OceanBench - Short-Term Global Ocean Forecasting

This paper compares the performance of operational short-range ocean forecast systems developed under the Global Ocean Data Assimilation Experiment (GODAE) – an international effort to demonstrate the feasibility of operational ocean forecasting. ‘Best estimates’ from four different operational forecast systems (either analyses, hindcasts, or nowcasts) are inter-compared for the Tasman and Coral Seas, off eastern Australia. Systems considered include those developed in Australia, France, the USA, and the UK. Each system is compared to observations of along-track sea-level anomaly, sea-surface temperature, near-surface velocity, and sub-surface temperature and salinity. All have their strengths and weaknesses, and each system out-performs all others in one aspect or another. With few exceptions, all systems demonstrate signal-to-noise ratios greater than one for all variables. Due to the Australian focus, in addition to the best estimates from the operational systems, operational forecasts and a delayed-mode reanalysis are also inter-compared using the Australian system. The Australian system generally performs the best for sea-level anomaly; the French system is best for near-surface velocities; the USA system generally performs the best for sea-surface temperature; and the UK system is best for sub-surface temperature and salinity. These findings provide useful indicators of deficiencies in each system and clear metrics by which future developments should be assessed. Based on these results and other practical considerations the adoption of multimodel consensus forecasting, using all available forecasts from all systems, is promoted as the most robust approach for the user community. Such developments are being pursued under GODAE OceanView – the successor to GODAE. The results show the success of GODAE in demonstrating the feasibility of operational oceanography.

Optimal interpolation method is applied to Geosat altimetry data both to remove orbit error and to separate temporal mean sea surface dynamic topography (SSDT) from temporal fluctuations around the mean. Loss of long-wavelength oceanic signals at orbit error reduction procedure is smaller in this method than that in conventional collinear methods, but the areal average height over the study domain is still removed as the orbit error. The fluctuation SSDT is quantitatively evaluated by sea level data from tide gauge stations at Japanese islands. The correlation coefficient of the two sea-level variations is 0.83 when the loss of the areal average is compensated by the seasonal variation of the areal average height determined from the climatological monthly-mean SSDT. In addition, the improvement of the geoid model by combined use of Seasat altimetry data and hydrographic data is validated through the estimated temporal mean SSDT. In a local area where hydrographic data contemporary with the Seasat mission exist, the geoid model has been significantly improved so that the absolute SSDT can be determined from combination of the altimetry data and geoid model; the absolute SSDT describes the onset event of a quasi-stationary large meander of the Kuroshio south of Japan very well. Outside this local area, however, errors of several tens of centimeters still remain in the improved geoid model.

Hydrologic uncertainty processor for probabilistic river stage forecasting: precipitation-dependent model

Over the last three decades, satellite altimetry has observed Sea Surface Height variations, providing a regular monitoring of the ocean circulation. Altimetry measurements have an intrinsic signal-to-noise ratio that strongly limits the space scales, and then the geophysical information that can be captured with this instrument. However, the recent progress made on both altimetry sensors and data processing, offers new perspectives in terms of research-oriented and operational applications. In this paper we present a methodological study that helps to better quantify the impact of this progress in terms of coastal circulation. We focus on a case study: the Northern Current, a narrow slope current (less than 60 km wide) located in the North Western Mediterranean Sea. We first use a high resolution numerical model validated with HF radars and underwater glider data to define the general characteristics of the Northern Current in terms of surface velocity and sea surface height signature. These characteristics are then compared with corresponding estimates of sea surface height velocity derived from 1-Hz altimetry data sets from three missions: Jason 2 (Ku-band LRM), SARAL (Ka-band LRM) and Sentinel-3A (SAR). The data from all missions were processed with the coastal-specific X-TRACK strategy. We show that near Toulon, the model is very close to the observations in terms of current estimates, providing a very good reference for altimetry data located in this area. The Northern Current is observed 15 km to the coast on average, with a mean core velocity of 0.44 m s−1. Its signature in sea level consists of a drop whose mean value at 6.14° E is 6.9 +/− 2.2 cm extending over 18 +/− 4 km. These variations show a clear seasonal pattern, but high frequency signals are also present most of the time. In 1-Hz altimetry data, the mean sea level drop associated with the Northern Current is overestimated by 3.6 cm for Jason 2, 0.3 cm for SARAL and 1.4 cm for Sentinel-3A. In terms of corresponding sea level variability, Jason 2 and SARAL altimetry estimates are larger than the model reference (+1.3 cm and +1 cm, respectively) whereas Sentinel-3A shows closer values (−0.4 cm). Without any sea level data filtering, the standard deviation of altimetry-derived velocity values is 3.7, 2.4 and 2.9 times too large for Jason-2, SARAL and Sentinel-3A, respectively. When filtering sea level data, the distribution of altimetry velocities tends to converge towards the model reference with a 50-km, 30-km and 40-km cutoff wavelength for Jason-2, SARAL and Sentinel-3A data, respectively.

