Drift Forecast Research Articles

Dead on the Beach? Predicting the Drift of Whale Remains Improves Management for Offshore Disposal

Whale mortality and strandings have increased in recent years, with deceased whales often brought to landfill. However, the disposal of whale remains offshore holds significant ecological importance and can be a culturally and ethically sensitive approach. Moreover, offshore disposal mitigates potential risks associated with onshore whale remains disposal, such as the spread of diseases and the logistical challenges of managing large carcasses. A challenge with offshore disposal is defining the best release location to avoid the remains drifting ashore or into shipping channels. Here we compared the drift model outputs using a drift forecast model (SARMAP) for a 14 m whale carcass that was moved offshore in southeast Queensland, Australia, and fitted with a satellite tracker over an observation period of 150 h until positioning signal ceased. The modelling was conducted using different ocean products (BLUElink, HYCOM, and Copernicus), which showed a good agreement with the tracked whale carcass, albeit with changing wind conditions and contrasting currents flowing northward along the coast and, further offshore, flowing south. This case study illustrated that wind was the foremost driver of carcass drift due to the surface area above the water surface. The drift forecast simulations allowed for a reliable prediction of the floating whale drift that can assist authorities with decision making. Offshore disposal of whale carcasses is a sustainable practice but requires good planning and scientific assessment.

The MOSAiC Distributed Network: Observing the coupled Arctic system with multidisciplinary, coordinated platforms

Central Arctic properties and processes are important to the regional and global coupled climate system. The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Distributed Network (DN) of autonomous ice-tethered systems aimed to bridge gaps in our understanding of temporal and spatial scales, in particular with respect to the resolution of Earth system models. By characterizing variability around local measurements made at a Central Observatory, the DN covers both the coupled system interactions involving the ocean-ice-atmosphere interfaces as well as three-dimensional processes in the ocean, sea ice, and atmosphere. The more than 200 autonomous instruments (“buoys”) were of varying complexity and set up at different sites mostly within 50 km of the Central Observatory. During an exemplary midwinter month, the DN observations captured the spatial variability of atmospheric processes on sub-monthly time scales, but less so for monthly means. They show significant variability in snow depth and ice thickness, and provide a temporally and spatially resolved characterization of ice motion and deformation, showing coherency at the DN scale but less at smaller spatial scales. Ocean data show the background gradient across the DN as well as spatially dependent time variability due to local mixed layer sub-mesoscale and mesoscale processes, influenced by a variable ice cover. The second case (May–June 2020) illustrates the utility of the DN during the absence of manually obtained data by providing continuity of physical and biological observations during this key transitional period. We show examples of synergies between the extensive MOSAiC remote sensing observations and numerical modeling, such as estimating the skill of ice drift forecasts and evaluating coupled system modeling. The MOSAiC DN has been proven to enable analysis of local to mesoscale processes in the coupled atmosphere-ice-ocean system and has the potential to improve model parameterizations of important, unresolved processes in the future.

CAR36, a regional high-resolution ocean forecasting system for improving drift and beaching of Sargassum in the Caribbean archipelago

Abstract. The stranding of Sargassum seaweed on the Caribbean archipelago beaches constitutes real socio-economic, ecological and health problems. Météo-France currently operates a model of Sargassum drift forecast (called MOTHY) forced by ocean currents from the global analysis and forecasting system GLO12 at 1/12° (∼9 km over the Caribbean) operated by Mercator Ocean International (MOi). In order to improve the Météo-France drift forecast, MOi has developed a regional high-resolution ocean forecasting system CAR36 at 1/36° (∼3 km) centred in the Caribbean archipelago region. In addition to a finer spatial resolution, this system was designed to resolve higher-frequency signals such as the tidal forcing and to use hourly atmospheric forcing including the inverse barometer effect. Here the added value of the CAR36 system relative to GLO12 is evaluated with particular focus on the reproduction of meso- and sub-mesoscale structures representing key features of the Caribbean region dynamics and therefore Sargassum transport. The realism of the local dynamics was examined with standard statistical validation diagnostics using satellite data (sea surface height, sea surface temperature, Sargassum detection) and drifting buoys, together with more process-oriented diagnostics such as eddy detection and tracking across the domain. GLO12 and CAR36 hindcast simulations were compared over the year 2019. CAR36 showed marginally better scores using OceanPredict diagnostics (https://oceanpredict.org/, last access: 10 April 2024). The dynamics of a westward-propagating North Brazil Current (NBC) eddy from its entry into the domain to its dissipation were found to be more realistic in CAR36, especially at the end of its lifetime when it collides with the Caribbean archipelago. The transfer of kinetic energy from the eddy dissipating westward into the Caribbean Sea was diagnosed as more pronounced for CAR36, corresponding to filamentary structures crossing the Caribbean archipelago and resulting in part from the friction of the NBC eddy along the islands to the east. Using detection from satellites, aggregation of Sargassum around eddies or along filaments suggests that CAR36 should improve the algae drift forecasts.

