This article investigates the potential of W-band radar reflectivity to
improve the quality of analyses and forecasts of heavy precipitation events
in the Mediterranean area. The “1D+3DVar” assimilation method,
operationally employed to assimilate ground-based precipitation radar data in
the Météo-France kilometre-scale numerical weather prediction (NWP)
model AROME, has been adapted to assimilate the W-band reflectivity measured
by the airborne cloud radar RASTA (Radar Airborne System Tool for Atmosphere)
during a 2-month period over the Mediterranean area. After applying a bias
correction, vertical profiles of relative humidity are first derived via a
1-D Bayesian retrieval, and
then used as relative humidity pseudo-observations in the 3DVar assimilation
system of AROME. The efficiency of the 1-D Bayesian method in retrieving
humidity fields is assessed using independent in-flight humidity
measurements. To complement this study, the benefit brought by consistent
thermodynamic and dynamic cloud conditions has been investigated by
separately and jointly assimilating the W-band reflectivity and horizontal
wind measurements collected by RASTA in the 3 h 3DVar assimilation system of
AROME.
The data assimilation experiments are conducted for a single heavy precipitation event
and then also for 32 cases. Results indicate that the W-band reflectivity has a larger
impact on the humidity, temperature and pressure fields in the analyses compared to the
assimilation of RASTA wind data alone. Besides, the analyses get closer to independent
humidity observations if the W-band reflectivity is assimilated alone or jointly with
RASTA wind data. Nonetheless, the impact of the W-band reflectivity decreases more
rapidly as the forecast range increases when compared to the assimilation of RASTA wind
data alone. Generally, the joint assimilation of the W-band reflectivity with wind data
results in the best improvement in the rainfall precipitation forecasts. Consequently,
results of this study indicate that consistent thermodynamic and dynamic cloud conditions
in the analysis leads to an improvement of both model initial conditions and forecasts.
Even though to a lesser extent, the assimilation of the W-band reflectivity alone also
results in a slight improvement of the rainfall precipitation forecasts.
Introduction
Kilometre-scale numerical weather prediction (NWP) models are now able to
explicitly resolve convection, and to represent clouds and precipitation with
a reasonable degree of realism . Doppler radar
observations are particularly well suited to initialize these NWP models
because they provide wind and reflectivity measurements at a comparable
spatial and temporal resolution. Consequently, observations from ground-based
precipitation radars are operationally assimilated in many kilometre-scale
NWP models to initialize precipitation areas . However,
ground-based precipitation radars have a very poor sensitivity to clouds.
Hence, the increased number of Doppler cloud-profiling radars
is
extremely appealing in data assimilation to initialize kilometre-scale NWP
models in cloudy areas. Indeed, cloud-profiling radars, either operating in
the Ka-band (≈35 GHz) or in the W-band (≈95 GHz), provide
valuable observations on cloud microphysical properties and light-to-moderate
precipitation . In addition, compared to
lower-frequency radars, they require small antennas to provide high spatial
resolution measurements, which makes them more easily deployable aboard
moving platforms such as spacecraft or aircraft .
Besides, recent technological breakthroughs might lead to a deployment of
lower-cost ground-based W-band radar networks .
Over the last few years, cloud radar data have been used several times for
kilometre-scale NWP model validation , but only a few studies were devoted to evaluate
their potential in data assimilation. In particular, within the Japan Meteorological
Agency's (JMA) non-hydrostatic model (JMA-NHM) with an ensemble
variational method , performed a
direct assimilation of vertical reflectivity profiles of the dual-frequency
precipitation radar (DPR) reflectivity observations from the
Global Precipitation Measurement (GPM) core observatory for the case of Typhoon Halong in
July 2014. The assimilation of DPR data resulted in an improvement in the
rain mixing ratio and updraught. However, because of negative model biases in
the ice regions, observations were discarded in and above the melting layer.
Therefore, did not take advantage of the cloud-affected
observations measured by the Ka-band radar, which are very sensitive to
clouds. In addition, ensemble variational methods are costly in terms of
computing time, which hampers their use in most current operational systems.
Assimilating reflectivity with traditional variational assimilation
techniques (3DVar and 4DVar) remains challenging. Indeed, the linearization
of the observation forward operator is not straightforward. In addition, it
is necessary to add hydrometeor contents in the control variables, which
requires us to specify the associated forecast error covariances. Besides,
the assimilation of humidity, wind and temperature variables have a larger
impact on the forecasts compared to hydrometeor observations
. Consequently, several studies used indirect
assimilation methods to assimilate cloud radar reflectivity measurements
. The reflectivity profiles are
first inverted into pseudo-observations that are closer to the control
variables of the NWP model (such as temperature or humidity) through the use
of either a 1-D variational assimilation technique
or a 1-D Bayesian retrieval
. These pseudo-observations are then assimilated into the
variational assimilation system like any other
conventional observation. In most
of these studies, cloud profiling radar (CPR) data on board CloudSat
satellite were assimilated in NWP models running at
coarse horizontal resolutions (larger than 10 km). For example, using a
technique combining a one-dimensional variational (1DVar) assimilation method
followed by a four-dimensional variational (4DVar) assimilation method,
performed several assimilation experiments with the
global-scale NWP model Integrated Forecasting System (IFS). To take full advantage of
the W-band reflectivity in cloudy areas, applied an
appropriate bias correction scheme which depends on the altitude and on the
temperature. Results suggest a slight positive impact on the subsequent
forecasts when appropriate bias correction, error estimates and quality
controls are performed. However, because of the inability of the reflectivity
forward operator to simulate the multiple scattering
effects, observations of the most convective situations were rejected from
the assimilation process. also employed a two-step method
consisting of a one-dimensional Bayesian retrieval of relative humidity
pseudo-observations, followed by a 3DVar assimilation method in the ALADIN
NWP model . Results show that, despite the small number of
assimilated observations, the impact of relative humidity pseudo-observations
is greater in areas poorly covered by the conventional observation networks,
such as over the oceans. However, failed to identify a
case study for which the humidity pseudo-observations led to a significant
impact on the analysis and on the subsequent forecasts.
So far, the impact of the assimilation of W-band radar reflectivity in a kilometre-scale
NWP model, with horizontal resolutions of less than 3 km, has never been investigated.
Therefore, the primary objective of this article is to investigate the benefits brought
by the assimilation of W-band radar reflectivity measurements to improve the forecasts of
the heavy precipitation events that regularly occur in the Mediterranean area. Indeed,
the accurate forecasting of the timing, position and intensity of such mesoscale
convective systems remains a challenge . Doppler cloud
radar data also provide valuable information on dynamical cloud properties.
highlighted a positive impact of the assimilation of such data in
a kilometre-scale NWP model. The joint assimilation of W-band reflectivity measurements
with wind data measured by Doppler cloud radar is expected to provide more consistent
thermodynamic and dynamic cloud conditions in the initial state. Nonetheless,
suggested that the joint assimilation leads to skill scores which
are comparable to the experiments in which reflectivity and Doppler velocity observations
are assimilated independently. However, their data assimilation experiments were
conducted in an idealized setup, and the observations were provided by ground-based
precipitation radar data. Therefore, to investigate the benefit brought by consistent
thermodynamic and dynamic cloud conditions in the initial state, the W-band reflectivity
will be separately and jointly assimilated with horizontal wind data measured by a
Doppler W-band radar.
