Operational Forecasting System Research Articles

The forecasting and analysis of oil spill drift trajectory during the Sanchi collision accident, East China Sea

On 06 January 2018, the Sanchi tanker collided with a Hong Kong-flagged cargo ship at 30°42′42″N, 124°56′42″E in the East China Sea (ECS). In this paper, we used a Northwest Pacific (NWP) operational oil spill forecasting system of National Marine Environmental Forecasting Center of China (NMEFC) to forecast the oil drifting trajectory during Sanchi collision accident. In order to reveal the major errors sources and improve the accuracy of the forecasting system, we designed five numeric simulation scenarios. In the five scenarios, we input the different current and wind forcing data into the forecasting system, and set the different oil released time. By the analysis and discussion of the five numeric simulation results, we drew the conclusions following: 1) The error of wind and the inaccuracy released time of oil were the major error sources of the NWP operational oil spill forecasting system of NMEFC during the accident. 2) We should further strengthen the observation of the complicated vortex region of Kuroshio Current to improve the prediction accuracy of material transport in ocean. 3) In the event that this information is unavailable, uncertainty analysis should be conducted by simulating the oil spill on all kinds of different released conditions.

Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation

Accurate water level forecasts during flood events are crucial to mitigate the loss of human lives and economic damages. However, the accuracy of flood models can be affected by various factors, including the complexity of the terrain geometry and bathymetry, imperfect physics as well as uncertainties in the inflows and parameters. This paper describes a practical implementation of an ensemble Kalman filter (EnKF) based data assimilation system that is aimed towards enhancing the forecasting skill of flood models. The system was implemented and tested with a real world dam break flood, based on the experimentally scaled Toce River valley flood that occurred on July 8th, 1996. Water depth data are available for assimilation from a network of 21 sensors distributed across the domain. Our results demonstrate that assimilating data into the flood model significantly improves the model prediction by up to 90% after assimilation and 60% during forecasting. Assimilating the data more frequently significantly enhances the system performances. Estimating the two-dimensional Manning coefficient together with the model’s dynamical variables (water depth and velocities) further improves the model prediction skill. Overall, our results suggest that assimilating data into the flood model, while jointly inferring the state and (poorly known) parameters, using an EnKF may provide an efficient framework for developing an operational flood forecasting system.

CALIOPE-Urban v1.0: coupling R-LINE with a mesoscale air quality modelling system for urban air quality forecasts over Barcelona city (Spain)

Abstract. The NO2 annual air quality limit value is systematically exceeded in many European cities. In this context, understanding human exposure, improving policy and planning, and providing forecasts requires the development of accurate air quality models at the urban (street level) scale. We describe CALIOPE-Urban, a system coupling CALIOPE – an operational mesoscale air quality forecast system based on the HERMES (emissions), WRF (meteorology) and CMAQ (chemistry) models – with the urban roadway dispersion model R-LINE. Our developments have focused on Barcelona city (Spain), but the methodology may be replicated for other cities in the future. WRF drives pollutant dispersion and CMAQ provides background concentrations to R-LINE. Key features of our system include the adaptation of R-LINE to street canyons, the use of a new methodology that considers upwind grid cells in CMAQ to avoid double counting traffic emissions, a new method to estimate local surface roughness within street canyons, and a vertical mixing parameterisation that considers urban geometry and atmospheric stability to calculate surface level background concentrations. We show that the latter is critical to correct the night-time overestimations in our system. Both CALIOPE and CALIOPE-Urban are evaluated using two sets of observations. The temporal variability is evaluated against measurements from five traffic sites and one urban background site for April–May 2013. While both systems show a fairly good agreement at the urban background site, CALIOPE-Urban shows a better agreement at traffic sites. The spatial variability is evaluated using 182 passive dosimeters that were distributed across Barcelona during 2 weeks for February–March 2017. In this case, the coupled system also shows a more realistic distribution than the mesoscale system, which systematically underpredicts NO2 close to traffic emission sources. Overall CALIOPE-Urban improves mesoscale model results, demonstrating that the combination of both scales provides a more realistic representation of NO2 spatio-temporal variability in Barcelona.

