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Environment and Climate Change Canada has initiated the production of a 1980–2018, 10 km, North American precipitation and surface reanalysis. ERA-Interim is used to initialize the Global Deterministic Reforecast System (GDRS) at a 39 km resolution. Its output is then dynamically downscaled to 10 km by the Regional Deterministic Reforecast System (RDRS). Coupled with the RDRS, the Canadian Land Data Assimilation System (CaLDAS) and Precipitation Analysis (CaPA) are used to produce surface and precipitation analyses. All systems used are close to operational model versions and configurations. In this study, a 7-year sample of the reanalysis (2011–2017) is evaluated. Verification results show that the skill of the RDRS is stable over time and equivalent to that of the current operational system. The impact of the coupling between RDRS and CaLDAS is explored using an early version of the reanalysis system which was run at 15 km resolution for the period 2010–2014, with and without the use of CaLDAS. Significant improvements are observed with CaLDAS in the lower troposphere and surface layer, especially for the 850 hPa dew point and absolute temperatures in summer. Precipitation is further improved through an offline precipitation analysis which allows the assimilation of additional observations of 24 h precipitation totals. The final dataset should be of particular interest for hydrological applications focusing on transboundary and northern watersheds, where existing products often show discontinuities at the border and assimilate very few – if any – precipitation observations.

International audience; In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2 optical images, with complementary contributions. Three conceivable scenarios are investigated with two sets of nomenclatures (17 and 43 classes): early, on-line, and late classifications. Experiments performed on 2017 show very satisfactory results (82–97%), locally almost on-par with state-of-the-art deep-based methods. We can conclude our framework offers a strong basis for country-scale operational deployment for 2020+CAP.

The scope of this work is to assess the progresses made in the warning alert system of Piedmont since the 1994 flood. We used the COSMO model at high horizontal resolution forced by ERA5 re-forecast to simulate the November 1994 event, performing also a simple sensitivity test regarding the parameterization of convection. We compared the results with the original forecast and with the available observations, in order to understand how the emission of the alert would have been affected using the current operational system.

Lanzhou Zhongchuan International Airport [International Civil Aviation Organization (ICAO) code ZLLL] is located in a wind shear prone area in China, where most low-level wind shear events occur in dry weather conditions. We analyzed temporal distribution and synoptic circulation background for 18 dry wind shear events reported by pilots at ZLLL by using the NCEP final (FNL) operational global analysis data, and then proposed a lidar-based regio-nal divergence algorithm (RDA) to determine wind shear intensity and location. Low-level wind shear at ZLLL usually occurs in the afternoon and evening in dry conditions. Most wind shear events occur in an unstable atmosphere over ZLLL, with changes in wind speed or direction generally found at 700 hPa and 10-m height. Based on synoptic circulations at 700 hPa, wind shear events could be classified as strong northerly, convergence, southerly, and weak wind types. The proposed RDA successfully identified low-level wind shear except one southerly case, achieving 94% alerting rate compared with 82% for the operational system at ZLLL and 88% for the ramp detection algorithm (widely used in some operational alert systems) based on the same dataset. The RDA-unidentified southerly case occurred in a near neutral atmosphere, and wind speed change could not be captured by the Doppler lidar.

Environmental control of historical sites in archaeological museums faces challenges of high-energy consumption and complex soil-air coupled preservation environment. Many historical sites are not well preserved and undergo deterioration because of the desiccation cracking and salt enrichment. Moreover, the desiccation cracking cannot be prevented even if the air relative humidity (RH) is sustained to near-saturated state. This is due to the reason that migration of moisture from the historical site to air environment is a spontaneous one-way process. In the current work, a protection strategy, dealing with the air supplied into a “super-saturation” state through indirect-direct evaporative cooling (IDEC) and ultrasonic atomizing humidification (UAH) units, was proposed to provide a stable preservation environment and suppress the relics’ deterioration. An experimental system, which consists of a simulated exhibition hall as well as an IDEC unit, was built. A series of tests were conducted to investigate the feasibility and performances of the system under different operational modes. The results show that: 1) the IDEC system could stabilize a preservation environment at the temperature of 24.4 °C, and relative humidity (RH) of saturation state (RH = 100%). The short-term fluctuations of temperature and RH are ±1.7 °C and ±0.0%, respectively, which conforms to the level-AA (risk-free) standard specified in ASHRAE. Moreover, it could effectively recover water content of the dried soil environment for the historical sites. 2) The system could manipulate precise temperature when the auxiliary surface air cooler (SAC) unit is switched on. Compared with a traditional return air operational system, it saves 40% of the energy consumed. The proposed system is of high-efficiency and provides energy-saving for the environmental control of historical sites.

The operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the ba...

The COSMO-Ru1Mp prototype of the numerical weather prediction system for the Moscow region with a grid spacing of 1 km and with the included and adapted TERRA_URB parameterization module for the urban areas is implemented. The module is provided with necessary information on the urban development and on the sources of anthropogenic heat based on the adaptation of open data. The module is interfaced with the COSMO-Ru operational system for the regional numerical weather prediction developed in the Hydrometcenter of Russia. The primary testing of the COSMO-Ru1Mp is performed, including daily forecasts based on operational data. The advantage of this forecast system and the prospects of its further development are revealed.

We developed a new system to monitor and forecast coastal and open-ocean states around Japan for operational use by the Japan Meteorological Agency. The system consists of an eddy-resolving analysis model based on four-dimensional variational assimilation and a high (2-km) resolution forecast model covering Japanese coastal areas that incorporates an initialization scheme with temporal and spatial filtering. Assimilation and forecast experiments were performed for 2008 to 2017, and the results were validated against various observation datasets. The assimilation results captured well the observed variability in sea surface temperature, coastal sea level, volume transport, and sea ice. Furthermore, the volume budget for the Japan Sea was significantly improved by the use of the 2-km resolution forecast model compared with the 10-km resolution analysis model. The forecast results indicate that this system has a predictive limit longer than 1 month in many areas, including in the Kuroshio current area south of Japan and the southern Japan Sea. In the forecast results of case studies, the 2017 Kuroshio large meander was well predicted, and warm water intrusions accompanying Kuroshio path variations south of Japan were also successfully reproduced. Sea ice forecasts for the Sea of Okhotsk largely captured the evolution of sea ice in late winter, but sea ice in early winter included relatively large errors. This system has high potential to meet operational requirements for monitoring and forecasting ocean phenomena at both meso- and coastal scales.

