The representation of boundary-layer processes in a high-resolution numerical
model: a sensitivity study from the COPS experiment
Burton, RR*, Gadian, A*, Blyth, A* and Mobbs, SD*.
* National Centre for Atmospheric Science, University of Leeds, Leeds, UK
Corresponding author: Ralph Burton ralph@env.leeds.ac.uk
Abstract
The ability of a state-of-the-art numerical model (WRF) to simulate an observed deep convective cloud (occurring
during the COPS campaign) is investigated, with particular reference to the model’s boundary-layer specification.
It is found that a combination of a TKE-based boundary-layer and a surface scheme that carries soil moisture is
necessary to simulate this observed cloud; in this case, a reservoir of moist air was allowed to develop, the energy
of which could be released via a suitable trigger. Boundary-layer schemes of the countergradient type were found
to produce a too-well-mixed boundary layer; and surface schemes which do not carry soil moisture were found to
produce an atmosphere that was too dry (and thus deficient in static energy) in the lower levels. This study aims to
illustrate deficiencies in current parametrisations of boundary-layer physics, and to determine the important
physical processes which need to be well represented in order to realistically simulate a deep convective cloud.
The results have important implications for the forecasting of deep convective events.
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1. Introduction.
In this study, it will be shown that the realistic representation of boundary-layer (BL) and land-surface schemes is
crucial when simulating a deep convective cloud. The description of the boundary layer is shown to be of critical
importance when modeling such an observed cloud from the COPS campaign: the ability of the model to correctly
represent boundary-layer processes such as turbulent mixing of moisture, and moisture supply by the surface, will
be investigated. The redistribution of moisture in the boundary layer is controlled primarily by turbulent mixing;
moisture itself can be supplied by the surface via soil and vegetative effects. If moisture is too well-mixed in the
boundary layer, (for example if moisture at lower levels is displaced large distances upward via large eddies, and
dryer air is displaced downward) then the amount of static energy available to a convective trigger is reduced. On
the other hand, if a combination of boundary-layer- and surface schemes allows a “reservoir” of moisture to
develop, energy is then available for convection, assuming a trigger such as surface convergence. Thus, the means
by which a numerical model represents processes such as turbulent mixing and evapotranspiration can result in
the simulation of convection, or its absence.
The use of the WRF (Weather Research and Forecasting) model (Skamarock et al. 2009) is widespread: its use in
meteorological modelling (both operational forecasting and analysis of test cases) is now extensive. However, the
behavior of the WRF boundary-layer schemes in representing the atmosphere above complex orography is not
well understood. This has important consequences for modelling work involving the use of WRF in such cases
The work presented here will illuminate the important physical processes that need to be represented well in any
numerical model, if the realistic simulation of deep convection is to be achieved. The ability of other numerical
models to reproduce this case of deep convection is described elsewhere in this issue (see B10).
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In this paper we shall concentrate upon the effect of (i) varying the different boundary-layer schemes and (ii)
changing the way in which the land surface is represented. From the groups of simulations, the potential problems
in the available boundary layer schemes (such as boundary layers being too well-mixed) will be demonstrated. In
Section 2, the COPS experiment is briefly described; Section 3 contains details of the WRF model and its
representations of the boundary layer and land surface models; results of the sensitivity tests are presented in
Section 4, and this study concludes with recommendations regarding the choice of physical schemes (Section 5).
2. The COPS experiment.
The convective and orographically induced precipitation study (COPS) has been described in detail elsewhere in
this volume (Wulfmeyer et al. 2010), and so will not be described further here. The subject of this paper is the
deep convective cloud observed on the 15th July (IOP8b). This isolated convective event has itself been described
elsewhere in this volume: the event is summarized in Wulfmeyer et al. (2010) and the mechanism of formation,
and observed and modelled response are described fully in Barthlott et al. (2010), hereafter B10. In the latter
paper, the ability of several numerical models to adequately represent this cloud are presented; it is seen that the
models differ widely in their representation of deep convection. Both Wulfmeyer et al. (2010) and Barthlott et al.
