Operational Weather Research Articles

The Weather On-Demand Framework

This paper describes the Weather On-Demand (WOD) forecasting framework which is a software stack used to run operational and on-demand weather forecasts. The WOD framework is a distributed system for the following: (1) running the Weather Research and Forecast (WRF) model for data assimilation and forecasts by triggering either scheduled or on-demand jobs; (2) gathering upstream weather forecasts and observations from a wide variety of sources; (3) reducing output data file sizes for permanent storage; (4) making results available through Application Programming Interfaces (APIs); (5) making data files available to custom post-processors. Much effort is put into starting processing as soon as the required data become available, and in parallel where possible. In addition to being able to create short- to medium-range weather forecasts for any location on the globe, users are granted access to a plethora of both global and regional weather forecasts and observations, as well as seasonal outlooks from the National Oceanic and Atmospheric Administration (NOAA) in the USA through WOD integrated-APIs. All this information can be integrated with third-party software solutions via WOD APIs. The software is maintained in the Git distributed version control system and can be installed on suitable hardware, bringing the full flexibility and power of the WRF modelling system to the user in a matter of hours.

The Observed and Simulated Evolution of a Microburst Using X‐Band Phased‐Array Radar Data Assimilation With EnKF

AbstractA microburst is a severe small‐scale meteorological event that develops rapidly, producing intense downdrafts that result in catastrophic divergent winds near the ground. The fine‐scale structure and evolution of real‐case microbursts are rarely analyzed due to the limitation of regular observational platforms and the computational capacity for numerical simulations. On 12 September 2020, a microburst was effectively captured by China's S‐band operational weather radar network and an X‐band polarimetric phased‐array radar (XPAR). XPAR observations can identify precursor signatures of the rapidly evolving microburst before the occurrence of surface wind disasters, demonstrating superior spatiotemporal resolution in monitoring compared to the S‐band radars. To disclose the evolution of small‐scale structure and the underlying physical processes, this study utilizes the Weather Research and Forecasting (WRF) model and the ensemble Kalman filter (EnKF) system to assimilate XPAR data. The simulation successfully reproduces the fine‐scale structure and evolution of the microburst with a misocyclone. The analysis indicates that the microburst's downdraft is primarily triggered by the middle‐level hydrometeor loading. The misocyclone generates a downward‐directed perturbation pressure gradient then accelerates the microburst's downdraft toward the surface. This study is the first observation and simulation of a real‐case microburst using the WRF‐EnKF system assimilating XPAR data. The investigation of the impact of misocyclone on the microburst enhances our understanding of microbursts' forcing mechanism.

A new verification approach for nowcasting based on intensity and spatial-temporal feature correction

Forecast verification is very important in the nowcasting operation and technical development of strong convective weather. The current conventional verification method for nowcasting uses a binary classification event verification method, which exists with double punishment, leading to low scoring issues. In order to make up for the shortcomings of conventional verification methods and explore the potential value of forecasting, based on the characteristics and requirements of strong convective weather nowcasting operations, this paper proposes a neighborhood verification method that considers spatial scale, time scale, and intensity error information simultaneously, based on the spatial neighborhood fraction skill score (FSS) verification method. The paper designs an intensity neighborhood correction scheme, a time neighborhood correction scheme, and a comprehensive correction scheme that considers both intensity and time neighborhoods. By introducing spatial neighborhood probability, the strict spatial matching requirement is weakened. Based on the Gaussian membership function in fuzzy logic, the value of forecasting grid below the threshold is explored. Time neighborhood adaptive weighting strategy is adopted to solve the problem of early and delayed forecasts. The evaluation and verification of the optical flow radar extrapolation nowcasting results show that compared to the traditional “point-to-point” verification indicators, the proposed indicators can increase the tolerance of verification, provide more useful information, and are more in line with the needs of practical application.

Probabilistic weather forecasting with machine learning.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range WeatherForecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude-longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.

