HESSHydrology and Earth System SciencesHESSHydrol. Earth Syst. Sci.1607-7938Copernicus PublicationsGöttingen, Germany10.5194/hess-21-5459-2017Impact of multiple radar reflectivity data assimilation on the
numerical simulation of a flash flood event during the HyMeX
campaignMaielloIdaida.maiello@aquila.infn.itGentileSabrinahttps://orcid.org/0000-0002-3892-970XFerrettiRossellaBaldiniLucahttps://orcid.org/0000-0001-5217-1205RobertoNicolettaPicciottiErricoAlberoniPier Paolohttps://orcid.org/0000-0003-2107-0289MarzanoFrank SilvioDepartment of Information Engineering, Electronics and
Telecommunications, Sapienza University of Rome, Rome, ItalyCETEMPS, Department of Physical and Chemical Sciences,
University of L'Aquila, L'Aquila, ItalyInstitute of Methodologies for Environmental Analysis, CNR
IMAA, Potenza, ItalyInstitute of Atmospheric Sciences and Climate, CNR ISAC,
Rome, ItalyHimet s.r.l., L'Aquila, ItalyArpae Emilia Romagna, Servizio Idro-Meteo-Clima, Bologna,
ItalyIda Maiello (ida.maiello@aquila.infn.it)7November201721115459547623June201626September201719August201715July2016This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/This article is available from https://hess.copernicus.org/articles/21/5459/2017/hess-21-5459-2017.htmlThe full text article is available as a PDF file from https://hess.copernicus.org/articles/21/5459/2017/hess-21-5459-2017.pdf
An analysis to evaluate the impact of multiple radar reflectivity
data with a three-dimensional variational (3-D-Var) assimilation
system on a heavy precipitation event is presented. The main goal is
to build a regionally tuned numerical prediction model and
a decision-support system for environmental civil protection
services and demonstrate it in the central Italian regions,
distinguishing which type of observations, conventional and not (or
a combination of them), is more effective in improving the accuracy
of the forecasted rainfall. In that respect, during the first
special observation period (SOP1) of HyMeX (Hydrological cycle in
the Mediterranean Experiment) campaign several intensive observing
periods (IOPs) were launched and nine of which occurred in
Italy. Among them, IOP4 is chosen for this study because of its low
predictability regarding the exact location and amount of
precipitation. This event hit central Italy on 14 September 2012
producing heavy precipitation and causing several cases of damage to
buildings, infrastructure, and roads. Reflectivity data taken from
three C-band Doppler radars running operationally during the event
are assimilated using the 3-D-Var technique to improve high-resolution
initial conditions. In order to evaluate the impact of the
assimilation procedure at different horizontal resolutions and to
assess the impact of assimilating reflectivity data from multiple
radars, several experiments using the Weather Research and Forecasting
(WRF) model are performed. Finally, traditional verification scores such
as accuracy, equitable threat score, false alarm ratio, and frequency
bias – interpreted by analysing their uncertainty through bootstrap
confidence intervals (CIs) – are used to objectively compare the
experiments, using rain gauge data as a benchmark.
Introduction
In the last few years, a large number of floods caused by different
meteorological events have occurred in Italy. These events mainly affected
small areas (few hundreds of square kilometres) making their forecast
very difficult. Indeed, one of the most important factors in producing
a flash flood was found to be the persistence of the meteorological
system over the same area in the presence of specific hydrological
conditions (the size and the topography of the drainage basin, the
amount of urban use within the basin, and so on), allowing for the
accumulation of a large amount of rain (Doswell et al., 1996). In complex
orography areas, such the Italian regions, this is largely due to the
barrier effect produced by the mountains, such as the
Apennines. Moreover, the Mediterranean Basin is affected by a complex
meteorology, due to the peculiar distribution of land and water, and due to
the Mediterranean Sea temperature, which is warmer than that of the
European northern seas (Baltic Sea and North Sea). These factors can
produce severe meteorological events: for example, if precipitation
persists over urbanized watersheds with steep slopes, devastating
floods can occur in a relatively short time.
The scientific community widely recognizes the need for numerical
weather prediction (NWP) models to be run at high resolution for
improving very short-term quantitative precipitation forecasts (QPFs)
during severe weather events and flash floods. The combination of NWP
models and weather radar observations has shown improved skill with
respect to extrapolation-based techniques (Sun et al.,
2014). Nevertheless, the accuracy of the mesoscale NWP models is
negatively affected by the “spin-up” effect (Daley, 1991) and is
mostly dependent on the errors in the initial and lateral boundary
conditions (IC and BC, respectively), along with deficiencies in the
numerical models themselves, and at the resolution of kilometres are even
more critical because of the lack of high-resolution observations,
apart from radar data. Several studies in the meteorological field
have demonstrated that the assimilation of appropriate data into the
NWP models, especially radar (Sugimoto et al., 2009) and satellite
ones (Sokol, 2009), significantly reduces the “spin-up” effect and
improves the IC and BC of the mesoscale models. Classical observations
such as TEMP (upper-level temperature, humidity, and wind
observations) or SYNOP (surface synoptic observations) do not have
enough density to describe for example local convection, while radar
measurements can provide a sufficient density of data. Maiello
et al. (2014) showed the positive effect of the assimilation of radar
data into the precipitation forecast of a heavy rainfall event
occurring in central Italy. The authors showed the improvement gained by using
assimilated radar data rather than a conventional approach. Similar
results are obtained for a case of severe convective storm in Croatia
by Stanesic and Brewste (2016).
