Experimental assimilation of synthetic bogus tropical cyclone pressure observations into a high-resolution rapid-update NWP model
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
CSIRO PUBLISHING
Journal of Southern Hemisphere Earth Systems Science, 2020, 70, 215–224
https://doi.org/10.1071/ES19036
Experimental assimilation of synthetic bogus tropical
cyclone pressure observations into a high-resolution
rapid-update NWP model
Susan Rennie A,B and Jim Fraser A
A
Australian Bureau of Meteorology, GPO Box 1289, Melbourne, Vic. 3001, Australia.
B
Corresponding author. Email: susan.rennie@bom.gov.au
Abstract. The effect of synthetic ‘bogus’ tropical cyclone (TC) central pressure observations on TC Owen was tested in
a convective-scale numerical weather prediction (NWP) system with hourly 4D-Var assimilation. TC Owen traversed the
Gulf of Carpentaria over 10–14 December 2018, entering from the east and briefly making landfall on the western edge
before reversing course and retracing its path east to cross the northern tip of Queensland. The Australian Bureau of
Meteorology runs a high-resolution NWP model centred over Darwin, which covers much of the Gulf of Carpentaria. The
next-generation developmental version of this model includes data assimilation. Therefore, when TC Owen presented
the opportunity to investigate the simulation of a TC within the domain, the developmental system was run as a case study.
The modelled cyclone initially failed to intensify. The case study was then repeated including assimilation of bogus central
pressure observations. This new run showed a large improvement in the intensity throughout the simulation; however, the
TC track was not substantially improved. This demonstration of the potential impact of using synthetic observations may
guide whether the development of a bogus observation source with sufficiently low latency for use in an hourly-cycling
system should be prioritised.
Keywords: Australian cyclones, central pressure observations, convective-scale numerical weather prediction, data
assimilation, Gulf of Carpentaria, synthetic observations, TC Owen, tropical cyclone.
Received 20 November 2019, accepted 3 June 2020, published online 28 August 2020
1 Introduction the northern tip of Queensland. ACCESS-DN covers three-
The Australian Bureau of Meteorology runs six high-resolution quarters of the Gulf of Carpentaria.
numerical weather prediction (NWP) configurations (1.5-km It became apparent during this run that the model did not
grid spacing) covering the major population areas of the country, adequately represent the TC intensity, likely due to a lack of
the ‘City’ versions of ACCESS (Australian Community Climate observations to inform the model about the TC structure. Hence
and Earth System Simulator, Puri et al. 2013). Occasionally, the warm-running version might compare poorly to the existing
tropical cyclones (TCs) occur in or pass through the Darwin operational downscaler, which benefits from re-initialising
domain, which covers the top end of the Northern Territory. The every 6 h from ACCESS-R. This regional model has 6-h 4D-
current operational high-resolution City NWP domains (Bureau Var with a larger domain and satellite observation input, and
National Operations Centre 2018) are run downscaled from the consequently may better represent cyclone central pressure and
regional model (12-km grid spacing) that covers the Aus- location. In comparison, the warm-running ACCESS-City relies
tralasian region (ACCESS-R; Bureau National Operations on a smaller set of observations (due to its smaller domain and
Centre 2016), initialised every 6 h. However, a warm-running time window) to analyse the cyclone, especially once it is away
rapid-update NWP system with hourly 4D-Var data assimilation from the boundaries where ACCESS-R provides information. It
is currently being developed to replace the downscaling system was decided to rerun the period, including synthetic central
as the operational high-resolution NWP in 2020. pressure observations, with the objective of analysing the model
During the development of the new ACCESS-City systems, a response to this additional information about the TC. This
request was made to initialise the Darwin domain (ACCESS- allowed a controlled experiment of observation impact.
DN) for the duration of TC Owen, so that forecasters could view TCs often originate over tropical oceans where meteorologi-
some of the new NWP output diagnostics in the presence of a cal observations are frequently insufficient to accurately define
cyclone. TC Owen was predicted to enter the Gulf of Carpen- vortex location and structure, particularly of the inner core.
taria from the east, and travel west for a period while intensify- Synthetic observations (commonly known as bogus
ing, before reversing course and passing eastward back across observations) derived from forecaster estimates of TC
Journal compilation Ó BoM 2020 Open Access CC BY-NC-ND www.publish.csiro.au/journals/es
216 Journal of Southern Hemisphere Earth Systems Science S. Rennie and J. Fraser
125°E 130°E 135°E 140°E
10°S 10°S
15°S 15°S
20°S 20°S
125°E 130°E 135°E 140°E
Fig. 1. Northern Australia showing the path of (ex-)TC Owen with the date at 00 UTC shown adjacent to its
location. The whole Darwin domain is shown by the solid box, with the inner fixed-resolution grid shown by the
dashed box. The model orography is also shown.
properties are often used in operational models (e.g. ACCESS- There are several caveats to be noted for this experiment.