Влияние климатических условий на вспышечную заболеваемость лихорадки Западного Нила на территории Российской Федерации в аспекте неспецифической профилактики заболевания

According to Federal Service for Hydrometeorology and Environmental Monitoring forecast, climatic conditions in Russia will stick to global warming trend which will contribute to further spread of WNV onto the northern areas. The ongoing changes of climatic conditions towards warming predetermine high probability of further WNV circulation in the environment and emergence of infection cases. Comparison of atmospheric air temperatures and abundance of precipitation with the incidence of West Nile virus (WNV) in Russia showed a direct correlation between them: the more precipitation and the higher spring-summer air temperatures, the higher the risk of WNV infection among the population. According to the data obtained from the Reference Centre for monitoring over WNV pathogen, WNV markers were detected in the territory of 61 constituent entities of the Russian Federation throughout the period of observation in 1999–2023 which testified to the existence of potential risk of human exposure during epidemic season in most of the parts of country. The climatic factor determines the probability of an outbreak developing in a certain area during a specific period of time. Disinsection treatments in the foci of LZN should be carried out every 2–3 weeks with adjustments for reliable and operational weather forecasts of Roshydromet in the subjects of the Russian Federation. Keywords: West Nile fever, West Nile virus, epidemic situation, non-specific prevention.

Role of Aerosol Ice‐Nucleus Effect in the Development of the “21⋅7” Henan Extreme Precipitation

AbstractThe aerosol ice‐nucleus (IN) effect, affecting heterogeneous ice nucleation and ultimately shaping cloud properties and precipitation efficiency, remains poorly understood and is overlooked in most operational numerical weather models. Therefore, this study aims to investigate the role of aerosol IN effect in the “21⋅7” Henan extreme precipitation event using two simulations based on an aerosol‐aware configuration of the WRF model. One prescribed spatially uniform ice nucleation particles (INP) concentration (labeled CTL), whereas the other incorporated MERRA2 aerosol concentration fields to represent a more realistic INP spatial distribution (labeled M2), which is marked by a west‐east gradient with higher INP concentration in the west. Results show that M2 simulated heavier precipitation near Zhengzhou closer to the observation regarding both spatial distribution and intensity than the CTL simulation. This improvement is attributed to an INP‐induced feedback akin to the aerosol lifetime effect. Specifically, the M2 simulation had higher INP concentration in the west, causing increased rates of heterogeneous ice nucleation as the convection progressed from west to east. This released additional latent heat, intensifying the convection and subsequently enhancing homogeneous ice nucleation, yielding more but smaller cloud ice particles in M2. These ice particles had reduced sedimentation speeds and lower aggregation rates, suppressing precipitation upstream of Zhengzhou. The intensified convection, carrying more water, propagated eastward, ultimately resulting in extremely heavy precipitation near Zhengzhou. This study underscores the aerosol IN effect on cloud microphysics, convection intensity, and regional rainfall distribution, elucidating the crucial role of aerosol‐meteorology coupling in accurately simulating moderate deep convective cloud precipitation.

Lightning-Fast Convective Outlooks: Predicting Severe Convective Environments With Global AI-Based Weather Models.

Severe convective storms are among the most dangerous weather phenomena and accurate forecasts mitigate their impacts. The recently released suite of AI-based weather models produces medium-range forecasts within seconds, with a skill similar to state-of-the-art operational forecasts for variables on single levels. However, predicting severe thunderstorm environments requires accurate combinations of dynamic and thermodynamic variables and the vertical structure of the atmosphere. Advancing the assessment of AI-models toward process-based evaluations lays the foundation for hazard-driven applications. We assess the forecast skill of the top-performing AI-models GraphCast, Pangu-Weather and FourCastNet for convective parameters at lead-times up to 10days against reanalysis and ECMWF's operational numerical weather prediction model IFS. In a case study and seasonal analyses, we see the best performance by GraphCast and Pangu-Weather: these models match or even exceed the performance of IFS for instability and shear. This opens opportunities for fast and inexpensive predictions of severe weather environments.

