Operational Forecasting System Research Articles

Optimal error analysis of MJO prediction associated with uncertainties in sea surface temperature over Indian Ocean

In this study, the predictability of the Madden–Julian Oscillation (MJO) is investigated using the coupled Community Earth System Model (CESM) and the climatically relevant singular vector (CSV) method. The CSV method is an ensemble-based strategy to calculate the optimal growth of the initial error on the climate scale. We focus on the CSV analysis of MJO initialized at phase II, facilitating the investigation of the effect of the initial errors of the sea surface temperature (SST) in the Indian Ocean on it. Six different MJO events are chosen as the study cases to ensure the robustness of the results. The results indicate that for all the study cases, the optimal perturbation structure of the SST, denoted by the leading mode of the singular vectors (SVs), is a meridional dipole-like pattern between the Bay of Bengal and the southern central Indian Ocean. The MJO signal tends to be more converged and significant in the Eastern Hemisphere while the model is perturbed by leading SV. The moist static energy analysis results indicate that the eastward propagation is much more evident in the terms of vertical advection and radiation flux than others. Therefore, the SV perturbation can strengthen and converge the MJO signal mostly by increasing the vertical advection of the moist static energy. Further, the sensitivity studies indicate that the structure of the leading SV is not sensitive to the initial states, which suggests that we might not need to calculate SVs for each initial time in constructing the ensemble prediction, significantly saving computational time in the operational forecast systems.

The 2018 fire season in North America as seen by TROPOMI: aerosol layer height intercomparisons and evaluation of model-derived plume heights

Abstract. Before the launch of the TROPOspheric Monitoring Instrument (TROPOMI), only two other satellite instruments were able to observe aerosol plume heights globally, the Multi-angle Imaging SpectroRadiometer (MISR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The TROPOMI aerosol layer height is a potential game changer, since it has daily global coverage, and the aerosol layer height retrieval is available in near real time. The aerosol layer height can be useful for aviation and air quality alerts, as well as for improving air quality forecasting related to wildfires. Here, TROPOMI's aerosol layer height product is evaluated with MISR and CALIOP observations for wildfire plumes in North America for the 2018 fire season (June to August). Further, observing system simulation experiments were performed to interpret the fundamental differences between the different products. The results show that MISR and TROPOMI are, in theory, very close for aerosol profiles with single plumes. For more complex profiles with multiple plumes, however, different plume heights are retrieved; the MISR plume height represents the top layer, and the plume height retrieved with TROPOMI tends to have an average altitude of several plume layers. The comparison between TROPOMI and MISR plume heights shows that, on average, the TROPOMI aerosol layer heights are lower, by approximately 600 m, compared to MISR, which is likely due to the different measurement techniques. From the comparison to CALIOP, our results show that the TROPOMI aerosol layer height is more accurate over dark surfaces, for thicker plumes, and plumes between approximately 1 and 4.5 km. MISR and TROPOMI are further used to evaluate the plume height of Environment and Climate Change Canada's operational forecasting system FireWork with fire plume injection height estimates from the Canadian Forest Fire Emissions Prediction System (CFFEPS). The modelled plume heights are similar compared to the satellite observations but tend to be slightly higher with average differences of 270–580 and 60–320 m compared to TROPOMI and MISR, respectively.

Satellite Sea Surface Salinity Observations Impact on El Niño/Southern Oscillation Predictions: Case Studies From the NASA GEOS Seasonal Forecast System

AbstractEl Niño/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would have great societal benefit. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near‐surface ocean salinity. Satellite sea surface salinity (SSS), combined with temperature, help to improve the estimates of ocean density changes and associated near‐surface mixing. For the first time, we assess the impact of satellite SSS observations for improving near‐surface dynamics within ocean reanalyses and how these initializations impact dynamical ENSO forecasts using NASA's coupled forecast system (GEOS‐S2S‐2). For all initialization experiments, all available sea level and in situ temperature and salinity observations are assimilated. Separate observing system experiments additionally assimilate Aquarius, SMAP, SMOS, and these data sets combined. We highlight the impact of satellite SSS on ocean reanalyses by comparing experiments with and without the application of SSS assimilation. Next, we compare case studies of coupled forecasts for the big 2015 El Niño, the 2017 La Niña, and the weak El Niño in 2018 that are initialized from GEOS‐S2S‐2 spring reanalyses that assimilate and withhold along‐track SSS. For each of these ENSO‐event case studies, assimilation of satellite SSS improves the forecast validation with respect to observed NINO3.4 anomalies (or at least reduces the forecast uncertainty). Satellite SSS assimilation improved characterization of the mixed layer depth leading to more accurate coupled air/sea interaction and better forecasts. These results further underline the value of satellite SSS assimilation into operational forecast systems.

