COAMPS Real-Time Dust Storm Forecasting During Operation Iraqi Freedom

192 WEATHER AND FORECASTING VOLUME 22

COAMPS Real-Time Dust Storm Forecasting during Operation Iraqi Freedom

MING LIU,DOUGLAS L. WESTPHAL,ANNETTE L. WALKER,TEDDY R. HOLT,KIM A. RICHARDSON, AND STEVEN D. MILLER Marine Meteorology Division, Naval Research Laboratory, Monterey, California

(Manuscript received 18 January 2006, in final form 19 May 2006)

ABSTRACT

Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.

1. Introduction quent weather phenomenon in these countries. The most severe dust storms usually occur in winter and Mineral dust is generated by wind erosion over arid spring and are associated with cold-air outbreaks from or semiarid land surfaces and is transported locally and Europe and central Asia (Perrone 1979; Walters and over vast distances, causing adverse environmental and Sjoberg 1988). These events can mobilize large amount weather problems over broad areas. Dust particles re- of dust and transport it far beyond the sources. On duce visibility and degrade air quality, thereby disrupt- regional or local scales, dust storms are associated with ing transportation and degrading health. As one of the strong winds and severe turbulence (Xu et al. 2000; Liu major components of natural aerosols, dust modifies et al. 2000). These factors influenced the outcome of the radiation budget directly by influencing solar and the hostage rescue mission in Iran in 1980. infrared radiation and indirectly by modifying cloud Real-time prediction of dust storms, especially quan- properties. titative forecasting of dust concentration and visibility, Deserts are widely distributed in the southwest Asian has become highly desirable as a meteorological service countries of Afghanistan, Iran, Iraq, Pakistan, Saudi to the public and military activities. The development Arabia, and Syria. Blowing or suspended dust is a fre- of advanced dust aerosol process-oriented numerical weather prediction models makes it possible to predict Corresponding author address: Ming Liu, Naval Research dust particle life cycles including emission, transport, Laboratory, 7 Grace Hopper Ave., Monterey, CA 93943. and removal at high spatial resolution on short to me- E-mail: [email protected] dium time scales. Dust storm prediction models have

DOI: 10.1175/WAF971.1

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WAF971 FEBRUARY 2007 L I U E T A L . 193 been used operationally in Europe (Nickovic et al. modeling system. Such verification will help to improve 2001), Australia (Cope et al. 2004), and East Asia the model and meet the U.S. Navy’s needs for higher (Shao et al. 2003; Park and In 2003; Tanaka et al. 2003; quality weather forecasting. In this paper, we apply, for Uno et al. 2004). At the U.S. Naval Research Labora- the first time, the standard statistical measures em- tory, the U.S. Navy Mesoscale Prediction System’s ployed in the verification of precipitation forecasts to Coupled Ocean–Atmospheric Mesoscale Prediction the verification of the forecasts of dust storms and vis- System (COAMPS), with an embedded dust aerosol ibility. The following sections describe the model, ob- model (Liu et al. 2003), has produced 3-day forecasts servations, forecasts, comparisons, statistical analysis, for southwest Asia in real-time runs since March 2003. evaluation, and conclusions. The focus was on the Iraq region including the Arabian Gulf and the nearby areas, providing dust concentra- 2. Model description tion, visibility, and optical depth in support of U.S. De- partment of Defense military operations. Real-time The U.S. Navy’s mesoscale meteorological system, COAMPS dust forecasting has been carried on since COAMPS, with embedded dust microphysics, is used to then in a combined research and operational mode. simulate dust storms during OIF. COAMPS is a non- This is the first mesoscale model being used in south- hydrostatic and compressible dynamic model applied in west Asia for operational dust forecasting. a terrain-following sigma vertical coordinate. It predicts In addition to the COAMPS dust model, the U.S. turbulent kinetic energy (TKE) for subgrid-scale diffu- Navy has also developed the only high-resolution dust sion, uses a force–restore method in the surface energy source database for southwest Asia that is compatible, budget, and contains explicit cloud microphysics. The in terms of resolution and accuracy, with COAMPS friction velocity, associated with the surface momentum dust forecasting. The previous dust source databases flux, is calculated based on Monin–Obukhov surface- have had a resolution of 1° or less. The work by Walker layer similarity theory. Ground wetness, an alternative et al. (2003) is based on years of data from surface of soil moisture, is calculated following the algorithm of observations, satellites, geography, and desertification Louis (1979) using precipitation, snow depth, ice cov- studies. A dust source database is the fundamental part erage, and evaporation, as well as soil latent heat flux of dust modeling and affects the modeling quality. and moisture capacity. The U.S. Geological Survey COAMPS becomes complete only with the support of (USGS) land-use 1-km resolution database is used to an appropriate database. obtain surface roughness length for various land sur- The U.S.-led coalition forces launched a military mis- faces. The complete details of the model structure, dy- sion called Operation Iraqi Freedom (OIF) from 18 namics, and physics can be found in Hodur (1997) and March to 30 April 2003. Weather maps, satellite obser- Chen et al. (2003). vations, and postdeployment reports reveal that dust The model domain extends vertically to an altitude of was one of the most important meteorological param- 35 km with 31 grid layers ranging in thickness from 10 eters in the Iraq region during OIF. Early in the mis- m at the surface to 6 km at the top. There are 13 grid sion, a strong dust storm swept across southwest Asia layers below 9 km. This resolution is used by FNMOC and visibility was reduced to less than 100 m. The dust in operational runs and therefore is employed in this storm dramatically impeded the military activities from dust forecasting. In the horizontal, the model uses the the ground to the midtroposphere on 25 and 26 March. three nested grid meshes shown in Fig. 1. The coarse Overall, five large-scale dust storms were observed dur- 81-km resolution domain of 92 ϫ 68 grid points is pur- ing the 43 days of OIF. The U.S. Navy’s Fleet Nu- posely chosen to cover the upstream deserts that might merical Meteorological and Oceanographic Center act as sources of dust. The middle 27-km grid nest with (FNMOC) used these COAMPS real-time dust fore- 127 ϫ 109 grid points is a transition grid from coarse casts in their daily dust weather discussions. to fine resolution. The 9-km resolution grid nest with Previous modeling studies have been either 1) short 181 ϫ 181 grid points covers the focus area of Iraq and term (for a field campaign), 2) not truly operational, 3) the eastern Persian Gulf. This combination of nested forecasts of only surface concentration or aerosol opti- grid meshes allows COAMPS to capture both synoptic cal depth, 4) lacking any statistical verification, 5) car- and mesoscale features of dynamical systems and dust ried out at synoptic-scale resolutions, or 6) operated storms. using a 1° resolution dust source database. The verifi- The data assimilation is performed at 12-h incremen- cation of COAMPS real-time dust forecasting for this tal update cycles using the meteorological data from the special period is a requirement for a better understand- world weather station network and satellite retrievals. ing of the strengths and weaknesses of the COAMPS The analysis and forecast fields of the U.S. Navy’s Op-

