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  • View in gallery

    Wind-retrieval locations (light blue crosses) from (a) 2DVAR and (b) VDRAS along with the locations of surface meteorological stations (red squares), rawinsonde (green dot), radar wind profilers (blue dots), and the KTLX NEXRAD (black dot). Gray shading denotes urban areas, and the modeling domain of the CALMET/CALPUFF is denoted by the large black square. Vertical coordinate for (c) 2DVAR and (d) VDRAS.

  • View in gallery

    Fraction of valid winds from the (a) 2DVAR wind retrievals and (b) VDRAS wind retrievals for a 2-week period in July during the JU2003 field campaign.

  • View in gallery

    Time series of ANL radar wind profiler wind speed and direction (blue dots) compared to values from (a) 2DVAR (red dots) at the 357-m range gate and (b) VDRAS (red dots) at the 192-m range gate. Dots denote 30-min averages, and vertical gray lines denote the range of wind retrieval values within a 30-min averaging period. Gray shading denotes night.

  • View in gallery

    Time series of ANL radar wind profiler wind speed and direction (blue dots) compared to values from (a) 2DVAR (red dots) at the 1897-m range gate and (b) VDRAS (red dots) at the 1677-m range gate. Dots denote 30-min averages, and vertical gray lines denote the range of wind retrieval values within a 30-min averaging period. Gray shading denotes night.

  • View in gallery

    Mean (dots) and std dev (lines) of the bias obtained from the 2DVAR (red) and VDRAS (blue) wind retrievals using only the NEXRAD data.

  • View in gallery

    Difference in the u and υ components of the wind speed at 550 m AGL between the PNNL and NOAA radar wind profiler sites for the (a) 2DVAR and (b) VDRAS wind retrievals.

  • View in gallery

    Observed (blue) and simulated (red) wind profiles over the ANL site on 5 Jul 2003, from four CALMET simulations that employed the following meteorological fields: (a) std observations, (b) std observations and radar wind profilers, (c) std observations and 2DVAR retrievals, and (d) std observations and VDRAS retrievals. Arrows indicate the direction in which the wind is blowing toward.

  • View in gallery

    As in Fig. 7, but for 10 Jul 2003.

  • View in gallery

    As in Fig. 7, but for 12 Jul 2003.

  • View in gallery

    Wind fields from two CALMET simulations at two altitudes on 0400 LST (1000 UTC) 5 Jul 2003. Color of arrow denotes wind speed: purple = <3, blue = 3–4, light blue = 4–5, green = 5–6, light green = 6–7, orange = 7–8, red = 8–9, pink = 9–10, and black > 10 m s−1.

  • View in gallery

    As in Fig. 10, but at 1000 LST (1600 UTC) 5 Jul 2003.

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    12-h-integrated concentration on 5 Jul 2003, from six CALPUFF simulations with the following meteorological inputs: (a) std observations, (b) std observations + profiler, (c) std observations + 2DVAR retrievals, and (d) std observations + VDRAS retrievals.

  • View in gallery

    As in Fig. 12, but for 10 Jul 2003.

  • View in gallery

    As in Fig. 12, but for 12 Jul 2003.

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An Evaluation of Two NEXRAD Wind Retrieval Methodologies and Their Use in Atmospheric Dispersion Models

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  • 1 Pacific Northwest National Laboratory, Richland, Washington
  • | 2 NOAA/National Severe Storms Laboratory, Norman, Oklahoma
  • | 3 National Center for Atmospheric Research,* Boulder, Colorado
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Abstract

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Jerome D. Fast, Pacific Northwest National Laboratory, P.O. Box 999, K9-30, Richland, WA 99352. Email: jerome.fast@pnl.gov

Abstract

Two entirely different methods for retrieving 3D fields of horizontal winds from Next Generation Weather Radar (NEXRAD) radial velocities have been evaluated using radar wind profiler measurements to determine whether routine wind retrievals would be useful for atmospheric dispersion model applications. The first method uses a physical algorithm based on four-dimensional variational data assimilation, and the second simpler method uses a statistical technique based on an analytic formulation of the background error covariance. Both methods can be run in near–real time, but the simpler method was executed about 2.5 times as fast as the four-dimensional variational method. The observed multiday and diurnal variations in wind speed and direction were reproduced by both methods below ∼1.5 km above the ground in the vicinity of Oklahoma City, Oklahoma, during July 2003. However, wind retrievals overestimated the strength of the nighttime low-level jet by as much as 65%. The wind speeds and directions obtained from both methods were usually similar when compared with profiler measurements, and neither method outperformed the other statistically. Within a dispersion model framework, the 3D wind fields and transport patterns were often better represented when the wind retrievals were included along with operational data. Despite uncertainties in the wind speed and direction obtained from the wind retrievals that are higher than those from remote sensing radar wind profilers, the inclusion of the wind retrievals is likely to produce more realistic temporal variations in the winds aloft than would be obtained by interpolation using the available radiosondes, especially during rapidly changing synoptic- and mesoscale conditions.

* The National Center for Atmospheric Research is sponsored by the National Science Foundation

Corresponding author address: Jerome D. Fast, Pacific Northwest National Laboratory, P.O. Box 999, K9-30, Richland, WA 99352. Email: jerome.fast@pnl.gov

1. Introduction

To accurately represent the mean transport and turbulent mixing of airborne materials in emergency response atmospheric dispersion models (ADMs) require the availability of real-time meteorological observations. In the United States, routine meteorological measurements from surface stations and rawinsondes are used frequently to drive ADMs. Large urban areas usually have a monitoring network that describes the spatial variations in the surface wind field (e.g., Horel et al. 2002). However, these observations are not typically representative of conditions above the surface, especially in the nighttime stable boundary layer (Doran et al. 2002). Vertical profiles of winds, temperature, and humidity are usually obtained from rawinsondes launched twice a day. The frequency of these measurements cannot fully describe temporal variations in the near-surface meteorological conditions, and only a fraction of rawinsonde sites are located adjacent to large urban areas. While a network of radar wind profilers provides hourly wind profiles, most of them have been deployed in rural areas of the central United States (Weber et al. 1993).