Development of a feature-oriented regional modelling system for the North Brazil Undercurrent region (1°–11°S) and its application to a process study on the genesis of the Potiguar Eddy

ABSTRACT Key elements of a Feature-oriented regional modelling system (FORMS) in the Western Equatorial Atlantic (1°–11°S) are developed for possible application in an operational forecast system as well as in process-oriented studies. This equatorial margin region is dominated by the northwestward flowing near surface North Brazil Undercurrent (NBUC) and the southeastward Deep Western Boundary Current (DWBC). Specifically, we investigate the genesis of a subsurface frontal meander of the NBUC, centred at about 4°S, 36.5°W, called the Potiguar Eddy (PE). The Feature Models (FMs) of NBUC and DWBC are first developed from observed velocity data, with temperature calculated through the thermal wind equation, and salinity from climatology. Two numerical experiments were then set up for the process study, (i) a realistic NBUC + DWBC experiment, with maximum depth of 5500 m, and (ii) a NBUC-only experiment, with maximum depth limited to 1500 m. Results conclude that the PE can be generated by a velocity field containing only the NBUC, and the DWBC – induced vertical shear seems to play a part on the eddy’s baroclinic characteristics (vertical extent and strength). This study highlights the potential of applicability of FORMS for operational forecasts and for process studies in similar regions of the world ocean where currents and topography interact to create permanent and/or semi-permanent eddies.

Antarctic Radiosonde Observations Reduce Uncertainties and Errors in Reanalyses and Forecasts over the Southern Ocean: An Extreme Cyclone Case

Cyclones with strong winds can make the Southern Ocean and the Antarctic a dangerous environment. Accurate weather forecasts are essential for safe shipping in the Southern Ocean and observational and logistical operations at Antarctic research stations. This study investigated the impact of additional radiosonde observations from Research Vessel “Shirase” over the Southern Ocean and Dome Fuji Station in Antarctica on reanalysis data and forecast experiments using an ensemble data assimilation system comprising the Atmospheric General Circulation Model for the Earth Simulator and the Local Ensemble Transform Kalman Filter Experimental Ensemble Reanalysis, version 2. A 63-member ensemble forecast experiment was conducted focusing on an unusually strong Antarctic cyclonic event. Reanalysis data with (observing system experiment) and without (control) additional radiosonde data were used as initial values. The observing system experiment correctly captured the central pressure of the cyclone, which led to the reliable prediction of the strong winds and moisture transport near the coast. Conversely, the control experiment predicted lower wind speeds because it failed to forecast the central pressure of the cyclone adequately. Differences were found in cyclone predictions of operational forecast systems with and without assimilation of radiosonde observations from Dome Fuji Station.

Operational Probabilistic Forecasting of Coastal Freak Waves by Using an Artificial Neural Network

Coastal freak waves (CFWs) are unpredictable large waves that occur suddenly in coastal areas and have been reported to cause casualties worldwide. CFW forecasting is difficult because the complex mechanisms that cause CFWs are not well understood. This study proposes a probabilistic CFW forecasting model that is an advance on the basis of a previously proposed deterministic CFW forecasting model. This study also develops a probabilistic forecasting scheme to make an artificial neural network model achieve the probabilistic CFW forecasting. Eight wave and meteorological variables that are physically related to CFW occurrence were used as the inputs for the artificial neural network model. Two forecasting models were developed for these inputs. Model I adopted buoy observations, whereas Model II used wave model simulation data. CFW accidents in the coastal areas of northeast Taiwan were used to calibrate and validate the model. The probabilistic CFW forecasting model can perform predictions every 6 h with lead times of 12 and 24 h. The validation results demonstrated that Model I outperformed Model II regarding accuracy and recall. In 2018, the developed CFW forecasting models were investigated in operational mode in the Operational Forecast System of the Taiwan Central Weather Bureau. Comparing the probabilistic forecasting results with swell information and actual CFW occurrences demonstrated the effectiveness of the proposed probabilistic CFW forecasting model.