Analysis of Gross Enrollment Ratio, Net Enrollment Ratio, and School Enrollment Ratio Trends at the Elementary School Level

This investigates points to decide forecasts of the accomplishment of Net Enrollment Rates (APK), Immaculate Enrollment Rates (APM) and School Enrollment Rates (APS) for basic school instruction in Pati Rule, Central Java Territory. The quantitative information in this inquire about was gotten from the websites of the Pati Rule Instruction and Culture Office, the Central Java Central Insights Office, and the Service of Instruction, Culture, Inquire about and Innovation. Slant Examination employments the Direct relapse strategy. The inquire about comes about appear that the normal APK drift expectation for 2023 to 2032 is 103.44%, the normal APM slant forecast for 2023 to 2032 is 100% and the normal APS drift forecast for 2023 to 2032 is 99.53%. The anticipated patterns in APK, APM and APS in 2023 to 2032 have come to and surpassed the government's target of 99%. The anticipated APK slant in Pati Rule from 2022 to 2032 will decrease, from 2022 to 2032 the decrease will be 4.21%. The anticipated NER drift in Pati Rule from 2022 to 2032 will increment, it can be seen that the increment from 2022 to 2032 is 5.88%. The anticipated APS slant in Pati Rule from 2022 to 2032 will increment, this could be seen from 2022 to 2032 encountering an increment of 1.31%.

Predictability of Arctic sea ice drift in coupled climate models

Abstract. Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of “perfect-model” ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least a 90 (45) d lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty in Eulerian drift vector predictions reaches the level of the climatological uncertainty within 4 weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to −0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models; they can be used to put current real-world forecast skill into perspective and highlight the model diversity of sea ice drift predictability.

지수평활법과 ARIMA를 이용한 전남 김 위판가격 예측모형 비교

This study conducted a price forecast of major Purple laver production areas, Jeollanam-do, in South Korea. Based on the reasoning that price forecast of Purple laver production regions could assist in controlling supply and demand, through the Exponential smoothing technique(Single exponential smoothing, Holt-Winters Additive and Multiplicative, Exponential smoothing method) and Auto regressive integrated moving average model(ARIMA), Purple laver producer price of Jeollanam-do, the largest wet Purple laver producing area, has been predicted. Moreover, the accuracy and stability of the relevant forecasting model have been reviewed through the Historical simulation method. Concerning Jeollanam-do Purple laver producer price forecast results, among the total models adopted through the utilization of the Historical simulation method, the Simple exponential smoothing with DRIFT forecast model, which uses a DRIFT forecast, was ultimately selected after going through stationary Time series and forecast model accuracy tests.

Calibration of sea ice drift forecasts using random forest algorithms

Abstract. Developing accurate sea ice drift forecasts is essential to support the decision-making of maritime end-users operating in the Arctic. In this study, two calibration methods have been developed for improving 10 d sea ice drift forecasts from an operational sea ice prediction system (TOPAZ4). The methods are based on random forest models (supervised machine learning) which were trained using target variables either from drifting buoy or synthetic-aperture radar (SAR) observations. Depending on the calibration method, the mean absolute error is reduced, on average, between 3.3 % and 8.0 % for the direction and between 2.5 % and 7.1 % for the speed of sea ice drift. Overall, the algorithms trained with buoy observations have the best performances when the forecasts are evaluated using drifting buoys as reference. However, there is a large spatial variability in these results, and the models trained with buoy observations have particularly poor performances for predicting the speed of sea ice drift near the Greenland and Russian coastlines compared to the models trained with SAR observations.