Recent Progress in Numerical Atmospheric Modeling in China

This review summarizes the scientific and technical progress in atmospheric modeling in China since 2011, including the dynamical core, model physics, data assimilation, ensemble forecasting, and model evaluation strategies. In terms of the dynamical core, important efforts have been made in the improvement of the existing model formulations and in exploring new modeling approaches that can better adapt to massively parallel computers and global multiscale modeling. With regard to model physics, various achievements in physical representations have been made, especially a trend toward scale-aware parameterization for accommodating the increase of model resolution. In the field of data assimilation, a 4D-Var system has been developed and is operationally used by the National Meteorological Center of China, and its performance is promising. Furthermore, ensemble forecasting has played a more important role in operational forecast systems and progressed in many fundamental techniques. Model evaluation strategies, including key performance metrics and standardized experimental protocols, have been proposed and widely applied to better understand the strengths and weaknesses of the systems, offering key routes for model improvement. The paper concludes with a concise summary of the status quo and a brief outlook in terms of future development.

Toward Convective-Scale Prediction within the Next Generation Global Prediction System

AbstractThe Geophysical Fluid Dynamics Laboratory (GFDL) has developed a new variable-resolution global model with the ability to represent convective-scale features that serves as a prototype of the Next Generation Global Prediction System (NGGPS). The goal of this prediction system is to maintain the skill in large-scale features while simultaneously improving the prediction skill of convectively driven mesoscale phenomena. This paper demonstrates the new capability of this model in convective-scale prediction relative to the current operational Global Forecast System (GFS). This model uses the stretched-grid functionality of the Finite-Volume Cubed-Sphere Dynamical Core (FV3) to refine the global 13-km uniform-resolution model down to 4-km convection-permitting resolution over the contiguous United States (CONUS), and implements the GFDL single-moment 6-category cloud microphysics to improve the representation of moist processes. Statistics gathered from two years of simulations by the GFS and select configurations of the FV3-based model are carefully examined. The variable-resolution FV3-based model is shown to possess global forecast skill comparable with that of the operational GFS while quantitatively improving skill and better representing the diurnal cycle within the high-resolution area compared to the uniform mesh simulations. Forecasts of the occurrence of extreme precipitation rates over the southern Great Plains are also shown to improve with the variable-resolution model. Case studies are provided of a squall line and a hurricane to demonstrate the effectiveness of the variable-resolution model to simulate convective-scale phenomena.

Explicit Prediction of Continental Convection in a Skillful Variable‐Resolution Global Model

AbstractWe present a new global‐to‐regional model, cfvGFS, able to explicitly (without parameterization) represent convection over part of the Earth. This model couples the Geophysical Fluid Dynamics Laboratory Finite‐Volume Cubed‐Sphere Dynamical Core (FV3) to the Global Forecast System physics and initial conditions, augmented with a six‐category microphysics and a modified planetary boundary layer scheme. We examine the characteristics of cfvGFS on a 3‐km continental U. S. domain nested within a 13‐km global model. The nested cfvGFS still has good hemispheric skill comparable to or better than the operational Global Forecast System, while supercell thunderstorms, squall lines, and derechos are explicitly represented over the refined region. In particular, cfvGFS has excellent representations of fine‐scale updraft helicity fields, an important proxy for severe weather forecasting. Precipitation biases are found to be smaller than in uniform‐resolution global models and competitive with operational regional models; the 3‐km domain also improves upon the global models in 2‐m temperature and humidity skill. We discuss further development of cfvGFS and the prospects for a unified global‐to‐regional prediction system.