The development of coupled atmosphere-ocean prediction systems with utility on the short-range Numerical Weather Prediction (NWP) and ocean forecasting timescales has accelerated over the last decade. This builds on a body of evidence showing the benefit, particularly for weather forecasting, of more correctly representing the feedbacks between surface ocean and atmosphere. It prepares the way for more unified prediction systems with the capability of providing consistent surface meteorology, wave and surface ocean products to users for whom this is important. Here we describe a coupled ocean-atmosphere system, with weakly coupled data assimilation, which was operationalised at the Met Office as part of the Copernicus Marine Environment Service (CMEMS). We compare the ocean performance to that of an equivalent ocean-only system run at the Met Office, and other CMEMS products. Sea surface temperatures in particular are shown to verify better than in the ocean-only systems, although other aspects including temperature profiles and surface currents are slightly degraded. We then discuss the plans to improve the current system in future as part of the development of a coupled NWP system at the Met Office.

A decision support system to aid in the risk evaluation of airborne animal diseases was developed for Ireland. The system's primary objective is to assist in risk evaluation of the airborne spread of Foot and Mouth Disease (FMD). The operational system was developed by Met Éireann – the Irish Meteorological Service and CVERA (Centre for Veterinary Epidemiology and Risk Analysis), in co-operation with NOAA-ARL (National Oceanic and Atmospheric Administration – Air Resources Laboratory) and ECMWF (European Centre for Medium-Range Weather Forecasts). The infrastructure largely relies on the HYSPLIT dispersion model driven by both ECMWF meteorological forecasts for longer range simulations, and HARMONIE-AROME meteorological forecasts, a high resolution local area meteorological model, ideal for shorter range national emissions. Following on from previous work by the Bureau of Meteorology, Australia as well as the Australian Department of Agriculture, Fisheries and Forestry, further modifications were made to the HYSPLIT source code to improve the model's characterisation of the Foot and Mouth Disease virus. FMD is a highly infectious disease among cloven hoofed animals that can transmit via airborne means. Biological characteristics related to temperature, humidity, lifespan as well as atmospheric washout were all incorporated either through new or existing functionality of the dispersion model. Combining the model dispersion capabilities of HYSPLIT with a virus emission model and GIS mapping software with farmland zoning, the disease dispersion system becomes a powerful analysis and decision support tool. This airborne animal disease atmospheric dispersion system helps improve emergency preparedness, as well as aid confinement and eradication strategies for relevant Irish authorities, during a disease outbreak.

Biogas Electrical Power Plant require a good operational system. Good operational system could be mean there is a clear information about the condition from field. To make easier to get information from field, a concept of signal reader which picturing actual condition can be build. In Biogas Electrical Power Plant from POME (Palm Oil Mill Effluent), there is a Waste Water Treatment Station. The Operation of Waste Water Treatment Station should be well and safe operated, to reducing hazardous content of the waste water and get Biogas as a side product. On that operation, every equipment in that field need the right information for control purpose. The require signal analysis in waste water treatment station to get information actual condition from field to the main control has been done. It needs some signal such as Actif-unactif Status Indicator, Level Indicator, Flow Meter Indicator, Temperature Indicator, Pressure Indicator for Liquid and Pressure Indicator for Gas, respectively. This kind of signal as an information from field (Waste Water Treatment Station) to make operation easier and safe.

A freely accessible model-aided satellite altimeter-based daily water level forecasting system using simple regression analysis is proposed for the Mekong River with feasibility study being performed. In the Mekong Delta (MD), where Regional Flood Management and Mitigation Center (RFMMC) does not provide forecasting, ocean tides strongly impact river levels and were specifically addressed by the sum of 5-term sinusoidal function. Forecasting skills of our system are quite promising in the MD, although RFMMC's forecasting system has better skills than ours in the Mekong mainstem upstream of MD. In contrast to current operational system, our system circumvents the need of frequent altimeter samplings in the upstream by using the Variable Infiltration Capacity (VIC) macroscale hydrologic model with 0.1° resolution. The proposed forecasting system is computationally efficient without the need of complex hydrodynamic modeling, making such approach globally applicable for river basins and deltas with comparable forecasting skill and minimal computational cost.

This study explored the suitability of simulation tools for accurately predicting fluidized bed gasification in various scenarios without disturbing the operational system, and dedicating time to experimentation, in the aim of benefiting the decision makers and investors of the low-carbon waste-based bioenergy sector, in accelerating circular bioeconomy solutions. More specifically, this study aimed to offer a customized circular bioeconomy solution for a rice processing residue. The objectives were the simulation and economic assessment of an air atmospheric fluidized bed gasification system fueled with rice husk, for combined heat and power generation, by using the tools of Aspen Plus V9, and the Aspen Process Economic Analyzer. The simulation model was based on the Gibbs energy minimization concept. The technological configurations of the SMARt-CHP technology were used. A parametric study was conducted to understand the influence of process variables on product yield, while three different scenarios were compared: (1) air gasification; (2) steam gasification; and (3) oxygen-steam gasification-based scenario. Simulated results show good accuracy for the prediction of H2 in syngas from air gasification, but not for the other gas components, especially regarding CO and CH4 content. It seems that the RGIBBS and Gibbs free minimization concept is far from simulating the operation of a fluidized bed gasifier. The air gasification scenario for a capacity of 25.000 t/y rice husk was assessed for its economic viability. The economic assessment resulted in net annual earnings of EUR 5.1 million and a positive annual revenue of EUR 168/(t/y), an excellent pay out time (POT = 0.21) and return of investment (ROI = 2.8). The results are dependent on the choices and assumptions made.

In the Mediterranean Sea unique environmental characteristics and sensitive assets coexist with intense maritime traffic that is represented by frequent daily passages of vessels along the main waterways. In order to assess the risk of oil stranding in case of at-sea emergencies and provide key products for environmental agencies or policymakers preparedness, a geographically relocatable, operational numerical system is implemented and tested. The system relies on the application of oceanographic and particle tracking models and is able to provide, on a high-resolution and unstructured computational grid, a 3-days forecast of those variables known as the main drivers of oil slicks at sea. The risk of potential oil stranding is computed through a combination of anthropogenic hazard and shoreline vulnerability. The sources of hazard vary on time and space in relation to local maritime vessel traffic. The shoreline vulnerability is based on the current knowledge of slope, main grain size, geology of rocks, and occurrence of manmade structures at coast. The operational system is enriched by a web graphical user interface and includes automatic and on-demand working modes. Its functionality is demonstrated in the Strait of Bonifacio (western Mediterranean Sea), area with a high potential risk of oil stranding due to an intense maritime traffic. Risk assessment is hence computed for a test year, the 2018. Critical values of risk are found in correspondence of long stretches of littoral while many of them are currently characterized by a low anthropogenic pressure. The results emphasize the geomorphological features of the shorelines as reducing or amplifying factors to any potential impact of oil stranding at coast.

