# Evaluation of Parameterization Schemes in the WRF Model for Estimation of Mixing Height

Authors: R. Shrivastava; S. K. Dash; R. B. Oza; D. N. Sharma
Journal: International Journal of Atmospheric Sciences
 (2014)

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## Abstract

This paper deals with the evaluation of parameterization schemes in the WRF model for estimation of mixing height. Numerical experiments were performed using various combinations of parameterization schemes and the results were compared with the mixing height estimated using the radiosonde observations taken by the India Meteorological Department (IMD) at Mangalore site for selected days of the warm and cold season in the years 2004–2007. The results indicate that there is a large variation in the mixing heights estimated by the model using various combinations of parameterization schemes. It was seen that the physics option consisting of Mellor Yamada Janjic (Eta) as the PBL scheme, Monin Obukhov Janjic (Eta) as the surface layer scheme, and Noah land surface model performs reasonably well in reproducing the observed mixing height at this site for
both the seasons as compared to the other combinations tested. This study also showed that the choice of the land surface model can have a significant impact on the simulation of mixing height by a prognostic model.


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## Body

## 1. Introduction

Prognostic atmospheric models are used as meteorological drivers to air pollution models in the absence of representative measured meteorological data for a site. These models generally provide wind speed, wind direction, temperature, humidity, rainfall, and mixing height values to the air pollution models. Many times, the resolution at which these models are integrated is too coarse to resolve the exchanges of heat, momentum, and moisture taking place at the air soil interface and hence these exchanges have to be parameterized in atmospheric models. Parameterization schemes may also be included in an atmospheric model for the representation of atmospheric phenomena whose explicit treatment may become too prohibitive due to cost and computer limitations. A weather model includes parameterizations for radiation, surface layer fluxes, turbulence, cumulus convection, and clouds. Generally there are six to seven schemes available for representation of each of these processes with its own merits and demerits depending upon the terrain, geography, and climate of the area under consideration. Mixing height is an important input to air pollution models since the transport and extent of mixing of pollutants depend on it. The mixing in the atmosphere primarily takes place through convective and mechanical processes. During the daytime, differential heating due to solar radiation sets up strong thermals in the atmosphere and the convective processes dominate whereas, during the nighttime, mechanical processes are responsible for the turbulent mixing. The variation of mixing height with varying parameterization schemes was studied by other investigators also. For example, Shin and Hong [1] intercompared five Planetary Boundary Layer (PBL) schemes in the WRF model for a single day from the Cooperative Atmosphere Surface Exchange Study (CASES-99) field program. They reported a large variation in the mixing height values computed by the five schemes in the daytime and nighttime. Similarly Han et al. [2] had also evaluated five PBL schemes in the MM5 model for the East Asian domain for March 2001 and reported a large difference in the mixing heights predicted by the various combinations of parameterization schemes. Hu et al. [3] compared the 3-month mean diurnal variations of the simulated mixing heights using different combination of parameterization schemes in the WRF model with the observed values for South East Texas.The present study focuses on the evaluation of parameterization schemes in the WRF model for estimation of mixing height for Kaiga site. Kaiga is one of the sites where nuclear power plants are operated for generation of electricity by Nuclear Power Corporation of India Ltd (NPCIL). It is a complex site with tall hills, evergreen forests, a reservoir, and so forth. Weather forecasting is important in the nuclear industry because of the aid it can provide in handling an emergency situation. However the weather forecast model needs to be validated/tuned with proper selection of parameterization schemes for it to be used in case of an emergency situation. Also many times a weather model is coupled with an offline atmospheric dispersion model for prediction of concentration. In order to validate the mixing height estimated by the WRF model, the radiosonde observations taken by the India Meteorological Department (IMD) are utilized. Since radiosonde data for Kaiga site are not available, the radiosonde data from the nearby radiosonde observation station, Mangalore, are utilized for this purpose. The Mangalore station is at a distance of ~300 km from Kaiga site. It should be noted that the model values were also extracted at this location for comparison with observed values. The cases selected for simulation correspond to selected days in the warm and cool season. Description of the model domain, numerical experiments, WRF model set-up, and physics parameterizations used is given in Section2 and the results are presented in Section 3.