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tion velocity u (m sϪ1) is set to 0.60 m sϪ1 based on *t field and laboratory experiments (Gillette and Passi 1988; Alfaro et al. 1997) and previous modeling work (Westphal et al. 1987, 1988). There are basically two types of dust emission formulas used in dust aerosol modeling. The first type is this u -dependent flux and * the other type is wind speed–dependent flux developed by Gillette (1978). Liu and Westphal (2001) conducted sensitivity studies of dust emission upon grid resolution by comparing these two types of dust flux formulas and with observations. They found that the u -driven flux is * more realistic and preferable than the wind speed– driven flux scheme. The latter method failed to predict the smaller dust events and diurnal variations during a 2-week period of April 1998 in East Asia due to the lack of a dependence on thermal stability and wind FIG. 1. Model domains of nested grids of 9-, 27-, and 81-km shear. The u formula has been used in several other resolution used in COAMPS dust forecasting for the Iraq region. * (The number of grid points in the x and y directions are given in dust forecast models (Uno et al. 2006). parentheses.) Coefficient A in (1) is the fraction of the area of each model grid box that is dust erodible, and thus capable of producing dust. It ranges from 0.0 to 1.0. The distribu- erational Global Atmospheric Prediction System tion of fractional erodibility for southwest Asia is (NOGAPS) (Hogan and Rosmond 1991) are used for shown in Figs. 2a and 2b. The erodibility for the 81-km the initial conditions and for updating the lateral grid is determined from a database that is built upon an boundary conditions every 6 h during the forecast. analysis of the 1° resolution Total Ozone Mapping However, there was no dust assimilation update: the Spectrometer (TOMS) aerosol index (e.g., Prospero et model begins each new forecast cycle with the previous al. 2002). The 81-km domain covers the deserts of Sa- 12-h forecast of dust as the initial condition. The real- hara, Libya, and Sudan in the west; Iraq and the Ara- time dust forecasting started 15 March 2003 and has bian Peninsula in the center; and Iran, Pakistan, Af- continued to the present, becoming operational at ghanistan, and India in the east. The erodibility for the FNMOC in 2004. Each day COAMPS produces a 72-h 27- and 9-km grids is derived from a high-resolution forecast beginning at 0000 UTC (0300 Baghdad local database developed by Walker et al. (2003) described time) and a 12-h forecast beginning at 1200 UTC (1500 earlier. Figure 2b, showing the distribution of erodibil- local time). ity for the 9-km grid, reveals detailed structures (espe- A dust aerosol model is fully embedded in COAMPS cially point sources) that the low-resolution TOMS as an in-line module of the prediction system using the aerosol-index analysis lacks. Examining Eq. (1), it is model’s exact meteorological fields at each time step clear that the accuracy of dust production depends on and at each grid point (Liu and Westphal 2001; Liu et both friction velocity and soil erodibility defined in the al. 2003). The dust module has the same vertical grid dust source database. The evaluations of dust forecasts structure, multiple nested grids, and grid nest interac- in sections 4–6 will show that the dust erodibility cur- tions. The mass conservation equation contains source rently used in SW Asia to be practical. emission, advection, sedimentation, mixing, dry depo- Dust emission is further restricted to the erodible sition at the surface, and wet removal by precipitation. areas that are predicted to have low values of ground The vertical dust flux F (kg mϪ2 sϪ1) of dust produc- wetness as predicted by COAMPS. In the real-time tion is proportional to the friction velocity (u ) raised runs, COAMPS performs one-way interaction to the * to the fourth power (or the square of surface wind winds and other dynamics fields of grid nests; for ex- stress) (Westphal et al. 1988; Nickling and Gillies 1993): ample, the coarse-grid data are passed to the lateral boundaries of the nested fine grids. The same dust F ϭ A1.42 × 10Ϫ5 ϫ u4 when u Ն u . ͑1͒ * * *t source function is used in all of the grid nests. Realizing that the dust source database is at very high resolution, This formulation accounts for both wind shear and a two-way interaction is enforced to the dust mass. The thermal stability effects on the momentum stress in the coarse-grid dust concentration is used as the lateral surface layer through the use of u . The threshold fric- boundary condition of the nested grid, while the inner- *

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plying Mie scattering theory, a specific extinction coefficient of 0.58 (m2 gϪ1) is derived for this effective particle size. The forecasted dust mass concentration is then converted to an extinction coefficient by multiplying this specific extinction. The forecasted dust mass load (mass vertical integral, in g mϪ2) is also directly converted to optical depth by multiplying this value. Dust advection in both the horizontal and vertical directions uses a fifth-order-accurate flux-form scheme developed by Bott (1989a,b). The algorithm performs a polynomial fitting to the upstream-advected field in each grid box to make the fitting curves approach the data at the grid points through a weighting flux treat- ment. Therefore, mass conservation and positive definite conditions can be effectively achieved. The process of dust subgrid-scale turbulent mixing is also important to the dust transport, so to the visibility. The eddy diffusion coefficient is the same as that used for moisture scalars and temperature in the dynamics model. COAMPS solves the TKE equation explicitly, from which the eddy diffusion coefficients are generated. The turbulent mixing is then solved implicitly to main- tain numerical stability. The details of the dust aerosol microphysics of gravitational sedimentation, dry deposition, and wet scavenging by convective and stable precipitation can be found in Liu et al. (2003).

