Central Weather Research Articles

May 11–12 Extreme Space Weather Events Brief and Dose Rate Model Response

In this brief paper, we analyze space weather events that occurred on May 11 and 12, 2024, from the perspective of an operational space weather center that provides advisories for civil aviation. One of the key metrics monitored by the center is the radiation dose rate at operational flight altitudes. A model implemented by the center provides the dose rate in real time. The model showed that dangerous levels were momentarily exceeded just above the usual 30,000 feet level during the events. This paper highlights differences in models used by various space weather centers, emphasizing the need for harmonization.

HEAT BUDGET OF THE BARENTS SEA SURFACE IN WINTER

Variability of the total heat balance (HB) of the Barents Sea during the cold period of the year has been studied. The cold period is that of cooling of the sea surface when the heat flux is permanently oriented towards the atmosphere. The contribution of two major components of the HB, i. e. sensible and latent heat fluxes, to the observed increase of the total winter heat transfer at the sea-atmosphere interface has been estimated. Data on short-wave and long-wave radiation fluxes, and sensible and latent heat values were obtained from the atmos-pheric reanalysis of the European Center for Medium-term Weather Forecasting ERA5. HB of the sea surface was calculated as a sum of short-wave and long-wave radiation fluxes and those of sensible and latent heat. The total HB, as well as the total flux of sensible and latent heat for the cold period were calculated by summing up the corresponding values between the start and end dates of the cooling season. Calculations demonstrated an increase in the sum of HB over the cold season for the northern part of the Barents Sea (up to 2000 MJ/m2 over 40 years), and a decrease in the southern part of the sea (up to 1000 MJ/m2 over 40 years). In the northern part of the sea, the contribution of sensible and latent heat fluxes decreases to 0,3-0,4. The observed trend of sum of HB over the cold season and its turbulent components could with high probability be explained by increasing difference between air temperature and sea surface temperature.

WeatherBench 2: A Benchmark for the Next Generation of Data‐Driven Global Weather Models

AbstractWeatherBench 2 is an update to the global, medium‐range (1–14 days) weather forecasting benchmark proposed by (Rasp et al., 2020, https://doi.org/10.1029/2020ms002203), designed with the aim to accelerate progress in data‐driven weather modeling. WeatherBench 2 consists of an open‐source evaluation framework, publicly available training, ground truth and baseline data as well as a continuously updated website with the latest metrics and state‐of‐the‐art models: https://sites.research.google/weatherbench. This paper describes the design principles of the evaluation framework and presents results for current state‐of‐the‐art physical and data‐driven weather models. The metrics are based on established practices for evaluating weather forecasts at leading operational weather centers. We define a set of headline scores to provide an overview of model performance. In addition, we also discuss caveats in the current evaluation setup and challenges for the future of data‐driven weather forecasting.

A Multi-Satellite Space Environment Risk Prediction and Real-Time Warning System for Satellite Safety Management

In response to the need for a space security situation assessment during orbit, the multi-satellite space environmental risk prediction and early warning system is based on the detection results of the space weather payload of the Fengyun 4A and 4B satellites, as well as the prediction results of the National Space Weather Center, for the first time. By comprehensively utilizing some open-source data, it is the first time that we have achieved a 24 h advanced prediction of the space environment high-energy proton, low-energy particle, and high-energy electron risks for the safety of the Fengyun-series high-orbit satellites, and a real-time warning of satellite single-event upset, surface charging, and deep charging risks. The automation system has preliminarily achieved an intelligent space risk assessment for the safety of multiple stationary meteorological satellites, effectively improving the application efficiency of the space environmental data and the products of Fengyun-series satellites. The business status is stable in operation, and the resulting error between the predicted results of various risk indices and the measured data was less than one level. The warning accuracy was better than 90%. This article uses the system for the first time, to use Fengyun satellite data to, accurately and in a timely manner, predict and warn us about the low-energy particles and surface charging high-risk levels of the Fengyun 4A and 4B satellites during the typical space weather event on 21 April 2023, in response to the impact of complex spatial environmental factors on the safety of Fengyun-series high-orbit satellites. The construction and operation of a multi-satellite space environmental risk prediction and early warning system can provide a reference for the safety work of subsequent satellite ground system operations.