ADMs currently employ two approaches to fill gaps in the available meteorological information. The first approach interpolates spatially and temporally between the observations using mass conservation and parameterizations of physical processes. The second approach supplements the measurements using gridded output from a forecast model. One disadvantage of the first diagnostic approach is that it does not necessarily represent the state of the atmosphere, especially for strong wind shears and rapidly changing meteorological conditions. While the second approach produces realistic wind shears and rapidly changing conditions, model predictions also contain uncertainties that can be large at times.

One potentially rich, yet untapped, source of meteorological data for routine use in ADMs is from the Next Generation Weather Radar (NEXRAD) network with 141 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars (Crum and Alberty 1993). Doppler velocities, the flow toward or away from the radar, are measured at ∼250-m increments along the radials. The radar’s range depends on the size, number density, and physical properties of scattering material, and consequently varies among NEXRAD sites (e.g., Kelleher et al. 2007). In clear-air scanning mode each volume coverage pattern (VCP) with five elevation angles ranging from 0.5° to 4.5° takes ∼10 min to complete, whereas in precipitation scanning mode each VCP with nine elevation angles ranging from 0.5° to 19.5° takes ∼6 min to complete (Brown et al. 2000). In contrast to weather forecasting and aviation applications, ADMs need clear-air mode data; otherwise, winds would be produced only when and where precipitation occurred. Near-surface data are obtained close to the radar site, but data are located at increasingly higher altitudes at farther distances. However, the horizontal extent of NEXRAD data within 1 km AGL encompasses many large urban areas, assuming the presence of sufficient scattering material. These data would be useful for ADMs because the ambient flow aloft influences near-surface meteorology.

If the horizontal wind components can be accurately derived from the measured radial velocities, then an unprecedented amount of data would be available to improve ADM predictions. In this study, we evaluate the accuracy of two variational techniques that derive two-dimensional wind components from radial velocities and determine the feasibility that data collected from the NEXRAD weather radar system can be used in emergency response ADMs. While the performance of each technique has been evaluated elsewhere, they have not been directly compared until now. Independent measurements are employed to quantify their performance. The effect of the additional information available on dispersion is examined by performing a series of sensitivity simulations using a widely used ADM. Finally, we discuss how NEXRAD wind retrievals could be used routinely for ADMs.

2. Overview of wind retrievals methodologies

Two entirely different methods for retrieving 3D fields of horizontal winds from NEXRAD radar radial velocity observations in this study are briefly described next.

a. VDRAS

The Variational Doppler Radar Analysis System (VDRAS) uses four-dimensional variational data assimilation (4DVAR) to retrieve three-dimensional time-varying wind and temperature fields from a sequence of volume scans. The basic concept behind 4DVAR is to fit the output of a prognostic model to spatially and temporally resolved observations. An early prototype of VDRAS was developed and demonstrated by Sun et al. (1991) using simulated single-Doppler radar data. The technique was later applied to a dry gust front case using Doppler radar data (Sun and Crook 1994), and then adapted to study the structure and dynamics of convective storms (Sun and Crook 1998). More recently, versions of VDRAS have been run operationally for demonstration projects, including the 2000 summer Olympics in Sydney, Australia (Crook and Sun 2004), and the 2004 Pentagon Shield Field Program (Warner et al. 2007).

At a minimum, VDRAS requires radial velocity data from a single Doppler radar as well as temperature soundings. VDRAS can also be configured to assimilate radar reflectivity, surface mesonet data, and data from multiple Doppler radars. The model’s boundary conditions are prescribed, so that the initial conditions uniquely determine the model solution. Thus, the fit is performed by variational adjustment of the model’s initial state. The initial conditions are iteratively adjusted in order to optimize the agreement between the model solution and the available observations over a given assimilation cycle that typically includes either two or three complete volume scans. Optimization is achieved using the adjoint of the prognostic model.

For this study, an operational version of VDRAS is used so that the prognostic model simulates dry, shallow incompressible flow. Fourteen vertical levels were used up to 5.06 km. The height of the first level was at 187.5 m and a grid spacing of 375 m was used for subsequent levels. The coordinate system does not include variations in terrain; therefore, model physics and parameterization schemes in the operational version are simpler than those implemented in most mesoscale models. These simplifications are essential to reduce the complexity of the adjoint code and the resulting computational expense of the cost function minimization algorithm (Crook and Sun 2004). The VAD wind profile from the radar provided the first guess of winds, and a climatological potential temperature sounding was used for the first guess of temperatures.

The following two quality control (QC) procedures were performed on the radar scans: one was velocity dealiasing and the other was a generalized check to remove remaining noisy and spatially inconsistent data and to clean any clutter-contaminated data that had a small velocity value.

b. 2DVAR

The two-dimensional variational data assimilation (2DVAR) is a statistical interpolation technique (Daley 1991) developed for radar wind analyses by Xu et al. (2006) based on the previous radar velocity analysis technique described by Xu and Gong (2003). This 2DVAR can be regarded as an extension of the traditional velocity–azimuth display (VAD) technique (Browning and Wexler 1968). This method uses radar radial velocity data to apply an optimal correction to a prior background field using assumed analytic forms for the error covariance of the background field. The background field, in this case, is determined from a VAD analysis of the conical scan data.

The method employed by 2DVAR is appealing from an operational point of view because of its relative simplicity and computational speed, its ability to retrieve velocities on a single conical scan surface, and its limited data requirements. However, 2DVAR is relatively new and has not been run operationally like VDRAS. As with VDRAS, the algorithm’s ability to handle processes influenced by complex terrain has not been developed. Currently, the method assumes that the canonical form of the background error covariance tensor function is homogeneous and isotropic in the horizontal following a constant height above the sea level or a flat ground surface. Another consideration is with the sensitivity of the retrieved wind fields to changes in the prescribed decorrelation length parameters, although these parameters should and can be estimated in principle from radar innovation data (Xu et al. 2007).

