Convection-permitting Model Research Articles

Combining a Cloud-Resolving Model with Satellite for Cloud Process Model Simulation Validation

AbstractAdvances in computer power have made it possible to increase the spatial resolution of regional numerical models to a scale encompassing larger convective elements of less than 5 km in size. One goal of high resolution is to begin to resolve convective processes, and therefore it is necessary to evaluate the realism of convective clouds resolved explicitly at this resolution. This paper presents a method that is based on satellite comparisons to examine the simulation of continental tropical convection over Africa, in a high-resolution integration of the Met Office Unified Model (UK UM), developed under the Cascade project. The spatial resolution of these simulations is 1.5 km, the temporal resolution is 15 min, and the convection is resolved explicitly. The Spinning Enhanced Visible and Infrared Imager (SEVIRI) radiometer measurements were simulated by the Radiative Transfer for the Television and Infrared Observation Satellite (TIROS) Operational Vertical Sounder (RTTOV) model, and then a comparison between the simulations and real SEVIRI measurements was performed. The analysis using the presented method shows that the UK UM can represent tropical convection dynamics realistically. However, an error has been found in the high-level humidity distribution, which is characterized by strong humidity gradients. A key point of this paper is to present a method for establishing the credibility of a convection-permitting model by direct comparison with satellite data.

Representation of model error in a convective-scale ensemble prediction system

Abstract. In this paper ensembles of forecasts (of up to six hours) are studied from a convection-permitting model with a representation of model error due to unresolved processes. The ensemble prediction system (EPS) used is an experimental convection-permitting version of the UK Met Office's 24-member Global and Regional Ensemble Prediction System (MOGREPS). The method of representing model error variability, which perturbs parameters within the model's parameterisation schemes, has been modified and we investigate the impact of applying this scheme in different ways. These are: a control ensemble where all ensemble members have the same parameter values; an ensemble where the parameters are different between members, but fixed in time; and ensembles where the parameters are updated randomly every 30 or 60 min. The choice of parameters and their ranges of variability have been determined from expert opinion and parameter sensitivity tests. A case of frontal rain over the southern UK has been chosen, which has a multi-banded rainfall structure. The consequences of including model error variability in the case studied are mixed and are summarised as follows. The multiple banding, evident in the radar, is not captured for any single member. However, the single band is positioned in some members where a secondary band is present in the radar. This is found for all ensembles studied. Adding model error variability with fixed parameters in time does increase the ensemble spread for near-surface variables like wind and temperature, but can actually decrease the spread of the rainfall. Perturbing the parameters periodically throughout the forecast does not further increase the spread and exhibits "jumpiness" in the spread at times when the parameters are perturbed. Adding model error variability gives an improvement in forecast skill after the first 2–3 h of the forecast for near-surface temperature and relative humidity. For precipitation skill scores, adding model error variability has the effect of improving the skill in the first 1–2 h of the forecast, but then of reducing the skill after that. Complementary experiments were performed where the only difference between members was the set of parameter values (i.e. no initial condition variability). The resulting spread was found to be significantly less than the spread from initial condition variability alone.

Preface: Forecast and projection in climate scenario of Mediterranean intense events: uncertainties and propagation on environment (the MEDUP project)

