Numerical Forecast System Research Articles

A 3D-Var assimilation scheme for vertical velocity with CMA-MESO v5.0

Abstract. Certain vertical motions associated with meso-microscale systems are favorable for convection development and maintenance. Correct initialization of updraft motions is thus significant in convective precipitation forecasts. A three-dimensional variational-based vertical velocity (w) assimilation scheme has been developed within the high-resolution (3 km) CMA-MESO (the Mesoscale Weather Numerical Forecast System of the China Meteorological Administration) model. This scheme utilizes the adiabatic Richardson equation as the observation operator for w, enabling the update of horizontal winds and mass fields of the model's background. The tangent linear and adjoint operators are subsequently developed and undergo an accuracy check. A single-point w observation assimilation experiment reveals that the observational information is effectively spread both horizontally and vertically. Specifically, the assimilation of w contributes to the generation of horizontal wind convergence at lower model levels and divergence at higher model levels, thereby adjusting the locations of convection occurrence. The impact of assimilating w on the forecast is then examined through a series of continuous 10 d runs. Further assimilation of w, in addition to the assimilation of conventional and radial wind data, significantly improves the forecast accuracy of precipitation, resulting in higher FSS (fractions skill score) values and higher ETS (equitable threat score) skills at higher thresholds (5 and 20 mm h−1). However, it should be noted that further assimilation of w can potentially lead to some false precipitation, resulting in slightly lower ETS values at lower thresholds (1 mm h−1) and a neutral impact on BIAS (bias score) skills. An individual case study conducted within the batch experiments reveals that assimilating w has a beneficial impact on the enhancement of vertical motion across different layers of the model, facilitating the transport of moisture from lower to middle–high model levels, thereby leading to an improvement in forecast skills.

Improvement in Storm Surge Numerical Forecasting Based on Wave Buoys Data

The maximum wind speed radius of a strong typhoon making landfall is an important factor influencing the numerical forecasting of storm surges. A method for inverting the maximum wind speed radius of typhoons based on wave buoys data was designed to significantly reduce the error in 24 h storm surge forecasting in this paper, and an operation scheme was proposed to enhance the storm surge numerical forecasting system based on this method. Hangzhou Bay and the Yangtze River Estuary, which have been frequently impacted by typhoons over the past five years, were selected as the research area. Common schemes for the maximum wind speed radius were analyzed, and five ladder schemes (10, 15, 20, 25, and 30 km) were established for wave and storm surge numerical model verification of Typhoon Muifa in 2022. Based on a comparison of the wave hindcast results and wave buoys observation data, the wave hindcast result of the commonly used scheme (30 km) was significantly greater than that of the observation data, and the optimal scheme (15 km) closest to the observation data could be determined during the 48 h warning period. Moreover, it was difficult to identify the optimal scheme during the 48 h warning period based on the storm surge hindcast results. A 24 h storm surge numerical forecasting test was performed with the commonly used scheme (30 km) and the optimal scheme (15 km). The results showed that the root mean square error (RMSE) of the optimal scheme (15 km) was 34% lower than that of the commonly used scheme (30 km), while the maximum storm surge error was also reduced from 47.7% for the commonly adopted scheme (30 km) to 11.8% for the optimal scheme (15 km). The maximum storm surges under the optimal scheme (15 km) along Hangzhou Bay and the Yangtze River Estuary ranged from 1.9 to 2.2 m, which were closer to the observation data, and the maximum storm surge under the commonly used scheme (30 km) was 0.8~1.2 m greater than that under the optimal scheme (15 km).

Knowledge Transfer Platform FindFISH – Numerical Forecasting System for the Marine Environment of the Gulf of Gdańsk for Fisheries

Fast access to expert knowledge is very valuable, especially in the context of decision-making. Fishermen can use this knowledge to diagnose hydrological and hydrochemical conditions in which fish stocks should be the most abundant. In response to this need, a digital service has been developed. It is a service created within the FindFISH project, which provides the results of all developed models: hydrodynamic, biochemical, and Fish – for selected commercially caught fish species (herring, sprats, cod, and flounder). Our research revealed that the FindFISH service can enhance fishing efficiency and quality by approximately 40%, significantly increasing expected profits. In practical applications, we observed a 50% to 70% concordance between the fishing grounds recorded by fishermen and those identified by the FindFISH Platform. The website runs dynamically in operational mode, allowing visualization of forecasts in the form of maps, time series, spatial data, and tables.

