Operational Weather Research Articles

Digital twins, the journey of an operational weather prediction system into the heart of Destination Earth

Moving a world leading numerical weather prediction system of the European Center for Medium-Range Weather Forecasts (ECMWF), that runs on a dedicated, bespoke, high performance computing cluster and supporting infrastructure, into the heart of the digital twin for climate change adaptation and extreme weather events, developed in the Destination Earth (DestinE) initiative of the European Commission, has been a challenging and exciting journey. In this paper we describe this journey with a focus on those aspects required to leverage the pre-exascale EuroHPC systems that have been made available to DestinE to run its computational representation [1]. EuroHPC systems can be effectively used for DestinE and are in fact key assets to deliver the computational power required for Earth system digital twins at global km-scale resolution. Each of these systems is operated by a national hosting entity that implements its own procedures, e.g. for identity and access management, specific system configuration like schedulers, filesystems, software management systems, and specific, sometimes vendor associated, toolchains, tooling, and container runtimes. In particular, the different scheduling policies encountered required us to adapt our workflows for each site. We found that having dedicated resources available, which was trialed in a period from 16th February to 14th April on LUMI, allowed to achieve high occupation rates, with 92% on the reserved GPU allocation and greater than 97% efficiency on the CPU reservation. Also, a stronger focus on federation of these systems, with a focus not only on federation of identities and accounts, but also in the areas of data ownership/transfer, observability, services and service accounts, maintenance coordination and performance portability could benefit DestinE. In general, it would be beneficial to make it easier to transfer a workload, or a digital twin system, from one EuroHPC site to the next and run and maintain them across several sites concurrently.

FY-3E Satellite Plasma Analyzer

The FY-3E satellite plasma analyzer marks China’s first detection of the characteristics, occurrence, and development of the typical plasma environment in the dawn–dusk orbit space. It provides data source support for operational space weather alerts and forecasts, helps ensure the in-orbit safety of the satellite, and accumulates space environment detection data for space environment modeling and space physics research. This paper gives a detailed introduction to the detection technology adopted by the FY-3E satellite plasma analyzer. We calibrated its performance through a calibration experiment and then analyzed and compared it with similar instruments in China. It is indicated that the instrument is capable of measuring an ion energy spectrum of 24 eV~32 keV and an electron energy spectrum of 23.7 eV~31.6 keV, its field of view reaches 180° × 90°, and the inversed measurement range of spacecraft absolute potential is better than −30 kV~+30 kV. All these contribute to a notably improved technology for plasma and satellite potential detection of China’s LEO satellites.

Deep Learning Based Effective Technique for Smart Grid Contingency Analysis Using RNN with LSTM

Smart grids play a crucial role in modernizing power systems, yet their susceptibility to disruptions necessitates effective contingency analysis for reliable operation. Traditional algorithms lack prediction accuracy for mitigation. To address this, a proposed method employs LSTM networks within a DL RNN framework, utilizing a logical collection of pertinent data—historical smart grid operation data, sensor measurements, weather conditions, and contingency events. This data undergoes preprocessing to train an LSTM-based DL RNN model capable of capturing temporal dependencies and complex dynamics within the smart grid system. The trained model yields a substantial improvement in prediction accuracy, increasing from 88% to 93%, while decreasing false positive and false negative rates from 3.2% to 1.25% with optimal time detection. Leveraging deep learning and recurrent neural networks, this approach offers accurate predictions, proactive decision support, and efficient restoration strategies, ultimately elevating the resilience and reliability of smart grid systems. The solution’s dataset encompasses a comprehensive array of relevant information. The neural network architecture integrates DL RNN with LSTM networks, showcasing its efficacy in enhancing prediction accuracy for smart grid contingency analysis. Evaluation metrics such as RMSE and MAPE providing a reliable foundation for proactive decision-making and fortifying the smart grid’s resilience against various contingencies.