On the Assessment of a Cooling Tower Scheme for High-Resolution Numerical Weather Modeling for Urban Areas

AbstractA cooling tower scheme that quantifies the sensible and latent anthropogenic heat fluxes released from buildings was coupled to an operational forecasting system [Rapid Refresh Multiscale Analysis and Prediction of the Beijing Urban Meteorological Institute (RMAPS-Urban)] and was evaluated in the context of the megacity of Beijing, China, during summer months. The objective of this scheme is to correct for underestimations of surface latent heat fluxes in regional climate modeling and weather forecasts in urban areas. The performance for surface heat fluxes by the modified RMAPS-Urban is greatly improved when compared with a suite of observations in Beijing. The cooling tower scheme increases the anthropogenic latent heat partition by 90% of the total anthropogenic heat flux release. Averaged surface latent heat flux in urban areas increases to about 64.3 W m−2 with a peak of 150 W m−2 on dry summer days and 40.35 W m−2 with a peak of 150 W m−2 on wet summer days. The model performance of near-surface temperature and humidity is also improved. Average 2-m temperature errors are reduced by 1°C, and maximum and minimum temperature errors are improved by 2°–3°C; absolute humidity is increased by 5%.

A novel stochastic modelling approach for operational real-time ice-jam flood forecasting

Forecasting ice jams and their consequential flooding is more challenging than predicting open water flood conditions. This is due to the chaotic nature of ice jam formation since slight changes in water and ice flows, location of the ice jam toe along the river and initial water levels at the time of jam formation can lead to marked differences in the outcome of backwater level elevations and flood severity. In this paper, we introduce a novel, operational real-time flood forecasting system that captures this stochastic nature of ice-jam floods and places the forecasts in a probabilistic context in the form of flood hazard maps (probability of flood extents and depths). This novel system was tested successfully for the ice-cover breakup period in the spring of 2018 along the Athabasca River at the Town of Fort McMurray, Canada.

Ensemble forecasts of air quality in eastern China – Part 2: Evaluation of the MarcoPolo–Panda prediction system, version 1

Abstract. An operational multimodel forecasting system for air quality has been developed to provide air quality services for urban areas of China. The initial forecasting system included seven state-of-the-art computational models developed and executed in Europe and China (CHIMERE, IFS, EMEP MSC-W, WRF-Chem-MPIM, WRF-Chem-SMS, LOTOS-EUROS, and SILAMtest). Several other models joined the prediction system recently, but are not considered in the present analysis. In addition to the individual models, a simple multimodel ensemble was constructed by deriving statistical quantities such as the median and the mean of the predicted concentrations. The prediction system provides daily forecasts and observational data of surface ozone, nitrogen dioxides, and particulate matter for the 37 largest urban agglomerations in China (population higher than 3 million in 2010). These individual forecasts as well as the multimodel ensemble predictions for the next 72 h are displayed as hourly outputs on a publicly accessible web site (http://www.marcopolo-panda.eu, last access: 27 March 2019). In this paper, the performance of the prediction system (individual models and the multimodel ensemble) for the first operational year (April 2016 until June 2017) has been analyzed through statistical indicators using the surface observational data reported at Chinese national monitoring stations. This evaluation aims to investigate (a) the seasonal behavior, (b) the geographical distribution, and (c) diurnal variations of the ensemble and model skills. Statistical indicators show that the ensemble product usually provides the best performance compared to the individual model forecasts. The ensemble product is robust even if occasionally some individual model results are missing. Overall, and in spite of some discrepancies, the air quality forecasting system is well suited for the prediction of air pollution events and has the ability to provide warning alerts (binary prediction) of air pollution events if bias corrections are applied to improve the ozone predictions.

Evaluating Operational Hydrodynamic Models for Real‐time Simulation of Evaporation From Large Lakes

AbstractMethods for simulating evaporative water loss from Earth's large lakes have lagged behind advances in hydrodynamic modeling. Here we explore use of oceanographic models to simulate lake evaporation from a long‐term water balance perspective. More specifically, we compare long‐term monthly simulations of latent heat flux from two configurations of a current operational hydrodynamic forecasting system (based on the Finite Volume Community Ocean Model, or FVCOM) for the Laurentian Great Lakes. We then compare these simulations to comparable simulations from a legacy conventional lake thermodynamics model, and from a recently developed statistical water balance model. We find that one of the FVCOM configurations that is currently used in operations for short‐term hydrodynamic forecast guidance is also suitable for real‐time simulation of evaporation from very large lakes. The operational versions of FVCOM should therefore be considered a readily available tool for supporting regional water supply management and, pending further research, extended water supply forecasting.