Extreme events in Venice and in the North Adriatic Sea: 28–29 October 2018

Predictions and monitoring of sea level events have always been very challenging issues aimed at protecting, enhancing and improving the marine environment, especially in the North Adriatic Sea and in the Venice Lagoon, that are increasingly exposed to the flooding risk from storm surges, well known as Acqua alta phenomenon. In this framework, a deep analysis of extreme events has been developed, to characterize this storm surge in terms of its rareness and intensity; the Generalised Pareto Distribution and the generalised extreme values methods have been applied to obtain sea level return period and return level. Moreover, the operational sea level forecasting system running at ISPRA in the framework of the SIMM—Sistema Idro-Meteo-Mare is here presented; it is based on two different methods, the deterministic hydrodynamic finite elements numerical model SHYFEM and a statistical one. It provides forecasts up to 96 h depending on the spatial resolution and input data configuration. In this work, a deep analysis of the 29th October 2018 storm surge event and the application of the described system are presented. This integrated system is, in fact, a very useful tool to support the management of the marine environment and its resource, in particular regarding the coastal zone planning, protection and management, and to support the mitigation and rescue actions.

Evaluation of four global ocean reanalysis products for New Zealand waters–A guide for regional ocean modelling

ABSTRACT A comparison between 4 (near) global ocean reanalysis products is presented for the waters around New Zealand. The objective is to provide information for an educated choice of ocean estate estimate. The simulations are compared to satellite and in situ observations, and vertical sections are extracted to evaluate the representation of the main regional boundary currents and their transport. Overall, the Copernicus GLORYS reanalysis exhibits the better performance, with more realistic ocean variability and smaller biases in the water column structure. However, the BlueLink Reanalysis (BRAN) provides more realistic transport estimates of the East Auckland Current, an important boundary current connecting New Zealand to the World Ocean. All simulations have important biases in both temperature and salinity, particularly in coastal regions. Moreover, they are not able to represent coastal currents and processes. Therefore, the present study results emphasise the need for a regional ocean reanalysis and a data assimilative operational forecast system.

Backward Adaptive Brightness Temperature Threshold Technique (BAB3T): A Methodology to Determine Extreme Convective Initiation Regions Using Satellite Infrared Imagery

Thunderstorms in southeastern South America (SESA) stand out in satellite observations as being among the strongest on Earth in terms of satellite-based convective proxies, such as lightning flash rate per storm, the prevalence for extremely tall, wide convective cores and broad stratiform regions. Accurately quantifying when and where strong convection is initiated presents great interest in operational forecasting and convective system process studies due to the relationship between convective storms and severe weather phenomena. This paper generates a novel methodology to determine convective initiation (CI) signatures associated with extreme convective systems, including extreme events. Based on the well-established area-overlapping technique, an adaptive brightness temperature threshold for identification and backward tracking with infrared data is introduced in order to better identify areas of deep convection associated with and embedded within larger cloud clusters. This is particularly important over SESA because ground-based weather radar observations are currently limited to particular areas. Extreme rain precipitation features (ERPFs) from Tropical Rainfall Measurement Mission are examined to quantify the full satellite-observed life cycle of extreme convective events, although this technique allows examination of other intense convection proxies such as the identification of overshooting tops. CI annual and diurnal cycles are analyzed and distinctive behaviors are observed for different regions over SESA. It is found that near principal mountain barriers, a bimodal diurnal CI distribution is observed denoting the existence of multiple CI triggers, while convective initiation over flat terrain has a maximum frequency in the afternoon.

Applications of the Gulf of Maine Operational Forecast System to Enhance Spatio-Temporal Oceanographic Awareness for Ocean Mapping

Despite recent technological advances in seafloor mapping systems, the resulting products and the overall operational efficiency of surveys are often affected by poor awareness of the oceanographic environment in which the surveys are conducted. Increasingly reliable ocean nowcast and forecast model predictions of key environmental variables – from local to global scales – are publicly available, but they are often not used by ocean mappers. With the intention of rectifying this situation, this work evaluates some possible ocean mapping applications for commonly available oceanographic predictions by focusing on one of the available regional models: NOAA’s Gulf of Maine Operational Forecast System. The study explores two main use cases: the use of predicted oceanographic variability in the water column to enhance and extend (or even substitute) the data collected on-site by sound speed profilers during survey data acquisition; and, the uncertainty estimation of oceanographic variability as a meaningful input to estimate the optimal time between sound speed casts. After having described the techniques adopted for each use case and their implementation as an extension of publicly-available ocean mapping tools, this work provides evidence that the adoption of these techniques has the potential to improve efficiency in survey operations as well as the quality of the resulting ocean mapping products.