Assessing Storm Surge Multiscenarios Based on Ensemble Tropical Cyclone Forecasting

AbstractEnsemble forecasting is a promising tool to aid in making informed decisions against risks of coastal storm surges. Although tropical cyclone (TC) ensemble forecasts are commonly used in operational numerical weather prediction systems, their potential for disaster prediction has not been maximized. Here we present a novel, efficient, and practical method to utilize a large ensemble forecast of 1,000 members to analyze storm surge scenarios toward effective decision making such as evacuation planning and issuing surge warnings. We perform the simulation of TC Hagibis (2019) using the Japan Meteorological Agency's (JMA) nonhydrostatic model. The simulated atmospheric predictions were utilized as inputs for a statistical surge model named the Storm Surge Hazard Potential Index to estimate peak surge heights along the central coast of Japan. We show that Pareto‐optimized solutions from an ensemble storm surge forecast can describe potential worst (maximum) and optimum (minimum) storm surge scenarios. These solutions exemplify a diversity of trade‐off surge outcomes across diverse coastal locations, reflecting variations in coastal geometry, including bathymetry. For example, some of the Pareto‐optimized solutions that illustrate worst surge scenarios for inner bay locations are not necessarily accountable for bringing severe surge cases in open coasts. We further emphasize that an in‐depth evaluation of Pareto‐optimal solutions can shed light on how meteorological variables such as track, intensity, and size of TCs influence the worst and optimum surge scenarios, which is not clearly quantified in current multiscenario assessment methods such as those used by JMA/National Hurricane Center in the United States.

Weather Radar Parameter Estimation Based on Frequency Domain Processing: Technical Details and Performance Evaluation

Parameter estimation is important in weather radar signal processing. Time-domain processing (TDP) and frequency-domain processing (FDP) are two basic parameter estimation methods used in the weather radar field. TDP is widely used in operational weather radars because of its high efficiency and robustness; however, it must be assumed that the received signal has a symmetrical or Gaussian power spectrum, which limits its performance. FDP does not require assumptions about its power spectrum model and has a seamless connection to spectrum analysis; however, its application performance has not been fully validated to ensure its robustness in an operational environment. In this study, we introduce several technical details in FDP, including window function selection, aliasing correction, and noise correction. Additionally, we evaluate the performance of FDP and compare the performance of FDP and TDP based on simulated and measured weather in-phase/quadrature (I/Q) data. The results show that FDP has potential for operational applications; however, further improvements are required, e.g., windowing processing for signals mixed with severe clutter.

Impacts of Dropsonde Observations on Forecasts of Atmospheric Rivers and Associated Precipitation in the NCEP GFS and ECMWF IFS Models

Abstract Atmospheric River Reconnaissance has held field campaigns during cool seasons since 2016. These campaigns have provided thousands of dropsonde data profiles, which are assimilated into multiple global operational numerical weather prediction models. Data denial experiments, conducted by running a parallel set of forecasts that exclude the dropsonde information, allow testing of the impact of the dropsonde data on model analyses and the subsequent forecasts. Here, we investigate the differences in skill between the control forecasts (with dropsonde data assimilated) and denial forecasts (without dropsonde data assimilated) in terms of both precipitation and integrated vapor transport (IVT) at multiple thresholds. The differences are considered in the times and locations where there is a reasonable expectation of influence of an intensive observation period (IOP). Results for 2019 and 2020 from both the European Centre for Medium-Range Weather Forecasts (ECMWF) model and the National Centers for Environmental Prediction (NCEP) global model show improvements with the added information from the dropsondes. In particular, significant improvements in the control forecast IVT generally occur in both models, especially at higher values. Significant improvements in the control forecast precipitation also generally occur in both models, but the improvements vary depending on the lead time and metrics used. Significance Statement Atmospheric River Reconnaissance is a program that uses targeted aircraft flights over the northeast Pacific to take measurements of meteorological fields. These data are then ingested into global weather models with the intent of improving the initial conditions and resulting forecasts along the U.S. West Coast. The impacts of these observations on two global numerical weather models were investigated to determine their influence on the forecasts. The integrated vapor transport, a measure of both wind and humidity, saw significant improvements in both models with the additional observations. Precipitation forecasts were also improved, but with differing results between the two models.