Forecast of oil slick drift from Ulysse/ CSL Virginia and Grande America accidents

Abstract number 1141410 Two recent accidents with a significant oil spill occurred near the French coast. One in the Mediterranean Sea and the other in the Bay of Biscay. On October 7, 2018, the Tunisian ro-ro vessel Ulysses collided with the Cypriot container ship CSL Virginia at anchor off northern Corsica. The spilled bunker oil could not be fully recovered by the French and Italian anti-pollution vessels due to unfavourable weather conditions. Pellets and highly viscous patties arrived on the beaches of the French Riviera on October 16, 2018. The beaching dates and locations of the main slicks were perfectly predicted using the MOTHY drift model combined with the currents of the CMEMS MED-Currents system. On March 12, 2019, the merchant ship Grande America sank at a depth of 4600 m, 350 km off the French coast, in the Bay of Biscay. It caused a spill of bunker oil and loss of containers. The MOTHY drift model is used daily during the aerial surveillance and recovery at sea period. It provides drift forecasts for oil slicks and containers up to 3 days in deterministic mode and up to 10 days in probabilistic mode. Long-term modelling of residual diffused pollution is also carried out, in particular to manage continuous leakage from the wreck. A technical committee of experts meets daily to evaluate drift observations and forecasts. It focuses on the best choices of available ocean models. Drift forecasts did not indicate any oil arrival to the coast. This allowed the authorities to organise the response at sea without mobilising resources ashore. Indeed, no pollution was observed on the coasts.

Evaluating numerical and free-drift forecasts of sea ice drift during a Southern Ocean research expedition: An operational perspective

ABSTRACT Antarctic sea ice is prevalently seen as a major player in the climate system, but it is also an important factor in polar maritime safety. Remote sensing and forecasting of Southern Ocean sea ice at time scales suitable for navigation and research planning remain challenging. In this study, numerical sea ice drift forecasts are assessed from the perspective of informing shipping operations. A series of tests is performed to ascertain whether an operational global ocean and sea ice model and a simple free-drift model can provide accurate drift estimates over short lead times. Both approaches are evaluated against ice drift measurements from buoys deployed during two research cruises in the Southern Ocean marginal ice zone during winter and spring. The numerical forecast model was able to forecast sea ice trajectories over 24 h with an average position error of 16.6 km during winter and 9.2 km during spring. The simpler free-drift model, using empirically optimised wind scaling, returned an average position error of 15.9 and 9.3 km during winter and spring respectively. Model skill for both the dynamical and free-drift models is lower in winter than in spring. Free-drift model skill appears linked with sea ice consolidation, which may assist in determining when and where this approach is fit for purpose. Lingering uncertainties regarding the rheological representation of sea ice in the dynamical model and the quality of the wind and ocean forcing remain, potentially affecting model skill over tactical navigation time frames.

Dispersion of Surface Drifters in the Tropical Atlantic

The Tropical Atlantic Ocean has recently been the source of enormous amounts of floating Sargassum macroalgae that have started to inundate shorelines in the Caribbean, the western coast of Africa and northern Brazil. It is still unclear, however, how the surface currents carry the Sargassum, largely restricted to the upper meter of the ocean, and whether observed surface drifter trajectories and hydrodynamical ocean models can be used to simulate its pathways. Here, we analyze a dataset of two types of surface drifters (38 in total), purposely deployed in the Tropical Atlantic Ocean in July, 2019. Twenty of the surface drifters were undrogued and reached only ∼8 cm into the water, while the other 18 were standard Surface Velocity Program (SVP) drifters that all had a drogue centered around 15 m depth. We show that the undrogued drifters separate more slowly than the drogued SVP drifters, likely because of the suppressed turbulence due to convergence in wind rows, which was stronger right at the surface than at 15 m depth. Undrogued drifters were also more likely to enter the Caribbean Sea. We also show that the novel Surface and Merged Ocean Currents (SMOC) product from the Copernicus Marine Environmental Service (CMEMS) does not clearly simulate one type of drifter better than the other, highlighting the need for further improvements in assimilated hydrodynamic models in the region, for a better understanding and forecasting of Sargassum drift in the Tropical Atlantic.

Integrated Analysis of Multisensor Datasets and Oil Drift Simulations—A Free‐Floating Oil Experiment in the Open Ocean

AbstractA free‐floating oil spill experiment (two oil types) in the open ocean is described, and the results from slick characterization through integrated analysis of drift simulations with remote sensing and in situ data are discussed. We compare oil drift simulations (OpenOil), applying various configurations of wind, wave, and current information, with the observed slick positions and shape. We describe trajectories and dynamics of the spills, slick extent, and their evolution, and the differences in detection capabilities in optical instruments versus multifrequency PolSAR acquired by Deutsches Zentrum für Luft‐und Raumfahrt F‐SAR. When using the best available forcing from in situ data and forecast models, good agreement with the observed position and extent was found. A fair agreement is obtained using only numerical forecast data as input. This case study reveals that the accuracy when using modeled current is relatively higher after one full local inertial period, as the effect of incorrectly modeled amplitude of the inertial current is then vanishing. This should be a general result applicable to any oceanic drift forecast based on the modeled current. A novelty is the comparison of viscous‐similar soybean and mineral oil. Our findings indicate that biological oil emulsions potentially could replace mineral oil emulsions in contingency and rehearsal campaigns, but this finding is only supported from an oil drift prediction perspective. Differences in mineral oil detection capabilities are found between synthetic aperture radar and optical imagery of thinner sheen regions. F‐SAR appears to be more sensitive to thinner oil and detect a larger extent, and differences between the thinner and thicker parts are observed.

