ASRAdvances in Science and ResearchASRAdv. Sci. Res.1992-0636Copernicus PublicationsGöttingen, Germany10.5194/asr-15-159-2018Current gust forecasting techniques, developments and challengesGust forecasting techniques, developments, challengesSheridanPeterpeter.sheridan@metoffice.gov.ukMet Office, FitzRoy Rd, Exeter, UKPeter Sheridan (peter.sheridan@metoffice.gov.uk)31July20181515917220February20188June201817July2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://asr.copernicus.org/articles/15/159/2018/asr-15-159-2018.htmlThe full text article is available as a PDF file from https://asr.copernicus.org/articles/15/159/2018/asr-15-159-2018.pdf
Gusts represent the component of wind most likely to be associated with
serious hazards and structural damage, representing short-lived extremes
within the spectrum of wind variation. Of interest both for short range
forecasting and for climatological and risk studies, this is also reflected
in the variety of methods used to predict gusts based on various static and
dynamical factors of the landscape and atmosphere. The evolution of Numerical
Weather Prediction (NWP) models has delivered huge benefits from increasingly
accurate forecasts of mean near-surface wind, with which gusts broadly scale.
Techniques for forecasting gusts rely on parametrizations based on a physical
understanding of boundary layer turbulence, applied to NWP model fields, or
statistical models and machine learning approaches trained using
observations, each of which brings advantages and disadvantages.
Major shifts in the nature of the information available from NWP models are underway with the advent of
ever-finer resolution and ensembles increasingly employed at the regional scale. Increases in the resolution of
operational NWP models mean that phenomena traditionally posing a challenge for gust forecasting, such as
convective cells, sting jets and mountain lee waves may now be at least partially represented in the model fields.
This advance brings with it significant new questions and challenges, such as concerning: the ability of traditional gust prediction
formulations to continue to perform as phenomena associated with gusty conditions become increasingly resolved; the extent to
which differences in the behaviour of turbulence associated with each phenomenon need to be accommodated in future gust prediction
methods. A similar challenge emerges from the increasing, but still partial resolution of terrain detail in NWP
models; the speed-up of the mean wind over resolved hill tops may be realistic, but may have negative impacts
on the performance of gust forecasting using current methods. The transition to probabilistic prediction using
ensembles at the regional level means that considerations such as these must also be carried through to the
aggregation and post-processing of ensemble members to produce the final forecast. These issues and their implications are discussed.
Gusts represent a brief, intense peak in wind typically responsible for the
worst damage caused by winds. This can constitute damage to structures such
as buildings, bridges and wind turbines, or arise from loss of control of air
or road vehicles, and gust prediction is a crucial element of weather
forecasting and climate services. A rigorous definition of a “gust” is
required in order for the results of research to be generally applicable, and
the World Meteorological Organization (WMO) recommends a definition based on
a 3 s running mean wind speed, with the maximum in a 10 min interval taken
as the gust for that interval .
This author previously conducted a survey of established gust forecasting
techniques which the present study extends. These are
based on physical reasoning and understanding of boundary layer turbulence,
empirical results or tuning, and statistical analysis. Most techniques
involve some combination of these bases, but generally the emphasis is on one
aspect, allowing rough categorisation of approaches ; the
same categorisation is used here. For instance, methods which will be termed
here “physically-based” interpret weather variables within a NWP model
column in light of knowledge and physical reasoning concerning boundary layer
turbulence and its manifestation at the surface in the form of gusts. The
most common example in operational forecasting employs Monin-Obukhov
similarity theory following ,
, , involving a scaling
approach (physical) to determine the relevant scales for wind variance and
height dependence, an empirically-determined universal function relating the
resulting non-dimensionalised versions of variance and height, and the gust
finally related to wind variance by assuming a 50 % exeedance probability
based on universal turbulence spectra and the 3 s definition above
(statistical). While not derived from first principles, the basis of this
method on a structured theoretical understanding of turbulent processes, and
measurements directed at constraining this understanding leads to the use of
the term “physically-based”. This is as opposed, for example, to simply
tuning a crude rule of thumb to observed gusts, or engineering fixes to
improve forecast error scores.
Methods which will be termed statistical predominantly employ extreme value
statistics provide an overview of these techniques as applied to
gust modelling, or models of the underlying wind
distribution as a function of predictor variables. They are data-driven, with
the statistical model fit to the available data, in a manner tailored to best
accommodate the structure within the data. Coupling of extreme value theory
with turbulence spectra provides a closed theory for turbulent gusts
seeand references therein, validating in principle
the extreme value approach. Statistical models are often used to define
return periods of damaging gusts in a given climate.
