ASRAdvances in Science and ResearchASRAdv. Sci. Res.1992-0636Copernicus PublicationsGöttingen, Germany10.5194/asr-14-157-2017GPS – Zenith Total Delay assimilation in different resolution simulations
of a heavy precipitation event over southern FranceCaldas-ÁlvarezAlbertoalberto.caldas-alvarez@kit.eduKhodayarSamiroBockOlivierInstitute of Meteorology and Climate Research (IMK-TRO), Karlsruhe
Institute of Technology (KIT), Karlsruhe, 76344, GermanyIGN LAREG, Univ Paris Diderot, Sorbonne Paris Cité, 5 rue Thomas Mann, 75205 Paris CEDEX 13, FranceAlberto Caldas-Álvarez (alberto.caldas-alvarez@kit.edu)13June20171415716215January201724April20174May2017This 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://asr.copernicus.org/articles/14/157/2017/asr-14-157-2017.htmlThe full text article is available as a PDF file from https://asr.copernicus.org/articles/14/157/2017/asr-14-157-2017.pdf
The aim of this study is to investigate the different
pathways of the interaction between an improved atmospheric moisture
distribution by Data Assimilation (DA) of Global Positioning System Zenith Total Delays (GPS-ZTD)
on the simulation of a selected Heavy Precipitation Event (HPE) across different
model horizontal resolutions (7 km, 2.8 km and 500 m). The initiation and
evolution of deep moist convection and heavy precipitation taking place on
the 24 September 2012, which had a dedicated Intensive Observation Period
(IOP6) during the Hydrological cycle in the Mediterranean eXperiment (HyMeX)
Special Observation period 1, are analysed. The results show an improvement
in the representation of the Integrated Water Vapour (IWV) spatial
distribution and temporal evolution when the data assimilation is applied as
well as through the refinement of the model grids. However, important
discrepancies between the simulated and the observed vertical profiles of
humidity still remain after the DA, thus affecting the representation of
convection and heavy precipitation. For the presented case study, the model
simulations exhibited a wet bias. The assimilation entailed a drying of the
low to middle troposphere over the study region during the 6 h prior
to the storm initiation for every horizontal resolution. This reduced the
instability present at the moment of storm initiation, weakening in return
the intensity of convection and the number of cells triggered. The
improvement observed in the atmospheric moisture content and distribution
was not followed by an improved precipitation representation closer to
observations. This highlights the relevance of correctly distributing the
assimilated IWV in the vertical direction in the models.
Introduction
The prediction of Heavy Precipitation Events (HPEs) that typically strike the
western Mediterranean region by late summer (Lee et al., 2016; Davolio et
al., 2016) is still a challenge for current Numerical Weather Prediction
(NWP) models. Under a weak synoptic forcing, the location and time of the
triggered convective cells is determined by mesoscale temperature and
humidity inhomogeneities that define unstable regions prone to lifting. If in
addition, sufficient moisture and a triggering mechanism are present,
convection will take place. This major role of water vapour in convection was
addressed in modelling studies where variations of 1 g kg-1 in
specific humidity were able to make a difference between intense convection
and its complete suppression (Crook, 1996). That is why the misrepresentation
of the moisture distribution within the models is an identified source of
error for heavy precipitation simulations. However, significant improvement
can be gained by refining the horizontal resolution in the models
(Hackenbruch et al., 2016) and through Data Assimilation (DA) of humidity
measurements. In particular, DA of Global Positioning System-derived Zenith Total Delays (GPS-ZTD) has
shown a positive impact in the prediction of strong precipitation (Boniface
et al., 2009). The presented research work aims at investigating the pathways
of the interaction between an improved atmospheric moisture distribution by
DA of GPS-ZTD and precipitation in NWP simulations of a selected HPE across
different grid spacings (7 km, 2.8 km and 500 m).
Methodology
The non-hydrostatic regional weather prediction model COnsortium for
Small-scale MOdelling (COSMO; Schättler et al., 2013) in its version 5.1
has been used to reproduce a HPE that took place on the night of the
24 September 2012 over southern Europe. This event was characterized by large
precipitation totals, measured at stations in the Cevennes-Avignon region
(named A1 with max. of 100 mm day-1), the Gulf of Genoa and the north
eastern Alpine region (A2 – max. of 60 mm day-1), and north eastern
Italy (A3 – max. of 160 mm day-1). In this study, only results on the
convection evolution and heavy precipitation over southern France (region A1)
are shown.
