98 R M S E 2 7
. 0 2 7
. 7 3 1
. 0 2 5
. 8 Wind direction Obs m e a n 2 0 4
. 2 1 9 9
. 0 2 0 3
. 5 2 0 7
. 0 (NW subdom a in) MM5 m ean 197.6 198.5 204.2 204.4 B i a s 7
. 2 4
. 5 6
. 4 6
. 0 A b s o l u t e e r r o r 2 9
. 4 2 8
. 1 2 9
. 6 2 7
. 0 R 2 0
. 3 6 0
. 3 4 0
. 2 7 0
. 3 7 Index of agreem ent 0.95 0.96 0.95 0.95 R M S E 4 5
. 4 4 3
. 6 4 5
. 6 4 2
. 5 Wind direction Obs m e a n 2 1 2
. 7 1 8 8
. 8 1 9 5
. 4 2 0 5
. 0 (NE subdomain) MM5 m ean 212.8 185.7 191.9 206.0 B i a s 2
. 3 0
. 2 0
. 9 3
. 0 A b s o l u t e e r r o r 1 7
. 0 1 8
. 7 1 9
. 4 1 5
. 3 R 2 0
. 5 5 0
. 6 0 0
. 5 4 0
. 6 3 Index of agreem ent 0.98 0.98 0.98 0.98 R M S E 2 7
. 2 2 8
. 9 2 9
. 7 2 3
. 6 Table 6.4 shows the statistical summary for wind speed. MM5 consistently overpredicts wind speed by 10-20%. This is not totally unexp ected, since the MM5 value is based on a 38-m layer and the observations are m a de around 10 m. As we saw with wind direction, perf orm a nce is better in the east than in the west. Seasonal effects vary from region to region, with the SE quadrant sh owing the most noticeab le eff ect, a perform a nce dip in th e su mmer.

33Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.4. Surface statistical summary for w i nd speed J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Wind speed O b s m e a n 3
. 4 1 3
. 0 9 2
. 5 2 3
. 1 5 (full dom ain) MM5 m ean 3.93 3.46 2.95 3.77 B i a s 0
. 5 1 0
. 3 8 0
. 4 4 0
. 6 2 A b s o l u t e e r r o r 1
. 0 6 0
. 9 2 0
. 8 6 1
. 0 7 R 2 0
. 6 3 0
. 6 4 0
. 6 1 0
. 6 2 Index of agreem ent 0.88 0.88 0.86 0.86 R M S E 1
. 4 7 1
. 2 5 1
. 1 5 1
. 4 6 Wind speed O b s m e a n 3
. 1 9 3
. 6 3 2
. 8 5 3
. 1 1 (SW subdom ain) MM5 m ean 3.72 3.90 3.12 3.74 B i a s 0
. 5 4 0
. 2 7 0
. 2 7 0
. 6 3 A b s o l u t e e r r o r 1
. 1 8 1
. 1 1 0
. 9 4 1
. 2 2 R 2 0
. 6 0 0
. 5 9 0
. 5 4 0
. 5 4 Index of agreem ent 0.86 0.87 0.85 0.84 R M S E 1
. 6 3 1
. 5 1 1
. 2 7 1
. 6 9 Wind speed O b s m e a n 3
. 4 1 2
. 7 2 2
. 0 7 2
. 9 9 (SE subdomain) MM5 m ean 3.78 3.12 2.56 3.58 B i a s 0
. 3 8 0
. 4 0 0
. 4 9 0
. 5 9 A b s o l u t e e r r o r 0
. 8 6 0
. 7 7 0
. 7 7 0
. 9 2 R 2 0
. 6 9 0
. 6 9 0
. 6 2 0
. 6 6 Index of agreem ent 0.90 0.89 0.85 0.87 R M S E 1
. 1 6 1
. 0 0 1
. 0 0 1
. 2 4 Wind speed O b s m e a n 3
. 2 6 2
. 9 8 2
. 5 8 3
. 1 4 (NW subdom a in) MM5 m ean 3.92 3.31 2.98 3.72 B i a s 0
. 6 6 0
. 3 3 0
. 4 0 0
. 5 8 A b s o l u t e e r r o r 1
. 2 8 0
. 9 7 0
. 9 0 1
. 1 7 R 2 0
. 5 7 0
. 6 1 0
. 6 0 0
. 6 1 Index of agreem ent 0.85 0.87 0.86 0.86 R M S E 1
. 7 3 1
. 3 3 1
. 2 1 1
. 5 8 Wind speed O b s m e a n 3
. 6 9 3
. 0 9 2
. 5 4 3
. 3 2 (NE subdomain) MM5 m ean 4.18 3.56 3.09 3.99 B i a s 0
. 4 9 0
. 4 7 0
. 5 5 0
. 6 7 A b s o l u t e e r r o r 0
. 9 7 0
. 8 7 0
. 8 3 0
. 9 9 R 2 0
. 7 0 0
. 6 7 0
. 6 8 0
. 6 8 Index of agreem ent 0.90 0.89 0.88 0.88 R M S E 1
. 3 2 1
. 1 5 1
. 1 0 1
. 3 3 Table 6.5 shows the statistical summary for wa ter vapor m i xing ratio. At low m i xing ratio values MM5 perform s rem a rkably well. At highe r values (>~ 10g/kg) MM5 tends to underpredict the observations. W e believe this could be cause d by the MRF PBL sche me being too efficient in its vertical m i xing during hot, hum i d conditions. This tendency leads to seasonal and geographical patterns. The northern quadrants show bette r perform a nce than the southern quadrants, and the summ er period show s poorer perform a nce th an other seasons. Overall, though, MM5 does a better job m odeling water vapor m i xing ratio than it does any other base variable.
34Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.5. Surface statistical summary for w a ter vapor mixing ratio J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Mixing ratio Obs m ean 3.5 8.0 10.9 5.0 (full dom ain) MM5 m ean 3.6 7.6 10.1 5.0 B i a s 0
. 1