An operational forecast modeling system for the Mississippi (MS) Sound/Bight has been developed. The system integrates a triple nested coastal ocean forecast modeling systems and a meteorological forecast model. The Mississippi Sound/Bight model based on ECOMSED, forms the central core of the operational forecast system. At its eastern and southern boundaries, the ECOMSED is coupled to a regional Gulf of Mexico (GOM) model in a manner that ensures seamless energy transfer between the two models. Meteorological forcing is provided by the Coupled Ocean/Atmospheric Mesoscale Prediction System, COAMPS. The forecast system automatically retrieves all available real-time river discharge data along the Gulf coast to be imposed as coastal boundary conditions. The operational MS Sound/Bight forecast model produces two 12-hour hindcast and two 48-hour forecasts everyday at 0000 and 1200 hours. The system is scheduled to run for 12 hours in a hindcast mode and then 48 hours in a forecast mode. However, these simulation periods can vary. Depending on the availability and lengths of inputs from the coupled GOM and COAMPS models, the operational system automatically sets the periods for hindcast and forecast simulations. The model saves the proper hydrodynamic information for a restart so that a smooth and seamless execution is possible to start the next cycle. All of the simulations of the model are performed and archived on the Major Shared Resource Center (MSRC) high-performance computers resident at NAVOCEANO, Stennis Space Center, MS. The archived model output includes hourly three-dimensional fields of salinity, temperature and currents and water level across the model domain. Quality control is performed before the results go to a post-processing phase. A post-processing routine, which runs autonomously, generates surface current, temperature and salinity distributions after the completion of each cycle of forecast. The model results are available on the NGLI website (www.navo.navy.mil/NGLI) for public use.

The altimetric data from the first 2.5‐year Geosat Exact Repeat Mission were used to estimate the mean sea surface height (SSH) field in the region of the Gulf Stream and its recirculation gyres. Assuming the instantaneous surface velocity field is composed of an eastward flowing jet and two westward recirculating flows, we used the time‐varying surface data from the altimeter to determine the shape of the along‐track mean SSH profiles and the historical hydrographic data to constrain the net SSH difference across the Gulf Stream system. The two‐dimensional mean SSH field was determined by objectively mapping the mean height profiles along the ascending and descending tracks. The SSH jump across the mean Gulf Stream has a maximum of 1.15m around 65°W and drops to an almost constant 0.9m downstream of the New England Seamount Chain (NESC). While the SSH jump associated with the mean northern recirculating flow is mostly uniform, we found that the Gulf Stream's southern recirculation has two local gyres that are separated by the NESC. An attempt was then made to estimate the mean deep circulation in this region by comparing the mean SSH field derived from the altimetry data and the surface dynamic height field based on the historical hydrographic data. Despite the large uncertainties, the mean deep flow pattern thus estimated agrees favorably with the overall circulation pattern from the long‐term current meter observations. Like the well‐defined northern recirculation gyre, we found that a continuous southward flow exists along 57.5°W, which follows closely along the deep layer potential vorticity contours. To the south of the Gulf Stream, the deep circulation consists of two separated recirculation gyres; the recirculation gyre to the east of the NESC appears to be trapped around the Corner Rise.

