conditions (nighttime - low boundary layer height, low wind speeds etc.) which allows a build up of methane concentrations. These instances are di cult to model and thus should have larger associated model uncertainties. This is why the incorporation of variability can be a relevant model to help capture time-dependent modelling errors. Applying a large, additive uncertainty which is set at a constant value de-weights the more certain observations. This results in the inversion assuming larger, more local sources. This suggests adding a constant value to represent model uncertainty is not an eective method to capture this error. Further experiments using pseudo observations could be conducted to better understand the dierences within these results. Experiment 9, keeps the relative distribution of uncertainties over the duration of the inversion, but still has almost double the uncertainty amount of experiment 1. The resulting regional emissions are similar. It appears that there are limitations to the additive method to create overall uncertainty. Addition of a constant model uncertainty desensitises the more precise observations giving a bias to the less precise observations. By including the standard deviation of the hourly concentrations acts as an indirect parameter to model uncertainty as the larger, short lived concentrations of methane usually occur at nighttime, when modelling uncertainty is higher.
The other aspects of measurement uncertainty are not correlated with errors in the model. Other metrics should be incorporated into the InTEM setup to represent modelling uncertainty to be more inclusive/representative. For example, continuous meteorological measurements could be made at all sites so when the measured and modelled values diverge (for example if the timing of an incoming front is wrong) additional uncertainty could be incorporated into the inversion setup. Results shown in Section 4.5 found that modelled winds can dier from measured values in the Haddenham case study. For these reasons a default value of 5 ppb was adopted to represent model uncertainty for the nal InTEM setup.
5.9 Daytime and nighttime inversions
To assess if there are optimal measurement times for the inversion setup, InTEM is run using observations split into daytime or nighttime values. These are de ned as measurements between 10:00 - 16:00 UTC for daytime and 22:00 - 04:00 UTC for nighttime values. The resulting emission maps and region totals can be found in Figure 5.11 and Table 5.6, respectively.
Chapter 5 Development of the inversion approach
The InTEM solution grid resolution maps look like near-opposites to each other in the EA region. Both maps still place large emissions over point sources and cities (London, Cambridge, Ipswich etc.) but the nighttime map (Figure 5.11.B) places more emissions in region 6 (Lincolnshire) and to the west of Weybourne.
Although the nighttime emissions seem more detailed in the EA area, the Norfolk and Suolk region totals are much less than the equivalent daytime values. The daytime maps place much larger emissions in the point sources but resolve few of the smaller emissions.
The dilution matrices used within the InTEM setup can explain why these dierences occur. Figure 5.12 shows the daytime (A) and nighttime (B) dilution matrices for the Haddenham site over a one year period (June 2013 - May 2014).
The spatial extent of the region of high sensitivity is larger for the dilution matrix for nighttime (Figure 5.12.B) than the daytime (Figure 5.12.A). This is due to the nighttime meteorological conditions, which usually experience lower wind speeds and more stagnant conditions. The lower nocturnal boundary layer heights and wind speeds will result in a less diuse dilution matrix compared to daytime equivalents. This could be why the nighttime maps produce a more detailed spatially distributed emission map.
Nighttime meteorology is more challenging to model in NAME due to localised subgrid scale in uences which are not fully captured in the UM's meteorology.
This is seen in several inversion studies which use dierent meteorological data, e.g. Geels et al. (2007); Chevillard et al. (2002); Elbern et al. (2007).
Geels et al. (2007) states that when running experiments similar to those described in the section (i.e. running inversions using solely day- or nighttime observations daytime values are quite well predicted, nighttime values are generally underpredicted.. Similarly, the results shown here produce much lower estimates using just nighttime observations. Nighttime timesteps should therefore be assigned a larger modelled uncertainty. As discussed in Section 5.8, speci c, time-dependent modelling uncertainty has not been adopted into the InTEM setup, which could mean that the nighttime local sources may be being modelled incorrectly. However, one alternative method of representing this increase model uncertainty in the nighttime observations can be to de-weight nighttime observations within the uncertainty cost function (Section 3.3.5). This pseudo-model uncertainty helps to represent the nighttime meteorological error.
5.9 Daytime and nighttime inversions
A) −0.5 0.0 0.5 1.0 1.5 2.0
51.552.052.553.0
Longitude (degrees)
Latitude (degrees)
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HD
TN WY TY
0 24.1 42.8 76 135.2 240.3 427.1 759.4 1350 2400
Max Value = 22596.4 ng s−1 m−2 Total Emissions = 300 kt yr−1
B) −0.5 0.0 0.5 1.0 1.5 2.0
51.552.052.553.0
Longitude (degrees)
Latitude (degrees)
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HD
TN WY TY
0 24.1 42.8 76 135.2 240.3 427.1 759.4 1350 2400
Max Value = 9352.17 ng s−1 m−2 Total Emissions = 362 kt yr−1
Figure 5.11: InTEM emission maps for the Haddenham site over a one year period (June 2013 - May 2014) using A) daytime (10:00-16:00) and B) nighttime (22:00-04:00) observation measurements.
Table 5.6 shows the regional daytime emission estimates are higher than the 2012 NAEI and the nighttime are lower, whereas emission maps produced using all measurements produce maps which are more similar to the NAEI. It was decided to incorporate all observations within the InTEM setup, as the resulting nighttime emission maps showed no obvious errors which could be identi ed as resulting from nocturnal meteorological uncertainty. The failure to fully capture the diurnal variability is a systematic error found in most inversion setups. By attempting to de-weight the nocturnal observations by increasing their associated uncertainties tries to asses this error but the method is not fully comprehensive.
Chapter 5 Development of the inversion approach
A)
B)
Figure 5.12: Daytime and nighttime dilution matrices for the Haddenham site over a one year period (June 2013 - May 2014). Figures show the average `dilution' values throughout 2013 and 2014 (s m-1). The Haddenham site location is marked with an X.