Ensemble forecasts of air quality in eastern China – Part 1: Model description and implementation of the MarcoPolo–Panda prediction system, version 1

Abstract. An operational multi-model forecasting system for air quality including nine different chemical transport models has been developed and provides daily forecasts of ozone, nitrogen oxides, and particulate matter for the 37 largest urban areas of China (population higher than 3 million in 2010). These individual forecasts as well as the mean and median concentrations for the next 3 days are displayed on a publicly accessible website (http://www.marcopolo-panda.eu, last access: 7 December 2018). The paper describes the forecasting system and shows some selected illustrative examples of air quality predictions. It presents an intercomparison of the different forecasts performed during a given period of time (1–15 March 2017) and highlights recurrent differences between the model output as well as systematic biases that appear in the median concentration values. Pathways to improve the forecasts by the multi-model system are suggested.

Современное состояние и перспективы совершенствования системы оперативного гидрологического прогнозирования в Гидрометцентре России

Современное состояние и перспективы совершенствования системы оперативного гидрологического прогнозирования в Гидрометцентре России

OPERATIONAL FORECASTING SYSTEM FOR DUBAI COASTAL WATERS

The abstract is based on decision support system for Dubai Coastal Zone and Waterway Operations. This is a sophisticated project to couple a suite of meteorology and ocean models with data management infrastructure and web-based tools to deliver data and decision support information to Dubai Municipality as well as public users. The latest open-source modeling technology available from the research and academic communities to build an operational forecast system that is a suite of interconnected components that allows for seamless interaction of different models, real-time data, and custom web-based reports and tools that access model forecasts and real-time observations. This included measurements taken in the Dubai coastal zone, satellite and atmospheric measurements across the Arabian Gulf, and from a variety of online global environmental models. The data specifically used to create each of the meteorological, hydrodynamic, spectral wave, inundation and oil-spill models. The circulation modeling studies of the Arabian Gulf were carried out by Kampf (2006), Thoppil (2010) and Xue and Eltahir (2015).

Impact of the Horizontal Heat Flux in the Mixed Layer on an Extreme Heat Event in North China: A Case Study

Extreme heat over the North China Plain is typically induced by anomalous descending flows associated with anticyclonic circulation anomalies. However, an extreme heat event that happened in the North China Plain region on 12–13 July 2015, with maximum temperature higher than 40°C at some stations, was characterized by only a weak simultaneous appearance of an anomalous anticyclone and descending flow, suggesting that some other factor(s) may have induced this heat event. In this study, we used the forecast data produced by the Beijing Rapid Updated Cycling operational forecast system, which predicted the heat event well, to investigate the formation mechanism of this extreme heat event. We calculated the cumulative heat in the mixed-layer air column of North China to represent the change in surface air temperature. The cumulative heat was composed of sensible heat flux from the ground surface and the horizontal heat flux convergence. The results indicated that the horizontal heat flux in the mixed layer played a crucial role in the temporal and spatial distribution of high temperatures. The horizontal heat flux was found to be induced by distinct distributions of air temperatures and horizontal winds at low levels during the two days, implying a complexity of the low-level atmosphere in causing the extreme heat.

Evaluating post-processing approaches for monthly and seasonal streamflow forecasts