Some recent studies of physically-based methods have focussed on evaluations
comparing pre-existing gust parametrisations. For instance
compare the COSMO gust diagnostic
(Monin-Obukhov-based, but with an added linear empirical sustained wind term
in some models to enhance stronger gusts, distinguishing it from other
M-O-based diagnostics) with that of .
compare the same diagnostics with the standard WRF
parametrisation and a simple empirical gust factor approach.
find that the COSMO diagnostic performs better than
the Brasseur method for a 2 km resolution model over Switzerland.
find little difference between diagnostics when applied
to a 20th century WRF model reanalysis, again over Switzerland. Interest
continues in developing better physically-based gust parametrisations
.
Recent examples of the use of statistical methods include
, whose study demonstrates the power of statistical
approaches in extending detailed but limited datasets to apply to much longer
periods. They use the maximum gust at a given model grid point in a 1 km
resolution dynamical downscaling simulation of the worst storm in each year
for 30 years to represent the annual maximum gust for that point, using this
to construct an extreme value analysis. In this way the gust strength for
different return periods was mapped over Germany at 1 km resolution using
just 87 day-long simulations. used the gustiness
analysis of and exposure correction of
to standardise wind and gust measurements between sites
in Germany. They then constructed relationships between Weibull distribution
parameters for extremes of the (sustained) wind and those for extreme gusts,
so that synthetic gusts could be obtained at further sites reporting only
sustained wind, enabling a more comprehensive gust return period analysis
using a 10-year dataset. Others include ,
, ,
, , ,
.
Crossing between physical and statistical approaches,
created a statistical model based on gust output from a continuous 30-year
run of the Met Office Unified Model (MetUM) on a 25 km grid over western
Europe, which was generated using the MetUM gust diagnostic. So, a
statistical gust model was created using a physically-based gust diagnostic,
applied to output from an NWP model. The data were used to create an open
access catalogue of the 50 most severe storms in the period (the eXtreme
WindStorms, or XWS, catalogue). Such flexibility in approach is a recurring
theme in gust prediction, in the effort to efficiently produce powerful
products.
More recently, new methods and applications have come to the fore. For
instance, machine learning methods are increasingly applied to modelling of
sustained winds, and to some extent gusts. Also, applications in high-rise
urban environments or for wind harvesting turbines have also taken the
emphasis away from a purely near-surface prediction to consideration of the
structure of a gust, as embodied in its vertical profile. Also new challenges
and opportunities associated with increasing NWP model resolution emerge:
small mesoscale structures associated with high winds and thought to be
sources of strong gusts, as well potentially as the largest elements of
boundary layer turbulence, begin to be resolved. A broad distinction can
usefully be made here between mesoscale phenomena and resolved turbulence.
The former involves a coherent, structured atmospheric flow induced by the
stability and wind structure of the atmosphere, such as a
microphysically-driven convective shower outflow, or a gravity wave, shaping
the winds and turbulent spectrum experienced below. The latter represents
part of the broad, self-similar spectrum of eddy motion itself. This is
likely to be more amenable to partitioning between resolved and unresolved
portions. Returning to the former case, meanwhile, where the true scale is
tightly defined, for instance a gravity wave wavelength or the size of a
convective shower, resolved motions may resemble the real structure in
simplistic terms, but will be constrained in scale and structural complexity
by the model grid and may thus behave and interact quite differently
depending on how well they are resolved. These newer aspects constitute the
main focus of this paper.
In the remainder of the paper, Sect. discusses some
growth areas in gust forecasting research, Sect.
describes challenges, for instance arising from increasing model resolution,
and the final section gives summary conclusions.
New developments
In recent years, studies in meteorology motivated by wind energy extraction
have moved from locating areas of high resource to considerations of
production efficiency, consistency and control of supply to energy grids, and
turbine fatigue, all of which depend on the relative gustiness and spatial
profile of the wind to which they are exposed
. The location of turbines at
different heights increasingly far from the surface further drives research
toward characterising the vertical profile of gust activity, rather than
merely a screen level or 10 m prediction. Other motivators for this
development are in large scale urban engineering, where for instance the
profile of wind and gust dictates the stress profile on a large building, and
similarly, in forestry. address the development of
improved physically-based gusts parametrisations, with an emphasis on the
gust profile.
characterise gust profiles using measurements and pursue the development of
gust parametrisations in this context.
model the underlying wind
distribution giving rise to gusts as extreme values, using direct numerical
simulation (DNS) and wind tunnel measurements to inform and validate the
model, which can be applied at any height, also testing at field sites.