Weather radar has a fundamental role in showing tri-dimensional
structures of convective storms and the associated mesoscale and
microscale systems (Nakatani, 2015). As an example, Xiao and
Sun (2007) showed that the assimilation of radar observation at high
resolution (2 km) can improve the prediction of convective systems. Recent research in meteorology has established that the
assimilation of real-time data, especially radar measurements (radial
velocities and/or reflectivities), into the mesoscale NWP models can
improve predicted precipitations for the next few hours (e.g. Xiao
et al., 2005; Sokol and Rezacova, 2006; Dixon et al., 2009; Salonen
et al., 2010).
The aim of this study is to investigate the potential of improving NWP
rainfall forecasts by assimilating multiple radar reflectivity data in
combination or not with conventional observations. This may also have
a direct benefit for hydrological applications, particularly for
real-time flash flood prediction and consequently for civil protection
purposes. Major obstacles, which make the assimilation of radar
reflectivities into NWP models a challenging problem both
mathematically and physically, lie in the nonlinear relationship between
radar reflectivity and precipitation intensity as well as in the rapid
evolution of mesoscale systems. While the radial velocities observation
operator is linear and based directly on prognostic model variables
(i.e. wind), the assimilation of radar reflectivity is more
challenging than radial velocity, because the observation operator of
radar reflectivity is highly nonlinear and has a non-Gaussian error
probability density function.
ECMWF (European Centre for Medium-Range Weather Forecasts)
analyses at 12:00 UTC on 14 September 2012: (a) mean sea
level pressure and (c) temperature (colour shades) and
geopotential height (black isolines) at 500 hPa; ECMWF
analyses at 12:00 UTC on 15 September 2012: (b) mean sea
level pressure and (d) temperature (colour shades) and
geopotential height (black isolines) at 500 hPa.
The novelty of the paper is in exploring the impact on the
high-resolution forecast of the assimilation of multiple radar
reflectivity data in a complex orography area, such as central Italian
regions, to predict intense precipitation. This aim is reached by
using the IOP4 of the SOP1 in the framework of the HyMeX campaign
(Ducrocq et al., 2014; Ferretti et al., 2014; Davolio
et al., 2015). The SOP1 was held from 5 September to 5 November 2012;
the IOP4 was issued for the central Italy target area on
14 September 2012 and it was tagged both as a heavy precipitation
event (HPE) and a flash flood event (FFE). The reflectivity measured
by three C-band weather radars was ingested together with traditional
meteorological observations (SYNOP and TEMP) using 3-D-Var to improve the
WRF model performance. So far, several studies about reflectivity data
assimilation in heavy rainfall cases have been performed (e.g. Ha
et al., 2011; Das et al., 2015) also including data of multiple radars
and in complex orography (e.g. Lee et al., 2010; Liu
et al., 2013). However, this is the first experiment conducted on the
Italian territory taking advantage of the reflectivity data collected
by all the radars that cover central Italy.
The paper is arranged as follows. Section 2 provides information
on the FFE and WRF model configuration. Section 3
presents the observations to be assimilated, the WRF 3-D-Var data
assimilation system, and the evaluation method used. The results are
presented and assessed in Sects. 4 and 5. Summary and
conclusions are given in the final section.
Interpolated map of 24 h accumulated rainfall from
00:00 UTC on 14 September 2012 over Abruzzo and Marche regions
taken from the DEWETRA system from rain gauge measurements. Black
contours are the administrative boundaries of regions, while the
coloured circles represent the warning pluviometric thresholds.
Study area and model setup
Flash floods are still one of the natural hazards producing human and
economic losses (Llasat et al., 2013). Moreover, an increasing trend
of the occurrence of severe events in the whole Mediterranean area has
been found by several authors (Hertig et al., 2012; Martin
et al., 2013; Diodato and Bellocchi, 2014). These open issues drove
the HyMeX programme (http://www.hymex.org) aiming at a better
understanding of the water cycle in the Mediterranean with a focus on
extreme weather events. The observation strategy of HyMeX is organized
into long-term (4 years) enhanced observation periods (EOPs) and
short-term (2 months) special observation periods (SOPs). During the
SOP1, which was held from 5 September to 5 November 2012 with the major
aim of investigating still-unclear mesoscale meteorological mechanisms
over the Mediterranean area, three Italian hydro-meteorological sites
were identified within the western Mediterranean target area (TA):
Liguria–Tuscany (LT), northeastern Italy (NEI), and central Italy
(CI). Several intensive observing periods (IOPs) were issued during
the campaign to document HPEs, FFEs, and orographic precipitation events (ORPs).
Rain gauge time series of some selected stations in Marche
(a, Fermo; b, Pintura di Bolognola) and Abruzzo
(c, Campo Imperatore; d, Atri; and e,
Pescara Colli) regions during the event of 14 September 2012. The
green histogram represents the hourly accumulated precipitation
(scale on the left); the blue line represents the incremental
accumulation within the 24 h (scale on the
right). (Courtesy of Italian Civil Protection Department.)
Case study
During the day of 14 September 2012 a deep upper-level trough entered the
Mediterranean Basin and deepened over the Tyrrhenian Sea slowly moving
southeastward. A cut-off low developed over central Italy (Fig. 1a and c)
advecting cold air along the central Adriatic coast producing instability
over central and southern Italy, and enhanced the Bora flow over the northern
Adriatic Sea. Convection with heavy precipitations occurred in the morning of
14 September mainly along the central eastern Italian coast (Marche and
Abruzzo regions), associated with the cut-off low over the Tyrrhenian Sea,
producing flood in the urban area of Pescara (central western coast of
Abruzzo region) where rainfall reached 150 mm in a few hours causing
several river overflows, a landslide, and much damage in the area of the city
hospital. Progressive motion southeastward of the cut-off and its filling
(Fig. 1b and d) gradually moved phenomena over the south of Italy, even if
some instability still remained over the mid-Adriatic until the afternoon of
Saturday 15 September. At the same time, a high-pressure ridge developed in
the western part of the western Mediterranean domain; this ridge slowly
drifts eastwards during the weekend.