Comparative study of time-series forecasting models for wind power generation in Gujarat, India

The rapid rate of transformation in the power sector of India has placed a significant emphasis on robust grids and distributed generation units. The observable shift in the energy sector, especially in wind and solar energy, also requires smooth integration of Distributed Generation units with the existing power grid. Precise wind power generation forecast, therefore, becomes an important and complex task for the strategic planning and management of the systems. We, thus, aim towards a system that can actually provide precise wind power forecasts by applying machine learning techniques. This work proposes a comparative and comprehensive analysis of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) model. The experimentations and modelling are performed considering meteorological and historical power generation data. The study is concentrated in Kutch, Gujarat and is validated on the data collected from the Central Electricity Authority (CEA), India for power generation data and weather data collected from regional weather centres. The findings show that ARIMA outperforms the other models for non-linear data in multivariate analysis, with a MAPE score of less than 5.5 on the prediction dataset.

Assimilation of new rocket dropsonde data using WRFDA and its impact on numerical simulations of typhoon NORU

On September 26 at 2100 UTC and September 27 at 0900 and 2300 UTC, three rockets platform carrying dropsondes (TFTC-400) devices were launched off the east coast of Hainan Island to conduct a launch experiment aimed at detecting Typhoon NORU (2216). The experiment yielded valuable data that were subsequently analyzed to ascertain temperatures, wind speeds, and relative humidity in the atmosphere. Of the four experiments conducted, employing three distinct control variable configurations (CVs), we utilized the 3DVAR of WRF Data Assimilation (WRFDA) to assimilate rocket sounding data and the NCEP ADP Global Upper Air Observational Weather Data from the research data archive dataset that was jointly produced by the Center for Weather and Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). In one experiment, no data assimilation was performed (CTL). These experiments were designed to assess the impact of these observational datasets on typhoon predictions of the Weather Research & Forecasting Model (WRF) numerical simulations. Utilizing the assimilated background field, a 24-hour forecast was conducted, and the assimilation simulation was analyzed with regard to typhoon path, intensity, precipitation, and improvements in the background field. The results reveal that, on average, the three-assimilation experiment led to a 30 % reduction in track error compared to the CTL. Additionally, the assimilation experiment for CV7 of control variable configurations brought the maximum wind speed closer to observed data than the CTL between 6 and 12 h. The TS (threat score) evaluation of simulated 24-hour precipitation in the model domain indicates that the three assimilation schemes exhibit a degree of improvement in the forecast scores for 24-hour cumulative typhoon precipitation. Nevertheless, the simulation results for minimum sea-level pressure are unsatisfactory. To establish statistical significance, additional cases within the relevant region are necessary for result validation.

The Role of Changbai Mountain in an Extreme Precipitation Event in Liaoning Province, China

Based on the half-hourly Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data product (0.1°), the fifth-generation European Center for Medium-Range Weather Forecasting atmospheric reanalysis dataset (ERA5), sounding data, and the Weather Research and Forecasting Model (WRF-ARW), this study explored the developmental process of a typical extreme precipitation event in Liaoning Province on 2 June 2021. This study focused on the impact of Changbai Mountain on this precipitation process and its corresponding physical mechanisms. The research findings revealed that Changbai Mountain significantly affected the precipitation event in three main aspects: blocking drag, forcing uplift, and leeside convergence. The blocking drag caused by the mountain topography led to an extension in the duration of heavy rainfall. The dynamic lifting and leeside convergence associated with the mountainous terrain also substantially increased the amount of precipitation. Furthermore, the topography hindered the movement of the Bohai Bay cold pool and enhanced the intensity of the cold pool, contributing to the sustained extreme precipitation in Liaoning Province. Lastly, the terrain sensitivity experiment demonstrated that when the height of Changbai Mountain was reduced, the convergence uplift, moisture condensation, and cold pool intensity were weakened, leading to significant changes in precipitation intensity and spatial distribution. These findings further confirm the crucial role of Changbai Mountain in the occurrence and development of local precipitation in Liaoning Province.

Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework

AbstractRecent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System.