Design and Visual Implementation of a Regional Energy Risk Superposition Model for Oil Tank Farms

Ensuring the safety of oil tank farms is essential to maintaining energy security and minimizing the impact of potential accidents. This paper develops a quantitative regional risk model designed to assess both individual and societal risks in oil tank farms, with particular attention to energy-related risks such as leaks, fires, and explosions. The model integrates factors like day–night operational variations, weather conditions, and risk superposition to provide a comprehensive and accurate evaluation of regional risks. By considering the cumulative effects of multiple hazards, including those tied to energy dynamics, and the stability and validity of the model are researched through Monte Carlo simulations and case application. The results show that the model enhances the reliability of traditional risk assessment methods, making it more applicable to oil tank farm safety concerns. Furthermore, this study introduces a practical tool that simplifies the risk assessment process, allowing operators and decision-makers to evaluate risks without requiring in-depth technical expertise. The methodology improves the ability to safeguard oil tank farms, ensuring the stability of energy supply chains and contributing to broader energy security efforts. This study provides a valuable method for researchers and engineers seeking to enhance regional risk calculation efficiency, with a specific focus on energy risks.

Dynamic simulation of roll bond PVT solar collector under Simulink/Matlab

Abstract This paper presents an innovative methodology to investigate dynamic roll bond PVT solar systems under real-world operational weather conditions using the Simulink/Matlab environment. The method is based on the MPPT electrical model of the solar cell and the differential energy equations of PVT at each of its layers. The effect of PV operating temperature, mass flow rate, and insulation medium on overall PVT thermal performance are all thoroughly examined. Results reveal that the annual average power delivered, in terms of both heat and electricity, is 352.8 W and 34.08 W, respectively. Furthermore, the addition of an insulating layer could lead to a significant improvement in PVT overall effectiveness. The present work offers a simple and flexible integration tool for roll bond PVT system simulation.

Design of a quick-connection device for installing pre-assembled offshore wind turbines

Higher wind velocities and lower wind shear are two motivations driving the development of floating offshore wind turbines (OWTs). However, such designs suffer from high expenses and complicated installation scenarios. Installation of offshore wind turbines is challenging due to the unpredictable nature of the environment and the technical complexities, especially at offshore sites. Mating of OWT on top of the pre-installed substructure is one of the critical stages of the installation operation. Grouted, welded, and bolted connections are utilized conventionally, but all have shortcomings. Welded and grouted connections suffer from fatigue forces, while a bolted connection requires minimal installation tolerances and sensitivity to impact forces. The design of a quick connection device (QCD) is expected to reduce the installation time, expand the operational weather window, and overcome the limitations of the earlier connection devices.The QCD described here comprises conic cross-sections, circular plates, and stiffeners connected to the floating substructure and OWT. This research uses a global model to estimate the relative velocities and displacements between the OWT and spar buoy. Furthermore, a local finite element model is developed to assess the influence of the impact forces and the design of the connection device. Implementing the hydrostatic stiffness of the floating spar within the impact simulations improved the simulation fidelity and reduced the impact damage. Different impact scenarios are performed, and the sensitivity of impact damage concerning the distribution of impact initiation points is assessed. Furthermore, an active control mechanism is used to reduce the relative motions between the installation vessel and the floating substructure. It is concluded that utilizing the anti-swing active control system minimizes the impact velocity and impact damage. This research can be extended by optimizing the design of the quick connection device.

Spatial Aligned Mean: A Method to Improve Consensus Forecasts of Precipitation from Convection-Allowing Model Ensembles