## 2.  Model and Methodology

The study domain covers south west India centered at the Kaiga Generating Station (KGS) (14°51′48′′ N, 74°26′31′′ E) as shown in Figure1. The WRF model version 3.1.1 (Skamarock et al. [4]; Wei et al. [5]) is integrated using three nested domains with grid spacing varying from 27 km, 9 km, and 3 km. The terrain and land use data for the innermost domain with 3 km grid resolution were taken from 30′′ data, for domain with 9 km resolution from 5′ data and for the domain with 27 km resolution from 10′ data available from the United States Geological Survey (USGS). The parameterization schemes tested in this study are defined in Table 1. The PBL schemes tested are Yonsei University, (YSU, Hong et al. [6]), Mellor Yamada Janjic Eta (MYJ, Janjic [7, 8]), Asymmetric Convective Model, (ACM2, Pleim [9]), and Quasi Normal Scale Elimination (QNSE, Sukoriansky et al. [10, 11]). The surface layer (SL) schemes used are Monin Obukhov (Monin and Obukhov [12]), Monin Obukhov Janjic Eta (MYJ, Monin and Obukhov [12], Janjic [13]), and Quasi-Normal Scale Elimination (QNSE, Sukoriansky et al. [10, 11]) and the land surface models (LSM) used are thermal diffusion (Dudhia [14]) and the Noah land surface model (Chen and Dudhia [15]). The other physical parameterizations are identical for all experiments which are Rapid radiation Transfer Model for long-wave radiation (RRTM, Mlawer et al. [16]), Dudhia (Dudhia [17]) for short-wave radiation, Kain Fritsch scheme for cumulus parameterization (Kain and Fritsch [18], J. S. Kain [19]) and Ferrier new Eta (Ferrier et al. [20]) scheme for representing microphysical processes in the clouds. A total of six combinations of parameterization schemes are considered which are defined in the Table 1. The National Centers for Environmental Prediction (NCEP) Final Analysis data available at 6 hourly interval on a 1° × 1° resolution are used to supply initial and boundary conditions. The model uses two-way nested boundary conditions and they are updated every 6 hours. A total of eight simulation cases are considered in the present study which are based on either maxima or minima in temperature and these are described in Table 2. This study focuses on the comparison of mixing height estimated by the model in the various combinations of parameterization schemes with the observed values. Each simulation is for a three-day period one on the day of occurrence, one day prior, and one day later to it. The mixing height computation by the model differs according to the PBL scheme used. The schemes YSU and ACM2 estimate mixing height based on the Richardson number whereas the remaining two PBL schemes namely MYJ and QNSE diagnose the mixing height on the basis of the turbulent kinetic energy (TKE). While both the approaches may be theoretically correct, the one which produces values of mixing height which compare well with the observation can be considered to be the better approach. In addition to the model’s own diagnosis of mixing height, the temperature profile given by the model was also used to estimate the mixing height based on the Holtzworth [21] method. In this case, the variation between different combinations of schemes due to varying methodologies is eliminated and the differences observed are only due to those in the temperature profile of the model. Both the methods of estimating mixing height are in turn compared with the observation.Table 1
Description of parameterization schemes.
Land surface
Surfacelayer
Planetaryboundary layer
Combination
Thermal diffusion
Monin Obukhov
YSU
A
Monin Obukhov
ACM2 (Pleim)
B
Monin Obukhov (Janjic Eta)
Mellor Yamada Janjic (Eta)
D
QNSE
QNSE
F
Noahland surface model
Monin Obukhov
YSU
C
Monin Obukhov (Janjic Eta)
Mellor Yamada Janjic (Eta)
E
YSU: Yonsei University; ACM2: Asymmetric Convective Model version 2; QNSE: Quasi-Normal Scale Elimination.Table 2
Description of experiments.
Simulation period
Case
Experiment
00Z 26 December 2004–00Z  29 December 2004
Minimumtemperature
TMN1
00Z 17 January 2005–00Z 20 January 2005
TMN2
00Z 26 January 2006–00Z 29 January 06
TMN3
00Z 14 November 2007–00Z 17 November 07
TMN4
00Z 13 March 2004 –00Z 16 March 2004
Maximumtemperature
TMX1
00Z 04 April 2005–00Z 07 April 2005
TMX2
00Z 23 February 2006–00Z 26 February 2006
TMX3
00Z 24 April 2007–00Z 27 April 2007
TMX4Figure 1
Model domains and the topography of the inner domain (red star denotes the plant site).


