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European Journal of Remote Sensing
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Effect of the ingestion in the WRF model of
different Sentinel-derived and GNSS-derived
products: analysis of the forecasts of a high impact
weather event
Martina Lagasio, Luca Pulvirenti, Antonio Parodi, Giorgio Boni, Nazzareno
Pierdicca, Giovanna Venuti, Eugenio Realini, Giulio Tagliaferro, Stefano
Barindelli & Bjorn Rommen
To cite this article: Martina Lagasio, Luca Pulvirenti, Antonio Parodi, Giorgio Boni, Nazzareno
Pierdicca, Giovanna Venuti, Eugenio Realini, Giulio Tagliaferro, Stefano Barindelli & Bjorn Rommen
(2019): Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived
products: analysis of the forecasts of a high impact weather event, European Journal of Remote
Sensing, DOI: 10.1080/22797254.2019.1642799
To link to this article: https://doi.org/10.1080/22797254.2019.1642799
© 2019 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group.
Published online: 17 Jul 2019.
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Effect of the ingestion in the WRF model of different Sentinel-derived and
GNSS-derived products: analysis of the forecasts of a high impact weather
event
Martina Lagasio
a
, Luca Pulvirenti
a
, Antonio Parodi
a
, Giorgio Boni
b
, Nazzareno Pierdicca
c
, Giovanna Venuti
d
,
Eugenio Realini
e
, Giulio Tagliaferro
e
, Stefano Barindelli
d
and Bjorn Rommen
f
a
CIMA Research Foundation, Savona, Italy;
b
DICCA, Department of Civil, Chemical and Environmental Engineering, University of Genoa,
Genoa, Italy;
c
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, Rome, Italy;
d
Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy;
e
Geomatics Research & Development srl,
Lomazzo, Italy;
f
European Space Agency (ESA-ESTEC), Noordwijk, The Netherlands
ABSTRACT
This paper presents the first experimental results of a study on the ingestion in the Weather Research
and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS)
derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in
September 2017. The rationale is that numerical weather prediction (NWP) models are presently
able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial
state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to
fully exploit the advances in numerical weather modelling, it is necessary to feed them with high
spatiotemporal resolution information over the surface boundary and the atmospheric column. In
this context, the Copernicus Sentinel satellites represent an important source of data, because they
can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea
surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially
dense network of GNSS stations is also exploited to assimilate water vapour content. Results show
that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce
the most positive effects on the performance of the forecast.
ARTICLE HISTORY
Received 24 December 2018
Revised 18 April 2019
Accepted 9 July 2019
KEYWORDS
Numerical weather model;
Sentinel-1; Sentinel-3; data
assimilation
Introduction
The progresses achieved in numerical weather prediction
(NWP) presently allow the models to produce forecasts
with a grid spacing of the order of 1 km (the so-called
cloud-resolving grid spacing) (e.g. Cardoso, Soares,
Miranda, & Belo-Pereira, 2013). High-resolution simula-
tions are required especially for complex topography
areas (e.g. Mediterranean Sea coastline), even though
they are very expensive from a computational point of
view, because some processes, such as strong convection,
can only be captured when the resolution of the numer-
ical model is higher than 2 km (Iriza, Dumitrache,
Lupascu, & Stefan, 2016). However, high-resolution
NWP models are generally fed by low-resolution and/or
not timely updated data and this implies a poor knowl-
edge of the initial state of both atmosphere and surface at
small scales. The uncertainties in the high-resolution
representation of the surface and the atmosphere may
result in an inaccurate simulation of the severe weather
phenomena in terms of timing, location and intensity.
The reliability of initial and boundary data may have
a large impact on NWP simulations, even larger than
that of the model grid spacing (Schepanski, Knippertz,
Fiedler, Timouk, & Demarty, 2015).
At present, NWP models operating at cloud-
resolving grid spacing can take advantage of the avail-
ability of several free of charge Earth Observation (EO)
data characterized by high spatial and/or temporal reso-
lution. In particular, the Copernicus Sentinel satellites are
able to provide information on the surface boundary and,
through the Synthetic Aperture Radar (SAR) interfero-
metry (InSAR) technique, on the atmospheric column
(Mateus, Catalao, & Nico, 2017), with a spatial resolution
consistent with that of cloud-resolving models (1 km).
Very high temporal resolution data about atmospheric
water vapour can be also derived from GNSS (Global
Navigation Satellite Systems) data (Bevis et al., 1994),
although they are basically point measurements. It can
be expected that ingesting products derived from the
aforementioned EO data into NWP models might sig-
nificantly reduce weather forecast uncertainties.
However, while some investigations on the assimilation
in NWP models of low resolution (tens of km) EO-
derived products (e.g. soil moisture extracted from the
Soil Moisture and Ocean Salinity mission data), are avail-
able in the literature (e.g. Muñoz Sabater, Fouilloux, & De
CONTACT Luca Pulvirenti luca.pulvirenti@cimafoundation.org CIMA Research Foundation, via Armando Magliotto 2, Savona 17100, Italy
EUROPEAN JOURNAL OF REMOTE SENSING
https://doi.org/10.1080/22797254.2019.1642799
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits
unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Rosnay, 2012), few studies were conducted on the inges-
tion of high-resolution EO products. Thus, the European
Space Agency (ESA) issued an intention to tender to
investigate whether the latter kind of products could be
useful to predict, at the high spatiotemporal resolution,
the atmospheric phenomena that lead to extreme events
and intense atmospheric turbulence phenomena.
This paper presents the first results of a set of
experiments carried out in the framework of the
STEAM (SaTellite Earth observation for
Atmospheric Modelling) research project, whose
objective was to answer to the aforementioned ESA
specific request. To pursue this goal, a NWP model,
namely the Weather Research and Forecasting (WRF)
model, was used at cloud-resolving scale to analyse
the impact of the ingestion of different Sentinel-
derived surface observations and of the integrated
precipitable water vapour derived from GNSS
measurements.
To the best of our knowledge, only a few papers on the
assimilation in NWP prediction models of water vapour
maps derived from SAR data by applying the InSAR
technique are available in the literature. Mateus et al.
(2018) used a horizontal grid spacing of the order of the
cloud permitting limit (3 km), while Mateus, Tomé, Nico,
Member, and Catalão (2016) and Pichelli et al. (2015)
worked at the cloud-resolving limit (1 km), but with
a quite small spatial domain (200 × 200 km
2
,or140×
203 km
2
, respectively). Furthermore, Panegrossi, Ferretti,
Pulvirenti, and Pierdicca (2011)presentedafirst attempt
to establish whether high-resolution SAR-derived soil
moisture data might be useful for operational NWP
applications; they used the Fifth Generation NCAR/
Penn State Mesoscale Model (MM5) at 1 km grid spacing
but, again, over a small domain (120 × 120 km
2
). Data
about the sea and land surfaces derived from Sentinel
data were never used for weather forecast applications
so far.
With respect to the aforementioned papers, in this
study a bigger spatial domain (1500 × 1500 km
2
)is
considered, still working at the cloud-resolving limit.
