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Evaluation and model/model comparisons for surface meteorology of the 4 IOPs

Evaluation and model/model comparisons for surface meteorology of the 4 IOPs. Framework. Presentation divided : - By parameters dynamics (wind speed/direction) thermodynamics (temperature) - For special various stations (close to the littoral) valley stations stations in altitude

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Evaluation and model/model comparisons for surface meteorology of the 4 IOPs

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  1. Evaluation and model/model comparisons for surface meteorology of the 4 IOPs

  2. Framework Presentation divided : - By parameters dynamics (wind speed/direction) thermodynamics (temperature) - For special various stations (close to the littoral) valley stations stations in altitude stations in the plains stations in high emission zones We principally focused on IOP 2 and 3 There are very few stations with meteorological measurements, but lots of data !!!

  3. Recalls

  4. Stations with meteorological measurements 1 – Vallée Huveaune (u,v) 2 – Martigues-ND-Marin (T,u,v) 3 – Martigues la Gatasse (T,u,v) 4 – Port de Bouc (P,RH,T,u,v) 5 – Martigues Pati (T) 6 – La fare les Oliviers (u,v) 7 – La Crau (u,v) 8 – Vitrol-Realtor (T,u,v) 9 – Biver (u,v) 10 – Marignanne JA (u,v)

  5. Some global statistics for wind direction • For Wind direction : • Zone 1 • IOP2a : all models retrieve successfully the wind direction, except for day 3. But if we look the whole IOP, differences between models are weak. • IOP2b : differences between models are more visible, maybe due to the fact that IOP2b is marked by sea-breezes phenomena. • Zone 3 • IOP2a : as for zone 1, all models are good. • IOP2b : impact of sea-breezes is weaker in this zone, so difference between models are less significant. • Zone 5 • IOP2a : as for zone 1, all models are good. • IOP2b : we can see a general under-estimation in the wind direction.

  6. For Wind direction : • Zone 1 • IOP3 : Wind direction is pretty well simulated. The mean error is about 10° • Zone 3 • IOP3 : Wind direction is also well simulated. Only UAM-Total made 50° error for day 2. The mean error is near -10° • Zone 5 • IOP3 : This IOP is worse. All models are wrong for day 2, with about 100° (!) difference with measurements.

  7. Some global statistics for wind speed • For Wind speed : • Zone 1 • IOP2a : Simulated wind speed is close to measurements. But some models overestimate it (Mocage) or underestimate it (Mapom, Chimere-acri, Mc2aq and Meso-nh). For worse models, mean error is about +/- 2m/s. For best model, it’s about .4m/s. • IOP2b : For this IOP, we have principally underestimation of wind-speed. • Zone 3 • IOP2a : In this box, Mocage and Rams-Chimie produce non-negligible overestimations (~1 m/s). • IOP2b : Overestimation is still present in this IOP for the same models than IOP2a. • Zone 5 • IOP2a : In this box, wind speed is hugely overestimated (+1200% for Mocage, +600% for other models !!). This phenomena can be explicated by the location of the used station (see later). • IOP2b : We still have overestimation, except for Chimere and Azur.

  8. For Wind speed : • Zone 1 • IOP3 : The mean trend is to underestimate wind speed in this box for this IOP. • Zone 3 • IOP3 : As for IOP2a and 2b, in this box, wind is overestimated (mean error : 1.5 m/s). • Zone 5 • IOP3 : As for IOP2a and 2b, in this box, wind is overestimated (mean error : 1 m/s).

  9. Some global statistics for temperature • For temperature : • Temperature is more or less well simulated by all models for all box during IOP2. We can see a general little overestimation by Mocage, and underestimation by Chimere-Acri.