Consideration of gust profile is already commonplace in the engineering
discipline. For instance report
results in profile terms when examining gust performance using various
engineering codes for wind modification over or in the lee of topography.
Not only the spatial but also the time profile of gusts is of interest for
similar reasons, since two gusts given equal numerical strength by some
definition may have different impacts dependent on the temporal envelope of
the wind fluctuation embodying the gust study this using Large Eddy
Simulation (LES).
Gust prediction is often motivated by the need to model impacts. Often this
is for the (re)insurance industry, where modelled gusts are coupled to some
“loss model” , but more
novel applications also exist. For instance, the Vehicle Overturning (VOT)
Model at the Met Office uses gusts diagnosed from NWP data to forecast hazard
risk for road users, where the risk measure is the combination of the hazard
(the gust strength), exposure (e.g. the extent to which a road is oriented
cross-wind), and vulnerability (how much the road is used by high-sided
traffic). , meanwhile, use a statistical gust model
mapped to a 50 m grid as input to a forest damage model.
Wind gust is generally not one of the primary factors considered in
assessments of climate change, though clearly an important potential impact.
Recent studies that do address climate impacts include
, who employ statistical downscaling from
a global circulation model (GCM) and a gust factor vs. wind speed
characteristic, (discussed above), and
, who use UK routine observation sites for historic
gust climate variability and a regional climate model's daily maximum wind as
a proxy for gust in projections to a future climate.
Examples of machine learning applied to general wind forecasting are common
e.g..
The agility of machine learning techniques in dealing with non-linear
behaviour of predictand variables as a function of predictors makes them well
suited to modelling extreme winds, which are frequently associated with
highly non-linear atmospheric phenomena (e.g. gravity wave breaking, deep
convection) that are difficult to parametrise using reductionistic
approaches. Application with gusts, however, often focusses on the detection
(identification) of gusts presently occurring, for mitigation in flight
control systems , exposure
of wind turbines to damage or power fluctuations , or
prediction of wind power variation as the result of turbulence and wind shear
. Studies in a meteorological context
prove harder to find, but there are examples, and an indication that there is
considerable promise in such approaches.
used a range of eighteen relevant synoptic (wind-,
stability-, and weather situation-related) and terrain factors of the
Colorado Front range, including parameters commonly associated with mountain
wave diagnosis, to attempt to statistically model gusts (peak wind within
consecutive 3 h periods in the day) associated with the famous Boulder
windstorm phenomenon as a function of these
predictors, by training using observed near-surface winds. A multiple linear
regression was used as a control and a neural network and support vector
regression (SVR) as two non-linear test methodologies. SVR using a polynomial
kernel function was found to yield significantly better predictions of Foehn
gusts than the other two (which performed roughly equally) when applied to
radiosonde profiles and output compared to measurements at surface wind
sites. also extended the approach to use 3-D data of
wind, temperature and humidity at a given height from the NAM model within
20 km of the site of interest as input to SVR to predict daily peak gusts
for 10 cities in the US, both in plain and complex terrain locations. Data
were reduced first using kernel principal component analysis (KPCA). The
results compared to conventional model output statistics (MOS) based on
linear regression show radical improvement in RMSE and biases. Tuning of
kernel functional type and other SVR parameters to each location was found to
be beneficial. The use of 3-D fields (albeit in a 20 km radius) suggests
that some benefit may also originate from the method being freed from the
columnar constraints common to many gust forecasting approaches. In a similar
vein, used the AdaBoost classification tree algorithm to
nowcast Foehn gusts in Switzerland using a set of predictor variables
typically associated with Foehn.
used a classification and regression tree methodology
(CART) performing predictions based upon current meteorological variables at
the site of interest (in New Zealand), finding that this performed better
than logistic regression and than other machine learning approaches such as
neural networks, and had some skill when used at very short (30 min)
nowcasting range. extend the study, using
some methods not considered in the first, finding that different
statistical/machine learning approaches emphasise different predictor
variables. For instance, the sustained wind speed was the primary predictor
in all methods apart from PCA, where relative pressure, temperature and
humidity are the primary predictors, and wind speed and direction were the
least important. Of course local pressure perturbations and wind gusts are
likely to be intimately linked.