TC and the UK Met Office global model) to help improve TC Firstly, the developmental ACCESS-City system is not in its
analyses and forecasts. In addition to using all available con- final form. Although no major changes are expected for the
ventional observations, the current operational 12-km NWP component, the operational data assimilation may include
ACCESS-TC Tropical Cyclone regional version of ACCESS additional observations, and updated background covariances
(Bureau National Operations Centre 2017) uses a radial array of used by the 4D-Var. Therefore, the data assimilation is expected
123 bogus surface pressure observations based on manual to be improved in future. Secondly, the source of the bogus
forecaster estimates of TC position, central pressure and size observations are TC advisories released every 6 h, around 1–3 h
to help specify the vortex position and structure (Davidson et al. after the valid time which is usually either 00, 06, 12 or 18 UTC.
2014). A new developmental ACCESS-TC version (National As the hourly 4D-Var relies on a latency (the delay after which
Operations Centre 2020) will run at 4-km horizontal grid observations are received) of at most 1 h, in real time the TC
spacing and similarly assimilates a radial array of bogus obser- advisories would generally arrive too late. The value of this
vations. In contrast, the UK Met Office global model (Heming experiment is that it can show how much value such additional
2016) and also the latest ‘APS3’ (third generation Australian observations can add, and if it is worth pursuing a bogus
Parallel Suite) version of the Bureau of Meteorology’s global observation source with lower latency.
model (National Operations Centre 2019) assimilate only sin-
gular hourly central pressure bogus observations derived from 2 TC Owen
international TC advisories. These observations are interpolated The following description of TC Owen gives all times in UTC.
to hourly intervals between the available advisories, assuming TC Owen formed in the northern Coral Sea on 2 December
there is more than one, and extrapolates up to 2 h past the final 2018 and reached category 2 before making its way westward
advisory. For simplicity, these hourly central pressure bogus while de-intensifying to a tropical low. It crossed the northern tip
observations were used in the ACCESS-DN experiments, rather of Queensland at Port Douglas on 9 December as a tropical low
than full radial arrays which were only available every 6 h. and entered the Gulf of Carpentaria late on 10 December. As it
Although within the last decade it has become more common travelled westward, it re-intensified to category 1 on 11 Decem-
for TC forecasts to be run at resolutions of 5 km or less, there has ber, category 2 by 12 December and category 3 by 13 December.
been little work done at ,2 km resolution, the convection- At this stage it had crossed the Gulf to touch land before turning
permitting scales. This is due to the computational expense of and returning eastwards. TC Owen lost strength and made
running a large domain that can capture the lifetime of a TC. landfall back on the Queensland coast as a category 2 cyclone
Experiments using bogus observations at these resolutions are late on 14 December, and traversed Cape York back to the Coral
similarly rare. Sea, rapidly returning to a tropical low. Fig. 1 shows TC Owen’s
Synthetic cyclone observations for high-res NWP Journal of Southern Hemisphere Earth Systems Science 217
path, at 6-h intervals, with the day of month marked at the
00 UTC point. The TC locations are taken from the TC
advisories published by the Bureau of Meteorology.
3 Method
The developmental ACCESS-City uses a stretched grid, with a
uniform resolution central region of 0.01358 (,1.5 km) which
then stretches out to 0.0368 (,4 km) at the boundaries (Fig. 1).