Comparison of different quantile regression methods to estimate predictive hydrological uncertainty in the Upper Chao Phraya River Basin, Thailand

AbstractThe estimation of predictive uncertainty and its application as a post‐processor of hydrological model output, such as water level, can provide additional information useful for short‐term hydrological forecasting. In this study, We applied quantile regression models for estimating predictive hydrological uncertainty and used it to derive probabilistic hydrological forecasts. Forecast water levels and associated forecast errors were used as predictor and predictand, respectively, to develop three regression models: (a) linear quantile regression (LQR), (b) weighted LQR and (c) LQR in Gaussian space using Normal Quantile Transformation. These different models for hydrological forecasting were developed for, and applied to, the operational flood forecasting system in the Upper Chao Phraya River, Thailand. The quality of these forecasts in terms of reliability, sharpness and overall skill were assessed using various graphical and numerical verification metrics. Results show that the improvement of forecast in terms of either reliability or sharpness depends upon the configurations used. With comparable overall performance, weighted LQR provided a relatively simple configuration, which can be used for estimating uncertainty in hydrological forecasting.

THE METHOD FOR CALCULATIONS THE VERTICAL DISTRIBUTION OF THE FIELDS OF THERMOHALINE CHARACTERISTICS FOR THE BLACK SEA DEEP-WATER AREA BY REMOTE MEASUREMENTS DATA

Problem Statement and Purpose. The results of studies on the development of methods for calculations of the vertical distribution of temperature and salinity fields in the active layer of the Black Sea by remote measurements data was presents in article. To solve the many practical problems, it is necessary to use vertical distribution of hydrological characteristics of the marine environment. Today there isn't an easy way to get a in situ data for evaluation of the vertical distribution of these characteristics. Indirect definitions of the vertical distribution of water temperature and salinity through the development of various methods for the calculation of the vertical profile of water temperature are used to date.Main purpose of this work is to estimate an ability of using of satellite data for calculation of the spatial distribution of thermohaline characteristics (on the example of the Black Sea).Data & Methods. The initial data of the actual water temperature and salinity values were station data or measurement from the ship data (OSD – Ocean Station data), data measured with the help of floats (PFL – Profiling float data) and satellite data of the sea surface temperature.Calculations of the vertical distribution of temperature and salinity fields in the active layer of the Black Sea by remote measurements data are based on original methods developed by the State Institution “Hydroacoustics Branch of Institute of Geophysics of NAS of Ukraine”. The development of methods was based on mathematical, physical and statistical methods of calculation.Study of vertical distribution of water temperature and salinity in the Black Sea were carried out in the deep-water part sea at standard levels (0, 10, 20, 25, 30, 50 meters) in spring – autumn period.Results. In the process of research, the following tasks were solved: the possibility of calculating the speed of sound from the values of water temperature calculated on the basis of satellite information are estimated; the possibility of calculating the salinity of water by the values of water temperature, sound speed in water and hydrostatic pressure are estimated; a method for calculations the vertical distribution of the fields of thermohaline characteristics in the active layer of the Black Sea by remote measurements data has been developed.A prototype of a computer program for calculations the vertical distribution of temperature and salinity fields in the Black Sea based on remote measurements data has been developed. The program automatically determines where and by what equations the vertical distribution of the fields of thermohaline characteristics in the active layer of the Black Sea can be calculated.The initial data for the calculations are only daily satellite data of the sea surface temperature and the month of the year.Designed by us, the prototype of a computer program to calculate the vertical distribution of the fields of thermohaline characteristics in the active layer of the Black Sea based on the remote measurements could serve as a basis for the establishment of a "Operational Forecast System of the fields of thermohaline characteristics in the Black Sea based on the remote measurements" (OFS). With the help of which the dynamics of characteristics and effects of their change on hydrobiological, hydrochemical, hydrophysical processes in the Black Sea and the ecosystem of the sea in general can be evaluated.In our opinion the developed method for calculating of the vertical distribution of the fields of thermohaline characteristics by remote measurements data can be applied to others water area of the World Ocean taking into account their hydrological conditions.