Post-processing of complex altimetry-derived echo shapes is necessary over the Caspian Sea, in which the altimetry-inferred level fluctuations, derived from on-board ‘retrackers’, represent errors within a wide boundary close to the coastlines, i.e. up to ~10–30 km. Four different techniques were implemented to retrack 10 Hz radar waveforms of the TOPEX/Poseidon (T/P) mission and 20 Hz waveforms of its follow-on mission Jason-1. These include three previously introduced algorithms of the Off Center of Gravity, the standard NASA β-5, and Threshold Retracking, after some improvements. We also introduced a new method called ‘Extrema Retracking (ExtR)’, which works based on edge detection and extracting extremum points of the returned waveforms. Before implementing each technique, a two-step preprocessing was applied to exclude unwanted waveforms. Then, an unsupervised classification approach was applied to classify the remaining waveforms into multiple groups, according to their shape and geometrical properties. Finally, the waveforms of each group were post-processed using an appropriate retracking algorithm, and subsequently, the derived corrected ranges were converted to sea surface height (SSH) values. Our post-processed results were evaluated (1) by comparing the time series of SSHs with in situ tide gauge measurements as well as (2) by comparing the along-track retracked SSHs to their corresponding geoid profiles, computed from the EGM2008 model. For each evaluation, in situ measurements or geoid profiles were subtracted from the retracked SSH values to compute residuals. Thereafter, the mean and root mean squares of the residuals were calculated and interpreted, respectively, as a measure of bias and noise level of the processed SSHs. Our results indicated a reduction in noise and bias after the application of retrackers. Specifically, the bias and noise of the ExtR-derived SSH time series were decreased at locations close to the Anzali, Noshahr and Neka tide-gauge stations (along the southern coast). For example, the correlation between altimetry observations and tide gauge measurements close to the Anzali Port over the T/P era was increased from 0.31 (using original altimetry data) to 0.83 (using ExtR), which is equivalent to a noise reduction of 0.58 to 0.39 m. Comparisons of the along-track SSHs with geoid profiles indicated a significant mitigation of land contamination over the coastal areas.

A benchmark of linear predictability of sea surface height (SSH) globally is presented, complementing more complicated studies of SSH predictability. Twenty years of the Estimating the Circulation and Climate of the Ocean (ECCOv4) state estimate (1992–2011) are used, fitting autoregressive moving average [ARMA([Formula: see text])] models where the order of the coefficients is chosen by the Akaike information criteria (AIC). Up to 50% of the ocean SSH variability is dominated by the seasonal signal. The variance accounted for by the nonseasonal SSH is particularly distinct in the Southern and Pacific Oceans, containing >95% of the total SSH variance, and the expected prediction error growth takes a few months to reach a threshold of 1 cm. Isolated regions take 12 months or more to cross an accuracy threshold of 1 cm. Including the trend significantly increases the time taken to reach the threshold, particularly in the South Pacific. Annual averaging has expected prediction error growth of a few years to reach a threshold of 1 cm. Including the trend mainly increases the time taken to reach the threshold, but the time series is short and noisy.

NOAA’s National Ocean Service (NOS) applies hydrodynamic models for the development, transition and implementation of operational forecast systems in US estuaries, ports, lakes and the coastal ocean. These models and systems have applications in the support of safe and efficient marine navigation and emergency response, as well as marine geospatial and ecological applications. As part of the forecast system development, skill assessment is required to evaluate each model system. A forecast model has to be ensured that it has been developed and implemented in a scientifically sound and operationally robust way, and its performance should meet or exceed target benchmarks before its products are approved for release to the public.This paper discusses a new approach for the development of standards and procedures for skill assessment of operational forecast systems, which are designed primarily to support marine transportation and emergency response. Accordingly, this skill assessment focuses on variables, statistics and targets that are of the most use to these communities. The components of skill assessment include: data analysis techniques, definition of model run scenarios and statistical variables, and NOS target values and acceptance criteria. In addition, a large suite of skill assessment software has been developed to compute the new skill assessment scores using data files containing modelled variables and observed data. This software ingests model forecasts and observational data, fills in gaps, creates time series, extracts skill-specific parameters, and computes the skill scores. The software has been tested and applied to all NOS operational forecast systems.