КРАТКОСРОЧНОЕ ПРОГНОЗИРОВАНИЕ СТОКА РЕК РОССИИ С ИСПОЛЬЗОВАНИЕМ МОДЕЛИ HBV-96 И СИСТЕМЫ COSMO-RU

A method for short-range forecasting of average daily streamflow is proposed. The method uses the HBV-96 conceptual model of river runoff formation and the COSMO-Ru operational numerical weather prediction system. The method has been implemented for 546 river basins located throughout Russia. An archive of the required hydrometeorological information for the period from 2010 to 2019 has been formed. The method has been developed using the data for the first seven years and verified on independent material with the ones for the last three years. The verification results have shown that the method allows obtaining satisfactory forecasts for a quite large number of river basins. The results make it possible to use the proposed method within the framework of an automated system for preparing and issuing short-range forecasts for the Russian river streamflow.

ProxyVis—A Proxy for Nighttime Visible Imagery Applicable to Geostationary Satellite Observations

Abstract Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center. Significance Statement This paper describes ProxyVis imagery, a new method for combining infrared channels to qualitatively mimic daytime visible imagery at nighttime. ProxyVis demonstrates that a simple linear regression can combine just a few commonly available infrared channels to develop a nighttime proxy for visible imagery that significantly improves a forecaster’s ability to track low-level oceanic clouds and circulation features at night, works for all current geostationary satellites, and is useful across a wide range of backgrounds and meteorological scenarios. Animated ProxyVis geostationary imagery has been operational at the National Hurricane Center since 2019 and is also currently being transitioned to operations at other NWS offices and the Joint Typhoon Warning Center.

A modified vertical eddy diffusivity parameterization in the HWRF model based on large eddy simulations and its impact on the prediction of two landfalling hurricanes

Vertical eddy diffusivity (VED) in the planetary boundary layer (PBL) has a significant impact on forecasts of tropical cyclone (TC) structure and intensity. VED uncertainties in PBL parameterizations can be partly attributed to the model’s inability to represent roll vortices (RV). In this study, RV effects on turbulent fluxes derived from a large eddy simulation (LES) by Li et al. (Geophys. Res. Lett., 2021, 48, e2020GL090703) are added to the VED parameterization of the PBL scheme within the operational Hurricane Weather Research and Forecasting (HWRF) model. RV contribution to VED is parameterized through a coefficient and varies with the RV intensity and velocity scale. A modification over land has also been implemented. This modified VED parameterization is compared with the original wind-speed-dependent VED scheme in HWRF. Retrospective HWRF forecasts of Hurricanes Florence (2018) and Laura (2020) are analyzed to evaluate the impacts of the modified VED scheme on landfalling hurricane forecasts. Results show that the modified PBL scheme with the RV effect leads to an improvement in 10-m maximum wind speed forecasts of 14%–31%, with a neutral to positive improvement for track forecasts. Improved wind structure and precipitation forecasts against observations are also noted with the modified PBL scheme. Further diagnoses indicate that the revised PBL scheme enhances moist entropy in the boundary layer over land, leading to improved TC intensity prediction compared to the original scheme.