applied an adaptive neuro-fuzzy inference system
(ANFIS) to forecast daily maximum gust in Kolkata in a given day based on
radiosonde profiles for a subset of 140 (70 for training, 70 for test)
thunderstorm days taken from 12 years of pre-monsoon season. A comprehensive
selection of stability indices (lifted index, CAPE, CIN, bulk Richardson
number, and others) potentially relevant to thunderstorm gusts are used as
input to the model (though the four named prove the most consistent and are
retained as the final model predictor set). term
ANFIS a hybrid method partly because a neural network occupies a central
layer, and inputs and outputs to/from this layer are fuzzy categories rather
than actual data (which has instead been processed into these categories
(input) or constructed from them (output)). A “backward and forward”
technique is used to optimise the system. ANFIS was found to perform better
than non-hybrid NN approaches (which lie closer to multiple linear regression
in performance) for Kolkata thunderstorms.
Other studies have used machine learning as part of a general approach, such
as , who employ the LSBoost regression tree
algorithm to optimise a relationship derived between parameters of a gust
statistical distribution and a set of external predictors. Though, as a
statistical model, their formulation does not contain explicit physical
reasoning, the study does benefit from an astutely chosen set of relevant
topographic, fetch and meteorological variables as model inputs. As was the
case for , this demonstrates the added value that
emerges from ensuring some physical awareness within the design (it seems
likely that additional variables not considered, e.g. relating to stability,
could add further value). discuss “physics
informed” machine learning for modelling turbulence (Reynolds stresses),
utilising DNS data of idealised flows.
It is interesting that the approach in some machine learning studies such as
those of , and is shaped by
the phenomenon causing the gust. Meanwhile, use a very
general approach applying to very diverse geographic and climatological
locations, but require a large (3-D) dataset as input, and consequently a
data reduction step, in their method.
The maximum gust over the previous hour, shown for an area west of
Shetland on 1 December 2011 at 10:00 UTC for four MetUM nested domain
resolutions: (a) 4.4 km, (b) 1.5 km, (c) 500 m
and (d) 200 m.
The applicability of machine learning to sustained wind (as opposed to gust)
modelling, on the one hand suggests that gusts should also be amenable to the
same kind of approach, but on the other that a direct approach to gust
forecasting using machine learning is not strictly necessary. Instead,
combined or hybrid approaches can be valuable; where an existing machine
learning method adds value, for instance to NWP forecasts of the sustained
wind (or other parameters upon which typical gust parametrisations depend),
any effective method could then be used to obtain the gust, such as a
physically-based parametrisation, propagating this added value to an improved
gust forecast.
As Fig. but for 3 January 2012 at 07:00 UTC
over Northern Ireland, during Cyclone Ulli.
Challenges
As NWP models on which gust forecasts are likely to be based move to
increasingly fine grids, they become more able to resolve terrain and
atmospheric phenomena that were previously firmly below the grid scale, and
by default part of the melange of sub-grid variability that parametrisations,
such as gust diagnostics, must try to accommodate. These atmospheric
phenomena include gravity waves, such as mountain lee waves and associated
rotors
,
sting jets
,
small convective cells such as those present in cold air outbreaks, that
typically deliver showery rain accompanied by gusty winds, and the largest
boundary layer eddies. Clearly this group straddles the categories of
turbulence and mesoscale variability, albeit including examples of the latter
associated with the local generation of increased winds and turbulence.
Models' new ability to begin to resolve these phenomena directly presents an
opportunity to improve associated gust forecasts. Meanwhile, wherever
localised turbulent effects or high winds induced by these phenomena are not
accommodated within the theoretical basis of a given gust diagnosis, there is
also the challenge to do so. Of course, resolution is at best partial
(structures “permitted” rather than “resolved”) and so modellers are
presented with a gust “grey zone” issue, with several turbulent or
turbulence/high wind-inducing phenomena occupying it, therefore creating a
set of “parallel” grey zones relating to wind extremes. The typical length
scales associated with these phenomena are listed in
Table .
List of boundary layer and mesoscale phenomena associated with high
or gusty winds, and the rough horizontal length scale ranges associated with
each. For structures with potentially high aspect ratios, the shortest length
scale is given (likely to be most relevant to the ability of NWP models to
resolve them).
