Atmospheric Moisture Effects on Heavy Precipitation During the HyMeX IOP16 Using GPS Nudging and Dynamical Downscaling

Gaining insight on the interaction between atmospheric moisture and convection is determinant to improve the model representation of heavy precipitation, a weather phenomenon that every year brings casualties and important monetary losses in the western Mediterranean region. Given the large variability of atmospheric moisture, an accurate representation of 10 its distribution is expected to reduce the errors related to the representation of moist convective processes. In this study, we assess the sensitivity of precipitating convection and underlying mechanisms during a heavy precipitation event (HyMeX intensive observation period 16) to corrections of the atmospheric moisture spatio-temporal distribution. Sensitivity experiments are carried out by nudging a homogenised data set of GPS-derived Zenith Total Delays (GPS-ZTD) with subhourly frequency (10 minutes) in 7km and 2.8 km simulations with the COSMO-CLM model over the western Mediterranean 15 region. The analysis shows that (a) large atmospheric moisture amounts (Integrated Water Vapour ~ 40 mm) precede heavy precipitation at the affected areas. This occurs 12 h before initiation over southern France and 4 h over Sardinia, north eastern Italy and Corsica (our main study area). (b) We found that the moisture is transported on the one hand, swept by a westerly large-scale front associated with an upper-level low and on the other hand evaporated from the Mediterranean Sea and north Africa. The latter moisture transport occurs in the <1 km to 4 km layer and has been identified for this event for the first time. 20 (c) COSMO-CLM overestimated the atmospheric humidity and precipitation amount over the study region (Corsica) and this was, to a good extent, corrected by the GPS-ZTD nudging by reducing noticeably both quantities, bringing results closer to observations. (d) The two processes that exerted the largest control on precipitation were the reduction of atmospheric instability over the island (CAPE -35 %) and the drying of the lower free troposphere bringing more dry air entrainment. Besides, the 7 km simulation showed a stronger impact for large-scale dynamical lifting at the target area, given a weakening 25 of the represented low-pressure system and the associated wind circulation. This reduced ultimately, the intensity and number of convective updrafts represented over the island. These results highlight the large impact exerted by moisture corrections on precipitating convection and the chain of related processes leading to it across scales. Additionally. The modelling experiments demonstrated the benefit of sub-hourly GPS-ZTD nudging to improve the modelling of precipitation. https://doi.org/10.5194/nhess-2019-319 Preprint. Discussion started: 19 November 2019 c © Author(s) 2019. CC BY 4.0 License.