- 0
. 4

- 0
. 8

- 0
. 0 A b s o l u t e e r r o r 0
. 5 1
. 0 1
. 3 0
. 6 R 2 0
. 9 1 0
. 9 2 0
. 8 6 0
. 9 3 Index of agreem ent 0.98 0.97 0.95 0.98 R M S E 0
. 8 1
. 4 1
. 7 0
. 9 Mixing ratio Obs m ean 4.1 8.0 11.5 5.7 (SW subdom ain) MM5 m ean 4.5 8.3 10.8 5.8 B i a s 0
. 4 0
. 3

- 0
. 6 0
. 1 A b s o l u t e e r r o r 0
. 9 1
. 4 1
. 6 0
. 9 R 2 0
. 8 1 0
. 8 7 0
. 7 9 0
. 8 9 Index of agreem ent 0.94 0.96 0.93 0.97 R M S E 1
. 2 1
. 8 2
. 0 1
. 2 Mixing ratio Obs m ean 5.3 11.8 14.9 7.6 (SE subdomain) MM5 m ean 5.5 10.8 13.3 7.4 B i a s 0
. 1

- 0
. 9

- 1
. 6

- 0
. 2 A b s o l u t e e r r o r 0
. 6 1
. 3 1
. 7 0
. 7 R 2 0
. 9 4 0
. 9 1 0
. 8 3 0
. 9 4 Index of agreem ent 0.98 0.96 0.90 0.98 R M S E 0
. 9 1
. 6 2
. 0 1
. 0 Mixing ratio O b s m e a n 2
. 8 5
. 8 8
. 0 3
. 4 (NW subdom a in) MM5 m ean 2.7 5.5 7.8 3.5 B i a s - 0
. 0

- 0
. 4

- 0
. 2 0
. 1 A b s o l u t e e r r o r 0
. 4 0
. 7 1
. 1 0
. 5 R 2 0
. 8 9 0
. 8 8 0
. 7 3 0
. 9 0 Index of agreem ent 0.97 0.96 0.92 0.97 R M S E 0
. 6 1
. 0 1
. 4 0
. 7 Mixing ratio O b s m e a n 2
. 2 6
. 4 9
. 4 3
. 9 (NE subdomain) MM5 m ean 2.1 6.4 9.4 3.9 B i a s - 0
. 1

- 0
. 4

- 0
. 8

- 0
. 2 A b s o l u t e e r r o r 0
. 3 0
. 8 1
. 1 0
. 4 R 2 0
. 9 0 0
. 9 3 0
. 8 3 0
. 9 3 Index of agreem ent 0.97 0.98 0.93 0.98 R M S E 0
. 5 1
. 0 1
. 4 0
. 6 Table 6.6 provides the statis tical sum m a ry for daily-averaged tem p erature. MM5 consistently shows a negative tem p erature bias that is m o st pronounced in the colder seasons. The spring/summ e r seasons show le ss bias, but also less skill. Th e eastern quadrants show better perform a nce than the western quadrants, m a tc hing what we saw in the wind statistics.

35Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.6. Surface statistical summar y for daily-averaged temperature J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Temperatu re Obs m e a n 2 7 4
. 3 2 8 9
. 0 2 9 3
. 4 2 7 8
. 8 (full dom ain) MM5 m ean 271.7 287.7 292.6 276.7 B i a s - 2
. 6

- 1
. 4

- 0
. 8

- 2
. 1 A b s o l u t e e r r o r 3
. 3 2
. 2 1
. 6 2
. 9 R 2 0
. 9 1 0
. 8 9 0
. 8 6 0
. 9 2 Index of agreem ent 0.96 0.96 0.96 0.97 R M S E 4
. 2 3
. 2 2
. 3 3
. 6 Temperatu re Obs m e a n 2 8 0
. 8 2 9 3
. 8 2 9 6
. 6 2 8 3
. 7 (SW subdom ain) MM5 m ean 278.4 291.8 295.3 281.4 B i a s - 2
. 4

- 1
. 9

- 1
. 3

- 2
. 2 A b s o l u t e e r r o r 3
. 1 2
. 7 2
. 2 2
. 8 R 2 0
. 8 6 0
. 8 1 0
. 8 0 0
. 9 0 Index of agreem ent 0.94 0.93 0.93 0.96 R M S E 4
. 0 3
. 7 2
. 9 3
. 6 Temperatu re Obs m e a n 2 8 1
. 1 2 9 4
. 2 2 9 7
. 2 2 8 5
. 4 (SE subdomain) MM5 m ean 278.6 293.5 296.9 283.6 B i a s - 2
. 5

- 0
. 7

- 0
. 3

- 1
. 8 A b s o l u t e e r r o r 3
. 1 1
. 4 1
. 1 2
. 5 R 2 0
. 8 9 0
. 9 2 0
. 8 6 0
. 9 2 Index of agreem ent 0.95 0.98 0.96 0.96 R M S E 3
. 9 1
. 8 1
. 4 3
. 1 Temperatu re Obs m e a n 2 6 9
. 1 2 8 4
. 6 2 9 0
. 4 2 7 2
. 5 (NW subdom a in) MM5 m ean 266.6 282.8 289.2 270.1 B i a s - 2
. 5

- 1
. 8

- 1
. 1

- 2
. 4 A b s o l u t e e r r o r 3
. 5 2
. 6 2
. 0 3
. 1 R 2 0
. 9 0 0
. 8 1 0
. 8 0 0
. 9 2 Index of agreem ent 0.96 0.93 0.94 0.97 R M S E 4
. 4 3
. 8 2
. 8 3
. 8 Temperatu re Obs m e a n 2 6 8
. 3 2 8 5
. 2 2 9 1
. 2 2 7 5
. 8 (NE subdomain) MM5 m ean 265.5 284.1 290.8 273.7 B i a s - 2
. 9

- 1
. 1

- 0
. 4

- 2
. 1 A b s o l u t e e r r o r 3
. 6 2
. 0 1
. 3 3
. 0 R 2 0
. 8 5 0
. 8 9 0
. 8 5 0
. 8 6 Index of agreem ent 0.93 0.96 0.96 0.94 R M S E 4
. 4 3
. 0 1
. 7 3
. 7 Table 6.7 shows the statistical summary for da ily m a xim u m t e m p eratur e. The statistics f

o llow the s a m e general patte rn seen in the da ily -averaged temperature st atis tic s. A model cold bias is evident for each region for each season. T h e bias is most severe d u ring th e cold seasons, and the northern subdom a ins show more bias th an do the southern subdom ains. This result probably stem s from the snow effects already di scussed. Even consideri ng these effects , the r 2 and IA indicate that the poorest perform a nce occurs in the summer. The best perform a nce for all regions is found in the O c tober-December period.
36Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.7. Surface statistical summar y for daily maximum temperature J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Max temp Obs m e a n 2 7 9
. 8 2 9 4
. 8 2 9 9
. 2 2 8 3
. 9 (full dom ain) MM5 m ean 276.6 293.5 298.7 281.4 B i a s - 3
. 3