Abstract Ensembles of convection-allowing model (CAM) forecasts are increasingly being used in operational numerical weather forecasting. Several approaches have been devised to find consensus among ensemble forecast fields, including the arithmetic ensemble mean and, more recently, the patchwise localized probability-matched (LPM) mean. However, differences in spatial distribution and intensity of precipitation features among ensemble members make it difficult to construct an ensemble mean product that characterizes the consensus while preserving precipitation structures forecasted by the individual ensemble members. To overcome this problem, this study aims to develop and test a method for improving ensemble consensus precipitation forecasts by directly considering the spatial offsets among ensemble members. This study uses a multiscale spatial alignment technique to align the precipitation features of each ensemble member to a common location, and the spatial aligned mean (SAM) is obtained by averaging the realigned members. It is shown that implementing SAM and subsequently applying the LPM technique to the average of all aligned members (SAM–LPM) can significantly improve the warm season precipitation forecast scores using common metrics such as equitable threat score (ETS). Also, improvement in the structure of features of heavy rainfall is shown from summer 2023 flash-flooding cases. Thus, SAM and SAM–LPM can be excellent candidate methods for calculating an ensemble consensus and providing ensemble consensus guidance to forecasters. Significance Statement High-impact rainfall events, such as flash floods, result in many billion-dollar loss events in the United States each year. This study seeks to improve the prediction of such events when using guidance from convection-allowing model (CAM) ensemble forecasts, such as the U.S. operational High-Resolution Ensemble Forecast (HREF) and the nascent Rapid Refresh Forecast System (RRFS). The proposed method, the spatial aligned mean (SAM), directly addresses the common issue of disparity in the predicted location of convective systems among ensemble members that confounds traditional ensemble consensus methods. In this study, it is found that SAM improves ensemble consensus guidance for high-impact rainfall events in both the HREF and the Center for Analysis and Prediction of Storms (CAPS) Finite-Volume Cubed-Sphere (FV3)-limited area model (LAM) CAM ensemble forecast system, a proxy for the future RRFS.

Toroidal modified Miller-Turner CME model in EUHFORIA: Validation and comparison with flux rope and spheromak

Context. Rising concerns about the impact of space-weather-related disruptions demand modelling and reliable forecasting of coronal mass ejection (CME) impacts. Aims. In this study, we demonstrate the application of the modified Miller-Turner (mMT) model implemented within EUropean Heliospheric FORecasting Information Asset (EUHFORIA) in forecasting the geo-effectiveness of observed coronal mass ejection (CME) events in the heliosphere. Our goal is to develop a model that not only has a global geometry, in order to improve overall forecasting, but is also fast enough for operational space-weather forecasting. Methods. We test the original full torus implementation and introduce a new three-fourths Torus version called the Horseshoe CME model. This new model has a more realistic CME geometry, and overcomes the inaccuracies of the full torus geometry. We constrain the torus geometrical and magnetic field parameters using observed signatures of the CMEs before, during, and after the eruption. We perform EUHFORIA simulations for two validation cases – the isolated CME event of 12 July 2012 and the CME–CME interaction event of 8–10 September 2014. We performed an assessment of the model’s capability to predict the most important Bz component using the advanced dynamic time-warping (DTW) technique. Results. The Horseshoe model predictions of CME arrival time and geo-effectiveness for both validation events compare well with the observations and are weighed against the results obtained with the spheromak and FRi3D models, which were already available in EUHFORIA. Conclusions. The runtime of the Horseshoe model simulations is close to that of the spheromak model, which is suitable for operational space weather forecasting. However, the capability of the magnetic field prediction at 1 AU of the Horseshoe model is close to that of the FRi3D model. In addition, we demonstrate that the Horseshoe CME model can be used for simulating successive CMEs in EUHFORIA, overcoming a limitation of the FRi3D model.

Impact of Directly Assimilating Radar Reflectivity Using a Reflectivity Operator Based on a Double-Moment Microphysics Scheme on the Analysis and Forecast of Typhoon Lekima (1909)