Conceptual design of a pilot assistance system for customised noise abatement departure procedures

The departure of an aircraft is commonly the flight phase with the highest thrust level in a flight, which leads to considerable noise levels on the ground. The departure procedures are characterised by the thrust reduction altitude, the acceleration altitude, and the control of airspeed and aircraft configuration within each take-off segment. Thrust reduction and acceleration altitudes are typically constant and are not adjusted to particular operational key parameters (e.g., take-off mass, reduced take-off thrust) or weather conditions. However, the parameters differ between individual flights and affect flight performance as well as the noise levels on the ground. This paper presents the conceptual design of a pilot assistance system which aims to reduce the noise on the ground by identifying a custom thrust reduction and acceleration altitude for existing noise abatement departure procedures. The pilot assistance system is based on an aircraft simulation model and a noise simulation and is aimed to be utilised during pre-flight planning on ground. A key part of the noise evaluation is that the local population distribution around the airport is considered. An overview of the ongoing research at the German Aerospace Center is provided. The conceptual design and preliminary results are presented and discussed using an exemplary take-off for three wind conditions.

Thermal environment and indices: an analysis for effectiveness in operational weather applications in a Mediterranean city (Athens, Greece)

The large number of thermal indices introduced in the literature poses a challenge to identify the appropriate one for a given application. The aim of this study was to examine the effectiveness of widely used indices in quantifying the thermal environment for operational weather applications within a Mediterranean climate. Eight indices (six simple and two thermo-physiological) were considered, i.e., apparent temperature, heat index, humidex, net effective temperature (NET), physiologically equivalent temperature (PET), universal thermal climate index (UTCI), wet-bulb globe temperature, and wind chill temperature. They were estimated using hourly meteorological data between 2010 and 2021, recorded in 15 stations from the Automatic Weather Station Network of the National Observatory of Athens in the Athens metropolitan area, Greece. The statistical analysis focused on examining indices’ sensitivity to variations of the thermal environment. NET, PET, and UTCI were evaluated as suitable for operational use, assessing both cool and warm environments, and extending their estimations to the entire range of their assessment scales. NET and PET often tended to classify thermal perception in the negative categories of their scales, with 63% of NET and 56% of PET estimations falling within the range of cool/slightly cool to very cold. UTCI estimations in the negative categories accounted for 25.8% (p < 0.001), while most estimations were classified in the neutral category (53.1%). The common occasions of extreme warm conditions in terms of both air temperature (Tair) and NET was 77.7%, Tair and UTCI 64.4%, and Tair and PET 33.6% (p < 0.001). According to the indices considered and the method followed, NET and UTCI satisfied sufficiently the requirements for operational use in the climate conditions of the Mediterranean climate.

Development and assessment of artificial neural network models for direct normal solar irradiance forecasting using operational numerical weather prediction data

Accurate operational solar irradiance forecasts are crucial for better decision making by solar energy system operators due to the variability of resource and energy demand. Although numerical weather prediction (NWP) models can forecast solar radiation variables, they often have significant errors, particularly in the direct normal irradiance (DNI), which is especially affected by the type and concentration of aerosols and clouds. This paper presents a method based on artificial neural networks (ANN) for generating operational DNI forecasts using weather and aerosol forecasts from the European Center for Medium-range Weather Forecasts (ECMWF) and the Copernicus Atmospheric Monitoring Service (CAMS), respectively. Two ANN models were designed: one uses as input the predicted weather and aerosol variables for a given instant, while the other uses a period of the improved DNI forecasts before the forecasted instant. The models were developed using observations for the location of Évora, Portugal, resulting in 10 min DNI forecasts that for day 1 of forecast horizon showed an improvement over the downscaled original forecasts regarding R2, MAE and RMSE of 0.0646, 21.1 W/m2 and 27.9 W/m2, respectively. The model was also evaluated for different timesteps and locations in southern Portugal, providing good agreement with experimental data.