Implementation and application of ensemble optimal interpolation on an operational chemistry weather model for improving PM2.5 and visibility predictions

Abstract. Data assimilation techniques are one of the most important ways to reduce the uncertainty in atmospheric chemistry model input and improve the model forecast accuracy. In this paper, an ensemble optimal interpolation assimilation (EnOI) system for a regional online chemical weather numerical forecasting system (GRAPES_Meso5.1/CUACE) is developed for operational use and efficient updating of the initial fields of chemical components. A heavy haze episode in eastern China was selected, and the key factors affecting EnOI, such as localization length scale, ensemble size, and assimilation moment, were calibrated by sensitivity experiments. The impacts of assimilating ground-based PM2.5 observations on the model chemical initial field PM2.5 and visibility forecasts were investigated. The results show that assimilation of PM2.5 reduces the uncertainty in the initial PM2.5 field considerably. Using only 50 % of observations in the assimilation, the root mean square error (RMSE) of initial PM2.5 for independent verification sites in mainland China decreases from 73.7 to 46.4 µg m−3, and the correlation coefficient increases from 0.58 to 0.84. An even larger improvement appears in northern China. For the forecast fields, assimilation of PM2.5 improves PM2.5 and visibility forecasts throughout the time window of 24 h. The PM2.5 RMSE can be reduced by 10 %–21 % within 24 h, and the assimilation effect is the most remarkable in the first 12 h. Within the same assimilation time, the assimilation efficiency varies with the discrepancy between model forecasts and observations at the moment of assimilation, and the larger the deviation, the higher the efficiency. The assimilation of PM2.5 further contributes to the improvement of the visibility forecast. When the PM2.5 increment is negative, it corresponds to an increase in visibility, and when the PM2.5 analysis increment is positive, visibility decreases. It is worth noting that the improvement of visibility forecasting by assimilating PM2.5 is more obvious in the light-pollution period than in the heavy-pollution period. The results of this study show that EnOI may provide a practical and cost-effective alternative to the ensemble Kalman filter (EnKF) for the applications where computational cost is the main limiting factor, especially for real-time operational forecast.

A Comparative Study on Four Methods of Boundary Layer Height Calculation in Autumn and Winter under Different PM2.5 Pollution Levels in Xi’an, China

In this paper, L-band sounding and surface observation data are used to calculate the boundary layer height (BLH) and evaluated CMA (China Metrological Administration Numerical Forecast System) and ERA5 in Xi’an for 2017–2021 using the Richardson (Ri) and Nozaki methods. For different PM2.5 pollution levels, the correlation between the vertical profile of meteorological factors and BLH is explored. There is a certain negative correlation between BLH and PM2.5 concentration. The BLH mean values of Nozaki, Ri, ERA5, and CMA from high to low are ~980 m, ~640 m, ~410 m, and ~240 m, respectively. The highest correlation is between ERA5 and CMA BLH with r2 > 0.85 for all pollution processes, while it between other methods is significantly lower (r2 < 0.58). The observational BLH is generally higher than the model results. Nozaki has a good adaptability on the light pollution, while Ri is more applicable to the stable boundary layer. In moderate and higher pollution, the ERA5 has a slightly better performance than CMA in BLH, while in light pollution there is a significant underestimation for both. Overall, the correlation between any two BLH methods gradually increases with increasing pollution level. In this study, there is about ~30% probability of polluted weather when BLH < 200 m and only <7% probability when BLH > 2000 m. It is difficult to simulate the neutral boundary layer and inversion processes for CMA and ERA5, but ERA5 has higher forecasting skills than CMA. This study can provide the data and theoretical support for the development of haze numerical forecast.