3. Observational data Miller (2003) has developed a dust enhancement product (DEP) that detects dust over surfaces in daytime using satellite radiances, provided the surface tem- peratures are not too cold. As an example, Fig. 3b shows the dust enhancement product using Terra Mod- erate Resolution Imaging Spectrometer (MODIS) radiances for 0745 UTC 26 March. The dust appears as FIG. 2. (a) Dust source distribution for the 81-km domain expressed as a grid erodible fraction (0.0–1.0) using the U.S. Navy’s pink shades in southern Iraq and northern Saudi Ara- high-resolution dataset in Iraq and north Saudi Arabia and the bia with a dust front (leading edge) in the east. This TOMS 1° database elsewhere. (b) Dust source distribution for the product is useful in locating dust fronts and in quali- 9-km domain expressed as a grid erodible fraction (0.0–1.0) using tative verification of the dust forecasts. Figure 3b will the navy’s high-resolution dataset. be discussed more in section 4 below, along with other observations, to compare and qualitatively verify grid dust concentration is passed to the coarse grids COAMPS modeled dust plumes. through averaging of the inner-grid values. On the The aerosol optical depth (AOD) can be retrieved other hand, dust mass has no impact on the COAMPS from satellite radiances during daytime over cloud-free dynamics. and glint-free oceans (Durkee et al. 2000). This type of Dust is modeled as a monodispersed aerosol, that is, retrieval can be used to quantitatively validate the a single particle size, with a diameter of 2.0 ␮m and a model predictions over the Persian Gulf. As another density of 2650 kg mϪ3. This effective size of the par- example, Fig. 4a shows the retrieved optical depth over ticles was chosen because it provided the closest match the Arabian Gulf at 1027 UTC 27 March. It reveals two to previous size-resolved simulations (10 size bins rang- areas of high optical depth in the Gulf with values of ing from 0.05 to 35 ␮m), both in terms of optical prop- 1.6–2.6 in the north and 1.0–2.0 in the south. Figure 4a erties and sedimentation fluxes (Liu et al. 2003). Ap- will be discussed more in section 4 below to compare

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Fig 2 live 4/C 196 WEATHER AND FORECASTING VOLUME 22 with modeled optical depth for a quantitative verifica- Visibility and current weather are routinely mea- tion. The AOD includes the effects of all aerosols, sured every 3 or 6 h at weather stations and are the only while the modeled AOD includes only the contribution synoptically reported quantities useful for verification by dust. This discrepancy can be neglected when spring of model dust forecasts. In a dust-storm-dominant sea- dust events dominate the optical depth. The forecasted son such as the spring in the Iraq region, dust particles dust AOD is calculated with a mass extinction effi- are the major factor in reduced visibility. Visibility re- ciency of 0.58 m2 gϪ1, based on Mie scattering theory ports are subjective but nevertheless clearly depict the for monodispersed dust particles of 2.0-␮m diameter passage of dust storms. For example, observed visibility and previous size-resolved simulations (Liu et al. 2003). from three stations of Arar, Hafr, and King Fahad in northern Saudi Arabia (whose locations are marked in Fig. 5) are shown in Figs. 6, 7, and 8. There is consis- tency among the observations at the three sites with similar timing at adjacent sites. Both Arar and Hafr show a dust frontal passage on 25 March following the cold front and surface temperature drop, while King Fahad shows it on early 26 March. The details of Figs. 6–8 will be discussed in section 4 below for the model verification at these three stations. Visibility is used in two ways. It can be used for direct comparison with the model-predicted visibility. Several relationships have been developed and used to convert dust mass concentration to visibility (Shao et al. 2003; Chung et al. 2003; Leys et al. 2002; McTainsh et al. 2001; Tews 1996; Patterson and Gillette 1977). We choose to use the relationship developed by Tews (1996) and Patterson and Gillette (1977) that expresses visibility (km) as inversely proportional to the square of the forecasted dust mass concentration C (mg mϪ3):

V ϭ 0.16րC2. ͑2͒ This relationship was derived from 30 yr of dust mass measurements and visibility observations made in Aus- tralia. Unfortunately, similar long-term data for southwest Asia do not exist. Due to the similarities in the optical properties of mineral dust aerosols throughout the world, this empirical equation is considered suit- able for southwest Asia. Given the observed and predicted visibility, we calculate the correlation, bias, rms, and relative error statistics for individual stations. COAMPS does not forecast other optically active aero-

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FIG. 3. (a) Weather map of observed surface wind (blue, full tick ϭ 10 m sϪ1), dust observation (red symbols), and cold front (heavy black dashed line) for 0600 UTC 26 Mar 2003. Simulated sea level pressure (green contours) and surface temperature (yellow contours) are also added to show synoptic forcing. (b) Satel- lite image of dust plumes (pink) in the Iraq region retrieved by the U.S. Naval Research Laboratory at 0745 UTC 26 Mar 2003. Heavy dashed line indicates the dust front. It is the same area, coverage is as in (a). (c) COAMPS 56-h forecasts of dust mass load (vertically integrated, mg mϪ2) and surface wind (full tick ϭ 10 m sϪ1) on the 9-km grid valid at 0800 UTC 26 Mar 2003.