Abstract. Streamflow forecasting is prone to substantial uncertainty due to errors in meteorological forecasts, hydrological model structure, and parameterization, as well as in the observed rainfall and streamflow data used to calibrate the models. Statistical streamflow post-processing is an important technique available to improve the probabilistic properties of the forecasts. This study evaluates post-processing approaches based on three transformations – logarithmic (Log), log-sinh (Log-Sinh), and Box–Cox with λ=0.2 (BC0.2) – and identifies the best-performing scheme for post-processing monthly and seasonal (3-months-ahead) streamflow forecasts, such as those produced by the Australian Bureau of Meteorology. Using the Bureau's operational dynamic streamflow forecasting system, we carry out comprehensive analysis of the three post-processing schemes across 300 Australian catchments with a wide range of hydro-climatic conditions. Forecast verification is assessed using reliability and sharpness metrics, as well as the Continuous Ranked Probability Skill Score (CRPSS). Results show that the uncorrected forecasts (i.e. without post-processing) are unreliable at half of the catchments. Post-processing of forecasts substantially improves reliability, with more than 90 % of forecasts classified as reliable. In terms of sharpness, the BC0.2 scheme substantially outperforms the Log and Log-Sinh schemes. Overall, the BC0.2 scheme achieves reliable and sharper-than-climatology forecasts at a larger number of catchments than the Log and Log-Sinh schemes. The improvements in forecast reliability and sharpness achieved using the BC0.2 post-processing scheme will help water managers and users of the forecasting service make better-informed decisions in planning and management of water resources. Highlights. Uncorrected and post-processed streamflow forecasts (using three transformations, namely Log, Log-Sinh, and BC0.2) are evaluated over 300 diverse Australian catchments. Post-processing enhances streamflow forecast reliability, increasing the percentage of catchments with reliable predictions from 50 % to over 90 %. The BC0.2 transformation achieves substantially better forecast sharpness than the Log-Sinh and Log transformations, particularly in dry catchments.

Model Development and Hindcast Simulations of NOAA’s Integrated Northern Gulf of Mexico Operational Forecast System

NOAA’s National Ocean Service is upgrading three existing northern Gulf of Mexico (GOM) operational nowcast/forecast systems (OFS) by integrating them into one single system (INGOFS) and developing additional domain coverage to encompass the lower Mississippi River, Lake Pontchartrain, Texas coastal embayments, and Mexican coastal waters. The system will produce real-time nowcast and short-range forecast guidance for water levels, 3-dimensional currents, water temperature, and salinity. INGOFS will be implemented using the Finite Volume Community Ocean Model (FVCOM). This paper describes the model configuration and results from a one-year (2 August 2016–1 August 2017) hindcast simulation. The model grid is composed of about 300,000 nodes and 600,000 elements, and has a spatial resolution ranging from 45 m near the coast to around 10 km on the open ocean boundary. It uses the FVCOM wetting and drying feature, the quadratic bottom friction scheme, and the two-equation model of the Mellor-Yamada Level 2.5 turbulence closure scheme. The hindcast results of water levels, surface temperatures, and salinity were verified by comparing the model time series with in situ observations. The root-mean-squared errors are about 0.08 m for water levels, about 1.1 °C for temperatures, and about 3.7 psu for salinity. The hindcast configuration will be further tested in a nowcast/forecast environment for a one-year period. The upgraded system is anticipated to be in operational production in mid-2020.

Improving Seasonal Forecasts for Basin Scale Hydrological Applications

Seasonal forecasting is a fast-growing climate prediction application that puts into practice the latest improvements in the climate modeling research. Skillful seasonal forecasts can drastically aid practical applications and productive sectors by reducing weather-related risks such as water availability. In this study two operational seasonal forecasting systems are tested in a water resource important watershed on the island of Crete. Hindcast precipitation and temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) System 4 and Met Office GloSea5 systems are tested for their forecast skill up to seven months ahead. Data of both systems are downscaled and corrected for biases towards the observations. Different correction methods are applied and evaluated. Post-processed data from these methods are used as an input to the hydrological model HYPE, to provide streamflow forecasts. Results show that a prior adjustment of the two systems’ precipitation and temperature may improve their forecast skill. Adjusted GloSea5 forecasts are slightly better estimates than the corresponding forecasts based on System 4. The results show that both systems provide a skillful ensemble streamflow prediction for one month ahead, with the skill decreasing rapidly beyond that. Update of the initial state of HYPE results in the reduction of the variability of the ensemble flow predictions and improves the skill but only as far as two months of forecast. Finally, the two systems were tested for their ability to capture a limited number of historical streamflow drought events, with indications that GloSea5 has a slightly better skill.