Convection was triggered over the affected regions as a result of mesoscale
instability and the advection of an upper-level trough towards the west
(Hally et al., 2014). An Intensive Observation Period (IOP6) was dedicated to
the study of this HPE in the framework of the Hydrological cycle in the
Mediterranean Experiment (HyMeX; Ducrocq et al., 2014). The COSMO simulations
span three days (22 September 2012 at 00:00 to 25 September 2012 at
00:00 UTC). Two approaches are applied in order to gain an improved
representation of the humidity fields and its related impact on convection.
On the one hand, a dense state-of-art GPS-derived ZTD data set (Fig. 1) is
continuously assimilated during the three-day simulation period with a
frequency of 10 min. This data set is provided by the Institut National de L'Information Géographique et Forestière (IGN; Bock et al., 2016) and has
been specially homogenized for 25 European GPS networks as a contribution to
the HyMeX community. Therefore, simulations (hereafter referred to as AS) are
obtained by assimilating only GPS-ZTD data by means of the Nudging towards
observations method (Schraff and Hess, 2012) using the operational nudging parameters employed at the German
Weather Service (DWD). These, are compared to control runs (CTRL), with no
assimilation of any type of observation. The second approach aims at
obtaining an improved moisture representation through refining the model
spatial resolution. To this end, the event is represented on three different
grid spacings (7 km, 2.8 km and 500 m) in a nested configuration
(simulation domains in Fig. 1). The simulations on the 7 km grid are forced
using analysis data from the Integrated Forecasting System (IFS) of the
European Centre for Medium-range Weather Forecasts (ECMWF) with a horizontal
resolution of 18 km. Settings close to the operational COSMO-EU set up
employed at the German Weather Service (DWD), including a convection
parametrization scheme following the mass-flux Tiedtke type (Tiedtke, 1989),
is used on a 40 level model configuration with an integration time of 60 s.
The 2.8 km runs are forced by the 7 km forecast runs, and the COSMO-DE
configuration with 50 model levels and a time step for integration of 20 s
was utilized. The deep convection parametrization scheme is not applied in
this COSMO-DE configuration. Finally, specific settings for simulations in
the lower limit of the mesoscale, including no convection parameterization
and the use of a 3-D closure scheme for vertical turbulent diffusion (Doms et
al., 2011) are used for the 500 m simulations. For this type of horizontal
resolution the forcing data were obtained from the forecast runs of the
2.8 km grid model output, the number of atmospheric levels is 80 and the
integration time step is 2 s. The external parameters of every simulation
include orography data obtained from the Global Land One-km Base Elevation
(GLOBE) Digital Elevation Model (Hastings et al., 2000). The Integrated Water Vapour (IWV) data set used
for comparison of our model results against observations is also provided by
the IGN and was derived from the ZTD data, employing surface pressure
operational analysis from the AROME model in its west-Mediterranean
configuration (AWMED) and the mean temperature computed from ERA-Interim pressure-level data following the algorithm described in Bevis et
al. (1992). The rain gauges precipitation data set and the radiosonde
profile shown in Sect. 3 are provided by the data base of HyMeX.
The 7 km, 2.8 km and 500 m simulation regions are denoted by the
blue, green and red frames respectively. The GPS stations location is denoted
by the orange dots and the location of the shown radiosonde measurement (see
Fig. 3b) is indicated by the purple dot.
Spatial distribution of the 24-Hourly averaged IWV differences (GPS
– COSMO). On the 23 September 2012 starting at 00:00 UTC.
ResultsAtmospheric moisture spatial distribution
Figure 2 represents the differences in the 24-hourly averaged IWV between
observations (GPS-derived IWV) and the values simulated by COSMO at different
horizontal resolutions. The nearest COSMO grid points to the stations
location are chosen for the differences. Investigation area A1 is shown on
the day prior to the storm initiation, the 23 September 2012, starting at
00:00 UTC. All CTRL runs show an overestimation of IWV by COSMO over the
mountain regions in the western part of the study area, as well as through
the Avignon valley reaching Lyon and the coast of the Gulf of Lion. This
generalized wet bias is stronger in the CTRL-7 and CTRL-2.8 simulations in
the northern part of the valley and over the upper left corner of the
simulation region as compared to the CTRL-500. Nevertheless, over the Gulf of Lion, south to area A1, the CTRL-500 run represents a larger area of
overestimation as compared to the CTRL-7 and CTRL-2.8 runs. Over the windward
slope of the Alps, and the oriental flank of the Massif Central the CTRL-2.8
and the CTRL-7 runs exhibit singular locations with a large underestimation
of the IWV values, not seen in the CTRL-500 runs. The general wet bias in the
model results are reduced as a consequence of the assimilation for every
model horizontal resolution. The mean of the IWV differences for the stations within area A1 is reduced from -0.6 mm (CTRL-7) to 0.5 mm (AS-7), from
-1.3 mm (CTRL-2.8) to 0.1 mm (AS-2.8) and from -1.4 mm (CTRL-500) to
0.01 mm (AS-500). This is in agreement with the decrease in IWV seen in
Fig. 2 over the mountain system and along the valley, however, the wet bias
over the Gulf of Lion coast remains in the AS-500 runs. This is probably due
to the proximity of the model boundaries that prevent any influence of the
assimilation on moist advected air masses coming from regions south to the
Gulf of Lion.