Introduction 30
Heavy precipitating convection causes yearly serious damages and casualties in countries of the Western Mediterranean (WMed) basin especially by autumn (Llasat et al., 2010;Gilabert and Llasat, 2017). During these events daily accumulated precipitation over 150 mm is not rare and precipitation rates can reach 20 mm h -1 . These are caused mainly by convective events ranging several temporal and spatial scales, from the mesoscale down to the micro-alpha Funatsu et al., 2018). Accurate representation of the convective processes interacting across-scales is crucial to support forecasters and 35 decision makers to prevent impacts on properties and communities. The WMed is especially prone to heavy precipitating convection by autumn because of the combination of the relatively high sea surface temperature of the Mediterranean and the Atlantic, the arrival of low-pressure systems such as extra-tropical cyclones or upper-level troughs and the interaction with the Mediterranean complex orography. Former studies pointed out the synoptic situation conducive to heavy precipitation as usually dominated by a low-pressure system, inducing a south-westerly warm and moist inflow, building sufficient instability 40 and moisture convergence (Jansa et al., 2001;Toreti et al., 2010;Nuissier et al., 2011;Ricard et al., 2012;Xoplaki et al., 2012. These studies also demonstrate the key role of atmospheric moisture at all phases of convective development and the need of gaining knowledge regarding its interaction with convection across scales to improve the modelling of extreme phenomena (Sherwood et al., 2010;Ahrens and Samson, 2010). Given the high spatio-temporal variability of atmospheric moisture, a deficient representation of its distribution (Steinke et al., 2015;Girolamo et al., 2016) 45 has been pointed out as a source of uncertainty in current predictions (Chazette et al., 2015;. That is why; there is growing interest in developing forecast systems that assimilate humidity observations with sub-hourly frequency (Guerova et al., 2016). Given the novelty of such assimilation frequencies and the multiple methodologies applied, new insights are needed on their impact on simulated atmospheric conditions leading to heavy precipitation.
Determinant for the development of precipitating convection in the WMed are the vast moisture amounts associated with the 50 heaviest precipitation events, which may originate from remote or local sources (Ricard et al., 2012;Krichak et al., 2014;. Depending on the synoptic conditions, the Mediterranean Sea can account for > 50 % of the transported moisture (Duffourg and Ducrocq, 2011). This is the case when an anticyclonic flow dominates the 3 to 4 days preceding heavy precipitation. Remote sources such as the Atlantic and the tropics also supply the needed moisture, especially for the heaviest precipitation events (Pinto et al., 2013;Winschall et al., 2014), whose transport is brought via tropical plumes (Chazette et al.,55 up to 10 g kg -1 below 850 hPa. A moist low-level, increases the Convective Available Potential (CAPE-ML) energy of the lifted parcel. A second factor, crucial for convection intensity is the moisture at the Lower Free Troposphere (LFT). This is the moisture transport that occurs above the Planetary Boundary Layer (PBL) where the influence of the surface roughness 65 can be considered negligible. Recent observational studies (Virman et al., 2018;Schiro and Neelin, 2019) concluded that the probability of intense convection increases rapidly with increasing LFT humidity, especially over land. In this regard, a more humid LFT prevents larger dry air entrainment from happening. Khodayar et al. (2018) quantified relative humidity to be > 75 % at 700 hPa in the location of all convective systems during a Heavy Precipitation Event (HPE). In addition to an increased probability of transition to deep moist convection, a more humid LFT enhances convection intensity (Zhuang et al., 2018). 70 Whether this sensitivity of heavy precipitation to LFT moisture variations is well represented by current atmospheric models has been investigated by past sensitivity modelling studies using fine model resolutions, from Δx~ 500 m to Δx~7 km (Keil et al., 2008;Honda and Kawano, 2015;Lee et al., 2018). They demonstrated that convection enhancing/weakening happened when increasing/diminishing moisture at the LFT in the simulations. These studies performed moisture sensitivity experiments modifying the water vapour distribution by adding or subtracting a prescribed water vapour amount at chosen heights. It is 75 thus, of particular interest to investigate the aforementioned issues by performing corrections toward observations instead of using idealized experiments.
Given the correlation between the location of moisture convergence and precipitating convection, the complex Mediterranean orography plays a decisive role in setting areas prone to heavy precipitation. The high mountain ridges constrain the moisture transports in the basin favouring moisture gathering at the mountain foothills, the coasts and the valleys. Moreover, the elevated 80 terrain provides dynamic lifting to the convergent moist air masses triggering convection. The mountain slopes bring the lowlevel air masses to the level where they become buoyantly unstable. Therefore, the Alps (Italy, Switzerland, and Austria), the Massif Central (France) and Corsica (France) are focal regions for precipitating convection events (Ducrocq et al., 2014). The case of Corsica is especially characteristic given the complex distribution of valleys and ridges, which induces diurnal variations in the mountain atmospheric boundary layer coming from processes related to the terrain (Adler et al., 2015). This 85 induces spatial inhomogeneities in the water vapour distribution that are crucial to determine the timing and location of deep convection (Adler et al., 2015) The linear composition of the highest peaks in the northwest to southeast direction render the island prone to heavy precipitation. Corsica is one of the main study regions of this paper where we assess relevant aspects of the moisture and convection interactions for a HPE coinciding the Intensive Observation Period (IOP) 16 of the Hydrological Cycle in the Mediterranean Experiment (HyMeX; Ducrocq et al. 2014Ducrocq et al. ) field campaign in autumn 2012 In relation to the problem of accurately representing heavy precipitation, the combination of recent advances in remote sensing techniques for atmospheric moisture measuring and the growing computational power has enabled the achievement of relevant improvements through data assimilation (Wulfmeyer et al., 2015). A well-established method to assimilate data is the Nudging scheme (Schraff and Hess, 2012), where the main advantages are its simplicity (Guerova et al., 2016) and that it has shown good results especially in analysing humidity fields as compared to other schemes (Schraff et al., 2016). Nudging can be used 95 to assimilate Global Positioning System (GPS) measurements that provide information on the total column atmospheric https://doi.org/10.5194/nhess-2019-319 Preprint. moisture. The demonstrated benefits of using GPS measurements are that it is an all-weather product (as opposite to other remote sensing integrated products), its large accuracy and its high temporal resolution (Cress et al., 2012;Guerova et al., 2016). The GPS data set used for nudging in this work is provided in the framework HyMeX. This unique HyMeX GPS product is particularly interesting given the common processing of data from more than 25 European and African networks 100 bringing a dense coverage of the area and its temporal resolution of minutes . The total number of stations included in the nudging sums up to over 900 in the whole WMed and specifically over Corsica up to 20. In this sense, an open question is what the different impacts of nudging GPS data across resolution simulations are. Especially after reaching grid lengths that explicitly resolve convection (< 3 km). With this purpose, we use two horizontal different horizontal resolutions (7 km and 2.8 km) to quantitatively asses the different impacts of correcting the atmospheric moisture distribution depending 105 on the corresponding model configurations.
Within this framework, this work is devoted to assessing the benefit of atmospheric moisture corrections with state-of-the-art GPS-derived measurements on sub-hourly time frequencies for the modelling of heavy precipitation through realistic sensitivity experiments.
We analyse this issue first by understanding the role of local and remote atmospheric moisture contributions to the convection-110 related processes leading to the occurrence of the event, and second through moisture sensitivity experiments nudging GPS information. The IOP16 of the HyMeX Special Observation Period (SOP) 1 has been extensively investigated in the past by e.g. Thévenot et al. (2015), Duffourg et al. (2016) and Martinet et al. (2017). This study complements those previous publications providing a detailed analysis of the relevance and characteristics of atmospheric moisture for the same case.
The organization of the paper is as follows. Section 2 describes the model set-ups and the modelling experiments and presents 115 the observational data sets used for model validation or nudging. Section 3 provides a description of the event including the synoptic situation, the convective evolution and the transport of moisture. Section 4 discusses the impact of the GPS nudging in precipitation, humidity and convective-related processes and Sect. 5 presents the conclusions.

GPS-Zenith Total Delay
The Zenith Total Delay (ZTD) is the "excess path length of GPS satellite emissions (in the L1 and L2-band) caused by the refractivity of the neutral atmosphere" (Businger et al., 1996). The refractivity definition for the neutral atmosphere depends on the partial pressures of water vapour and dry air and on the temperature as introduced in ground papers of GPS meteorology (Bevis et al., 1994). The ZTD is proportional to the Integrated Water Vapour (IWV) in the zenith direction. The ZTD is given 125 in length units and the delay in the Zenith direction is usually preferred given it shows the shortest delays. It is obtained from the slanted path delays by means of mapping functions, ( ) dependent on the curvature of the Earth and the elevation angle (Duan et al., 1996). The dataset used for the sensitivity experiments, is provided by the LAboratoire de Recherche en Géodésie (LAREG) and the HyMeX community and its specifications can be found in Bock et al. (2016). It merges data from more than 25 European and African networks, with over 900 stations, is made available in temporal resolutions up to five minutes and it 130 has a dense coverage of the western European countries (see Fig. 1). All networks have been commonly processed by the GIPSY-OASIS II software to guarantee homogeneity. Data screening includes outliers, range and ambiguity checks to increase the accuracy. The comparison against radiosonde IWV measurements has shown no significant biases during night-time and biases in the range 0.5 -1.4 mm during daytime .