(2010) contain numerous figures depicting the observed cloud and, to avoid any repetition, the reader is referred
to those papers for a full description. The present study seeks to determine the response from a single model
(WRF) by varying the representation of boundary-layer processes in that model. It will be seen that convection
occurs only when a specific model boundary-layer-configuration is satisfied. The nature of this particular
configuration allows insight into the controlling factors regarding the simulation of deep convection in this case.
3
3. Description of WRF.
The Weather and Research Forecasting model (WRF) is a state-of-the art numerical model originally developed
by NCAR and other US institutes and agencies. Highly portable and designed to run under various (easily
changed) configurations, the model includes a full suite of physical packages.. The version of WRF used in this
study is Version 3.1 (released April 2009); however, this study has relevance to earlier (and presumably,
subsequent) releases of the model which include the boundary-layer and surface-scheme representations described
below. Additionally, the findings of this study could apply equally to any model using these (generic) types of
surface- and boundary-layer-schemes. With an isolated cloud forming over complex terrain, during a period of
high pressure, the 15th July cloud event represents a particularly challenging test case and constitutes an ideal
scenario for model assessment1.
3.1. Description of the general model configuration.
In the simulations described in this paper, WRF was set up with the physical schemes as displayed in Tables I and
II. Table I shows the physical schemes that are common to all the runs described in this paper; Table II describes
the differences between model runs.
Figure 1 shows the three nests used in the model simulations. The ratio of grid sizes in parent:child nests is a
constant 1:3, and the timestepping in the model integrations follows the same rule. The feedback between nests is
a two-way process, with information being fed from the smaller scales to the larger, in addition to the usual
The term assessment is used purposefully here, instead of the more common alternative, verification – since there is no
direct attempt here to compare model results with observations. This seeming deficiency is intentional, as only one of the
simulations presented here produced any deep convective cloud at all – and this particular simulation is described as part of a
separate model intercomparison in this issue (Barthlott et al. 2010).
1
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downscaling of energy. The model was initialized with NCEP GFS analyses, freely available on the internet
(NOAA National Operational Model Archive & Distribution 2009). The analyses used were at 1 degree by 1
degree resolution and with 27 vertical levels; these 6-hourly analyses were employed to both initialize the model
simulation, and to provide lateral boundary conditions (on the outer domain); . On the inner domain, the boundary
conditions are derived from the outer domain. The WRF domain configuration was chosen to be the same as that
used in both the WRF simulations in B10. The Morrison microphysics scheme was chosen for all runs; this was
(until Version 3.1 of WRF) the only double-moment scheme available and is considered to represent ice processes
well (Morrison et al. 2009); vapour, cloud droplets, cloud ice, rain, snow and graupel are included in the scheme.
In the latest version the Thompson microphysics scheme is also of a double-moment formulation. It is worth
noting that previous studies using WRF have found that “problems exist in all the microphysics schemes” (Gallus
et al. 2008). While further simulations to determine the sensitivity of the present model configuration to the
choice of microphysics scheme would be desirable, such simulations are beyond the scope of this study. The
radiation schemes used are typical choices for this model. Further details of all these schemes can be found in
Skamarock et al. (2009).
Numerical and dynamical parameters
Grid sizes and resolution
Nest number
1
2
550x550 x 50
301x241
vertical levels,
x 50
3.6km
vertical
levels,
1.2km
5
Time step
18s
6s
Cumulus parametrisation
None
None
Microphysics
Morrison
Morrison
Radiation (SW)
Dudhia
Dudhia
Radiation (LW)
RRTM
RRTM
Table I. Properties of the simulations that are common to all runs. For further details of the physical schemes, see the WRF
Technical Note ( Skamarock et al. 2009)
Run name
BL scheme
Land surface
Comments
THERM-YSU
Yonsei University
Thermal diffusion
Very shallow, very isolated
cloud.
THERM-MYJ
Yonsei University
Thermal diffusion
Very shallow cloud.
NOAH-YSU
Mellor-Yamada-Janjic
NOAH LSM
Very shallow cloud.
NOAH-MYJ
Mellor-Yamada-Janjic
NOAH LSM
Deep cloud extending to
approx 12km in altitude.