(a) Hourly IWV temporal evolution at a radiosonde site
measured by the GPS and obtained with COSMO at the nearest grid points
(b) Skew-T log at the same site on the 23 September 2012 at
12:00 UTC
Figure 3a represents the temporal evolution of the IWV for the set of model
simulations and the GPS-derived IWV measurements at a selected site (see
Fig. 1). Discrepancies up to ca. 4 mm are found between the simulated and
the measured IWV for every different CTRL run (7 km, 2.8 km and 500 m). As
expected, the assimilation is able to correct these differences and its
impact is strongest when the discrepancies between CTRL and the observations
are largest. Such is the case on the 22 September 2012 at 02:00 UTC and during the 23 September 2012,
between 08:00 and 18:00 UTC, most remarkably for the convection-permitting grids (AS-2.8 and CTRL-500). During the hours of
convective precipitation three relative maxima of IWV are seen. The first
maximum, within the precipitation time window (23 September 2012, 21:00 UTC
to 24 September 2012, 10:00 UTC), is associated with the constant feeding of
humidity, for at least 6 h prior to the arrival of the front by a south to
south westerly flow. This increase is larger for the CTRL-2.8 and the
CTRL-500 runs due to the advection of a more moist air mass originating at
the Iberian Peninsula not seen in the AS-2.8, AS-500 runs neither in the CTRL-7
and AS-7 runs. The second maximum is associated to the arrival of the
upper-level trough. Finally, the late increase in specific humidity at the
24 September 2012 09:00 UTC of about 1.5 mm for every model resolution is
originated by the wake low of the front, enhanced in the AS runs as a result
of the assimilation. Figure 3b shows the vertical profiles of temperature and
dew point temperature of the set of simulations as well as measured by a
radiosonde at the site (Fig. 1) on the 23 September at 12:00 UTC. Small
differences in the temperature profiles exist between the CTRL simulations
and their assimilated counterparts for every model resolution. The agreement
with observations is usually good for this variable. The dew point
temperature profiles are, however, strongly impacted by the assimilation. Between
08:00 and 18:00 UTC of 23 September 2012, the decrease in IWV in the AS runs
caused by the assimilation is seen as a decrease in the mixing ratio between the 400 and 850 hPa levels,
reaching differences even higher than 1 g kg-1. Regarding the agreement with observations, important differences in
the dew point temperature profile still remain after the assimilation.
Figure 3b illustrates these existing discrepancies, with differences as large
as 2 g kg-1 in the mixing ratio between the AS-2.8 and AS-500 runs and the observations around the 800 hPa level. The tendency for a drying of the
middle to low levels of the troposphere is well represented.
Spatial averages temporal evolution over area A1. (a) CAPE
and (b) precipitation from rain gauges and COSMO at the nearest grid
points.