In this study, the QC package described in Gong et al. (2003), Zhang et al. (2005), and Liu et al. (2005) was applied to the level-2 raw data.

3. Approach

a. JU2003 case study

The NEXRAD wind retrievals from 2DVAR and VDRAS were evaluated using radar wind profiler data obtained during the 2003 Joint Urban (JU2003) field campaign in Oklahoma City, Oklahoma (Allwine et al. 2004). Our evaluation employs data between 3 and 14 July 2003 from the four radar wind profilers shown in Fig. 1a, along with the operational surface meteorological stations in the region, and radial velocities from the KTLX NEXRAD radar located ∼20 km southeast of Oklahoma City. The Argonne National Laboratory (ANL) profiler was located about 6 km north of downtown Oklahoma City and the Pacific Northwest National Laboratory (PNNL) profiler was located just south of downtown. The University of Oklahoma (OU) profiler was located in Norman, Oklahoma, in the vicinity of the National Weather Service (NWS) rawinsonde about 25 km south of downtown Oklahoma City. An existing radar wind profiler operated by the National Oceanic and Atmospheric Administration (NOAA) was located about 50 km south of Oklahoma City. The PNNL, ANL, and OU profilers measured 30-min-average wind speed and direction profiles up to ∼4 km AGL and the NOAA profiler measured hourly average wind speed and direction profiles up to ∼10 km AGL.

Prior to 10 July southeasterly-to-southwesterly ambient winds were usually observed over Oklahoma City during JU2003 as a result of the Bermuda high pressure system that was located over the southeastern United States. Downward momentum transfer resulted in southerly winds at the surface as well during most of the period (Allwine et al. 2004). Diurnal variations in wind speed and direction above the surface were largely the result of the dynamics associated with the nocturnal low-level jet (Bonner 1968). The meteorological conditions associated with the Bermuda high were disrupted only once during JU2003 as a result of a cold front that produced northerly winds and showers over the region on 10 July.

Both 2DVAR and VDRAS were set up to compute wind retrievals over an area centered over KTLX that encompassed Oklahoma City and the surrounding region using a grid spacing of ∼2 km, as shown in Figs. 1a,b, respectively. The spatial extent of the domains differs slightly; 2DVAR had 80 × 80 points over a 160 km × 160 km domain and VDRAS had 75 × 75 points over a 150 km × 150 km domain, and 2DVAR computed wind components along the radar elevation scans (Fig. 1c) while VDRAS computed wind components along constant-elevation surfaces (Fig. 1d).

b. Available wind retrievals

The lack of radar scans produced gaps in the wind retrievals during the 2-week period, but this was a small percentage of the total time as shown in Fig. 2. Even though both 2DVAR and VDRAS employ the same radar data, the data gaps are somewhat different, presumably because of the different QC techniques that the two models employ. For 2DVAR, winds are only obtained for those grid points with valid data. The fraction of valid data points in Fig. 2a reflects that the radar data quality varies in time and depends on the meteorological conditions and the scattering efficiency of the atmosphere. For this period, the fraction of data availability on the 2DVAR grid ranged from 0.35 to 0.85. Because VDRAS employs 4DVAR, it fills in information for all the grid points when pieces of the radar scans are missing so that the fraction of valid points for VDRAS is always 1.0 (Fig. 2b).

c. Computational requirements

The 2DVAR and VDRAS codes were run on a Red Hat Linux cluster that consisted of 2.4-GHz dual P4 processors, with Myrinet interconnect and 3 Gb of memory per node. The 2DVAR was compiled with version 6.0 of the Portland Group compiler and VDRAS was compiled using version 9.0 of the Intel compiler, but we expect differences in computer time not to be affected significantly by the compilers.

On average, 2DVAR retrievals were completed in ∼2 min while each retrieval from VDRAS was completed in ∼5 min. The combination of 2DVAR’s simpler statistical interpolation technique and computing retrievals only along the elevation scans results in less computer time than VDRAS. The computational time for both systems did not vary significantly during this period. Because a complete VCP is obtained in about 6 min, both codes can be run for real-time operational use.

4. Evaluation of wind retrievals

a. Temporal and vertical variations

The radar wind profiler range gate closest to the wind retrieval levels was chosen for the evaluations of 2DVAR and VDRAS, rather than vertically interpolating either the wind retrievals to the profiler range gates or the profiler winds to the wind retrieval levels. For the PNNL, ANL, and OU profilers, the largest difference in the altitude of the wind profiler range gates and retrieval levels was 34 m. The difference at the NOAA profiler was as large as 146 and 97 m for 2DVAR and VDRAS, respectively. Both the radar wind profiler and the radar sample volumes of air, so that the height differences should not significantly affect the conclusions regarding the differences between measurements and wind retrievals. Radar wind profiler measurements also contain uncertainties up to ∼0.5 m s−1 (Office of the Federal Coordinator for Meteorology Services and Supporting Research 1998).

An example time series comparing the wind retrievals and radar wind profiler measurements near the surface at the ANL site is shown in Fig. 3. The 2DVAR 0.5° level over the ANL site was closest to the 357-m range gate (Fig. 3a) and the lowest VDRAS level was closest to the 192-m range gate (Fig. 3b). The measurements show a diurnal variation in wind speed and direction, primarily from the development of a low-level jet nearly every night. Both wind retrieval techniques reproduced this feature, although the retrieval nighttime wind speeds were frequently higher than observed. The daytime values were usually closer to the profiler measurements, perhaps because of the stronger backscattering during the day. At times, the radar wind profiler measurements exhibited strong variations in wind direction when the wind speeds were the lowest. Neither wind retrieval method reproduced this feature.