Impacts of FY-4A GIIRS Water Vapor Channels Data Assimilation on the Forecast of “21·7” Extreme Rainstorm in Henan, China with CMA-MESO

A record-breaking extreme rainstorm occurred in Henan Province of China on 20 July 2021. To investigate the impacts of the Geostationary Interferometric Infrared Sounder (GIIRS) data assimilation on model analysis and forecasts of this rainfall event, the high temporal resolution GIIRS water vapor (WV) channel data were assimilated in the high-resolution CMA-MESO (Mesoscale Weather Numerical Forecast System of China Meteorological Administration) in this study. The results showed that the GIIRS WV radiance assimilation could improve the model WV analysis, which in turn adjusted the distributions of hydrometeors (radar composite reflectivities) and wind field, and finally improved the precipitation forecast. Additionally, although barely any GIIRS observations were assimilated over the cloudy area, the precipitation forecast errors of “21·7” extreme rainstorm events could be reduced by improving the structure of atmospheric circulations through the assimilation of neighboring data around Henan, especially over the upstream region. With the GIIRS WV data assimilation, the location error of maximum 24-h accumulated precipitation forecasts decreased from 128.48 km to 28.97 km (improved by 77.45%) for the cold start at 0000 UTC (Universal Time Coordinated) on 19 July 2021, and it was also reduced by about 60.52% for the warm start experiment at 0600 UTC on 19 July 2021. In addition, the GIIRS assimilation experiment showed an extraordinarily heavy rainfall area (above 250 mm/24 h) around Zhengzhou station, which did not appear in the control experiment, and was closer to the observed extreme precipitation. This study demonstrates the potential value of geostationary hyperspectral infrared sounders data assimilation in extreme weather early warning and forecasting.

Testing a Novel Aggregated Methodology to Assess Hydrodynamic Impacts on a High-Resolution Marine Turtle Trajectory

We designed a novel aggregated methodology to infer the impact of ocean motions on the movements of satellite-tracked marine turtles adopting available oceanographic observations and validated products of a numerical oceanographic forecasting system. The method was tested on an 11-months trajectory of a juvenile loggerhead turtle (LT) wandering in the Tyrrhenian Sea (Mediterranean Sea) that was reconstructed with a high-resolution GPS tracking system. The application of ad-hoc designed metrics revealed that the turtle’s route shape, ground speed and periodicities of its explained variance mimic the inertial motions of the sea, showing that this methodology is able to reveal important details on the relation between turtle movements and oceanographic features. Inertial motions were also identified in the observed trajectory of a surface drifting buoy sampling the Tyrrhenian Sea in a common period. At each sampling point of the turtle trajectory, the sea current eddy kinetic energy (EKE) and a Sea Current Impact index were computed from a validated set of high-resolution ocean modeling products and their analysis showed the relevant effects of the highly variable local sea currents mechanical action. Specifically, the metric we adopted revealed that the turtle trajectory was favorably impacted by the encountered sea current advection for about 70% of its length. The presented oceanographic techniques in conjunction with high-resolution tracking system provide a practicable approach to study marine turtle movements, leading the way to discover further insights on turtle behavior in the ocean.

Dynamics and predictability of cold spells over the Eastern Mediterranean

The accurate prediction of extreme weather events is an important and challenging task, and has typically relied on numerical simulations of the atmosphere. Here, we combine insights from numerical forecasts with recent developments in dynamical systems theory, which describe atmospheric states in terms of their persistence (θ−1) and local dimension (d), and inform on how the atmosphere evolves to and from a given state of interest. These metrics are intuitively linked to the intrinsic predictability of the atmosphere: a highly persistent, low-dimensional state will be more predictable than a low-persistence, high-dimensional one. We argue that θ−1 and d, derived from reanalysis sea level pressure (SLP) and geopotential height (Z500) fields, can provide complementary predictive information for mid-latitude extreme weather events. Specifically, signatures of regional extreme weather events might be reflected in the dynamical systems metrics, even when the actual extreme is not well-simulated in numerical forecasting systems. We focus on cold spells in the Eastern Mediterranean, and particularly those associated with snow cover in Jerusalem. These rare events are systematically associated with Cyprus Lows, which are the dominant rain-bearing weather system in the region. In our analysis, we compare the ‘cold spell Cyprus Lows’ to other ‘regular’ Cyprus Low days. Significant differences are found between cold spells and ‘regular’ Cyprus Lows from a dynamical systems perspective. When considering SLP, the intrinsic predictability of cold spells is lowest hours before the onset of snow. We find that the cyclone’s location, depth and magnitude of air-sea fluxes play an important role in determining its intrinsic predictability. The dynamical systems metrics computed on Z500 display a different temporal evolution to their SLP counterparts, highlighting the different characteristics of the atmospheric flow at the different levels. We conclude that the dynamical systems approach, although sometimes challenging to interpret, can complement conventional numerical forecasts and forecast skill measures, such as model spread and absolute error. This methodology outlines an important avenue for future research, which can potentially be fruitfully applied to other regions and other types of weather extremes.