In previous studies, radar reflectivity is often directly assimilated using reflectivity operators based on a single-moment (SM) microphysics scheme, though the forecast model uses a double-moment (DM) microphysics scheme. With the fixed number concentrations, only the mixing ratios of hydrometeors are directly updated during the assimilation, which leads to a mismatch between the analyzed microphysical state variables and the microphysics scheme of the forecast model. In this study, the radar reflectivity is directly assimilated through an observation operator consistent with the DM Thompson microphysics scheme used in numerical integrations, and the impact of reflectivity operators based on SM and DM schemes on the analysis performance of the ensemble Kalman filter for typhoon Lekima on 9 August 2019 is evaluated. Reflectivity observations from a single operational weather radar in Wenzhou City, Zhejiang Province of China are assimilated. In addition, the dual-polarization observations from the same radar are used to evaluate the quality of the analysis. The analyzed reflectivity and dual-polarization characteristics obtained by different reflectivity operators are examined in detail. Compared with the experiments applying the reflectivity operator based on the SM Lin scheme, the use of a reflectivity operator consistent with the DM Thompson scheme adopted in the forecast model results in analyzed reflectivity and polarization characteristics that are more consistent with the observed characteristics in terms of general structure, location, and intensity. Forecasted reflectivity, 3 h accumulated precipitation, and typhoon intensity and track are also evaluated. The application of the reflectivity operator based on the DM scheme makes better forecasts of typhoon intensity, precipitation, and reflectivity, which also improves the forecast skills on typhoon tracks to a certain extent.

Studies on Heavy Precipitation in Portugal: A Systematic Review

This systematic review, based on an adaptation of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement from 2020, focuses on studies of the atmospheric mechanisms underlying extreme precipitation events in mainland Portugal, as well as observed trends and projections. The 54 selected articles cover the period from 2000 to 2024, in which the most used keywords are “portugal” and “extreme precipitation”. Of the 54, 23 analyse trends and climate projections of precipitation events, confirming a decrease in total annual precipitation, especially in autumn and spring, accompanied by an increase in the frequency and intensity of extreme precipitation events in autumn, spring and winter. Several articles (twelve) analyse the relationship between synoptic-scale circulation and heavy precipitation, using an atmospheric circulation types approach. Others (two) establish the link with teleconnection patterns, namely the North Atlantic Oscillation (NAO), and still others (three) explore the role of atmospheric rivers. Additionally, five articles focus on evaluating databases and Numerical Weather Prediction (NWP) models, and nine articles focus on precipitation-related extreme weather events, such as tornadoes, hail and lightning activity. Despite significant advances in the study of extreme precipitation events in Portugal, there is still a lack of studies on hourly or sub-hourly scales, which is critical to understanding mesoscale, short-lived events. Several studies show NWP models still have limitations in simulating extreme precipitation events, especially in complex orography areas. Therefore, a better understanding of such events is fundamental to promoting continuous improvements in operational weather forecasting and contributing to more reliable forecasts of such events in the future.

LiveWire: Horizontal Geoelectric Field Prediction With 1‐hr Lead‐Time Using Multi‐Fidelity Boosted Neural Networks

AbstractGeomagnetically Induced Currents (GICs) are electrical currents generated by rapid changes in the geomagnetic field during space weather events, posing risks to power grids and pipelines. Traditional approaches predict GICs indirectly by forecasting , the temporal variation of the geomagnetic field, which is proportional to the induced electric field via Faraday's law. However, current physics‐based models driven by in situ solar wind measurements offer only 10–30 min lead times, insufficient for power grid operators to take mitigating actions. Additionally, grid operators prefer direct forecasts of the geoelectric field, which directly influences GICs, rather than relying on intermediate predictions of that require complex and time‐consuming calculations. We present a novel approach that directly forecasts the horizontal geoelectric field with a one‐hour lead time, bypassing predictions. Our method combines magnetometer data, magnetotelluric survey data, and solar wind inputs into a new probabilistic multi‐fidelity machine learning technique, ProBoost, resulting in the LiveWire model. Using data from the Boulder Geomagnetic Observatory (BOU) since 2002, we trained and validated LiveWire on the top 50 geoelectric field events during geomagnetic storms. Our results show that LiveWire outperforms both a persistence forecast and the operational Space Weather Modeling Framework (SWMF) by at least 31% and 23%, respectively. This advancement in geoelectric field forecasting promises more accurate GIC predictions, helping enhance the resilience of critical infrastructure to space weather.