An example of qualitative comparison at a higher altitude is shown in Fig. 4. The 2DVAR 2.5° level over the ANL site was closest to the 1897-m range gate (Fig. 4a), and the fifth VDRAS level was closest to the 1677-m range gate (Fig. 4b). At this altitude, which is above the nighttime low-level jets, the radar wind profiler measurements and wind retrievals indicate much smaller diurnal variations in speed and direction. Both retrievals produced speeds that were in better agreement with the data and directions that differed from the data more frequently than the values closer to the surface (Fig. 3).

The average values, bias, root-mean-square error (RMSE), index of agreement (IA), and correlation coefficient (R) that quantify the performance (Willmott 1982) of the wind retrievals are listed in Tables 1 and 2 for 2DVAR and VDRAS, respectively. To be consistent with the averaging period of the radar wind profiler measurements, 30-min averages of the wind retrievals were computed for ANL, PNNL, and OU sites and 1-h averages of the wind retrievals were computed for the NOAA site. The number of retrieval measurement pairs decreased with height because radar wind profiler data were not as complete aloft as they were near the surface, and the range of the wind retrievals from 2DVAR varied in time (Fig. 2a) and did not always extend to the profiler sites.

As shown in Table 1, the wind speed bias for 2DVAR at the ANL, PNNL, and OU sites ranged from −1.4 to 2.7 m s−1 and the RMSE ranged from 2.4 to 4.7 m s−1. While the bias at the NOAA profiler was only 0.7 m s−1 near the surface, a bias of ∼5 m s−1 and an RMSE of ∼8 m s−1 was produced above 2800 m AGL. These altitudes were higher than those available from the other radar wind profilers. The wind direction bias at all profiler sites ranged from −16° to 7° and the RMSE ranged from 18° to 67°. The IA and R for both speed and direction that was often greater than 0.8 suggests that the wind retrieval produced temporal variations that were similar to the radar wind profiler measurements. In general, IA and R were lower at the higher altitudes, so that the wind retrieval had more skill near the surface than aloft. One reason for this is that radar beamwidth is narrower when the range gate is closer to the radar. The statistics at the OU profiler (closest to KTLX) were generally good at all altitudes because the radar elevation angles passed over this site within ∼1700 m of the ground. As shown in Table 2, the wind speed bias for VDRAS at all profilers ranged from −1.9 to 2.4 m s−1 and the RMSE ranged from 2.2 to 3.8 m s−1. The wind direction bias ranged from −23° to 18° and the RMSE ranged from 15° to 85°. As with 2DVAR, the IA and R for both wind speed and direction was often greater than 0.8 and the values near the surface were greater than those at higher altitudes.

To directly compare the statistics of 2DVAR and VDRAS on the same scale, the biases in the wind speed and direction are shown in Fig. 5. At the OU profiler, the wind speed and direction bias from 2DVAR was closer to zero than from VDRAS. At the PNNL and ANL sites, both retrievals usually overestimated the wind speeds near the surface and underestimated the wind speeds between 0.5 and 2 km AGL, but mean bias from 2DVAR was usually closer to zero. Farther aloft, both retrievals had higher wind speeds than the profiler measurements. At the NOAA site, the bias from VDRAS was closer to zero than from 2DVAR at all altitudes. While both retrievals produced wind speeds larger than the profiler measurements above 2.5 km AGL, the bias from 2DVAR was much larger than from VDRAS. Both retrievals produced a larger range in the bias for speed and direction at higher altitudes, indicating a larger uncertainty aloft.

The differences in the performance of the two retrieval techniques can be attributed to the different approaches employed by 2DVAR and VDRAS. In general, the performance of VDRAS was better aloft because it is based on a 3D mesoscale model that permits the dynamic and thermodynamic fields to adjust realistically. The first guess in 2DVAR is based solely on the VAD wind profile. Despite the differences in the level of complexity in the techniques employed by 2DVAR and VDRAS, both retrievals produced similar results within 1–2 km of the surface. While one model may have performed better than another at a specific place or time, one model did not outperform the other in terms of statistical measures.

b. Spatial variations

We also evaluated how well the wind retrievals reproduced the spatial variability among the radar wind profiler sites at three altitudes: 550, 900, and 1400 m. The radar wind profiler data were interpolated to the three altitudes and to the time of the retrieved wind field. Then, the retrieved wind profile, nearest to the radar wind profilers, was interpolated to the three altitudes.

An example of the u and υ wind component differences between the PNNL and NOAA sites at the 550-m level is shown in Fig. 6 for the 30-min-averaged data over the 2-week period. Differences in the profiler wind components between the sites were usually within 3 m s−1. The differences from 2DVAR and VDRAS usually did not follow the temporal variations in the wind profiler differences when the wind profiler differences were less than 3 m s−1. There were a few instances in which the radar wind profilers indicated strong horizontal gradients (e.g., 10, 12, and 13 July). On 10 July, a front moved through the region that produced differences between the PNNL and NOAA sites as large as 10 m s−1. The wind retrievals from 2DVAR and VDRAS both produced larger spatial variations, and their temporal evolution was not exactly the same as the measurements. In general, the peak differences in the wind components were larger than observed, especially after 10 July. The 2DVAR produced larger peak spatial variations in both the u and v components than VDRAS.

The correlation coefficients between the measured and retrieved velocity differences (Table 3) are usually higher with larger separations, that is, between PNNL and NOAA or between ANL and NOAA (Fig. 1a). By contrast, the distance between the PNNL and ANL profilers is only ∼7 km. The observed velocity differences between the PNNL and ANL sites were very noisy, and thus the retrieved and observed velocity differences are poorly correlated. One would expect the velocity differences between the sites to be small (especially aloft) because of their proximity. VDRAS performed somewhat better than 2DVAR in terms of representing the spatial wind variations; however, the correlation coefficients from both methods were low.

5. Evaluation of wind retrievals within an ADM

As shown previously, the 2DVAR and VDRAS wind retrievals differ from radar wind profiler measurements and from each other at times. If these wind retrievals are incorporated into ADMs, then the simulated dispersion patterns will differ to some extent.