The development of numerical forecasting systems of primary sources of gold on the results of placer sampling in the example Vagran placer cluster (North Urals)

Based on the results of field research, as well as data from stock reports, two types of placer gold were identified within the Vagran placer cluster, which are indicators of primary mineralization. They are used as benchmarks for developing a digital system for predicting parameters and localizing primary sources of placer gold. Formalized typomorphic characteristics of placer gold (size, roundness, fineness, sorting and content of impurity elements), combined in multiplicative indicators, make it possible to forecast the composition and localization of the primary mineralization with greater confidence than ordinary parameters separately. The data required for such an assessment do not require additional field and highly qualified laboratory studies, they are contained in standard reports on the heavy minerals testing, and, in contrast to the characteristics of individual indicator types of placer gold, they give more stable results. The study of the correlation system allowed to identify characteristic indicators for the primary mineralization of gold-sulfide-quartz and hypogenic-hypergenic types, and to give recommendations for conducting prospecting and exploration in order to identify the primary gold content of the cluster. The proposed method of creating forecast estimates allows to computerize the process of determining the prospects for primary mineralization of territories.

Observing system experiments over the Atlantic Ocean with the REMO ocean data assimilation system (RODAS) into HYCOM

The Oceanographic Modeling and Observation Network (REMO) focuses on scientific and technological development of operational oceanography in Brazil considering both numerical forecasting and observational systems. A key component of the forecasting system is the recently constructed REMO Ocean Data Assimilation System (RODAS) into the HYbrid Coordinate Ocean Model (HYCOM). Here, RODAS is presented for the first time with its full capability. RODAS employs a multivariate ensemble optimal interpolation scheme that is able to assimilate Argo temperature (T) and salinity (S) profiles, sea surface temperature (SST) analyses, and satellite along-track or gridded sea-level anomaly (SLA) data. RODAS is presented together with a series of Observing System Experiments (OSEs), in which components of the Global Ocean Observing System (GOOS) were systematically withheld from 3-year assimilation runs over the Atlantic Ocean. Using the same initial condition from a full assimilation run, OSEs were performed from 1 January 2010 to 31 December 2012 withholding (i) only Argo data; (ii) only UK MetOffice OSTIA SST analyses; (iii) only satellite along-track altimetry data; and (iv) all observation types. These runs were also compared with the full assimilation run and the model free run to evaluate the impact of different observations into the model state. The results show that each observation type brings complementary information into the analyses. Assimilation of SST is needed to better constrain the mixed layer temperature, while assimilation of SLA mainly improves the representation of circulation by adding mesoscale-like features, such as those found in the Gulf Stream and the Brazil-Malvinas Confluence. In the subsurface, only Argo observations are able to constrain the thermohaline state. When Argo data are withheld, the quality of S is seriously compromised and becomes worse than the free run in the upper ocean. Additionally, the run withholding all observation types shows that the model state in the surface almost reaches the free run state by the end of the third year. However, below 300 m, the memory of the Argo data assimilation is longer and the quality of T and S is only degraded by 35% in comparison with the full assimilation run.