Moreover, not only one variable, but an ensemble of
variables (derived from GNSS and Sentinel data), are
ingested. The ingestion of multiple high spatial and/
or temporal resolution EO products, together with
GNSS (Global Navigation Satellite System) data, into
a cloud-resolving NWP model, operated at 1.5 km
grid spacing over a wide region, represent, therefore,
the key novel contribution brought by this study.
Several model runs have to be accomplished to pro-
duce a complete set of results and each high-resolution
run is very expensive from a computational point of view.
Consequently, the number of case studies analysed
throughout the whole duration of the STEAM project
(18 months) was necessarily limited to 2–3events.The
first set of STEAM experiments, presented in this paper,
were conducted considering a major flash-flood event
occurred in the Livorno town (Tuscany, Central Italy)
in September 2017 with a dramatic deaths toll (9 people),
and severe economic impacts. The experiments included
the control simulation (i.e., no ingestion of any variable in
the WRF model) used as benchmark, the ingestion of one
variable at a time and the ingestion of a combination of
multiple variables (Sentinel-derived products and/or
GNSS observations). The analysis was based on the com-
parison between the forecasted rain rate (cumulated in
a 12-h interval) and the spatially interpolated rain rate
measured by a network of rain gauges.
The paper is organized as follows. The Materials
and Methods section firstly discusses the rationale
behind the choice of the physical variables ingested
in the WRF model and presents the synoptic back-
ground and the meteorological characteristic of the
Livorno case study. The adopted model setup, the
assimilation methods and the validation method are
also described in this section. The Variables retrieval
section outlines the methods adopted to retrieve the
physical variables that are not already available as
high-level products and introduces the data used to
carry out the experiments. The Results and
Discussion section is dedicated to the description of
the experiments and the analysis of their results. The
final section draws the main conclusions of the study.
Materials and methods
Selection of the variables to be ingested in WRF
To select the variables to be ingested into the WRF
model, the Observing Systems Capability Analysis
and Review (OSCAR) tool, developed by the World
Meteorological Organization (WMO) in support of
Earth Observation applications, studies and global
coordination (https://www.wmo-sat.info/oscar/) was
adopted. Through OSCAR, a set of requirements for
the observation of meteo-hydrological variables of
interest in WMO programs and different application
areas is available. Requirements are expressed for
geophysical variables in terms of the following cri-
teria: A) uncertainty, B) horizontal and (when applic-
able) vertical resolutions, C) observing cycle (i.e., the
temporal resolution), D) timeliness, E) stability
(when applicable). One of the OSCAR application
areas is represented by high-resolution NWP and its
relevant variables are listed in Table 1.
Looking at Table 1, it can be deduced that, in
principle, several variables can be relevant for high-
resolution NWP applications. Then, an analysis was
performed with the aim of selecting a subset of these
variables. The analysis was based on the following
points: I) possibility to estimate the variable using
satellite EO data (in particular Sentinel ones); II)
utility of the variable for the specific case study con-
sidered here (e.g. snow cover, or sea ice were
2M. LAGASIO ET AL.
obviously not considered because the event occurred
in the Mediterranean area during the summer); III)
compliance with the OSCAR requirements.
Regarding point III), for each criterion OSCAR pro-
vides three values: 1) threshold, which is the mini-
mum requirement to be met to ensure that data are
useful; 2) goal, which is an ideal requirement above
which further improvements are not necessary; 3)
breakthrough, which is an intermediate level between
threshold and goal and that, if achieved, would result
in a significant improvement for the targeted applica-
tion. Among the criteria listed above, the priority was
given to horizontal resolution, in agreement with the
discussion done in the introduction of this paper.
The outcomes of the analysis led us to select the
following variables: Integrated Water Vapour (that
can be retrieved using Sentinel-1 or GNSS data),
Soil Moisture at the surface and Wind Vector over
the surface (that can be retrieved using Sentinel-1
data), Land and Sea Surface Temperature (that can
be retrieved using Sentinel-3 data).
To briefly outline the analysis carried out to select
the variables to be ingested in WRF, Soil Moisture
(SM) is taken as an example. Table 2 is again derived
from OSCAR and shows the requirements for SM.
Firstly, the horizontal resolution requirement was
considered and the literature regarding SM retrieval
using SAR (and Sentinel-1 in particular) was exam-
ined. It was found that Sentinel-1 (S1) derived SM
maps are generally produced with a spatial resolution
of the order of 1 km (Balenzano et al., 2013;
Hornacek et al., 2012; Pierdicca, Pulvirenti, & Pace,
2014). It must be pointed out that, although the
nominal S1 resolution (5 × 20 m
2
single look) could
in principle allow for producing SM maps with
a higher resolution, a multi-look processing using
a large number of looks and/or a smoothing/low
pass filter are generally applied to cope with the
speckle noise characteristic of SAR images.
Nonetheless, a resolution of 1 km enables to fulfil
the OSCAR requirement at the goal level. For what
concerns the uncertainty, it is difficult to provide
a reliable score for SM, because reference (ground
truth) data are available in few geographic areas
(e.g. the ground stations belonging to the
International Soil Moisture Network, or ad-hoc cam-
paigns). In the literature, it was shown that it is
possible to estimate SM with accuracy (root mean
square error –RMSE) from 0.04 to 0.08 m
3
/m
3
(El
Hajj et al., 2016), thus meeting the OSCAR require-
ments at least at threshold level. However, these
scores often refer to specific test sites, so that at larger
scales (e.g. national scale), higher RMSE can be
expected, especially if SAR observations are
Table 1. Variables relevant for high-resolution NWP according to the WMO-OSCAR database (https://www.wmo-sat.info/oscar/).
Subdomain Variables
Basic Atmospheric Air pressure (at surface) Air specific humidity (at surface) Air temperature (at surface)
Atmospheric temperature Specific humidity Integrated Water Vapour (IWV)
Wind (horizontal) Wind (vertical) Wind speed over the surface (horizontal)
Wind vector over the surface
(horizontal)
Clouds and
precipitations
Accumulated precipitation (over 24 h) Cloud base height Cloud cover
Cloud drop effective radius Cloud ice
Cloud ice (total column) Cloud liquid water (CLW) Cloud liquid water (CLW) total column
Cloud top height Cloud type Precipitation intensity at surface (liquid or
solid)
Precipitation intensity at surface (solid) Precipitation type at the surface
Aerosols and radiation Earth surface albedo Fraction of Absorbed PAR (FAPAR) Long-wave Earth surface emissivity
Upward short-wave irradiance at TOA Upward long-wave irradiance at
TOA
Ocean Dominant wave direction Dominant wave period Sea surface temperature
Significant wave height
Land surface Land surface temperature Leaf Area Index (LAI) Normalised Difference Vegetation Index
(NDVI)
Snow cover Snow water equivalent Soil moisture at surface
Atmospheric chemistry O3 O3 (Total column)
Sea Ice Sea-ice cover Sea-ice surface temperature Sea-ice thickness
Table 2. Requirements defined for soil moisture at the surface for high-resolution NWP according to the WMO-OSCAR database
(https://www.wmo-sat.info/oscar/).