To assess the potential of Doppler W-band radar data to improve short-term forecasts of
heavy precipitation events, we take advantage of the data collected by the airborne
Doppler W-band radar RASTA Radar Airborne System Tool for
Atmosphere; in 2012 over the western Mediterranean area during the HyMeX
first “special observing period” HyMeX-SOP1; dedicated to
heavy precipitation events. The W-band reflectivity and wind measurements collected by
RASTA are separately and jointly assimilated into the 3DVar assimilation system of a
special version, named AROME-WMed , of the Météo-France
operational convective-scale model AROME . The impact of the
assimilation of RASTA data in synergy with all other conventional assimilated data is
first evaluated for one of the most significant rainfall events which occurred during the
“intensive observing period” 7a IOP7a of the HyMeX-SOP1. Next, the
experiments are run for 32 case studies of the HyMeX-SOP1 in which RASTA data are
available. The “1D+3DVar” assimilation method of , used
operationally to assimilate ground-based precipitation radars in AROME
, is particularly well suited for vertically pointing radars and is
thus employed to assimilate the W-band reflectivity observed by RASTA. Vertical profiles
of relative humidity are first derived via a 1-D Bayesian retrieval, and then used as
pseudo-observations in the 3DVar assimilation system of AROME. For the first time, a
validation of the 1-D Bayesian retrieval with in situ humidity measurements is performed
in this study.
This article is organized as follows. In Sect. , the data collected
by the RASTA airborne Doppler W-band radar are presented, followed by a brief description
of the NWP model AROME-WMed with its 3 h 3DVar assimilation system.
Section provides a full description of the 1D+3DVar
assimilation method used to assimilate the W-band radar reflectivity. The different data
assimilation experiments are presented in Sect. . These different
experiments are evaluated in Sect. , followed in
Sect. by an evaluation of the 32 assimilation cases of the SOP1.
Conclusions are drawn in Sect. .
NWP model and radar data
This study takes advantage of the data collected by the RASTA
Doppler W-band radar during the HyMeX first special observing period (HyMeX-SOP1), which
took place from 5 September to 5 November 2012 over the western Mediterranean
. The main goal of the HyMeX-SOP1 was to document the heavy
rainfall events that regularly affect the Mediterranean area.
The Doppler W-band radar RASTA during the HyMeX-SOP1
The airborne cloud radar RASTA is a monostatic Doppler multi-beam antenna system
operating in the W band at 95 GHz .
The aircraft platform used is the French Falcon 20 research aircraft from the SAFIRE unit
(Service des Avions Français Instrumentés pour la Recherche en Environnement).
RASTA has six Cassegrain antennas to measure the reflectivity and radial velocity in
three non-collinear directions above and below the aircraft. The maximum unambiguous
distance is 15 km with an unambiguous velocity of 7.8 m s-1 (the pulse repetition
frequency equals 10 kHz).
After processing, the Doppler velocities of the three upward-looking and
downward-looking antennas are combined to retrieve the three components of
the wind field above and below the aircraft .
The measurements are collected at a time resolution of 250 m s-1 (i.e.
1.5 s between two measurements of the same antenna) and at a vertical
resolution of 60 m. In addition, this study takes advantage of the
reflectivity measurements collected by the nadir- and zenith-pointing
antennas. The zenith-pointing antenna is slightly less sensitive than the
nadir-pointing antenna (-26 dBZ versus -27 dBZ at 1 km).
Therefore, this unique instrument allows for the recording of the microphysical and
dynamic properties of clouds in the vertical at a high resolution of 60 m and
quasi-continuously in time (≈1.5 s) during the flights. In particular, during
the HyMeX-SOP1, RASTA collected data during 18 flights in stratiform (72.6 %),
convective (14.3 %) and clear-sky (13.1 %) columns over land, sea and complex
terrain . RASTA flight tracks during the HyMeX-SOP1 are represented
by the black lines in Fig. . Further details about the RASTA configuration
during the HyMeX-SOP1 are given by .
The Falcon 20 flight tracks (black lines) during the HyMeX first special
observing period over the AROME-WMed domain. The Falcon 20 flight track during IOP7a
(flight 15) is indicated by the red line. IOP7a case study is delimited by the red box.
The altitude of ground above sea level (in metres) is represented by the colour shades.
The rain gauges are represented by the blue markers. The black box shows the domain used
for the impact study in Sect. .
The AROME-WMed NWP model
This study is performed with a special version of the Météo-France operational
kilometre-scale NWP model AROME , named AROME-WMed .
AROME-WMed, which covers the entire northwestern Mediterranean Basin, was specially
designed for the HyMeX-SOP1 and ran in real time to plan the airborne operations,
especially in the mesoscale convective systems. The AROME-WMed domain is displayed in
Fig. . AROME-WMed runs at a horizontal resolution of 2.5 km with 60
vertical levels ranging from approximately 10 m a.g.l. to 1 hPa. The deep convection
is explicitly resolved and the microphysical processes are governed by the ICE3
one-moment bulk microphysical scheme . Six water species are
predicted by AROME-WMed (water vapour, rain, cloud liquid droplets, snow, pristine ice
and graupel). The particle size distributions (PSDs) are expressed as generalized gamma
distributions multiplied by the total number concentrations. PSDs are reduced to
exponential distributions for snow, graupel and rain.
The analyses of the ARPEGE global operational NWP model are used to provide boundary
conditions. AROME-WMed has a 3 h 3D variational (3DVar) data assimilation system
based on an incremental formulation . The
control variables of this system are temperature, specific humidity, surface pressure,
vorticity and divergence. The resolution of the analysis grid is the same as that of
AROME-WMed. Following the results of , the incremental analysis
update IAU; is not used for the 3DVar assimilation scheme.
Background error covariances were computed using a period characterized by convective
systems in October 2010 over the northwestern Mediterranean region .
Every 3 h, an analysis is computed by using all observations available within a ±1 h 30 min assimilation window and a 3 h forecast is produced to provide a background
for the next cycle. The assimilation system ingests a wide variety of observations from
satellite, ground-based GPS, aircraft, radiosondes, drifting buoys, balloons and wind
profilers, automatic land and ship weather stations, and ground-based precipitation
radars of the French network ARAMIS (reflectivity and radial velocity). The purpose of
this study is to assess the impact of the assimilation of RASTA data in addition to this
already dense observing network.
RASTA data pre-processing
RASTA data are discarded between 250 m above and 250 m below the aircraft,
which is the minimal measuring range of the zenith- and nadir-pointing
antennas. Ground clutter is also removed. To reduce observation and
representativeness errors, RASTA data are interpolated in the model vertical
and horizontal resolutions. For the reflectivity measurements, this
interpolation is done by taking the average value (in mm6 m-3) of
all data available along the aircraft track within a box of 2.5 km length
between the two half model levels surrounding each model level. From a given
range from the radar, when the aircraft roll and/or pitch angles are greater
than a threshold (|roll| >7∘ at 10 km range), some measurements
might come out of the grid box of interest . Therefore,
these data are removed from the interpolation. The same interpolation is done
for the retrieved horizontal wind component except that a median filter is
employed. Indeed, applying a median filter instead of averaging allows us to
reduce the influence of outliers, due to the difficulty of having
high-quality measurements for airborne Doppler radar .