Parametric Postprocessing of Dual-Resolution Precipitation Forecasts

Abstract All major weather centers issue ensemble forecasts, which differ both in ensemble size and spatial resolution, even while covering the same domain. These parameters directly determine both the forecast skill of the prediction and the computation cost. In the past few years, the plans for upgrading the configuration of the Integrated Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF) from a single forecast with 9-km resolution and a 51-member ensemble with 18-km resolution induced an extensive study of the forecast skill of both raw and postprocessed dual-resolution predictions comprising ensemble members of different horizontal resolutions. We investigate the predictive performance of the censored shifted gamma (CSG) ensemble model output statistic (EMOS) approach for statistical postprocessing with the help of dual-resolution, 24-h, precipitation accumulation ensemble forecasts over Europe with various forecast horizons. We consider the operational 50-member ECMWF ensemble as of high resolution and extend it with a low-resolution (29-km grid), 200-member experimental forecast. The investigated dual-resolution combinations consist of subsets of these two forecast ensembles with equal computational cost, which is equivalent to the cost of the operational ensemble. Our case study verifies that, compared with the raw ensemble combinations, EMOS postprocessing results in a significant improvement in forecast skill and that skill is statistically indistinguishable between any of the analyzed mixtures of dual-resolution combinations. Furthermore, the semilocally trained CSG EMOS provides an efficient alternative to the state-of-the-art quantile mapping without requiring additional historical data.

Retrieving Atmospheric Gas Profiles Using FY-3E/HIRAS-II Infrared Hyperspectral Data by Neural Network Approach

The observed radiation data from the second-generation Hyperspectral Infrared Atmospheric Sounder (HIRAS-II) on the Fengyun-3E (FY-3E) satellite contain useful vertical atmosphere information which can distinguish and retrieve vertical profiles of atmospheric gas components including ozone (O3), carbon monoxide (CO), and methane (CH4). This paper utilizes FY-3E/HIRAS-II observational data to optimize each gas channel using the improved Optimal Sensitivity Profile method (OSP) channel algorithm and establishes a typical convolutional neural network model (CNN) and a representative U-shaped network model (UNET) with deep features and shallow feature links to perform atmospheric profile retrieval calculations of O3, CO, and CH4. We chose the clear sky data of the Indian and its southern seas in December 2021 and January 2022, with reanalysis data from European Center for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and European Center for Medium-Range Weather Forecasts Atmospheric Composition Reanalysis v4 (EAC4) serving as the reference values. The retrieval outcomes were then compared against advanced numerical forecast models including the Whole Atmosphere Community Climate Model (WACCM), Global Forecast System (GFS), and satellite products from an Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI). Experimental results show that the generalization ability and retrieval accuracy of CNN are slightly higher compared with UNET. For O3 profile retrieval, the mean percentage error (MPE) of the whole layers for CNN and UNET data in relation to ERA5 data was less than 8%, while the root-mean-square error (RMSE) was below 1.5 × 10−7 kg/kg; for CH4 profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 0.7%, while the RMSE was below 1.5 × 10−8 kg/kg. The retrieval of O3 and CH4 are resulted in a significant improvement compared to the forecast data and satellite products in most pressure levels; for CO profile retrieval, the MPE of the whole layers for CNN and UNET data in relation to EAC4 data was less than 11%, while the RMSE was below 4 × 10−8 kg/kg. The error of the CO retrieval results was higher than that of the forecast data at the pressure level of 200~500 hPa and lower than that of similar satellite products with most pressure levels. The experiments indicated that the neural network method effectively determines the atmospheric gas profiles using infrared hyperspectral data, exhibiting a positive performance in accuracy and retrieval speed.

The Predicition of Solar Flares Using Millimeter Radio Brightenings

Abstract Solar activity could have significant impacts on various Earth and near-Earth space systems, such as satellite communication and power grid systems. The prediction of solar activity and active solar events plays a major role when preparing for these disturbances. Various satellite-based instruments constantly observe the Sun. However, only a few ground-based solar instruments could provide versatile enough information for the space weather prediction. Metsähovi Radio Observatory of Aalto University (Finland) has a unique collection of millimetre (8 mm) solar radio maps over the past 40 years, and even denser observational solar radio data catalogue since 2011. About 75–80 % days yearly are covered nowadays. This gives opportunity to make statistical estimation of solar flare occurrence based on solar radio maps. In this study, we had 2253 days when both solar radio map and GOES (Geostationary Operational Environmental Satellites) classified solar flare were observed. In this work, we used solar flare classification done by the Space Weather Centre (SWC) of the National Oceanic and Atmospheric Administration (NOAA). The data were observed between 1 January 2011 and 12 September 2022. Our study shows that the maximum intensity of radio brightenings is a good indicator to tell which kind of GOES classified solar flare could be expected to happen. The article presents that intense radio brightening is needed to produce a certain GOES classified solar flare.