This is intended to improve the transmission of information
from the coarser-resolution parent model through the lateral
boundaries, and also to move boundary effects away from the
inner region that will be used by forecasters. The grid has 80
vertical levels up to 38.5 km, with higher resolution near the
surface and terrain-following coordinates. For this experiment
the grid is nested inside the operational ACCESS-R, which is
also used to initialise the run. This ‘cold start’ initialisation is
done a couple of days before the event to allow for spin-up. Soil Doppler radar winpro groundgps
moisture is updated daily from the operational global ACCESS sonde aws buoy
version, and sea surface temperatures are updated daily from
RAMSSA (Regional Australian Multi-Sensor Sea surface tem- Fig. 2. Location of surface and upper air observations (excluding aircraft
perature Analysis, Beggs et al. 2011). observations). Doppler radars are shown with a dotted 100-km circle
The NWP component uses the Met Office Unified Model marking the radius within which Doppler radial winds were assimilated.
based on version 10.6 with additional bug fixes and enhance- Shaded areas show where latent heat nudging from rainfall estimates was
ments to produce new diagnostics, all of which are available in available. ‘sonde’ indicates radiosonde observations, ‘winpro’ indicates
later Unified Model versions. The science configuration is the wind profiler observations, ‘aws’ indicates automatic weather station
tropical Regional Atmosphere and Land version 1 (RAL1-T) observations, and ‘groundgps’ indicates ground based global position
system observations.
(Bush et al. 2020). To date, most RAL1 testing of TC modelling
has been done with downscalers nested in global deterministic
or ensemble systems, without any vortex initialisation. These
Number of satellite observations
tests showed over-deepening of the cyclone central pressure, but
the corresponding winds were weaker than observed in intense 500 ATOVS IASI AIRS AMV
cyclones.
Observations assimilated in these simulations came from 400
Observation count
automatic weather stations, wind profilers and radiosondes,
aircraft, total precipitable water from ground-based GNSS 300
(Global Navigation Satellite System), radial velocity from the
Bureau of Meteorology weather radars and AMVs (atmospheric 200
motion vectors, i.e. satellite winds) and sporadic satellite
radiances from AIRS, ATOVS and IASI. Observations were 100
extracted from 55 min after the nominal time of each hourly
cycle. Additionally, latent heat nudging was applied to the first 0
hour of the model run using half-hourly rainfall accumulations 00 UTC 00 UTC 00 UTC 00 UTC
from the national Australian radar composite. Fig. 2 shows 11/12 12/12 13/12 14/12
spatial coverage of conventional observations (excluding ships
and aircraft), Fig. 3 shows temporal availability of satellite Fig. 3. The number of satellite observations assimilated for each cycle
observations and Fig. 4 shows the spatial coverage of satellite during the control run.
observations.
The hourly central pressure bogus observations were inter- corrupted and contain no useful information (Lorenc and
polated from international TC advisories, as is done for the Hammon 1988), was set to 0 for the bogus observations.
APS3 version of the Bureau of Meteorology’s global model The data assimilation uses the Met Office Observation
(National Operations Centre 2019). The advisories give a TC Processing System (OPS) and variational analysis system
location in degrees to one decimal place, and a central pressure (VAR) software for observation processing and assimilation
in whole hPa. The bogus observations are interpolated hourly respectively. The hourly 4D-Var (Rawlins et al. 2007) uses an
between two advisories 6 h apart (Heming 2016). To ensure their assimilation window spanning between T30 and T þ 30 min
assimilation, even if the bogus observations are very different for each cycle time (T). It uses static background error co-
from the model field, the initial Probability of Gross Error, a variances as described in Lorenc et al. (2000), but in a model-
prior estimate of the probability that an observation might be level formulation arising from swapping the order of vertical
218 Journal of Southern Hemisphere Earth Systems Science S. Rennie and J. Fraser
Observation coverage
Satwind IASI
–8 –8
–10 –10
–12 –12
–14 –14
–16 –16
–18 –18
126 128 130 132 134 136 138 140 126 128 130 132 134 136 138 140
AIRS ATOVS
–8 –8
–10 –10
–12 –12
–14 –14
–16 –16
–18 –18
126 128 130 132 134 136 138 140 126 128 130 132 134 136 138 140
Fig. 4. The spatial coverage of assimilated satellite observations for satwind (AMVs) and the three radiance types. The intensity of the
shade indicates the occurrence of an observation at a location, where black indicates at least 20 observations at that location, during the
duration of the trial.
