Avichal Mehra

Status and future of global and regional ocean prediction systems

Operational evolution of global and regional ocean forecasting systems has been extremely significant in recent years. Global Ocean Data Assimilation Experiment (GODAE) Oceanview supports the national research groups providing them with coordination and sharing expertise among the partners. Several systems have been set up and developed pre-operationally, and the majority of these are now fully operational; at the present time, they provide medium- and long-term forecasts of the most relevant ocean physical variables. These systems are based on ocean general circulation models and data-assimilation techniques that are able to correct the model with the information inferred from different types of observations. A few systems also incorporate a biogeochemical component coupled with the physical system, while others are based on coupled ocean–wave–ice–atmosphere models. The products are routinely validated with observations in order to assess their quality. Data and product implementation and organization, as well as service, for users have been well tried and tested, and most of the products are now available to users. The interaction with different users is an important factor in the development process. This paper provides a synthetic overview of the GODAE OceanView prediction systems.

GODAE OceanView Class 4 forecast verification framework: global ocean inter-comparison

As part of the work of the GODAE OceanView Inter-comparison and Validation Task Team (IV-TT), 6 global ocean forecasting systems spread across 5 operational oceanography forecast centres were inter-compared using a common set of observations as a proxy for the truth. The ‘Class 4’ in the title refers to a set of forecast verification metrics defined in the MERSEA-IP/GODAE internal metrics document (Hernandez 2007), the defining feature of which is that comparisons between forecasts and observations take place in observation space. This approach is seen as a departure from other diagnostic approaches such as analysing model trends or innovation statistics, and is commonly used in the atmospheric community. The physical parameters involved in the comparison are sea surface temperature (SST), sub-surface temperature, sub-surface salinity and sea level anomaly (SLA). SST was measured using in-situ observations obtained from USGODAE, sub-surface conditions were compared to Argo profiles, while sea level anomaly was measured by several satellite altimeters courtesy of AVISO. The 5 forecast centres involved in the project were Met Office, Australian Bureau of Meteorology, Mercator Océan, Environment Canada and NOAA/NWS/NCEP. Combining Met Office, Mercator Océan and Environment Canada forecasts into a mixed resolution multi-model ensemble produces estimates of the ocean state which have better accuracy and associativity properties for SST, SLA and temperature profiles than any individual ensemble component.

Recent progress in performance evaluations and near real-time assessment of operational ocean products

Operational ocean forecast systems provide routine marine products to an ever-widening community of users and stakeholders. The majority of users need information about the quality and reliability of the products to exploit them fully. Hence, forecast centres have been developing improved methods for evaluating and communicating the quality of their products. Global Ocean Data Assimilation Experiment (GODAE) OceanView, along with the Copernicus European Marine Core Service and other national and international programmes, has facilitated the development of coordinated validation activities among these centres. New metrics, assessing a wider range of ocean parameters, have been defined and implemented in real-time. An overview of recent progress and emerging international standards is presented here.

Progress and challenges in short- to medium-range coupled prediction

The availability of GODAE Oceanview-type ocean forecast systems provides the opportunity to develop high-resolution, short- to medium-range coupled prediction systems. Several groups have undertaken the first experiments based on relatively unsophisticated approaches. Progress is being driven at the institutional level targeting a range of applications that represent their respective national interests with clear overlaps and opportunities for information exchange and collaboration. The applications include forecasting of the general circulation, hurricanes, extra-tropical storms, high-latitude weather and coastal air–sea interaction. In some cases, research has moved beyond case and sensitivity studies to controlled experiments to obtain statistically significant metrics and operational predictions.

S4: An O2R/R2O Infrastructure for Optimizing Satellite Data Utilization in NOAA Numerical Modeling Systems: A Step Toward Bridging the Gap between Research and Operations

AbstractIn 2011, the National Oceanic and Atmospheric Administration (NOAA) began a cooperative initiative with the academic community to help address a vexing issue that has long been known as a disconnection between the operational and research realms for weather forecasting and data assimilation. The issue is the gap, more exotically referred to as the “valley of death,” between efforts within the broader research community and NOAA’s activities, which are heavily driven by operational constraints. With the stated goals of leveraging research community efforts to benefit NOAA’s mission and offering a path to operations for the latest research activities that support the NOAA mission, satellite data assimilation in particular, this initiative aims to enhance the linkage between NOAA’s operational systems and the research efforts. A critical component is the establishment of an efficient operations-to-research (O2R) environment on the Supercomputer for Satellite Simulations and Data Assimilation Studies ...