Massively parallel implicit equal-weights particle filter for ocean drift trajectory forecasting

Forecasting of ocean drift trajectories are important for many applications, including search and rescue operations, oil spill cleanup and iceberg risk mitigation. In an operational setting, forecasts of drift trajectories are produced based on computationally demanding forecasts of three-dimensional ocean currents. Herein, we investigate a complementary approach for shorter time scales by using the recently proposed two-stage implicit equal-weights particle filter applied to a simplified ocean model. To achieve this, we present a new algorithmic design for a data-assimilation system in which all components – including the model, model errors, and particle filter – take advantage of massively parallel compute architectures, such as graphical processing units. Faster computations can enable in-situ and ad-hoc model runs for emergency management, and larger ensembles for better uncertainty quantification. Using a challenging test case with near-realistic chaotic instabilities, we run data-assimilation experiments based on synthetic observations from drifting and moored buoys, and analyze the trajectory forecasts for the drifters. Our results show that even sparse drifter observations are sufficient to significantly improve short-term drift forecasts up to twelve hours. With equidistant moored buoys observing only 0.1% of the state space, the ensemble gives an accurate description of the true state after data assimilation followed by a high-quality probabilistic forecast.

Characterizing Drift Behavior in Type S Thermocouples to Predict In-use Temperature Errors

The Type S thermocouple continues to be used as a primary reference for industrial applications and as a secondary reference in many national metrology institutes (NMI). The Type S thermocouple, in a well annealed state, can yield temperature measurements accurate to better than ± 0.2 °C up to approximately 1100 °C. Metallurgical changes, even in the best cared for thermocouples, quickly lead to drift in use. The main causes of drift are reversible processes occurring in the Pt/Rh alloy thermoelement. Most NMIs will periodically apply a high-temperature (1100 °C) anneal to restore the thermocouple to a state close to that of the original. But, for many second-tier laboratories and industrial users, this annealing procedure is not available or is impracticable. Therefore, these users are faced with measurements that are subject to accumulated or continuous drift errors. Fortunately, recent research has shown that the Pt-10%Rh thermoelement changes with temperature in a predictable way, enabling forecasting of drift as a function of exposure time and temperature. This study aggregates drift results for several Type S thermocouples from different manufacturers after exposure to MSL’s gradient furnace in the range 100 °C to approximately 900 °C. Numerical techniques are then used on these data to anticipate the likely in-use temperature errors for other Type S thermocouples. The method can provide improved confidence for end-users of Type S thermocouples where regular annealing is either difficult or impossible.

Extrapolating Eulerian ocean currents for improving surface drift forecasts

ABSTRACT Predictions of drift trajectories based on four drift models were compared with observations from drifting buoys deployed in 2014 and 2015 in the Estuary and Gulf of St. Lawrence to show the impact of the current vertical shear on the surface drift predictions. Input of ocean currents and winds are obtained from ISMER's 5 km resolution ocean circulation model and from the Canadian Regional Deterministic Prediction System, respectively. The control drift model A considers depth-averaged near-surface currents (0–5 m) provided by the top grid cell of the ocean circulation model. Model B performs a linear extrapolation assuming a constant vertical shear equal to that between the first two cells of the ocean model. Models C and D perform a dynamic extrapolation assuming an Ekman layer with a constant or linearly increasing vertical viscosity, respectively. Model performance is evaluated based on several error metrics. Drift models based on extrapolated surface currents reduce separation distances relative to the control model by 25% (model B), 31% (model C) and 35% (model D) on average, for lead times from 3 h to 72 h. We thus recommend the use of extrapolation methods of near-surface ocean currents for improving surface drift forecasting skills in support of emergency response.