Table 1. The long forecast run lengths at different hours
Analysis hour (UTC) 00 03 06 09 12 15 18 21
Run length (h) 16 13 27 24 21 15 22 19
Run end time (UTC) 16 16 09 09 09 06 16 16
and horizontal transform (Wlasak and Cullen 2014). Model- entered the domain late on 10 December 2018 and exited late on
level formulation ensures that length scales characterising the 14 December 2018. Therefore, the model, while having little
training set are preserved for each model level resulting in the time to spin up, was initialised before the TC entered the
analysis increments appropriately accounting for observational domain, and the only information from the parent model about
information at both small and large scales. The background error the TC came from the lateral boundary conditions.
covariances determine how far observation information is The hourly data assimilation cycles include short runs of 3 h
spread within the domain, but a spectral formulation is used duration to allow the data assimilation cycling, and longer runs
which does not directly yield the spatial scales. every 3 h of variable duration. The longer runs usually finished at
The model was cold-started from downscaled ACCESS-R either 1600 UTC (which captures the end of the convective
initial conditions valid for 0300 UTC 10 December 2018 with diurnal cycle) for the daytime runs, or at 0900 UTC for the
data assimilation cycles continuing hourly from 0600 UTC 10 evening runs (to keep the run length shorter). Long run lengths are
December 2018 to 2300 UTC 17 December 2018. TC Owen shown in Table 1. Due to resource constraints, forecast run lengths
Synthetic cyclone observations for high-res NWP Journal of Southern Hemisphere Earth Systems Science 219
Table 2. Overview of experiments and operational NWP system
Control With bogus Operational
Parent ACCESS-R ACCESS-R ACCESS-R
Assimilated observations Standard observations Standard observations plus bogus central pressure No assimilation
Long forecasts 3 hourly 3 hourly 6 hourly
were kept shorter than might be used operationally. Note that the movement to make landfall suggests the steering flow was too
forecast initiates at 30 min before the specified time and runs for weak in the model.
the time shown in Table 1. However, forecast lengths in the results The mean track errors as a function of forecast length were
section are referred to from the ‘analysis time’, i.e. the middle of calculated for every forecast base time (eight times per day).
the assimilation window, following normal NWP conventions. Fig. 8 shows a box plot of the track errors. The mean values
The basic differences between the two trials and the opera- (horizontal lines) show that the control runs without bogus have
tional high-resolution NWP system used for comparison in the smaller errors on average but the spread of the errors is large and
results are detailed in Table 2. the differences are not significant. The large range of errors is
Cyclone tracks are an important method of cyclone verifica- likely due to the difficulty in forecasting this cyclone, with weak
tion. Although there exist some complicated algorithms to esti- steering from the lateral boundary conditions. It should be noted
mate the cyclone location, a simple method was chosen for this that the operational ACCESS models had similar difficulty in
study. The model forecast tracks are derived by finding the point of forecasting the TC track.
lowest pressure in the Gulf, ignoring any low pressures associated The inclusion of the bogus resulted in a much more realistic
with small-scale mesocyclones (which can appear in the bands of central pressure. However, the intensification occurred later and
convection associated with the vortex) and assuming a central generally finished earlier than was observed (Fig. 9). The timing
pressure location similar to that of the previous time. Plots of of the weakening may have been influenced by the proximity of
forecast tracks are smoothed by a seven-point (1 h) running mean the TC to the eastern boundary. Overall the central pressure was
to aid visibility, as the mean sea level pressure (MSLP) field is generally 25 hPa higher than observed for the non-bogussed
output every 10 min. The location and central pressure from the control run at its most intense, while for the bogussed run the
bogus observations are used to represent the ‘truth’. central pressure was 10 hPa higher than observed. The cyclone
in the non-bogussed control run showed very little deepening,
despite the expectation that a mesoscale-resolution cyclone
4 Results modelling experiment should be capable of producing an intense
An example of the MSLP and a cross-section of wind is shown in cyclone. Although the deepening from the inclusion of the bogus
Fig. 5. This demonstrates the effect of assimilating central is not unexpected, it should be recognised that the result was
pressures on the intensity of the cyclone. fairly successful, with only a single observation added to the
The location of the TC at analysis time (T ¼ 0), essentially input of the OPS in each cycle.
the start location of each forecast, was extracted to produce a The tracks and central pressures were also compared against
path of initial or analysis locations. The analysed paths for the the operational 1.5-km resolution downscaled system
two experiments are plotted along with the path from the bogus (ACCESS-C2), as this is the system to be replaced by the
observations in Fig. 6. The cyclones appear at the eastern developmental system. This uses a fixed grid with a smaller
boundary at around the right time, influenced by the lateral domain than the developmental system, where the easternmost
boundary information from the parent model. Both simulated boundary is 18 further west than the boundary of the variable
cyclones lag in travelling westward and do not go as far as the grid. The TC paths from each base time (ACCESS-C2 is
true cyclone. On returning eastward, the simulated cyclones are initialised at 00 UTC, 06 UTC, 12 UTC and 18 UTC) are shown
slower to move, and by 00 UTC 14 December 2018 the cyclones in Fig. 10.