In reviewing the development of extended range forecast, it is not only a complicated scientific problem, but also a technical difficulty, which is challenging in meteorological research. The grid weather operational forecast system for extended range at National Meteorological Center in China is introduced. Based on the ECMWF ensemble prediction system, 5-km grid forecast operational system is developed with these methods including back propagation neural network-self Memory method (BP-SM), Delaunay triangulated method and moving window ordinary kriging method (MWK). Compared daily maximum 2-m temperature forecast of the extended range with ensemble mean and BP-SM method at 2454 stations over China in summer 2018, the results show that the calibrated daily 2-m temperature forecast is significant with the BP-SM method especially over the regions of complex topography. The prospect of research for calibrated technology and development trend in extended range forecast are discussed.

Radioxenon activity concentrations are monitored globally using the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organisation, improving the monitoring community's ability to detect radionuclide signatures from an underground nuclear test (UNT). An IMS-like noble gas system is in operation at AWE (Aldermaston, UK) and can collect and measure radioxenon isotopes in environmental air samples. When operated in this mode, data produced is analysed at the UK National Data Centre (NDC) and significant detection events are flagged for further investigation. This work discusses a number of significant detection events analysed using the operational system deployed at the UK NDC, which includes atmospheric transport simulations and a real-time stack-monitoring data feed from the nearest medical isotope production facility in Belgium. A comparison of the expected radionuclide contributions with measured detections is presented, including a comparison of the isotopic ratios for the radioxenon isotopes of interest (133Xe, 131mXe, 133mXe, 135Xe).

This study estimates the photovoltaic (PV) energy production from the rooftop solar plant of the National Institute of Technology Karnataka (NITK) and the impact of clouds and aerosols on the PV energy production based on earth observation (EO)-related techniques and solar resource modeling. The post-processed satellite remote sensing observations from the INSAT-3D have been used in combination with Copernicus Atmosphere Monitoring Service (CAMS) 1-day forecasts to perform the Indian Solar Irradiance Operational System (INSIOS) simulations. NITK experiences cloudy conditions for a major part of the year that attenuates the solar irradiance available for PV energy production and the aerosols cause performance issues in the PV installations and maintenance. The proposed methodology employs cloud optical thickness (COT) and aerosol optical depth (AOD) to perform the INSIOS simulations and quantify the impact of clouds and aerosols on solar energy potential, quarter-hourly monitoring, forecasting energy production and financial analysis. The irradiance forecast accuracy was evaluated for 15 min, monthly, and seasonal time horizons, and the correlation was found to be 0.82 with most of the percentage difference within 25% for clear-sky conditions. For cloudy conditions, 27% of cases were found to be within ±50% difference of the percentage difference between the INSIOS and silicon irradiance sensor (SIS) irradiance and it was 60% for clear-sky conditions. The proposed methodology is operationally ready and is able to support the rooftop PV energy production management by providing solar irradiance simulations and realistic energy production estimations.

Flooding is a rapidly growing concern in West Africa. Several floods have occurred in recent years with severe consequences including loss of lives and damaged infrastructure. Flooding is also projected to increase with climate change. Access to operational forecasts is a critical component in addressing these challenges. This study presents results from our joint efforts to co-design, co-adapt, and co-operate a short- and medium-term operational hydrological forecasting and alert pilot system for West Africa, within the FANFAR project (www.fanfar.eu).The system has been co-developed through a cycle of workshops, training sessions, and expert exchanges involving representatives from hydrological services, emergency management agencies, river basin organisations, and expert agencies in 17 countries in West and Central Africa. Multi-criteria decision analysis was employed to clarify and prioritize system objectives and configurations. We found that the most highly prioritized objectives were: high accuracy, clear flood risk information, reliable access, and timely production and distribution of the information. Our agile development approach also provided ample opportunities to focus development efforts on the most highly prioritized components, and incorporate stakeholder feedback in the development process.The system is built on an ICT cloud platform that employs a daily forecasting chain including meteorological reanalysis and forecasting, data assimilation of gauge observations and satellite altimetry, hydrological initialisation and forecasting, flood alert derivation, and distribution through e-mail, SMS, web visualisation and API. The system is designed to enable multiple configurations and integration of several information sources (e.g. different hydrological models, observations, flood hazard thresholds etc.). We present the system configurations, stakeholder-driven adaptations, challenges, and current forecast performance. To our knowledge, the FANFAR system constitutes a significant advancement toward the vision of achieving efficient flood management in West Africa.

In Peru, heavy precipitations (PR) are the second natural phenomenon with the greatest number of people affected in recent decades. Landslides (known as “huaycos” in Peru) are mostly produced by PR and located overall on the Andes mountains. In this regard, to monitoring and inform in advance about the most susceptible regions to landslides, the National Service of Meteorology and Hydrology of Peru (SENAMHI) has launched the national system for monitoring of landslides produced by PR, called SILVIA (“Sistema de Monitoreo de Movimientos en Masa generados por Lluvias Intensas” in spanish).The methodology couple PR thresholds (7 days of antecedent PR) from PISCO operational precipitation (a gridded daily precipitation product of SENAMHI) with the susceptibility map for landslide hazard produced by the Peruvian Geological, Mining and Metallurgical Institute (INGEMMET). Both inputs products are combined in a purposely-built hazard matrix to get a spatially and temporally variable for landslide hazard: while statistical PR thresholds are used to accomplish a temporal definition with very coarse spatial resolution, landslide susceptibility maps provide static spatial information about the probability of landslide occurrence at fine spatial resolution. The hazard matrix combines three susceptibility classes (S1, low susceptibility; S2 medium susceptibility; S3 high and very high susceptibility) and three PR rate classes (L1, L2, L3), defining three hazard classes, from P1 (low hazard) to P3 (high hazard).SILVIA has been launched by SENAMHI (https://www.senamhi.gob.pe/?p=monitoreo-silvia) at a national scale. The implementation of SILVIA as a warning system has been improved using precipitation daily forecasting generating a daily-time forecast system to cope with streams activation in subbasins (“Activación de quebradas”, https://www.senamhi.gob.pe/?p=aviso-activacion-quebrada).