Figure 2, produced using the DEWETRA operational platform, shows the
interpolated map of 24 h accumulated rainfall recorded from the
rain gauge network from 14 September to 15 September
(00:00–00:00 UTC) with a maximum accumulated rainfall on the highest
peak of the Abruzzo region (Campo Imperatore) reaching approximately
300 mm in 24 h. DEWETRA (Italian Civil Protection
Department, CIMA Research Foundation, 2014) is an operational web
platform used by the Italian Civil Protection Department (DPC) and
implemented by CIMA Research Foundation
(http://www.cimafoundation.org/en/). DEWETRA allows the synthesis,
integration, and comparison of information necessary for instrumental
monitoring and model forecasting, and to construct real-time risk scenarios
and their possible evolution. Rain gauge time series of some selected
stations in the Marche and Abruzzo regions, where the most significant amount
of rainfall accumulated, are presented in Fig. 3: Fermo and Pintura
di Bolognola (Marche region), respectively, with nearly 130 mm
in 24 h (Fig. 3a) and 180 mm in 24 h
(Fig. 3b); Campo Imperatore, Atri, and Pescara Colli (Abruzzo region)
with respectively nearly 300 mm (Fig. 3c), 160 mm
(Fig. 3d), and 140 mm (Fig. 3e) in 24 h. It is clearly
shown (Fig. 3) that the accumulation started around 02:00 UTC on 14
September: in Fermo, Atri and Pescara Colli most of rainfall was
concentrated in the first half of the day, whereas in Pintura di
Bolognola and Campo Imperatore, precipitation fell all day long. The
large amount of hourly precipitation for Atri and Pescara Colli,
respectively, at 06:00 UTC and 05:00 UTC (red ovals in Fig. 3d and e)
reached 45 mmh-1, indicating convective precipitation,
whereas rainfall at Campo Imperatore rain gauge (Fig. 3c) was much
weaker but lasting longer, reaching an accumulated
amount of approximately 300 mm in 24 h.
Figure 4 shows the vertical maximum intensity (VMI) reflectivity
product from the Italian radar network (Vulpiani et al., 2008a)
superimposed onto the Meteosat second generation (MSG)
10.8 µm image (in normalized inverted greyscale). A close-up
over the central Italy target area highlights a line of convective
cells along the Apennines in central Italy due to the western flow
approaching the orographic barrier. VMI values above 45 dBZ are
associated with intense precipitation that occurred during convective
events.
Close-up over central Italy of the reflectivity on
14 September 2012 at 08:00 UTC from the Italian radar network
overlapped with the MSG (IR 10.8) at 07:30 UTC. (Courtesy
of Italian DPC.)
WRF model setup
The numerical weather prediction experiments are performed in this
work using the non-hydrostatic Advanced Research WRF (ARW) modelling
system V3.4.1. It is a primitive equations mesoscale meteorological
model, with terrain-following vertical coordinates and options for
different physical parameterizations. Skamarock et al. (2008) provide
a detailed overview of the model.
In this study, a one-way nested configuration using the ndown
program is used: a 12 km domain (263×185) that covers
central Europe and the western Mediterranean Basin (referred to as D01) is
initialized using the European Centre for Medium-Range Weather
Forecasts (ECMWF) analyses at 0.25∘ horizontal
resolution; an innermost domain that covers the whole of Italy (referred to
as D02), with a grid space of 3 km (445×449) using as
BC and IC the output of the previous forecast at 12 km. Both
domains run with 37 unequally spaced vertical levels, from the surface
up to 100 hPa (Fig. 5).
WRF ndown domain configuration: the two domains
have resolution of 12 and 3 km, respectively. The high-resolution D02 over Italy includes Mt Midia (MM), ISAC-CNR (P55C)
and San Pietro Capofiume (SPC) radars (red dots in the figure).
Technical characteristics of the three radars whose reflectivity
have been assimilated during IOP4.
FeaturesUnitsMM radarP55C radarSPC radarOwnerCF Abruzzo RegionISAC-CNR of RomeArpae Emilia RomagnaLocationMonte MidiaRomeSan PietroCapofiumeLatitude(deg)42.05741.84044.6547Longitude(deg)13.17712.64711.6236Height (a.s.l.)(m)176013131DopplerYESYESYESDual polarizationNOYESYESRange resolution(m)50075250Half-power beam width(deg)1.610.9Temporal resolution(min)15515Elevations angles used in PPI scans(deg)0, 1, 2, 30.6, 1.6, 2.6, 4.4, 6.2, 8.3, 11.0, 14.60.53, 1.4, 2.3, 3.2, 4.1, 5.0Maximum range(km)120 or 240120125
Taking into account that the performance of a mesoscale model is
highly related to the parameterization schemes, the main physics
packages used in this study are set as for the operational
configuration (Ferretti et al., 2014) used at the Centre of Excellence
CETEMPS (http://cetemps.aquila.infn.it/). They include
(Skamarock et al., 2008): the “New” Thompson et al., 2004,
microphysics scheme, the MYJ (Mellor–Yamada–Janjić) scheme for the
PBL (planetary boundary layer), the Goddard shortwave radiation scheme
and the RRTM (rapid radiative transfer model) longwave radiation
scheme, the Eta similarity scheme for surface layer formulation and
the Noah LSM (land surface model) to parameterize the physics of land
surface. A few preliminary tests are performed to assess the best
cumulus parameterization scheme to be used both for the coarse and
finest resolution domain for this event. Hence, the following
parameterizations are tested: the new Kain–Fritsch and the Grell 3-D
schemes. The latter is an enhanced Dudhia of the Grell–Deveneyi
scheme, in our simulations only used on the lowest resolution domain,
where the option cugd_avedx (subsidence spreading) is
switched on. Based on the results of these two cumulus
parameterization schemes, the one producing the best precipitation
forecast will be used to evaluate the impact of data assimilation.