We propose a new deep-learning architecture HIDRA2 for sea level and storm tide modeling, which is extremely fast to train and apply and outperforms both our previous network design HIDRA1 and two state-of-the-art numerical ocean models (a NEMO engine with sea level data assimilation and a SCHISM ocean modeling system), over all sea level bins and all forecast lead times. The architecture of HIDRA2 employs novel atmospheric, tidal and sea surface height (SSH) feature encoders as well as a novel feature fusion and SSH regression block. HIDRA2 was trained on surface wind and pressure fields from a single member of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric ensemble and on Koper tide gauge observations. An extensive ablation study was performed to estimate the individual importance of input encoders and data streams. Compared to HIDRA1, the overall mean absolute forecast error is reduced by 13 %, while in storm events it is lower by an even larger margin of 25 %. Consistent superior performance over HIDRA1 as well as over general circulation models is observed in both tails of the sea level distribution: low tail forecasting is relevant for marine traffic scheduling to ports of the northern Adriatic, while high tail accuracy helps coastal flood response. To assign model errors to specific frequency bands covering diurnal and semi-diurnal tides and the two lowest basin seiches, spectral decomposition of sea levels during several historic storms is performed. HIDRA2 accurately predicts amplitudes and temporal phases of the Adriatic basin seiches, which is an important forecasting benefit due to the high sensitivity of the Adriatic storm tide level to the temporal lag between peak tide and peak seiche.

<p>corresponding author: N.Flokos</p><p>flksnik@gmail.com</p><p>ABSTRACT</p><p>Sea level change is one of the key indicators of climate change with numerous effects such as flooding, erosion of beaches, salt intrusion.  The detailed global picture of sea level and the monitoring of its spatial-temporal changes is performed by Satellite Altimetry (SA). Nowadays, SA data compare well with measurements from the global tide gauge network, but the aim of 0.3 mm/year accuracy in the altimeter derived rate of global mean sea level rise is still not fully met. </p><p>Whilst the precise determination of global and regional sea level rise from SA data is promising, there is however an observational gap in our knowledge regarding the coastal zone. While Tide Gauges (TG) are usually located at the coast, therefore providing coastal sea level measurements, altimeters have difficulties there. Filling this gap becomes important when considering that the impact of sea level rise can be devastating on the coast with effects on society and ecosystems. This makes it even more significant knowing that there are many stretches of the world’s coast that still do not possess in situ level measuring devices.  </p><p>This work aims to discuss the available data and methods that link the SA measurements of sea level rise with TG measurements. Whilst there is rich literature on relevant applications, it is important to have a clear and concise methodology on this.</p><p>Tide gauge data</p><p>Several post processing steps need to be applied to the raw TG data to enrich the raw Sea Surface Heights (SSH) values and make them comparable with SA data. There are several geophysical corrections, such as pressure and wind effects, which can be applied to TG data in order to deduce  Sea Level (SL) and be consistent with altimeter data. High frequency atmospheric effects on TG data are corrected using the Dynamic Atmospheric Correction (DAC) provided by AVISO. One other large uncertainty is the vertical stability of the TG benchmark over time. TG data must be corrected for the Vertical Land Motion (VLM) to enable the comparison of two sea level measurements (TG and SA) and their later integration within the surfaces of the absolute sea heights. The main VLM dataset can be obtained from SONEL database (SONEL 2016) which provides crustal velocities from the continuous GNSS measurements at sites collocated to the TG.</p><p>Satellite altimetry data</p><p>Whilst Satellite Altimetry over the open ocean is a mature discipline, global altimetry data collected over the coastal ocean remain still largely unexploited. This is because of intrinsic difficulties in the corrections and issues of land contamination in the footprint that have so far resulted in systematic flagging and rejection of these data. In this work, the relevant methodology to overcome these problems and extend the capabilities of current and future altimeters to the coastal zone (coastal altimetry) will be discussed and a number of coastal altimetry data sets will be used (eg SARvatore, X-TRACK, RADS etc). Finally, a practical example using real data sets over the Aegean Sea will be presented. </p><p> </p><p> </p>