Quantifying stratosphere-troposphere transport of ozone using balloon-borne ozonesondes, radar windprofilers and trajectory models

In a series of 10-day campaigns in Ontario and Quebec, Canada, between 2005 and 2007, ozonesondes were launched twice daily in conjunction with continuous high-resolution wind-profiling radar measurements. Windprofilers can measure rapid changes in the height of the tropopause, and in some cases follow stratospheric intrusions. Observed stratospheric intrusions were studied with the aid of a Lagrangian particle dispersion model and the Canadian operational weather forecast system. Definite stratosphere-troposphere transport (STT) events occurred approximately every 2–3 days during the spring and summer campaigns, whereas during autumn and winter, the frequency was reduced to every 4–5 days. Although most events reached the lower troposphere, only three events appear to have significantly contributed to ozone amounts in the surface boundary layer. Detailed calculations find that STT, while highly variable, is responsible for an average, over the seven campaigns, of 3.1% of boundary layer ozone (1.2 ppb), but 13% (5.4 ppb) in the lower troposphere and 34% (22 ppb) in the middle and upper troposphere, where these layers are defined as 0–1 km, 1–3 km, and 3–8 km respectively. Estimates based on counting laminae in ozonesonde profiles, with judicious choices of ozone and relative humidity thresholds, compare moderately well, on average, with these values. The lamina detection algorithm is then applied to a large dataset from four summer ozonesonde campaigns at 18 North American sites between 2006 and 2011. The results show some site-to-site and year-to-year variability, but stratospheric ozone contributions average 4.6% (boundary layer), 15% (lower troposphere) and 26% (middle/upper troposphere). Calculations were also performed based on the TOST global 3D trajectory-mapped ozone data product. Maps of STT in the same three layers of the troposphere suggest that the STT ozone flux is greater over the North American continent than Europe, and much greater in winter and spring than in summer or fall. When averaged over all seasons, magnitudes over North America show similar ratios between levels to the previous calculations, but are overall 3–4 times smaller. This may be because of limitations (trajectory length and vertical resolution) to the current TOST-based calculation.

Implementation and validation of a new operational wave forecasting system of the Mediterranean Monitoring and Forecasting Centre in the framework of the Copernicus Marine Environment Monitoring Service

Abstract. Within the framework of the Copernicus Marine Environment Monitoring Service (CMEMS), an operational wave forecasting system for the Mediterranean Sea has been implemented by the Hellenic Centre for Marine Research (HCMR) and evaluated through a series of preoperational tests and subsequently for 1 full year of simulations (2014). The system is based on the WAM model and it has been developed as a nested sequence of two computational grids to ensure that occasional remote swell propagating from the North Atlantic correctly enters the Mediterranean Sea through the Strait of Gibraltar. The Mediterranean model has a grid spacing of 1∕24∘. It is driven with 6-hourly analysis and 5-day forecast 10 m ECMWF winds. It accounts for shoaling and refraction due to bathymetry and surface currents, which are provided in offline mode by CMEMS. Extensive statistics on the system performance have been calculated by comparing model results with in situ and satellite observations. Overall, the significant wave height is accurately simulated by the model while less accurate but reasonably good results are obtained for the mean wave period. In both cases, the model performs optimally at offshore wave buoy locations and well-exposed Mediterranean subregions. Within enclosed basins and near the coast, unresolved topography by the wind and wave models and fetch limitations cause the wave model performance to deteriorate. Model performance is better in winter when the wave conditions are well defined. On the whole, the new forecast system provides reliable forecasts. Future improvements include data assimilation and higher-resolution wind forcing.

Ice Forecasting in the Next-Generation Great Lakes Operational Forecast System (GLOFS)

Ice Cover in the Great Lakes has significant impacts on regional weather, economy, lake ecology, and human safety. However, forecast guidance for the lakes is largely focused on the ice-free season and associated state variables (currents, water temperatures, etc.) A coupled lake-ice model is proposed with potential to provide valuable information to stakeholders and society at large about the current and near-future state of Great Lakes Ice. The model is run for three of the five Great Lakes for prior years and the modeled ice cover is compared to observations via several skill metrics. Model hindcasts of ice conditions reveal reasonable simulation of year-to-year variability of ice extent, ice season duration, and spatial distribution, though some years appear to be prone to higher error. This modeling framework will serve as the basis for NOAA’s next-generation Great Lakes Operational Forecast System (GLOFS); a set of 3-D lake circulation forecast modeling systems which provides forecast guidance out to 120 h.