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FIG. 5. A map showing the locations of 19 weather stations surrounding Iraq. Stations Arar, Hafr al Batin, and King Fahad are marked in dashed circles. Stations marked with solid dots have observed high frequency of dust storms and are near dust source areas. Solid triangles are the radiosondes in the Iraq region. The order of the stations in the statistics plots (Figs. 10–13) from left to right begins at Siirt in eastern Turkey and continues counterclockwise around Iraq, ending with Orumieh in northern Iran.

case studies, model verifications, and operational fore- FIG. 4. (a) Satellite-retrieved optical depth in the Arabian Gulf casts have been proven to be a successful approach in by U.S. Naval Research Laboratory at 1027 UTC 27 Mar 2003. the dust modeling community in recent years (Shao et Two dashed circles indicate the maximum areas of optical depth. al. 2003; Chung et al. 2003; Leys et al. 2002; McTainsh (b) COAMPS 58-h forecast of dust optical depth in the 27-km domain valid at 1000 UTC 27 Mar 2003 and surface dust obser- et al. 1998, 2001). Other weather types may be the cause vations (dollar symbols) in the Arabian Gulf. for reported visibilities less than the threshold. For the weather stations in the Iraq region, less than 10% of the observed visibility measured to less than 3.5 km was sol species, such as smoke, pollution, sea salt, or dust caused by precipitation, fog, smoke, and haze. Since from anthropogenic or military activities. Hence, the COAMPS and Eq. (2) do not model these effects, a few forecasted visibilities may have a slightly high bias. nondust78 contributions would cause a small reduction Second, visibility can be used as a threshold for de- in the skill scores for the forecasted visibilities. fining dust storm occurrence. For this analysis, an effective dust storm is assumed when the reported visibility is less than 3.5 km. This choice was based on the 4. Comparisons of model forecasts with visibilities reported when the current weather was re- observations ported as any of the “dust storm” codes (e.g., 6–9 and a. Verification for the 25–27 March dust storm 30–35), which are defined by the World Meteorological Organization for weather station reports. Based on this The strongest dust storm of the OIF period occurred conditional test for a dust storm, we can calculate bi- on 25–27 March at the beginning, and is called the OIF nary measures of model skill, such as storm frequency, dust storm. The storm grounded the U.S. Air Force for threat scores, prediction rates, missing rates, and false a day and impeded other military operations in and alarm rates. Similar visibility comparisons in dust storm around Iraq. The ability to forecast this type of dust

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FIG. 6. Time series of (a) observed visibility, (b) COAMPS FIG. 7. Same as in Fig. 6 but for station Hafr al Batin. 0–72-h forecasted dust visibility on the 9-km grid, and (c) 0–72-h forecasts of surface temperature and wind barbs (a full tick ϭ 10 Ϫ ms 1) at Arar weather station from 24 to 27 Mar 2003. Note the Arabian Peninsula with the dust front at the leading, or inverted visibility (y) axis. eastern, edge just reaching the Arabian Gulf. For model verification, we show in Fig. 3c the 56-h forecast event is critical and the evaluation of COAMPS’ per- of dust mass load (the vertical integral of concentra- formance for this case is of particular significance. tion) and surface wind vectors from the 9-km grid for On 24 March, a low pressure system and an associ- 0800 UTC 26 March for comparison with the surface ated cold front moved to the eastern Mediterranean winds and the satellite DEP shown in Figs. 3a and 3b. Sea, where the cold air from Europe encountered the The modeled plume covers most of the same large ar- warm and dry air from the Sahara. The frontal system eas of Iraq and Saudi Arabia, except western Saudi swept westward across Syria and Jordan and arrived in Arabia. The location of the modeled dust front agrees Iraq and northern Saudi Arabia on 25 March. The well with that of the observed front (Figs. 3a and 3b). strong northwesterly winds behind the cold front raised The forecasted winds also compare favorably with the dust storms as the front moved over the source regions observations. in the deserts. Dust mobilization continued on 26 Over the following 24 h, the front moved south- March, and the dust plumes followed the cold front, southeast, reaching the southern Arabian Gulf and the reaching the Arabian Gulf on 27 March. Straight of Hormuz by 1027 UTC 27 March, as seen in Figure 3a shows the observed surface winds and dust the satellite-retrieved optical depth of Fig. 4a. A further storm observations at 0600 UTC 26 March. While the model verification is done by comparing this satellite wind shift alone justifies the location of the analyzed AOD with the 58-h forecast of dust optical depth of the cold front, we also overlay the COAMPS 6-h forecasts 9-km grid for 1000 UTC 27 March (Fig. 4b). The fore- of sea level pressure and surface temperature to more cast shows maxima over the northern and southern clearly reveal the structure of the weather system. The Arabian Gulf, as is seen in the retrieved AOD (Fig. 4a). Terra MODIS DEP for 0745 UTC 26 March (Fig. 3b) The spatial distribution agrees well with the retrieval. shows the dust covering most of Iraq and the northern In particular, the modeled optical depths show two lo-