We therefore employed the widely used the California Meteorological Model/California Puff Model (CALMET/CALPUFF) system to examine the sensitivity of the simulated transport and mixing of a passive scalar when the 2DVAR and VDRAS wind retrievals are used as input. CALMET (Scire et al. 1997) is a diagnostic, mass-conserving meteorological model that generates wind and temperature fields via interpolation from standard, routinely available meteorological observations. CALPUFF (Scire et al. 2000) is a Lagrangian Gaussian puff model that uses CALMET meteorology to transport and disperse material released to the atmosphere. The 110-km-wide domain for the CALMET/CALPUFF computations, shown in Figs. 1a,b, encompasses Oklahoma City and the radar wind profiler sites with a 2-km horizontal grid spacing.

A series of 12-h simulations were performed for each day between 3 and 17 July that started at 0300 LST, and for the six cases listed in Table 4. Case 1 represents the 3D wind fields that would be produced using operational data that are normally available at most urban areas in the United States. Case 2 represents a situation in which an urban area happens to have additional meteorological information aloft, such as those produced by a radar wind profiler. Cases 3 and 4 were made to demonstrate how the contribution of the wind retrievals to the overall wind field could affect the prediction of near-surface dispersion when they are compared to cases 1 and 2. Cases 5 and 6 include all of the data and were made to determine whether the large amount of data produced by the wind retrievals overwhelms the influence of the radar wind profiler data in the diagnostic model predictions.

a. Evaluation of CALMET winds

Before examining the effect of the six cases on the predicted dispersion, we first present comparisons of the observed winds and the winds derived from CALMET. As with any diagnostic model, interpolation techniques and adjustment for mass conservation will result in wind fields that are similar to, but not exactly the same as, the input data. Figure 7 shows an example of the CALMET wind fields for cases 1–4 on 5 July compared to the ANL wind profiler data interpolated to the CALMET vertical levels.

For case 1 (Fig. 7a), the simulated winds from CALMET were qualitatively similar to the radar wind profiler because the observed winds did not change significantly on this day. Therefore, the temporal interpolation of the twice-daily rawinsonde wind profiles was a reasonable approximation of the observed variation. However, the simulated wind speeds for the low-level jet during the morning before 0600 LST were lower than those observed, and the directions aloft differed by as much as 50°. For case 2 (Fig. 7b), the CALMET winds closely followed the profiler winds as expected because those winds were employed by CALMET, indicating that the interpolation and mass adjustment did not lead to a wind field that deviated significantly from the observed winds. For cases 3 and 4 (Figs. 7c,d) the wind speeds and directions were close to the measurements within 2 km AGL and are similar to case 2, suggesting that the wind retrievals provided information comparable to the radar wind profiler. However, the wind speeds and directions above 2 km AGL were often very different from the profiler.

A second example, shown in Fig. 8, is for 10 July when the observed winds were stronger and a front moved through the region so that the westerly winds during the morning shifted to northeasterly by the afternoon. In contrast to Fig. 7a, the CALMET and observed winds aloft were very different for case 1 (Fig. 8a) because the temporal interpolation did not work well for the rapidly changing meteorological conditions associated with the front. The simulated winds were in better agreement with the profiler data close to the surface because CALMET employed the hourly surface observations. As expected, a good agreement between the observed and simulated winds was produced for case 2 (Fig. 8b). The CALMET fields that employed the wind retrievals for cases 3 and 4 (Fig. 8c,d) were better than those from case 1, but relatively large errors in both speed and direction still occurred at the time of the frontal passage. On this day, VDRAS produced better winds above 2 km AGL than 2DVAR.

A stationary front was located north of Oklahoma City on 12 July for the third example shown in Fig. 9. The behavior of the CALMET wind fields was similar to the 10 July period (Fig. 8). While the observed upper-air directions remained westerly throughout the period, the wind speeds decreased so that the temporal interpolation between the twice-daily rawinsonde profiles did not work well (Fig. 9a). The cases that employed the NEXRAD wind retrievals (Figs. 9c,d) were better than case 1, but not as good as case 2 (Fig. 9b).

Cases 5 and 6 produced winds that were very similar to cases 3 and 4 in general (not shown), and the incorporation of radar wind profiler data did not reduce the large differences between the observed and simulated winds above 2 km AGL. The similarity between cases 5 and 6 with cases 3 and 4 occurred because CALMET weights all of the input data equally, and there were far more wind retrieval profiles near the radar wind profiler site.

For case 1, which employed only the standard observations, the wind speed bias, RMSE, IA, and R over all altitudes and sites range from −1.5 to 0.99, from 1.38 to 3.91, from 0.76 to 0.91, and from 0.66 to 0.88, respectively. As with the graphical depictions, case 2, which employed the radar wind profiler data, had the best performance, as expected. The index of agreement and correlation coefficient was usually close to 1, and biases and root-mean-square errors were usually less than a few tenths of a meter per second within 2 km of the ground. The statistics for cases 3–6 that include the NEXRAD wind retrievals were better than that from case 1. The differences in the statistics between cases 3 and 5 and between cases 4 and 6 were small, but the statistics for cases 5 and 6, which included the radar wind profiler data, were slightly better.

Because of the different vertical coordinates employed by 2DVAR and VDRAS (Figs. 1c,d), it was also useful to identify problems in the retrievals by directly comparing the horizontally varying wind fields generated by CALMET. An example when the wind field derived from 2DVAR varied more spatially than VDRAS is shown in Fig. 10 at 0400 LST (1000 UTC) 5 July. Both models produced higher wind speeds at 450 m AGL (Figs. 10a,b) than aloft at 2600 m AGL (Figs. 10c,d) as a result of the nocturnal low-level jet. Wind speeds over the domain at 450 m AGL ranged from 3 to 10 m s−1 from 2DVAR and from 7 to 10 m s−1 for VDRAS. The locations of the peak wind speeds were different as well; however, the wind speed and direction at the profiler sites were quite similar. The wind fields farther aloft at 2600 m AGL were qualitatively similar, except that the wind speeds derived from VDRAS were 1–2 m s−1 higher than from 2DVAR.