## 3. Results and Discussion

The mixing heights obtained in all combinations of parameterization schemes tested are shown in Tables3 and 4 for the experiments TMN1–TMN4 and TMX1–TMX4, respectively, with the best performing combinations presented in bold font. The observed mixing heights are calculated from the radiosonde temperature profile following the method of Holtzworth [21]. For a three-day simulation, six radiosonde profiles are available for comparison (one each at 00Z and 12Z). However, sometimes, a particular sounding may not be available and hence that time is not considered in the analysis for example, 12Z on January 28, 2006. In order to remove the dependence of mixing height on parameterization schemes, the mixing height values are also generated by using Holtzworth method on the model derived temperature profiles and the results are presented in Table 5 for experiments TMN1–TMN4 and in Table 6 for experiments TMX1–TMX4. Here also, the best performing combinations are presented in bold font. The Holtzworth method works on the basis of intersection of the temperature profile with the dry adiabatic line drawn from the surface with the maximum temperature of the day for the 12Z mixing height and with the minimum temperature +5°C for the 00Z mixing height. In cases where the two curves do not intersect, the mixing height cannot be determined. These are represented by a “—” in Tables 5 and 6. The model derived mixing heights are shown in columns A–F and the observed mixing height is presented in column O. From Tables 3 and 4, one can estimate the magnitude of variation among the different parameterization schemes for estimation of mixing height at a given time making a parametric study so very essential. The differences in the model estimates of mixing height arise because of the various methods of calculation used in the schemes. For example at 12Z 26th December 2004, the observed mixing height from the radiosonde data was 300 m. The model generated values range from 77 m to 800 m indicating a large variation as compared to the observation. Similar variation is noted at other times also. It is also important to note the role of the land surface model in estimation of mixing height. In an atmospheric model, the land surface model is used to calculate the exchange coefficients of heat, momentum, and moisture at the air soil interface which is used in the estimation of heat, momentum, and moisture fluxes. These fluxes provide the lower boundary condition for vertical transport in the PBL. The five-layer thermal diffusion model is a simple soil temperature model. The layers are 1 cm, 2 cm, 4 cm, 8 cm, and 16 cm thick. Below this the temperature is fixed at a deep layer average value. The soil moisture values are season dependent constant values based on land use categorization without considering the effects of vegetation. The Noah land surface model on the other hand predicts soil temperature and moisture in 4 layers extending 10 cm, 30 cm, 60 cm, and 100 cm from the surface and summing up to 2 m below the surface. It includes land use, monthly vegetation fraction, and evapotranspiration in the estimation of sensible and latent heat fluxes. In general, it is seen that the values of mixing height obtained using the Noah land surface model are better than those obtained with the thermal diffusion model for identical combinations of PBL/SL schemes (i.e., E is better than D and C is better than A) due to better representation of physical processes leading to transfer of heat, momentum, and moisture at the air soil interface. In fact Stensrud [22] says that “if variations in surface conditions occur over small scales, then the resulting rapid horizontal changes in the values of surface fluxes can lead to the development of non-classical mesoscale circulations, such as vegetation or inland sea breezes.” The model’s representation of such processes are important for weather prediction for a complex terrain sites. For a given land surface model, say the Noah LSM, the results with the Monin Obukhov (Janjic Eta) PBL scheme and the Mellor Yamada Janjic (Eta) surface layer scheme are better than those with the YSU PBL and Monin Obukhov surface layer scheme (i.e., E is better than C). Hence it is seen that it is a combined effect of all parameterization schemes which translates into a good simulation of mixing height by an atmospheric model. As already mentioned, the Holtzworth method was also used to estimate the mixing height based on the model derived temperature profile under various combinations of parameterization schemes. For a comparison of the two methods, the scatter plots of the model generated mixing heights with the observations are shown in Figures 2 and 3 for the case studies TMN1–TMN4 and TMX1–TMX4, respectively. Also, many statistical parameters like average of model, average of observation, mean absolute error, index of agreement, and root mean square error were computed and are presented in Table 7. From Figure 2, it is seen that the model’s own diagnosis of mixing height is underpredicted whereas the same obtained using Holtzworth method on the model generated temperature profile is overpredicted. However the Holtzworth method used on the model derived temperature profile has a smaller intercept as compared to that of the model’s own diagnosis of mixing height. The correlation coefficient and index of agreement obtained in both the cases are comparable. From Table 7, referring to the errors, namely, mean absolute and root mean square error, it is seen that the model’s own estimation of mixing height is better than that obtained using the Holtzworth method on the model generated temperature profile on the basis of lower values of errors. The difference in the mean absolute error between the two methods is ~12% in an average mixing height of 320 m. Hence making a definite statement about any of the two methods being superior is not possible for the case studies TMN1–TMN4 based on the current simulations carried out. Similar analysis was done for the case studies TMX1–TMX4 also, with the scatter plot being shown in Figure 3. From the plot and the statistical analysis in Table 7, it can be concluded that for these cases, the mixing height computed using the Holtzworth method on the model generated temperature profile is better than the model’s own estimation of mixing height based on the turbulent kinetic energy. All the statistical parameters computed for the case TMX_HE (referring to Holtzworth method on the model derived temperature profile for experiments TMX1–TMX4) are superior to the case TMX_E (referring to experiments TMX1–TXM4). From Table 4, it is seen that on many occasions the mixing height at 12Z is underestimated by the model. Even if the land surface model chosen accounts for the physical processes occurring in the atmosphere, the coefficients/constants being used in the respective algorithms may require further refinement. When the Holtzworth method is applied on the model derived temperature profile in combination E, this underestimation is eliminated. Hence for the experiments TMX1–TXM4 corresponding to selected days in the warm season, the mixing height estimated using the Holtzworth method on the model derived temperature profile is better than the model’s own estimation of mixing height. It is also to be noted that the application of the Holtzworth method on other combinations of parameterization schemes A, B, D, and F in this study leads to deterioration of model results and should be avoided. Finally to summarize, this study has shown that the set of parameterizations schemes defined by the combination E gives reasonable results for 13 out of 23 case studies for the cool season and 18 out of 24 case studies for the warm season mainly due to the Noah land surface model used. Nevertheless, this mixing height should still be verified by using the Holtzworth method on the model derived temperature profile obtained using the same combination of parameterization schemes for those locations where radiosonde data are available. This study also shows that the state-of-the-art parameterizations schemes in the WRF model still have scope for improvement.Table 3