Data and methodology
This section is focused on the description of types of
observations ingested into the assimilation procedure, namely both
conventional and radar, and on the 3-D-Var methodology as well as the
observation operator used for the calculation of the
reflectivity. Also, a brief overview of the evaluation method
adopted to assess the performance of numerical weather predictions
will be given.
Observations to be assimilated
Conventional observations SYNOP and TEMP were retrieved from the ECMWF
Meteorological Archival and Retrieval System (MARS). They have been
packed in a suitable format for incorporation into the assimilation procedure
using the Observation Preprocessor (OBSPROC) module provided by the
3-D-Var system. Among its main functions are to perform
a quality control check and to assign observational errors based on
a pre-specified error file. In short, a total of 983 observations
(967 SYNOP and 16 TEMP) are incorporated into the coarse-resolution
domain, whereas a total of 338 (333 SYNOP and 5 TEMP) observations are incorporated
into the high-resolution one.
Reflectivities taken from three C-band Doppler radars operational
during the IOP4 have been assimilated to improve IC. The radars have
different technical characteristics and were operated with different
scanning strategies and operational settings as shown in Table 1: each
radar has a half-power beam width of 1.6, 1, and 0.9∘,
respectively, for Monte Midia (MM), Polar55C (P55C), and San Pietro
Capofiume (SPC), and a range resolution of 500, 75, and 250 m.
MM and SPC radars are included in the Italian weather radar network,
while P55C radar is a research radar working on demand, which was
operational during the IOPs of the HyMeX campaign (Roberto et al.,
2016).
It is worth mentioning that radar data can be affected by numerous
sources of errors, mainly due to ground clutter, attenuation due to
propagation or beam blocking, anomalous propagation, and radio
interference. This is the reason why a preliminary “cleaning”
procedure is applied to the measured radar reflectivity from the three
radars before the assimilation process, consisting of the following
three steps:
A first quality check of radar volumes to filter out radar
pixels affected by ground clutter and anomalous
propagation; furthermore, Z was corrected for attenuation using
a methodology based on the specific differential phase shift
(Kdp) available for dual polarization radars (Vulpiani
et al., 2015); moreover, reflectivity is not corrected for partial
beam blocking: all the data that are affected by partial beam
blocking and clutter have been filtered out.
Volume reflectivity radar data are converted from their native
polar coordinates (range, azimuth and altitude) into geographical
Cartesian ones (latitude, longitude and altitude).
The minimum assimilated reflectivity is set to -20 dBZ.
Moreover, no observation thinning is performed because this procedure
has not yet been developed into the 3-D-Var system for radar data. Instead,
an iterative approach has been applied to extract more information
from radar data during the assimilation procedure: this is the
multiple outer loops technique explained in Sect. 4.
3-D-Var data assimilation method
Data assimilation (DA) is a technique employed in many fields of
geosciences, perhaps most importantly in weather forecasting
and hydrology. In this context it is the procedure by which
observations are combined with the product (first guess or
background forecast) of a NWP model and their corresponding
error statistics, to produce a bettered estimate (the
analysis) of the true state of the atmosphere (Skamarock
et al., 2008). The variational DA method realizes this through the
iterative minimization of a penalty function (Ide et al., 1997):
J(x)=Jb(x)+J0(x)=12[y0-H(x)]TR-1[y0-H(x)]+(x-xb)TB-1(x-xb),
where xb is the first guess state vector, y0 is the
assimilated observation vector, H is the observation operator that links
the model variables to the observation variables and x is the unknown
analysis state vector to be found by minimizing J(x). Finally, B
and R are the background covariance error matrix and the
observation covariance error matrix, respectively.
The minimization of the penalty function J(x), displayed by
Eq. (), is the a posteriori maximum likelihood estimate of
the true atmosphere state, given the two sources of a priori data that
are xb and y0 (Lorenc, 1986).
In this study the 3-D-Var system developed by Barker et al. (2003,
2004) is used for assimilating radar reflectivity and conventional
observations SYNOP and TEMP. The penalty function minimization is
performed in a preconditioned control variable space, where the
preconditioned control variables are pseudo-relative humidity, stream
function, unbalanced temperature, unbalanced potential velocity, and
unbalanced surface pressure. Because of radar reflectivity
assimilation is considered, the total water mixing ratio
qt is chosen as the moisture control variable. The
following equation presents the observation operator used by the
3-D-Var to calculate reflectivity for the comparison with the observed
one (Sun and Crook, 1997):
Z=43.1+17.5logρqr,
where ρ and qr are the air density in
kgm-3 and the rainwater mixing ratio in gkg-1,
respectively, while Z is the co-polar radar reflectivity factor
expressed in dBZ. Since the total water mixing ratio qt
is used as the control variable, a warm rain process (Dudhia, 1989) is
introduced into the WRF-3-D-Var system to allow for producing the
increments of moist variables linked to the hydrometeors.