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are shown in Fig. 6 and depict the dust arriving on the morning of 25 March in the large-scale southerly winds that developed in the warm sector ahead the cold front. The main dust front arrived after the passage of the cold front that caused a large temperature drop in the afternoon. Strong westerlies across northern Saudi Arabia continued after the passage of the front until late on 26 March, in response to a surface low that developed over Syria. After a short period of moderate clearing early on 27 March, the second dust storm arrived. COAMPS accurately predicted the two events but the forecasted visibility during the clearing episode was higher than observed. Figure 7 shows the comparison of observed visibility with COAMPS forecasts at station Hafr al Batin for the same forecast period. The observations and COAMPS forecasts display the same events as observed at Arar, except the cold front arrived several hours later and the forecasted visibilities during the clearing event before the cold front are lower than observed. Additionally, there is a smaller event in the afternoon of 24 March that occurs in moderate southeasterlies. COAMPS predicted this event, but with lower visibilities than observed. Figure 8 is similar to Figs. 4 and 5 but for station King Fahad and for a single 72-h forecast from 0000 UTC 25 FIG. 8. Same as in Fig. 6 but for station King Fahad from 25 to March to 0000 UTC 28 March. Both observations and 28 Mar 2003. model forecasts show the low, prefrontal visibilities on 25 March, the cold front passage on 26 March followed cal maxima, as did the retrieval. The values of 0.8–2.0 at by the low visibilities in the north and westerly winds. both ends of the Gulf agree with the retrieved values in While the winds over northern Saudi Arabia diminish the south but are 25% low in the north. The surface by the end of 26 March, dust remains in suspension over dust observations in Fig. 4b confirm the modeled dust the Arabian Gulf and coastal areas throughout 27 plumes in the area. March. Surface visibility observations at Arar and Hafr al b. Overview of dust activity during OIF Batin in northern Saudi Arabia and King Fahad on the Arabian Gulf (see Fig. 5 for the locations) are com- Weather reports and satellite imagery indicate that pared to the modeled visibility of the 9-km grid from a there were numerous dust storms during OIF. For ex- single 72-h model forecast in Figs. 6–8 to examine the ample, in Fig. 9a we show the time series of the total accuracy of the COAMPS dust forecast at specific sites. number of low-visibility (Ͻ3.5 km) reports for all the These sites were chosen because they are located along surface weather stations within the 9-km model domain the path of the dust front. Lerner et al. (2004) con- (Fig. 1) from 16 March to 30 April. The data reveal five ducted a study of the quality of weather station visibil- large events centered around 20 and 26 March, and 8, ity reports in several southwest Asia countries for 2001– 16, and 27 April that were produced by frontal passages 03. These three stations and the 16 other stations shown and deep low pressure systems with each episode last- in Fig. 5 (and used in section 5) were found to report ing 2–3 days. In Fig. 9b we show the time series of regularly and to provide consistent data. Iraq did not COAMPS total dust mass load (Mt) of the 9-km grid in report their meteorological observations to the World the entire 9-km domain. Though a different quantity, Meteorological Organization (WMO) before or during the simulated mass load shows good qualitative agree- the war. ment in the timing and duration of the five large events The visibility observations and forecasted visibility as of the observations in Fig. 9a. The relative magnitudes well as the forecasted winds and temperature from appear closely correlated, with the event on the 25–27 Arar for 0000 UTC 24 March to 0000 UTC 27 March March being the largest.

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nity and is applied here to evaluate the accuracy of COAMPS real-time dust forecasts throughout the OIF period in the focus area. Some representative statistical errors and forecast rates are calculated at 19 weather stations surrounding Iraq (see Fig. 5). These stations are chosen according to the data quality (Lerner et al. 2004), frequency of observations, and proximity to Iraq. In this analysis, the dust storm frequency is defined as the ratio of the number of observations of low visibility Յ3.5 km, to the total number of weather reports at each station in the OIF period. The stations having the high- est dust storm frequency (i.e., Ն6%) are found to the south and southeast of Iraq (in Fig. 5) and at or downwind of dust source areas (Fig. 2b). Three distinct and commonly used errors are calculated for the statistical analysis of modeled visibility: bias error ͑km͒ ϭ ͚ ͑F Ϫ O͒րN,

͚ ͑F Ϫ O͒2րN͔0.5, and͓ ס rms error ͑km͒ FIG. 9. (a) Time series of the total number of weather stations relative error ͑%͒ ϭ ͚|F Ϫ O|/͚|O|, that observed dust storms (defined as visibility Ͻ 3.5 km) in the 9-km model domain. (b) Time series of modeled total dust where F is the 3-, 6-, 9-, or 12-h forecasts of dust vis- mass load in the air (Mt) in the 9-km domain for the 9-km grid. ibility generated at 0000 and 1200 UTC, obtained by Maximums are circled to show the timing of storms between (a) bilinear interpolation from the grid points to the and (b). weather stations; O is the observed visibility; and N the The observations and COAMPS forecasts (Figs. 6–9) number of observations. Bias error indicates the mean reveal clear diurnal signals with the onset in lifting oc- direction of deviation from observations, but does not curring soon after sunrise, or between 0000 and 0600 tell the magnitude of the errors. The rms error gives the UTC, when vertical mixing of momentum occurs. The magnitude of the error that is weighted according to the minima occur in the late-night hours, or before 0000 quadratic scoring rule and thus emphasizes large errors. UTC, when nocturnal inversions minimize the vertical Relative error is the ratio of the linearly weighted error exchange and surface winds are lowest. The dust emis- to the mean observation and thus indicates the impor- sion flux [Eq. (1)] is a function of u and therefore tance of mean error. The modeled visibilities at 3-h * accounts for both thermal stability and wind shear ef- intervals are compared with the corresponding 3-h ob- fects on the momentum exchange in the surface layer. servations. The stations report visibility ranging from The large-scale dynamic forcing modulates this diurnal 50 m up to a maximum of 10, 12, or 15 km, depending pattern. on the reporting convention of the station. Since the In summary, the direct comparisons of the modeled model generates a continuous range of visibilities, results with surface dust and visibility observations, sat- reaching very high values during clear conditions, the ellite images, and optical depth retrievals illustrate that upper limit of the modeled dust visibility is restricted to COAMPS accurately predicts the timing, strength, and the appropriate reporting maxima for each station. spatial coverage of major dust events for the Iraq re- Figure 10 shows the bias error of the modeled vis- gion. In the next section, we perform a more quantita- ibility for the 9-km grid at each of the 19 stations tive evaluation of model performance by conducting a throughout the OIF period (16 March–30 April). The statistical analysis of the visibility forecasts for the re- stations are ordered by their locations in Fig. 5 from gion. Siirt in northern Turkey, counterclockwise around the Iraqi border to Orumieh in northwestern Iran. The bias 5. Statistic analysis of model forecasts over the error is both positive and negative and varies from sta- OIF period tion to station, with nine stations showing positive bias, nine stations negative, and one station zero. The one a. Evaluation of statistical errors pattern that emerges is for negative biases (forecasted Statistical analysis is an effective verification method visibility too low) along southern Iraq from Truaif to widely used in the numerical weather forecast commu- King Fahad and positive biases elsewhere. The negative