Forecasts of redshift drift constraints on cosmological parameters

ABSTRACT Cosmological observations usually map our present-day past light cone. However, it is also possible to compare different past light cones. This is the concept behind the redshift drift, a model-independent probe of fundamental cosmology. In simple physical terms, this effectively allows us to watch the Universe expand in real time. While current facilities only allow sensitivities several orders of magnitude worse than the expected signal, it should be possible to detect it with forthcoming ones. Here, we discuss the potential impact of measurements by three such facilities: the Extremely Large Telescope (the subject of most existing redshift drift forecasts), but also the Square Kilometre Array and intensity mapping experiments. For each of these we assume the measurement sensitivities estimated respectively in Liske et al. (2008), Klockner et al. (2015), and Yu, Zhang & Pen (2014). We focus on the role of these measurements in constraining dark energy scenarios, highlighting the fact that although on their own they yield comparatively weak constraints, they do probe regions of parameter space that are typically different from those probed by other experiments, as well as being redshift dependent. Specifically, we quantify how combinations of several redshift drift measurements at different redshifts, or combinations of redshift drift measurements with those from other canonical cosmological probes, can constrain some representative dark energy models. Our conclusion is that a model-independent mapping of the expansion of the universe from redshift z = 0 to z = 4 – a challenging but feasible goal for the next generation of astrophysical facilities – can have a significant impact on fundamental cosmology.

An iceberg forecast approach based on a statistical ocean current model

This article proposes a statistical model for short-term iceberg drift forecasts by transforming the problem of forecasting the iceberg velocity into a problem of forecasting the ocean current velocity. A Vector-autoregression model is identified using historical ocean current data as a training set. The proposed forecast scheme is tested and analysed on four real iceberg drift trajectories. Based on these results, recommendations about the forecast horizon, the filter horizon and model order are given. Moreover, it is shown that the statistical forecast approach presented in this article offers superior performance to a conventional dynamic iceberg forecast model for short-term drift forecasts.

Comparison of statistical iceberg forecast models

Short-term iceberg drift prediction is challenging. Large uncertainties in the driving forces – current, wind and waves – usually prevent accurate forecasts. Recently several statistical iceberg forecast models have been proposed by the authors, which use iceberg position measurements to improve the short-term drift forecast. In this article these statistical forecast methods and models are briefly reviewed. An extensive comparison between the statistical models, in addition to a dynamic iceberg forecast model, is performed on several iceberg drift trajectories. Based on this comparison a new statistical forecast scheme is proposed that combines some of the advantages of the other methods.

Application of a free drift tactical ice forecast model in pack ice conditions

The extension of oil and gas exploration in High Arctic offshore locations will likely require floating drilling capability in the presence of high-concentration sea ice (pack ice) at the beginning and end of the open water season. The detection and drift forecasting of potentially unmanageable ice features (PUIF) will be key components of an ice management system designed to manage risk while drilling during pack ice intrusions. In September 2015 during the Oden Arctic Technology Research Cruise 2015 (OATRC 2015), an ExxonMobil Upstream Research Company (EMURC) free drift tactical ice forecast model designed to forecast the drift of individual ice floes in low concentration ice conditions was applied in near real-time to high concentration pack ice with predominant thicknesses of medium first-year ice in the Arctic Ocean. The description and application of the proposed ice drift forecast model is presented in this paper. In addition, forecast results are compared to drift data collected during OATRC 2015. It is shown that the free drift forecasting tool produces reasonably accurate and useful forecasts in the high concentration ice observed.

Analysis and forecast of the surface currents in the Okhotsk Sea on the base of observations on drift of buoys

Data of observations on drifting buoys in the western Okhotsk Sea are presented. Quasi-stochastic mode of the buoys drift under forcing of atmospheric cyclone is noted. The drift is analyzed jointly with analysis of the wind field and the sea surface satellite altimetry. The buoy drift trajectories are modeled under separate influence of the wind-driven and geostrophic flows. There is concluded that both wind-driven and geostrophic currents at the sea surface should be accounted for forecasting of drift for buoys or any floating objects.

A GIS-based software for forecasting pollutant drift on coastal water surfaces using fractional Brownian motion: A case study on red tide drift

For ocean pollution emergencies, decision-makers need to quickly know the location of the pollutant for quick assessment and response strategies. In this study, an integrated operational forecasting model coupling a non-Fickian particle-tracking diffusion model based on fractional Brownian motion and geographic information system (GIS) has been developed to implement an operating system for pollutant drift forecasting. The software was developed in C# and C++ language using ArcGIS Engine functions which provides improved visualization and user-friendly and automatic tools for simulation in a geographically referenced environment. The capabilities and effectiveness of the developed software were illustrated by predicting red tide drift through calibration with field observations. This visualized operational forecasting software provides a quick and easy deployable tool for decision-makers in quick response to emergency ocean pollution events.