The performance of the DA system strongly depends on the quality of
the B matrix in Eq. (). In this study, a specific
background error statistics is computed for both domains for the
entire SOP1 duration using the National Meteorological Centre (NMC)
method (Parrish and Derber, 1992). This technique estimates the
initial state error using differences of couples of forecasts valid at
the same time, but with one of them having a delayed start time. One
of the advantage of this method is that it maintains information on
the dynamic of the model itself, but it may not give the proper
correlation structure on data-sparse observations. Commonly, for
regional applications and to remove the diurnal cycle, a delay of
24 h between the forecasts (T+24 minus T+12) is used;
nevertheless, this delay can produce overestimated correlation length
scales compared to those needed by a variational data assimilation
technique, because of too dynamically evolved structures (Sadiki
et al., 2000). Since 3-D-Var is applied to the Mediterranean area,
B has to take into account the scale of the motions of this
orographic and meteorologically complex area: the model grid
resolution ranges between 12 and 3 km, therefore the errors
have to describe the physical phenomena relative to these scales.
Evaluation
The Point-Stat Tool of MET (Model Evaluation Tools) application (DTC,
2013), developed at the DTC (Developmental Testbed Centre, NCAR), has
been used to objectively evaluate the 12 h accumulated precipitation
produced by WRF on both domains. The interpolation method used to
match the gridded model output to the point observation is the
distance weighted mean in a 3×3 square of grid points. The
observations used for the statistical evaluation were obtained from
the DEWETRA platform of the Department of Civil Protection and the
comparison has been performed over the central Italy target area using
about 3000 rain gauges with a good coverage throughout the Italian
territory. Moreover, for interpreting results from the verification
analysis bootstrap, confidence intervals (CIs) have been used to
analyse the uncertainty associated with the score's
values. Bootstrapping is a non-parametric, computationally expensive,
statistical technique (Efron and Tibshirani, 1993) for estimating
parameters and uncertainty information, which allows us to make inferences
from data without making strong distributional assumptions about the
data or the statistic being calculated. Therefore, the idea was to
estimate CIs to set some bounds (bootstrap upper and lower confidence
limits) on the expected value of the verification score helping to
assess whether differences between competing forecasts are
significant.
List of experiments to test the impact of data assimilation.
ExperimentCumulusGrid resolutionAssimilation SYNOPAssimilation radar+TEMPCTLGRELL3D+CUGD12KM/3KMNONOCONGRELL3D+CUGD12KM/3KM/BOTHYESNOCONMMGRELL3D+CUGD12KM/3KM/BOTHYESMMCONMMPOLGRELL3D+CUGD12KM/3KM/BOTHYESMM+POLCONMMPOLSPCGRELL3D+CUGD12KM/3KM/BOTHYESMM+POL+SPCCONMMPOLSPC3OLGRELL3D+CUGD12KM/3KM/BOTHYESMM+POL+SPC with 3 outer loopsDesign of the numerical experiments: discussion of the
results
The simulations on the coarser-resolution domain (D01) are run from
12:00 UTC on 13 September 2012 and integrated for the following 96 h,
whereas runs on the finest-resolution domain (D02) started at 00:00 UTC on
14 September for a total of 48 h of integration. The previous
coarser-resolution WRF forecast at 00:00 UTC is used as the first guess in
the 3-D-Var experiment, because 00:00 UTC has been selected as the
“analysis time” of the assimilation procedure. After assimilation, the
lateral and lower boundary conditions are updated for the high-resolution
forecast. Finally, the new IC and BC are used for the model initialization
(in a warm start regime) at 00:00 UTC. As already pointed out, a set of
preliminary experiments are performed using different cumulus convective
schemes to assess the best one to be used. The following experiments are
performed without assimilation and using the convective scheme on the
coarser-resolution domain only: KAIN-FRITSCH (KF_MYJ); GRELL3D
(GRELL3D_MYJ); GRELL3D associated with the CUGD factor (GRELL3D_MYJ_CUGD).
The best performance is obtained by the Grell3D scheme which is able to
simulate the peak of precipitation cumulated in 24 h over Campo
Imperatore, whereas KAIN-FRITSCH completely misses it (not shown here). The
MET statistical analysis supports the previous finding and the simulation
with cugd_avedx activated shows a significant
performance improvement in terms of
uncertainty of the calculated scores than the other two simulations (not
shown). Hereafter GRELL3D_MYJ_CUGD is referred to as the control experiment
(CTL) performed without any data assimilation.
At this point analysis of a new set of simulations is performed
allowing us to establish the best model configuration for the radar
reflectivity assimilation. The DA experiments aim to investigate
the impact of the assimilation at low and high resolution by
assimilating both conventional and non-conventional data at both
resolutions;
the impact of the assimilation of different types of
observations;
the impact of the different radars, which is investigated by
performing experiments by assimilating conventional data and then
adding radar one by one.
Therefore in Table 2, together with CTL simulation, the following DA
experiments are summarized: (i) the assimilation of conventional data
only (CON), (ii) the assimilation of reflectivity data from MM only is added
(CONMM), (iii) the assimilation of P55C radar reflectivity
is added to the previous experiments (CONMMPOL), and (iv) the assimilation
of the third radar reflectivity data is also added
(CONMMPOLSPC). Finally, an experiment to assess the role of the outer
loops is performed (CONMMPOLSPC3OL): to include nonlinearities into
the observation operator and to evaluate the impact of reflectivity
data entering for each cycle, the multiple outer loops strategy is
applied (Hsiao et al., 2012). According to this approach, the
nonlinear problem is solved iteratively as a progression of linear
problems: the assimilation system is able to ingest more observations
by running more than one analysis outer loop, allowing observations
rejected in the previous loop to be entered into the subsequent
one. Since radar data are nonlinearly related to the analysis control
variables, the outer loops method is particularly helpful to extract
more information from such data. For example, over a total amount of
518 400 radar data (considering all the three radars), the fraction
of radar observations assimilated into the 3 km domain is 32 986 at the
first outer iteration, 33 001 at the second outer iteration, and 33 027 at the last one.