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FIG. 10. Bias error (km) of modeled visibility on the 9-km grid FIG. 11. The rms error (km) and relative error (%) of the mod- at 19 stations from 16 Mar to 20 Apr 2003. The order of the eled visibility on the 9-km grid at 19 stations from 16 Mar to 20 stations begins with Siirt in eastern Turkey and moves counter- Apr 2003. Heavy arrow bar covers the stations of high dust fre- clockwise around Iraq, ending with Orumieh in Iran (see Fig. 5). quency. Relative error is multiplied by 10 to better show station- dependent distribution. biases are likely due to overestimation of the dust source strength. The positive biases may be due to the (Gyakum and Samuels 1987; Hamill and Colucci 1998; absence of other optically active aerosols in COAMPS, Colle et al. 1999; Colle and Mass 2000). A visibility such as smoke, pollution, and dust generated by anthro- threshold is required to delineate between a dust storm pogenic activities. The Aerosol Robotic Network and a clear sky for a binary comparison between ob- (AERONET) measurement from Bahrain has shown servations and the model. A threshold of 3.5 km again persistently nonzero AOD (low visibility) even during is chosen based on the visibilities reported during dust dust-free conditions (Smirnov et al. 2002). storms in the Iraqi region over the OIF period. A dust Figure 11 shows the rms and relative errors of mod- storm is presumed to have occurred when the observed eled visibility for the 9-km grid at the 19 stations. Com- visibility is less than the threshold. Dust-free, or clear- pared with Fig. 5, it is clear that the rms error depends sky, conditions are assumed when the observed visibil- on dust storm frequency with error of 3.0–3.5 km at the ity is greater than the threshold. For a given station, stations of high dust frequency in the south and error of COAMPS is assumed to have predicted a dust storm if 1.5–2.0 km at the stations of low dust frequency in the any of the hourly, predicted visibilities fall below 3.5 km north. This is because the stations in the south experi- during the 3-h window centered on the observation ence more low-visibility days and are likely to have time. Otherwise, it is considered a clear-sky prediction. larger errors, while at the low-frequency stations, both In this way, each 3-h window is counted as one incident the observed and predicted visibilities often reach the of either a dust storm or clear skies, based on the 3.5- maximum reported values so individual errors are often km threshold. The forecast rates are calculated as fol- small or even zero. The relative error has a similar lows: dependency on dust frequency. It is about 20% high on 1) dust storm prediction rate ϭ number of correctly average at the dust-frequent stations (e.g., an average predicted dust incidents/number observed dust inci- 2.0 in Fig. 11) and 7% low on average elsewhere (e.g., dents, on average about 0.7 in Fig. 11). 2) dust storm false alarm rate ϭ number of falsely predicted dust incidents / number of observed clear-sky, b. Evaluation of forecast rates incidents Another statistical tool used to quantify COAMPS 3) dust storm threat score ϭ (number of predicted dust accuracy is the calculation of forecast rates in four cat- incidents)/(predicted dust ϩ missed dust ϩ false egories: dust storm prediction rate, missed dust storm alarm dust incidents), and rate, clear-sky (e.g., no dust) prediction rate, and dust 4) total prediction rate ϭ (number of correctly pre- storm false alarm rate. A linear threshold method is dicted dust incidents ϩ correctly predicted clear-sky adapted from the evaluations of precipitation forecasts incidents)/(total observations).

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FIG. 12. COAMPS dust storm prediction rate, false alarm rate, FIG. 13. COAMPS total prediction rate of correctly predicted and threat score on the 9-km grid at 19 stations from 16 Mar to 20 dust storm and correctly predicted clear sky on the 9-km grid at 19 Apr 2003. Heavy arrow bar covers the stations of high dust fre- stations from 16 Mar to 20 Apr 2003. quency.

frequency stations located near the dust source areas. The threat score measures the forecast accuracy The dust storm threat score is accordingly high at high- when the clear-sky incidents are removed from consid- dust-frequency stations due to the high dust prediction eration. The total prediction rate is the overall accuracy rate, having scores ranging from 0.3 to 0.55, whereas it of dust storm and clear-sky incidents that are correctly is below 0.15 at the stations far from the dust source predicted. areas. Figure 12 shows the dust storm prediction rate and Figure 13 shows the total model prediction rate of the false alarm rate of the 9-km grid at the 19 stations for 9-km grid at the 19 stations. More than 85% of the the period, as well as the dust storm threat score. As observed weather incidents (dust storm and clear sky) before, the forecast rates strongly depend on dust storm were correctly predicted everywhere throughout the frequency with the high dust-frequency stations exhib- OIF period. COAMPS appears to have provided accu- iting high dust storm prediction rates of 50%–95%. rate real-time forecasts to the U.S. Navy and the U.S.- (See the stations of high and low dust frequency in Fig. led coalition forces. 5.) Due to the nature of dust particle sedimentation and the high mountains located in the north of the Iraq 6. Statistical evaluation of 12–72-h forecasts for region, most of the dust mass is deposited in the south the 26 March dust storm near the deserts, while a small amount of dust is transported downwind, mostly being lifted high up in the air As described in section 4 and seen in Figs. 7 and 9, 25 for long-distance transport. Therefore, the surface sta- and 26 March experienced the strongest dust storm in tions in the north, far away from the source areas, ex- the 9-km domain during the OIF period. In this section, perience much lower dust storm activity than those in we calculate the forecast rates of dust storm and clear- the south. At these low- or no-dust incidents stations, sky prediction for the 12-, 24-, 36-, 48-, 60-, and 72-h this type of linear calculation method results in appar- forecasts at the single verification time of 1200 UTC ently poor forecast rates because the number of ob- 26 March and averaged over 95 weather stations in served dust incidents is very small. As seen in Fig. 12, the 9-km domain of the Iraqi region. The same lin- these stations in the north have a 20% or lower dust ear method as used above is used here. The forecast storm prediction rate, for example, an 80% or higher rates of dust storm prediction and clear-sky prediction missed dust storm rate. On average, the dust storm false (ϭ1.0 Ϫ dust false alarm rate) for the 9-km grid aver- alarm rate is low at all 19 stations, for example, high aged in the 9-km domain are plotted in Fig. 14 for the clear-sky prediction rate, because the number of no- 12–72-h forecasts, valid at 1200 UTC 26 March. dust or clear-sky weather incidents is much larger than COAMPS generates nearly constant high values of that of dust storm incidents. Therefore, the false alarm prediction rates of both dust storms (78%) and clear rate is low everywhere, even below 10% at high-dust- sky (95%) with little change in the forecast skill for the