The Mediterranean basin is a particularly vulnerable region to climate change, partly due to its quite unique character that results both from physiographic conditions and societal development. The region features indeed a near-closed sea surrounded by very urbanised littorals and mountains from which numerous rivers originate. This results in a lot of interactions and feedbacks between oceanic–atmospheric–hydrological processes that play a predominant role on climate and extreme events that frequently cause heavy damages and human losses in the Mediterranean. These highimpact weather events are heavy precipitation and flash flooding during the fall season, severe cyclogenesis associated with strong winds and large swell or heat waves and droughts accompanied by forest fires during summer. If during the last decade, numerical weather prediction (NWP) have made considerable progress in terms of realistic modelling of intense events, especially with the implementation by national weather services of convection-permitting models, they still have difficulties to predict the precipitation location and amount with the precision required to drive hydrological models, and more generally, to meet societal demands in the field of early warning and risk prevention. One of the current challenges is to quantify the uncertainties associated with these atmospheric model forecasts and to study their spread throughout the forecasting and warning chains. Also, the Mediterranean region is qualified as a “hot spot” for climate change. However, the evolution of intense events in the Mediterranean with climate change is still an open question. Studies on the evolution of extremes with climate change rely on a cascade of models and methods; all sources of uncertainty that need to be better quantified to assess the likelihood of climate projections and of their impacts. The MEDUP project (2008–2011), sponsored by the French National Research Agency (ANR) Vulnerabilite Milieux et Climat program, focused on these issues. Its objectives were to quantify and reduce the uncertainties associated with NWP and regional climate models and to study how they propagate on the environment and may combine with the intrinsic uncertainties of the vulnerability and risk analysis methods. An original feature of MEDUP was to address these questions at one and the same time for weather forecasting (1–4 day range), seasonal forecasting (1–3 month range) and climate scenario simulations (50–100 yr range). MEDUP was also innovative in considering the whole uncertainty chain, from the atmospheric modelling of high-impact weather events to their consequences on the environment. The region of interest was the northwestern Mediterranean in southern France, a region with complex coast shapes and mountains. Highimpact weather events considered in MEDUP are heavy precipitation, severe winds and long dry weather periods (favouring droughts). This special issue of Natural Hazards and Earth System Sciences contains 14 articles presenting most of the studies carried out within the MEDUP project. In the following, we summarize, for each of the prediction ranges, the content and main results of the papers published in this special issue together with references to some other studies performed within MEDUP.

Combining probabilistic precipitation forecasts from a nowcasting technique with a time-lagged ensemble

A probabilistic nowcasting technique based on the Local Lagrangian method is combined with probabilistic forecasts derived from a time-lagged convection-permitting model to produce seamless short-term probabilistic precipitation forecasts. The fraction, the neighbourhood and the mean method are used to derive probabilistic information from this eight member ensemble. The model-based forecasts are calibrated with the reliability diagram statistics method. The skill of the probabilistic nowcasts and forecasts is evaluated with three quality measures. Probabilistic model-based forecasts are found to outperform probabilistic radar-based nowcasts after 2.25–3.5 h. Weighting functions derived in a lead time dependent evaluation of forecast skill are used to combine nowcasts and forecasts additively. The resulting seamless blended forecasts maintain or exceed the skill of the respective best component. In comparison with similar studies, the application of the time-lagged approach increases the skill of the numerical forecasts and hence the blended forecasts. Copyright © 2013 Royal Meteorological Society

Statistical Assessment of Tropical Convection-Permitting Model Simulations Using a Cell-Tracking Algorithm

Abstract This study presents a method for comparing convection-permitting model simulations to radar observations using an innovative object-based approach. The method uses the automated cell-tracking algorithm, Thunderstorm Identification Tracking Analysis and Nowcasting (TITAN), to identify individual convective cells and determine their properties. Cell properties are identified in the same way for model and radar data, facilitating comparison of their statistical distributions. The method is applied to simulations of tropical convection during the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) using the Weather Research and Forecasting Model, and compared to data from a ground-based radar. Simulations with different microphysics and model resolution are also conducted. Among other things, the comparisons between the model and the radar elucidate model errors in the depth and size of convective cells. On average, simulated convective cells reached higher altitudes than the observations. Also, when using a low reflectivity (25 dBZ) threshold to define convective cells, the model underestimates the size of the largest cells in the observed population. Some of these differences are alleviated with a change of microphysics scheme and higher model resolution, demonstrating the utility of this method for assessing model changes.