Goal Breakthrough Threshold
Uncertainty 0.02 m
3
/m
3
0.04 m
3
/m
3
0.08 m
3
/m
3
Stability/decade (if applicable)
Horizontal Resolution 1 km 5 km 40 km
Vertical Resolution
Observing Cycle 60 min 3 h 6 h
Timeliness 30 min 60 min 6 h
EUROPEAN JOURNAL OF REMOTE SENSING 3
performed under dense vegetation conditions (e.g.
Hajnsek et al., 2009). In any case, the fulfilment of
the spatial resolution and, to some extent, accuracy
requirements led us to select SM among the variables
ingested by WRF. It must be however underlined that
the observation cycle can represent a critical aspect.
Indeed even considering S1 ascending and descend-
ing orbits, the observation cycle varies between 12
h (best case) and six days (worst case). Similar lines
of reasoning were followed to select Integrated Water
Vapour (IWV), Wind speed (W
S
) and direction (W
D
)
(i.e., the wind vector), Land Surface Temperature
(LST) and Sea Surface Temperature (SST).
Case study
To comply with the objective of the STEAM project
(discussed in the Introduction), a critical aspect was
the choice of the case studies. It was necessary to
search for high impact weather events (HIWEs)
observed as close as possible to their onset by both
S1 and Sentinel-3 (S3) and occurred in a geographic
area where reliable validation data were available. To
fulfil these very strict requirements, a review of the
HIWEs happened in Italy (where a dense rain gauge
network useful for validation purposes is available) in
recent years (2016–2018), on the basis of European
Severe Weather Database and the corresponding
Sentinel data availability was carried out. This review
led us to choose the Livorno flash flood as the first
case study to be analyzed in the framework of the
STEAM project.
Starting from the afternoon-evening of Saturday,
9 September, a large trough deepened on the western
Mediterranean, recalling an intense flow of currents
from the south, mild and extremely humid, on all the
Tyrrhenian sectors and on the part east of the
Ligurian Sea. From the evening of Saturday
9 September, the freshest airflow associated with
vorticity at 500 hPa was supportive of instability
conditions on the Tuscany region (not shown). The
environment was also conducive to the development
of intense local convective precipitation systems per-
sistent, not only because of the slow evolution of the
depression area, but also because of the shear of the
winds (variation of the intensity and direction of the
wind along the vertical column) well highlighted by
the “deep level shear”which determined a separation
between the updraft area (rising currents that feed the
storms) and that of downdraft (descending currents
that generate the wind band), favouring locally sta-
tionary storms. However, it is very important to
consider, from a predictability standpoint, how the
entire central Tyrrhenian sea, and large part of cen-
tral Italy were prone to the potential occurrence of
very intense, persistent, and self-regenerating thun-
derstorm phenomena as confirmed by the values of
the Severe Weather Threat Index (SWEAT) that mea-
sures thunderstorm potential by examining low-level
moisture, convective instability, jet maxima, and
warm advection (Figure 1). These conditions resulted
in about 250 mm of rain fell in 2 h between 02:00 and
04:00 UTC of 9/10 September over the Livorno city
area causing the death of nine people.
Available data
For the Livorno event, S1 and S3 data acquired
between the afternoon of September 8 and the eve-
ning of 9 September 2017 were available. In particu-
lar, the following data were directly available through
the Sentinel catalogues: 1) LST and SST derived from
the Sea and Land Surface Temperature Radiometer
(SLSTR) onboard S3, generated on a 1 × 1 km
2
grid;
2) wind vector (W
S
and W
D
at 10 m asl) included in
the Ocean Wind Field (OWI) component of the
Sentinel 1 Level-2 Ocean (OCN) product (again gen-
erated on a 1 × 1 km
2
grid). More specifically, for
Figure 1. SWEAT index at 07 UTC of September 10, 2017 (ECMWF 25 km run, 12 UTC September 9, 2017).
4M. LAGASIO ET AL.
SST, the Sentinel 3 data acquired on
9 September 2017 at 20:36 UTC were gathered,
while for LST, the Sentinel 3 data acquired on
9 September 2017 at 09:50 UTC were used. For
what concerns SM, W
D
and W
S
, S1 observed the
Livorno area on 8 September 2017 at 17:14 UTC.
To derive SM from S1 data and IWV from GNSS
data we relied on retrieval algorithms (described in
the Variables retrieval section).
WRF model setup and data assimilation methods
The WRF model (Skamarock et al., 2008) was selected
as the numerical weather model to accomplish all the
experiments carried out within the framework of the
STEAM project. It is a compressible non-hydrostatic
model that was developed at the National Center for
Atmospheric Research (NCAR) in collaboration with
several institutes and universities for operational
weather forecasting and atmospheric science research.
For this work, WRF version 3.8.1 was adopted. Three
domains with two-way nesting setup (Figure 2)were
used, at 13.5 (250 × 250 horizontal grid points), 4.5
(451 × 450) and 1.5 km (943 × 883) spatial resolution,
with 50 vertical levels (all domains top reached 50
hPa). The selected WRF model setup was based on
previous results for this kind of events (Fiori et al.,
2014,2017; Lagasio, Parodi, Procopio, Rachidi, &
Fiori, 2017). The two inner domains (4.5 and
1.5 km) allow solving explicitly many convective pro-
cesses (Kain et al., 2008; Kain, Weiss, Levit, Baldwin, &
Bright, 2006) so an explicit treatment of convection is
chosen. As for the 13.5 km domain, a parameterization
scheme closest to the one used in the GFS global
model is chosen (Han & Pan, 2011)
Concerning the surface layer, the MM5 scheme
was adopted; this scheme uses stability functions
from Dyer and Hicks (1970), Paulson (1970), Webb
(1970) to compute surface exchange coefficients for
heat, moisture, and momentum. A convective velo-
city following Beljaars (1995) was used to enhance
surface fluxes of heat and moisture. The Rapid
Update Cycle (RUC) was chosen for land processes;
the scheme has a multi-level soil model with higher
resolution in the upper soil layer (0, 5, 20, 40, 160,
300 cm is default). The planetary boundary layer
(PBL) scheme is based on the diagnostic non-local
Yonsei University PBL (Hong, Noh, & Dudhia, 2006)
scheme, as the next generation of the Medium-Range
Forecast (MRF) PBL, also using the countergradient
terms to represent fluxes due to non-local gradients.
As for the microphysics, the WRF single-moment
6-class (WSM6) scheme was used; it extends the
WSM5 scheme to include graupel and its associated
processes. Of the three WSM schemes, the WSM6
scheme is the most suitable for cloud-resolving
grids, considering the efficiency and theoretical back-
grounds (Hong & Lim, 2006). Finally, the radiative
processes were parameterized by means of the long-
wave and shortwave Rapid Radiative Transfer Model
(RRTMG) schemes (Iacono et al., 2008). The initial
and lateral boundary conditions were derived from
NCEP-GFS (National Centers for Environmental
Prediction Global Forecast System) analysis and fore-
cast data available at a horizontal resolution of 0.25°×
0.25° and a time resolution of 3 h.