Modeling of 137Cs as a Tracer in a Regional Model for the Western Pacific, after the Fukushima–Daiichi Nuclear Power Plant Accident of March 2011

AbstractIn this study, results are presented from the first operational ocean tracer dispersion model operated by the National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction (NOAA/NWS/NCEP). This study addresses the dispersion of radionuclide contaminants after the Fukushima–Daiichi nuclear accident that was triggered by the 11 March 2011 earthquake and tsunami. The tracer capabilities of the Hybrid Coordinate Ocean Model (HYCOM) were used in a regional domain for the northwestern Pacific, with nesting lateral boundary conditions using daily nowcast–forecast fields from the global operational Real-Time Ocean Forecast System (RTOFS-Global), a ° HYCOM global forecast from NCEP, based on data-assimilative ° HYCOM Global Ocean Forecast System (GOFS) analyses from the Naval Research Laboratory/Naval Oceanographic Office (NRL/NAVOCEANO). This regional model, RTOFS Episodic Tracers for a region of the North West Pacific (RTOFS-ET_WPA), was in operati...

GODAE OceanView Inter-comparison for the Australian Region

This paper compares the performance of short-range operational ocean forecasts, using ‘observational space’ metrics developed under GODAE OceanView (GOV). Best estimates (behind the real-time analysis) and forecasts are inter-compared for the Australian region (0-50S, 90-180E) for 2013. Systems considered include those developed in Australia, France, Canada, United Kingdom and USA. Each system is compared to observations of along-track sea level anomaly, sea surface temperature observations from surface drifters and sub-surface Argo profiles of temperature and salinity. The UK operational system generally has the smallest errors for sea surface temperature and sea level anomaly for the Australian region. However, the French systems outperform others in sub-surface temperature and salinity for the region. Of the two products provided by the Australian centre, an ensemble based approach is found to perform better than the deterministic system, having higher skill and lower root mean square errors. Some of the ‘better’ results of systems can be attributed in part to the lack of independence of the reference observations; however the study does demonstrate the feasibility and robustness of GOV global ocean inter-comparison efforts for regional applications.

Neural networks technique for filling gaps in satellite measurements: application to ocean color observations

A neural network (NN) technique to fill gaps in satellite data is introduced, linking satellite-derived fields of interest with other satellites and in situ physical observations. Satellite-derived “ocean color” (OC) data are used in this study because OC variability is primarily driven by biological processes related and correlated in complex, nonlinear relationships with the physical processes of the upper ocean. Specifically, ocean color chlorophyll-a fields from NOAAs operational Visible Imaging Infrared Radiometer Suite (VIIRS) are used, as well as NOAA and NASA ocean surface and upper-ocean observations employed—signatures of upper-ocean dynamics. An NN transfer function is trained, using global data for two years (2012 and 2013), and tested on independent data for 2014. To reduce the impact of noise in the data and to calculate a stable NN Jacobian for sensitivity studies, an ensemble of NNs with different weights is constructed and compared with a single NN. The impact of the NN training period on the NNs generalization ability is evaluated. The NN technique provides an accurate and computationally cheap method for filling in gaps in satellite ocean color observation fields and time series.

Improving NCEP HWRF Simulations of Surface Wind and Inflow Angle in the Eyewall Area

AbstractThis technical note describes a modification of the boundary layer parameterization scheme in the hurricane weather and research forecast (HWRF) model, which improves the simulations of low-level wind and surface inflow angle in the eyewall area and has been implemented in the HWRF system and used in the operational system since 2016. The modification is on an observation-based adjustment of eddy diffusivity previously implemented in the model. It is needed because the previous adjustment resulted in a discontinuity in the vertical distribution of eddy diffusivity near the surface-layer top, which increases the friction within the surface layer and compromises the surface-layer constant-flux assumption. The discontinuity affects the simulation of storm intensity and intensification, one of the main metrics of model performance, particularly in strong tropical cyclones. This issue is addressed by introducing a height-dependent adjustment so that the vertical profile of eddy diffusivity is continuou...

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (KdPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological KdPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (bb) coefficients) and apparent optical properties (downwelling attenuation coefficient (Kd) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the KdPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and KdPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.