After this pre-processing, a thinning is applied to RASTA data to decrease
observation density and satisfy assumptions about observation error
covariances, which are supposed to be 0 dB2. It is particularly true for
measurements made by different instruments, which have independent physical
errors. However, this hypothesis might be no more valid if the observations
are collected very close to each other by the same instrument. Applying a
thinning to the observations is therefore necessary for having satisfactory
assumptions about observation error covariances .
Therefore, RASTA data are assimilated every 3 time steps, which is equivalent
to a distance of approximately 5 to 9 km depending on the aircraft speed.
The data are not thinned vertically because the vertical forecast error
covariances are less marked than the horizontal ones
and it is thus not useful to apply any thinning in that case
.
Here, we employ the 1D+3DVar assimilation method
used operationally to assimilate ground-based
precipitation radar data in AROME. This data assimilation technique allows us to shift a
pattern that was well simulated by the model but at a wrong location. It relies on the
ability of the model to create consistent moisture and reflectivity profiles. Indeed,
cloudy areas are generally associated with relative humidity close to the saturation and
high reflectivity values. This method is particularly well suited for vertically pointing
radars because the first step of the assimilation method is based on the differences
between different vertical profiles of reflectivities. For instance, since March 2016,
this assimilation method is operationally employed to assimilate vertical profiles of DPR
reflectivity data in the Japanese kilometre-scale NWP model (JMA-NHM) .
The first step consists of a 1-D Bayesian retrieval of the best estimate of relative
humidity (RH) profiles, named hereafter pseudo-observations (PO), given the observed
vertical profile of reflectivity Zo. For each observed column of reflectivity
Zo, the corresponding vertical profile of RH pseudo-observation
yPORH is given by
yPORH=∑ixiRHexp-12JPOxi∑jexp-12JPOxj,
with
JPO=1no∑knoZok-Hzxk-bk2σo2,
where
subscript i denotes the index of the model profile in the vicinity of the
observed profile of reflectivity;
xiRH is the vertical column of relative humidity from the model background;
Hzxk is the simulated reflectivity (in dBZ) at the model
level k, given the model state xk; Hz being the forward operator;
no is the number of valid observed reflectivity data in the column;
bk is the bias correction value used at the altitude k (in dB),
described in Sect. ; and
σo is the standard deviation of observation and forward operator errors (in dB).
The W-band reflectivity forward operator Hz described by is used
to simulate the reflectivity. It is consistent with the ICE3 one-moment microphysical
scheme of AROME and takes the hydrometeor contents of the five hydrometeor species (rain,
snow, graupel, cloud liquid water and pristine ice), temperature, pressure and relative
humidity as input parameters. The T-matrix method is employed to
compute the single scattering properties. Following the results of ,
the graupel axis ratio is set to 0.8, snow axis ratio to 0.7 and pristine ice axis ratio
to 1. The forward operator returns the simulated reflectivity at each range gate from the
radar and accounts for hydrometeors and water vapour attenuation.
According to Eq. (), for each observed vertical profile
Zo, the vertical column of RH pseudo-observation is a linear
combination of the neighbouring RH profiles taken from the model background
xiRH.
The xiRH neighbouring profiles are located in a 160 km wide square centred
on the aircraft location. For the AROME-WMed model, this size is sufficient to reduce the
effects of spatial mismatches between model and observations and to
gather a database of xiRH which are consistent with the meteorological
situation. In addition, the xiRH profiles would become less representative
with a larger size since meteorological environments can change over ≈100 km.
In Eq. (), the xiRH profiles are weighted by a
function (JPO) of the difference between the observed
Zo and simulated Hzxi column of reflectivities
(cf. Eq. ). Thus, larger weights are given to the
neighbouring columns that most closely resemble the observations. To ensure
equivalent weights regardless of the number of altitude levels used for each
neighbourhood profile, the square difference in Eq. ()
is divided by the number of valid data over the observed column of
reflectivity.
The square difference is also divided by the observation error variance
σo2. A small σo will favour the neighbouring columns
that most closely resemble the observation. However, if there is no simulated profile of
reflectivity which is close enough to the observed one, there will be no retrieval since
the weight tends towards a value close to 0. Hence, a small σo either
leads to an accurate retrieval or to no retrieval at all. On the other hand, a large
σo would give similar weights and smooth the neighbourhood
xiRH profiles, regardless of the extent to which they resemble the observed
profile of reflectivity . Therefore, a sensitivity study is performed
in Sect. to σo values.
The Bayesian retrieval is not applied in the case of clear sky, i.e. when all the
reflectivities over the whole vertical column are below the radar sensitivity in both the
simulations and the observations. However, if the simulations indicate cloud or
precipitation, the closest “clear-sky” profile in the vicinity of the radar is selected
for the retrieval.
In the second step of the 1D+3DVar assimilation approach, the retrieved
vertical profiles of relative humidity pseudo-observations yPORH are
assimilated in the 3DVar assimilation system of AROME-WMed, like any other
conventional observations.
Bias correction
The Bayesian retrieval can also be applied to
other variables using the same weights as those from the retrieval of RH
profiles (in Eq. ), for example to retrieve
reflectivity pseudo-observations yPOZ:
yPOZ=∑iHzxiexp-12JPOxi∑jexp-12JPOxj.
The reflectivity pseudo-observation yPOZ can be used as an indicator to
evaluate the quality of the 1-D Bayesian retrieval and to estimate the biases that can
arise between observations and simulations. Indeed, in data assimilation it is necessary
to have unbiased quantities and to remove these systematic errors . These biases can arise from the observations, the ICE3 microphysical scheme
and/or the forward operator formulations. showed that the biases
between observations and simulations depend on the temperature and on the altitude. Since
this study is focused on one specific area of interest during the same season, the bias
is mainly a function of the altitude. The bias is also a function of the error standard
deviation σo. Indeed, while a small value of σo favours
the simulated columns that most closely resemble the observations, a larger value of
σo smooths the simulated reflectivity profiles.
Therefore, a bias b was determined from the statistics between
Zo and yPOZ using the altitude and the error
standard deviation σo as predictors. Calculations were
performed for all flights during the HyMeX-SOP1 every 4 time steps. The
background states of a control (CTRL) experiment were used as a
reference to simulate the reflectivity pseudo-observations and to estimate
the biases. The CTRL experiment was run during a 45-day cycled period from
00:00 UTC 24 September 2012 – which is the day when the Falcon 20 first
flew during HyMeX-SOP1 – to 5 November 2012, after the last flight. It
includes all the observations that are operationally assimilated (see
Sect. ).
The bias between RASTA observations and the reflectivity pseudo-observations is depicted
in Fig. as a function of the altitude for different values of
σo (see legend). Calculations were only performed if both the
observation and the reflectivity pseudo-observation are above the radar sensitivity. The
number of observations used for the calculations is also shown in Fig. b.
This number is smaller for small values of σo (red curve) because it
constrains the amount of retrieved profile of reflectivity pseudo-observations to only
those which most closely resemble the observations.
Bias (a) between RASTA reflectivity and the reflectivity
pseudo-observation as a function of the altitude for different values of error standard
deviation σo (see legend box). Panel (b) shows the number of
observations used for the calculations as a function of the altitude for the different
values of error standard deviation σo.