The data ingestion was performed according to
three different methodologies: direct insertion,
a nudging-like technique, and finally a 3DVAR
assimilation. Direct insertion is meant hereafter as
the substitution of a given variable in the NWM fields
with the corresponding one retrieved by EO sensors;
it was applied for SST and LST. The nudging-like
technique was applied for the SM observations; it
included different steps. Firstly, a difference map
between the S1-derived SM (reprojected to the WRF
grid) and the WRF SM from the first level of soil
model (in correspondence with the points observed
by S1) was produced; then the resulting map was
interpolated to fill the gaps in the S1-derived maps
and added to the original WRF SM field in the first
(superficial) level. Finally, the new SM field was pro-
pagated in the underlying vertical levels by a vertical
profile correction through the linear interpolation of
the difference between the observed and simulated
SM assuming a difference equal to 0 in the deepest
level (the S1 observation is only superficial, in the
deepest layers the value of the model is more reli-
able). Figure 3 shows the SM maps resulting from the
procedures described above. It can be noted that in
the lowest layer there is no influence of the satellite
observation and that this influence increases in the
upper layers.
Figure 2. WRF nested domains used for the simulations
EUROPEAN JOURNAL OF REMOTE SENSING 5
W
S
and W
D
from Sentnel-1 and GNSS were assimi-
lated using the 3 DVAR technique. In particular, for
GNSS a 3-h cycle technique was implemented (to take
advantage of the high temporal resolution). The 3DVAR
main purpose is to provide an optimal estimate of the
current status of equation (1) (Ide, Courtier, Ghil, &
Lorenc, 1997).
JðxÞ¼1
2xxb
TB1xxb
þ1
2yy0
TR1yy0
(1)
where xis the analysis to be found that minimizes the
cost function J(x), x
b
is the first guess of the NWP
model, y° is the assimilated observation, and y=H(x)
is the model-derived observation transformed from
the analysis xby the observation operator Hfor
comparison against y°. The solution of (1) represents
a posteriori maximum likelihood (minimum var-
iance) estimate of the true state given two sources
of data: the first guess x
b
and the observation y°. The
analysis fit to this data is weighted by estimates of
their errors Band R, which denote the background
error covariance matrix and the observation error
covariance matrix, respectively.
The version 3.9.1 of the Data Assimilation system
built within the WRF (WRFDA) was chosen. The
Control Variable option 5 (CV5) of the WRFDA
package was used for the Bmatrix calculation using
the National Meteorological Center (NMC) method
(Wang et al., 2014). The NMC method was applied
over the entire month of October 2013 with a 24-h
lead time for the forecasts starting at 00:00 UTC and
a 12-h lead time for the ones initialized at 12:00 UTC
of the same day. The differences between the two
forecasts (t+ 24 and t+ 12) valid for the same refer-
ence time were used to calculate the domains specific
error statistics.
The Rmatrix was assumed to be diagonal, as done
in most of the models (Bouttier & Courtier, 1999).
Thus, observation error correlations are often
assumed to be zero, considering distinct measure-
ments affected by physically independent errors.
Conversely, observation error variances are assumed
equal to the instrumental errors, thus varying
between different variables.
Validation method
To validate the model output for each experiment the
Method for Object-Based Diagnostic Evaluation
(MODE) tool (Davis, Brown, Bullock, & Halley-
Gotway, 2009) was used. Simply speaking, MODE is
a basic algorithm for image segmentation and object
Figure 3. First row: S1 derived soil moisture; second and third rows: six layers SM maps used as WRF input
6M. LAGASIO ET AL.
matching, specifically developed for meteorological
applications. After the identification of the objects in the
precipitation maps (observed and forecasted), MODE
assignstotheobjectaseriesofattributesdefined both
geometrically (e.g. size, orientation) and based on the
precipitation values inside the objects. Then, to summar-
ize the performance of forecasts, it compares the attri-
butes between matched forecasted and observed objects.
The use of this validation method is mainly due to the fact
that when comparing high-resolution observational data
analysis and cloud-resolving meteorological forecast,
especially in case of deep moist convective and highly
localized phenomena, the traditional verification meth-
ods suffer from the so-called “double penalty”issue and
hence alone cannot provide a measure of spatial and
temporal match between the forecast and observed
meteorological patterns. In this respect, it is then prefer-
able to take advantage of features based verification tech-
nique, such as MODE. For sake of clarity and
conciseness, the MODE evaluation in terms of pairs of
objects attributes is hereafter summarized using three
representative output indices: centroid distance, angle
difference and area ratio (Table 3).
Among the different parameters, MODE provides also
some classical statistical scores. Then to assess the perfor-
mance of the forecasts also the probability of detection
(PODY), the false alarm ratio (FAR) and the frequency
bias (FBIAS) were chosen. These parameters were
derived from a contingency table that shows the fre-
quency of “yes”and “no”rain forecasts and occurrences.
The four combinations of forecasts (yes or no) and
observations (yes or no) are: 1) hit: rain forecasted and
actually occurring; 2) miss: rain not forecasted and actu-
ally occurring; 3) false alarm: rain forecasted and actually
not occurring; 4) correct negative: rain not forecasted and
actually not occurring.
PODY is given by the number of hits divided by
the total number of observed rainy events:
PODY ¼hits
hits þmisses perfect score ¼1ðÞ(2)
FAR is given by the number of false alarms divided
by the total number of forecasted rainy events:
FAR ¼false alarms
hits þfalse alrms perfect score ¼0
ðÞ
(3)
FBIAS is given by the total number of forecasted
rainy events divided by the total number of observed
rainy events:
FBIAS ¼hits þfalse alarms
hits þmisses perfect score ¼1ðÞ(4)
Variables retrieval
As previously pointed out, SST and LST were directly
available as Sentinel-3 (S3) level-2 products (currently
through the Sentinel-3 Pre-Operations Data Hub),
while W
S
and W
D
were directly available as S1
level-2 products (through the Copernicus Open
Access Hub). Hence, for these variables, there was
no need to use a retrieval algorithm. Conversely, SM
and IWV had to be retrieved. As for the latter vari-
able, the quantity that can be estimated from satellite
data is the Zenith Total Delay (ZTD) from GNSS and
the Slant Total Delay (STD) from S1 using the InSAR
technique. ZTD represents the delay induced by the
troposphere on GNSS signals in the zenith direction,
while STD is the difference between the slant range
atmospheric delays of the master and the slave images
of an Interferometric pair. Since it was found that, for
the Livorno event, there was little water vapour in the
atmosphere at the S1 acquisition time, only GNSS-
derived ZTD were considered in this study. The
methods used to retrieve SM and ZTD are briefly
described hereafter.
Soil moisture retrieval from sentinel-1 data
The algorithm developed to estimate SM was con-
ceived to combine a multi-temporal approach with
a good computational efficiency. The latter character-
istic is particularly suitable for an operational appli-
cation, such as the use of SM maps in NWP models.
A multi-temporal approach to estimate SM from SAR
assumes that the temporal scale of variation of soil
roughness is considerably slower than that of SM.
Hence, if a dense time-series of SAR data is available,
as expected using S1, short-term changes in the back-
scattering coefficient σ° (that represents the SAR
measurement) are basically related to SM variations.