Bayesian Model Verification of NWP Ensemble Forecasts

Abstract Forecasts of convective precipitation have large uncertainties. To consider the forecast uncertainties of convection-permitting models, a convection-permitting ensemble prediction system (EPS) based on the Consortium for Small-scale Modeling (COSMO) model with a horizontal resolution of 2.8 km covering all of Germany is being developed by the Deutscher Wetterdienst (DWD). The deterministic model is named COSMO-DE. Vertical structures of temperature and humidity affect the potential for convective instability. For verification of vertical model profiles, radiosonde data are used. However, the observed state is uncertain by itself because of the well-known limits in observing the atmosphere. In this work the authors use a probabilistic approach, which considers the observation error as well as the model uncertainty to validate multidimensional state vectors (e.g., temperature profiles) of the COSMO-DE-EPS and of two mesoscale ensembles with horizontal resolution of 10 km and parameterized convection. The mesoscale ensembles are the COSMO short-range EPS (COSMO-SREPS) and the COSMO limited-area EPS (COSMO-LEPS). The approach is based on Bayesian statistics and allows for both verification and comparison of ensembles. The investigation period comprises August 2007 for a comparison of the COSMO-DE-EPS with the COSMO-SREPS. A period of 5 days in July 2007 is used to demonstrate the potential of the Bayesian approach for verification by evaluating the COSMO-SREPS and COSMO-LEPS against COSMO-EU analyses. Based on the Bayesian approach, it is shown that the temperature profiles modeled by the COSMO-DE-EPS are more consistent with the observed profiles than those of COSMO-SREPS.

Uncertainty of lateral boundary conditions in a convection-permitting ensemble: a strategy of selection for Mediterranean heavy precipitation events

Abstract. This study examines the impact of lateral boundary conditions (LBCs) in convection-permitting (C-P) ensemble simulations with the AROME model driven by the ARPEGE EPS (PEARP). Particular attention is paid to two torrential rainfall episodes, observed on 15–16 June 2010 (the Var case) and 7–8 September 2010 (the Gard-Ardèche case) over the southeastern part of France. Regarding the substantial computing time for convection-permitting models, a methodology of selection of a few LBCs, dedicated for C-P ensemble simulations of heavy precipitation events is evaluated. Several sensitivity experiments are carried out to evaluate the skill of the AROME ensembles, using different approaches for selection of the driving PEARP members. The convective-scale predictability of the Var case is very low and it is driven primarily by a surface low over the Gulf of Lyon inducing a strong convergent low-level flow, and accordingly advecting strong moisture supply from the Mediterranean Sea toward the flooded area. The Gard-Ardèche case is better handled in ensemble simulations as a surface cold front moved slowly eastwards while increasing the low-level water vapour ahead is well reproduced. The selection based on a cluster analysis of the PEARP members generally better performs against a random selection. The consideration of relevant meteorological parameters for the convective events of interest (i.e. geopotential height at 500 hPa and horizontal moisture flux at 925 hPa) refined the cluster analysis. It also helps in better capturing the forecast uncertainty variability which is spatially more localized at the "high-impact region" due to the selection of more mesoscale parameters.

Realism of Rainfall in a Very High-Resolution Regional Climate Model

AbstractThe realistic representation of rainfall on the local scale in climate models remains a key challenge. Realism encompasses the full spatial and temporal structure of rainfall, and is a key indicator of model skill in representing the underlying processes. In particular, if rainfall is more realistic in a climate model, there is greater confidence in its projections of future change.In this study, the realism of rainfall in a very high-resolution (1.5 km) regional climate model (RCM) is compared to a coarser-resolution 12-km RCM. This is the first time a convection-permitting model has been run for an extended period (1989–2008) over a region of the United Kingdom, allowing the characteristics of rainfall to be evaluated in a climatological sense. In particular, the duration and spatial extent of hourly rainfall across the southern United Kingdom is examined, with a key focus on heavy rainfall.Rainfall in the 1.5-km RCM is found to be much more realistic than in the 12-km RCM. In the 12-km RCM, heavy rain events are not heavy enough, and tend to be too persistent and widespread. While the 1.5-km model does have a tendency for heavy rain to be too intense, it still gives a much better representation of its duration and spatial extent. Long-standing problems in climate models, such as the tendency for too much persistent light rain and errors in the diurnal cycle, are also considerably reduced in the 1.5-km RCM. Biases in the 12-km RCM appear to be linked to deficiencies in the representation of convection.