  10. For temperature : • For this IOP, there is a general underestimation of the temperature (~2°C).

  11. Stations in plain • IOP2b : zonal wind is well represented by all models (mean gross error from .5 to 1 m/s, correlation near .80 for all the iop)), except by Chimere-Acri (correlation near .4). Dispersion of model value is about 1-2 m/s. • Meridian wind is overestimated the 1st day by all models. Then all models simulate successfully wind speed, but Chimere-Acri overestimate night values. • Correlations are between .6 to .9, with 1 m/s mean gross error). • In this plot, we can see alternately land-breezes the night, and sea-breezes the day. • Mocage overestimates night temperature of about 5°C. • All models underestimate day temperature by 4°C. However, correlation is near .7 • IOP3 : Winds seems to be simulated less accurately. We see dispersion in models values. Correlations are near .8 for meridian and .7 for zonal winds. • The problem is for temperature. All models hugely underestimate day temperature (2.5°C of mean error). • Correlations are only near .6. • We should try to understand why temperatures for IOP 3 aren’t well simulated, whereas they are good for IOP2. • IOP2a : the main part of the models well simulate zonal wind. Mocage overestimates a little zonal wind the 3rd day, and Mapom underestimates it the 2nd day. Only Chimere-acri totally underestimate zonal wind the 2nd day. Correlations are near .75 with 1 m/s mean error. • Concerning meridian wind, we haven’t see breeze during this IOP. All models simulate quite well the variations in wind speed, with however some large overestimations the 1st day. So correlations are near .6 with 2.5 mean error. • Temperature is mainly well simulated. Correlation : .9

  12. Stations in valley • IOP2a : With the station, witch is located in a Est-West oriented valley, dispersion in models values is very large for meridian wind. Mesurements are always near 0 m/s. Models seem to have some problem to simulate wind when topography is complex and/or when wind speed is very low. (correlation : .2 !) • For zonal wind, model are better, with however some dispersion. (correlation : .7) • IOP3 : Models simulate better zonal (.75) and meridian wind (.65) for this IOP, but nevertheless, we can see some overestimations for meridian wind (1.5 m/s). • This station is very particular, and not very representative to determinate models “scores”. But she’s very interesting about impact of ground forcing. • IOP2b : With this IOP, we see the same problem with low wind speed. This station isn’t affected by sea-breeze. However, models try to simulate this phenomena (visible on meridian wind). Dispersion in models values is not so big (1-2 m/s) (be careful to the scale). • Correlations are near .45 (ZW) and .55 (MW)

  13. Stations in altitude • IOP2a : This station is not really in altitude, but she’s at 200m, just behind the Estaque. • Wind is quite well simulated (corr .8 zw and .6 mw), except some overestimations in north wind speed for Mocage. • Concerning temperature, we can see some huge underestimation during days, for Mapom and Chimere-Acri (corr .9) • IOP2b : zonal wind is quite well simulated (correlation between .7 to .8 • We don’t see in this station any transition between sea-breeze and land-breeze. • Temperatures are more or less well simulated (corr .85), except day 3 (3.5°C mean error) • IOP3 : Zonal wind is good (corr .8), but meridian wind isn’t really well simulated, especially day 3. Mean correlations for meridian wind are near .7 (only .5 for day 3). • Day temperatures are always underestimated, as for Port de Bouc (corr .7).

  14. Stations in high emissions zone • This station is located near Martigues and all the oil refineries. So it’s important to analyze meteorology in this area marked by high emissions of pollutants. • IOP2a : zonal wind is well represented, except by Mocage, witch overestimate night values. However, correlations are good for this IOP (.7) • Meridian wind is more difficult to analyze. Models are dispersed, especially during days 1 and 2. Mocage overestimates night values and RAMS-Chimie is wrong during all the beginning of this IOP. Correlations are only near .55. • IOP2b : zonal wind is not bad represented. Models trend to overestimate day values. Correlations are near .8 • As for zonal wind, meridian wind is overestimated by all models, but especially by Rams-Chimie and Rams-Camx. Correlations are near .65 with 1 m/s of mean error. • IOP3 : Zonal wind is quite well simulated, except by Mapom for the first night, and by meso-NH, witch don’t produce as much as variations in wind speed. Correlations are good : .85 • Meridian wind is worse. All models overestimate speed (1.5 to 2 m/s of mean error). Correlations are near .7

  15. Global view with 2D map • We have seen large differences between all models if we analyze station by station. • But for air quality models, if local values are important, it’s as important to simulate global tendencies on large domains. And in this cases, differences between models are less visible, and of course less important. • If we look some 2D map, for zonal wind by example, we can see that some models are good for some stations, worse for others. Another model will give opposite results. • But on large domains, all models give comparable results.

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