Near-Real-Time Tephra Fallout Assessment at Mt. Etna, Italy

During explosive eruptions, emergency responders and government agencies need to make fast decisions that should be based on an accurate forecast of tephra dispersal and assessment of the expected impact. Here, we propose a new operational tephra fallout monitoring and forecasting system based on quantitative volcanological observations and modelling. The new system runs at the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo (INGV-OE) and is able to provide a reliable hazard assessment to the National Department of Civil Protection (DPC) during explosive eruptions. The new operational system combines data from low-cost calibrated visible cameras and satellite images to estimate the variation of column height with time and model volcanic plume and fallout in near-real-time (NRT). The new system has three main objectives: (i) to determine column height in NRT using multiple sensors (calibrated cameras and satellite images); (ii) to compute isomass and isopleth maps of tephra deposits in NRT; (iii) to help the DPC to best select the eruption scenarios run daily by INGV-OE every three hours. A particular novel feature of the new system is the computation of an isopleth map, which helps to identify the region of sedimentation of large clasts (≥5 cm) that could cause injuries to tourists, hikers, guides, and scientists, as well as damage buildings in the proximity of the summit craters. The proposed system could be easily adapted to other volcano observatories worldwide.

Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance. Much of the rainfall in this region during June is contributed by the mei-yu rain band. We find that similar skill exists for predicting the East Asian summer monsoon index (EASMI) on monthly time scales, and that the latter could be used as a proxy to predict the regional rainfall. However, there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly. Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations, which may be inherently unpredictable on the seasonal forecasting time scale, the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high. The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.

Improvement of an Operational Forecasting System for Extreme Tidal Events in Santos Estuary (Brazil)

Forecasting estuarine circulation is a hot topic, especially in densely populated regions, like Santos (Brazil). This paper aims to improve a water-level forecasting system for the Santos estuary, particularly the physical forcing determining the residual tide, which in extreme cases increase the predicting errors. The MOHID hydrodynamic model was implemented with a nested downscaling approach. All automatic procedures to provide a high-resolution real-time forecast system are managed by the AQUASAFE software. Water-level observation and prediction datasets (2016–2017) of five tide gauges in the Santos channel were analyzed, resulting in distinct model configurations, aiming to minimize forecasting inaccuracies. Current MOHID open boundary reference solutions were modified: the astronomical solution was updated from FES2012 to FES2014 whereas the meteorological component (Copernicus Marine Environment Monitoring Service (CMEMS) global solution) time resolution was altered from daily to hourly data. Furthermore, the correlation between significant wave height with positive residual tide events was identified. The model validation presented a minimum Root Mean Square Error (RMSE) of 12.5 cm. Despite FES2014 solution improvements at the bay entrance, errors increase in inner stations were maintained, portraying the need for better bathymetric data. The use of a CMEMS hourly resolution decreased the meteorological tide errors. A linear regression method was developed to correct the residual tide through post-processing, under specific wave height conditions. Overall, the newest implementation increased the water-level forecast accuracy, particularly under extreme events.

An operational forecasting system for physical processes in the Santos-Sao Vicente-Bertioga Estuarine System, Southeast Brazil

We describe the design, implementation, and performance of a fully automated Santos Operational Forecasting System (SOFS), built to monitor and predict short-term (< 3 days) sea surface elevations, currents, temperature, and salinity in the Santos-Sao Vicente-Bertioga Estuarine System (SSVBES). The SSVBES located at 24.0∘S, 46.3∘W is a complex estuarine system with many interconnected channels and two connections with the open sea. The system is prone to storm tides that bring coastal flooding to and interrupt ship traffic through Santos Port. The SOFS hydrodynamic module is based on the Princeton Ocean Model (POM) version POM-rain. The SSVBES model grid is forced by tides, winds, and river runoff and is nested into a coarse-resolution South Brazil Bight (SBB) grid. The SBB grid is forced by winds, density gradients, and the Brazil Current flowing offshore. Within SSVBES, SOFS works in parallel with three real-time observation stations. The model performance was tested against observed data with a best Willmott skill of 0.97 and root mean square error (RMSE) of 13.0 cm for tidal sea level (15.9% of the mean tidal range). For tidal currents, the best skill and RMSE were above 0.99 and 3.9 cm/s (4.3% of the mean tidal current range), respectively. The coupled system was able to simulate seven storm tides with average skill of 0.95 and average RMSE of 17.0 cm. The good agreement with observed data shows the potential use of the designed system to protect both human life and assets.

