El Plan de Ordenamiento Territorial de Manizales

In this section we aim to find a configuration for the UKV data assimilation that processes Mode-S EHS derivedobservationswith a similar performance to that achieved without them. Our aim is to obtain a data assimilation performance that is similar in terms of the computational time and to ensure that adding Mode-S EHS observations does not negatively impact on the fit to other less numerous observations. We do this by running short trials of the UKV. We assess the performance of the data assimilation processing by checking the number of iterations, niter, required for the cost function (eq.

(2.8)) to reach a minimum value. This would be an important factor for operational implementation. The assimilation is stopped when

• k∇J (xa)k ≤ Q or

• the change in the cost function |J(xn+1)−J (xn)|

• niter > nmax, where niter is the actual number of iterations and nmax is the

threshold for stopping the data assimilation process; for the parallel suite UKV- ps37, nmax = 100.

A large number of iterations implies slow convergence to the minimum value which may not be suitable for operational implementation.

Table 7.1 lists the technical trials for assimilation of Mode-S EHS observations. The notation for these trials is as follows:

1. CONT is the control experiment where no aircraft observations are assimilated. 2. A is for AMDAR, S is for Mode-S EHS aircraft observations

3. For all trials the time window of accepted observations for AMDAR is ±90 min- utes. Assimilation of AMDAR includes AIREP.

4. The restricted time window for data assimilation of observations (d) for Mode-S EHS observations is ±90 minutes of the analysis time unless otherwise stated. The letter combination dyy indicates that a different data assimilation window is used, where yy is the time in minutes.

5. The time window for the spatial-temporal thinning window (t), described in sec- tion 7.3.4 is 300 seconds unless otherwise stated. The letter combination txxx indicates the time window used, where xxx is the time in seconds.

6. Where thinning is applied it applies to all aircraft observations between the sur- face and 11 km. This is a separate process that is applied after the data assimi- lation time window.

These trials ran the data assimilation in a continuous cycle starting from 0300 UTC and finishing at 0900 UTC 2nd January 2015. This would provide at least three data assimilation cycles in which Mode-S EHS data would be assimilated. For these technical trials we use the AMDAR winds σAOBS profile for both AMDAR and Mode-S EHS

winds; and the profile for AMDAR temperature σAOBS T1 for both AMDAR and

Mode-S EHS temperatures, these are shown in figure 7-1 (page 161). (In our longer trials we use the revisedσAOBS T2profile for Mode-S EHS temperatures.)

The UKV-ps37 was configured to initialise its run from the operational analysis obtained 3 hours earlier at 0000 UTC. This was to allow the NWP model a short spin- up time before starting to assimilate Mode-S EHS observations. Moreover, the start time of the trial was chosen to allow for the gradual increase in the number of available Mode-S EHS observations. This is because there is minimal air traffic operating within

UK airspace between the hours from 2300 to 0500 UTC. During this time any Mode-S EHS observations are likely to be from air traffic transiting UK airspace at high altitude (≈ 10 km).

Trial runs were conducted as follows: (a)without aircraft-based observations (CONT),

(b)with AMDAR only, (c) with AMDAR and Mode-S EHS winds and temperatures,

(d) with AMDAR plus Mode-S EHS winds and (e) with AMDAR plus Mode-S EHS temperatures. AMDAR wind and temperature observations were used in all these tri- als. The number of aircraft-based observations was controlled either by changing the data assimilation window for the type of observation or applying temporal and spa- tial thinning. In all trials where AMDAR reports were assimilated this also included AIREPS. The number of available AIREPS is around 100 per day. The number of AMDAR reports can be around 1000 per day.

The results of these trials are listed in table 7.2. The table shows the number of iterations required at each data assimilation cycle (0300, 0600 and 0900 UTC) to achieve minimisation of the cost function and the corresponding number of aircraft reports assimilated for temperature (T) and wind (W) observations. We see that for experiment CONT the number of iterations decreases over the three cycles, with the average number of iterations being 20. Trial CONT has not assimilated aircraft observations, so provides a metric for assessing the impact of assimilating aircraft observations on the number of iterations.

Trial AO assimilated only AMDAR and AIREP reports which were available to the operational version of the UKV. The number of observations increased from around 250 at 0300 UTC to around 900 at 0900 UTC. The average number of iterations to minimise the cost function is 18, which is comparable to the trial CONT.

Trial AS was designed to assimilate all available aircraft-based observations, AM- DAR, AIREP and Mode-S EHS. We can see that the number of assimilated aircraft- based observations is 100 times greater than for trial AO. At the beginning of the experiment the number of iterations is three-times that of AO. At subsequent cycles, where the number Mode-S EHS observations increases by a factor of 10, the maximum number of iterations, nmax, has been reached. Also the assimilated aircraft obser-

vations are dominated by the Mode-S EHS. From this trial, it is clear that for the UKV-ps37 using 3-hourly 3-D Var, assimilating all available Mode-S EHS observations is not practicable.

Trials AStxxxdyy (see table 7.1), were used to explore the options available to thin the available aircraft-based observations. We begin with spatial thinning, indicated with txxx then change the time range for the time-window for accepting observations, indicated with dyy. The spatial thinning control was set to prioritise assimilation of

Mode-S EHS reports. For spatial thinning alone we note that the number of iterations to reach minimisation is two- to four-times that of AO. The cost of spatial thinning is to reduce the number of assimilated observations by a factor of 10 when compared with trial AS. However, whilst thinning has achieved the desired effect on the number of iterations required to reach minimisation of the cost function it is still too high when compared to the trial AO. The trial ASt300d30 uses a time window of ±30 minutes around the analysis validity time to accept Mode-S EHS observations. We see that the trial ASt300d30 does reduce the number of iterations to that comparable to the operational version, AO. The number of iterations is halved when thinning of Mode-S EHS observations is included.

Trials ASt300d30W and ASt300d30T were used to assess which of the Mode-S EHS observation types causes the increase in the number of iterations. For these experiments the impact on the data assimilation for assimilating only winds or only temperatures is about the same. The last trial, AOsto, was performed due to a major technical change that was made to the UKV-ps37 prior to its operational deployment. This involved a change in the order in which the background error covariance transformation operations were performed from vertical then horizontal to horizontal then vertical. The reasons for this change are beyond the scope of this study. Suffice to say trial AOsto shows that the effects of these changes appear to have had only a small effect on the number of iterations when compared to the trial AO.

Based on the number of iterations required to achieve a minimisation we conclude from these technical trials that spatial thinning and reducing the data assimilation window would provide a UKV-ps37 suite that would be comparable to the operational version of the UKV in terms of the number of iterations required for the cost function to be minimised. The number of Mode-S EHS observations would be reduced to around 10% of those available, this is still around 20 times more than the available AMDAR.

This reduction is comparable to previous studies (2.13, page 27) which used Mode-S

EHS reports of 5% within SSR range of Schipol Airport (de Haan & Stoffelen 2012);

10% and 50% within German airspace (Lange & Janjic 2016), supplied by Maastrict

Upper Air Centre; and between 5% and 15% for Mode-S MRAR (Strajnar et al. 2015).

The limitations of these technical trials is that we used a version of the UKV suite that mirrored closely the version to be used in operational weather forecasting, UKV- ps37. There are technical limitations with using UKV-ps37. The data assimilation system for UKV-ps37 does not distinguish between AMDAR and Mode-S EHS obser- vations in its diagnostic output. So it is not possible to assess the impact of assimilating Mode-S EHS observations separately from the assimilation of AMDAR observations. Nonetheless useful information can still be obtained by performing an extended run

using the configuration of the trial ASt300d30.