In the following section the results will be presented and discussed
following the rationale of the previously introduced experiments and
analysing the uncertainty (confidence level of 95 %) in the
realized scores (forecast accuracy (ACC), frequency bias (FBIAS),
equitable threat score (ETS), and false alarm ratio (FAR)) for performance
quantitative assessment.
Impact of conventional measurements and radar reflectivity
assimilation on rainfall forecast: low vs. high resolution
In Fig. 6, a preliminary comparison among low-resolution (LR)
simulations is shown. The control simulation (CTL) without data
assimilation is shown in Fig. 6a; the other panels
(Fig. 6b–f) show the experiments performed using the data
assimilation.
WRF D01 accumulated 24 h rainfall forecast over
central Italy from 00:00 UTC on 14 September 2012: (a) WRF
D01 CTL; (b) WRF D01 CON_LR_12KM; (c) WRF D01
CONMM_LR_12KM; (d) WRF D01 CONMMPOL_LR_12KM; (e) WRF
D01 CONMMPOLSPC_LR_12KM; (f) WRF D01
CONMMPOLSPC3OL_LR_12KM.
The outputs of different experiments in Fig. 6 have been eyeballed and
we found that CONMMPOLSPC_LR_12KM (black arrow in Fig. 6e) shows the
most encouraging performance compared to the observed accumulated
rainfall of Fig. 2: the rainfall maximum over Campo Imperatore is very
well simulated; however a slight cell displacement at the border
between Marche and Abruzzo regions is noticeable. The rain cumulated
by the gauges in 24 h related to this cell is around
300 mm (Fig. 3c); in the simulations shown in Fig. 6b and f
this cell is reproduced, although its position is shifted in another
region. Furthermore, the precipitation pattern along the northern
coasts of Abruzzo (black oval in Fig. 6e) is also quite well
forecasted. At an objective comparison of the statistical indices (not
shown here) with their relative upper and lower confidence limits for
the 12 h accumulated precipitation and for two thresholds (1 and
40 mm for light and heavy rain regimes, respectively), we
obtained likely good values for ACC and FAR for all the experiments
and for heavy rain regimes, strengthened by a small uncertainty
interval. On the other hand, for the lower threshold the values of
FBIAS for all simulations, considering also the confidence intervals,
are greater than one. One possible interpretation of the impact of the
lower threshold is that with 95 % confidence all the experiments
are overestimating the frequency of precipitation around
1 mm(12h)-1.
Similarly to the above comparison, in Fig. 7 high-resolution results (HR)
obtained performing reflectivity assimilation on the 12 km domain
(column 1), on the 3 km (column 2), and on the 12 and 3 km
together (column 3) are presented; at the top of Fig. 7 the CTL experiment on
D02 is shown. Figure 7 is organized as follows: viewing panels by line, on
line 1 all the simulations with conventional data assimilation only (CON*)
are found; on line 2 all the experiments with the assimilation of the
reflectivity data from MM radar added (CONMM*); on line 3 all the experiments
with the assimilation of the reflectivity data from two C-band radars added
(CONMMPOL*); on line 4 all the experiments with the assimilation of the
reflectivity data from all three C-band radars added (CONMMPOLSPC*); on
line 5 the simulations where the strategy of outer loops is adopted
(CONMMPOLSPC3OL*). In order to quantify the uncertainty associated with these
experiments, the bootstrap 95 % confidence intervals for verification
statistics ACC, FBIAS, ETS, FAR have been summarized over Tables 3–5
reporting the two thresholds of precipitation for light and heavy rain
regimes: 1 and 40 mm(12h)-1, respectively.
WRF D02 accumulated 24 h rainfall forecast over
central Italy from 00:00 UTC on 14 September 2012: CTL simulation
(top centre); on each column are simulations obtained performing
reflectivity assimilation at different resolutions (*12KM, *3KM,
*12KM_3KM); on each line are simulations performed assimilating
different kinds of data (CON*, CONMM*, CONMMPOL*,CONMMPOLSPC*,
CONMMPOLSPC3OL*).
Bootstrap 95 % confidence intervals for verification statistics
forecast accuracy (ACC), frequency bias (FBIAS), equitable threat score
(ETS), false alarm ratio (FAR), and referring to experiments in columns 2 and 3 of
Fig. 7,
respectively. They are considered as a function of thresholds (1 and 40 mm(12h)-1). The experiments are CTL, CON_3KM and
CON_12KM_3KM, CONMM_3KM and
CONMM_12KM_3KM, CONMMPOL_3KM
and CONMMPOL_12KM_3KM,
CONMMPOLSPC_3KM and CONMMPOLSPC_12KM_3KM, CONMMPOLSPC3OL_3KM and
CONMMPOLSPC3OL_12KM_3KM. The numbers in bold font represent the value between bootstrap upper and lower confidence limits.
In order to investigate the impact of the assimilation at different
resolutions, we examine Fig. 7 by column and also compare with the
available observations (Fig. 2) using the statistical analysis:
Column 1 (12KM): CTL produces an overestimation of the rainfall
that is not corrected by the assimilation of conventional data, but
assimilating the reflectivity from the three radars (column 1 line 4)
and also introducing the three outer loops (column 1 line 5) the main
cells are better reproduced. MET indices (not shown here) suggest
that CTL and CON_HR_12KM have the largest difference between the
CIs bounds for higher thresholds of FBIAS: this result suggests that
the remaining simulations, with smallest difference in CI limits
and with both bounds lower than 1, surely underestimate the
frequency of heavy precipitating events. Another aspect to point
out is that some indices for all simulations are quite close to each
other and within the CIs, so it is not possible to discern which is
the best experiment over all.
