Abstract. The Special Observation Period (SOP1

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Abstract. Black carbon (BC), brown carbon (BrC), and soil dust are the most important radiation-absorbing aerosols (RAAs). When RAAs are deposited on the snowpack, they lower the snow albedo, causing an increase in the solar radiation absorption. The climatic impact associated with the snow darkening induced by RAAs is highly uncertain. The Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) attributes low and medium confidence to radiative forcing (RF) from BrC and dust in snow, respectively. Therefore, the contribution of anthropogenic sources and carbonaceous aerosols to RAA RF in snow is not clear. Moreover, the snow albedo perturbation induced by a single RAA species depends on the presence of other light-absorbing impurities contained in the snowpack. In this work, we calculated the present-day RF of RAAs in snow starting from the deposition fields from a 5-year simulation with the GEOS-Chem global chemistry and transport model. RF was estimated taking into account the presence of BC, BrC, and mineral soil dust in snow, simultaneously. Modeled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed that BC, BCE, and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global-average all-sky total RAA-, BC-, BrC-, and dust-snow RF were 0.068, 0.033, 0.0066, and 0.012 W m−2, respectively. At a global scale, non-BC compounds accounted for 40 % of RAA-snow RF, while anthropogenic RAAs contributed to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W m−2). In the middle latitudes of Asia, the forcing from dust in spring accounted for 50 % (+0.24 W m−2) of the total RAA RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction resulted in an overall uncertainty of −50 %/+61 %, −57 %/+183 %, −63 %/+112 %, and −49 %/+77 % in BC-, BrC-, dust-, and total RAA-snow RF, respectively. Uncertainty upper bounds of BrC and dust were about 2 and 3 times larger than the upper bounds associated with BC. Higher BrC and dust uncertainties were mainly due to the presence of multiple absorbing impurities in the snow. Our results highlight that an improvement of the representation of RAAs in snow is desirable, given the potential high efficacy of this forcing.

Abstract. Black carbon (BC), brown carbon (BrC) and soil dust are the most radiation absorbing aerosols (RAA). When RAA are deposited on the snowpack, they lower the snow albedo, increasing the absorption of the solar radiation. The climatic impact associated to snow darkening induced by RAA is highly uncertain. In this work, a 5-years simulation with GEOS-Chem global chemistry and transport model was performed, in order to calculate the present-day radiative forcing (RF) of RAA in snow. RF was estimated taking simultaneously into account the presence of BC, BrC, and mineral soil dust in snow. Modelled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed as BC, BCE and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global mean all sky total RAA, BC, BrC and dust snow RF are 0.068, 0.033, 0.0066, and 0.012 W/m2, respectively. At global scale, non-BC compounds account for 40 % of RAA snow RF, while anthropogenic RAAs contribute to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W/m2). In the middle latitudes of Asia, dust forcing in spring accounts for the 50 % (+0.24 W/m2) of the total RAAs RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction result in an overall uncertainty of −50% / +61 %, −57 % / +183 %, −63 % / +112 %, and −49 % / +77 % in BC, BrC, dust and total RAAs snow RF, respectively.

During the first Hymex campaign (5 September–6 November 2012) referred to as Special Observation Period (SOP-1), dedicated to heavy precipitation events and flash floods in Western Mediterranean, three Italian hydro-meteorological monitoring sites were activated: Liguria-Tuscany, North-Eastern Italy and Central Italy. The extraordinary deployment of advanced instrumentation, including instrumented aircrafts, and the use of several different operational weather forecast models has allowed an unprecedented monitoring and analysis of high impact weather events around the Italian hydro-meteorological sites. This activity has seen the strict collaboration between the Italian scientific and operational communities. In this paper, an overview of the Italian organization during the SOP-1 is provided, and selected Intensive Observation Periods (IOPs) are described. A significant event for each Italian target area is chosen for this analysis: IOP2 (12–13 September 2012) in North-Eastern Italy, IOP13 (15–16 October 2012) in Central Italy and IOP19 (3–5 November 2012) in Liguria and Tuscany. For each IOP the meteorological characteristics, together with special observations and weather forecasts, are analyzed with the aim of highlighting strengths and weaknesses of the forecast modeling systems. Moreover, using one of the three events, the usefulness of different operational chains is highlighted

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011-June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98 % accuracy in correctly identifying rainy and nonrainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42-0.45 Heidke skill score) or rainy areas only (0.27-0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial-temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0-15 mm h −1 range suggests that OPEMW tends to slightly overestimate sri values below 6-7 mm h −1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h −1 with respect to rain gauges (respectively −2-0 and 4 mm h −1 with respect to weather radars).

Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows for the use of both a higher spatial resolution and the parameterization schemes specifically developed for representing sub-grid scale physical processes at high resolution. However, precipitation estimation is still affected by errors that can impact the response of hydrological models. To the aim of improving the hydrological forecast and the characterization of related uncertainties, a regional-scale meteorological–hydrological ensemble is presented. The uncertainties in the precipitation forecast and how they propagate in the hydrological model are also investigated. A meteorological–hydrological offline coupled ensemble is built to forecast events in a complex-orography terrain where catchments of different sizes are present. The Best Discharge-based Drainage (BDD; both deterministic and probabilistic) index, is defined with the aim of forecasting hydrological-stress conditions and related uncertainty. In this context, the meteorological–hydrological ensemble forecast is implemented and tested for a severe hydrological event which occurred over Central Italy on 15 November 2017, when a flood hit the Abruzzo region with precipitation reaching 200 mm (24 h)−1 and producing damages with a high impact on social and economic activities. The newly developed meteorological–hydrological ensemble is compared with a high-resolution deterministic forecast and with the observations (rain gauges and radar data) over the same area. The receiver operating characteristic (ROC) statistical indicator shows how skilful the ensemble precipitation forecast is with respect to both rain-gauge- and radar-retrieved precipitation. Moreover, both the deterministic and probabilistic configurations of the BDD index are compared with the alert map issued by Civil Protection Department for the event showing a very good agreement. Finally, the meteorological–hydrological ensemble allows for an estimation of both the predictability of the event a few days in advance and the uncertainty of the flood. Although the modelling framework is implemented on the basins of the Abruzzo region, it is portable and applicable to other areas.

Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows to use both a higher spatial resolutions and the newly developed parameterization schemes for representing sub-grid scale physical processes. However, precipitation estimation is still affected by errors that can impact on the response of hydrological models. To the aim of considering the uncertainties in the precipitation forecast and how they propagate in the hydrological model, an ensemble approach is investigated. A meteo-hydro ensemble system is built to forecast events in a complex orography terrain where catchments of different size are present. In this context, the meteo-hydrological forecast system is implemented and tested for a severe hydrological event occurred over Central Italy on November 15, 2017. During this period, a flash flood hit the Abruzzo region causing precipitation up to 200 mm/24 hours and producing damages with a high impact on social and economic activities.The newly developed meteo-hydro ensemble system is compared with a high resolution deterministic forecast and with the observations over the same area, showing a very good response. In addition, the ensemble allows for an estimation of the predictability of the event a few days in advance and of the uncertainty of this flood. Although the modelling framework is implemented on the basins of Abruzzo region, it is portable and applicable to other areas.