Preventive and Predictive CNN Based Solution for Pipeline Leak, Blockage and Corrosion Detection

Using Artificial Intelligence (AI) in Internet of Things (IoT) environments has shown great potential to transform many industries. As oil and gas industries continue to push for digital transformation, safety critical applications such as pipeline leak detection, corrosion and blockage detection could also benefit from the adoption of AI and IoT. In our day-to-day life, we come across pipeline accidents and their after-effects. Structural deterioration of pipes in urban distribution network has presented great challenges to the utilities all over the world. When the deterioration exceeds its resiliency, the pipes leak or burst, leading to significant socio-economic overhead. The harsh operating environment, extreme temperature and weather conditions increases the potential for corrosion induced damages to the pipelines. The extreme conditions where the pipelines are located also make it difficult to rely on human operators to physically monitor these pipelines and respond to observed leaks. We propose a preventive and predictive AI-based solution that can continuously monitor the health of a complex pipeline infrastructure. It is a complete solution where pipelines will be scrutinized for blockage, leakage, corrosion, and defects. We propose a novel approach in which video images from IoT cameras installed across various locations on the pipelines are continuously analyzed and a convolutional neural network model is implemented for detecting leaks from the pipeline. Flow sensors will be mounted on the hardware to measure the fluid flow rate. This approach is proposed to deliver greater benefits in terms of accuracy and efficiency.

Estimating Full Longwave and Shortwave Radiative Transfer with Neural Networks of Varying Complexity

Abstract Radiative transfer (RT) is a crucial but computationally expensive process in numerical weather/climate prediction. We develop neural networks (NN) to emulate a common RT parameterization called the Rapid Radiative Transfer Model (RRTM), with the goal of creating a faster parameterization for the Global Forecast System (GFS) v16. In previous work we emulated a highly simplified version of the shortwave RRTM only—excluding many predictor variables, driven by Rapid Refresh forecasts interpolated to a consistent height grid, using only 30 sites in the Northern Hemisphere. In this work we emulate the full shortwave and longwave RRTM—with all predictor variables, driven by GFSv16 forecasts on the native pressure–sigma grid, using data from around the globe. We experiment with NNs of widely varying complexity, including the U-net++ and U-net3+ architectures and deeply supervised training, designed to ensure realistic and accurate structure in gridded predictions. We evaluate the optimal shortwave NN and optimal longwave NN in great detail—as a function of geographic location, cloud regime, and other weather types. Both NNs produce extremely reliable heating rates and fluxes. The shortwave NN has an overall RMSE/MAE/bias of 0.14/0.08/−0.002 K day−1 for heating rate and 6.3/4.3/−0.1 W m−2 for net flux. Analogous numbers for the longwave NN are 0.22/0.12/−0.0006 K day−1 and 1.07/0.76/+0.01 W m−2. Both NNs perform well in nearly all situations, and the shortwave (longwave) NN is 7510 (90) times faster than the RRTM. Both will soon be tested online in the GFSv16. Significance Statement Radiative transfer is an important process for weather and climate. Accurate radiative transfer models exist, such as the RRTM, but these models are computationally slow. We develop neural networks (NNs), a type of machine learning model that is often computationally fast after training, to mimic the RRTM. We wish to accelerate the RRTM by orders of magnitude without sacrificing much accuracy. We drive both the NNs and RRTM with data from the GFSv16, an operational weather model, using locations around the globe during all seasons. We show that the NNs are highly accurate and much faster than the RRTM, which suggests that the NNs could be used to solve radiative transfer inside the GFSv16.

Heavy Rainfall Forecast of Landfalling Tropical Cyclone Over China With an Upgraded DSAEF_LTP Model

AbstractSince the release of the Dynamical‐Statistical‐Analog Ensemble Forecast for Landfalling Typhoon Precipitation (DSAEF_LTP) model, a series of upgrades has been made to improve its heavy rainfall forecast performance for different cases and regions of China. However, little effort has been given to systematic evaluation of its multi‐upgraded version with a large sample size, especially for all the coastal regions of China. This study evaluates the performance of Version 1.0 (V1.0) and its improved Version 1.1 (V1.1) of the DSAEF_LTP model in simulating heavy rainfall amounts associated with 76 landfalling tropical cyclones (LTCs) over China during 2004–2016. The optimized schemes from these simulations are then applied to the heavy rainfall forecasts of 37 LTCs during 2017–2020. To further improve the forecast performance of V1.1, a subregional re‐ensemble scheme, referred to as V1.1R, is introduced after examining its performances over three coastal subregions of China. A comparison of the forecast performances of V1.0, V1.1, V1.1R, and four operational numerical weather prediction (NWP) models for the 37 LTCs shows that the performance of V1.1 is much better than that of V1.0, with a 65% higher summed threat score (TS) for over 250‐mm (TS250) and 100‐mm (TS100) rainfall; and that V1.1R's TS100 and TS250 values are further improved by 8% and 20%, respectively, as compared to V1.1. These values are superior to those of the four operational NWP models. Additionally, case studies reveal that the track distributions and intensities of LTCs may significantly impact the forecast performance of heavy rainfall.