A case of offshore convective initiation by interacting land breezes near Darwin, Australia

A case of offshore convective initiation by interacting land breezes near Darwin, Australia is investigated using convection-permitting model simulations, radar-derived precipitation observations, thermodynamic profiles from radiosonde soundings and surface measurements. These analyses elucidate the convergence of two land breezes in the Van Diemen Gulf, one originating from the Tiwi Islands and the other from mainland Australia; the convergence is sufficient to initiate a line of convection that forms parallel to the mainland coast in the early morning. While differing in small-scale features, the modeled system shows reasonably good agreement with the observed precipitation accumulations. However, using simulations with different initialization times and examining a second case, it is shown that the representation of the land-breeze system and subsequent convective initiation is very sensitive to the upstream wind and thermodynamic conditions, making correct simulation of these processes challenging.

PDF Parameterization of Boundary Layer Clouds in Models with Horizontal Grid Spacings from 2 to 16 km

Abstract Many present-day numerical weather prediction (NWP) models are run at resolutions that permit deep convection. In these models, however, the boundary layer turbulence and boundary layer cloud features are still grossly underresolved. Underresolution is also present in climate models that use a multiscale modeling framework (MMF), in which a convection-permitting model is run in each grid column of a global general circulation model. To better represent boundary layer clouds and turbulence in convection-permitting models, a parameterization was developed that models the joint probability density function (PDF) of vertical velocity, heat, and moisture. Although PDF-based parameterizations are more complex and computationally expensive than many other parameterizations, in principle PDF parameterizations have several advantages. For instance, they ensure consistency of liquid (cloud) water and cloud fraction; they avoid using separate parameterizations for different cloud types such as cumulus and stratocumulus; and they have an appropriate formulation in the “terra incognita” in which updrafts are marginally resolved. In this paper, an implementation of a PDF parameterization is tested to see whether it improves the simulations of a state-of-the-art convection-permitting model. The PDF parameterization used is the Cloud Layers Unified By Binormals (CLUBB) parameterization. The host cloud-resolving model used is the System for Atmospheric Modeling (SAM). SAM is run both with and without CLUBB implemented in it. Simulations of two shallow cumulus (Cu) cases and two shallow stratocumulus (Sc) cases are run in a 3D configuration at 2-, 4-, and 16-km horizontal grid spacings. Including CLUBB in the simulations improves some of the simulated fields—such as vertical velocity variance, horizontal wind fields, cloud water content, and drizzle water content—especially in the two Cu cases. Implementing CLUBB in SAM improves the simulations slightly at 2-km horizontal grid spacing, significantly at 4-km grid spacing, and greatly at 16-km grid spacing. Furthermore, the simulations that include CLUBB exhibit a reduced sensitivity to horizontal grid spacing.

Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME

AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

Evaluation of soil moisture ensemble runs to estimate precipitation variability in convection-permitting model simulations for West Africa