An Analysis of the Reliability of a New Dataset of Transmission Line Icing Thickness in Southern China

AbstractBased on ERA-Interim data, gauge observations, transmission line icing observational data, and hindcasted predictors from a numerical forecast system of transmission line icing, a new transmission line icing thickness (TLIT) dataset was constructed to solve the problem of limited historical data. The reliability of the dataset was analyzed using case studies and climate data. The results showed that the descriptions of three icing events in southern China by the TLIT were consistent with the actual observational data, and the icing thickness differences were less than 2 mm. The spatial distribution of annual icing days and icing thickness calculated using meteorological observation station icing data (OIT) and the TLIT data had a similar pattern, with small differences in the numerical values. A rotated empirical orthogonal function (REOF) decomposition was conducted for 67 transmission line icing events. It was found that the spatial distributions of the first three characteristic vectors of the TLIT and OIT data were similar, and the correlation coefficients for the time coefficients of the first three characteristic vectors were 0.801, −0.443, and 0.576, respectively. Three key areas were identified based on the first three patterns of REOF, and the average icing thickness of 67 events in southern China and the three key areas was calculated. The correlation coefficients of icing thickness calculated by the TLIT and OIT data for these areas were 0.648, 0.384, 0.565, and 0.599, respectively. The results illustrate that the TLIT data can reflect the temporal and spatial variations of ice thickness in southern China.

Structure of the FindFish Knowledge Transfer Platform

Abstract As consumer awareness increases in Poland, fish consumption is on an upward trend. While new technologies permit meeting increasing demand, they significantly increase the pressure on fish supplies. As a result, many fish stocks are over-exploited, which threatens marine ecosystems. Declining fisheries, increasing fishing operating costs, and the necessity of making longer fishing expeditions at greater distances that often end with catches of fewer or less valuable fish mean that in some fisheries sectors are currently running at the margins of profit or at a loss. At the same time, the European Union’s fisheries policy aims to create more selective and sustainable fisheries, to implement limitations through fishing quotas, and to ban the recognition of discards. The aim is to ensure that individual fish species are protected while providing the European Union with stable, secure supplies of fresh fish. The FindFish Knowledge Transfer Platform project was launched to provide solutions for challenges facing commercial fisheries. Its purpose is to provide fishers with a knowledge transfer platform and a numerical forecasting system for the marine environment of the Gulf of Gdansk. By combining in-situ research, environmental data, fish catch data, and numerical calculations, this tool will improve the accuracy of targeted fishing while reducing by-catch. The system will provide more reliable data on fish stocks and facilitate more efficient resource management.

Minimum-cost numerical prediction system for wind power in Uruguay, with an assessment of the diurnal and seasonal cycles of its quality

This work presents the results of a numerical forecast system of minimum cost for the electric power generated by wind farms in Uruguay. By keeping at minimum levels both the computational costs and the complexity of the empirical corrections of the numerical results, we obtain a benchmark for the forecast skill of more complex forecast systems, that is easily available during their calibration stages and operative functioning. The work also aims to explore the diurnal and seasonal cycle of the forecasts quality. It is found that this simple forecast system produces very good results, albeit the dependencies of the forecast skill and errors respect to the season of the year and the time of the day are distinguishable. It is also found that it is necessary to take into account the diurnal and the seasonal cycles during the calibration of the empirical corrections. The good results of this simple technique might had been possible due to the relative smooth topography of Uruguay.

China–Sri Lanka Joint Center for Education and Research for the 21st Century Maritime Silk Road

The China–Sri Lanka Joint Center for Education and Research for the 21st Century Maritime Silk Road (CSL-CER) was established in August 2015. The Joint Center is an overseas science and education platform established by Chinese Academy of Sciences. CSL-CER has conducted a series of research activities, including an annual scientific expedition to the eastern Indian Ocean, with many kinds of instruments installed for monsoon and ocean observation. The ‘Numerical Forecast System for the Surrounding Area of Sri Lanka,’ which was developed by the Joint Center, can provide rapid mapping on multiple scales and at multiple resolutions for numerical forecast products serving local society. The annual China–Sri Lanka Joint Workshop on Monsoon Climate and Ocean Environment was successfully held in 2015 and 2016. According to the Memorandum of Understanding (MOU) on joint Master’s degrees, 15 new Master’s students were accepted by University of Chinese Academy of Sciences (UCAS) in 2015.