Figure 11 shows the winds 6 h later at 1000 LST (1600 UTC) 5 July. At this time, the winds at 450 m AGL (Figs. 11a,b) between 2DVAR and VDRAS were quite similar. In contrast with Fig. 10, spatial variations from 2DVAR were only ∼1 m s−1 greater than from the VDRAS at this time. The shift from large to small spatial wind speed variations between 0400 and 1000 LST was also evident at the ANL profiler for the time series shown in Fig. 6. The quasi-symmetrical perturbation pattern in the wind speed and direction at 2600 m AGL (Fig. 11c) is an example of an artifact produced by 2DVAR that was usually produced only at high altitudes. This artifact also explains the larger errors in 2DVAR wind speeds and directions when compared with the radar wind profiler data above 2 km AGL (Table 1). The 2DVAR wind field at this time was unrealistic because there were no sudden shifts in the ambient synoptic conditions, and one would expect horizontally homogenous winds aloft like the wind field produced from VDRAS (Fig. 11d). A rapid decrease in the available data occurred between 0400 and 1000 LST 5 July, as shown in Fig. 2; therefore, the variational approach in 2DVAR may produce unrealistic results when relatively few NEXRAD radial velocity data points are available.

b. CALPUFF sensitivity simulations

CALPUFF simulations were performed based on the six CALMET wind field cases. Instead of using the JU2003 tracer experiment release site and intermittent release periods, we employed release characteristics in the model that would better illustrate the differences between the wind fields. A constant release rate (10 g s−1) of material between 0300 and 1500 LST was made near the OU profiler site (Fig. 1) from a stack. The release height, exit velocity, and temperature were 40 m AGL, 5 m s−1, and 355 K, respectively. This site was chosen because the southerly-to-southwesterly winds usually observed during the 2-week period would transport the plume through a large fraction of the modeling domain. Time-integrated concentrations (i.e., exposure) defined as the time integral of the surface concentration at a point, plume centerline locations, and plume widths were computed to illustrate differences in dispersion.

Figure 12 shows the 12-h-accumulated concentration of the predicted plumes on 5 July for cases 1–4. The plume for case 1 was transported to the northeast during the period (Fig. 12a). When the radar wind profiler data were included in CALMET (Fig. 12b), the plume was transported farther to the east, and the footprint’s bifurcation indicates a wind shift during the period that transported material toward the north for a short period of time. The northward transport occurred between 1130 and 1230 LST when winds from case 1 were southwesterly (Fig. 7a) and the observed winds and winds from case 2 were southerly (Fig. 7b). The wind retrievals in CALMET resulted in bifurcations in the footprints for cases 3 and 4 (Figs. 12c,d) that were qualitatively similar to the footprint from case 2. The plume centerlines (defined in the appendix) from cases 2, 3, and 4 differed by as much as 10° from case 1, which included only the standard observations. The differences in the plume width (defined in the appendix) among the cases were small near the release site and became as large as 2 km at 50 km downwind of the release site.

The dispersion of the downwind plume during the frontal passage on 10 July is shown in Fig. 13. The footprint of the plume from case 1 (Fig. 13a) shows that the variable winds transported the tracer over a wide region toward the east and south. Before 0900 LST, the simulated winds were southwesterly so that the plume was transported toward the northeast. As the front moved through the region, new material, as well as previously released material over the northeastern portion of the domain, was transported to the south and southwest. The footprint from case 2 (Fig. 13b) was remarkably similar to that of case 1, even though there were large differences in the wind fields as shown in Figs. 8a,b, suggesting that transport was governed more by the winds within a few hundred meters of the surface than the winds aloft. After a frontal passage, the air would likely be more stable and would result in less coupling with the wind aloft. The simulated winds from 0900 to 1500 LST from case 2 better represented the northeasterly near-surface winds and transported more material farther to the southwest than that from case 1. Cases 3 and 4 produced footprints (Figs. 13c,d) that were qualitatively similar to both cases 1 and 2; however, the 2DVAR wind fields in case 3 produced some transport to the north that was not seen in cases 1, 2, and 4. The plume centerline from cases 3 and 4 with the wind retrievals was located farther to the south than cases 1 and 2, especially near the release site. In contrast to 5 July, differences in the plume width among cases 1–4 on 10 July were much larger—as much as 8 km at 22 km downwind of the release site.

Figure 14 shows the footprint of the plume predicted on 12 July. The stronger and near-constant wind directions in the CALMET fields using the standard observations in case 1 (Fig. 14a) produced a downwind plume that was straight and narrow. The footprint produced by case 2 (Fig. 14b) was wider as a result of transport to the east from 0700 to 1200 LST (Fig. 9b). While cases 3 and 4 also produced downwind transport toward the northeast (Figs. 14c,d), both cases had more meandering of the plume and a distinct bifurcation in the plume with the smaller branch transported to the east. On this day, the plume footprint using the wind retrievals was significantly different than case 2, which employed the radar wind profiler data. Cases 3 and 4 were more similar to one another than to case 2. The centerline of the plume predicted by cases 3 and 4 were again farther south of cases 1 and 2 on 12 July. On this day, the plume width from case 1 was narrow and only 3 km wide, and 50 km downwind of the release site. However, the plume widths from the other cases were much larger (between 6 and 13 km).

In general, the shapes of the plume from 2DVAR and VDRAS differed somewhat, but the results suggest that the choice of wind retrieval would not significantly affect the predicted dispersion for the meteorological during JU2003. The plume footprint from cases 5 and 6 were similar to those of cases 3 and 4 for all the dispersion runs (not shown) because the wind fields in CALMET were also very similar. The similarity of 2DVAR and VDRAS in terms of dispersion is also based on the predicted exposure for other days not shown in Figs. 12 –14. There was only one day—10 July (Fig. 13)—for which the exposure using the VDRAS wind retrievals performed better than 2DVAR. As discussed previously, the mesoscale model in VDRAS is able to better represent the complex winds, and thus the dispersion, associated with the frontal passage on that day. Following this line of reasoning, VDRAS may be more appropriate to simulate dispersion for other sites and time periods with rapidly changing meteorological conditions; however, additional tests comparing VDRAS and 2DVAR are necessary for verification.