Comparison of model generated mixing height with the observed values for cases TMN1–TMN4.
A (m)
B (m)
C (m)
D (m)
E (m)
F (m)
O (m)
Case study
12Z 26 December 2004
77
136
112
145
146
800
300
TMN1
00Z 27 December 2004
248
77
339
391
392
565
150
12Z 27 December 2004
227
169
107
145
398
1069
300
00Z 28 December 2004
405
159
546
395
393
2577
150
12Z 28 December 2004
34
164
118
146
399
400
400
00Z 29 December 2004
305
103
375
144
396
1064
100
12Z 17 January 2005
256
293
493
401
808
580
900
TMN2
00Z 18 January 2005
29
33
30
145
145
144
350
12Z 18 January 2005
112
189
297
1568
580
2073
600
00Z 19 January 2005
30
38
32
145
144
144
200
12Z 19 January 2005
215
213
360
400
806
807
700
00Z 20 January 2005
159
50
30
144
145
144
100
12Z 26 January 2006
350
258
417
146
575
575
550
TMN3
00Z 27 January 2006
133
53
115
142
142
253
100
12Z 27 January 2006
237
192
236
146
399
258
650
00Z 28 January 2006
121
39
234
394
143
1540
100
00Z 29 January 2006
232
84
269
142
252
2041
200
00Z 14 November 2007
309
203
347
145
398
397
200
TMN4
12Z 14 November 2007
137
54
171
142
143
253
100
00Z 15 November 2007
133
206
105
146
146
258
100
12Z 15 November 2007
101
57
29
142
143
565
480
00Z 16 November 2007
35
231
202
145
145
395
100
12Z 16 November 2007
40
37
29
141
141
787
400
3/23
3/23
1/23
1/23
13/23
2/23
The bold values refer to the best match between model and observation.Table 4
Comparison of model generated mixing height with the observed values for cases TMX1–TMX4.
A (m)
B (m)
C (m)
D (m)
E (m)
F (m)
O (m)
Case study
12Z March 2004
256
293
493
401
808
580
900
TMX1
00Z 14 March 2004
29
33
30
145
145
144
350
12Z 14 March 2004
112
189
297
1568
580
2073
900
00Z 15 March 2004
30
38
32
145
144
144
200
12Z 15 March 2004
215
213
360
400
806
807
700
00Z 16 March 2004
159
50
30
144
145
144
100
12Z 04 April 2005
257
365
399
580
809
809
900
TMX2
00Z 05 April 2005
30
39
30
145
146
145
150
12Z 05 April 2005
86
237
338
1575
581
2084
900
00Z 06 April 2005
30
59
30
145
146
145
350
12Z 06 April 2005
117
272
226
147
403
587
800
00Z 07 April 2005
30
39
30
146
147
259
300
12Z 23 February 2006
117
153
276
146
577
580
600
TMX3
00Z 24 February 2006
89
35
42
144
143
1062
100
12Z 24 February 2006
84
1017
206
146
576
2058
400
00Z 25 February 2006
134
86
29
143
571
254
100
12Z 25 February 2006
331
223
355
398
575
398
900
00Z 26 February 2006
198
52
29
395
144
2049
100
12Z 24 April 2007
429
409
533
811
809
1083
800
TMX4
00Z 25 April 2007
30
53
41
145
579
258
150
12Z 25 April 2007
214
263
350
588
584
819
700
00Z 26 April 2007
58
54
30
147
146
261
50
12Z 26 April 2007
422
436
443
585
585
1088
900
00Z 27 April 2007
30
57
30
146
146
260
100
No. of successes
—
2/24
—
1/24
18/24
3/24
The bold values refer to the best match between model and observation.Table 5
Comparison of model generated mixing height (using Holtzworth’s method) with the observed values for experiments TMN1–TMN4.
A (m)
B (m)
C (m)
D (m)
E (m)
F (m)
O (m)
Case study
12Z 26 December 2004
1300
1400
550
1200
450
1250
300
TMN1
00Z 27 December 2004
100
100
—
150
50
200
150
12Z 27 December 2004
1350
1300
650
1300
400
1450
300
00Z 28 December 2004
—
80
—
50
80
—
150
12Z 28 December 2004
1100
800
500
1200
500
1300
400
00Z 29 December 2004
—
80
—
70
80
50
100
12Z 17 January 2005
600
600
600
580
550
625
900
TMN2
00Z 18 January 2005
200
180
200
150
100
150
350
12Z 18 January 2005
1300
1200
1000
1400
950
1350
600
00Z 19 January 2005
250
300
150
200
100
250
200
12Z 19 January 2005
1100
1050
900
1150
850
1150
700
00Z 20 January 2005
200
200
80
150
80
200
100
12Z 26 January 2006
1400
1800
1050
2000
900
2000
550
TMN3
00Z 27 January 2006
50
200
50
200
100
200
100
12Z 27 January 2006
1450
950
750
1400
700
1400
650
00Z 28 January 2006
50
50
—
50
100
100
100
00Z 29 January 2006
100
80
—
100
80
120
200
00Z 14 November 2007
150
—
—
—
—
—
200
TMN4
12Z 14 November 2007
550
650
750
1250
600
1300
100
00Z 15 November 2007
50
120
—
120
100
180
100
12Z 15 November 2007
500
490
400
900
700
950
480
00Z 16 November 2007
110
120
50
150
100
200
100
12Z 16 November 2007
1250
1100
1000
1200
900
1200
400
No. of successes
2/23
1/23
1/23
2/23
16/23
1/23