Radiosondes
In the framework of the HyMeX SOP1 MétéoFrance provides the operational soundings containing more than 30 atmospheric parameters, including temperature, dew point temperature, wind speed, geopotential height, air pressure, wind direction and wind speed. In average, they contain ca. 30 levels between the surface and the 300 hPa level with about one measurement every 250 m. In addition to the operational soundings, supplementary soundings were launched during the HyMeX IOPs. 140 Hence, the temporal resolution of the soundings lies between 12 h and 6 h. In total, 10 stations are used among which 3 (Gibraltar, Mallorca and Dar El Beïda) are used for process-understanding and 7 over the Italian Peninsula, Croatia, Corsica and Sardinia are used for validation of the specific humidity and IWV simulations. We perform the validation of the model data obtaining the nearest grid points to the location of the radiosondes. No height correction is applied for this purpose since the difference in height between the neighbouring grid points and the height of the radiosonde stations does not exceed 30 m 145 in any case. The data is accessible at http://mistrals.sedoo.fr/?editDatsId=595&datsId=595&project_name=HyMeX.

Rain gauges
Météo-France and the HyMeX program provide the HyMeX domain precipitation amount (Nuret, 2013;SEDOO OMP. doi: 10.6096/mistrals-hymex.904) data set with hourly accumulated precipitation measured by rain gauges. Over 5000 stations are 150 deployed over western Mediterranean land parts with about 30 stations placed over the island of Corsica. The version used (V4) enjoys the newest quality control and checks for double stations. The data set spans the Sep-2012 to Mar-2013 period.

The NOAA CPC Morphing Precipitation (CMORPH)
CMORPH makes available precipitation measurements in a rectangular grid merging satellite microwave observations. These 155 are combined using the Morphing technique (Joyce et al., 2004, https://doi.org/10.5065/D60R9MF6), that uses motion vectors, derived from infrared data to transport the microwave information to spots where no microwave data were available. It has a broad coverage (60°S -60°N), and its spatial and temporal resolution at the equator is of 8 km and 30 minutes. The Climate Prediction Center (CPC) of the National Weather Service (NWS) in the USA provides the data and it spans the period 1998 to 2015. CMORPH has shown a good detection skill in validation studies (Bitew and Gebremichael, 2011;Habib et al., 2012) 160 and high correlation rates with sub-daily rain gauge data (Sapiano and Arkin, 2009). open-water evaporation (Ew), Snow sublimation (Es), snow sublimation (Es) and interception loss (Ei), as described in Martens et al. (2017) and Miralles et al., (2011). Four interconnected modules dealing with the rainfall interception, soil stress, soil state and the evaporation calculation, compute the aforementioned contributions. The four modules are forced by gridded global data which, in the version used in this work (v3b), is obtained mostly from remote sensing products, such as the Clouds and the Earth's Radiant Energy System (CERES) for radiation, the Tropical Rainfall Measurement Mission (TRMM) for 170 precipitation, the Atmospheric Infrared Sounder (AIRS) for air temperature or European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) for soil moisture. GLEAM version v3b has shown an average correlation with in-situ measurements of 0.78. In the validation, only 2 out of 63 stations showed differences with a level of significance of 10 % (Martens et al., 2017).

The Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT)
The HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) computes air parcels, dispersion and chemical transformations (Stein et al., 2015;Rolph et al., 2017). In this paper, we use HYSPLIT to compute backward trajectories of moisture sources. The HYSPLIT model uses a hybrid approach combining lagrangian trajectories with the Eulerian methodology, using a fixed three-dimensional grid as a frame of reference (Stein et al., 2015). The free access internet-based 180 platform READY (https://www.ready.noaa.gov/index.php) offers HYSPLIT trajectories calculation using eight different atmospheric model analyses of meteorological data. In this work, we use the half-degree archive of the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) that spans the period 2007 to present and has a global coverage. The dataset is accessible in https://www.ready.noaa.gov/HYSPLIT_traj.php, last accessed 18-July-2019.

Consortium for Small-scale Modelling (COSMO) in Climate Mode
The COSMO model is based on the fully compressible, nonhydrostatic, hydro-thermodynamical equations of the atmosphere.
Where the latter is considered as a multicomponent continuum constituted by, liquid water, dry air, water vapour and solid water in the form of cloud droplets ice crystals, raindrops, rimed aggregates, hail and graupel (Schättler et al., 2016). The COSMO version used in this study is the 5.00 and the model is used in climate configuration (COSMO-CLM). This implies 190 that the slow-changing variables (ozone concentration, aerosol concentration and canopy variables) evolve in time. This brings a more realistic representation for seasonal simulations such as the ones presented in this work. The dynamic solver is a third order Runge-Kutta split-explicit scheme following Wicker and Skamarock (2002). It uses an Arakawa-C/Lorenz grid with scalars defined at the centre of the grid box and the normal velocity components defined on the corresponding box faces. The grid is rotated, and the height coordinate shows a Gal-Chen terrain-following grid stretching. The model uses a sponge layer 195 with Rayleigh damping at the top boundary and three grid point lines for adaptation at the lateral boundaries. The boundary and initial states of the atmospheric prognostic variables are obtained by coarser resolution forcing models in a one-way nesting approach. The soil state and the surface-atmosphere interactions are simulated through the TERRA-ML model (Doms et al., 2011). TERRA-ML has eight soil layers and is responsible for issuing the temperature and humidity conditions at the ground and considers the processes of evaporation, runoff, snow storage and interception storage. COSMO-CLM in the used 200 configurations for the resolutions of this work (for a 7 km and a 2.8 km grid resolution), parameterizes the turbulent diffusion using a 1D diagnostic closure for the turbulent kinetic energy (Doms et al., 2011). The grid-scale clouds and precipitation are parameterized using a bulk scheme including several hydrometeor types (Doms et al., 2011). The radiation is parameterized following the formulation after Ritter and Geleyn (1992). In the case of grid spaces larger than 3 km sub-grid deep moist convection is parameterized using a mass-flux, low-level scheme with the equilibrium closure based on moisture convergence 205 (Tiedtke, 1989). Shallow convection is parameterized using an adaptation of the Tiedtke scheme in the simulations using a 7 km and a 2.8 km grid. performed following a second-order autoregressive function of the distance between the location of the observation and the target point (∆ / ; see Eq. 1.b). The vertical interpolation of the observed data is performed assuming a Gaussian decay in height differences.