- 1
. 4

- 0
. 5

- 2
. 5 A b s o l u t e e r r o r 4
. 0 2
. 9 2
. 2 3
. 2 R 2 0
. 8 7 0
. 8 2 0
. 8 1 0
. 9 2 Index of agreem ent 0.95 0.94 0.95 0.97 R M S E 5
. 4 4
. 4 2
. 9 4
. 1 Max temp Obs m e a n 2 8 8
. 2 3 0 0
. 9 3 0 3
. 3 2 9 0
. 4 (SW subdom ain) MM5 m ean 285.3 299.6 303.1 288.2 B i a s - 2
. 9

- 1
. 3

- 0
. 1

- 2
. 3 A b s o l u t e e r r o r 3
. 9 3
. 1 2
. 6 3
. 2 R 2 0
. 7 7 0
. 7 0 0
. 7 2 0
. 8 5 Index of agreem ent 0.91 0.90 0.92 0.94 R M S E 5
. 3 4
. 6 3
. 3 4
. 3 Max temp Obs m e a n 2 8 6
. 8 2 9 9
. 8 3 0 2
. 5 2 9 0
. 8 (SE subdomain) MM5 m ean 283.7 299.1 302.4 288.4 B i a s - 3
. 1

- 0
. 6

- 0
. 1

- 2
. 4 A b s o l u t e e r r o r 3
. 6 2
. 0 1
. 6 2
. 9 R 2 0
. 8 5 0
. 8 4 0
. 7 1 0
. 9 0 Index of agreem ent 0.93 0.95 0.92 0.95 R M S E 4
. 7 2
. 7 2
. 1 3
. 5 Max temp Obs m e a n 2 7 4
. 2 2 9 0
. 3 2 9 7
. 0 2 7 7
. 1 (NW subdom a in) MM5 m ean 270.5 288.2 295.8 274.2 B i a s - 3
. 7

- 2
. 0

- 1
. 2

- 2
. 9 A b s o l u t e e r r o r 4
. 6 3
. 5 2
. 5 3
. 5 R 2 0
. 8 2 0
. 7 2 0
. 8 1 0
. 9 1 Index of agreem ent 0.92 0.90 0.94 0.96 R M S E 6
. 1 5
. 4 3
. 3 4
. 5 Max temp Obs m e a n 2 7 2
. 9 2 9 0
. 3 2 9 6
. 2 2 7 9
. 7 (NE subdomain) MM5 m ean 269.4 288.7 295.7 277.3 B i a s - 3
. 5

- 1
. 5

- 0
. 5

- 2
. 4 A b s o l u t e e r r o r 4
. 1 3
. 0 2
. 0 3
. 0 R 2 0
. 7 8 0
. 7 9 0
. 7 5 0
. 8 8 Index of agreem ent 0.89 0.93 0.92 0.95 R M S E 5
. 4 4
. 5 2
. 8 3
. 8 Table 6.8 shows the statistical summary for m i nimum te m p erature. A slight negative 1- 2 o C bias is seen for all regions for all of our summary periods. Th e snow issue is not as great a concern for m i ni m u m temperatures as it is fo r maxim u m temperatu r es, an d the perform a nce values hav e increased accordin gly. Summ er perform a nce once agai n is poorer th an the other seasons.

37Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 Table 6.8. Surface statistical summary for daily minimu m temperature J a n - M a r A p r - J u n J u l

- S e p O c t
- D e c Min temp Obs m e a n 2 6 8
. 8 2 8 3
. 2 2 8 8
. 0 2 7 4
. 1 (full dom ain) MM5 m ean 267.2 281.9 287.0 272.7 B i a s - 1
. 6

- 1
. 3

- 1
. 0

- 1
. 4 A b s o l u t e e r r o r 3
. 4 2
. 5 2
. 3 3
. 1 R 2 0
. 8 7 0
. 8 7 0
. 7 7 0
. 8 7 Index of agreem ent 0.96 0.96 0.93 0.96 R M S E 4
. 3 3
. 3 3
. 1 4
. 0 Min temp Obs m e a n 2 7 4
. 1 2 8 6
. 5 2 9 0
. 8 2 7 7
. 8 (SW subdom ain) MM5 m ean 272.8 284.7 288.6 276.4 B i a s - 1
. 3

- 1
. 9

- 2
. 2

- 1
. 4 A b s o l u t e e r r o r 3
. 1 3
. 0 3
. 0 2
. 9 R 2 0
. 8 3 0
. 8 0 0
. 7 4 0
. 8 6 Index of agreem ent 0.95 0.93 0.89 0.96 R M S E 3
. 9 3
. 8 3
. 8 3
. 6 Min temp Obs m e a n 2 7 5
. 5 2 8 8
. 7 2 9 2
. 8 2 8 0
. 5 (SE subdomain) MM5 m ean 273.7 287.9 291.7 279.3 B i a s - 1
. 8