In this investigation, the impact of soil moisture on the precipitation amount and distribution in model simulations for West Africa was assessed. This included the examination of the dependence of precipitation on soil moisture, boundary layer parameters or convective conditions. For this purpose, different soil moisture fields from the rainy season of the year 2006 in West Africa were collected. These included three analysis fields, satellite measurements, and model output from the African Monsoon Multidisciplinary Analysis (AMMA) Land Surface Model Intercomparison Project. The different soil moisture fields were analyzed and compared first; large differences in the mean values, the north–south gradients, and the heterogeneity were revealed. The distributions were taken to initialize five convection-permitting model runs performed with the limited-area model COSMO (Consortium for Small-scale Modeling), forced by reanalyses from the European Centre for Medium-Range Weather Forecasts. A case study of a period with intense convective activity in the Sudanian and Sahelian zone was selected for the simulations. The simulated precipitation was compared with data from two satellite-based precipitation analyses and with forecasts from two global models of lower resolution. The model spread encompassed the precipitation analyses in the southern subdomain, while the latter lay far above the model results in the northern subdomain. The different soil moisture fields also influenced the turbulent fluxes at the surface, the boundary layer state and height as well as convective instability. Standard deviation of the simulations was highest for convective available potential energy, while the impact on convective inhibition was less pronounced. Only a weak negative relation of precipitation to soil moisture or conditional instability could be discerned. In this case, dry soil favored a higher boundary layer with better developed convective cells contributing to larger precipitation sums. The spread between the five precipitation forecasts suggests that a high-resolution limited-area ensemble, built by applying atmospheric variations as well as variations of the initial land surface conditions, will be a proper method to improve the prediction of mesoscale convective systems and convective precipitation for West Africa.

Ensemble prediction for nowcasting with a convection-permitting model – II: forecast error statistics

A 24-member ensemble of 1-h high-resolution forecasts over the Southern United Kingdom is used to study shortrange forecast error statistics. The initial conditions are found from perturbations from an ensemble transform Kalman filter. Forecasts from this system are assumed to lie within the bounds of forecast error of an operational forecast system. Although noisy, this system is capable of producing physically reasonable statistics which are analysed and compared to statistics implied from a variational assimilation system. The variances for temperature errors for instance show structures that reflect convective activity. Some variables, notably potential temperature and specific humidity perturbations, have autocorrelation functions that deviate from 3-D isotropy at the convective-scale (horizontal scales less than 10 km). Other variables, notably the velocity potential for horizontal divergence perturbations, maintain 3-D isotropy at all scales. Geostrophic and hydrostatic balances are studied by examining correlations between terms in the divergence and vertical momentum equations respectively. Both balances are found to decay as the horizontal scale decreases. It is estimated that geostrophic balance becomes less important at scales smaller than 75 km, and hydrostatic balance becomes less important at scales smaller than 35 km, although more work is required to validate these findings. The implications of these results for high-resolution data assimilation are discussed.

Ensemble prediction for nowcasting with a convection-permitting model—I: description of the system and the impact of radar-derived surface precipitation rates

A key strategy to improve the skill of quantitative predictions of precipitation, as well as hazardous weather such as severe thunderstorms and flash floods is to exploit the use of observations of convective activity (e.g. from radar). In this paper, a convection-permitting ensemble prediction system (EPS) aimed at addressing the problems of forecasting localized weather events with relatively short predictability time scale and based on a 1.5 km grid-length version of the Met Office Unified Model is presented. Particular attention is given to the impact of using predicted observations of radar-derived precipitation intensity in the ensemble transform Kalman filter (ETKF) used within the EPS. Our initial results based on the use of a 24-member ensemble of forecasts for two summer case studies show that the convectivescale EPS produces fairly reliable forecasts of temperature, horizontal winds and relative humidity at 1 h lead time, as evident from the inspection of rank histograms. On the other hand, the rank histograms seem also to show that the EPS generates too much spread for forecasts of (i) surface pressure and (ii) surface precipitation intensity. These may indicate that for (i) the value of surface pressure observation error standard deviation used to generate surface pressure rank histograms is too large and for (ii) may be the result of non-Gaussian precipitation observation errors. However, further investigations are needed to better understand these findings. Finally, the inclusion of predicted observations of precipitation from radar in the 24-member EPS considered in this paper does not seem to improve the 1-h lead time forecast skill.