The Nudging Scheme
Regarding temporal weighting, for hourly or even more frequent data measured from a stationary platform, the data are temporally interpolated linearly to the model time. The observations are assigned to a grid point in the spatio-temporal space and the body of the report is evaluated. This is the step where gross error and consistency checks, quality control and redundancy checks dismiss suspicious observations.
In the case of GPS-ZTD observations, these are converted to Integrated Water Vapour (IWV) following Bevis et al. (1994). altitude difference of the GPS station and model surface lays within the range -150 -600 m to allow for extrapolation and interpolation, respectively and are converted to a specific humidity profile ( ). This is needed given IWV is not a model prognostic variable. The profile is constructed ( ) by means of an iterative process that scales the observed IWV ( ) 230 with the modelled IWV ( ) until a sufficiently low error is reached, see Eq.
(2). The first constructed profile ( ) for the iterative process, is the modelled specific humidity profile. Hence, the profile used for nudging depends on the vertical humidity distribution simulated by the model at the beginning of the nudging time-window.

The GPS-ZTD Nudging Sensitivity Experiments 235
The Nudging scheme is used to assimilate GPS-ZTD data to assess the sensitivity of heavy precipitating convection to corrections of the spatio-temporal distribution of atmospheric moisture. The methodology is described as follows, we perform reference runs, hereafter referred to as CTRL, of the period 1-Sep 0000 UTC to 20-Nov 0000 UTC using two different horizontal resolutions (7 km and 2.8 km). Subsequently, we simulate the same period, keeping the same settings but nudging GPS-ZTD data continuously every 10 minutes. These runs are called NDG-7 and NDG-2.8. The 7 km runs (CTRL-7 and 240 NDG-7) have been forced by European Centre for Medium-Range Weather Forecasts (ECMWF) analyses. The 2.8 km runs (CTRL-2.8 and NDG-2.8) are forced by the CTRL-7 simulation in a one-way nesting strategy. The simulation domains are contained in Fig. 1. Within the 80-day period of simulation, there are several events, which are largely affected by the GPS-ZTD nudging. IOP16 is one of them, which is especially interesting given the large precipitation reductions (-20 %) and the important role of the local orographic and instability factors in triggering and maintaining convection rather than the large-245 scale upper level forcing. Under a weak synoptic forcing, the impact of the GPS-ZTD is larger given the strongest correction of the lower to middle tropospheric humidity. We validate the model output against in-situ humidity measurements quantifying the Mean Absolute Error (MAE) and Mean Bias (MB) and the Agreement Index (AI) as described in González-Zamora et al. (2015). The precipitation fields are validated against rain gauges and the evapotranspiration over land using spatial averages of the GLEAM product. 250 To investigate the impact of moisture variations on convection-related processes, such as atmospheric latent and potential instability conditions, several convective related indices are examined. The CAPE-ML, providing information about the latent instability, is obtained through the mean layer parcel method, as described in (Leuenberger et al., 2010). KO-index is obtained as the differences in θe between several levels of the atmosphere up to 500 hPa (Andersson et al., 1989) hence it bears information on potential instability and how the upper-levels introduce atmospheric instability. Finally, the moisture flux is 255 obtained by multiplying specific humidity and the horizontal wind following Ricard et al. (2012)

Synoptic conditions
The synoptic situation during the IOP16 was characterized by a cut-off low displacing westerly from the Iberian Peninsula toward southern France between 25-Oct 1200 UTC and 27-Oct 0000 UTC (Thévenot et al., 2015). The upper levels showed an associated diffluent flow with a south-westerly to southerly circulation at the low levels over the western part of the basin.
Such a synoptic situation is prototypical for HPEs in the WMed (Jansa et al., 2001;Duffourg and Ducrocq, 2011). Over the 270 Thyrrenean Sea on the morning of 26-Oct, the low-level induced convergence to the south of France. Figure 2 shows the geopotential height of the 500 hPa level (FI500), the Pressure at the Mean Sea Level (PMSL) and the spatial distribution of the Equivalent Potential Temperature ( ) at 850 hPa at three hours of the event as represented by CTRL-7. On the 26-Oct 1800 UTC, the high values of (> 320 K) finally reached Corsica as well as extensive parts of the Thyrrenean Sea (see Fig. 2.c). Northerly cold winds terminated the event in the early morning of 27-Oct.

Convective evolution over Corsica
In the early morning of 26-Oct convection triggered and organized into a V-shape MCS close to the north-eastern coast of 280 Spain. This MCS was named MCS0 by Thévenot et al. (2015) and hereafter we adopt the same nomenclature. This is shown by Fig. 3. region in north-western Italy around 0730 UTC named MCS2 after Thévenot et al. (2015), not shown here. This area shows the highest precipitation rates of the event with over 245 mm in 24h (Duffourg et al., 2016). High convective cloud tops are 290 also observed over the mid Mediterranean west of Corsica at 0730 UTC as shown by the brightness temperature ( Fig. 3.a).
This shows that convection is already happening offshore before the cells arrive at the island. At 1400 UTC over the island, the offshore convection is reinforced by orographic lifting of the moist low-level air masses.
Over Corsica, which is our study region, precipitation total values reach maximum accumulations between 75 mm and 100 mm, over the windward side of the mountains and over the mountain crests, between 50 mm and 75 mm (see Fig. 3.b). At the 295 lee side of the mountain, the accumulated precipitation reaches 30 mm. The first convective cells occur over the island between 1300 UTC -1500 UTC on the 26-Oct, forced by orographic lifting precipitating with intensities up to 11.5 mm h -1 over the windward side of the mountains (not shown). Between 26-Oct 1900 UTC and 27-Oct 0100 UTC, offshore-size convective systems arrive at the island (see Fig. 3.a). This stage has the largest precipitation intensities of the event (up to 16 mm h -1 , not shown) with precipitation falling mostly over the western part of the island, transitioning from the north at 2100 UTC to the 300 south at 2300 UTC.