Tailings dams and storage facilities store toxic mine waste and effluent. Failure of a tailings storage facility can cause dramatic local ecosystem damage, water contamination and, if a tailings dam fails, loss of life due to inundation of the downstream area. The failure rate of tailings dams is known to be significantly greater than that of conventional water retention dams, but monitoring all tailings dams and storage facilities through frequent site visits could be an expensive and resource-demanding task.Monitoring tools based on remote sensing and internet of things (IoT) sensors have the potential to reduce the risk from tailings storage failures by enabling the organisations responsible to conduct some monitoring remotely, and hence direct their resources for detailed monitoring more efficiently.We present an overview of DAMSAT (Dam Monitoring from SATellites), an operational tool for monitoring tailings dams, tailings deposit areas and water dams. The tool consists of several different modules. Radar and optical satellite remote sensing data, and in situ internet of things (IoT) sensors are used to monitor surface movement and indicators of pollution at tailings storage sites. Meteorological forecasts are coupled to hydrological models in order to forecast changes in water level at the dams. DAMSAT presents the monitoring information together with risk information from hazard, consequence and evacuation models of possible dam failures in one integrated platform. The project is a partnership between UK and Peruvian organisations. This approach, alongside proactive user engagement activities and user requirements analysis, is designed to ensure that the system is developed with the needs of the user community in mind.

The leakage of any substances at the sea bottom consequently leads to contamination. The polluted area will strongly depend on the type of pollution which can be dissolved, material and the physical state of the bottom layers. Although the systems that monitor the particles or pollution on the sea surface already exists, there is no similar tool for the bottom area. This work aims at developing the High Resolution Dispersion Model (HRDM) that calculates transport of the pollution in case of potential leakage from chemical ammunition dumped into the Baltic Sea. The model has been embedded into existing operational system that covers the whole region of the Baltic Sea. Thus, it consists of the three main elements: operational atmospheric model that makes calculations every day, coupled ice-ocean model covering the whole Baltic Sea area and HRDM. The horizontal resolution of the atmospheric part is about 10 km, the coupled system is driven on 2.3 km grid and HRDM is implemented on 50 m horizontal resolution. The clients are able to make integration since 2014. Access to the system is granted via REpresentational State Transfer (REST) API that separates user interface from the server site. It is possible to make simulations for instantaneous and continuous type of leakage. The results are served in binary form (as a netcdf file) and as a georeferenced tiff picture. The temporal resolution of the output data is 1 h. The HRDM consists of half-life of sulfur mustard and the results for both – with and without half-life calculations are included in the output files. The HRDM has been created as a part of Decision Support System and is not (and will not be) available for public.

The impact of forthcoming wide-swath altimetry missions on the ocean analysis and forecasting system was investigated by means of OSSEs (observing system simulation experiments). These experiments were performed with a regional data assimilation system, implemented in the Iberian–Biscay–Ireland (IBI) region, at 1∕12∘ resolution using simulated observations derived from a fully eddy-resolving free simulation at 1∕36∘ resolution over the same region. The objective of the experiments was to assess the ability of different satellite constellations to constrain the ocean analyses and forecasts, considering both along-track altimeters and future wide-swath missions; consequently, the capability of the data assimilation techniques used in the Mercator Ocean operational system to effectively combine the different kinds of measurements was also investigated. These assessments were carried out as part of a European Space Agency (ESA) study on the potential role of wide-swath altimetry in future versions of the European Union Copernicus programme. The impact of future wide-swath altimetry data is evident for investigating the reliability of sea level values in OSSEs. The most significant results were obtained when looking at the sensitivity of the system to wide-swath instrumental error: considering a constellation of three nadir and two “accurate” (small instrumental error) wide-swath altimeters, the error in ocean analysis was reduced by up to 50 % compared to conventional altimeters. Investigating the impact of the repetitivity of the future measurements, the results showed that two wide-swath missions had a major impact on sea-level forecasting – increasing the accuracy over the entire time window of the 5-day forecasts – compared with a single wide-swath instrument. A spectral analysis underlined that the contributions of wide-swath altimetry data observed in ocean analyses and forecast statistics were mainly due to the more accurate resolution, compared with along-track data, of ocean variability at spatial scales smaller than 100 km. Considering the ocean currents, the results confirmed that the information provided by wide-swath measurements at the surface is propagated down the water column and has a considerable impact (30 %) on ocean currents (up to a depth of 300 m), compared with the present constellation of altimeters. The ocean analysis and forecasting systems used here are those currently used by the Copernicus Marine Environment and Monitoring Service (CMEMS) to provide operational services and ocean reanalysis. The results obtained in the OSSEs considering along-track altimeters were consistent with those derived from real data (observing system experiments, OSEs). OSSEs can also be used to assess the potential of new observing systems, and in this study the results showed that future constellations of altimeters will have a major impact on constraining the CMEMS ocean analysis and forecasting systems and their applications.

A novel methodology is introduced for the spatial indexing of groundwater drought ‘risks’ (GDRs). It combines reliability analysis and standardised water-level index (SWI), which is readily applicable to areas with sparse data on groundwater depth (GWD) measurements. In reliability analysis, GWDs are reformulated in terms of load, which accounts for external effects, e.g. withdrawals and recharge, as well as resistance, which accounts for system capacity with regard to drought intensities (mild, moderate, severe and extreme). Reliability analysis formulates a novel procedure by using loads and resistance to formulate a performance function, which can be treated by statistical techniques, and thereby derives values of GDR, defined as failure of an operational system but without considering consequences. GDRs at observation wells are spatially distributed by using an interpolation technique. The methodology allows for estimating time variability in GDR to derive an environmental/ecological hazard indicator (EHI), which can serve in the management and planning of predicting groundwater drought. A Graphical User Interface (GDR V.1.0) is developed to serve as a decision support system and to derive GDR and EHI values.

Quantitative precipitation forecast (QPF) is still a challenge for numerical weather prediction (NWP), despite the continuous improvement of models and data assimilation systems. In this regard, the assimilation of radar reflectivity volumes should be beneficial, since the accuracy of analysis is the element that most affects short-term QPFs. Up to now, few attempts have been made to assimilate these observations in an operational set-up, due to the large amount of computational resources needed and due to several open issues, like the rise of imbalances in the analyses and the estimation of the observational error. In this work, we evaluate the impact of the assimilation of radar reflectivity volumes employing a local ensemble transform Kalman filter (LETKF), implemented for the convection-permitting model of the COnsortium for Small-scale MOdelling (COSMO). A 4-day test case on February 2017 is considered and the verification of QPFs is performed using the fractions skill score (FSS) and the SAL technique, an object-based method which allows one to decompose the error in precipitation fields in terms of structure (S), amplitude (A) and location (L). Results obtained assimilating both conventional data and radar reflectivity volumes are compared to those of the operational system of the Hydro-Meteo-Climate Service of the Emilia-Romagna Region (Arpae-SIMC), in which only conventional observations are employed and latent heat nudging (LHN) is applied using surface rainfall intensity (SRI) estimated from the Italian radar network data. The impact of assimilating reflectivity volumes using LETKF in combination or not with LHN is assessed. Furthermore, some sensitivity tests are performed to evaluate the effects of the length of the assimilation window and of the reflectivity observational error (roe). Moreover, balance issues are assessed in terms of kinetic energy spectra and providing some examples of how these affect prognostic fields. Results show that the assimilation of reflectivity volumes has a positive impact on QPF accuracy in the first few hours of forecast, both when it is combined with LHN or not. The improvement is further slightly enhanced when only observations collected close to the analysis time are assimilated, while the shortening of cycle length worsens QPF accuracy. Finally, the employment of too small a value of roe introduces imbalances into the analyses, resulting in a severe degradation of forecast accuracy, especially when very short assimilation cycles are used.