Segmentation of polarimetric radar imagery using statistical texture

Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a diverse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: (1) the introduction of secondary, texture-based fields, calculated using statistical properties of gray-level co-occurrence matrices (GLCMs), and (2) a Gaussian mixture model (GMM), used to classify echo sources by combining radar variables with texture-based fields from (1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying-window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors' plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors' plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes or by scientists for case study analyses or historical-event identification.

2022 real-time Hurricane forecasts from an experimental version of the Hurricane analysis and forecast system (HAFSV0.3S)

During the 2022 hurricane season, real-time forecasts were conducted using an experimental version of the Hurricane Analysis and Forecast System (HAFS). The version of HAFS detailed in this paper (HAFSV0.3S, hereafter HAFS-S) featured the moving nest recently developed at NOAA AOML, and also model physics upgrades: TC-specific modifications to the planetary boundary layer (PBL) scheme and introduction of the Thompson microphysics scheme. The real-time forecasts covered a large dataset of cases across the North Atlantic and eastern North Pacific 2022 hurricane seasons, providing an opportunity to evaluate this version of HAFS ahead of planned operational implementation of a similar version in 2023. The track forecast results show that HAFS-S outperformed the 2022 version of the operational HWRF model in the Atlantic, and was the best of several regional hurricane models in the eastern North Pacific for track. The intensity results were more mixed, with a dropoff in skill at Days 4–5 in the Atlantic but increased skill in the eastern North Pacific. HAFS-S also showed some larger errors than the long-time operational Hurricane Weather Research and Forecasting (HWRF) model in the radius of 34-knot wind, but other radii metrics are improved. Detailed analysis of Hurricane Ian in the Atlantic highlights both the strengths of HAFS and opportunities for further development and improvement.

Displacement Error Characteristics of 500-hPa Cutoff Lows in Operational GFS Forecasts

Abstract Cutoff lows are often associated with high-impact weather; therefore, it is critical that operational numerical weather prediction systems accurately represent the evolution of these features. However, medium-range forecasts of upper-level features using the Global Forecast System (GFS) are often subjectively characterized by excessive synoptic progressiveness, i.e., a tendency to advance troughs and cutoff lows too quickly downstream. To better understand synoptic progressiveness errors, this research quantifies seven years of 500-hPa cutoff low position errors over the globe, with the goal of objectively identifying regions where synoptic progressiveness errors are common and how frequently these errors occur. Specifically, 500-hPa features are identified and tracked in 0–240-h 0.25° GFS forecasts during April 2015–March 2022 using an objective cutoff low and trough identification scheme and compared to corresponding 500-hPa GFS analyses. In the Northern Hemisphere, cutoff lows are generally underrepresented in forecasts compared to verifying analyses, particularly over continental midlatitude regions. Features identified in short- to long-range forecasts are generally associated with eastward zonal position errors over the conterminous United States and northern Asia, particularly during the spring and autumn. Similarly, cutoff lows over the Southern Hemisphere midlatitudes are characterized by an eastward displacement bias during all seasons. Significance Statement Cutoff lows are often associated with high-impact weather, including excessive rainfall, winter storms, and severe weather. GFS forecasts of cutoff lows over the United States are often subjectively noted to advance cutoff lows too quickly downstream, and thus limit forecast skill in potentially impactful scenarios. Therefore, this study quantifies the position error characteristics of cutoff lows in recent GFS forecasts. Consistent with typically anecdotal impressions of cutoff low position errors, this analysis demonstrates that cutoff lows over North America and central Asia are generally associated with an eastward position bias in medium- to long-range GFS forecasts. These results suggest that additional research to identify both environmental conditions and potential model deficiencies that may exacerbate this eastward bias would be beneficial.