For all of the CALPUFF simulations, the plume widths from case 1 were narrower than those from case 2, which included radar wind profiler data in addition to the standard observations. The inclusion of hourly wind profiles introduced more temporal variability into the CALMET wind fields that spread out the plume. The plume widths were even wider for cases 3 and 4, demonstrating that the additional spatial variations in the winds derived from the NEXRAD data resulted in increased dispersion downwind of the release site that was not produced by inclusion of the radar wind profiler data alone in CALMET.

The way in which the NEXRAD retrievals increase the downwind dispersion of a plume is reasonable, but would need a tracer experiment to adequately quantify the improvement in the representation of dispersion. The SF6 tracer experiment conducted during JU2003 obtained measurements only up to 5 km downwind of the downtown release site, and therefore would not be suitable to examine how the spatial variations in the NEXRAD wind retrievals would affect dispersion.

Other parameters that drive ADMs, such as temperature gradients, mixing depth, and turbulence, are often not observed at sufficient spatial and temporal scales near the surface and within the boundary layer. Uncertainties in these quantities will also affect the spatial distribution and concentration of predicted atmospheric contaminates.

c. Effect of temporal resolution of meteorological inputs

To be consistent with most operational dispersion model simulations, CALMET wind fields were produced at hourly intervals, and wind retrievals were averaged over each 1-h period. The NEXRAD wind retrievals can be computed in near–real time; therefore, it is possible to obtain wind retrievals at a rate of approximately once every 10 min.

To examine the effects of the time-varying NEXRAD wind retrievals on the simulated dispersion patterns, the CALPUFF simulations were repeated for 5, 10, and 12 July, which were based on CALMET wind fields at 15-min intervals. The spatial distribution and magnitude of the predicted surface instantaneous concentrations and exposure that employed the 15-min wind fields were very similar to the 1-h wind fields. The difference in the maximum exposure was less than ∼6%. The similar dispersion patterns are consistent with Figs. 3 and 4, which show gradual changes in wind speed and direction from both wind retrievals. The number of periods with rapidly changing winds over subhourly intervals was relatively few for the present cases. Nevertheless, there are likely to be meteorological conditions in which subhourly wind fields would significantly improve the transport and mixing of atmospheric contaminants.

6. Discussion

The ambient daytime winds observed during JU2003 were rather simple, except on a few days. This is one factor that contributed to the similar performance of VDRAS and 2DVAR in this study. The 4DVAR systems are designed to handle large temporal changes of the atmosphere. However, when the winds neither change rapidly in time nor contain large horizontal gradients, simpler techniques such as those employed by 2DVAR can perform equally well.

For example, both wind retrievals performed similarly in representing the low-level jet as shown in Fig. 3. In this case the mesoscale model did not add enough information to significantly improve the peak nocturnal winds speeds. The presence of the low-level jet was widespread as indicated by the radar wind profiler measurements and had lower horizontal wind shears than the cold front. The simpler 3D wind fields likely lead to the similar performance of 2DVAR and VDRAS in representing low-level jets. Conversely, VDRAS better represented the evolving vertical wind shears associated with the passage of a cold front between 0900 and 1200 LST 10 July as shown in Fig. 8. This is likely the result of the mesoscale model’s governing equations that permit a realistic representation of the 3D wind fields associated with the propagation of the front. While the retrieved winds from 2DVAR contain some information on wind shifts, 2DVAR had far fewer data points to represent those variations than available from the mesoscale model predictions in VDRAS.

Our comparison of simple versus complex wind retrieval techniques illustrates that their relative performance depends on the ability of a mesoscale model that forms the basis of 4DVAR systems to accurately represent the wind fields. The 4DVAR system employed by the version of VDRAS used in this study does not include certain physics that most mesoscale models employ. This simplification enables the 4DVAR system to run in near–real time. Including additional physics, such as a more detailed treatment of land surface exchange, terrain effects, and cloud physics, would enable VDRAS to better represent 3D circulations and vertical mixing needed by ADMs.

While 4DVAR systems will likely be used routinely by ADMs in the future, the simplicity of 2DVAR makes it attractive for current ADM applications. The 3D wind fields from 2DVAR can be ingested as another data source, along with traditional meteorological measurements without replacing the meteorological portion of the ADM. However, an important factor to consider for real-time applications is how to handle wind retrievals that are clearly unrealistic, such as the one produced by 2DVAR shown in Fig. 11. For operational applications, filters will need to be developed for this new technique that remove wind retrievals if the number of measured radial velocity data points fall below some criterion or eliminate all values at altitudes higher than 2 km AGL. Another consideration for operational ADMs is assigning either 2DVAR or VDRAS wind retrievals a lesser weight than other meteorological measurements that are known to a higher degree of accuracy.

Our results also show that when the winds change very slowly in time, interpolation of standard meteorological conditions in space and in time may be adequate for emergency response dispersion calculations. However, there are many situations in which there is not enough standard meteorological data available to represent rapidly changing synoptic- and mesoscale conditions. For example, at night, the formation of a low-level jet results in strong vertical wind shears that can not be adequately represented by standard observations. If atmospheric contaminants are lofted into this jet, they could be transported large distances in a short period of time.

The simple meteorological conditions during the daytime resulted in dispersion patterns that were similar for wind fields based on the standard observations and those that included the wind retrievals. Nevertheless, there were still subtle differences in the surface concentration magnitude and the plume footprint that would affect decisions regarding the actions taken by emergency responders. Additional tests are needed over a wider range of meteorological conditions to obtain a more complete assessment of how the NEXRAD wind retrievals would affect ADM predictions. During periods with large space and time variations of the winds, such as those observed in regions of complex terrain and during rapidly propagating synoptic systems, the effect of including the NEXRAD wind retrievals on dispersion could be much larger than that demonstrated in this study.