- 0
. 8

- 1
. 1

- 1
. 2 A b s o l u t e e r r o r 3
. 3 2
. 0 2
. 0 2
. 8 R 2 0
. 8 5 0
. 8 8 0
. 7 5 0
. 8 5 Index of agreem ent 0.94 0.96 0.91 0.95 R M S E 4
. 2 2
. 5 2
. 5 3
. 7 Min temp Obs m e a n 2 6 4
. 3 2 7 8
. 8 2 8 3
. 9 2 6 8
. 0 (NW subdom a in) MM5 m ean 263.0 277.1 283.2 266.8 B i a s - 1
. 2

- 1
. 7

- 0
. 7

- 1
. 3 A b s o l u t e e r r o r 3
. 3 2
. 8 2
. 5 3
. 1 R 2 0
. 8 9 0
. 7 8 0
. 6 5 0
. 8 8 Index of agreem ent 0.96 0.92 0.89 0.96 R M S E 4
. 2 3
. 7 3
. 3 3
. 9 Min temp Obs m e a n 2 6 3
. 4 2 8 0
. 0 2 8 6
. 3 2 7 1
. 9 (NE subdomain) MM5 m ean 261.4 279.0 286.0 270.3 B i a s - 2
. 0

- 1
. 0

- 0
. 4

- 1
. 6 A b s o l u t e e r r o r 3
. 8 2
. 4 2
. 1 3
. 5 R 2 0
. 7 8 0
. 8 6 0
. 6 6 0
. 7 4 Index of agreem ent 0.93 0.96 0.90 0.92 R M S E 4
. 7 3
. 2 2
. 8 4
. 4
Appendix B contains a full suite of statistical ti me series plots. From these plots it is clear that MM5 does a good job in resolving the transient synoptic features (i.e. fr onts) that existed in 1996.

38Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## 7. CLOSING OBSERVATIONS In general, MM5 perform e d reasonably well. Major synoptic features were captured, and only a couple errors stood out. Below is our summary. 1. Near-surface tem p eratures are generally underpredicted. The inability of MM5 to dynam i cally build/m e lt snowfields is the biggest concern we have with this m odeling, along with the lack of proper air m a ss m odi fication over these fields. The snow keeps the air m a ss above it from war m ing much above 0 o C, which leads to trem endous underpredictions of tem p erature in the late winter/early spring. W e have seen no evidence that this problem significantly affects m o del precipit ation or wind flow. Even when snow is not present, the model consistent ly underpredicts temperatures (m ean, m a xi mum , and minim u m )
. 2. PBL heights are capped at ~2000 m
. The PBL height problem seem s to stem from the selection of the MRF schem e . We prefer a m o re sophisticated schem e , but the preferred schem e s either do not output K v ’s, or they are too experim e ntal for tasks such as this one, at least in MM5 vers io n 2. This is prim arily a summertim e concern, an d it affects the western s t ates m o re so than the eastern s t ates. 3. W i nd fields problem atic over the wes t ern s t ates. The 36-km r e solution used in this modeli ng is clearly insuf f i cient to re so lve the com p licated orographically-induced flows near the surface o v er the wes t ern United S t ates. Elsewhere the m odel does a really good job in re plicating the m e an flow on a cell-to-cell basis. The flows aloft are m ode led well everywhere. 4. Lake tem p eratures are too high. MM5 version 2.12 contains a bug that cau ses it to som e ti m e s m i sdiagnose the tem p eratures of certain sm aller lakes. The bug only shows itself whenever the 24-category land use option is requested, as we have employed in this m odeling. The effect is to create warm erthan-reasonable lak e tem p eratu r es, an d error is largest in the spring/summer periods. T h e Great Salt Lake is most susceptible to this bug, though certain Canadian lakes may also exhibit the problem . The Great Lak e s are un affected by th e error. 5. Water vapor m i xing ratio is m odeled very well. In general, the surface moisture field s ar e m odeled exceptionally well. The only concern with the m i xing ra tio pr e d ictions is th at they te nd to be negatively bias ed during the summ er in the southern states.

39Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## REFERENCES
1. Dudhia, J. A nonhydrostatic version of the Pe nn State/NCAR m e soscale model: Validation tests and simulation of an Atla ntic cyclone and cold front. Mon. Wea. Rev. , 121 , 1493-1513, 1993. 2. Grell, G.A.; Dudhia, J. and Stauffer, D.R. A Description o f

th e Fifth Gene ration Penn State/NCAR Mesosc al e Model (MM5 ). NCAR Tech. Note, NCAR TN-398-STR, 138 pp., 1994. 3. SAI. 1996. "User' s Guide to the Variable-Gri d Urban Airshed Model (U AM-V)" System s Applications, Inc., San Rafael , California (SYSAPP-96-95/27r). 4. ENVIRON (1997) User' s Guide to the Com p rehe nsive Air Q u ality Model with Extensions (CAMx), Version 1.10. E nviron Intern ational Corporation, Novato, CA. 5. Guthrie, P.G., C.A. Em er y, M.P. Ligocki, G.E. Mansell, A.K. Kuklin and D. Gao. 1995. “Developm e n t and Prelim inary Testing of the Regulatory Modeling System for Aerosols and Deposition (REMSAD).” Technical m e morandum prep ared for R. Scheffe, U.S. EPA Office of Policy, Planning, and Evaluation by System s Applications Interna tional, San Rafael, California. 6. Byun, D.W. and J.K.S. Ching, Eds, “Science Al gorithm s of the EPA Models-3 Community Multiscale Air Quality (C MAQ) Modeling System ”, EPA/600/R-99/030 , Office of Research and Developm ent, U.S. EPA, W a shi ngton, D.C., March, 1999. 7. MCNC, The Multisca l e Air Quality Sim u lation Pl atform (MAQSIP): An Overview of Model Developm ent and Initial App lications, in preparation, 2000. 8. Zhang, D. L. and Anthes, R.A. A high-resolu tion model of the planetary boundary layer - Sensitiv ity tests and co mparisons with SESAME-79 data. J. Appl. Meteor ., 21, 1594-1609, 1982. 9. Gayno, G.A. Development of a High er-Order, F og-Producin g Boundary Layer Model Suitable for Use in Numerica l Wea t her Predic tion . M.S. Thesis, The Pennsylvania State Univ., 104 pp. 10. Mellor, G.L. and Ya m a da, T. A Hierarchy of Turbulence Closure Models for Planetary Boundary-Layers. J. Atmos. Sci., 31 , 1791-1806, 1974. 11. Hong S.Y. a nd H. L. Pan, H.L. Nonlocal boundary layer vertical diffusion in a m e dium -range forecast m odel. Mon. Wea. Rev ., 124 , 2322-2339, 1996. 12. Dudhia, J. Num e rical study of convection obs erved during the winter m onsoon experim e nt using a m e soscale two-dim e nsional model. J. Atmos.Sci ., 46 , 3077-3107, 1989 13. Chen, S., J. Dudhia, D. Gill, et al, 1 995: PSU/NCAR Mesoscale Mod e ling System – Tutorial Class Notes. Available fro m NC AR ( http://www.mmm.ucar.edu/mm5)
.

40Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 

## APPENDICES
APPENDIX A – M O DEL CONFIGURATION FILE
3 domains (1st number refers to 108 km domain, 2nd number to 36 km domain, 3 rd to the 12-km domain) Sigmas 1.,0.995,.988,.98,.97,.956,.938,.916,.893,.868,.839,.808,.777,.744,.702,.648, .582,.5,.4,.3,.2,.12,.052,.0, KXS 23 # number of half-sigma layers NESTIX "69,115,190," # domain heights in cells NESTJX "89,169,166," # domain widths in cells DIS "108.,36.,12.," # grid sizes (km) MTHRD "1,1,2," # mother domain IDs NESTI "1,15,35," # lower left I's of nests NESTJ "1,18,21," # lower left J's of nests NHYDRO = 1, #nonhydrostatic LEVIDN "0,1,2," # level of nest for each domain IMPHYS "5,5,5," # mixed phase ice processes MPHYSTBL 0 # do not use look-up tables for moist physics ICUPA "2,6,6," # Kuo,KF,KF IBLTYP "5,5,5," # MRF FRAD "1,1,1," # simple ISOIL "1,1,1," # multi-layer soil temperature model (must use for MRF pbl) ISHALLO "0,0,0," # No shallow convection RADFRQ "30.,20.,10.," atmospheric radiation calculation freq (in minutes) IMVDIF 1,1,1, # moist vertical diffusion in clouds IVQADV 1,1,1, # linear vertical moisture advection IVTADV 1,1,1, # linear vertical temperature advection ITHADV 1,1,1, # advection of temperature uses potential temperature ITPDIF 1,1,1, # diffusion using perturbation temperature ICOR3D 1,1,1, # 3D Coriolis force IFUPR 1,1,1, # upper radiative boundary condition IFDRY 0,0,0, # Don't do fake-dry run (no latent heating) ICUSTB 1,1,1, # stability check for Anthes-Kuo CPS only IBOUDY 3,3,3, # time and inflow/outflow relaxation IFSNOW 1,1,1, # SNOW COVER EFFECTS ISFFLX 1,1,1, # surface fluxes ITGFLG 1,1,1, # surface temperature prediction ISFPAR 1,1,1, # surface characteristics ICLOUD 1,1,1, # cloud effects on radiation ICDCON 0,0,0, # Don't use constant drag coefficients IVMIXM 1,1,1, # vertical mixing of momentum HYDPRE 1,1,1, # HYDRO EFFECTS OF LIQ WATER (HY run only) IEVAP 1,1,1, # EVAP OF CLOUD/RAINWATER TISTEP 300.,100.,36., # Adv time steps (s) I4D 1,1,1, # GRID 4DDA RUN
41Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5
DIFTIM 720.,720.,720., # 3D ANALYSIS NUDGING 180.,180.,180., # SFC ANALYSIS NUDGING IWIND 1,1,1, # 3D ANALYSIS NUDGING of winds 1,1,1, # SFC ANALYSIS NUDGING of winds GV 3.0E-4,2.5E-4,1.0E-4, # 3D ANALYSIS NUDGING COEFFICIENT of winds 3.0E-4,2.5E-4,1.0E-4, # SFC ANALYSIS NUDGING COEFFICIENT of winds ITEMP 1,1,1, # 3D ANALYSIS NUDGING of temperature 0,0,0, # SFC ANALYSIS NUDGING of temperature GT 3.0E-4,2.5E-4,1.0E-4, # 3D ANALYSIS NUDGING COEFFICIENT of temps 0,0,0, # SFC NUDGING of temps IMOIS 1,1,1, # 3D ANALYSIS NUDGING of moisture mixing ratio 0,0,0, # SFC ANALYSIS NUDGING of moisture mixing ratio GQ 1.0E-5,1.0E-5,1.0E-5, # 3D ANALYSIS NUDGING COEFFICIENT of moisture 0,0,0, # SFC NUDGING of moisture IROT 0,0,0, # GRID NUDGE THE ROTATIONAL WIND FIELD GR 0,0,0, # NUDGING COEFFICIENT FOR THE ROTATIONAL COMPONENT INONBL 0,0,0, # U WIND INCLUDE BOUNDARY LAYER NUDGING 0,0,0, # V WIND INCLUDE BOUNDARY LAYER NUDGING 1,1,1, # TEMPERATURE EXCLUDE BOUNDARY LAYER NUDGING 1,1,1, # MIXING RATIO EXCLUDE BOUNDARY LAYER NUDGING RINBLW 350.,250.,150., # RADIUS OF INFLUENCE FOR SURFACE ANALYSIS (KM) ??? I4DI 0,0,0, # No obs nudging 

### APPENDIX B – SUPPLEMENTAL PLOTS While only a sm all sample of plots is included in the m a in text, a m o re com p lete se t is found here. These plots are also larg er and m o re readable than thos e in the text. They do not have individu al captions. Instead, a summary cap tion is includ ed b e fore each s e ries of plo t s is presented. Dates and tim es are found in the plot labels. These plots follow : 1. W i nds at ~400 m AGL on the 15 th of each m onth (12 plots ) 2. W i nds at ~1400 m AGL on the 15 th of each m ont h (12 plots ) 3. W i nds at ~3400 m AGL on the 15 th of each m ont h (12 plots ) 4. Daily-av e raged surface winds on the 15 th of each m onth (12 plots) 5. Daily m a ximum PBL heights on the 14 th of each m onth (12 plots) 6. Daily accum u lated rainfall on th e 15 th of each m o nth (12 plo t s ) 7. Tim e series for selected surface sites (36 plots ) 8. W i nd direction statistical tim e series (5 dom ains, 4 seasons) (20 plots) 9. W i nd speed statistical tim e series (5 dom a ins, 4 seasons) (20 plots) 10. Water vapor m i xing ratio statistical tim e se ries (5 dom a ins, 4 seasons) (20 plots) 11. Daily-av e ra ged tem p erature s t atistic a l tim e series (5 dom ains, 4 seasons) (20 plots)
42Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5 12. Daily m a ximum tem p erature s t a tis tical tim e series (5 dom ains, 4 seasons) (20 plots) 13. Daily m i nimum tem p erature sta t istica l tim e series (5 dom ains, 4 seasons) (20 plots)

43Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and observed horizontal w i n d vectors at ~400 m AGL (Sigm a =0.938; Level=6). The plots are at 1200 UTC or 000 0 UTC (alte r nating) for the 15 th day of each m onth in 1996.

44Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and observed horizontal w i n d vectors at ~1400 m AGL (Sig ma=0.808; Level=11). The plots are at 1200 UTC or 000 0 UTC (alte r nating) for the 15 th day of each m onth in 1996.

45Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and observed horizontal w i n d vectors at ~3400 m AGL (Sig ma=0.582; Level=16). The plots are at 1200 UTC or 000 0 UTC (alte r nating) for the 15 th day of each m onth in 1996.

46Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and observed dail y -avera ged horizontal w i nd v ectors near the surface. The plots are made for the 15 th da y of each month in 1996.

47Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and “Observed” day time maximum mixed-la y e r heights for the 14 th of each month of 1996.

48Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Modeled and observed accumulated rainfall for the 24 hours ending at 12 UTC on the 15 th of each month in 1996. The numbers indicate observed rainfall in tenths of a mm/da y . The “0” nu mber denotes no observed rainfall and “1” d enotes trace-amount of observed rainfall.

49Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Temporal variation of modeled and observed dail y -avera ged w i nd di rection, w i n d speed, surface temperature, w a ter vapo r mixing ratio, dail y maximum temperature and dail y minimum temperature . These sites, in alphab etical order b y site na me, are: Atlanta, GA (ATL) Dallas / Ft W o rth, TX (DFW ) Los Angeles (LAX) Minneapolis/St Paul, MN (MSP) Missoula, MT (MSO) Syracuse, N Y (SYR)

50Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y - avera ged w i nd di rection statistical plots. The plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and b i as, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w h ole and for four subdomains.

51Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y - avera ged w i nd s peed statistical plots. T he plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and bias, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w h ole and for four subdomains.

52Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y - avera ged w a ter vapor mixing ratio statistical plots. The plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and bi as, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w h ole and for four subdomains.

53Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y - avera ged surface temperature statistical plots. The plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and bias, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w hol e and for four subdomains.

54Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y maxi mum temperature statistical plots. The plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and b i as, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w h ole and for four subdomains.

55Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5

Dail y mini mum temperature statistical plots. The plots represent the temporal variation of (a) domain-averaged modeled and observed means, (b) absolute error and bias, and (c) index of agreement and coefficient of determination. The plots are in seasonal segments, and the y are created for the domain as a w h ole and for four subdomains.

56Meteorol ogic a l Modeling of 1996 f o r t h e Uni t ed St ates with MM5


### APPENDIX C – S U PPLEMENT AL COLOR GRAPHIC PLOTS A file containing color gifs is avai lable. It is a tar file nam e d gifs 96.tar which contain 48 gif files. These files show the m o del fields with m odel- observation dif f

erences overlaid. There are four categories of plots: 1) Daily averaged tem p erature (ta vg_01.gif, tavg_02.gif, ..., tavg_12.gif) 2) Daily m a ximum te m p erature (t m a x_01.gif, tm ax_02.gif, ..., tm ax_12.gif) 3) Daily m i ni mum tem p erature (tm i n_01.gif, tm in_02.gif, ..., tm in_12.gif) 4) Daily averaged water vapor m i xing ra tio (qavg_01.gif, qavg_02.gif, ..., qavg_12.gif) The plots are m a de for t h e 15 th of ea ch m onth, with the num be r in each file representing a particular month. (01=Jan, 02=Feb, etc...) The differe nces follow a color scale repres ented in the following table: Difference color sca l e D i f f

e r e n c e C o l o r s c a l e # < -5 Deep Blue -5 < # < -3 Medium Blue -3 < # < -1 Light Blue -1 < # < 1 White 1 < # < 3 Yellow 3 < # < 5 Orange

# > 5 Red

57