Column 2 (3KM): a partial correction of the rainfall
overestimation compared to column 1 is observed especially if
reflectivity from all the radars are assimilated together with
conventional data and the outer loops strategy is applied (column 2
line 5); the statistical indices in Table 3 show the most
competitive experiment among the assimilated ones to be the
CONMMPOLSPC3OL_3KM for lower threshold of rain for ACC (0.83) and
FBIAS (0.96). On the other hand CONMM_3KM is the most promising
simulation for heavy rain threshold for the indices FBIAS (0.31) and
ETS (0.13).
Column 3 (12KM_3KM): rainfall overestimation was partially
corrected compared to columns 1 and 2 by all experiments; the MET
statistics in Table 3 shows that CTL and CONMMPOLSPC3OL_12KM_3KM
are the experiments with encouraging values and small uncertainty
for ACC and ETS especially for light rain regimes, although there is
a quite broad spread in FBIAS for CTL experiment (score 0.47, with
a lower and upper CI limit of respectively 0.14 and 1.61) if we
consider higher thresholds.
Bootstrap 95 % confidence intervals for verification statistics
forecast accuracy (ACC), frequency bias (FBIAS), equitable threat score
(ETS), false alarm ratio (FAR), and referring to experiments in lines 1–4 of Fig. 7. They are considered as a function of thresholds (1 and 40 mm(12h)-1).
The experiments are CTL, CON_3KM/CONMM_3KM/CONMMPOL_3KM/CONMMPOLSPC_3KM,
CON_HR_12KM/CONMM_HR_12KM/CONMMPOL_HR_12KM/CONMMPOLSPC_12KM,
CON_12KM_3KM/CONMM_12KM_3KM/CONMMPOL_12KM_3KM/CONMMPOLSPC_12KM_3KM. The numbers in bold font represent the value between bootstrap upper and lower confidence limits.
Bootstrap 95 % confidence intervals for verification statistics
forecast accuracy (ACC), frequency bias (FBIAS), equitable threat score
(ETS), false alarm ratio (FAR), and referring to experiments in line 5 of Fig. 7. They
are considered as a function of thresholds (1 and 40 mm(12h)-1). The
experiments are CTL, CONMMPOLSPC3OL_3KM,
CONMMPOLSPC3OL_HR_12KM,
CONMMPOLSPC3OL_12KM_3KM. The numbers in bold
font represent the value between bootstrap upper and lower confidence limits.
The frequency of rainfall underestimation for higher thresholds found in the
mother domain when radar reflectivity data are assimilated in D01 only has
been reduced by switching to a higher-resolution domain; moreover, the
overestimation of the frequency for lower thresholds has been corrected
because the FBIAS, previously systematically above 1 is found to be
approximately 1 (indices not shown). Furthermore, general improvements
(especially for FBIAS and ETS) come out for heavy rain regimes when radar
reflectivity assimilation has been performed on the highest resolution
domain, whereas the ingestion of conventional observations produces the worst
results for FBIAS and ETS since a smaller number of them were assimilated
into the finest-resolution domain (for instance one sounding out of five
total) than that the coarser one. Data assimilation, operated on both the 12 and
3 km domains, shows similar
performances to the experiments where assimilation is performed only on D01,
but a worse response for higher thresholds (Table 3) than the ones where
assimilation is carried out on D02.
In order to examine the impact of the assimilation of different data
and radars, we can now analyse the experiments shown in Fig. 7 line
by line. The results are compared with the observations of
Fig. 2. The following considerations are worth discussing:
Line 1 (CON): a strong reduction of the rainfall is found with
respect to CTL if conventional data are assimilated, but the
rainfall pattern remains unchanged. Statistical indices of CON
experiment (Table 4) do not improve the performances of CTL (despite
a reduction in some cases of the spread between the CI limits for
higher thresholds of the FBIAS). Some indices values suggest
a slightly better performance when the conventional observations are
assimilated only on the bigger domain and for higher thresholds
(FBIAS 0.49), together with an improvement of FAR index for heavy
rain regime (FAR 0.001).
Line 2 (CONMM): a further reduction in the precipitation
overestimation is found as well as some variations in the pattern of
the rainfall; the scores in Table 4, together with their bootstrap
upper and lower limits, show that MM radar reflectivity and
conventional observations assimilation, improves the model
performance above all for lower thresholds with respect to the
experiments where only SYNOP and TEMP were incorporated. It also applies
for some of the scores at higher thresholds (for example for ETS).
Line 3 (CONMMPOL): a quite strong improvement in the rainfall
amount is found for all simulations. However, from the statistics of
Table 4, we found a general worsening of the results both for light
and heavy rain regimes when POL is added (especially for FBIAS and
ETS, in some cases also for ACC and FAR at lower thresholds).
Line 4 (CONMMPOLSPC): a clear correction of the rainfall pattern
is found; the overestimation produced by the simulation where the
reflectivity from all the radars are assimilated on the 3 km
domain has been corrected by the experiment in which the
reflectivity is assimilated both on D01 and D02; the uncertainty in
the realized scores of Table 4 suggests that the addition of SPC
radar improves the results; furthermore, they are not better than
those where only MM is ingested.
Line 5 (CONMMPOLSPC3OL): the outer loops experiment confirms the
strong overestimation reduction by *12KM_3KM; from Table 5 it seems
that the introduction of 3OL improves the indices estimate and
bounds above all when the 12 km domain is considered (see
FBIAS and ETS for both rain regimes and FAR for lower thresholds).