The development of tornadic storms on the cold side of a front favoured by local enhancement of moisture and CAPE

In the afternoon of 28 July 2005, a damaging F2 tornado in Birmingham, United Kingdom, was one of three tornadoes developing on the immediate cool side of a surface warm front. An analysis is performed to find out why the narrow zone on this side of the front was apparently so favourable for tornadoes in spite of lower surface temperatures. It is investigated how three ingredients for tornadogenesis are distributed and how these distributions evolve. These ingredients — the presence of a convective storm, low-level wind shear, and a low LCL height — and the presence of SBCAPE, which is a prerequisite for surface-based convective storms, are studied using surface, upper-air and satellite data, as well as a convection-permitting model. At some distance on the cool side of the front, strong wind shear existed across the lowest 1 km (around 15 m s −1 bulk shear), but no surface-based instability to support surface-based convection. In contrast, the air mass on the front's warm side exhibited weak SBCAPE (50–200 J kg −1) but only fairly weak low-level shear (0–1 km bulk shear ~ 6 m s −1). The analysis suggests that between these regions, within a narrow zone on the cold side of the front, surface-based instability was considerably higher (~ 450 J kg −1 of SBCAPE) and the 0- to 1-km bulk wind shear was estimated to be 10.5 m s −1. Moreover, the higher relative humidity in this zone resulted in a lower lifted condensation level (~ 200 m in this zone, compared to ~ 700 m on the warm side of the front). It is concluded that an overlap of all these four ingredients only existed within this zone that was only about 30 km wide.

Assessing the Benefits of Convection-Permitting Models by Neighborhood Verification: Examples from MAP D-PHASE

Abstract High-resolution numerical weather prediction (NWP) models produce more detailed precipitation structures but the real benefit is probably the more realistic statistics gained with the higher resolution and not the information on the specific grid point. By evaluating three model pairs, each consisting of a high-resolution NWP system resolving convection explicitly and its low-resolution-driving model with parameterized convection, on different spatial scales and for different thresholds, this paper addresses the question of whether high-resolution models really perform better than their driving lower-resolution counterparts. The model pairs are evaluated by means of two fuzzy verification methods—upscaling (UP) and fractions skill score (FSS)—for the 6 months of the D-PHASE Operations Period and in a highly complex terrain. Observations are provided by the Swiss radar composite and the evaluation is restricted to the area covered by the Swiss radar stations. The high-resolution models outperform or equal the performance of their respective lower-resolution driving models. The differences between the models are significant and robust against small changes in the verification settings. An evaluation based on individual months shows that high-resolution models give better results, particularly with regard to convective, more localized precipitation events.

Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations

Abstract This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts. Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

High tropical winds

Strong and persistent low-level winds blowing over the Adriatic basin are often associated with intense precipitation events over Italy. Typically, in case of moist southeasterly wind (Sirocco), rainfall affects northeastern Italy and the Alpine chain, while with cold northeasterly currents (Bora) precipitations are localized along the eastern slopes of the Apennines and central Italy coastal areas. These events are favoured by intense air-sea interactions and it is reasonable to hypothesize that the Adriatic sea surface temperature (SST) can affect the amount and location of precipitation.High-resolution simulations of different Bora and Sirocco events leading to severe precipitation are performed using a convection-permitting model (MOLOCH). Sensitivity experiments varying the SST initialization field are performed with the aim of evaluating the impact of SST uncertainty on precipitation forecasts, which is a relevant topic for operational weather predictions, especially at local scales. Moreover, diagnostic tools to compute water vapour fluxes across the Italian coast and atmospheric water budget over the Adriatic Sea have been developed and applied in order to characterize the air mass that feeds the precipitating systems. Finally, the investigation of the processes through which the SST influences location and intensity of heavy precipitation allows to gain a better understanding on mechanisms conducive to severe weather in the Mediterranean area and in the Adriatic basin in particular.Results show that the effect of the Adriatic SST (uncertainty) on precipitation is complex and can vary considerably among different events. For both Bora and Sirocco events, SST does not influence markedly the atmospheric water budget or the degree of moistening of air that flows over the Adriatic Sea. SST mainly affects the stability of the atmospheric boundary layer, thus influencing the flow dynamics and the orographic flow regime, and in turn, the precipitation pattern.