Impact on precipitating convection
In order to assess the impact on atmospheric latent instability as an
ingredient of convection, Fig. 4a shows the spatially averaged temporal
evolution of Convective Available Potential Energy (CAPE). Figure 4b represents the
spatial averages of the hourly precipitation observations obtained from rain
gauge measurements and the COSMO simulated precipitation at the nearest grid
points. We can see three periods during the simulation period with existing
CAPE, the first two coinciding with the diurnal cycle and the last one
(starting at 24 September 2012, 00:00 UTC) induced by the arrival of the
front. The changes seen in the spatially averaged CAPE between the CTRL and
AS runs for the different model resolutions arise predominantly from changes
in the moisture amount, given the weak impact of the assimilation on
temperature. During the hours of the second CAPE maxima, on the
23 September 2012 at 18:00 UTC, the spatially averaged specific humidity was
reduced about 0.25 g kg-1 for the simulations on the 7 km grid and
0.75 g kg-1 on the 2.8 km and 500 m grid simulations at the 850 hPa
level (not shown). This drying caused a reduction in areal-mean CAPE of about
10 J Kg-1 in the simulations with the 7 km grid and 45 and
25 J Kg-1 in the simulations on the 2.8 km and 500 m grid
respectively. During the precipitating hours the spatially averaged CAPE was
reduced by the assimilation during the first hours of the event but was
enhanced, most strongly in the 2.8 km and 500 m simulations, during the
last hours. This happened as a result of a drying followed by wetting of the
lowest tropospheric levels. These changes in CAPE affected the represented
convection since for the CTRL runs, in this case study, less cells were
triggered, the convection was found to be less organized and the intensity of
the updrafts was weakened (not shown). The temporal evolution of
precipitation for every grid spacing is consequently affected and lower
precipitation totals for every model resolution are found as shown by
Fig. 4b. On the 24 September 2012 at 04:00 UTC the hourly precipitation was
decreased 1 mm in the 7 km and 500 m runs and 2 mm in the 2.8 km and
500 m runs. The structure and location of the precipitation maxima were
likewise modified by the assimilation (not shown). Only the AS-2.8 run shows
a late increase in precipitation as compared to its control homologue, this
was due to the triggering of two isolated convective cells lasting for two
hours over the north western part of A1, over the Alps, as a result of the
increased CAPE over the area seen in Fig. 4a during the last hours of
precipitation.
Conclusions
The aim of this study is investigating the influence on heavy precipitation
during HyMeX IOP 6 of an improved moisture distribution by assimilating
GPS-ZTD across different horizontal resolutions (7 km, 2.8 km and 500 m).
The NWP model COSMO was employed to simulate this HPE and results on the
simulation of deep moist convection impacting the southern France region (A1)
were shown.
The exclusive assimilation of GPS-ZTD data continuously every 10 min
improved the representation of the amount, timing and distribution of IWV
over the study region as demonstrated by the temporal evolution and the
24-hourly averaged spatial distribution of IWV for all three model
resolutions. The impact on modelled convection of the humidity fields
modified by the assimilation was strong for this case study as shown by the
reduction of the updrafts intensity and of its organization and by the shift
of the preferred spots for convection (not shown). However, relevant
differences in the vertical profile of humidity still remain after the
assimilation. Even though during the 6 h prior to storm initiation a
tendency for a decrease in humidity below the 500 hPa level was well
represented, the discrepancies in the stratification shown by the model and
observations impede a clear improvement in the representation of heavy
precipitation.
For this case study in southern France, the GPS-ZTD assimilation brought a
decrease in IWV with the consequent decrease of atmospheric humidity,
predominantly in heights below the 500 hPa level for every model horizontal
resolution. As a consequence, the instability over the region was reduced as
illustrated by CAPE, thus reducing the amount of convective precipitation
over the area and inducing relevant differences in the precipitation location
and structure.
In the future more case studies will be investigated to assess a more
generalized response of modelled convection to the assimilation of GPS-ZTD.
The model data employed in this study are property of the hosting research institution and are not publicly available.
The GPS-derived datasets for ZTD and IWV are provided the LAboratoire de Recherche en Géodesie (LAREG) as part of L'Institut National de L'Information Géographique et Forestière (Bock and Bosser, 2014).
The high-resolution satellite precipitation data set is provided by the Climate Prediction Center/National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce (CMORPH, 2017).
The rain gauges and radiosonde data sets are provided by HyMeX and are made available for the HyMeX community.
The authors declare that they have no conflict of
interest.
Acknowledgements
This research project is funded by the Bundesministerium für Bildung und
Forschung (BMBF). It is a contribution to the HyMeX program supported by CNRS
MISTRALS, ANR IODA-MED Grant ANR-11-BS56- 0005 and ANR REMEMBER Grant
ANR-12-SENV-001.The authors thank the HyMeX database teams (ESPRI/IPSL and
SEDOO/Observatoire Midi-Pyrenées) for their help in accessing the data.
Finally, we thank Christoff Schraff (DWD) for his support in defining an
appropriate model set-up for the implementation of the GPS-ZTD data
assimilation. The article processing charges
for this open-access publication were covered by a Research
Centre of the Helmholtz Association.Edited by: A. Cress Reviewed by: three
anonymous referees
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