A case study highlighting this issue occurred when gusts in the operational
1.5 km resolution Met Office UKV during a cold air outbreak on 1 December
2011 west of Shetland were found to be overestimated. Subsequently, the case
was modelled using successively finer horizontal grids using the MetUM in a
1-way nested configuration (Mark Weeks, Met Office). A second case, Cyclone
Ulli (3 January 2012), was also simulated. Nests at resolutions of 4.4 km,
1.5 km, 500 m and 200 m were used. Figures and
show the operational 10 m gust diagnostic in the four
nested domains for the two cases, over the area of the 200 m resolution
domain. The operational output consisted of the maximum over the previous
hour of the MetUM gust diagnostic . There is a clear
trend as resolution increases of stronger gusts covering a wider area, and
especially at 200 m resolution, larger peak gust values (point observation
time series were also used to substantiate this). Figure
shows cross-flow sections of wind from the 1 December 2011 case, at the
section location shown in Fig. . Filamentary structures
viewed from above in Fig. are found to be related to
convective overturning at low levels to form boundary layer rolls (hook-like
structures in cross-section in the figure, which precipitation maps show
represent showers) and accompanying areas of strong wind reaching the surface
in downdrafts, which become more apparent with increasing resolution.
Effectively, flow structures previously parametrised within the gust
diagnostic become partially resolved in the model. This is illustrated in
Fig. , which shows areas of locally much stronger winds
for the finer domains. Applying the gust diagnostic in these downdraft areas
represents a “double-counting” of the perturbation constituting the gust,
with erroneously large gust values possible. The use of the hourly maximum
gust exacerbates this, where small areas of overlarge gusts can contaminate a
long “smear” of the domain. Instantaneous diagnosed gusts were found to be
much less excessive in time series comparisons; using an hourly mean instead
of maximum of the gust diagnostic was also found more representative. This
can be seen as a “grey zone” problem, encountered depending on the
resolution of the model and the scale of the convective structures.
2.5 m (level 1) winds from a 200 m resolution MetUM nested domain
on 1 December 2011 at 10:00 UTC, in an area West of Shetland. The straight
line in the middle of the plot gives the location of vertical cross-sections
shown in Fig. .
Sting jets are another phenomenon involving relatively localised vertical
motion, to which have been attributed a number of cases involving extreme
gust damage, such as the “Great Storm” in 1987 in the UK. These represent a
descending jet originating from the cloud head behind a bent-back cold front,
driven by slantwise CAPE, and possibly evaporation from the cloud head, and
distinct from jets associated with the warm and cold conveyor belts;
typically sting jets are found in explosively developing cyclones
.
Vertical cross-sections of zonal wind component in each of four
nested MetUM domains, (a) 4.4 km, (b) 1.5 km, (c) 500 m and (d) 200 m.
Cross-section position indicated in Fig. .
give useful
schematic depictions of the jet's integration within the cyclone system.
and use back
trajectories to confirm this conceptual picture, while
demonstrate how lesser sting jets may be able to enhance winds in weaker
storms. use a 12 km model grid, demonstrating that
very high resolution is not needed for sting jets to be present.
As Fig. , but showing the instantaneous
sustained wind.
Extreme winds associated with sting jets may reach the surface as a result of
the weakness of low level stability, and thus current gust diagnostic
formulations sensitive to this might be suitable to predict associated gusts.
The slantwise route of these winds, meanwhile, casts some doubt. Gust
diagnostics generally operate only using data from the current model column,
while gusts in reality may occur effectively due to conditions in adjacent
columns, and more research is needed to test the significance of this. The
“grey zone” with respect to this phenomenon concerns the ability of the
model to sufficiently resolve the jet itself in the first instance, but also
may concern the representation of the jet's interaction with the surface; the
ability of typical NWP gust diagnostics to perform relies on a good
representation of the vertical profile of temperature and wind, and in turn
on the performance of vertical mixing parametrisations responding to the
presence of the jet and the resolution of its intensity and structural
detail. and develop a
precursor diagnostic for the likelihood of sting jets in a given cyclone
(detection using back trajectories is likely to be involved and uncertain on
a routine automated basis). This suggests it may be possible to designate
sting jets so that conditional/adaptive methods could be used in forecasting
associated gusts.