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FIG. 14. Dust storm forecast rate and clear-sky (ϭ1.0 Ϫ false alarm rate) forecast rate of the 12-, 24-, 36-, 48-, 60-, and 72-h FIG. 15. Surface visibility statistics for the 9-km grid of bias error forecasts valid at 1200 UTC 26 Mar 2003, averaged in the Iraq (km), rms error (km), and relative error (%) of 12–72-h visibility region of the 9-km domain for the 9-km grid. forecasts valid at 1200 UTC 26 Mar 2003 in the Iraq region for the 9-km domain. different length forecasts. This means COAMPS 3-day ␷ In the wind vector error, uf and f are the modeled wind dust forecasts are as accurate as the 12-h forecasts. In ␷ component speeds, uo and o the observed speeds, and addition to the forecast rates, the bias error, rms error, N the total number of weather stations. This error in- and relative error at the 9-km grid are calculated and dicates the magnitude of wind direction error. Mean averaged over the 9-km domain to have a further ex- absolute error is a linear scoring of scalar error that is amination. A clear trend is also absent in these three always less than or equal to rms error, in which F and O statistical errors (Fig. 15). The 27- and 81-km grid nests are the modeled and observed values of either wind also had nearly invariable dust forecast rates and sta- speed or temperature. tistic errors at this particular time of 1200 UTC 26 Figure 16 shows the statistics of bias, rms, relative, March over the 9-km domain (not shown). and absolute errors, as well as vector error, at the 9-km The absence of a trend in the forecast skill is unex- grid for surface wind speed and direction calculated at pected and may be due to three factors. 1) The 72-h the surface stations within the 9-km model domain, for forecasts of both dust and dynamical fields are as accu- 12–72-h forecasts valid at 1200 UTC 26 March. None of rate as the 12-h forecasts with good statistics. 2) The the errors show any trend. The relative error is between data assimilation has little effect on the dynamics be- 25% and 28%, while the bias error is always positive, cause there are so few upper-air observations in the indicating COAMPS overpredicts wind speeds in this Iraq region (see the radiosonde sites in Fig. 5); hence, region. The 27- and 81-km grids in the 9-km domain the dynamical variables are little changed from one also show no trend in forecast skill (not shown). forecast to the subsequent analysis. 3) There is no dust Figure 17 shows the bias, rms, relative, and absolute data assimilation process so that the dust forecast is errors at the 9-km grid of surface temperature in the essentially a single long forecast with no incremental 9-km domain. Both rms error and absolute error de- corrections. These effects are demonstrated by exam- cline very slightly from 12 to 72 h, but the differences ining the statistical errors of COAMPS surface winds are only about 5%–7% and are not significant enough and temperature, both of which affect dust production to conclude that the COAMPS temperature forecast and deposition via the surface momentum and heat accuracy has changed. The bias error indicates a warm fluxes, and are the indicators of the accuracy of the bias for a short forecast, changing to a cold surface dynamical model forecast: temperature bias for longer forecasts. The same trends are found in the 27- and 81-km grids (not shown). The ͑ Ϫ1͒ ϭ ͕͚͓͑ Ϫ ͒2 wind vector error ms uf uo constant statistical scores in the 3-day forecasts of winds ϩ ͑␷ Ϫ ␷ ͒2͔րN͖0.5 and and temperature as forecast length increases in this case f o of 26 March implies a consistently accurate forecast, Ϫ (͚|F Ϫ O|րN. and that observational data (especially radiosondes ס mean absolute error ͑ms 1 or ЊC͒

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7. Summary and conclusions

Dust storms are a significant weather phenomenon in the Iraq region. We have conducted real-time dust forecasting for the U.S. Navy using the COAMPS Meso- scale Prediction System with an in-line dust aerosol model during the OIF period in March and April 2003. By simulating the dust life cycle of emission, transport, and deposition, and using a high-resolution dust source database for southwest Asia, we were able to forecast dust storms in 1–3 days in advance along with the con- ventional meteorological variables. Our daily 12–72-h forecasts were used in the weather discussion produced by the U.S. Navy Fleet Numerical Meteorological and Oceanographic Center during OIF. COAMPS real- time runs provided valuable products for the U.S. Navy FIG. 16. Surface winds statistics on the 9-km grid of bias error and contributed to the U.S.-led OIF mission. (m sϪ1), rms error (m sϪ1), absolute error (m sϪ1), vector error Ϫ COAMPS real-time forecasts have been analyzed (m s 1), and relative error (%) for the 12–72-h forecasts valid at 1200 UTC 26 Mar 2003 in the Iraq region for the 9-km domain. and verified by comparing with available observations of enhanced satellite images and retrievals, surface weather reports, and visibility measurements. We show are insufficient in the Iraq region to show the benefit of that COAMPS predicted the five major dust storms data assimilation for COAMPS. Since observational that occurred in the OIF period in good agreement with data were consistently missing during the Iraq war pe- observations in terms of timing, strength, duration, and riod, it is likely that data assimilation did not contribute spatial coverage. With grid nesting (triple nested) and throughout the period and the performance of dust high-resolution modeling (9 km), COAMPS predicted forecasting is similar to this case. The same model was the dust front passage of the strong dust storm on 25–27 evaluated in another study for the Middle East (Na- March. Three weather stations located along the Iraqi chamkin and Hodur 2001). That study showed similar border and the Arabian Gulf were specifically verified statistical scores of surface wind and temperature to the for the 3-day forecasts of visibility, revealing consistent ones found in this study, but forecast skill improved forecasts of the passage of dust storms with accurate with decreasing forecast length because of sufficient ob- timing and intensity of dust plumes and visibility. servations and effective data assimilation. We use 19 weather stations surrounding the Iraqi border to calculate statistical errors and forecast rates of modeled dust visibility for a quantitative evaluation of COAMPS dust forecasting for the OIF period. The bias error varies from positive (due to possible overestimation of dust fluxes) to negative (due to the lack of other aerosols in the model). It was found that both rms and relative errors depend on the frequency of dust storms and the locations of the stations. The stations located in the south near the source areas experience more dust storms and a larger range in visibilities, and tend to have higher statistical errors. While the stations in the north often have visibilities reaching the maximum reported and a smaller range of visibilities, so individual statistical errors are smaller. Both the dust storm prediction rate and threat score were high at the stations of high dust frequency in the south, reaching 50%–90% accuracy and a 0.3–055 score FIG. 17. Surface temperature statistics on the 9-km grid of bias error (K), rms error (K), absolute error (K), and relative error for the OIF period, respectively. The false alarm rate is (%) for the 12–72-h forecasts valid at 1200 UTC 26 Mar 2003 in low everywhere because the number of clear-sky the Iraq region for the 9-km domain. weather incidents is much larger than that of dust