Validation of OceanSat-3 sea surface winds for their utilization in the NCMRWF NWP assimilation systems

The Indian Space Research Organization (ISRO) launched OceanSat-3, the third-generation satellite in the OceanSat series, carrying a Ku-band scatterometer in November 2022. The novelty of the OceanSat-3 scatterometer (OSCAT-3) is its high-resolution sea surface wind products generated at 12.5 km in addition to 25 km. This study aims to check the quality of OSCAT-3 winds for their possible assimilation in the National Centre for Medium Range Weather Forecasting (NCMRWF) operational Numerical Weather Prediction (NWP) models. The newly available high-resolution OSCAT-3 wind products are validated against in-situ buoy winds and NCMRWF NWP model equivalents for six months from July to December 2023. The validation results of OSCAT-3 winds are also compared with those of the Advanced Scatterometer (ASCAT) onboard the Meteorological Operational Satellite-C (MetOp-C) using the same method. Validation of OSCAT-3 winds against buoy winds shows that the Root Mean Square Errors (RMSEs) in wind speed (direction) is less than 2 m/s (15°) over the Tropics (20° N − 20° S) and slightly higher than 2 m/s (20°) over the Northern Hemisphere (20° N − 90° N), which is closer to the satellite’s mission specifications of 1.8 m/s and 20°. The RMSE in the wind speed/direction in both the scatterometer winds are less than 2 m/s (20°) over the three different geographical regions: the Tropics, Northern Hemisphere, and Southern Hemisphere, but with lower errors for ASCAT winds. The better agreement of ASCAT winds with the NWP equivalents can be attributed partially to the routine assimilation of ASCAT winds in the NCMRWF operational NWP models and also due to the frequency of operation, C-band compared to the OSCAT-3 operated in the Ku band. Since there are three distinct datasets, scatterometer winds, buoy winds, and the NWP equivalents, triple collocation is also carried out in this study. Triple collocation also shows that the errors in OSCAT-3 winds are within the mission goal. The findings from this study indicate that the OSCAT-3 vector winds are of similar quality as its predecessors (OSCAT-2 and SCATSAT-1) and can be assimilated operationally in the NCMRWF NWP models as well as in other global operational NWP models.

Terminal Doppler Weather Radar Retrievals in Complex Terrain during a Summer High Ozone Period

Abstract Out of the 45 radars composing the Terminal Doppler Weather Radar (TDWR) network, 21 are located in areas of complex terrain. Their mission to observe low-level wind shear at major airports prone to strong shear-induced accidents puts them in an ideal position to fill critical boundary layer observation gaps within the NEXRAD network in these regions. Retrievals such as velocity azimuth display and velocity volume processing (VVP) are used to create time-height profiles of the boundary layer from radar conical scans, but assume that a wide area around the radar is horizontally homogeneous. This assumption is rarely met in regions of complex terrain. This paper introduces a VVP retrieval with limited radius to make these profiling techniques informative for flows affected by topography. These retrievals can be applied to any operational radar to help examine critical boundary layer processes. VVP retrievals were derived from the TDWR for Salt Lake City International Airport, TSLC, during a summertime high ozone period. These observations highlighted thermally driven circulations and variations in boundary layer depth at high vertical and temporal resolution and provided insight on their influence on air quality. Significance Statement Residents in many urban areas of the United States are exposed to elevated ozone concentrations during the summer months. In complex terrain, thermally driven circulations and terrain-forced flows affect chemical processes by modulating mixing and transport. A novel technique to monitor local boundary layer conditions on small horizontal length scales was applied to data from the Terminal Doppler Weather Radar located near Salt Lake City International Airport during a multiday high ozone event, and effects of these flows on ozone concentrations are illustrated. This technique can be applied to other operational weather radars to create long-term and real-time records of near-surface processes at high vertical and temporal resolution.