Subseasonal tropical cyclone forecasts from two operational forecast models are verified for the 2017/18 and 2018/19 Southern Hemisphere cyclone seasons. The forecasts are generated using the ECMWF’s Medium- and Extended-Range Ensemble Integrated Forecasting System (IFS), and the Bureau of Meteorology’s seasonal forecasting system ACCESS-S1. Results show the IFS is more skillful than ACCESS-S1, which is attributed to the IFS’s greater ensemble size, increased spatial resolution, and data assimilation schemes. Applying a lagged ensemble with ACCESS-S1 increases forecast reliability, with the optimum number of lagged members being dependent on forecast lead time. To investigate the impacts of atmospheric assimilation at shorter lead times, comparisons were made between the Bureau of Meteorology’s ACCESS-S1 and ACCESS-GE2 systems, the latter a global Numerical Weather Prediction system running with the same resolution and model physics as ACCESS-S1 but using an ensemble Kalman filter for data assimilation. This comparison showed the data assimilation scheme used in the GE2 system gave improvements in forecast skill for days 8–10, despite the smaller ensemble size used in GE2 (24 members per forecast compared to 33). Finally, a multimodel ensemble was created by combining forecasts from both the IFS and ACCESS-S1. Using the multimodel ensemble gave improvements in forecast skill and reliability. This improvement is attributed to complementary spatial errors in both systems occurring across much of the Southern Hemisphere as well as an increase in the ensemble size.

Incorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time (tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

A new marine forecasting system for the New Zealand region has been developed, including global and regional wave models, and regional tide and storm surge models. These form part of an integrated weather-related hazards forecasting capability, which also includes an accurate, data-assimilating high-resolution weather forecasting system and a national-scale flood forecasting system. Predicted variations in sea surface height and depth-averaged current due to tides and storm surge are provided in twice-daily 48-hour regional forecasts. Storm surge is predicted for the New Zealand region using the RiCOM hydrodynamic model on an unstructured grid in depth-averaged mode, using semi-implicit integration in time and a semi-langrangian scheme for advection. The model reproduced the storm surge event resulting from the passage of cyclone “Funa” across New Zealand during January 2008. Root-mean-square (RMS) errors in predicted storm surge height were 4-13 cm. For a second simulated storm, which occurred in Marlborough in late July/early August 2008, the model predicted the storm surge height with RMS errors at Kaikoura and Lyttelton of 7 and 4 cm, respectively. Wave-breaking also contributes to sea levels close to the coast through radiation stresses. The forecast system runs Wavewatch III on nested global and regional structured grids, providing twice-daily 48-hour forecasts. Model developments incorporating wave radiation stresses into the hydrodynamic model forcing are described, and some investigations into the effects of wave forcing on surface setup and coastal inundation are presented. A case study of inundation around the fringes of Hawkes Bay in response to extreme sea levels exacerbated by wave-induced stresses is presented. The case study used the SWAN wave model to provide the near-shore wave characteristics and radiations stresses, and demonstrates the ability of the coupled tide-surge-wave model to simulate and predict coastal flooding on a spatial resolution of a few metres.

This paper discusses the needs to establish a capability to provide real-time regional ocean forecasts and the feasibility of producing them on an operational basis. Specifically, the development of a Regional Ocean Forecast System using the Princeton Ocean Model (POM) as a prototype and its application to the East Coast of the U.S. are presented. The ocean forecasts are produced using surface forcing from the Eta model, the operational mesoscale weather prediction model at the National Centers for Environmental Prediction (NCEP). At present, the ocean forecast model, called the East Coast-Regional Ocean Forecast System (EC-ROFS) includes assimilation of sea surface temperatures from in situ and satellite data and sea surface height anomalies from satellite altimeters. Examples of forecast products, their evaluation, problems that arose during the development of the system, and solutions to some of those problems are also discussed. Even though work is still in progress to improve the performance of EC-ROFS, it became clear that the forecast products which are generated can be used by marine forecasters if allowances for known model deficiencies are taken into account. The EC-ROFS became fully operational at NCEP in March 2002, and is the first forecast system of its type to become operational in the civil sector of the United States.