Hydropower makes up nearly half of Sweden's electrical energy production. However, the distribution of the water resources is not aligned with demand, as most of the inflows to the reservoirs occur during the spring flood period. This means that carefully planned reservoir management is required to help redistribute water resources to ensure optimal production and accurate forecasts of the spring flood volume (SFV) is essential for this. The current operational SFV forecasts use a historical ensemble approach where the HBV model is forced with historical observations of precipitation and temperature. In this work we develop and test a multi-model prototype, building on previous work, and evaluate its ability to forecast the SFV in 84 sub-basins in northern Sweden. The hypothesis explored in this work is that a multi-model seasonal forecast system incorporating different modelling approaches is generally more skilful at forecasting the SFV in snow dominated regions than a forecast system that utilises only one approach. The testing is done using cross-validated hindcasts for the period 1981–2015 and the results are evaluated against both climatology and the current system to determine skill. Both the multi-model methods considered showed skill over the reference forecasts. The version that combined the historical modelling chain, dynamical modelling chain, and statistical modelling chain performed better than the other and was chosen for the prototype. The prototype was able to outperform the current operational system 57 % of the time on average and reduce the error in the SFV by ∼ 6 % across all sub-basins and forecast dates.

Surface soil moisture (SSM) content plays an important role in a number of physical processes involving the energy and mass exchanges between the land and atmosphere interactions. Determination of ...

With the international goals of the Paris Agreement and the growing number of time-bound national goals for emissions reductions, reliable estimates of CO2 emissions are becoming more and more important. In particular, reducing the time lag of these estimates and producing short-term projections are gaining importance as the remaining time until mitigation deadlines becomes shorter. The Global Carbon Project has been producing a current-year projection of global CO2 emissions since 2012, introducing a sub-projection for the European Union in 2018. The success of this EU projection has been variable, and in this article I explore how the projections in 2019 were made along with some of the reasons why the projections have high uncertainty and bias. About 84% of the total error in the projection of EU emissions in 2019 was because of a poor projection for coal consumption, which was a result of poor estimates of sub-annual observations, a misunderstanding of conflicting information, and poor assumptions applied to the remainder of the year. The correction of the errors identified here will go some way to improving future short-term projections of the European Union's CO2 emissions, paving the way for a low-maintenance, operational system.

The Arctic Ocean has drawn major attention in recent years due to its rich natural resources and shorter navigational routes. Arctic development and transportation involve significant risk caused by the unique features of this region, such as ice, severe operating conditions, unpredictable climatic changes, and remoteness. Considering the high degree of uncertainty in the performance of vessel operating systems and humans, robust risk analysis and management tools are required to provide decision-support to prevent accidents and ensure safety at sea. This paper proposes an Object-Oriented Bayesian Network model to dynamically predict ship-ice collision probability based on navigational and operational system states, weather and ice conditions, and human error. The model, when integrated with potential consequences, may help estimate risk. A case study related to oil tanker navigation on the Northern Sea Route (NSR) is used to show the application of the proposed model to predict oil tanker collision with sea ice.

Extreme temperature anomalies such as heat and cold waves may have strong impacts on human activities and health. The heat waves in Western Europe in 2003 and in Russia in 2010, or the cold wave in South-Eastern Europe in 2012, generated a considerable amount of economic loss and resulted in the death of several thousands of people. Providing an operational system to monitor extreme temperature anomalies in Europe is thus of prime importance to help decision makers and emergency services which are responsive to an unfolding extreme event. In this study, the development and the validation of a monitoring system of extreme temperature anomalies are presented. The first part of the study describes the methodology based on the persistence of events exceeding a percentile threshold. The method is applied to three different observational datasets, in order to assess the robustness and highlighting uncertainties in the observations. The climatology of extreme events from the last 21 years is then analysed to highlight the spatial and temporal variability of the hazard and discrepancies amongst the observational datasets are discussed. In the last part of the study, the products derived from this study are presented and discussed regarding previous studies. The results highlight the accuracy of the developed index and the statistical robustness of the distribution used to calculate the return periods.

A brief description of the MTVZA-GYa microwave radiometer installed on the Meteor-M No. 2 Russian polar orbiting meteorological satellite is presented. The procedures of bias correction and quality control indispensable in satellite data assimilation are considered. The operational system of global data assimilation by the Hydrometcenter of Russia is briefly described. Results of numerical experiments on the assimilation of MTVZA-GYa observations in six temperature-sensitive channels are given. It is demonstrated that the assimilation of these data significantly improves the accuracy of short-range weather forecasts in the Southern Hemisphere. The effect of the use of MTVZA-GY data in the Northern Hemisphere is neutral.

Real-time prediction of vegetation phenology is critical for assisting crop monitoring, natural resource management, and land modeling in weather prediction systems. However, due to the lack of timely available satellite datasets and the inherent noise in time series, little attention has been paid to real-time and short-term predictions of vegetation phenology. The successful launch of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard operational Suomi National Polar-orbiting Partnership (Suomi NPP) satellite makes this research possible because it can provide land surface observations in a timely fashion. This study introduces an operational system that provides real-time and short-term predictions of vegetation phenology. Specifically, the system integrates timely available VIIRS observations and the climatology (expectation and standard deviation) of vegetation phenology from long-term MODIS data to simulate a set of potential temporal trajectories of greenness development at a given time for each pixel. These potential trajectories are then applied to identify spring green leaf development in real time, predict the occurrence of greenup onset, mid-greenup phase and maturity onset, and analyze the uncertainty of the prediction across a variety of ecosystems in North America. The accuracy of real-time and short-term predictions was evaluated by comparing with standard VIIRS detections and near-surface PhenoCam observations in both 2014 and 2015 across North America. The results showed that the real-time prediction of spring phenological metrics from VIIRS were all significantly correlated with those derived from PhenoCam datasets (R 2 > 0.96, P

In the last decades since the dramatic increase in flood frequency and magnitude, floods have become a crucial problem in West Africa. National and international authorities concentrate efforts on developing early warning systems (EWS) to deliver flood alerts and prevent loss of lives and damages. Usually, regional EWS are based on hydrological modeling, while local EWS adopt field observations. This study aims to integrate outputs from two regional hydrological models&mdash, Niger HYPE (NH) and World-Wide HYPE (WWH)&mdash, in a local EWS developed for the Sirba River. Sirba is the major tributary of Middle Niger River Basin and is supported by a local EWS since June 2019. Model evaluation indices were computed with 5-day forecasts demonstrating a better performance of NH (Nash&ndash, Sutcliffe efficiency NSE = 0.58) than WWH (NSE = 0.10) and the need of output optimization. The optimization conducted with a linear regression post-processing technique improves performance significantly to &ldquo, very good&rdquo, for NH (Heidke skill score HSS = 0.53) and &ldquo, good&rdquo, for WWH (HSS = 0.28). HYPE outputs allow to extend local EWS warning lead-time up to 10 days. Since the transfer informatic environment is not yet a mature operational system 10&ndash, 20% of forecasts were unfortunately not produced in 2019, impacting operational availability.