(Balenzano, Mattia, Satalino, & Davidson, 2011; Kim
et al., 2012; Pierdicca, Pulvirenti, & Bignami, 2010).
The algorithm is based on a multi-temporal max-
imum likelihood (ML) approach (e.g. Kim et al.,
2012) used to invert a direct scattering model of
backscattering from a bare soil, namely that proposed
by Oh (2004). The Oh Model relates the state of the
soil, described by the pair (SM,s), where sdenotes the
height standard deviation of the rough surface, to the
Table 3. Description of the MODE-derived spatial indices used in this work.
Index Description
Centroid Distance Centroid Distance: Provides a quantitative sense of spatial displacement of forecast (Best score 0).
Angle Difference For non-circular objects, gives measure of orientation errors (Best score 0)
Area Ratio Provides an objective measure of whether there is an over- or under-prediction of areal extent of forecast (Best score 1).
EUROPEAN JOURNAL OF REMOTE SENSING 7
backscattering coefficient σ°. This relationship is
represented in the form of lookup table (LUT) (Kim
et al., 2012; Pierdicca, Pulvirenti, Bignami, & Ticconi,
2013; Pierdicca et al., 2014), which was generated by
applying the Oh model considering as inputs 50
values of s(between 0.5 e 4.5 cm with
a discretization of 0.0816 cm), 100 values of SM
(between 0.05 e 0.4 m
3
/m
3
with a discretization of
0.0035 m
3
/m
3
) and 13 values of incidence angle θ
(between 26° and 50° with a discretization of 2°).
The range of SM and svalues is approximately the
same used by Pierdicca et al. (2013), (2014) and
accounts for the range of validity of the Oh model.
As for the θrange, with respect to the S1
Interferometric Wide Swath (IWS) acquisition mode
nominal incidence angle range, a larger interval was
considered for the LUT to account for topography.
For each pair (SM, s), 13 values of σ° at VV polariza-
tion (one for each θvalue) were computed (indicated
as σ0
VV;model). Note that VV is the default co-polarized
channel of S1.
The retrieval algorithm assumes that a time series
of M+ 1 measurements (at the current time tand
at Mprevious times t−1, ..., t −M) is available. It
performs a least square search minimizing the square
difference between measured and modelled values of
σ°:
d½SMðtÞ;SMðt1Þ; :::; SMðtMÞ;s;θ
¼σ0
VV;soil ðtÞjdB σ0
VV;model ½SMðtÞ;s;θjdB
no
2
þσ0
VV;soil ðt1ÞjdB σ0
VV;model ½SMðt1Þ;s;θjdB
no
2
þ::: ::: þσ0
VV;soil ðtMÞjdB σ0
VV;model ½SMðtMÞ;s;θjdB
no
2
(5)
where dis the cost function that has to be minimized
and the symbol jdB indicates that the backscatter
values are expressed in logarithmic units. In (5),
σ0
VV;soil refers to the soil contribution to the S1 mea-
surements, so that, if vegetation is present, its effects
on the S1 backscattering measurements must be cor-
rected before determining the cost function d. The
choice of Mis a compromise between SM estimation
accuracy and computational time; M= 4 was chosen
as done by Pierdicca et al. (2014).
The correction of the vegetation effects on σ°is
a very critical point for any SM retrieval approach. In
fact it is usually based on semi-empirical models that
may lack generality. For this study, the Water Cloud
Model (WCM, Attema & Ulaby, 1978) was used, with
the parameters proposed by Bindlish and Barros
(2001) for all land uses. In the WCM, the vegetation
is simply represented by a “bulk”parameter, such as
the vegetation water content W. Previous studies (e.g.
Jackson et al., 2004,1999) have shown that there
exists a direct relationships between Wand the
Normalized Difference Vegetation Index (NDVI),
which can be derived from optical data such as,
Sentinel-2, Landsat, or MODIS ones. Here the
empirical relationship found by Pierdicca et al.
(2013) was used (and NDVI data were extracted
from the MOD13Q1 MODIS product, see Huete
Didan, van Leeuwen, Miura, & Glenn, 2011):
W¼11:92 NDVI 2:73 kg=m2
(6)
Note that if W< 0.25 kg/m
2
, the influence of vegeta-
tion on the radar signal is considered as negligible
and the S1 backscatter is not corrected (Pierdicca
et al., 2013). Instead, if W>5 kg/m
2
the retrieval of
SM is assumed to be totally insensitive to SM, so that
pixels are masked in this case.
Zenith total delay retrieval from GNSS data
Water vapour content of the atmosphere low layer
(up to about 10 km), known as troposphere or neu-
tral atmosphere, affects GNSS signals by changing
their propagation velocities with respect to that of
the light in vacuum. A diminished speed results in
a larger observed time Δtto cover the satellite-
receiver distance, and eventually to a larger satellite-
receiver observed distance SobscΔt. The path delay in
length (Slant Total Delay –STD) can be therefore
defined as follows (here we disregard the ray
bending):
STDs
r¼Sobs S¼òs
c
vsðÞ1
ds ¼òsnsðÞ1ðÞds
¼106òsNsðÞds
(7)
where sis the actual signal path here considered equal
to the straight satellite-receiver line, nis the refrac-
tion index and N106n1ðÞis the refractivity. By
assuming a horizontally layered water vapour distri-
bution, one can express such slant delay as a linear
function of the delay along the vertical direction, i.e.,
the ZTD, above each receiver:
STDs
r¼ZTDrmf (8)
where mf is the known mapping function, that pro-
jects the unknown zenith delay onto the satellite-
receiver slant path. Most of the modern mapping
functions are expressed using a continued fraction
in terms of 1/sin(ε) (Marini, 1972), εbeing the satel-
lite elevation, truncated after the third term (Niell,
1996):
mf εðÞ¼ 1
sin εðÞþ a
sin εðÞþ b
sin εðÞþc
(9)
The a;band cparameters are usually taken from
a global model, the most used being Niell
(Niell,1996), GMF (Böhm, Niell, Tregoning, &
Schuh, 2006) and VMF (Böhm & Schuh, 2004). The
8M. LAGASIO ET AL.
parameters of the VMF (Vienna Mapping Function)
are estimated from ECMWF numerical weather
model (Kouba, 2008) with a temporal resolution of
6 h and a spatial resolution of 2°in longitude and 2.5°
in latitude.
It is worth reminding here that the tropospheric
delay (the word delay is usually referred to the extra
distance and is expressed in meters) due to water
vapour, is just one out of many other systematic
errors affecting GNSS observations which are to be
accounted for in order to achieve sub-centimetre
accuracy positions. Two different strategies are cur-
rently used to deal with those systematic effects: one
is based on a combination of the observations of two
receivers to a satellite pair, allowing for the removal
of the common systematic effects, the other models
most of the errors and relies on the currently
achieved high accuracy of satellites ephemerides
(Héroux & Kouba, 2001). This last strategy, known
as Precise Point Positioning (PPP) has been adopted
for the STEAM experiment.