Figure shows that the bias increases with the altitude, which is consistent
with the existence of model biases in cloudy areas in the ICE3 microphysical scheme of
AROME-WMed . Figure highlights the fact
that, because of the smoothing effect, the bias increases with the error standard
deviation σo. Indeed, at approximately 6 km altitude, the bias can
reach up to 6 dB if σo equals 9 dB, and only ≈1.5 dB if
σo equals 2 dB.
The effect of the bias correction is shown in Fig. , in which a contoured
frequency by altitude diagram (CFAD) of the differences between the observed reflectivity
and the bias-corrected reflectivity pseudo-observations are shown for a
σo of 2 dB. The residual bias is indicated by the black line.
Figure demonstrates that, after applying the bias correction in
Eq. (), the residual bias is close to 0 dB except above an
altitude of approximately 10 km, which is probably due to the smaller number of points
used to calculate the bias correction. As explained by , the use of
additional predictors, such as temperature or hydrometeor contents, could lead to an
improvement in the bias correction at higher altitude.
Contoured frequency by altitude diagram (CFAD) of the differences between the
observed reflectivity and the bias-corrected reflectivity pseudo-observations. The
residual bias after applying the bias correction is indicated by the black line.
Observation error within the Bayesian inversion
As explained in Eq. (), the quality of the 1-D Bayesian retrieval
relies on the specification of standard deviation of observation and forward operator
errors σo (in dB). In-flight water vapour mixing ratio measurements,
ro, are available at the flight level and can be used to estimate
σo and to evaluate the quality of the retrieval. These data present the
advantage of being completely independent from the retrieval and they allow the
evaluation of the humidity pseudo-observations which will then be assimilated in the
3DVar assimilation system of AROME-WMed.
The 1-D Bayesian retrieval is applied to the CTRL background states for error standard
deviations σo ranging from 0.6 to 9 dB. The bias correction, which has
been calculated for each σo in Sect. , is
applied in Eq. (). The retrieved pseudo-observations,
rm, of water vapour mixing ratio at the flight level are then compared with
the in-flight measurements, ro, over 32 flights of the HyMeX SOP1. The
comparison is done as follows. First, a manual data quality control is applied to in situ
humidity observations in order to remove the poor-quality measurements that can arise
from instabilities or periods of malfunctioning during the flights. After this quality
control, 24 flights out of 32 remain. Second, water vapour mixing ratio measurements are
averaged over 12 time steps to reduce observation noise and representation errors.
Figure shows the standard deviations (Fig. b) and biases
(Fig. a) between the observed in-flight water vapour mixing ratio and the
retrieved ones (red curve) as a function of the error standard deviations
σo. The standard deviations between in-flight measurements and the
background water vapour mixing ratio are also represented by the black data points.
Standard deviations (b, in g kg-1) and biases
(a, in g kg-1) of the water vapour mixing ratio differences
between in-flight measurements, rq, and the retrieved ones,
rm (red), as a function of the error standard deviations
σo (in dB). The standard deviations and biases before
applying the 1-D Bayesian retrieval are represented by the black data
points.
The standard deviation values in Fig. demonstrate that the
retrieved water vapour mixing ratios are always in better agreement with the
in-flight measurements compared to the background state. This improvement
highlights the ability of the 1-D Bayesian method to retrieve humidity fields
that are closer to independent observations. The variation in the standard
deviation indicates the existence of an optimal value of σo
of approximately 2 dB. Indeed, below 2 dB, the standard deviation increases
with decreasing σo. This is due to the tendency of the
retrieval to be more selective for small values of σo, which
results in using the background state instead of applying the retrieval. On
the contrary, above 2 dB, the standard deviation increases with
σo. Indeed, a large σo increases the number
of successful inversions, but smooths them to produce the resulting humidity
pseudo-observations. Finally, it should be noted that the bias is also
improved with a σo of 2 dB
(Fig. a). Hence, we decided to
use an error standard deviation σo of 2 dB for the rest of
this study.
Time–height cross section of the reflectivity observed by RASTA (a),
simulated from the background (b) and pseudo-observations (c). The
differences between the RH pseudo-observations and the relative humidity from the
background state are shown in (d). Aircraft altitude is indicated by the black
line.
Data assimilation experiments
To assess the potential of RASTA data to improve analyses and forecasts of
heavy precipitation events, a total of 4 experiments is conducted. The CTRL
experiment includes all the observations that are operationally assimilated
(see Sect. ). Three additional RASTA experimental designs
(Z, V, ZV) share the same configuration as CTRL, except that they also
include the assimilation of RASTA data. The reflectivity observed by RASTA is
assimilated alone in the Z experiment, and jointly with RASTA horizontal wind
components in the ZV experiment. The 1D+3DVar assimilation method described
in Sect. is employed to assimilate RASTA
reflectivity observations in the Z and ZV experiments. In addition, the V
experiment includes the assimilation of RASTA wind data alone. As explained
by , the assimilation of RASTA wind data is
straightforward and does not require the use of a radial wind observation
operator. Indeed, the Doppler multi-beam antenna system of RASTA allows the
retrieval of the horizontal wind components (u, v), which are directly
linked to two control variables of the AROME model (vorticity and
divergence).
RASTA data are not measured simultaneously but over the flight leg.
Consequently, at each assimilation cycle, the 3DVar assimilation system of
AROME-WMed ingests all the RH pseudo-observations and/or RASTA wind data
available during a 2 h assimilation window centred on the assimilation time
T, as if they were valid at time T. A larger assimilation window
increases the number of observations and results in larger coverage. However,
since RH pseudo-observations vary with convective systems, which can evolve
quickly in time, a larger assimilation window would result in assimilating
data that are no longer valid at the current assimilation time T. Besides,
conducted a sensitivity study to the length of the
assimilation window by assimilating airborne Doppler wind radar data in the
3DVar assimilation system of AROME-WMed. Results indicated that, even though
the best scores were reached with a 3 h assimilation window, a slight
positive improvement of the 8 h precipitation forecasts was also evidenced
with a 2 h assimilation window. Therefore, a 2 h assimilation window is a
good compromise to assimilate a larger number of observations, which are
nearly valid at the assimilation time, without adding any detrimental
observation in the assimilation system. Hence, the length of the assimilation
window has been set to 2 h in this study.
Relative humidity (RH, in %) for (a)–(e): the
pseudo-observations, the CTRLIOP7, the ZIOP7, the VIOP7 and
the ZVIOP7 experiments.
The observation errors for the RH pseudo-observations
yPORH and RASTA wind data are the same as the one used
for the radiosonde measurements. It is set to 12 % for the RH
pseudo-observations. RASTA wind observation error increases with the altitude
from approximately 1.8 m s-1 at 900 hPa to approximately
2.5 m s-1 at 200 hPa. Finally, in addition to the pre-processing
described in Sect. , a quality control is also
performed prior to the assimilation: observations with innovation
(observations minus background) greater than
a threshold are rejected. This threshold depends on both the observation and
background errors. It has a constant value of approximately 55 % for the RH
pseudo-observations. It increases with the altitude for RASTA wind data
because the error standard deviation is a function of the altitude
(approximately 25 m s-1 at the maximum).