Urban green infrastructure plays an increasingly significant role in sustainable urban development planning as it provides important regulating and cultural ecosystem services. Monitoring of such dynamic and complex systems requires technological solutions which provide easy data collection, processing, and utilization at affordable costs. To meet these challenges a pilot study was conducted using a network of wireless, low cost, and multiparameter monitoring devices, which operate using Internet of Things (IoT) technology, to provide real-time monitoring of regulatory ecosystem services in the form of meaningful indicators for both human health and environmental policies. The pilot study was set in a green area situated in the center of Moscow, which is exposed to the heat island effect as well as high levels of anthropogenic pressure. Sixteen IoT devices were installed on individual trees to monitor their ecophysiological parameters from 1 July to 31 November 2019 with a time resolution of 1.5 h. These parameters were used as input variables to quantify indicators of ecosystem services related to climate, air quality, and water regulation. Our results showed that the average tree in the study area during the investigated period reduced extreme heat by 2 &deg, C via shading, cooled the surrounding area by transferring 2167 &plusmn, 181 KWh of incoming solar energy into latent heat, transpired 137 &plusmn, 49 mm of water, sequestered 8.61 &plusmn, 1.25 kg of atmospheric carbon, and removed 5.3 &plusmn, 0.8 kg of particulate matter (PM10). The values of the monitored processes varied spatially and temporally when considering different tree species (up to five to ten times), local environmental conditions, and seasonal weather. Thus, it is important to use real-time monitoring data to deepen understandings of the processes of urban forests. There is a new opportunity of applying IoT technology not only to measure trees functionality through fluxes of water and carbon, but also to establish a smart urban green infrastructure operational system for management.

Two important communities related to oceanography in the Baltic Sea are those working on operational oceanography and Earth system science, with focusing on the same water body but different temporal scales. They have been coordinated through two organizations/programs: the Baltic Sea Operational Oceanographic System (BOOS) and the Baltic Sea Experiment (BALTEX) and its successor, the Baltic Earth Program (Earth system science for the Baltic Sea region), respectively. Although the two communities have archived significant progresses in their own fields since early 1990s, there were few interactions between the communities. Rapid advancements of operational oceanography on ocean monitoring, data sharing, modelling and historical ocean state reconstruction in the last decade have provided a wide range of data, products and modelling tools which may be used in Earth system and climate change research. This is especially true when operational oceanography in the Baltic Sea is in a transition to a seamless service, i.e., from basin to local scales, from synoptic to climate scales and from physical to biogeochemical and biological systems. On the other hand, the Baltic Sea Earth system research can help to improve operational oceanography by contributing research observations and transferring their research achievements to the operational system. Based on a review of state-of-the-art of BOOS monitoring and modelling capabilities and on-going BOOS research, this paper will highlight topics and areas which are related to the Baltic Earth Grand Challenges, i.e., salinity dynamics, land-sea biogeochemical linkages, natural hazards and extreme events, sea level dynamics, coastal morphology and erosion, regional variability of water and energy exchanges, and multi-drivers of regional Earth system changes. Potential win-win cooperation and interaction between the BOOS and the Baltic Earth communities are also proposed and discussed.

The National Oceanic and Atmospheric Administration (NOAA) has pioneered the development and application of high-performance meteorological radar for public weather warning and forecasting services. The current operational system, the dual-polarization WSR-88D weather surveillance radar, has led to significant capability enhancements, for example, increased accuracy and lead time for severe weather and flash flood warnings. This paper summarizes research to determine whether the eventual replacement for the WSR-88D should exploit rapidly maturing phased array radar (PAR) technology. Key benefits for the rapid, adaptive scanning so enabled have been identified and work continues to mature associated data processing and numerical weather prediction (NWP) assimilation techniques. In parallel, the considerable engineering challenges associated with meteorological PAR are being addressed through modeling, hardware demonstration and data analysis. The Advanced Technology Demonstrator (ATD) - an S-band, dual polarization PAR - is described in this paper. ATD puts NOAA in a position to fully expose operational benefits and substantially advance the technical readiness level of meteorological PAR.

Electrical systems with high penetration of renewable energies are increasingly common. Among these sources, wind energy is the one that has most propagated with the technology in great development and the fact that much of the world population density is located in the coastal where there is strong incidence of winds, favoring the installation of large parks that will not need of long transmission lines for delivery this energy. Power through tidal current streams is a promising source as it presents as one of the advantages the high predictability of energy resources. Power systems analysis tools need to include these technologies as they are a worldwide trend. This paper aims to present a probabilistic modeling to include these sources in the optimal power flow problem. For this, wind farms, tidal currents and hybrids ones were modeled. They were included in the network as a form of power injection to analyze the operational system costs. The methodology was applied in the WSCC test system and had shown that the costs are significantly reduced, and that complementarity among the sources is an important factor for the constancy of the output power of the source.

Forecasting the height of new snow (HN) is crucial for avalanche hazard forecasting, road viability, ski resort management and tourism attractiveness. Météo-France operates the PEARP-S2M probabilistic forecasting system, including 35 members of the PEARP Numerical Weather Prediction system, where the SAFRAN downscaling tool refines the elevation resolution and the Crocus snowpack model represents the main physical processes in the snowpack. It provides better HN forecasts than direct NWP diagnostics but exhibits significant biases and underdispersion. We applied a statistical post-processing to these ensemble forecasts, based on non-homogeneous regression with a censored shifted Gamma distribution. Observations come from manual measurements of 24 h HN in the French Alps and Pyrenees. The calibration is tested at the station scale and the massif scale (i.e. aggregating different stations over areas of 1000 km2). Compared to the raw forecasts, similar improvements are obtained for both spatial scales. Therefore, the post-processing can be applied at any point of the massifs. Two training datasets are tested: (1) a 22-year homogeneous reforecast for which the NWP model resolution and physical options are identical to the operational system but without the same initial perturbations; (2) 3-year real-time forecasts with a heterogeneous model configuration but the same perturbation methods. The impact of the training dataset depends on lead time and on the evaluation criteria. The long-term reforecast improves the reliability of severe snowfall but leads to overdispersion due to the discrepancy in real-time perturbations. Thus, the development of reliable automatic forecasting products of HN needs long reforecasts as homogeneous as possible with the operational systems.