Table II. The four runs considered in this paper, and their boundary-layer and land-surface schemes. The names in column 1
constitute the descriptive labels used in the remainder of this paper.
With reference to Table II, there are four different simulations presented in this paper; only one of which managed
to produce the observed deep convective cloud. The four different runs, which are combinations of two different
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boundary-layer (BL) schemes and two different land-surface models (LSM), were otherwise identical. The BL
and LSM schemes used in this study are described below.
3.2. Description of the boundary- and surface-layer schemes.
In WRF the boundary layer (BL) schemes can be broadly classified according to the way in which the turbulence
(especially the representation of larger scale eddies) is treated. Any sophisticated BL scheme should possess the
property that even when the vertical gradient of the mean value of θ is zero, the turbulent flux of heat can be nonzero (this then allows large-scale eddies to develop over large depths in the atmosphere: see, e.g. Stull 1991).
There are two principal means of ensuring this.
3.2.1. BL: “Countergradient” schemes.
The motivation behind this type of BL scheme is that turbulent fluxes are allowed to depend upon local mean
gradients with the addition of an extra, or countergradient, term (see, for example, Stensrud 2007):
=
where C is a prognostic field such as u, v or θ.
(The terminology “counter” applied since the corrective term acts in the opposite direction to the mean gradient
of the field.). This formal statement of the fluxes ensures that the desirable property noted above is satisfied (if γ
is non-zero). The countergradient term, denoted by
, can then be derived as a function of (for example) the field
flux at the surface and a suitable velocity scale, with the inevitable introduction of a number of constants. As will
be seen below, this approach to the representation of the boundary layer leads to biases in BL depth and mixing;
due to the instantaneous mixing throughout the depth of the BL.
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3.2.2. BL: TKE-based schemes.
In this representation of the BL scheme, an extra equation is incorporated into the numerical model. This extra
equation determines the production and dissipation of turbulent kinetic energy (TKE). There are numerous means
by which the equations of TKE can be presented (see, for example, Stull 1991) but all have the required property
that turbulent fluxes do not vanish in a region of zero mean gradient. TKE-based BL schemes are often thought of
as being more sophisticated, since the TKE equations are derived from a direct treatment of the equations of
motion. . In spite of this, TKE schemes are still subject to some assumptions and depend upon certain
empirically-determined constants (the equation set itself is still not closed).
Note that for both the countergradient and the TKE-based schemes, the numerical implementation is onedimensional in the vertical. In the simulations described below, the Yonsei University (YSU) scheme (Hong et al.
2006) is of the countergradient type, and the Mellor-Yamada-Janjic (MYJ) scheme (Mellor and Yamada 1982,
Janjic 2006) is of the TKE type.
Given that we are seeking to simulate a case with known deep convection, it would seem to be imperative that the
surface is represented accurately in the model: if the surface heat and moisture budgets, for example, are not
adequately represented, then there may be an inadequate forcing of convective processes. The part of the WRF
model that deals with these surface fluxes, and feeds the latter into the lower levels of the BL scheme, are the land
surface models, and these are described in the next section.
3.2.3. Thermal Diffusion model.
The following is a summary and paraphrasing of information derived from A Description of the Advanced
Research WRF Version 3 (Skamarock et al. 2009), which constitutes a technical companion to WRF and includes
a comprehensive list of references.
8
In the thermal diffusion treatment of the surface heat and moisture fluxes, there is no explicit vegetation included.
Soil temperatures are prescribed at five discrete levels, plus a deep-layer average; soil moisture is derived from
seasonal averages. This model is one-dimensional. For further details see Skamarock et al. (2009).
3.2.4. NOAH LSM.
This parametrisation (Skamarock et al. 2009) of the surface is more sophisticated than the thermal diffusion
scheme described in 3.2.3. The possibility of, and allowance for, evapotranspiration and vegetative effects
(including root zone), in addition to drainage and runoff effects (utilizing vegetation categories and soil texture) is
a major feature of the model. Soil temperature and soil water and ice are kept at four model levels. As for the
(simpler) thermal diffusion approach, the NOAH model is effectively a column model.