Wind speed (m s-1) for (a)–(e): the observations, the
CTRLIOP7, the ZIOP7, the VIOP7 and the ZVIOP7
experiments.
The four different experiments are first conducted for a heavy precipitation
event which occurred during the intensive observing period 7a (IOP7a) over
south-eastern France on 26 September 2012. During this case study, RASTA data
were collected during flight 15 between 06:10 and 09:45 UTC (red line in
Fig. ). Therefore, RASTA data are assimilated for the first time
at the 06:00 UTC analysis. The different experiments are named
CTRLIOP7, ZIOP7, VIOP7 and ZVIOP7.
They share the same background field to compute the 06:00 UTC analysis. They
start at 00:00 UTC 26 September 2012 and end at 12:00 UTC
26 September 2012. Next, in order to study the impact of the assimilation of
RASTA data in various conditions during the whole HyMeX-SOP1, the four
experiments are run for the 32 analysis cases in which RASTA data are
available. For this configuration, the CTRL experiment is the same as the one
used in Sect. , which was run during a 45-day cycled
period from 00:00 UTC 24 September to 5 November 2012. For the sake of
simplicity, the CTRL experiment is named CTRLSOP1. The three RASTA
experiments are respectively named ZSOP1, VSOP1 and
ZVSOP1. In order to disentangle the cycling effect from the impact
of the assimilation of RASTA data on the analyses, the ZSOP1,
VSOP1 and ZVSOP1 experiments are not cycled and share the
same background fields as the CTRLSOP1 experiment ones.
The 12 h accumulated rainfall between 06:00 and 18:00 UTC 26 September 2012
(IOP7a) for radar observations, the CTRLIOP7, the ZIOP7, the
VIOP7 and the ZVIOP7 experiments.
Impact on the IOP7a case study
To assess the potential of RASTA observations to improve short-term forecasts, focus is
first concentrated on one of the most significant precipitation events which occurred
during IOP7a. More than 100 mm of rain was observed between 00:00 UTC on 26 September
and 00:00 UTC on 27 September in the area delimited by the red box in Fig. . As mentioned in Sect. , RASTA data are
assimilated for the first time at the 06:00 UTC analysis in the ZIOP7,
VIOP7 and ZVIOP7 experiments. Most of these data are located upwind
of where the rainfall event took place later in the morning at approximately 08:00 UTC.
Such a configuration is required to evaluate the impact of the assimilation of RASTA data
to improve heavy precipitation events.
1-D Bayesian retrieval
As explained in Sect. , the first step to
assimilate the reflectivity consists of a 1-D Bayesian retrieval of RH
pseudo-observation profiles, given the vertical profile of reflectivity
observed by RASTA. Since no direct RH observations are available with such a
high vertical resolution as the one of RASTA data, the method is validated by
comparing the reflectivity pseudo-observations yPOZ with RASTA
Zo observations. Figure shows RASTA observations
(interpolated on the vertical grid model; (a), the simulated profile of
reflectivities from the background (b) and the retrieved reflectivity
pseudo-observations (c). The differences between the RH pseudo-observations
and the background RH profiles are also shown in panel (d). Differences are
displayed in red (blue) if RH pseudo-observations are larger (smaller) than
the background.
Figure highlights the capability of the 1-D Bayesian method to
retrieve profiles which are in better agreement with the observations than
the background. For example, at approximately 06:30 UTC, the observation
profiles indicate clouds below an altitude of 6 km, as opposed to the
simulated profiles from the background which only indicate clear-sky
profiles. This has been rectified in the reflectivity pseudo-observation
profiles, and in the corresponding RH pseudo-observation profiles. Indeed,
the RH pseudo-observation values are larger than the background RH values
(red values in d), and are thus more representative of the presence of
cloud. Inversely, at approximately 06:25 UTC, the Bayesian retrieval has
been able to remove the low-level clouds present in the background, and to
add clouds above an altitude of about 4 km. Between 06:50 and 07:00 UTC,
the reflectivity pseudo-observations are also in much better agreement with
the observations than the background. The corresponding RH
pseudo-observations values are also larger than the background, which is
consistent with the fact that larger RH values are usually associated with
larger reflectivity values. Hence, Fig. demonstrates the
ability of the Bayesian retrieval to pick up vertical profiles in the
neighbourhood which are more consistent with the observations. Indeed, this
retrieval successfully dried areas associated with low reflectivity values,
and moistened areas associated with high
reflectivity values. These retrieved RH pseudo-observation profiles are then
assimilated in the 3DVar assimilation system of AROME-WMed in the
ZIOP7 and ZVIOP7 experiments.
Impact on analyses
Figure shows (from the top to the bottom
panels), the relative humidity for the pseudo-observations, the CTRLIOP7, the
ZIOP7, the VIOP7 and the ZVIOP7 analyses. Similarly,
Fig. represents the wind speed for the observations and the different
experiments. The four different analyses were computed using the same background state.
Temperature NT(a), surface pressure
Np(b), humidity Nv(c) and kinetic
Ns(d) contribution terms of the exergy distance as a
function of the altitude for the ZSOP1 (red curve), VSOP1
(blue curve) and ZVSOP1 (black curve) analyses.
As shown in Figs. a and a, the number of RH pseudo-observations
which have been assimilated is larger than the number of RASTA wind data. Indeed,
contrary to RASTA wind data, the reflectivity is also assimilated in the case of clear
sky. Besides, airborne Doppler velocity measurements are contaminated by the aircraft
motion (roll/pitch/drift angles, ground speed, etc.). Therefore, because of the
difficulty to have high-quality measurements , RASTA wind data have
been more frequently rejected (cf. between 06:42 and 06:50 UTC in Fig. ).
In addition, contrary to the W-band reflectivity measurements, RASTA horizontal wind
components are obtained through a retrieval, which might also explain the smaller number
of assimilated horizontal wind data. Finally, since RH pseudo-observations are
assimilated in the same way as radiosonde observations are
(cf. Sect. ), they are rejected above an altitude of
approximately 9 km because the values are very small.
Compared to the RH pseudo-observations in Fig. , RH is overestimated in the
CTRLIOP7 and in the VIOP7 analyses. Except at approximately 8 km of
altitude, the RH profiles are much more similar to the RH pseudo-observations in the
ZIOP7 and ZVIOP7 analyses. Conversely, in Fig. , the
VIOP7 and ZVIOP7 analyses are in much better agreement with RASTA
wind observations compared to the CTRLIOP7 and the ZIOP7 analyses.
Figure shows that the VIOP7 analysis is very similar to the
CTRLIOP7 one in terms of humidity. Similarly, in Fig. , the
ZIOP7 analysis is very similar to the CTRLIOP7 one in terms of wind
speed. Therefore, the assimilation of RASTA wind data (respectively RH
pseudo-observations) does not impact the humidity (respectively wind) field in the
analysis, probably because wind and humidity are not highly correlated in the
assimilation process through the background error covariances. However, the joint
assimilation of the RH pseudo-observations with RASTA wind data (ZVIOP7
experiment) results in a positive impact in terms of both the wind and the humidity
fields.