Unauthenticated | Downloaded 09/29/21 11:08 PM UTC FEBRUARY 2007 L I U E T A L . 205 storms. Overall, COAMPS correctly predicted more Bott, A., 1989a: A positive definite advection scheme obtained by than 85% of the observed dust storm and clear-sky nonlinear renormalization of the advective fluxes. Mon. Wea. weather at all stations in the Iraq region. Rev., 117, 1006–1015. ——, 1989b: Reply. Mon. Wea. Rev., 117, 2633–2636. Dust forecasts of various lengths (12–72 h), averaged Chen, S., and Coauthors, 2003: COAMPS 3.0 model description— over 95 stations in the 9-km domain of the Iraqi region, General theory and equations. NRL Tech. Note NRL/PUB/ were evaluated for the strongest dust event on 26 7500-0-3-448, 143 pp. March to examine the model forecast skill. COAMPS Chung, Y. S., H. S. Kim, K. H. Park, J. G. Jhun, and S. J. Chen, generates nearly constant high values of prediction 2003: Atmospheric loadings, concentration and visibility associated with sandstorms: Satellite and meteorological analy- rates of dust storms (78%) and clear sky (95%) for all sis. Water Air Soil Pollut.: Focus, 3, 21–40. forecast lengths. We also found that both the statistical Colle, B. A., and C. F. Mass, 2000: The 5–9 February 1996 flood- errors of visibility and the errors of the corresponding ing event over the Pacific Northwest: Sensitivity studies and dynamic forcing of winds and temperature present little evaluation of the MM5 precipitation forecasts. Mon. Wea. change in different length forecasts. The constantly Rev., 128, 593–617. ——, K. J. Westrick, and C. F. Mass, 1999: Evaluation of MM5 high forecast rates and the lack of improvement in fore- and Eta-10 precipitation forecasts over the Pacific Northwest cast performance imply that the model forecasts were during the cool season. Wea. Forecasting, 14, 137–154. consistent, and the lack of radiosondes data in the Iraqi Cope, M. E., and Coauthors, 2004: The Australian air quality region eliminates the potential benefit of data assimi- forecasting system. Part I: Project description and early out- lation to COAMPS dust modeling. comes. J. Appl. Meteor., 43, 649–662. Durkee, P. A., and Coauthors, 2000: Regional aerosol optical This paper has presented the new capability of depth characteristics from satellite observations: ACE-1, COAMPS in dust modeling and the new skill in opera- TARFOX and ACE-2 results. Tellus, 52B, 1–14. tional forecasting. The study of real-time dust forecast- Gillette, D. A., 1978: A wind tunnel simulation of the erosion of ing and verification has given us several insights into soil: Effect of soil texture, sandblasting, wind speed, and soil operational dust forecasting. 1) Dust source specifica- consolidation on dust production. Atmos. Environ., 12, 1735– tion and resolution are a fundamental part of dust mod- 1743. ——, and R. Passi, 1988: Modeling dust emission caused by wind eling and greatly affect the accuracy of forecasting. erosion. J. Geophys. Res., 93, 14 233–14 242. Work is on going on high-resolution dust source iden- Gyakum, J. R., and K. J. Samuels, 1987: An evaluation of quan- tifications for the rest of the globe. 2) Dust aerosol titative and probability of precipitation forecasts during the modeling with a single particle size is practical and ac- 1984–85 warm and cold seasons. Wea. Forecasting, 2, 158– curate in operational runs where computational re- 168. Hamill, T. M., and S. J. Colucci, 1998: Evaluation of Eta–RSM sources are limited. 3) The surface weather station ob- ensemble probabilistic precipitation forecasts. Mon. Wea. servations and satellite retrievals are invaluable to the Rev., 126, 711–724. dynamics and dust modeling verification. 4) The im- Hodur, R. M., 1997: The Naval Research Laboratory’s Coupled provement of forecast accuracy may depend on the Ocean/Atmosphere Mesoscale Prediction System (COAMPS). data assimilation of the dust component using satellite Mon. Wea. Rev., 125, 1414–1430. Hogan, T. F., and T. E. Rosmond, 1991: The description of the retrievals in the future. The COAMPS dust forecast U.S. Navy Operational Global Atmospheric Prediction Sys- model became operational at FNMOC for the Iraq re- tem’s Spectral Forecast Model. Mon. Wea. Rev., 119, 1786– gion in 2004. 1815. Lerner, J. A., D. L. Westphal, and J. S. Reid, 2004: Quality con- Acknowledgments. We thank Dr. Jason Nachamkin trolled surface visibility observations used to validate pre- for the discussions about statistical analyses. We also dicted surface aerosol concentration for southwest Asia. Pre- prints, 20th Conf. on Weather and Forecasting, Seattle, WA, thank Mr. Arunas Kuciauskas for providing archived Amer. Meteor. Soc., CD-ROM, P4.3. satellite images. 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