A forecasting and hazard assessment system for oil dispersion from Italian oil rigs was set up within the framework of a national research project. The system is based on 3D hydrodynamic, mesoscale resolving models providing forcing fields for a Lagrangian module of oil dispersion and slick evolution (oil transport and transformation). The tool provides,daily, the outputs of numerical simulations of possible oil spills from extraction platform sites. In this work we present the results for the 3 platforms closest to the Italian coast. Aside the operational usage, the numerical outputs are also stored and adopted to compute statistics of the slick distributions in the coastal and marine areas of interest. The hazard was assessed by means of two different indices (Hazard Index and Occurrence Index) based on the operational system outputs, for the estimation of the hazard at sea (marine hazard or aerial hazard). As short forecasts (2 days long) often do not allow the oil to reach the coast, an extra set of long-period simulations has been performed in order to compute a third Hazard Index (Coastal Hazard Index) suitable to estimate the hazard along the shorelines.\\ The adopted methodology allowed, as a whole, to assess both coastal and marine oil spill hazard due to oil spill extraction activities. The indices constitute a basic informative layer on which the environmental risk could be also evaluated, once opportunely combined with coastal vulnerability and sensitivity layers.

The hybrid two-way coupled 3DEnsVar assimilation system was tested with the NCMRWF global data assimilation forecasting system. At present, this system consists of T574L64 deterministic model and the grid-point statistical interpolation analysis scheme. In this experiment, the analysis system is modified with a two-way coupling with an 80 member Ensemble Kalman Filter of T254L64 resolution and runs are carried out in parallel to the operational system for the Indian summer monsoon season (June–September) for the year 2015 to study its impact. Both the assimilation systems are based on NCEP GFS system. It is found that hybrid assimilation marginally improved the quality of the forecasts of all variables over the deterministic 3D Var system, in terms of statistical skill scores and also in terms of circulation features. The impact of the hybrid system in prediction of extreme rainfall and cyclone track is discussed.

An ocean state monitor and analysis radar (OSMAR), developed by Wuhan University in China, have been mounted at six stations along the coasts of East China Sea (ECS) to measure velocities (currents, waves and winds) at the sea surface. Radar-observed surface current is taken as an example to illustrate the operational high-frequency (HF) radar observing and data service platform (OP), presenting an operational flow from data observing, transmitting, processing, visualizing, to end-user service. Three layers (systems): radar observing system (ROS), data service system (DSS) and visualization service system (VSS), as well as the data flow within the platform are introduced. Surface velocities observed at stations are synthesized at the radar data receiving and preprocessing center of the ROS, and transmitted to the DSS, in which the data processing and quality control (QC) are conducted. Users are allowed to browse the processed data on the portal of the DSS, and access to those data files. The VSS aims to better show the data products by displaying the information on a visual globe. By utilizing the OP, the surface currents in East China Sea are monitored, and hourly and seasonal variabilities of them are investigated.

The automatic system of operational forecasting of the Black Sea state which functions at the Marine Hydrophysical Institute is presented. Principles of the system construction are considered; the marine environment models used for forecasting, the data streams required for the system functioning, and tools for validating and visualizing the results of sea-state calculations are described. Some examples of investigating a number of processes and phenomena in the Black Sea are given.

This research was conducted to analyze balancing the storage capacity of the Bendo Reservoir with the quantity of water needed. Bendo Reservoir located in Nginden Sawo Village, Ponorogo Regency is one of the multipurpose reservoirs in Indonesia designed for irrigation, water supply, and simultaneously hydroelectric power. The water available is observed to be decreasing along with climate change, thereby, requiring an appropriate reservoir operational system. Several methods have been designed to achieve this purpose through the use of vast and complex variables but this study used a Dynamic Systems approach for analysis. Hhowever, involved the use of two scenarios in the simulation model to obtain the most optimum results. A period of 5 years was simulated and the result showed water shortage started in the 54th month for scenario 2, 49th month for scenario 1, and 46th month for the base model. Therefore, means scenario 2 has the most optimum conditions and recommended as the appropriate solution for Bendo Reservoir water distribution.

The quality of the upgraded version of the CMEMS biogeochemical operational system of the Mediterranean Sea (MedBFM) is assessed in terms of consistency and forecast skill, following a mixed validation protocol that exploits different reference data from satellite, oceanographic databases, Biogeochemical Argo floats, literature. We demonstrate that the GODAE metrics paradigm can be efficiently applied to validate an operational model system for biogeochemical and ecosystem forecasts. The accuracy of the CMEMS biogeochemical products for Mediterranean Sea can be achieved from basin-wide and seasonal scale to mesoscale and weekly scale, and its level depends on the specific variable and the availability of reference data. In particular, the use of the Biogeochemical Argo floats data allows for a relevant enhancement of the validation framework of operational biogeochemical models, providing new skill metrics for key biogeochemical processes and dynamics (e.g. deep chlorophyll maximum depth), which can be easily implemented to routinely monitor the quality of the products and highlight any possible anomaly.

The paper presents the flow chart of the internal power supply source of oil and gas processing plants (OGPP) with combined generation of heat and electric power, synthesis gas and complex waste disposal: low-potential hydrocarbon gases of the thermal utilization unit, black oil fuel of the gasification unit. The considered flow chart implements the principles of energy and resource efficiency, environmental safety and makes possible to get closer to the maximum closed-loop process cycles. The energy efficiency criteria system was developed to evaluate the efficiency of the proposed scheme integration with the energy complex and OGPP operational system. The most significant criteria of the energy and technical and economic efficiency are represented. On the basis of the mentioned criteria, the efficiency of the internal power supply source integration into the energy complex (EC) of OGPP was evaluated.

© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 This paper presents the development of a model based on efficiency equations to evaluate the performance of an HCPV/T system and compares its outputs with data of an operational case-study system installed in Palermo, Italy. The model is validated with data of the operational system to show real performance. The model can evaluate (a) the electric efficiency of the InGaP/InGaAs/Ge TJ solar cell and (b) electrical and thermal power/energy production potential of one module. The model predictions are compared with experimental electric and thermal data by obtaining linear regression plots of experimental results vs. analytical results; the R2 for experimental electrical and thermal results are 0.91 and 0.87 respectively. Using the model, the evaluated average daily analytical and experimental InGaP/InGaAs/Ge TJ solar cell efficiencies are 33 % and 25 % respectively; with a maximum daily experimental value of 30 %. It was found that the annual analytical and potential (based on derived equations from experimental data) electric energy produced by one module are 158 kWh/m2/year and 144 kWh/m2/year respectively, while the annual analytical and potential thermal energy are 375 kWh/m2/year and 390 kWh/m2/year respectively. SMART GEMS project funded by H2020-MSCA-RISE-2014 (GA No 645677) and FAE project, PO FESR Sicilia 2007/2013 4.1.1.1.