PPP mainly models each satellite-receiver observed
distance as a function of the unknown station coordi-
nates, receiver clock offset, ZTD (a proper combination of
dual frequency data, iono-free combination, gets rid of
the ionospheric contribution) and the phase ambiguities
(Héroux & Kouba, 2001). A lot of effort has been put in
the modelling of the systematic effect, nowadays
accounted for by all software implementing GNSS data
adjustment for high accuracy positioning purposes.
The observations collected by the GNSS station
from all the satellites simultaneously in view at
a given epoch involve the same ZTD parameter
and receiver clock, while the phase ambiguities
and the coordinates of a fixed station are common
to all the different epochs of a daily session. The
parameters can be retrieved by applying either
aKalmanfilter approach, thus updating their esti-
mates epoch by epoch based on the past observa-
tions only, or a joint least squares adjustment of all
the session observations. In the STEAM experi-
ment, the free and open source software goGPS
(Realini & Reguzzoni, 2013) was adopted. It was
developed by GReD srl, implementing a PPP strat-
egy with a multi-epoch joint least squares adjust-
ment of the observations. The processing settings
are summarized in Table 4.
Results and discussion
The experiments
The WRF simulations were started at the
synoptic hour temporally closest to the time of the
S1 acquisition, i.e., on 8 September 2017 at 18:00
UTC and lasted 48 h (until 10 September 2017 at
18:00 UTC).
To apply the multi-temporal SM retrieval algo-
rithm previously described, not only the S1 data
acquired on 8 September 2017, but also those
acquired on September 2, August 27, August 21 and
15 August 2017 were used and the ground range
detected (GRD) products were chosen. The GRD
data were multi-looked (10 × 10), calibrated and
geocoded using the 30 m SRTM DEM. All the
Sentinel-derived data were resampled and reprojected
to a reference WRF grid (that of the inner domain:
Geographic Lat/Lon, WGS84, ~1.5 km of pixel size,
corresponding to 0.0135°). The nearest neighbour
approach was applied for this purpose, except for
the SM maps for which a pixel aggregate resampling,
which averages all the pixel values that contribute to
the output pixel, was adopted.
ZTD time series at 30-s resolution were estimated
for all the geodetic permanent stations within the
WRF domain providing free raw GNSS observations.
For the selected event, the data of 375 stations were
available. An accuracy assessment was performed by
comparing the GNSS-derived ZTD to the eight Italian
radiosounding datasets, yielding a mean difference of
3 mm and a standard deviation of 14.6 mm (a total of
43 radiosonde launches has been considered).
Firstly, the sensitivity of the WRF model to each
single variable was analysed, then the ingestion of one
variable at a time was accomplished (besides the
control simulation run, as underlined in the
Introduction). The values of SM and wind field
were assumed as acquired at 18:00 UTC (although
S1 acquisition time was 17:40), i.e., at the starting
time of the simulations.
S3 derived SST and LST data were ingested into
the model via a direct insertion, as previously pointed
out. For this purpose, the WRF simulation was
stopped at the acquisition time of each variable and
then restarted after having replaced the WRF derived
variable with the S3 derived one in a warm restart
mode. Concerning GNSS-derived ZTD, it was assimi-
lated in WRF using the 3DVAR technique. In parti-
cular, the assimilation was cyclically performed every
3 h (throughout the 48h-simulation) in two different
ways: 1) considering either a GNSS observation
every minute (in a 30-min time window centred at
the analysis time), 2) considering only the GNSS
Table 4. GPS processing settings.
Satellite elevation cut-off:7 degrees
ZTD random walk regularization: 0.015 m/ ffiffiffi
h
p
ZTD gradients random walk
regularization:
0.0015 m/ ffiffiffi
h
p
Geophysical corrections: IERS 2010 convention
Ambiguity: Float
Mapping function: VMF gridded mapping function
Orbits and satellite clocks: International GNSS Service (IGS)
final products
EUROPEAN JOURNAL OF REMOTE SENSING 9
observation temporally closest to the analysis time.
The assimilation configuration for each experiment is
summarized in Table 5.
Analysis of the results of the experiments
As validation data, the rain rate measurements pro-
vided by the rain gauge network managed by the
Italian Department of Civil Protection (DPC) were
chosen. It includes more than 5000 rain gauges cover-
ing all the Italian territory. The rain rate measure-
ments were spatially interpolated to be compared
with the model forecasts. To evaluate the forecast
performances, the rain rate cumulated between
September 9 at 18:00 UTC and September 10 at
06:00 UTC (time period when most of the rain fell)
was considered, as the sub-daily period including the
most intense part of the observed flash-flood-
producing storm. Two different thresholds on the
cumulated rain (THR hereafter) were considered to
identify the objects in the forecast and observation
maps by using the MODE tool, namely THR ≥48 mm
and THR ≥72 mm. Figure 4 and Figure 5 show the
values of the scores computed through MODE
obtained in the experiments of ingestion of one vari-
able at a time
In terms of pairs of objects indices (Figure 4), an
improvement in terms of the centroid distance
(CENTR_DIST) with respect to the control run
(CTRL) is achieved through the assimilation of SST,
SM and the wind field (W
S
and W
D
) for THR≥72mm
(the assimilation of W
S
and W
D
improves the result
for THR>48mm too). Furthermore, the assimilation
of ZTD decreases the angle difference
(ANGLE_DIFF) for both the thresholds. Finally,
almost all the performed simulations show an
improvement in terms of AREA_RATIO with respect
to CTRL forecast.
Looking at Figure 5, in terms of classical statistical
indices, it can be seen that the ingestion of LST
implied an improvement of the forecast performances
for all the three parameters considered in this analysis
(increase of FBIAS and PODY, decrease of FAR).
However, this improvement did not turn out to be
very significant except for FBIAS and THR of 48 mm.
Even the ingestion of SST did not produce significant
impacts on the WRF forecasts. As for SM, its inges-
tion led to an improvement of all the indices except
FBIAS for a threshold of 48 mm. It must be under-
lined that by ingesting SM, the lowest values of FAR
were obtained for both the thresholds.
The most positive impact on the forecast perfor-
mances was obtained by assimilating the wind field
(W
S
and W
D
). In this case, despite of a slight increase
of FAR, a FBIAS close to the perfect score (1, see
Figure 5) was obtained and also PODY improved
with respect to the CTRL. Furthermore, in terms of
rainfall pattern location, the assimilation of the wind
field-produced improvements in terms of
CENTR_DIST and AREA_RATIO for both the THR
(Figure 4). In this case, the role of high-resolution
observation assimilation seems to be very important.
In fact, the assimilation of high-resolution wind field
correcting the original WRF wind field at initializa-
tion time that was forced by the low resolution
(~25 km) GFS data allowed the model to evolve
more in agreement with the physics of the observed
convective system. Figure 6 shows the wind field
extracted from the S1 OWI level-2 product
(8 September 2018–17:40 UTC).