The bold values refer to the best match between model and observation.Table 6
Comparison of model generated mixing height (using Holtzworth method) with the observed values for experiments TMX1–TMX4.
A (m)
B (m)
C (m)
D (m)
E (m)
F (m)
O (m)
Case study
12Z 13 March 2004
1200
950
950
1200
920
1450
900
TMX1
00Z 14 March 2004
220
150
250
250
200
200
350
12Z 14 March 2004
350
300
500
900
500
950
900
00Z 15 March 2004
150
180
180
200
200
250
200
12Z 15 March 2004
1150
950
800
1450
750
1400
700
00Z 16 March 2004
300
200
100
300
100
400
100
12Z 04 April 2005
950
950
950
1300
900
1300
900
TMX2
00Z 05 April 2005
100
200
350
250
300
250
150
12Z 05 April 2005
100
—
500
700
550
800
900
00Z 06 April 2005
250
180
100
200
180
200
350
12Z 06 April 2005
300
400
200
250
300
250
800
00Z 07 April 2005
200
300
200
200
150
200
300
12Z 23 February 2006
1050
1150
750
1600
700
1600
600
TMX3
00Z 24 February 2006
—
100
80
80
—
100
100
12Z 24 February 2006
1400
1300
700
1500
600
1500
400
00Z 25 February 2006
80
150
50
80
80
150
100
12Z 25 February 2006
1300
1350
950
1450
850
1450
900
00Z 26 February 2006
—
50
50
80
50
150
100
12Z 24 April 2007
700
700
750
700
750
700
800
TMX4
00Z 25 April 2007
100
120
200
150
150
200
150
12Z 25 April 2007
400
350
450
400
450
500
700
00Z 26 April 2007
50
100
80
80
50
150
50
12Z 26 April 2007
800
700
700
1050
700
1100
900
00Z 27 April 2007
120
100
120
100
120
180
100
No. of successes
2/24
3/24
—
2/24
15/24
2/24
—
The bold values refer to the best match between model and observation.Table 7
Statistical analysis.
Statistic
TMN_E
TMN_HE
TMX_E
TMX_HE
AVG_WRF (m)
318
385
423
415
AVG_O (m)
320
320
498
493
MAE (m)
123
159
179
125
IOA
0.86
0.83
0.84
0.91
CORR
0.74
0.76
0.75
0.86
RMSE (m)
160
222
229
186
AVG_WRF: average of model, AVG_O: average of observation, MAE: mean absolute error, IOA: index of agreement, CORR: correlation coefficient, RMSE: root mean square error, TMN_E: experiments TMN1–TMN4, TMN_HE: experiments TMN1–TMN4 but using Holtzworth method, and TMX_E, TMX_HE: same as TMN_E, TMN_HE for the case studies TMX1–TMX4.Figure 2
Comparison of model generated (in combination E) mixing height with the observed values for experiments TMN1–TMN4.Figure 3
Comparison of model generated (in combination E) mixing height with the observed values for experiments TMX1–TMX4.Index of agreement(1)
IOA
=
1
-
∑
i
=
1
N
(
P
i
-
O
i
)
2
∑
i
=
1
N
(
|
P
i
-
O
mean
|
+
|
O
i
-
O
mean
|
)
2
.Pearson correlation coefficient(2)
CORR
=
(
N
(
∑
i
=
1
N
O
i
P
i
)
-
(
∑
i
=
1
N
O
i
)
(
∑
i
=
1
N
‍
P
i
)
)
×
(
[
N
(
∑
i
=
1
N
O
i
2
)
-
(
∑
i
=
1
N
O
i
)
2
]
hhhhhhh
×
[
N
(
∑
i
=
1
N
P
i
2
)
-
(
∑
i
=
1
N
P
i
)
2
]
[
N
(
∑
i
=
1
N
O
i
2
)
-
(
∑
i
=
1
N
O
i
)
2
]
)
-
1
/
2
.Mean absolute error(3)
MAE
=
1
N
∑
i
=
1
N
|
P
i
-
O
i
|
.Root mean square error(4)
RMSE
=
1
N
∑
i
=
1
N
(
P
i
-
O
i
)
2
.


## 4. Conclusions

The numerical experiments carried out for the selection of the optimum combination of parameterization schemes for estimation of mixing height have shown that the physics option consisting of Mellor Yamada Janjic (Eta) as the PBL scheme, Monin Obukhov Janjic (Eta) as the Surface Layer scheme, and Noah land surface model performs reasonably well in reproducing the observed mixing height for almost all the cases considered for simulation as compared to the other combinations tested. The role played by the land surface model for a good simulation of mixing height was also emphasized. The application of the Holtzworth method on the model derived temperature profile to estimate mixing height as opposed to the model diagnosed mixing height suggests that during the warm season, the model derived mixing height based on the Holtzworth method matches reasonably well with the observations as opposed to the model diagnosed mixing height. However, for the cold season no such conclusive statement can be made. Such studies on the proper choice of parameterization schemes at a power plant site are useful when a prognostic weather model like WRF is to be coupled with an air quality model for atmospheric dispersion studies.



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*Source: 451578-2014-02-26.xml*