Because seasonal rainfall is the largest driver of grain production in Australia, forecasts of can increase the profitability of grain farms when used to inform on-farm decision making. Seasonal forecasting in the grainbelt of Western Australia (WA) is difficult because climate drivers that affect the Australian continent have little influence on the south-west corner. The Statistical Seasonal Forecast system was developed to provide skilful forecasts to assist on-farm decision making in the south-west of WA. It has now been operational from 2013 to 2018. This article describes the models and methods used to create forecasts, the results of forecast validation, and verification of the accuracy of six years of operational seasonal forecasts. Forecast validation was performed using leave-one-out cross-validation. It showed positive skill, lower for late spring and summer forecasts that rises in autumn and is highest for forecasting winter rainfall. Skill for forecasting growing season (May to October) is good for lead times of up to three months. Verified skill of the operational forecasts is lower than estimated using cross-validation. The operational system performed reasonably well at forecasting 3-month periods, and for most years it showed better skill than climatology. Verified skill for growing season forecasts is higher than that of forecasting any 3-month period, with higher skill in the south. With only a small sample of six years of forecasts, longer-term accuracy may be better or worse. To test whether declining accuracy is a risk, use of a less generous measure of forecast validation is recommended.

Uncertainties of snowpack models and of their meteorological forcings limit their use by avalanche hazard forecasters, or for glaciological and hydrological studies. The spatialized simulations currently available for avalanche hazard forecasting are only assimilating sparse meteorological observations. As suggested by recent studies, their forecasting skills could be significantly improved by assimilating satellite data such as snow reflectances from satellites in the visible and the near-infrared spectra. Indeed, these data can help constrain the microstructural properties of surface snow and light absorbing impurities content, which in turn affect the surface energy and mass budgets. This paper investigates the prerequisites of satellite data assimilation into a detailed snowpack model. An ensemble version of Meteo-France operational snowpack forecasting system (named S2M) was built for this study. This operational system runs on topographic classes instead of grid points, so-called ‘semi-distributed’ approach. Each class corresponds to one of the 23 mountain massifs of the French Alps (about 1000 km2 each), an altitudinal range (by step of 300 m) and aspect (by step of 45°). We assess the feasability of satellite data assimilation in such a semi-distributed geometry. Ensemble simulations are compared with satellite observations from MODIS and Sentinel-2, and with in-situ reflectance observations. The study focuses on the 2013–2014 and 2016–2017 winters in the Grandes-Rousses massif. Substantial Pearson R2 correlations (0.75–0.90) of MODIS observations with simulations are found over the domain. This suggests that assimilating it could have an impact on the spatialized snowpack forecasting system. However, observations contain significant biases (0.1–0.2 in reflectance) which prevent their direct assimilation. MODIS spectral band ratios seem to be much less biased. This may open the way to an operational assimilation of MODIS reflectances into the Meteo-France snowpack modelling system.

Monitoring Sea Surface Salinity (SSS) is important for understanding and forecasting the ocean circulation. It is even crucial in the context of the acceleration of the water cycle. Until recently, SSS was one of the less observed essential ocean variables. Only sparse in situ observations, most often closer to 5 meters deep than the surface, were available to estimate the SSS. The recent satellite missions of ESA's SMOS, NASA's Aquarius, and now SMAP have made possible for the first time to measure SSS from space. The SSS drivers can be quite different than the temperature ones. The model SSS can suffer from significant errors coming not only from the ocean dynamical model but also the atmospheric precipitation and evaporation as well as ice melting and river runoff. Satellite SSS can bring a valuable additional constraint to control the model salinity. In the framework of the SMOS Nino 2015 ESA project (https://www.godae-oceanview.org/projects/smos-nino15/), the impact of satellite SSS data assimilation is assessed with the Met Office and Mercator Ocean global ocean analysis and forecasting systems with a focus on the Tropical Pacific region. This article presents the analysis of an Observing System Experiment (OSE) conducted with the 1/4° resolution Mercator Ocean analysis and forecasting system. SSS data assimilation constrains the model SSS to be closer to the observations in a coherent way with the other data sets already routinely assimilated in an operational context. Globally, the SMOS SSS assimilation has a positive impact in salinity over the top 30 meters. Comparisons to independent data sets show a small but positive impact. The sea surface height (SSH) has also been impacted by implying a reinforcement of TIWs during the El-Niño 2015/16 event. Finally, this study helped us to progress in the understanding of the biases and errors that can degrade the SMOS SSS performance.

Highlights • GloFAS was calibrated using daily streamflow data from 1287 stations worldwide. • Evolutionary Algorithm with parallel co-evolution of multiple simulations was used. • Parameters will be used in operational flood forecasting (www.globalfloods.eu). • Bias reduction in the forcings is needed to further improve the forecast skill., This paper presents the calibration and evaluation of the Global Flood Awareness System (GloFAS), an operational system that produces ensemble streamflow forecasts and threshold exceedance probabilities for large rivers worldwide. The system generates daily streamflow forecasts using a coupled H-TESSEL land surface scheme and the LISFLOOD model forced by ECMWF IFS meteorological forecasts. The hydrology model currently uses a priori parameter estimates with uniform values globally, which may limit the streamflow forecast skill. Here, the LISFLOOD routing and groundwater model parameters are calibrated with ECMWF reforecasts from 1995 to 2015 as forcing using daily streamflow data from 1287 stations worldwide. The calibration of LISFLOOD parameters is performed using an evolutionary optimization algorithm with the Kling-Gupta Efficiency (KGE) as objective function. The skill improvements are quantified by computing the skill scores as the change in KGE relative to the baseline simulation using a priori parameters. The results show that simulation skill has improved after calibration (KGE skill score > 0.08) for the large majority of stations during the calibration (67% globally and 77% outside of North America) and validation (60% globally and 69% outside of North America) periods compared to the baseline simulation. However, the skill gain was impacted by the bias in the baseline simulation (the lowest skill score was obtained in basins with negative bias) due to the limitation of the model in correcting the negative bias in streamflow. Hence, further skill improvements could be achieved by reducing the bias in the streamflow by improving the precipitation forecasts and the land surface model. The results of this work will have implications on improving the operational GloFAS flood forecasting (www.globalfloods.eu).