Atmospheric moisture transport
The transport of moisture feeding the convective systems along Corsica and at southern France and north-eastern Italy arises from the action of the upper-level pressure low through two mechanisms. First, the associated front swept atmospheric moisture from the Atlantic to the Mediterranean in the course of 36 h. Second, from intense evaporation over the Mediterranean and 305 north-Africa between 25-Oct 1800 UTC and 26-Oct 1200 UTC transported by the southwesterly flow.
In this section, we use observations from radiosondes, the Evapotranspiration product GLEAM (see Sect. 2.1), CMORPH precipitation estimates, backward trajectories and the COSMO-CLM CTRL-7 simulation for understanding of the transport and distribution of moisture towards the WMed region. We use the CTRL-7 simulation given the good agreement against radiosonde measurements from the HyMeX database (discussed later in Sect. 4.2). 310 Figure 4 shows the CTRL-7 representation of IWV between 25-Oct 1200 UTC and 26-Oct 1200 UTC in the western Mediterranean at three hours with the simulated wind fields at 850 hPa. At 25-Oct 1200 UTC (Fig. 4.a), the front associated with the pressure low west of the Iberian Peninsula swept large IWV amounts, up to about 40 mm over the strait of Gibraltar and along the southern Portuguese coast. Local areas at the Gulf of Lions (southern France) also show values as large as 40 mm at about 1200 UTC before precipitation initiation. At 26-Oct 0000 UTC, the Atlantic moisture is already located over the 315 Algerian coast and at the Gulf of Lions (see Fig. 4.b). As introduced in Thevenot et al. (2015) and Martinet et al. (2015), the large moisture amount present at the Gulf of Lions originates partly from the Mediterranean Sea due to evaporation along the Spanish eastern coast. Along the Algerian coast, these high moisture amounts at 26-Oct 0000 UTC were a combination of moisture swept by the low-pressure system from the Atlantic and moisture evaporated from north African land. At the hour of precipitation initiation over Corsica (26-Oct 1200 UTC), vast IWV amounts surrounded the western and southern coasts of the 320 island (see Fig. 4.c). These large IWV values (~ 40 mm) surrounded the island about 4 h prior to precipitation initiation. We Mallorca, a similar vertical distribution can be observed (see Fig. 5.c.). On the 25-Oct 0000 UTC, specific humidity values as 335 high as 6 g kg -1 exist between 500 hPa and 800 hPa. Twelve hours later, the high and specific humidity can be found in the layer 700 hPa to 800 hPa due to the delayed arrival of moisture at the low-levels. Finally, at 26-Oct 0000 UTC high and specific humidity is located at the marine boundary layer over Mallorca. UTC and 1800 UTC and the vertical updrafts of wind speed larger than 0.25 m s -1 (Fig 6.a). Hourly evaporation rates of 0.3 345 mm h -1 took place at the southern part of the NA box and of 0.2 mm h -1 over the Algerian Atlas (northern part of the NA black box). The moisture gathers in the PBL for several days until the first convective updrafts take place over the area (25-Oct 1800 UTC, see Fig. 6.b). The radiosondes over Dar el Beïda (Fig. 5.b) show the accumulation of moisture in the lower-atmosphere (about 10 g kg -1 close to 1000 hPa on the 25-Oct 1200 UTC). The first convective activity over NA starts at about 25-Oct 1800 UTC (see Fig. 6.b). Vertical transport of humidity is promoted by convection and continues during the evening of 25-Oct (see 350   confirm the moisture transport from northern Africa, as they are located over the intense evapotranspiration area "NA" black 365 box on the 25-Oct 1800 UTC. This is the hour when convective activity occurs over "NA" lifting the humid air masses (see Fig. 6.b). Two sets of trajectories are distinguishable. The first set (ellipse A in Fig. 7) shows faster trajectories, whose starting point is located over Morocco on the 25-Oct 1800 UTC and whose transport occurs at higher levels (between 2000 and 3000 m a.s.l). The second set (ellipse B in Fig. 7) shows trajectories which are slower, that originate over northern Algeria at a height < 1000 m, representative of the well mixed diurnal layer (Garratt, 1994) and rise to 4000 m over the island. 370

Nudging Effects on Convection
The present section examines the sensitivity of the precipitation field and the underlying convection-related processes responsible for the IOP16 event, to realistic atmospheric moisture corrections through GPS-ZTD nudging.

Precipitation
The COSMO-CLM simulations were able to represent the event over the island on both the 7 km and the 2.8 km configurations 375 at the right time, albeit they overestimated the amount. Indeed, the maximum accumulated precipitation simulated by CTRL-7 was between 125 mm and 200 mm and for CTRL-2.8 was between 100 mm and 125 mm, both of which are too large in comparison with the measurements (between 75 mm and 100 mm, see Fig. 3.b). CTRL-7 performed well in representing the location of maximum precipitation; over the windward slope of the Corsican mountains (see Fig. 7.a). The represented precipitation was mostly orographically triggered together with an offshore 380 convective line west of the island triggered by low-level convergence (not shown). Offshore precipitation accumulations at this location brought by the convective line are between 50 mm and 75 mm. CTRL-2.8 showed a worse representation of the location of the maximum as it shifted it towards the crests of the mountain mainly and also to the lee side (see Fig. 7.c), but represented better the amount than CTRL-7. CTRL-2.8 represented more isolated precipitation structures, located over the mountain crests.
The main differences in precipitation representation of CTRL-2.8 (Fig. 7.c) in comparison with CTRL-7 ( Fig. 7.a) are the location of maximum precipitation over the crests, the missing of the offshore convective line at 26-Oct 2100 UTC and the much more localized heavy precipitation structures. The latter is a well identified effect of reaching convection permitting resolutions (Chan et al., 2012;Fosser et al., 2016).
The GPS-ZTD nudging induced for both model resolutions a decrease in the accumulated totals, bringing values closer to the 390 observations. In the case of NDG-7, it showed maximum precipitation totals between 100 mm and 125 mm (-20 % variation), rain gauges showed precipitation between 75 mm and 100 mm (see Fig. 7.b). The location of the maximum was very similar to that of CTRL-7, over the windward side of the mountain, in good agreement with the observations. However, the convective line ahead of the island is not captured by the NDG-7 simulation because of relevant changes in the low-level mesoscale wind circulation (not shown). These differences in wind circulation arise partly due to changes in the pressure distribution of the 395 event, as explained in Sect. 4.4. The NDG-2.8 showed maximum accumulated precipitation in the range 75 mm to 100 mm (-25 %) over one of the mountain peaks in better agreement with observations ( Fig. 7.d). The location of precipitation maxima did not change however significantly as it erroneously remained over the mountain crests.