Cross-sections through idealised 3-D simulations of rotors of
(a) type 1 and (b) type 2, reproduced from
, showing horizontal wind component along the
plane of the cross-section. Flow is from right to left.
Lee waves are gravity waves initiated by hilly or mountainous terrain, ducted
in some layer downstream in the lower troposphere
, and can be accompanied by severe
perturbation of the near-surface flow . This
constitutes alternating areas of accelerated and decelerated flow.
Deceleration, occurring beneath a wave crest, may be sufficient to cause
reversal and separation of the flow from the surface, to form a “rotor” –
conceptually a rough, horizontal-axis vortex of recirculation
.
Idealised simulations using the terrain of the Falkland Islands demonstrate
this in Fig. , reproduced from
. High levels of turbulence and strong winds are
typically associated with rotors, and rotors can occur on quite different
scales depending on the mountain range and atmospheric conditions that give
rise to the associated lee waves compare
Fig. with the order-of-magnitude larger structures
in the Owens Valley, USA, .
Figure shows areas of rotor flow reversal in blue
for the two common “types” of rotor, “1” and “2”. The rotor vortex and
its constraint by the quasi-stationary wave structure direct and confine
turbulence in a coherent way that does not occur for a horizontally isotropic
boundary layer. model how sub-vortices in the main
recirculation separate with the flow and intensify, and return to the surface
at the reattachment point in type 1 rotors, with the most gusty winds at the
separation point and light winds and turbulence beneath the remainder of the
crest. Accelerated but relatively less gusty winds occur on the downslope and
beneath wave troughs (tending to be stronger in the type 2 case). The
non-local source of rotor turbulence calls the single-column approach of
typical gust diagnostics into question for rotor situations, as does the
departure of the turbulence generation mechanism in rotors from the basic
model implicit in such diagnostics, and again research is needed to see if
they are adequate.
(a) UK area MSG visible satellite image at 14 Z
16 March 2017, showing distinctive banded cloud associated with lee waves
over Wales and Northern England ahead of a cold front. (b) Output
from the Met Office Vehicle Overturning (VOT) model valid 11–19 Z
16 March 2017, colouring of major routes follows a four-stage “traffic
light” designation of risk from green through yellow and amber to red.
The “grey zone” issue associated with lee wave rotors concerns, in the
first instance, the model grid spacing compared to the possible range of lee
wave wavelengths, but subsequently also the relative ability of the model to
resolve the sub-rotor turbulence structure that gives rise to the formation
of gusts, which may differ significantly from the structure assumed in
typical turbulence parametrisations.
As Fig. , but showing the instantaneous gust.
The ability to resolve lee waves is nevertheless valuable for forecasting
their impacts. For instance, Fig. demonstrates output from the
Met Office VOT Model, which takes input gust information from the Met Office
MOGREPS-UK 2.2 km resolution ensemble forecast. Despite the relatively
marginal ability to resolve waves at this grid spacing, hazards to high-sided
vehicles are highlighted on various routes in the lee of large terrain in
England and Wales during a moderate lee wave case.
The common theme so far is a failure of the simple columnar approach to take
into account the lagrangian aspects that take turbulence from its source to
where it is felt, and which are particular to the structure of the phenomenon
in question in more complex meteorological or terrain-influenced flows.
The immediate influence of terrain on the wind flow results in speed-up over
hill tops and can result in flow separation and wakes behind obstacles
see for idealised hills. In engineering, such
terrain influence is commonly accounted for. For instance,
present comparisons of sustained winds and gusts
diagnosed in conjunction with various heuristic engineering codes for
speed-up over terrain obstacles. evaluate the
schemes in relation to wind tunnel measurements of the sustained wind and
gust profile with height.
Direct terrain influence on gust is often not explicitly dealt with in
meteorological contexts, meanwhile. As NWP model resolution improves, larger
variations in resolved wind occur within a given area. Gust formulations at
many Met services take resolved (given to imply “sustained”) wind as input
so that any increase in wind is amplified in the output
gust field. This is unlikely to be representative since the cause of the gust
is turbulence resident in the boundary layer as a whole, while local
features, unless very steep, have more influence on the modulation of the
sustained wind. For instance, quote
, that gust speed over hill tops is not enhanced to the
same degree as wind speed. With the NWP approach, as terrain is more finely
resolved, associated resolved wind maxima increase, and the parametrised gust
will also continue to increase, so that gusts may locally be increasingly
overestimated. This is demonstrated in Figs. and
showing instantaneous wind and instantaneous gust
respectively for a snapshot from the nested simulations of cyclone Ulli,
already mentioned.