Impact on rainfall forecasts
Figure shows the 12 h accumulated rainfall between 06:00 and
18:00 UTC 26 September 2012 (IOP7a) for radar observations, the
CTRLIOP7, the ZIOP7, the VIOP7 and the
ZVIOP7 experiments.
First, the predicted rainfall pattern is well reproduced in the four
different experiments. As shown by , the maximum
rainfall accumulation is overestimated in the CTRLIOP7 experiment
(≈142 mm versus 93 mm in the radar observations), but is better
reproduced in the ZIOP7 experiment (130 mm). In addition, the
assimilation of RH pseudo-observations jointly with RASTA wind data in the
ZVIOP7 experiment also results in a decrease (133.5 mm) of the
predicted maximum rainfall accumulation. Finally, the experiment in which
RASTA wind data are assimilated alone in the VIOP7 leads to the
better agreement with the radar observations. Indeed, the maximum rainfall
forecast accumulation has been reduced to only 118 mm.
Results on the HyMeX SOP1
The impact of the assimilation of RASTA data is now assessed over
the 32 cases in which RASTA data were assimilated during the HyMeX-SOP1. In order to use
the same background fields, we use the ZSOP1 , VSOP1 and
ZVSOP1 experiments, which are not cycled. An exergy-distance-based approach
is first employed to measure the relative impact of the assimilation
of RASTA observations on the analysis and forecast fields. Then, the added value of the
assimilation of RASTA data on the analyses is evidenced by using independent humidity
measurements. Finally, the subsequent forecasts are validated against rain-gauge
measurements.
Kinetic (Ns; c and d) and
humidity (Nv; a and b) contribution terms of
the exergy distances as a function of the forecast term over land
(a, c) and over sea (b, d) for the ZSOP1 (red
curve), VSOP1 (blue curve) and ZVSOP1 (black curve)
experiments.
Impact study using an exergy-distance-based approach
The moist-air available-enthalpy (exergy)
distance is first briefly described, and then calculated
to measure the relative impact of the assimilation of RASTA data on analyses
and short-term forecasts.
The moist-air available-enthalpy (exergy) distance
Traditionally, the impact of the assimilation of a new observation type and its
synergistic effect with other observations are assessed through verification scores of a
long data-denial assimilation experiment . These approaches are very
expensive from a numerical point of view. The new type of observation needs to be
assimilated in a large number of analysis cases, which is not affordable for airborne
radar measurements since the availability of the new observations depends on the aircraft
flights. By contrast, energy-based approaches are
cost-effective methods for evaluating the impact of the assimilation of a new observing
system in a NWP model. The idea is to combine thermodynamic variables of the atmosphere
into a model space-based measure , which avoids the use of long
data-denial experiments and adjoint-based methods that rely on strong linearity
assumptions which are not valid at the convective scale. These approaches provide a
measure of the relative impact of the observations on the analysis and forecast fields.
For example, employed the moist total energy norm
MTEN; to evaluate the loss of quality in the forecasts when an
observation type is not assimilated. A similar methodology was employed by
to characterize model errors in winds, temperature, humidity and
precipitation.
Based on results of , defined a
moist-air available-enthalpy (exergy) distance, which provides a more general
and comprehensive metric between a perturbed thermodynamic state (here the
RASTA experiments) and a reference one (here the CTRL experiment). It is
defined by the integration over the 2-D domain of the sum of four quadratic
terms in horizontal wind components U,V (Ns), temperature T
(NT), surface pressure ps (Np) and water vapour mixing
ratio rv (Nv). The four contribution terms of the
exergy distance are then given by
4NT=∫DCpdTr2TCTRL-Ti2TCTRL‾2dD,5Np=∫DRdTr2psCTRL-psi2psCTRL‾2dD,6Nv=∫DRvTr2rvCTRL-rvi2rvCTRL‾dD,7Ns=∫DUCTRL-Ui2+VCTRL-Vi22dD,
where the superscript i denotes the RASTA experiments (ZSOP1,
VSOP1 or ZVSOP1), Cpd is the specific
heat of dry air, Rd is the dry air constant, Rv is
the water vapour gas constant and Tr is the reference temperature
(taken to be 300 K). The total exergy distance is then given by the sum of
the four quadratic terms NT, Np, Nv and Ns.
Standard deviation (g kg-1) of the water vapour mixing ratio differences
between in-flight humidity measurements ro and the analyses rm
for the different experiments (CTRLSOP1, ZSOP1, VSOP1 and
ZVSOP1) over the 24 analyses. The standard deviation differences between the
measurements and the background state are also shown by the black data points
(N=6307).
In Eqs. (), () and (),
the contribution terms of the exergy distance are divided by the weighting
factors TCTRL‾, psCTRL‾
and rvCTRL‾, which correspond to the average
values over the 2-D domain of TCTRL, psCTRL
and rvCTRL, respectively. Hence, as defined by
, the weighting factors TCTRL‾
and rvCTRL‾ are a function of the altitude,
where an arbitrary factor “ϵ” was introduced but with unknown
values between 0.1 and 10. This
arbitrariness is removed by where
rvCTRL‾ varies significantly with height,
since the water vapour mixing ratio decreases by 3 orders of magnitude
between the surface and the stratosphere. Therefore, moisture analysis and
forecast impacts between the different atmospheric levels are fully taken
into account through the use of these altitude-dependent weighting factors.
Hence, the use of the exergy distance is expected to more fairly rank the
different observing systems through the use of more balanced contributions
between wind, temperature and water vapour.
Differences in the average Heidke skill score
(HSS) of the 6 h cumulated precipitation forecasts versus rain gauge
measurements, between the three RASTA experiments and the
CTRLSOP1 experiment (a) ZSOP1,
(b) VSOP1 and (c) ZVSOP1. Calculations
were performed over the 32 runs in which RASTA data were assimilated. The
error bars represent the 95 % bias-corrected and accelerated (BCa)
bootstrap confidence intervals (see ).
In this study, the four different contribution terms of the exergy distance
will be studied independently in order to evaluate the respective impact of
the assimilation of the RH pseudo-observations and/or RASTA wind components
on temperature (Eq. ), surface pressure
(Eq. ), water vapour mixing ratio (Eq. )
and wind (Eq. ) fields.
Impact on analyses
The temperature NT, surface pressure Np, humidity Nv and kinetic
Ns contribution terms of the exergy distance are calculated over the domain
defined by the black box in Fig. . Figure represents NT
(a), Np (b), Nv (c) and Ns (d) as a function of the altitude
for the ZSOP1 (red curve), VSOP1 (blue curve) and ZVSOP1
(black curve) analyses. The different contribution terms are averaged over the 32
analyses in which RASTA data have been assimilated.
First, Fig. demonstrates that the assimilation of RH pseudo-observations
and/or RASTA wind data has a small impact on the temperature NT (a) and surface
pressure Np (b) contribution terms of the exergy distance. Indeed, even though there
is a correlation between the different variables through the background error covariance
matrix , there is a larger impact on the contribution terms
(Nv and/or Ns) that are associated to the variables (wind and/or
humidity) directly linked to the assimilated observations. On the analyses, the
experiment which has the smallest impact on NT (a) and Np (b) is the
VSOP1 experiment (blue curve), followed by the ZSOP1 experiment.