are similarly located. The analysis location for the bogussed All simulated cyclones initially have a slow, meandering
cyclone stays closer to the truth from 14 December but does not westward path, turn around sometime around 0000 UTC 13
go as far westward on 12 and 13 December. December 2018, and have a more rapid progression eastward
The forecast tracks are shown in Fig 7. The cyclone was late (Fig. 10). All simulations fail to go far enough west, possibly
to enter the domain and was slow to move westward initially. because the parent model, ACCESS-R, also fails to produce the
The simulated TCs showed a strong tendency to make westward movement. In the panels for 14 December 2018 it is
landfall too far south-east, which was more pronounced with also clear the TC does not cleanly exit the domain and some
the bogus run. The control-run cyclone essentially dissipated on lingering low pressure regions remain near the boundary.
landfall, but reformed closer to the true location, while the bogus The central pressures for the ACCESS-C2 downscaled runs
run stayed south-east, not quite making landfall (Fig. 7b). The are shown in comparison to the experiments in Fig. 11. This
bogus-run cyclone location only improved a day later when it shows that the ACCESS-C2 central pressures are lower than for
shifted northwards, although the turn around to an eastward the control run. The parent ACCESS-R model does not have a
movement was generally correct. The insufficient westward sufficiently deep central pressure, so the initial ACCESS-C2
220 Journal of Southern Hemisphere Earth Systems Science S. Rennie and J. Fraser
1200 UTC 20181212
(a) Mean sea level pressure (hPa) (b) Mean sea level pressure (hPa)
–12 1005 –12 1005
–13 1000 –13 1000
–14 995 –14 995
Latitude
Latitude
–15 990 –15 990
–16 985 –16 985
–17 980 –17 980
136 137 138 139 140 136 137 138 139 140
Longitude Longitude
(c) Meridional wind (ms–1) (d ) Meridional wind (ms–1)
34 34
6 22 6 22
10 10
Km
Km
4 4
–2 –2
–14 –14
2 2
–26 –26
–38 –38
0 0
137.0 137.5 138.0 137.0 137.5 138.0
Longitude Longitude
Fig. 5. (a) and (b) Mean sea level pressure. (c) and (d) Meridional wind component through the transects shown in (a) and (b).
(a) and (c) Control run. (b) and (d) ‘Bogus’ experiment run with central pressure assimilation.
Control
Bogus
Truth
Fig. 6. TC track and central pressure at analysis time for each hourly cycle. The panel on the left shows the larger
area for context, while the panel on the right is the area enclosed by the dashed box. In the right panel, the markers
indicate the location at 00 UTC and the number in the marker indicates the day of the month. The red circles are
locations for the control run, the blue squares are markers for the run with bogus observations and the black
diamonds indicate the true location.
pressures are not very deep. However, the cyclone may be better forecast can require a period of 12–36 h to spin up some features.
represented overall, which allows the cyclone to develop and The bogussed runs are still much better and produce a realistic
deepen more during the forecast. Note that a downscaled central pressure.
Synthetic cyclone observations for high-res NWP Journal of Southern Hemisphere Earth Systems Science 221
Paths for control Paths for bogus
11/12 00 to 21 UTC 11/12 00 to 21 UTC
12/12 00 to 21 UTC 12/12 00 to 21 UTC
00 UTC
03 UTC
06 UTC
09 UTC
12 UTC
15 UTC
18 UTC
13/12 00 to 21 UTC 13/12 00 to 21 UTC
21 UTC
14/12 00 to 21 UTC 14/12 00 to 21 UTC
Fig. 7. Forecast TC tracks for forecasts from each day, from base times every 3 h. The forecast base time is
indicated by the colour, where 00 UTC is red and the colours progress through the HSV rainbow. The no-bogus
control-run forecast tracks are in the left column and the bogus-run tracks are in the right column. The start of
each track is indicated by a coloured dot and the end of the track by a coloured cross. The truth shown by the
black line includes grey and black dots to indicate the true locations at 00 UTC on 11 (easternmost), 12
(northernmost), 13 (westernmost) and 14 December 2018. The black dots represent the location at the start of the
day for each panel.222 Journal of Southern Hemisphere Earth Systems Science S. Rennie and J. Fraser
5 Conclusions large computing resource requirements, so opportunities have
The assimilation of bogus observations has been a tactic long been few.