4. Results of the sensitivity tests.
4.1 The mechanism of cloud formation on the 15th July.
In all four runs, a clear signal is derived from a convergence line which formed over much of the Black Forest on
the afternoon of the 15th July. In the north of the COPS region, this convergence line was observed in surface,
aircraft and upper-air instrumentation (Kottmeier et al. 2009) and is thought to be the driving mechanism behind
cloud formation in that region. Due to the lack of any observing stations in the region of the observed deep cloud
considered here, however, the connection (or rather extension) of the convergence line to the south cannot be
confirmed experimentally (although radar evidence in B10 seems to suggest that it is); the numerical simulations
presented below do suggest, however, that after 12Z a convergence line stretched from the north to the south of
the Black Forest. Figure 2 shows the vertical velocities using for the Thermal-YSU case.
At 10Z, zones of convergence can be seen forming on the western slopes of the Black Forest, which grow and
nearly join by 11Z; to the east, a line of convergence (oriented in a roughly west-east direction) is apparent at 10Z.
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By 13Z, the line of convergence appears to be a coherent structure, covering nearly the whole of the region from
south to north; and the previously existing, separate line to the east has dissipated. As the afternoon progresses,
the line is seen to move to the east; while preserving its rather sinuous structure. By 18Z the line has fragmented
and lost all coherency. The signature of the convective line can also be seen at lower levels; thus, Fig. 3 shows
the lowest-level vertical velocity and divergence field for the NOAH-MYJ run. Although the line of convergence
appears more complex, the line is still clearly defined. This convergence line appears in all the runs in this study.
The more coherent convergence line in the YSU-type runs is attributable to the deep, well-mixed boundary-layer,
coupled with a coarse representation of soil moisture, as will be demonstrated in the following sections.
While the YSU-THERMAL combination of physical schemes is, prima facie, the simplest simulation (in terms of
the complexity of the physical treatment of the underlying processes), it nevertheless offers insights into the
dynamical situation. No significant cloud was observed in this run, therefore it can function as a type of idealised
simulation, the dynamical processes appearing more clearly, as they are not disturbed by the effects of clouds
(such as downdrafts). Thus, in analyzing the simulations, the YSU-THERMAL case allows the determining
physical processes to become apparent, the details (and hopefully more realistic properties) of which are
illustrated by the NOAH-MYJ simulation (the only simulation to produce deep cloud).
In the following, it is instructive to examine a north-south cross-section (defined in Figs. 2 and 3 and subsequent
figures) and investigate the boundary-layer properties along this transect as the convergence line moves across it.
The southeast-northwestern oriented convergence line noted above moves eastward across the cross-section (as
shown in Figs. 2.) Due to this orientation, the region of upward motion moves southwards across the cross-section
(see Fig. 4 which shows the cross-section for the NOAH-MYJ run) giving the impression that clouds are moving
to the south; this is not the case, and is simply a consequence of the orientation of the convergence line as it
passes through the cross-section. Between 14 and 15Z, the convergence line is stationary near the location of the
deep convective updraft; this stationarity allows the potential for updrafts to develop in the NOAH-MYJ
simulation. In the other simulations (Fig 5), the same progression of southward moving updrafts can be seen along
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the cross-section. However in these cases the vertical velocities are not as significant, and updrafts do not
penetrate beyond the LFC. By 16Z and beyond, the convergence line has, in all simulations, weakened and
fragmented, and the deep cloud rapidly dissipates.
The cloud obtained in the NOAH-MYJ run (Fig. 6) shows a quite realistic structure, with a large anvil spreading
to the north (as observed in reality); patterns of updrafts and downdrafts (including a rear inflow) that would be
associated with such a deep convective cloud; and high values of w associated with the large value of CAPE for
the nearby sounding (see below).
4.2 Boundary-layer response.
Skew-t plots are shown in Fig. 7 for a point near the site (48.XW, 8.XE) of greatest convective activity (see B10).
For all the measure of convective potential (such as LI, SWEAT, TT) the NOAH-MYJ run displays the highest
values, and thus the potential for the severest convective activity, at least as measured by these metrics; and does,
indeed, produce the greatest response to the passing of the convergence line.