Objectively Assessing Characteristics of Mesoscale Convective Organization in an Operational Convection-Permitting Model

Abstract The realism of convective organization in operational convection-permitting model simulations is objectively assessed, with a particular focus on the mesoscale aspects, such as convective mode. A tracking and classification algorithm is applied to observed radar reflectivity and simulated radar reflectivity from the operational ACCESS-C convection-permitting forecast domain over northern Australia between October 2020 and May 2022, and the characteristics of real and simulated convective organization are compared. Mesoscale convective systems from the operational forecast model are approximately twice as likely to be oriented parallel to the ambient wind and ambient wind shear than those observed by radar, indicating a bias toward the “training line” systems typically associated with more extreme rainfall. During highly humid active monsoon conditions, simulated convective systems have larger ground-relative speeds than systems observed in radar. Although there is less than 5% difference between the ratios of simulated and observed trailing, leading and parallel stratiform system observations, significant differences exist in other wind shear–based classifications. For instance, in absolute terms, simulated systems are 10%–35% less likely to be upshear tilted, and 15%–30% less likely to be downshear propagating than observed systems, suggesting errors in simulated cold pool characteristics. Significance Statement Remarkable progress has been made simulating thunderstorms in operational weather forecasting computer models. While some details of individual storm clouds may be unrealistic, how these storm clouds self-organize, i.e., cluster and regenerate, can be explicitly simulated, with this organization often appearing realistic. However, assessing the realism of this organization in an objective, systematic way has proven challenging. Here we assess organized convection in Australia’s current high-resolution weather prediction model. In some respects, simulated storm clouds organize realistically. High-altitude icy cloud mostly trails behind groups of storm clouds in both simulations and reality. In other respects, organization is unrealistic. Simulated storm clouds are twice as likely to orient along the mean wind direction than in reality, likely contributing to extreme rainfall biases.

Effect of Refrigerated Inlet Cooling on Greenhouse Gas Emissions for a 250 MW Class Gas Turbine Engine

In this study, the effect of inlet air cooling on greenhouse gas (GHG) emissions and engine performance for a land-based gas turbine engine was investigated under varying ambient temperatures (15–55 °C). The study aimed to reduce GHG emissions while improving output power and fuel efficiency during hot weather operating conditions. For illustrative purposes, a representative gas turbine engine model, approximating the 250 MW class General Electric (GE) engine, was analyzed in a simple cycle. A refrigeration process was integrated with a turboshaft gas turbine engine to chill the incoming air, and the power required for cooling was extracted from the gas turbine’s output power. This mechanical chiller was assumed to provide a 15 °C inlet air temperature. Without inlet air cooling, at 55 °C ambient temperature, the engine’s power output was calculated to decrease by 15.06%, while power-specific fuel consumption and GHG emissions increased by 6.09% and 5.84%, respectively. However, activating the refrigeration or cooling system in the inlet made it possible to mitigate most of the adverse effects of hot weather on the engine’s performance and GHG emissions. Therefore, with inlet air cooling, the power output loss reduces to 3.28%, indicating an 11.78% recovery compared to the 15.06% loss without cooling. Similarly, the rise in power-specific fuel consumption caused by high ambient temperature decreases from 6.09% to 3.43%, reflecting a 2.66% improvement. An important finding of the study is that with inlet air cooling, the increase in GHG emissions reduces from 5.84% to 3.41%, signifying a 2.43% improvement on a hot day with a temperature of 55 °C.