used to improve the representation of TCs in global and In this study the effect of using bogus observations is
regional NWP systems, but is not common in convective-scale assessed for a TC that entered the ACCESS-DN domain in
NWP. This is in part because operational high-resolution December 2018. The addition of bogus central pressure obser-
simulations using data assimilation over a large tropical vations to the experimental high-resolution, hourly-cycling
region have only become common in recent years, due to the ACCESS NWP system resulted in marked improvement to the
representation of cyclone intensity. The cyclone tracks are
roughly accurate in the slower westward movement, turn around
Mean forecast track error and traverse back eastwards along a similar path at a faster rate.
However, all simulated paths, whether they are from a down-
300 Control
Bogus scaled run, or with data assimilation and with or without bogus
observations, fail to capture the full westward extent of the path.
250
Forecast track error (km)
Indeed, the bogussed experiment had the least westward move-
ment. The depth of the cyclone may have actually made it harder
200 to correct the location of the cyclone, as the amount by which the
model fields would need to change becomes larger.
150 Although the track performance for the experiments both
with and without bogus observations was somewhat disappoint-
100 ing, it should be kept in mind that the tracks in a high-resolution
nested model are to a large extent dependent on the steering flow
50 imposed by the larger-scale nesting model that provides the
boundary conditions. In this case the parent ACCESS-R model
0 (31) (29) (27) (15) (3) forecast tracks suffered similar limitations, suggesting this case
0 6 12 18 24 has a particularly challenging track to forecast. The ACCESS-R
Forecast length (h)
tracks (not shown) followed a similar trajectory to the opera-
tional ACCESS-C tracks. Nevertheless, the cyclone with the
Fig. 8. Plot of mean forecast errors (green triangles) with box plots bogus observations appeared more realistic in terms of cyclone
showing the median (horizontal bar) and quartiles (range of box). The intensity, which should be reassuring for forecasters. This
whiskers represent the first and third quartiles, and the black circles are flier suggests that finding a way to produce bogus observations with
points. The number of samples is shown in brackets near the x axis. short latency would be beneficial for instances when a TC enters
0000 0100 0200 0300 0400 0500 0600 0700 0800 0900 1000 1100
1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300
1000
900
Minimum pressure (hPa)
980
970
960
11/12 11/12 12/12 12/12 13/12 13/12 14/12 14/12
00 UTC 12 UTC 00 UTC 12 UTC 00 UTC 12 UTC 00 UTC 12 UTC
Fig. 9. Forecast TC central pressure for the ‘true’ (forecaster analysed) pressure (black dot–dash line), the no-
bogus control runs (dashed line) and the runs with bogus (solid line). The colour represents the base time of the
run, using the same colour scheme as Fig. 7.Synthetic cyclone observations for high-res NWP Journal of Southern Hemisphere Earth Systems Science 223
20181211 0000 20181211 0600 20181211 1200 20181211 1800
20181212 0000 20181212 0600 20181212 1200 20181212 1800
20181213 0000 20181213 0600 20181213 1200 20181213 1800
20181214 0000 20181214 0600
Operational
Bogus
Control
Truth
Fig. 10. Tracks starting every 6 h compared with the operational downscaled run. The operational domain boundary is marked with the dashed grey
line. The initial track position is marked by a dot. Note that the initial vortex position shown in the first panel may not reflect the true centre of the TC due
to close proximity to the model boundary. The track durations for the operational and truth forecasts are 36 h. The track durations for the experiments are
16, 27, 21 and 22 h for 00 UTC, 06 UTC, 12 UTC and 18 UTC respectively.224 Journal of Southern Hemisphere Earth Systems Science S. Rennie and J. Fraser
Available at: http://www.bom.gov.au/australia/charts/bulletins/
1000 apob107-external.pdf.
Bureau National Operations Centre (2017). APS2 Upgrade to the ACCESS-
Minimum pressure (hPa)
TC Numerical Weather Prediction System. Melbourne, Australia.
990 Available at: http://www.bom.gov.au/australia/charts/bulletins/BNOC_
Operations_Bulletin_112.pdf.