For the CCL in each of the soundings, it is apparent that the CCL is lowest for the NOAH-MYJ run; the two
highest (in altitude) values of the CCL are THERM-YSU and NOAH-YSU, suggesting that the use of this BL
scheme is causing the PBL to be too dry; the use of the NOAH scheme mitigates this to some extent. The YSU
scheme redistributes surface moisture too readily to upper levels of the PBL, where it can be detrained into the
free atmosphere. This is illustrated in Fig. 8 which shows the difference in mixing ratio between the NOAH-MYJ
and NOAH-YSU simulations. The only significant negative areas in this plot are above 2km, suggesting that not
only is the YSU boundary-layer of a larger vertical extent, it has also redistributed the moisture too evenly. The
counter-gradient term γ (see Eqn. 1) is seen here to be responsible for transferring moisture too readily away from
the lower levels in the simulations where the YSU scheme is used. In the latter, as was noted in § 3.2.1, turbulent
transfer is allowed between levels which are not adjacent (in order to simulate large-scale eddies); in this case this
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has the deleterious effect of transporting moisture away to upper levels. It is possible that some tuning of the
countergradient term may improve the resulting simulations when using the YSU boundary-layer scheme; this is,
however, beyond the scope of this brief study.
In the MYJ cases, turbulent mixing is on adjacent levels, and the moisture is kept to a large extent within the
lower levels, effectively retaining a reservoir of high-energy moist air in the lower levels, where its buoyancy
leads to large updrafts once the convergence line passes over. Fig. 9 shows that the difference in mixing ratio
between NOAH-MYJ and THERM-MYJ has a different structure, as would be expected. Here, the differences
are caused by parcels of higher surface moisture in NOAH-MYJ rising; the thermal “bubbles” are clearly seen. At
an earlier time in the run (Fig. 9(a)), the lower-level moisture field is uniformly higher in the NOAH-MYJ
simulation.
Fig. 10 shows the boundary-layer profiles of θ for the four simulations for the Achern site (48.63N, 8.07E);
sondes were launched from here during IOPs, and the observed profile is plotted also. It can be seen that when the
YSU scheme is used, the BL is far too deep; the use of the NOAH LSM with the YSU BL scheme improves the
situation somewhat. When the MYJ scheme is used, the depth of the mixed layer is closer to that observed. It
cannot be said, though, that any of the combinations produces a boundary layer profile of θ that is close to the
observed profile. Although the lower-level temperatures for the NOAH runs are cooler than the thermal-diffusion
runs (and also cooler than the observations), the lifted indices (LI) for these cases (see Fig. 7) are still the high; in
particular, the NOAH-MYJ run has a LI of -6.6, the highest of any model run. This suggests that, for the NOAHMYJ run, the increase in CAPE due to the increase in lower-level moisture (attributed to more realistic soil
moisture fields) for this run is more significant than the decrease in CAPE linked to the cooling effect: a similar
result to that noted in Pielke and Zeng (1989) when discussing the difference in convective response between dry
and irrigated soil.
It has been found in previous studies (e.g. Kain et al 2005, Weisman et al. 2008) that the YSU BL scheme
produces biases. In the latter paper it is stated that “the YSU scheme tends to create boundary layers that are
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deeper and drier, and is also very aggressive in eliminating capping inversions…the MYJ scheme tends to deepen
the boundary layer more slowly, resulting in [boundary layers] that are characteristically cooler, moister, and
more strongly capped”. This discrepancy between the two BL schemes originates in the means by which air is
mixed: the YSU scheme instantaneously mixes air throughout the boundary layer as a whole, whereas the MYJ
scheme mixes adjacent (model) levels. As is seen in the present results also, this leads in the former case to a too
deep, too dry boundary layer.
It is also found that for various other numerical models (including the Met Office UM, Meso-NH, Arome and
Cosmo models: see B10, Sect. 4.4) the BL for this event is not adequately simulated, suggesting that much work
still needs to be done in parametrising the BL in such numerical models, and that there needs to be an
improvement in the understanding of the underlying physical processes, in order to improve the numerical
representation of the latter.