However, this rank order is reversed after only 1 h forecast (not shown). The larger
impact on NT and Np is obtained if RH pseudo-observations are assimilated jointly
with RASTA wind data (ZVSOP1 experiment, black curve).
As expected, since RH pseudo-observations are linked to the humidity fields, the impact
of the assimilation of RH pseudo-observations (ZSOP1) is larger on
Nv than on the other contribution terms. Similarly, since RASTA wind
observations are linked to the horizontal wind components, their assimilation
(VSOP1) result in a larger impact on Ns. Next, the assimilation of
RH pseudo-observations (respectively RASTA wind data) does not impact significantly
Ns (respectively Nv). Therefore, humidity and horizontal wind
data do not seem to be highly correlated with one another, which is consistent with the
results of Sect. . A larger impact on both the Nv
and Ns contribution terms is obtained if RH pseudo-observations and RASTA
wind data are assimilated jointly (ZVSOP1, black curve), along with a larger
impact on all the contribution terms. Consequently, this result indicates that the joint
assimilation is required to have an impact on both the wind and humidity fields in the
analyses.
Impact on short-term forecasts
Equations () and () are now integrated over the
two-dimensional domain and the vertical levels for different forecast terms. Results are
only shown for the kinetic (Ns) and humidity (Nv) contribution
terms of the exergy distances because the major differences have mainly been evidenced on
these two terms (see Fig. ). Figure represents Ns
(c and d) and Nv (a and b) as a function of the forecast term over
land (a and c) and over sea (b and d) for the ZSOP1 (red curve),
VSOP1 (blue curve) and ZVSOP1 (black curve) experiments.
Generally, Fig. shows that the impact is larger over sea (b
and d) than over land (a and c). Indeed, ground-based precipitation radar
data (reflectivity and Doppler velocity) are also assimilated over land. Therefore, there
is a lack of wind and humidity observations over sea, which is partly compensated by the
assimilation of RASTA data. This is particularly evidenced for the humidity contribution
term Nv of the exergy distance (a and b panels). However, after 2 h forecast
term, the impact of the VSOP1 and ZVSOP1 experiments on
Ns is of the same order of magnitude over land and over sea.
Except at the analysis time on the humidity contribution term Nv, the impact
of the assimilation of RH pseudo-observations (ZSOP1 experiment) is always
smaller than the impact of RASTA wind data (VSOP1 experiment). This can be
attributed to the fact that the forecast system seems to have a short memory of RH
pseudo-observations, which is consistent with the findings of . The
impact of the assimilation of RASTA wind data has a larger impact on the humidity
forecasts, probably by adjusting large structures, and by modifying in return the frontal
and/or convective features. In addition, the impact is always larger when RH
pseudo-observations are assimilated jointly with RASTA wind data (ZVSOP1
experiment, black curve). This result was expected because the number of assimilated
observations has been increased in the ZVSOP1 experiment. Besides, the
ZVSOP1 experiment seems to take the benefits (or disadvantages) of both the
ZSOP1 and the VSOP1 experiments. The small impact of the
ZSOP1 experiment seems to indicate that it is pointless to assimilate RH
pseudo-observations without modifying the wind field in a consistent way. Finally, the
ZVSOP1 experiment leads to a larger impact on the kinetic contribution term
Ns than on the humidity contribution term Nv. This is can be
explained by the fact that the VSOP1 experiment has more impact on
Ns than the ZSOP1 experiment has on Nv.
To conclude, the relative impact of the assimilation of RASTA data on the
analysis and forecasts fields has been evidenced using the exergy distance.
This impact study highlighted that RH pseudo-observations have a modest
impact on the analyses on the humidity field, which vanishes soon as the
forecast term increases compared to the experiment in which RASTA wind data
are assimilated alone. The impact on the subsequent forecasts is more
important if both data are assimilated jointly. The benefit brought by this
impact will be evaluated in the next sections.
Analyses evaluation: comparisons against in situ measurements
The aim of this section is to assess the added value of the assimilation of RASTA data on
the analyses. The evaluation is not shown against other conventional assimilated
observations, because, as expected, the fit to observations is always better in
CTRLSOP1 than in the RASTA experimental analyses. However, in-flight humidity
measurements at flight level are not assimilated in any of the experiments, and are used
as independent observations to assess the impact of the assimilation of RASTA data on the
humidity analyses. As explained in Sect. , poor quality
measurements are removed for the comparisons. Hence, after the manual quality control,
only 24 analysis cases remain. Figure shows the standard deviation between
humidity mixing ratio measurements and the analysed ones for the different experiments
(ZSOP1, VSOP1 and ZVSOP1) during the 24 analysis cases. The
standard deviation between the measurements and the water vapour mixing ratios from the
background state is also represented by the black data points, which is a constant value
because the same background states are used in all the different experiments.
First, it should be noted that the analysed water vapour mixing ratios are always in
better agreement with the observations compared to the background field, which is quite
reassuring. Next, the standard deviation is slightly larger for the VSOP1 than
for the CTRLSOP1 experiment. Hence, the assimilation of RASTA wind data alone
(VSOP1) does not improve the analysis in terms of humidity, which was expected
because RASTA wind data are only slightly related to humidity, so it is likely that the
humidity analysis field moves away from humidity observations. The experiment that
reduces the standard deviation the most is the ZSOP1 experiment, which
indicates that the assimilation of RH pseudo-observations alone positively impacts the
analysis in terms of humidity.
Even though slightly less pronounced, the joint assimilation of RH pseudo-observations
with RASTA wind data (ZVSOP1 experiment) also leads to an improvement of the
analysed humidity field. The respective impacts of the ZSOP1 and
VSOP1 experiments are both present in the ZVSOP1 experiment.
Therefore, since the standard deviation is slightly larger for the VSOP1
experiment, it seems logical that the standard deviation in Fig. is larger
for the ZVSOP1 experiment than for ZSOP1. In addition, it has been
demonstrated in Fig. that the humidity field in the analysis is more
impacted by the assimilation of RH pseudo-observations (ZSOP1) than RASTA wind
data (VSOP1). Consequently, the ZVSOP1 experiment inherits more from
the benefits of the ZSOP1 experiment than from the disadvantages of the
VSOP1 experiment in the humidity analysis.
Rainfall forecast evaluation
Forecast scores are now validated using the rain-gauges, whose locations are
indicated by the blue markers in Fig. . For the comparisons,
model outputs are interpolated to the rain-gauge station locations using a
linear interpolation. Heidke skill score (HSS) is calculated for the 6 h
accumulated rainfall forecasts for the CTRLSOP1 and the three
RASTA experiments (ZSOP1, VSOP1 and ZVSOP1).
HSS is calculated for the 32 assimilation cases in which RASTA data have been
assimilated. Figure represents the mean HSS differences in the
6 h accumulated rainfall forecasts between the RASTA and the
CTRLSOP1 experiment as a function of the rainfall accumulation
threshold (millimetres). The bootstrap confidence intervals are also shown
for each threshold. They are quite large because HSS has only been calculated
over 32 cases. The impact of the assimilation of RASTA wind data is positive
if the differences are above 0.
Figure indicates that the benefit of the RH pseudo-observations
(ZSOP1) is neutral to slightly positive above the threshold of
approximately 25 mm. Besides, the impact of the assimilation of wind
vertical profiles (VSOP1) is larger than that of RH
pseudo-observations (ZSOP1), especially for the larger rainfall
accumulation thresholds. This is consistent with the fact that the impact of
the RH pseudo-observations is less pronounced than the impact of RASTA wind
data as the forecast term increases (see Sect. ).