The above underlines the care needed if neighbourhood processing is used to
create a PDF for probabilistic forecasting, since localised, erroneously
large gusts on terrain peaks would then adversely influence the forecast for
locations well away from the peaks. One solution would be to develop rules
such as those described by to
diagnose and so mitigate the issue more sophisticated,
physically-based approaches also exist, e.g. .
An alternative is to use terrain elevation masking, so that the neighbourhood
of a site of interest is filtered to only include other model points of
similar elevation (Nigel Roberts, personal communication, 2017).
It seems possible that a method similar to the TKE-based method of
, which is sensitive to the wind momentum aloft which may
be transferred to the surface, rather than primarily the wind pattern
at the surface, may be less subject to the problems cited above, which
are most often due to strong resolved near-surface winds which were not
present in coarser resolution NWP model predecessors.
Two distinct approaches to tackling the “grey zone” challenge were proposed
in another contribution (Mylne and Roberts, poster P85 in session UP1.4 of
this conference). The first involves neighbourhood processing to determine a
PDF of 10 m the model wind speed and MetUM gust diagnostic over the
surrounding points. For typical shear-driven gusts, the mode or median of the
gust diagnostic PDF is assumed to be representative. For convective gusts,
some high percentile of the 10 m wind PDF is taken to represent the gust;
the percentile used would be “calibrated” to reflect the relative realism
of convective intensity permitted in the model. No attempt is made to
determine which conditions actually apply in the case in question, and the
maximum of these two values is taken as the gust prediction. Elevational
masking is used to ensure only grid points with like topographic character
are included in the neighbourhood.
The second involves instead an explicit diagnosis of convectively active
areas (for instance using the sharpness of precipitation gradients or
gradients in the 10 m wind). In convective areas, the 10 m neighbourhood
winds are used to calculate the gust, while the gust diagnostic is used in
non-convective areas. This could be extended to also treat areas affected by
sting jets or lee waves separately if necessary, for instance by diagnosing
the appropriate precursors in the large scale flow. Otherwise, lee waves may
not be well treated by the neighbourhood approach since the neighbourhood may
unhelpfully mix the very different wind and gust behaviour of trough and
crest areas. And while sting jets are technically akin to convective gusts,
particularly if low-level stability mediates whether or not touch-down
occurs, their slanted structure may mean areas of strong winds and
precipitation gradients do not coincide, so that they also need a separate
diagosis method.
In the case of large boundary layer eddies the grey zone problem is perhaps
more straightforward. Here, the turbulent spectrum could be truncated at the
effective model resolution and extreme value theory applied to the remainder
of the spectrum. Analogous approaches have been made in other applications
involving small scale variability, such as to combine and weight a 1-D
turbulence closure and 3-D sub-grid turbulence scheme within a single
parametrisation for mixing , by gauging the effective
resolved scale for a particular quantity, depending on the expected eddy
scale for a given weather regime. The study in turn follows similar studies
regarding, for instance, parametrisation of sub-grid updraft velocity for
cloud activation .
Another challenge regards the accuracy of ancillary data and how it
influences the forecast of gust in NWP models. Friction velocity depends on
the effective surface roughness, diagnosed imperfectly from land surface
datasets, which themselves may be interpreted for instance using
generalisations concerning satellite data, and may be of inferior resolution
or out-of-date. As the grid scale decreases, the appropriate effective
roughness may not relate directly to the model grid box in which the gust is
predicted, but to the upwind fetch also. Grid boxes may be small enough that
turbulence in the column would not be expected to have adjusted from its
state in the upwind column.
The transition away from deterministic forecasts towards ensembles and
probabilistic products is a further new challenge – how best to aggregate
NWP model output information on gusts in the final product. The statistical
elements of gust diagnosis (basis in turbulence spectra and extreme values),
suggest amenability to aggregation in this way. Questions remain, however,
around the incorporation of outlying members, which may represent a different
regime to the dominant group at a given location. The combination of
neighbourhood aggregation, lagged ensembles, and post-processing with the
basic ensemble idea, adds complexity to the task of ensuring correct
aggregation and transmission of gust information end-to-end in NWP systems.