One possible reason for this inability to correctly capture the BL structure is that all these models use terrainfollowing coordinates (ALAN G. REF or PERSONAL COMM.) more here
4.3 Surface response.
It has been found (Crook 1996) that numerical tests are highly sensitive to the surface values of surface moisture
and temperature, and that “convection initiation is very sensitive to variations in boundary layer thermodynamics”
and that “variations in boundary layer temperature and moisture that are within typical observational ability
(1deg. C and 1g/kg respectively) can make the difference between no initiation and intense convection” (Crook
1996). Thus it is no surprise that these runs have different convective responses, since the surface temperatures
and moistures, in addition to BL structure and depth, are different. Additionally, the necessity of including
vegetative effects in simulations of a pre-storm environment have been noted previously by Chang and Wetzel
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(1991); in that study, the enhanced evapotranspiration, coupled with spatial variations of soil moisture and
temperature, were found to be essential in capturing the observed surface properties.
Fig. 11 shows the difference between the 2m mixing ratio for NOAH-MYJ and that for THERM-MYJ, illustrating
the difference that results in the use of the NOAH land surface model. In these figures, the coloured contours
represent the difference in mixing ratios; isosurfaces of cloud water mixing ratio = 1E-4 kg/kg (derived from
NOAH-MYJ) are displayed on top of these contours, to illustrate where the cloud is situated. In addition, the 10m
wind vectors for NOAH-MYJ are also plotted. It is clear that from 13Z-15Z the deep cloud in NOAH-MYJ
appears where the difference in surface mixing ratio is greatest. The cloud can be seen to form along the (rather
well-defined) borders of this higher difference, i.e. along the steep gradient in surface moisture, with surface
convergence supporting the vertical transport of moist, buoyant air. This steep gradient in surface moisture may
itself induce extra circulations of the types described by Pielke (2001). This would appear to be confirmed by Fig.
12, which shows a cross-section (defined in Fig. 11) of the difference in mixing ratio across the cloud. As can be
seen, the steep gradient in mixing ratio difference between the thermal-MYJ and NOAH-MYJ case is collocated
with a strong updraught in the NOAH-MYJ run, suggesting the importance of low-level moisture convergence in
this case. Also worth noting is the fact that the mixing ratio is nearly always higher in the NOAH-MYJ than in the
thermal-MYJ case.
These plots suggest that the presence of deep clouds in NOAH-MYJ, and not in THERM-MYJ, is due to the
enhanced surface moisture in the former; this itself due to the use of the NOAH land surface model (as opposed to
climatological soil moisture data). It has been noted in previous studies (e.g. Trier et al. 2004) that slight
variations in the soil moisture content can affect the results of numerical models, via the means by which surface
sensible heat and latent heat fluxes are partitioned. Trier et al. (2004) also found that a coarser representation of
soil moisture field resulted in a less accurate forecast of convection initiation, compared with a fine scale soil
moisture field. They add that “...the initial soil moisture distribution…can significantly influence convection
initiation through its effect on the subsequent thermodynamic stability and lower-tropspheric convergence…” The
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inclusion of soil moisture is also seen, in the present results, to make a difference between convective initiation
and no convection, presumably by the same mechanism as noted in Trier et al (2004). In a further study, Pielke et
al. (1997) found that modelled thunderstorm development only occurred when a realistic representation of soil
and vegetative effects was included in the model; for the same model with a “dry” surface, no thunderstorms were
simulated. This was explained by the enhanced transpiration of water vapour into the atmosphere in the former
simulation. Thus, the inclusion of appropriate soil moisture fields is crucial in supplying the lower levels of the
atmosphere with moisture, if a convective storm is to be realistically simulated. In the NOAH runs, soil moisture
is allowed to evolve in time, and leads to a moister surface than that obtained by seasonal (climatological)
moisture fields.
5. Conclusions and recommendations.
In this study a number of sensitivity tests have been conducted in order to determine which combination of
boundary layers scheme and land surface model produces a deep convective cloud. Of the four simulations
conducted, the combination of the TKE-based BL scheme and the soil-moisture-carrying land surface model is
seen to be the only combination which produced a deep convective cloud. This study also shows that there is
much room for improvement in the parametrisation of boundary-layer processes in numerical models: all the
boundary-layer schemes tested here produce a boundary layer that is too deep. In the case of the YSU-type
scheme, some adjustment to the countergradient term may prove necessary, and relatively simple.