980 Bureau National Operations Centre (2018). APS2 upgrade of the ACCESS-
C Numerical Weather Prediction system. Melbourne, Australia. Avail-
Operational able at: http://www.bom.gov.au/australia/charts/bulletins/BNOC_Oper-
970
Control ations_Bulletin_114.pdf.
Bogus Bush, M., Allen, T., Bain, C., et al. (2020). The first Met Office
960 Unified Model/JULES Regional Atmosphere and Land configuration,
RAL1. Geosci. Model Dev. 13, 1999–2029. doi:10.5194/GMD-13-
1999-2020
11/12 11/12 12/12 12/12 13/12 13/12 14/12 14/12 Davidson, N. E., Xiao, Y., Ma, Y., et al. (2014). ACCESS-TC: Vortex
00 UTC 12 UTC 00 UTC 12 UTC 00 UTC 12 UTC 00 UTC 12 UTC
Specification, 4DVAR Initialization, Verification, and Structure Diag-
nostics. Mon. Wea. Rev. 142, 1265–1289. doi:10.1175/MWR-D-13-
Fig. 11. Central pressure of TC for forecasts from the base times of
00062.1
00 UTC, 06 UTC, 12 UTC and 18 UTC, the green lines show the control
Heming, J. T. (2016). Met Office Unified Model Tropical Cyclone Perfor-
run without bogus, the blue lines show the experiment with bogus and the
mance Following Major Changes to the Initialization Scheme and a
red lines show the operational downscaler. Truth is the black line. To help
Model Upgrade. Wea. Forecasting 31, 1433–1449. doi:10.1175/WAF-
distinguish the lines, the colour intensity is dependent on base time, with the
D-16-0040.1
most intense colour at 00 UTC and the least intense colour at 18 UTC.
Lorenc, A. C., Ballard, S. P., Bell, R. S., et al. (2000). The Met. Office global
three-dimensional variational data assimilation scheme. Quart. J. Roy.
one of the ACCESS-City domains, as other available observa- Meteor. Soc. 126(570), 2991–3012. doi:10.1002/QJ.49712657002
Lorenc, A. C., and Hammon, O. (1988). Objective quality control of
tions may not be sufficient to define the vortex location and
observations using Bayesian methods: theory, and a practical implemen-
structure. However, this conclusion should be confirmed in
tation. Quart. J. Roy. Meteor. Soc. 114, 205–239. doi:10.1002/QJ.
other cases, especially as the role that the steering flow infor- 49711447911
mation from the parent model plays in cyclone development is National Operations Centre (2019). APS3 upgrade of the ACCESS-G/GE
not quantified. Numerical Weather Prediction system. Melbourne, Australia. Available
at: http://www.bom.gov.au/australia/charts/bulletins/opsbull_G3GE3_
Conflicts of interest external_v3.pdf.
National Operations Centre (2020). APS3 upgrade of the ACCESS-TC
The authors declare no conflict of interest.
Numerical Weather Prediction system. Melbourne, Australia. Available
at: http://www.bom.gov.au/australia/charts/bulletins/opsbull_ACCESS_
Acknowledgements
TC3_External.pdf.
This research did not receive any specific funding. Puri, K. A., Dietachmayer, G., Steinle, P. J., et al. (2013). Implementation of
the initial ACCESS numerical weather prediction system. Aust.
References Meteorol. Ocean. 63, 265–284. doi:10.22499/2.6302.001
Beggs, H., Zhong, A., Warren, G., et al. (2011). RAMSSA - An Operational Rawlins, F., Ballard, S. P., Bovis, K. J., et al. (2007). The Met Office global
High-Resolution Multi-Sensor Sea Surface Temperature Analysis over four-dimensional variational data assimilation scheme. Quart. J. Roy.
the Australian Region. Aust. Meteorol. Ocean. 61, 1–22. doi:10.22499/2. Meteor. Soc. 133, 347–362. doi:10.1002/QJ.32
6101.001 Wlasak, M. A., and Cullen, M. J. P. (2014). Modelling static 3-D spatial
Bureau National Operations Centre (2016). APS2 Upgrade to the ACCESS- background error covariances – the effect of vertical and horizontal
R Numerical Weather Prediction System. Melbourne, Australia. transform order. Adv. Sci. Res. 11, 63–67. doi:10.5194/ASR-11-63-2014
www.publish.csiro.au/journals/esYou can also read