The high-resolution observation assimilation at the
initialization time helped the forecast because quanti-
tative precipitation NWP models predictive ability
challenges can derive from the poor knowledge of
the initial state of the atmosphere at small scales lead-
ing to an inevitable model spin-up that often results in
an inaccurate simulation of the convective system in
terms of timing, location and intensity (Sugimoto,
Crook, Sun, Xiao, & Barker, 2009). Thus, the capability
to capture small-scale phenomenon is likely the reason
of the good impact of the S1-derived wind field on the
forecast, even though these data were assimilated more
than 24 h before the onset of the event. Usually the
data assimilation impacts more the first 12 h; however,
this is not the first time in literature that for some
modelling and assimilation setup the impact of obser-
vations assimilation lasts more than 12 h. Koizumi,
Ishikawa, and Tsuyuki (2005) found a positive impact
of rain assimilation on the precipitation up to 18 h;
Dash, Sahu, and Sahu (2013) found out that in a set of
Table 5. Experiment setup summary.
Experiment Assimilation timing Assimilation methods
LST 10 UTC 09/10/2017 Direct Insertion
SST 21 UTC 09/10/2017 Direct Insertion
SM 18 UTC 08/10/2017 Nudging-like
WIND 18 UTC 08/10/2017 3dvar
ZTD3h 3 hour cycling starting at 18 UTC 08/10/2017 3h cycling 3dvar considering GNSS observation every minute in a 30
minutes time window centered at the analysis time
ZTD3h_1ist 3 hour cycling starting at 18 UTC 08/10/2017 3h cycling 3dvar considering GNSS observation temporally closest to the
analysis time
WIND+SM
+ZTD
Assimilation at the acquisition time of SM and WIND with
GNSS-ZTD with a 3h cycling 3dvar
3dvar + nudging-like
CTRL No assimilation No assimilation
10 M. LAGASIO ET AL.
experiments, 3DVAR improvements in simulation can
be significant up to 24 h and that the impact of
assimilation is visible up to 72-h simulation, although
with less magnitude. In this case, the choice of the
domain setup (two-way nested with outer domains
boundary very far from the inner one and updated
every 3 h) could help the assimilation impacts to last
more than the usual time range.
To further investigate the impact of wind field
assimilation at the model initialization, a reference
time step at 02UTC on 10 September 2017, corre-
sponding to the most intense phase of the observed
event, was considered. Then, using the VAPOR
(Visualization and Analysis Platform for Ocean,
Atmosphere, and Solar Researchers, www.vapor.
ucar.edu) software, the atmospheric flow field was
analysed compared to the CTRL experiment
(Figure 7). 3D isosurfaces (5 × 10
−5
kg/kg) for the
rainwater, snow and graupel variables have been
rendered in combination with the wind field at
10 m in case of the CTRL run (panel a) and the
WRF run with the assimilation of the wind field
(panel c) at the same time instant (02 UTC of
10 September). By inspecting the 10-min horizontal
wind field, it is possible to argue that the assimila-
tion of the S-1 derived wind field (Figure 6)con-
tributed to improve, in comparison to the control
run, the persistence and intensity of the conver-
gence responsible for the triggering of the observed
highly-precipitating back-building Mesoscale
Convective System (MCS). Consequently, the pre-
diction obtained by assimilating the wind field was
definitely more in agreement with the physics of
the observed MCS. Furthermore, while reflectivity
Figure 4. Results of the comparison between reference and WRF-derived cumulated rain rate for the Livorno event obtained
using the MODE tool. CENTR_DIST, ANG_DIFF and AREA_RATIO are considered. Left panels: THR ≥48 mm; right panels: THR≥72
mm. Blue bars: control run; red bars: ingestion/assimilation of: LST (1st row), SM (2nd row), SST (3rd row), WS and WD (4th row),
ZTD using a GNSS observation every minute; green bar (last row): assimilation of ZTD using only the GNSS observation
temporally closest to the assimilation time (last row). The dark blue lines indicate the perfect scores (0 for CENTR_DIST and
ANG_DIFF, 1 for AREA_RATIO).
EUROPEAN JOURNAL OF REMOTE SENSING 11
is nearly absent nearby the Tuscany coastlines
(Livorno area) for the OL experiment (panel b),
conversely stronger activity is apparent in panel
b over the Livorno area and downshear the main
convective system.
From Figure 4 and Figure 5, it can be deduced that
the assimilation of the ZTD derived from a GNSS
observation every minute (ZTD3h red bars in the
last row of the figures) and that of the ZTD obtained
using only the GNSS observation temporally closest
to the analysis time (ZTD3h_1ist, green bars) gave
rise to quite similar results. A significant improve-
ment of FBIAS was achieved with both forecasts,
although associated with a slight increase of FAR
(slightly lower in the ZTD3h_1ist simulation).
PODY slightly improved with respect to the control
simulation (CTRL) only for the ZTD3h simulation. In
terms of spatial pattern location, the CENTROID_
DIST evidences a worsening in the rainfall location
with respect to CTRL simulation, but big improve-
ments were achieved in terms of AREA_RATIO and
ANGLE_DIFF of the convective structure.
From the previous analysis, it emerges that the
WRF forecasts showed a large sensitivity to the
assimilation of the wind field and water vapour.
For both the thresholds used to identify objects
through the MODE tool, the assimilation of the
wind field produced the best performances for
FBIAS, PODY and AREA_RATIO, the assimilation
of ZTD produced the best results for
ANGLE_DIFF, while the best performances for
what concerns FAR were obtained by initializing
the WRF run with the S1-derived SM,aspreviously
pointed out. Hence, a further experiment was
Figure 5. Same as Figure 4, but considering FBIAS (perfect score: 1), PODY (perfect score: 1) and FAR (perfect score: 0)
12 M. LAGASIO ET AL.
carried out performing the combined assimilation
of the wind field and the ZTD (derived from
a GNSS observation closest to the analysis time)
inaWRFruninitializedwiththeS1-derivedSM.
This experiment will be synthetically denoted as
WIND+SM+ZTD hereafter. The results are shown
in Figure 8, where the scores obtained in this
experiment are compared with those obtained in
the CTRL run, as previously done, as well as with
the best scores (e.g., highest FBIAS and PODY and
lowest FAR, CENTROID_DIST and ANGLE_DIFF)
obtained in the previous experiments of ingestion/
assimilation of one variable at a time. It can be
seen that this experiment of combined assimilation
of WIND, ZTD and SM enabled to improve the
performances obtained in the previous experiments
except for the FAR for a THR of 48 mm and the
ANGLE DIFF. Overall, the WIND+SM+ZTD
experiment produced better results with respect to
the experiments of ingestion of a single variable.
Figure 6. Wind field estimated from the Sentinel-1 observation performed on September 8, 2018 at 17:40 UTC and included in
the OWI level-2 product
Figure 7. 3D simulated structure composed by rainwater (cyan), graupel (yellow) and snow (grey) microphysics species
respectively for the CTRL run (a) and the run with the assimilation of the wind field (c); the horizontal 10m wind intensity
represented by red vectors. The red line in panels a and c indicates the location of the vertical section of the two structures to
investigate the reflectivity values in the middle of the observed convective structure reported in panels b (for CTRL) and d (for
WIND)
EUROPEAN JOURNAL OF REMOTE SENSING 13
Figure 8. Same as Figure 4 (upper panel) and Figure 5 (lower panel), but considering the combined ingestion of WS and WD,
ZTD using a GNSS observation every minute and SM (red bars); green bars: best results obtained with the ingestion of a single
variable.