Atmospheric Moisture
To assess the accuracy of model moisture outputs and the impact of nudging GPS-ZTD, independent humidity measurements 400 from radiosonde profiling of the atmosphere are compared with the CTRL and NDG simulations. In total, we selected 55 soundings from 7 stations (blue squares within the 2.8 km simulation domain in Fig. 1), during the period 26-Oct 0000 UTC to 28-Oct 0000 UTC. The temporal resolution of the radiosondes is between 6 h and 12 h depending on the considered station.  . We can see that the 2.8 km slightly outperforms 7 km in representing IWV.
Nudging GPS-ZTD data, improves the scores. The MAE of IWV is 2 mm for NDG-7 and NDG-2.8 and the MB is of -0.04 mm and -0.08 mm, respectively. In this sense, both the 7 km and the 2.8 km simulations endure an improvement 410 Figure 8.a shows the spatially averaged temporal evolution of IWV over Corsica. The hours prior to precipitation initiation (26-Oct 1300 UTC) were characterized by an IWV pick up starting at 26-Oct 0000 UTC. All simulations show this, albeit the IWV amount over Corsica for NDG-7 and NDG-2.8 was 5 mm higher than for CTRL-7 and CTRL-2.8. This was due to represented precipitation over the island until the night of 24-Oct in the NDG runs, hence inducing a much wetter boundary layer (not shown). By 26-Oct 1000 UTC, an intense moisture increase takes place over the island. As described in Sect Sea, the south-westerly flow is only weakly impacted by the GPS-ZTD nudging during the first stages of the event over Corsica. At 26-Oct 1400 UTC, the CTRL and NDG runs start to diverge and between 26-Oct 1600 UTC and 27-Oct 0600 420 UTC, NDG-7 and NDG-2.8 show ca. 4 mm less than their CTRL counterparts do. This has stringent consequences for the intensity of convection and precipitation with a vast decrease of precipitation amount, as discussed in Sect. 4.1.
The humidity reduction between 26-Oct 1600 UTC and 27-Oct 0600 UTC takes place below 500 hPa. Fig. 8.b shows how median IWV decreases from 30 mm to 27 mm as a result of the GPS-ZTD nudging in the 7 km simulations (-10 %) and from 30 mm to 28 mm in the 2.8 km (-7 %). At 500 hPa, a specific humidity reduction of 0.2 g kg -1 took place for median values in 425 the 7 km simulation (-13 %). The decrease was weaker in the 2.8 km grid with a reduction of 0.5 g kg -1 (33 %). At 500 hPa the specific humidity decrease ranged between 0.5 g kg -1 and 1 g kg -1 for median values (-8 %) for both resolutions. At 950 hPa, the humidity reduction was larger in the 7 km (-8 %) than in the 2.8 km run (-2%). Figure 9 represents the MAE and the MB of specific humidity profiles between 500 hPa and 950 hPa for the same set of radiosondes. Between 600 hPa and 950 hPa, the MAE of specific humidity of CTRL-7 and CTRL-2.8 is between 0.7 g kg -1 430 (600 hPa) and 1.3 g kg -1 (925 hPa). The MB of the profile shows that this error comes from an underestimation of specific humidity by COSMO-CLM below 650 hPa, which is largest below 900 hPa. Over 650 hPa, the simulations overestimated the specific humidity. The GPS-ZTD nudging improves the MAE of the humidity profile between 650 hPa and 925 hPa for both resolutions. The MAE of NDG-7 is now within the range 0.6 g kg -1 (600 hPa) and 1.1 g kg -1 (925 hPa) and the improvement reaches the 950 hPa level. For NDG-2.8, the MAE is between 0.8 g kg -1 (650 hPa) and 1.2 g kg -1 (900 hPa) but an improvement 435 is only achieved down to 925 hPa. The MB is closer to zero at the same atmospheric layers (650 hPa to 900 hPa) for both resolutions albeit showing better results for the 7 km simulation. The correction for LFT moisture is larger in the 7 km runs than in the 2.8 km, probably due to the larger number of observations included in the nudging in this simulation because of larger simulation domains (see Fig. 1). These values of the MAE and Mean Bias are of the same order as the validation of the RMSE of specific humidity profiles between reanalyses data and Lidar measurements from Duffourg et al. (2016). Below 900 440 hPa, the GPS-ZTD nudging was not able to bring such a clear correction, especially for NDG-2.8 where the MAE and MB showed very similar values to the CTRL-2.8 runs. The GPS-ZTD is not able to correct sufficiently the dry bias of the model below 900 hPa because the radiosondes showed a steeper gradient of increasing humidity at the lowest levels. Both CTRL runs show difficulties in representing this gradient and the correction induced by the GPS-ZTD nudging is not enough to moist sufficiently the PBL during this event. Overall, COSMO-CLM shows a good performance in representing the integrated 445 atmospheric moisture fields and humidity over 900 hPa at both model resolutions. The 2.8 km simulation was initially more accurate, but the nudging brings both to similar accuracy rates.