This may unhelpfully constrain the level of sophistication that can be
practically justified in the gust diagnosis. Would a change of philosophy be
useful in undertaking this task, such as preserving the statistical nature of
turbulence in some form until an output is required at some level of
processing (e.g. raw model data, filtered data, gridded products,
site-specific products)? Should a stochastic physics approach extend to gust
formulations? The ever-present difficulty of verifying gusts, which are due
to their sporadic and stochastic nature difficult to model with point
accuracy, with sparse observations will presumably be greater when
probabilistic forecasts must be verified. Nevertheless, given this stochastic
nature, a probabilistic approach seems to offer some advantages.
Conclusions
Gust impacts continue to broaden as human activities diversify and their
forecasting and mitigation is perhaps more valuable than ever. Growth in wind
energy harvesting, and knowledge of the implications of gusty winds for
turbine lifespan, energy efficiency and generation reliability has emerged as
a major motivator for research. Large scale and high rise urban planning also
accelerates as economies develop, and both of these areas require
higher-dimensional information on wind variability and its impacts, than
simply a low level gust. The use of UAVs which may be sensitive to gusts and
turbulence is ever increasing.
Machine learning has been relatively sparsely used in gust forecasting, and
opportunities remain for its exploitation, which seems to hold promise of
significant improvements in accuracy. The variety of techniques is abundant,
and while this may make the best method harder to isolate, it also allows for
flexibility of approach, with the best technique perhaps depending on
geography and the gust generaton mechanism in question.
Statistical methods continue to offer new ways of efficiently creating
powerful results concerning gust risk by extending limited model or
observational datasets. The climatological emphasis of most studies extends
more and more towards climate change guidance.
Physically-based approaches continue to have value, but increasing NWP model
resolution represents a challenge as well as an opportunity. The improving
representation of small-scale atmospheric phenomena allows for a better
identification of weather situations likely to give rise to gusty conditions
(lee wave rotors, convective showers, sting jets), and a better
representation of the distribution and variability of the mean wind as a
function of terrain exposure. Meanwhile, current gust diagnostic formulations
typically assume gusts originate from isotropic turbulence associated with a
boundary layer over flat terrain, and so gusts can be diagnosed from within
the model column. But when turbulence is controlled by a structured low-level
phenomenon such as lee wave rotors or convective showers, this assumption is
flawed. Research is needed to understand the extent of this problem and
suggest modified approaches.
The problem is exacerbated since the phenomenon in question is likely to be
partially resolved, and to a different extent for a given phenomenon,
climate, geography, case study or NWP model. This can be thought of as a
“grey zone” for a given mesoscale structure type. Two approaches to
mitigate this have been suggested in a separate conference paper. The first
involves calculating two gust predictions, one based on the resolved
variability of wind in the model grid boxes surrounding a particular
location, and one on existing gust formulations, looking at some average over
the surrounding points. The second involves an automated diagnosis of the
weather situation to decide which approach to use.
Increasingly high resolution creates higher demands on the representativity
of land surface datasets, which govern the crucial quantity of (effective)
surface roughness. For sufficiently small grid boxes, the turbulence in a
given column may represent a fetch of a number of grid boxes, so that local
parameters are not sufficient for its characterisation.
The use of ensemble and probabilistic products should improve the quality and
relevance of forecasts but also brings another layer of challenge for optimal
processing of model data and forecast verification, where the devil may be in
the detail.
These problems aside, physically-based approaches can have a “plug and
play” quality that allow them to be combined with other approaches to create
a useful product, for instance to create statistical models
or provide input to hazard models. Combined
methodologies in general are frequently powerful
.
No data sets were used in this article.
The author declares that there is no conflict of
interest.
This article is part of the special issue “17th EMS Annual
Meeting: European Conference for Applied Meteorology and Climatology 2017”.
It is a result of the EMS Annual Meeting: European Conference for Applied
Meteorology and Climatology 2017, Dublin, Ireland, 4–8 September 2017.
Acknowledgements
The author is very grateful to a number of people at the Met Office for
discussions, ideas and contributing content: Mark Weeks, Rebecca Hemingway,
Jess Standen, Ken Mylne and Nigel Roberts. DWD/Hans Ertel centre
(Sabrina Wahl, Martin Goeber) very kindly funded conference attendance. The
author thanks reviewers Anton Beljaars and Ben Wichers Schreur, whose useful
and knowledgeable comments helped improve and round out the manuscript.
Edited by: Sabrina Wahl
Reviewed by: Ben Wichers Schreur and Anton Beljaars
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