Figure 9. Panel a: Tuscany region hydro-meteorological alert areas; panel b: areas of interest (S3 in green and A6 in red) for this
event; panels c and d: hourly mean rainfall over the S3 and A6 alert areas, respectively, comparing the observed rainfall (OBS),
the CTRL run, the WIND+SM+ZTD run and a control simulation initialized at 00 UTC on September 9, 2017 (CTRL_00UTC).
14 M. LAGASIO ET AL.
In view of an operational forecasting framework,
hourly rainfall forecast produced by the WIND+SM
+ZTD experiment and the CTRL simulation were
compared with the corresponding hourly observed
data. To pave the way to this analysis, it is worth to
notice that the Italian Civil Protection Department
manages the hydro-meteorological warning in terms
of alert areas. The Tuscany region is divided into 26
Alert Areas (Figure 9, panel a), but for this event,
only two areas were largely affected (A6 and S3 high-
lighted in Figure 9, panel b). The hourly rainfall
obtained for these areas is shown in panels c and
d of Figure 9. In case of the S3 area, the WIND+SM
+ZTD simulation produced an improvement in terms
of rainfall peaks (e.g., underestimation of the second
peak is reduced from about 20 mm to about 7 mm)
with respect to the CTRL simulation (panel c), while
the improvement is less pronounced over the A6 area
(panel d). More specifically, concerning the A6 area,
the WIND+SM+ZTD simulation enabled
a quantitative precipitation forecast (QPF) improve-
ment for the first rainfall peak, but without succeed-
ing in mitigating the underestimation of the second
peak. Thus, despite the cyclic update of the forecast
every 3 h with ZTD observations, the forecast is not
able to fully capture the convective system dynamic.
This can be due to the initialization time far from the
event and to the GNSS network not particularly
dense over the Tuscany region (see Figure 10), then
not able to steer the simulated event, triggered over
the sea area, towards the A6 area.
It is worth noting that the Italian Civil Protection
(and regionals civil protection departments) alert sys-
tem is based on a daily bulletin issued each day before
13 UTC reporting the forecasted situation for the
following 36 h for each Alert Area of each Region
(see Figure 9, panel A for the Tuscany region). In this
context, it is important to consider that the 18 UTC
GFS global model is available at 21.45 UTC and the
forecast runtime is 2h30, so that the forecast forced
by the 18 UTC GFS is available at 00:15/00:30 UTC.
Along the same lines, the forecast initialized with 00
UTC GFS is available at 6:30 UTC. In an operational
framework only these two initializations are relevant
for issuing the daily alert bulletin, while subsequent
initializations at 06 UTC and 12 UTC (available,
respectively, at about 12.30 and 20.30 UTC) can be
used only for an alert update in a nowcasting frame-
work. Then, to gain a further insight in the effect of
the ingestion of the EO-derived products, the lower
panels of Figure 10 include also the hourly mean
rainfall for a new CTRL run started at 00UTC on
9 September 2017, i.e., close to the event onset
(CTRL_00UTC hereafter).
It can be seen that even the CTRL_00UTC was not
able to fully capture the observed rainfall peaks and
their timing, over the target areas S3 and A6.
Moreover, the WIND+SM+ZTD ingestion setup fol-
lowed more closely the CTRL_00UTC simulation
than the CTRL one. Finally, and more importantly,
the WIND+SM+ZTD ingestion setup produced
higher rainfall peaks with respect to the
CTRL_00UTC simulation, especially over the S3
area. Hence, the WIND+SM+ZTD experiment
resulted into a win-win situation being able to pro-
vide a better QPF over the target Alert Areas even in
advance with respect to one that would have been
available by forcing WRF with a more recent GFS
model instance.
Conclusions
The assimilation of Sentinel-derived products into
numerical weather prediction models was rarely
Figure 10. GNSS and EPN (European Permanent Network) stations used in this work with zoom on the stations placed in
Tuscany
EUROPEAN JOURNAL OF REMOTE SENSING 15
attempted so far and can be considered as a challenging
problem related to the improvement of the quality of the
predictions of high-resolution numerical weather mod-
els. In the framework of the STEAM project, a number of
experiments was accomplished, making reference to
adisastrousflood occurred in Italy in 2017, to verify the
effect of the ingestion of surface soil moisture, wind speed
and direction, land and sea surface temperature, as well as
precipitable water vapour in the WRF model. The work
has been performed trying to answer to the ESA investi-
gation about the value added obtained from the synergic
use of Sentinel-derived observation and NWP models.
By analyzing a set of sensitivity experiments con-
sidering one variable at a time, the most positive
impact on the WRF forecasts was achieved by assim-
ilating the wind speed and direction included in the
Ocean Wind Field component of the Sentinel-1
Level-2 Ocean product. This finding is likely related
to the fact that the triggering of the considered event
is strictly due to the presence of a convergence line
over the sea area in front of Livorno city. Thus, the
assimilation of a high spatial resolution wind field
observation over the sea area allowed the model
dynamic to evolve in a way more similar to the
observed convective event with respect to the CTRL
simulation. The assimilation of GNSS-derived zenith
total delay turned out to have a positive impact as
well, except for an increase of the false alarm rate. For
the latter parameter, a decrease was obtained by
ingesting the Sentinel-1-derived SM. The ingestion
of LST produced a slight improvement of the quality
of the WRF predictions, while SST did not show any
positive influence on the forecasts.
It must be underlined that the results of the assim-
ilation of the GNSS-derived ZTD are promising even
for the assimilation of high-resolution water vapour
maps derived from atmospheric phase screen maps
generated by applying the interferometric technique
to Sentinel-1 data. This application was not carried
out in this study, because of the very low values of the
water vapour in the atmosphere at the time of the
Sentinel-1 observation of the area of interest.
A further improvement of the accuracy of the
forecasts was achieved through a combined ingestion
of W
S
and W
D
, ZTD and SM. LST was not considered
in this experiment, despite its positive impact on the
WRF simulations, because, when high impact
weather events occur, cloud cover often hampers the
retrieval of this variable.
The outcomes of this study seem to confirm the use-
fulness of the synergy between high-resolution numerical
weather models and a set of Earth Observation products
at least for the forecast of high impact weather events.
Nonetheless, further investigations are required and will
be accomplished in the framework of the STEAM project.
Firstly, more than one case study has to be considered,
although high-resolution numerical weather predictions
are very expensive from a computational point of view.
Secondly, an assessment of the value added assimilating
high-resolution water vapour maps derived from atmo-
spheric phase screen maps generated by applying the
interferometric technique to Sentinel-1 data will be inves-
tigated in different cases to see the impact of higher spatial
resolution ZTD observations.
Disclosure statement
No potential conflict of interest was reported by the
authors.
Funding
This work was supported by the European Space Agency
[4000121670/17/NL/AF].
ORCID
Martina Lagasio http://orcid.org/0000-0002-2468-3577
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