While the wind retrievals contain uncertainties in wind speed and direction that are larger than obtained from traditional instrumentation, we have shown that the two variational methods qualitatively reproduced the observed spatial and temporal variations, although some filtering is needed to eliminate occasional bad wind retrieval data. If either 2DVAR or VDRAS are run operationally, then 3D wind fields could be produced in the vicinity of the NEXRAD sites across the United States at 10–15-min intervals. A modest amount of computational resources would be required: one dedicated processor per NEXRAD site. Only the latest 3D wind components would need to be saved because they could always be generated from the raw NEXRAD data. This information could be used by a variety of applications, and would greatly increase the amount of data used to drive emergency response dispersion models.

Acknowledgments

We thank Jeremy Rishel for his assistance with CALMET operations. This research was supported by the U.S. Department of Homeland Security through the Science and Technology Directorate, Office of Systems Engineering and Development under a Related Services Agreement with the U.S. Department of Energy (DOE) under Contract DE-AC05-76FL01830. Pacific Northwest National Laboratory is operated by Battelle for DOE.

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APPENDIX

Formulation of Plume Width and Centerline

The plume width and centerline position is estimated as a function of distance from the source. The plume, in this case, is defined by the total ground level exposure (after say 12 h). The plume centerline is computed from the exposure-weighted position vector along an arc of radius r from the source, and its position vector is defined as
i1558-8432-47-9-2351-ea1
where
i1558-8432-47-9-2351-ea2
and E(r, ϕ) is the exposure. The width of the plume about the centerline position is then estimated from
i1558-8432-47-9-2351-ea3
where
i1558-8432-47-9-2351-ea4
The above expression for wc represents the exposure-weighted plume width along an arc of radius r from the source. The angle ϕ′ used in the evaluation of E is the angle measured from the plume centerline vector ϕ′.

Fig. 1.
Fig. 1.

Wind-retrieval locations (light blue crosses) from (a) 2DVAR and (b) VDRAS along with the locations of surface meteorological stations (red squares), rawinsonde (green dot), radar wind profilers (blue dots), and the KTLX NEXRAD (black dot). Gray shading denotes urban areas, and the modeling domain of the CALMET/CALPUFF is denoted by the large black square. Vertical coordinate for (c) 2DVAR and (d) VDRAS.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 2.
Fig. 2.

Fraction of valid winds from the (a) 2DVAR wind retrievals and (b) VDRAS wind retrievals for a 2-week period in July during the JU2003 field campaign.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 3.
Fig. 3.

Time series of ANL radar wind profiler wind speed and direction (blue dots) compared to values from (a) 2DVAR (red dots) at the 357-m range gate and (b) VDRAS (red dots) at the 192-m range gate. Dots denote 30-min averages, and vertical gray lines denote the range of wind retrieval values within a 30-min averaging period. Gray shading denotes night.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 4.
Fig. 4.

Time series of ANL radar wind profiler wind speed and direction (blue dots) compared to values from (a) 2DVAR (red dots) at the 1897-m range gate and (b) VDRAS (red dots) at the 1677-m range gate. Dots denote 30-min averages, and vertical gray lines denote the range of wind retrieval values within a 30-min averaging period. Gray shading denotes night.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 5.
Fig. 5.

Mean (dots) and std dev (lines) of the bias obtained from the 2DVAR (red) and VDRAS (blue) wind retrievals using only the NEXRAD data.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 6.
Fig. 6.

Difference in the u and υ components of the wind speed at 550 m AGL between the PNNL and NOAA radar wind profiler sites for the (a) 2DVAR and (b) VDRAS wind retrievals.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 7.
Fig. 7.

Observed (blue) and simulated (red) wind profiles over the ANL site on 5 Jul 2003, from four CALMET simulations that employed the following meteorological fields: (a) std observations, (b) std observations and radar wind profilers, (c) std observations and 2DVAR retrievals, and (d) std observations and VDRAS retrievals. Arrows indicate the direction in which the wind is blowing toward.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 8.
Fig. 8.

As in Fig. 7, but for 10 Jul 2003.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 9.
Fig. 9.

As in Fig. 7, but for 12 Jul 2003.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 10.
Fig. 10.

Wind fields from two CALMET simulations at two altitudes on 0400 LST (1000 UTC) 5 Jul 2003. Color of arrow denotes wind speed: purple = <3, blue = 3–4, light blue = 4–5, green = 5–6, light green = 6–7, orange = 7–8, red = 8–9, pink = 9–10, and black > 10 m s−1.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 11.
Fig. 11.

As in Fig. 10, but at 1000 LST (1600 UTC) 5 Jul 2003.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 12.
Fig. 12.

12-h-integrated concentration on 5 Jul 2003, from six CALPUFF simulations with the following meteorological inputs: (a) std observations, (b) std observations + profiler, (c) std observations + 2DVAR retrievals, and (d) std observations + VDRAS retrievals.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 13.
Fig. 13.

As in Fig. 12, but for 10 Jul 2003.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Fig. 14.
Fig. 14.

As in Fig. 12, but for 12 Jul 2003.

Citation: Journal of Applied Meteorology and Climatology 47, 9; 10.1175/2008JAMC1853.1

Table 1.

Statistics that quantify the performance of 2DVAR wind retrievals using radar wind profiler measurements.

Table 1.
Table 2.

Statistics that quantify the performance of VDRAS wind retrievals using radar wind profiler measurements.

Table 2.
Table 3.

Correlation coefficients between time series of velocity differences at locations of the NOAA Purcell and PNNL profilers. The second (third) column displays the correlation between the difference in u (υ) measured at the profiler locations and the difference in u (υ) from the VDRAS retrieval at the same locations. The fourth (fifth) column displays the correlation between the difference in u (υ) measured at the profiler locations and the difference in u (υ) from the 2DVAR retrieval at the same locations.

Table 3.
Table 4.

Types of simulations performed using CALMET/CALPUFF.

Table 4.
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