Instability reduction and increase of free-tropospheric mixing
The two affected processes, which exerted the largest control on precipitation reduction, were atmospheric latent instability reduction and dry air entrainment, both investigated in this section. The drying brought by the GPS-ZTD nudging over Corsica 450 dried the atmosphere below 500 hPa. In this section, we will discuss how the impact was primarily reducing CAPE-ML and additionally enhancing mixing with dry air above the PBL. The changes in these two processes start to play a role immediately after the first hour of large IWV differences i.e. after 26-Oct 1600 UTC this is so for both the 7 km and the 2.8 km simulations. Figure 10 shows the height-surface cross-sections of Equivalent Potential Temperature ( ), specific humidity and the wind in the vertical-horizontal direction along the direction of the mean wind (purple transect in Fig. 1) over the island at 26-Oct 1700 455 UTC. CTRL-7 and CTRL-2.8 show values over 322 K from the surface up to 500 hPa showing the upward transport of moist low-level air masses. After applying the GPS-ZTD nudging, NDG-7 shows reduced values of (310 K) close to the ground over the island and at 700 hPa (312 K) showing a less favourable environment for convection development. The 2.8 km simulation, for its part, showed a weak reduction of at the windward side of the mountain (316 K) as a result of the GPS-ZTD nudging compared to the NDG-7. However, at the lee side between 600 hPa and 900 hPa, is reduced in NDG-460 2.8 by -8 K (compared to CTRL-2.8, 318 K), this is shown in Fig. 10.c and Fig. 10.d. The consequence for the updrafts was a change in their timing location and intensity (see Fig. 10.d). For the time shown, the drier environment in the NDG-7 and NDG-2.8 runs impedes the development of deeper updraughts. Figure 11 shows that median CAPE-ML is reduced as a result of the GPS nudging for both resolutions from 310 J kg -1 in CTRL-7 to 190 J kg -1 in NDG-7 (-39 %) and from 600 J kg -1 in CTRL-2.8 to 410 J kg -1 in NDG-2.8 (-32 %). Since COSMO-465 CLM selects the lowest 50 hPa as the mean layer to compute CAPE-ML (mixed layer), a decrease of humidity close to ground implies a relevant impact on atmospheric instability conditions. COSMO-CLM in the 2.8 km resolution represented larger latent instability than 7 km for this event. Median KO-index increased from -2.7 K, in CTRL-7 to -1.5 K in NDG-7 (+ 44 %) where lower KO-index indicates more potential for storm development under favourable large-scale conditions. The narrower simulation domains of the 2.8 km simulations (see Sect. 2.2.2) render the impact of the GPS nudging on KO-index weaker 470 given the inability to represent changes on the large-scale pressure distribution. The overall decrease in the median moisture flux implies a drier ground level and a drier LFT. This means that the air entrained in the convective updrafts is drier than that of the reference runs (CTRL). The median moisture flux is reduced by about 13 % in NDG-7 and about 5% in CTRL-2.8 at 700 hPa. At the PBL, the moisture flux is also reduced. The changes in moisture flux between CTRL-7 and NDG-7 are larger than their 2.8 km counterparts. This is due to two factors, first, the changes in specific humidity are slightly weaker in the 2.8 475 km runs as compared to 7 km and second, the wind speed and direction in the 7 km runs are modified as a result of the GPS-ZTD nudging. For instance, at 950 hPa, extreme horizontal wind speeds are reduced by -8 % from CTRL-7 to NDG-7. This impact is not observed in the 2.8 km runs.
Overall, the humidity reduction caused by the GPS-ZTD nudging, locally over Corsica, reduced the amount of instability (as shown by CAPE-ML and KO-index) as well as humidity at the LFT (demonstrated by the changes in specific humidity and 480 moisture flux).

Impact on the low-pressure system and mesoscale winds
Besides impacting the representation of the local conditions of humidity, instability and buoyancy, the GPS-ZTD nudging affected the representation of the low-pressure system. This section shows how a large humidity reduction over the Iberian Peninsula and France weakened the intensity of the pressure Low and its associated circulation. This brought, in turn, stringent 485 modifications of the wind fields close to Corsica down to the ground and hence on dynamic lifting. This effect was observed in the 7 km resolution runs exclusively given the broader extent of the simulation domains. Hence, for the analysis of the impact of the GPS-ZTD nudging on the large-scale surface pressure distribution we focus in the 7 km resolution simulations.
In the early morning of 26-Oct-2016, the centre of the upper-level low was located over the north-western part of the Iberian Plateau. The GPS nudging induced moisture reductions of 7 mm in IWV at that location in the NDG-7 simulations, with very 490 large reductions in the range 1-2 g kg -1 from the ground up to 700 hPa (not shown). The progression of the pressure-low toward southern France was effective in twelve hours and at 1500 UTC, the PMSL was of 995hPa at the Rhône Valley (CTRL-7).
The centre of the Low extended toward the Alps at 2300 UTC. Drying of the atmospheric column, due to the GPS-ZTD nudging, also took place at this region between the 25-Oct and the 28-Oct (not shown). At 2300 UTC on the 20-Oct, over the Cévennes-Vivarais area, differences in IWV were of 3 mm between CTRL-7 and NDG-7. Figure 12 shows the differences in 495 PMSL on the 26-Oct 2300 UTC between NDG-7 and CTRL-7 as well as the wind fields at 950 hPa. The GPS-ZTD nudging increased by 10 hPa the PMSL at the centre of the system and up to 2 hPa between Brittany (France) and the Balearic Islands (Spain). The impact for the cyclonic wind circulation was a veering from a south-westerly to west-south-westerly flow and a reduction of the wind speeds. The largest impact was observed at the 950 hPa level albeit relevant differences affecting Corsica exist between 850 hPa and 1000 hPa. The reduction of the wind speeds is demonstrated through box-whisker plots in Fig. 11. 500 This difference in wind speed does not exist in the 2.8 km runs. The consequence for convection initiation was that weaker low-level convergence was represented in the NDG-7. Either for offshore convergence or convergence forced by the orography. This hampered orographic lifting at the mountain foothills and ahead of the island reducing triggering of new cells and weakening the convective updrafts.