Similar results were also obtained in prior studies
. Finally, the best results are obtained for
the ZVSOP1 experiment, which suggests that the accumulated rainfall
forecasts benefit more from the assimilation of the W-band reflectivity
jointly with RASTA wind data. Similar results were also obtained with other
categorical scores (FAR and POD), and for the 9 and 12 h
rainfall accumulation forecasts.
Discussions and conclusions
The primary objective of this article was to assess the impact of the assimilation of
W-band radar reflectivity in a kilometre-scale NWP model, and specifically to improve
analyses and short-term forecasts of heavy precipitation events in the Mediterranean
area. The W-band reflectivity measurements collected by the RASTA airborne Doppler W-band
radar during the HyMeX-SOP1 were assimilated into the 3 h 3DVar assimilation system of
the NWP model AROME. To complement this study, the benefit brought by consistent
thermodynamic and dynamic cloud conditions has also been investigated by assimilating
separately and jointly the horizontal wind measurements retrieved by RASTA. Results of
this study will provide guidance for future observing systems by assessing whether it is
more relevant to improve the current technology for cloud radars measuring horizontal
wind profiles or only reflectivity profiles. The data assimilation experiments were first
conducted for one of the most significant heavy precipitation events of the HyMeX-SOP1
(IOP7a). Then, to cover a larger number of meteorological situations, the different
experiments have been run for the 32 cases in which RASTA data were available during the
HyMeX SOP1.
The 1D+3DVar assimilation method, operationally employed to assimilate ground-based
precipitation radar data in AROME, has been adapted to assimilate the W-band
reflectivity. Vertical profiles of relative humidity are first derived via a 1-D Bayesian
retrieval, and then used as pseudo-observations in the 3DVar assimilation system of
AROME. In order to fully take advantage of the W-band reflectivity in cloudy areas, a
bias correction scheme was applied. The error standard deviation σo was
estimated by minimizing the standard deviation between the retrieved humidity fields and
independent in situ humidity measurements. Results indicate that the best estimate of the
error standard deviation is close to 2 dB. The comparison with in situ humidity
measurements highlighted the ability of the 1-D Bayesian method to retrieve the humidity
field, which are in better agreement with completely independent humidity measurements.
After validating the first step of the 1D+3DVar assimilation method, the
exergy distance was calculated for each experiment to measure the relative
impact of the assimilation of RASTA data on the analyses and the subsequent
forecasts. This method allows one to independently assess the impact of the
new observation type on the temperature, surface pressure, wind and humidity
fields, independently. In particular, this impact study demonstrated that RH
pseudo-observations have a larger impact on the humidity, temperature and
pressure variables on the analyses, compared to the assimilation of RASTA
wind data alone. However, after 1 h forecast, this rank order is reversed,
probably because the forecast system has a short memory of the changes made
by the RH pseudo-observations on the humidity in the analysis. This result is
consistent with the findings of , who employed a similar
method to assimilate CPR data
on-board the CloudSat satellite in the ALADIN NWP model. The impact on the
analyses and forecasts is always larger if the W-band reflectivity is jointly
assimilated with RASTA wind data, probably because the two observations
complement each other and lead to a more consistent thermodynamic and dynamic
cloud or frontal conditions in the initial state. In addition, it has been
demonstrated that the impact of the assimilation of RH pseudo-observations
and/or RASTA wind data is more important over sea than over land, probably
because these areas are poorly covered by the conventional network.
To evaluate the benefits brought by these impacts on the analyses, all assimilation
experiments have been compared by calculating the standard deviation between the humidity
analysis fields and in situ humidity measurements. The comparisons demonstrated that the
experiment in which RH pseudo-observations are assimilated alone improves the analyses in
terms of humidity the most, slightly followed by the experiment in which RASTA wind data
are also assimilated jointly.
Generally, results of this study indicate that the W-band reflectivity leads to a slight
positive improvement in the rainfall precipitation forecasts. Nonetheless, the impact is
even more positive if RASTA wind data are assimilated alone. Finally, the best scores are
reached if the W-band reflectivity is jointly assimilated with RASTA wind data. Even
though for precipitation Doppler radars and for cyclone studies, similar results were
also obtained in prior studies . Consequently,
the results suggest that the joint assimilation of the two observations leads to a slight
improvement of both moisture initial conditions and precipitation forecasts.
In the future, the impact of the assimilation of the W-band reflectivity will also be
investigated for other meteorological situations, such as fog. Indeed, since W-band radar
are very sensitive to cloud liquid water, their assimilation in kilometre-scale NWP model
should improve fog forecasts. In particular, the lower-cost W-band radar BASTA
will be employed during dedicated field campaigns.
The current 1D+3DVar assimilation method requires us to define the error standard
deviation for the retrieved RH pseudo-observations. One perspective might be to prescribe
observation errors that vary in space. In addition, it is possible that the limited
impact of RH pseudo-observations as the forecast term increases is due to the fact that
hydrometeors are not initialized: the condensation consumes the moisture which has just
been injected in the analysis. In a near future, it will be possible to add the
hydrometeor-specific contents in the control variables with a flow-dependent component in
the background-error covariances. Indeed, an EnVar data assimilation system is currently
being developed for the AROME model . The direct assimilation of the
W-band reflectivity should be favoured by this future implementation.
Data availability
RASTA data and aircraft in situ data are all available from
the HyMeX database (10.17616/R3M34X). Rain-gauge data are also
available from the HyMeX database
(https://doi.org/10.6096/MISTRALS-HyMeX.904, ). The
simulation data that support the findings of this study are available from
the corresponding author on reasonable request.
Author contributions
This work was carried out by MB as part of her PhD thesis
under the supervision of VD and OC. JD processed and provided RASTA data. NF
provided the files and setup that are necessary to run the different
assimilation experiments with AROME-WMED. PM provided the formulation of the
exergy distance and helped use it in the impact study. All co-authors
collaborated, interpreted the results, wrote the paper and replied to the
comments from the reviewers.
Competing interests
The authors declare that they have no conflict of
interest.
Special issue statement
This article is part of the special issue “Hydrological cycle
in the Mediterranean (ACP/AMT/GMD/HESS/NHESS/OS inter-journal SI)”. It is
not associated with a conference.
Acknowledgements
This work is a contribution to the HyMeX programme supported by MISTRALS, ANR
IODA-MED grant ANR-11-BS56-0005 and ANR MUSIC grant ANR-14-CE01-0014. This
work was supported by the French national programme LEFE/INSU. The authors
acknowledge the DGA (Direction Générale de l'Armement), a part of the
French Ministry of Defense, for its contribution to Mary Borderies's PhD. The
authors thank SAFIRE for operating the French Falcon 20 research aircraft
during HyMeX-SOP1. Pierre Brousseau, Thibaut Montmerle and Philippe Chambon
are also acknowledged for their technical support and scientific advise. The
authors would also like to thank the two anonymous reviewers for their
comments, which significantly improved the paper.
Review statement
This paper was edited by Eric Martin and reviewed by two anonymous referees.
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