For different reasons, the use of the YSU boundary layer scheme and the thermal-diffusion land model do not
represent well the moisture in the lower levels of the system; and they do not allow a reservoir of moist air to
develop, the distinguishing feature of the NOAH-MYJ run, which, under the action of the convergence line,
allows deep cloud to form. The use of the YSU boundary layer scheme was found to redistribute moisture too
15
evenly within the boundary layer; the use of the thermal diffusion land-surface scheme produced lower levels that
were too dry. The following summary of the four cases may prove useful:
(i) In the Thermal – YSU case, the realistic supply of moisture to the lower levels is absent (due to the use of
climatological soil moistures); any available moisture is too readily mixed throughout the depth of the boundary
layer (caused by the instantaneous mixing throughout the BL depth via the countergradient BL scheme); this leads
to lower CAPE values in the region of the observed cloud (indeed, the lowest of the four cases. This combination
of BL scheme and land-surface representation is therefore not recommended in this case). No deep cloud
simulated
(ii) In the Thermal-MYJ case, the redistribution of moisture was less vigorous than in (i) (due to the adjacentlevel-mixing of the TKE scheme), leading to relatively high CAPE values; but as in (i) the supply of moisture to
the lowest levels was reduced. No deep cloud simulated.
(iii) In the NOAH-YSU case, although the surface was well supplied with moisture, the countergradient BL
scheme mixes available moisture too evenly in the atmosphere, leading to a deeper BL depth and only moderate
CAPE values. No deep cloud simulated.
(iv) In the NOAH-MYJ case, the surface was well supplied with moisture, which remained in the lower levels of
the atmosphere, due to the less vigorous mixing of the MYJ scheme; this provided high CAPE (the highest of all
runs) upon the arrival of the convergence line, and subsequently deep convective cloud developed.
The mechanism of cloud formation suggested here is of a suitable reservoir of moist air, the energy of which can
be released upon a suitable trigger. In this test case, the combination of a soil-moisture carrying land surface
model (NOAH), allowing a moister surface, and a TKE-based BL scheme (MYJ), permitting moisture to remain
in the lower levels of the atmosphere, produced a reservoir of moist air; this was lifted by the action of the
convergence line, and deep convective cloud resulted. Thus, it is crucial that the moisture in the lower levels of a
numerical model be adequately represented, and that the boundary-layer scheme does not distribute this moisture
16
throughout the BL (and beyond); illustrating the important physical processes which need to be adequately
represented.
6. References.
Barthlott, C., Richard, E., Chaboureau, J.-P., Burton, RR, Gadian, A, Blyth, A, Mobbs, S, Bauer, H.-S.,
Schwitalla, T., Keil, C., Trentmann, J., Kern, B., Seity, Y., Kirshbaum, D., Hanley, K., Flamant, C., Handwerker,
J. (2010): “Initiation of deep convection at marginal instability in an ensemble of mesoscale models: a case study
from COPS”. Q. J. Roy. Met. Soc., 2010
Chang, J.-T. and Wetzel, PJ (1991): “Effects of spatial variations of soil moisture and vegetation on the evolution
of a prestorm environment: a case study”, Mon. Wea. Rev. 119, 1368-1390
Crook, NA. (1996). “Sensitivity of moist convection forced by boundary layer processes to low-level
thermodynamic fields.” Mon. Wea. Rev. 124, 8, 1767-1785
Gallus, WA and Pfeifer, M. (2008): “Intercomparison of simulations using 5 WRF microphysics schemes with
dual-polarization data for a German squall line”, Adv. Geosci., 16, 109-116
Hong, S.-Y. and Noh, Y. and Dudhia, J. (2006): “A new vertical diffusion package with an explicit treatment of
entrainment processes”, Mon. Wea. Rev., 134, 2318-2341
Janjic, Z. I. (1996): “The step-mountain eta coordinate model: further developments of the convection, viscous
sublayer and turbulence closure schemes”, Mon. Wea. Rev., 122, 927-945
Kain, JS, Weiss, SJ, Baldwin, ME, Carbin, GW, Bright, DR, Levit, JJ and Hart, JA: (2005): “Evaluating the highresolution configurations of the WRF model that are used to forecast severe convective weather: the 2005
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List of Figures.