Conclusions 505
Further knowledge of the pathways of moisture and convection interaction is needed (Stevens, 2005;Sherwood et al., 2010;Ahrens and Samson, 2010). A deeper understanding of moist processes is relevant to improve the representation of heavy precipitation by numerical atmospheric models in order to support the mitigation and prevention of its hazards. The presented work aimed at assessing the sensitivity of the precipitating convection and underlying mechanisms to realistic corrections of the atmospheric moisture distribution. We did this by, first understanding the role of local and remote atmospheric moisture 510 contributions to the occurrence of the event and second, through moisture sensitivity experiments nudging GPS-ZTD observations. The unique opportunity provided by the synergy of high-resolution atmospheric modelling, very frequent data nudging and high-resolution humidity datasets enables the study of moisture and convection interactions in a selected case study of heavy precipitation. With this purpose, we presented an in-depth analysis of HyMeX IOP16 with special focus on the complex orographic region of Corsica.
The results showed novel insights on the role of remote and local moisture transports and the moisture distribution preconditioning heavy precipitation at the WMed and over Corsica during IOP16. These results supplement the findings by (Thévenot et al., 2015;Duffourg et al., 2016;Martinet et al., 2017), focusing in southern France and the Gulf of Lions as study region. The main findings of this study are summarized in the following  Large atmospheric moisture amounts (IWV~ 40 mm) precondition the areas of convective activity, namely, southern 520 France and the Gulf of Lions, Corsica and Sardinia, the middle Mediterranean and northeastern Italy, in agreement with previous investigations in the region . These very wet air masses reach southern France The CTRL-7 and NDG-7 better captured the location of precipitation than the 2.8 km simulations. The nudging did not improve this aspect in the 2.8 km resolution.
The impact of the GPS-ZTD nudging has been assessed with the following conclusions:  Heavy precipitation showed a large sensitivity to the moisture variations, implying a strong reduction of the maximum 555 totals (-20 %for 7 km and -25 % for 2.8 km) arising from less intense convection and a lower number of triggered cells. This is related to the reduction of specific humidity below 500 hPa by -10 % in the 7 km and by -7 % in the 2.8 km.
 The two affected processes which exerted the largest control for precipitation reduction were the reduction of atmospheric instability over the island (-35 % CAPE-ML) and the drying of the LFT bringing more dry air 560 entrainment into the convective updrafts (-13 % moisture flux at 700 hPa for 7 km and -5 % for 2.8 km).
 Additionally, the 7 km simulations showed an impact on the large-scale surface pressure and the associated circulation given the larger simulation domains. The GPS-ZTD nudging dried the atmospheric levels over Iberia and France, weakening the Low (~10 hPa higher for PMSL). This induced in turn a decrease in wind speed (-7 %) and a veering 565 of the direction toward a west-south-westerly.
This study highlights the added value of adequate corrections of atmospheric moisture for modelling of convective-processes, in this case through sub-hourly GPS-ZTD nudging. The high-temporal resolution of the GPS-ZTD observations facilitate a better representation of the water vapour variability and a better regulation of the accumulated precipitation. This was shown to be the case for HyMeX IOP16 at convection permitting and convection parameterized grid lengths. This is especially 570 relevant since, in spite of the consensus in the scientific community that convection permitting is the future of NWP, coarser resolution simulations will still be needed as providers of forcing data and therefore reducing moisture uncertainties at these grid lengths is needed to improve the precipitation forecasts. Also noteworthy is the large sensitivity to variations of the LFT moisture showed by the model. Recent observational studies have highlighted the linkage between intense convective precipitation and a humid free troposphere (Schiro and Neelin, 2019;Virman et al., 2018), hence the relevance of the ability 575 to represent such sensitivity. Just as the high-temporal resolution, the dense spatial coverage and the accuracy are the clear benefits of GPS-ZTD nudging, this study also points out one of its drawbacks. Being an integrated quantity, GPS-ZTD nudging struggles to correct the vertical distribution of humidity, in this case particularly in the lower-troposphere. Lastly, this study focuses on a single case study; therefore, the results presented here should be extended to other events of the region to prove their general applicability. In a further publication, the authors evaluate the impact of GPS-ZTD nudging on the SOP1 period 580 of the HyMeX campaign considering the sensitivity of all IOPs in this autumn season.

Code Availability
The COSMO-CLM model is only accessible to members of the Climate Limited-area Modelling Community and access is granted upon request. Parts of the model documentation are freely available at http://www.cosmomodel.org/content/model/documentation/core/default.htm 585

Data Availability
Two further publications using these nudging simulations are on-going. Therefore, these are not yet available for the open public. However, the data used to produce the figures showing results on the nudging simulations (Figures 2,4,6.a,6.b,6.c,7,8,9,10,11,12) are accessible in at https://doi.org/10.5445/IR/1000097457. The observational data used in the figures within this manuscript are obtained from the referenced data sets and their access depends on the restrictions of the producing 590 institutions.

Author Contributions
SK designed and planned the experiments. ACA carried out as part of his PhD the nudging experiments under the supervision of SK. ACA and SK analysed the results and wrote the manuscript.

Competing Interests 595
The authors declare that they have no conflict of interest.

Acknowledgements
We acknowledge Oliver Bock and the LAboratoire de Recherche En Géodésie (LAREG) of the French Institute of the Geographic and Forest Information (IGN) for their providing of the GPS-ZTD data set. We also acknowledge Météo-France and the HyMeX program for supplying the Rain gauges and radiosonde data supported by grants MISTRALS/HyMeX and 600 ANR-11-BS56-0005 IODA-MED. We would like to thank the German Weather Service (DWD) and the CLM-Community for their providing of the COSMO-CLM model, and especially Ulrich Schättler and Christoph Schraff for their support in carrying out the nudging experiments. We are also thankful to the European Centre for Medium-Range Weather Forecasts (ECMWF) for their Integrated Forecasting System (IFS) analyses. We thank as well the teams of HYSPLIT at the NOAA Air Resources Laboratory, CMORPH at the Climate Prediction Center (CPC) and GLEAM for their data sets. Finally, we would 605 like to thank the hosting institution, the Karlsruhe Institute of Technology (KIT).