Review on the Application of Numerical Weather Prediction in the Simulation Research of Atmospheric Dispersion

The spatial and temporal variations of wind field and the atmospheric turbulence characteristics are important factors that affect the migration and dispersion of local atmospheric pollutants. Since the numerical meteorological forecasting system with high resolution is the foundation of atmospheric dispersion simulation, more and more attention has been drawn to the application of numerical weather prediction systems in wind field prediction and atmospheric simulation. This paper reviewed the widespread application of numerical weather prediction systems in wind field prediction and atmospheric simulation with the focus on the mesoscale weather forecasting system with high resolution, for instance WRF and MM5, as well as the requirement for numerical weather prediction in the simulation research of wind field prediction and atmospheric dispersion.

The Influence of Soil Moisture Cotent on the Numerical Simulation of Dust Storms

In this paper, the influence of the soil moisture content on the the dust emission proceed is discussed by using an integrated numerical simulation and forecast system of dust storms based on the physical mechanisms of sand and dust emissions by wind erosion. The results show that the influence of soil moisture content on the dust emission is very distinguished, and soil moisture content is one of the most important parameters as input conditions in the forcasting model systems of dust storm. The prediction capability of numerical dust model is improved by estimated the surface soil moisture content for dust model system.

An assessment of the diversity in scenario-based tsunami forecasts for the Indian Ocean

This work examines the extent to which tsunami forecasts from different numerical forecast systems might be expected to differ under real-time conditions. This is done through comparing tsunami amplitudes from a number of existing tsunami scenario databases for eight different hypothetical tsunami events within the Indian Ocean. Forecasts of maximum tsunami amplitude are examined at 10 output points distributed throughout the Indian Ocean at a range of depths. The results show that there is considerable variability in the forecasts and on average, the standard deviation of the maximum amplitudes is approximately 62% of the mean value. It is also shown that a significant portion of this diversity can be attributed to the different lengths of the scenario time series. These results have implications for the interoperability of Regional Tsunami Service Providers in the Indian Ocean.

Application of Four Schemes for Sand and Dust Emissions in China

A serial of numerical experiments were conducted with four representative sand and dust emission schemes using an integrated numerical simulation and forecast system of dust storms. The comparison between the simulation results and the observation data shows that the vertical dust flux and the dust concentration of the scheme developed by Marticorena and Bergametti is the biggest, while those of the scheme developed by Lu and Shao is the smallest. The simulation effect of the dust emission scheme developed by Shao is similar to the observation, so the dust model system of Shao is preferably used to predict the dust events in China.

Calibration of a coupled biological–physical model for prediction in a coastal inlet

We describe a numerical forecast system designed for prediction of physical and biological dynamics of a coastal inlet. It is based on a coastal ocean observatory that was located at Lunenburg Bay, Nova Scotia, Canada. Biological, chemical, optical, and physical measurements were collected from instrumented moorings, weekly sampling and detailed surveys from 2002 through 2007. Here we present a framework for calibration and evaluation of an ecosystem model using data from the summer of 2007. A three-dimensional hydrodynamic model was coupled to a simple biological (Nutrients–Phytoplankton–Detritus) model; a simple model was used so results could be compared directly to observed biological and chemical variables using skill scores as a first step toward data-assimilation modeling. As a complement to this analysis, variability of model output, e.g., the nutrient limitation term, was examined to understand the modeled biological response to the simulated physical environment. Skill scores based on variances in observed and simulated time-series of biological components were also investigated. Coastal upwelling/downwelling simulated through this model has been found to increase modeled biological activity in the bay. Also model skill in reproducing the observed patterns in nutrients and phytoplankton has been increased due to the restoring conditions for biology set up at the open ocean boundaries of the bay.

Assimilation of image sequences in numerical models

Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. Forecasting algorithms should take into account all the available information on the considered dynamic system. The variational data assimilation (VDA) technique combines all these informations in an optimality system (O.S.) in a consistent way to reconstruct the model inputs. VDA is currently used by the major meteorological centres. During the last two decades about 30 satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular, the dynamic evolution of certain meteorological or oceanic features (such as eddies, fronts, etc.) that the human vision may easily detect is not optimally taken into account in realistic applications of VDA. Image Assimilation in VDA framework can be performed using ‘pseudo-observation’ techniques: they provide apparent velocity fields, which are assimilated as classical observations. These measurements are obtained by certain external procedures, which are decoupled with the considered dynamic system. In this paper, we suggest a more consistent approach, which directly incorporates image sequences into the O.S.