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Fig. 1. Topography (coloured contours, m ASL) f the two domains used in the model simulations; the outer box is
at 3.6km resolution and the inner box, centred over the Black Forest, is at 1.2km resolution.
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(a) 10Z
21
(d) 14Z
(b) 11Z
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(e) 15Z
(c) 13Z
(f) 18Z
Fig. 2. Vertical velocities (coloured contours, cm/s) at z=1.5km for the THERM-YSU run, for selected times during the 15th
July. The red line defines the location of a cross-section which will be used in subsequent figures.
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(a)
(b)
Fig.3. (a) Vertical velocity (coloured contours, cm/s) at z=1.5km for the NOAH-YSU run; (b) divergence of the NOAH-YSU
run at the lowest model level.
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(a) 10Z
25
(d) 14Z
(b) 12Z
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(e) 15Z
(c) 13Z
(f) 16Z
Fig. 4. Sequence of vertical velocities (coloured contours, cm/s) through the cross-section defined in Fig. 2 for the NOAHMYJ run, at selected times. Also shown are the wind vectors and the 100% RH contour (solid line).Wind vectors are
constructed such that if the head of one vector touches the tail of another, the horizontal windspeed is 5m/s. Note that the
colour bar, denoting the range of vertical velocities in each cross-section, changes in each plot.
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(a)
28
(b)
29
(c)
Fig. 5. Vertical velocities (coloured contours) for the cross-section defined in Fig. 2. for (a) the THERM-YSU, (b) THERMMYJ and (c) NOAH-YSU runs at 15Z. Also shown are the wind vectors and the 100% RH contour (solid line). Wind vectors
are constructed such that if the head of one vector touches the tail of another, the horizontal windspeed is 5m/s
30
Fig. 6. Vertical velocities and wind vectors along the cross-section defined in Fig. 2. for the NOAH-MYJ run at 15Z. Also
shown are the 95% and 100% RH contours (solid lines). Wind vectors are constructed such that if the head of one vector
touches the tail of another, the horizontal windspeed is 5m/s
31
(a)
32
(c)
(b)
(d)
Fig. 7. skew-T plots at 15Z for a location close to the location of deep convection. (a) THERM-YSU, (b) THERM-MYJ,(c)
NOAH-YSU, (d) NOAH-MYJ.
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(a)
(b)
Fig. 8. Difference plots, showing q(NOAH-MYJ) - q(NOAH-YSU) for (a) 11Z and (b) 15Z (coloured contours, g/kg). Also
shown are the 95% and 100% RH contours for the NOAH-MYJ run.
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(a)
(b)
Fig. 9. Difference plots, showing q(NOAH-MYJ) - q(THERM-MYJ) for (a) 08Z and (b) 11Z. (coloured contours, g/kg)
35
Fig. 10. Comparison between boundary-layer potential temperature at the Achern site for the four simulations
(coloured lines), together with observations (dotted line), for 14Z on the 15th July 2007.
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(a)
(b)
37
(c)
Fig. 11. Difference plots of q2(NOAH-MYJ)-q2(THERM-MYJ) for (a) 13Z, (b) 14Z and(c) 15Z. Also shown are isosurfaces of
cloud-water mixing ratio at a value of 0.1g/kg. The range of displayed values ranges from -0.001kg/kg (coloured deep blue)
to 0.001kg/kg (coloured red). The solid black line denotes a cross-section shown in Fig. 12.
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Fig. 12. Cross section (defined by the straight black line in Fig. 11a) of the difference in mixing ratio q(NOAHMYJ) – q(thermal-MYJ). Coloured contours represent differences in mixing ratio; solid (dashed) black lines
represent contours of positive (negative) vertical velocity; and the solid red lines represent the 95% and 100%
relative humidirt contours.
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40