Assimilating atmospheric motion vector winds using a feature track correction observation operator

AbstractAtmospheric motion vector (AMV) winds have positive impacts in operational numerical weather prediction (NWP) systems. These impacts might be improved with better treatment of the following error characteristics of AMVs. First, AMVs may have wind errors due to height assignment errors. Second, AMVs may have additional wind‐speed biases in addition to those due to height assignment errors. Third, AMVs are representative of motion in a possibly thick atmospheric layer, not a single atmospheric level. Previous work proposed a variational feature track correction (FTC) method in which an observation operator is implemented that averages the NWP background winds optimally in the vertical. Here, a prototype feature track correction observation operator (FTC‐OO) is implemented in the NOAA/NCEP data assimilation (DA) system. The parameters describing the vertical averaging are determined offline based on previous DA cycles. The FTC‐OO reduces the observation minus background standard deviation by about 4%. Global observing‐system experiments (OSEs) are performed comparing the FTC‐OO with the operational observation operator. The forecast verification sample is 41 10‐day forecasts. The OSEs show that the FTC‐OO improves forecast skill, primarily for tropical geopotential height. Additional OSEs are performed that include Aeolus wind observations. The hypothesis that the Aeolus winds would enhance the impact of the FTC‐OO was not borne out in these experiments—the Aeolus observations alone have a significant positive impact, but the impact of the FTC method in the presence of the Aeolus observations is neither enhanced nor degraded compared with the impact of the FTC method alone.

Sensitivity of horizontal resolution and land surface model in operational WRF forecast for Online Nuclear Emergency Response System (ONERS)

Accurate Meteorological forecasts are crucial for the assessment of plume dispersion and dose prediction in nuclear power plant (NPP) sites. In this work the forecast sensitivity of the Weather Research and Forecasting (WRF) model is tested by running a series of forecast simulations for horizontal resolution, and land surface models (LSM) in the context of Online Nuclear Emergency Response System (ONERS) for Indian NPP sites. 72 h forecast simulations are made for three seasons viz. summer, southeast and northeast monsoon using the Global Forecast data. Three simulation experiments, namely 2 km-NOAH, 3 km-NOAH and 3 km-NOAHMP are conducted using two different nested domain configurations (18–6–2 km and 9–3 km) and two LSM schemes (NOAH and NOAH-MP) and tested at four different NPP sites. Forecast comparison of surface winds, relative humidity, temperature, heat fluxes and planetary boundary layer heights with data from meteorological tower, radiosonde and the Modern-Era Retrospective analysis for Research and Applications 2 (MERRA-2) shows 3 km-NOAH is equally capable in predicting surface parameters as well as vertical profiles compared to 2 km-NOAH with marginal differences. 3 km-NOAHMP shows less mean bias and better correlation for boundary layer height and heat fluxes. Comparison of spatial flow-field with 5th generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) data shows synoptic scale seasonal winds, sea level pressure systems and temperature hot-spots are better captured by 3 km-NOAHMP compared to 6 km coarse domain in the 18–6–2 km configuration. The daily accumulated rainfall by all simulations is overestimated compared to ERA5 data. The predictions by 3 km-NOAHMP better agree with Integrated Multi-satellite Retrievals for Global Precipitation Measurement (GPM-IMERGE) data whereas 2 km-NOAH predicts delayed rainfall occurrence. Dispersion simulations of hypothetical plume release from a coastal NPP site with all three forecasts properly show the influence of local scale diurnal land-sea breeze and seasonal winds on the plume movement. Therefore the 9–3 km domain with NOAHMP LSM is found to be a suitable choice for operational weather forecast in ONERS for Indian NPP sites.

Extreme low‐level wind jets in Storm Ciarán

AbstractSatellite imagery of Storm Ciarán as it approached the UK early on 1 November 2023 showed characteristic banding that fuelled media reports that Ciarán could contain a sting jet, with enhanced likelihood of damaging surface winds and gusts. Here operational numerical weather prediction model output is used with trajectory analysis to reveal the existence of at least two wind speed maxima attributed to sting jets. The small‐scale sting jets are located beneath, and likely affected by, far broader overrunning strong wind regions attributed to a deeply descending dry intrusion, thus highlighting the likely interaction of these airstreams at low levels.