In summary, simulation results show that assimilation of conventional
data shows better performance on the lowest resolution domain because more
observations were used in the coarser domain, whereas when the
assimilation is performed on the highest-resolution domain only few
SYNOP and even less TEMP fell down in the 3 km domain at the
analysis time of the assimilation procedure. The impact of
conventional observations are expected to be lower than those of non-conventional ones, because most of them have already been used by
ECMWF to produce their analysis and they are here used as first
guess, even if at lower resolution (0.25∘). Therefore, they
result in being correlated with the background and the improvements of
those experiments where they are assimilated are expected to be low.
With regard to the assimilation of reflectivity radar data, it should
be noted that P55C radar observations of the event considered is
shielded at the lowest elevation angles by the Apennines range and
provides a limited contribution to reflectivity data that are
assimilated. Also, the outer loops strategy could have an important
role in the assimilation procedure, but this needs further
investigation (for example, additional work needs to be dedicated to
testing the different tuning factors for both observation and
background during each outer loop) because a general rainfall
underestimation for higher thresholds is found.
The results of this section confirm that when there is a correlation
between the observations and the first guess used, the results of the
data assimilation are poor, especially if no “special” observation
is available on a wide area. The assimilation of a large amount of
surface data together with the radiosonde ones decreases the quality
of the final analysis produced. It probably depends on the different
density of the surface and the three-dimensional data of radiosondes,
as assessed by Liu and Rabier (2002), the former being much larger
than the latter.
Conclusions
In this paper the effects of multiple radar reflectivity data assimilation on
a heavy precipitation event occurred during the SOP1 of the HyMeX campaign
have been evaluated: the aim is to build a regionally tuned numerical
prediction model and decision-support system for environmental civil
protection services within the central Italian regions. A sensitivity study
at different domain resolution and using different types of data to improve
initial conditions has been performed by assimilating into the WRF model
radar reflectivity measurements, collected by three C-band Doppler weather
radars operational during the event that hit central Italy on
14 September 2012. The WRF assessment tools used are 3-D-Var and
MET. The study is performed on
the complex basin, both for the orography and physical phenomena, of the
Mediterranean area. First of all, the WRF model responses to different types
of cumulus parameterizations have been tested to establish the best
configuration and to obtain the control simulation. The latter has been
compared with observations and other experiments performed using 3-D-Var. The
set of assimilation experiments have been conducted following two different
strategies: (i) data assimilation at low and high resolution or at both
resolutions simultaneously; (ii) conventional data against radar reflectivity
data assimilation. Both have been examined to assess the impact on rainfall
forecast.
The major findings of this work are the following:
Grell 3-D parameterization improves the simulations both on
D01and D02 and the use of the spreading factor is an added value in
properly predict heavy rainfall over inland of Abruzzo and the
rainfall pattern along the northeast coast.
Investigating the impact of the assimilation at different
resolutions, positive results are shown by the experiments where
the data assimilation is performed on both domains 12 and
3 km.
The impact of the assimilation using different types of
observations shows improvements if reflectivity from all the radars,
along with SYNOP and TEMP are assimilated; furthermore, MM is the
one that gives more optimistic results due to its excellent
monitoring of the whole event.
The outer loops strategy allows for further improving positive
impact of the assimilation of multiple reflectivity radars
data; moreover, a deeper investigation of this approach is required
to assess its impact, above all concerning the running time in
an operational context.
We have seen that there are thresholds where the WRF 3-D-Var is
statistically significant, with 95 % confidence, while for other
thresholds we have to be careful in drawing conclusions above all in
the face of large uncertainty or when the score values are quite
close to each other.
From the results obtained in this study, it is not possible to assess,
in general terms, which is the best model configuration. In fact, this
analysis should be performed systematically with a significant number
of flash flood case studies before one can claim with certainty the
positive impact of multiple reflectivity radar observations
assimilation upon the forecast skill. Nevertheless, this work has
pointed out aspects in 3-D-Var reflectivity data assimilation that
encourages us to investigate more FFEs which have occurred over
central Italy, in order to make the proposed approach suitable to
provide a realistic prediction of possible flash floods both for the
timing and localization of such events. To confirm and consolidate
these initial findings, apart from analysing more case studies,
a deeper analysis of the meteorology of the region and of the
performance of the data assimilation system throughout longer trials
in a “pseudo-operational” procedure is necessary. Moreover, a more
sophisticated spatial verification technique (MODE, Method for
Object-Based Diagnostic Evaluation; Davis et al., 2006a, b) which
focuses on the realism of the forecast, by comparing features or
“objects” that characterize both forecast and observation fields,
could be investigated in the future. In fact, spatial verification
methods are particularly suitable to address the model capability to
reproduce structures like the convective systems responsible for the
high precipitation events as considered in the present research,
which, because of their typical dimensions, need high-resolution
simulations to be predicted (Gilleland et al., 2009). These
new-generation spatial verification methods, through the
identification and the geometrical description of “objects” in
forecast and observation fields (e.g. accumulated precipitation or
radar reflectivity), permit an evaluation of the forecast skill in
a more consistent way.
The authors declare that the underlying
research data generated and/or analysed during the current study are only
partially publicly accessible, with some of them are available on request: for the
analyses and conventional observations please contact ECMWF, for radar data
please refer to their specific owner, and for rain gauges data please get in
touch with the National Civil Protection Department and CIMA Research
Foundation.
The authors declare that they have no conflict of
interest.
Acknowledgements
We are grateful to the Gran Sasso National Laboratories for support in
computing resources, as well as the National Civil Protection Department and
CIMA Research Foundation for rain gauge data used for the model validation.
NCAR is also acknowledged for the WRF model, the 3-D-Var system, and the MET tool. This
work aims at contributing to the HyMeX programme. Edited by: Hannah Cloke Reviewed by: two
anonymous referees
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