Evaluation of GloFAS-Seasonal Forecasts for Cascade Reservoir Impoundment Operation in the Upper Yangtze River

Standard impoundment operation rules (SIOR) are pre-defined guidelines for refilling reservoirs before the end of the wet season. The advancement and availability of the seasonal flow forecasts provide the opportunity for reservoir operators to use flexible and early impoundment operation rules (EIOR). These flexible impoundment rules can significantly improve water conservation, particularly during dry years. In this study, we investigate the potential application of seasonal streamflow forecasts for employing EIOR in the upper Yangtze River basin. We first define thresholds to determine the streamflow condition in September, which is an important period for decision-making in the basin, and then select the most suitable impoundment operation rules accordingly. The thresholds are used in a simulation–optimization model to evaluate different scenarios for EIOR and SIOR by multiple objectives. We measure the skill of the GloFAS-Seasonal forecast, an operational global seasonal river flow forecasting system, to predict streamflow condition according to the selected thresholds. The results show that: (1) the 20th and 30th percentiles of the historical September flow are suitable thresholds for evaluating the possibility of employing EIOR; (2) compared to climatological forecasts, GloFAS-Seasonal forecasts are skillful for predicting the streamflow condition according to the selected 20th and 30th percentile thresholds; and (3) during dry years, EIOR could improve the fullness storage rate by 5.63% and the annual average hydropower generation by 4.02%, without increasing the risk of flooding. GloFAS-Seasonal forecasts and early reservoir impoundment have the potential to enhance hydropower generation and water utilization.

Filtering techniques to enhance optical turbulence forecast performances at short time-scales

ABSTRACT The efficiency of the management of top-class ground-based astronomical facilities supported by adaptive optics (AO) relies on our ability to forecast the optical turbulence (OT) and a set of relevant atmospheric parameters. Indeed, in spite of the fact that the AO is able to achieve, at present, excellent levels of wavefront corrections (a Strehl ratio up to 90 per cent in H band), its performances strongly depend on the atmospheric conditions. Knowing in advance the atmospheric turbulence conditions allows an optimization of the AO use. It has already been proven that it is possible to provide reliable forecasts of the OT (${C_N^2 }$ profiles and integrated astroclimatic parameters such as seeing, isoplanatic angle, wavefront coherence time, etc.) for the next night. In this paper, we prove that it is possible to improve the forecast performances on shorter time-scales (order of 1 or 2 h) with consistent gains (order of 2–8) employing filtering techniques that make use of real-time measurements. This has permitted us to achieve forecasts accuracies never obtained before and reach a fundamental milestone for the astronomical applications. The time-scale of 1 or 2 h is the most critical one for an efficient management of the ground-based telescopes supported by AO. We implemented this method in the operational forecast system of the Large Binocular Telescope (LBT), named Advanced LBT Turbulence and Atmosphere (ALTA) Center that is, at our knowledge, the first operational system providing forecasts of turbulence and atmospheric parameters at short time-scales to support science operations.

Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits

ABSTRACTThe operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the basis for information services about the marine environment and its ecosystem, and can provide boundary data for weather predictions. Bluelink information services are available to marine industries (e.g. commercial fishing, aquaculture, shipping, oil and gas, renewable energy), government agencies (e.g. search and rescue, defence, coastal management, environmental protection), and other stakeholders (e.g. recreation, water sports, artisanal and sport fishing) who depend on timely and accurate information about the marine environment. This review highlights the last 15 years of Bluelink achievements delivering mesoscale (eddy-resolving) to sub-mesoscale and short- to medium-range (days to weeks) ocean forecasts and reanalyses. Key achievements include the development of a global ocean forecasting and reanalysis system, a relocatable ocean-atmosphere model and a littoral zone analysis and forecasting capability. Beyond the traditional short-term forecasting of physical ocean properties (temperature, salinity, surface height, currents, waves), marine activities such as water quality and habitat management as well as climate monitoring increasingly rely on operational oceanographic data and products. These are areas of active research of the Bluelink team in collaboration with national and international partners.

OPENCoastS: An open-access service for the automatic generation of coastal forecast systems

Coastal forecast systems are used for many purposes, including harbor management, search and rescue operations, and response to extreme events. However, the generation and operation of these systems is time-consuming, requires expertise in both information technologies and modeling of coastal processes, and needs dedicated computational power. The new service OPENCoastS overcomes these difficulties by generating on-demand coastal circulation forecast systems through a web platform with minimal user intervention. Using a web platform, the user is guided through seven simple steps to generate an operational forecast system for any coastal region. The only requirements are an unstructured grid of the study area and information on river flow, if applicable. The platform provides ocean and atmospheric forcings and data for model validation, and includes interfaces for results visualization and forecasts management. Forecasts are generated with the community model SCHISM, and computing resources are provided through the European Open Science Cloud.