Improved space weather observations and modeling for aviation radiation

In recent years there has been a growing interest from the aviation community for space weather radiation forecasts tailored to the needs of the aviation industry. In 2019 several space weather centers began issuing advisories for the International Civil Aviation Organization alerting users to enhancements in the radiation environment at aviation flight levels. Due to a lack of routine observations, radiation modeling is required to specify the dose rates experienced by flight crew and passengers. While mature models exist, support for key observational inputs and further modeling advancements are needed. Observational inputs required from the ground-based neutron monitor network must be financially supported for research studies and operations to ensure real-time data is available for forecast operations and actionable end user decision making. An improved understanding of the geomagnetic field is required to reduce dose rate uncertainties in regions close to the open/closed geomagnetic field boundary, important for flights such as those between the continental US and Europe which operate in this region. Airborne radiation measurements, which are crucial for model validation and improvement, are lacking, particularly during solar energetic particle events. New measurement campaigns must be carried out to ensure progress and in situ atmospheric radiation measurements made available for real-time situational awareness. Furthermore, solar energetic particle forecasting must be improved to move aviation radiation nowcasts to forecasts in order to meet customer requirements for longer lead times for planning and mitigation.

The CAESAR Project for the ASI Space Weather Infrastructure

This paper presents the project Comprehensive spAce wEather Studies for the ASPIS prototype Realization (CAESAR), which aims to tackle the relevant aspects of Space Weather (SWE) science and develop a prototype of the scientific data centre for Space Weather of the Italian Space Agency (ASI) called ASPIS (ASI SPace Weather InfraStructure). To this end, CAESAR involves the majority of the SWE Italian community, bringing together 10 Italian institutions as partners, and a total of 92 researchers. The CAESAR approach encompasses the whole chain of phenomena from the Sun to Earth up to planetary environments in a multidisciplinary, comprehensive, and unprecedented way. Detailed and integrated studies are being performed on a number of well-observed “target SWE events”, which exhibit noticeable SWE characteristics from several SWE perspectives. CAESAR investigations synergistically exploit a great variety of different products (datasets, codes, models), both long-standing and novel, that will be made available in the ASPIS prototype: this will consist of a relational database (DB), an interface, and a wiki-like documentation structure. The DB will be accessed through both a Web graphical interface and the ASPIS.py module, i.e., a library of functions in Python, which will be available for download and installation. The ASPIS prototype will unify multiple SWE resources through a flexible and adaptable architecture, and will integrate currently available international SWE assets to foster scientific studies and advance forecasting capabilities.

COSMIC-2 Mission Summary at Three Years in Orbit

We summarize the status of the FORMOSAT-7/COSMIC-2 (COSMIC-2) mission which has completed its first three years in orbit. COSMIC-2 is a joint U.S./Taiwan program consisting of six satellites in low-inclination orbits with the following payloads: Global Navigation Satellite System radio occultation, in-situ ion velocity meter, and tri-band radio frequency beacon. The constellation is in its final orbit configuration and reached mission full operating capability in September 2021. An extensive calibration/validation campaign has to date enabled the release of all baseline neutral atmosphere products and nearly all baseline ionosphere products. The mission is providing usually more than 5000 neutral atmosphere RO profiles per day with a precision better than 2 μrad from 30–60 km altitude. Each day, nearly 12,000 combined total electron content occultations and arcs are generated with absolute accuracy of better than 3 TECU. IVM density precision is at or below the 1% requirement. Neutral atmosphere and ionosphere latency, measured from time of observation to product creation time, is below 30 min median. Data products are delivered in near real-time to operational weather and space weather centers and made available openly to the research community. New ionosphere products specifying the presence and absence of scintillation are under development and planned for future release.

Development of Thermostat for the Fluxgate Magnetometer in Icheon Geomagnetic Observatory and Stability Evaluation after Installation

This paper reports on the design and installation of a thermostat to keep the temperature of the fluxgate magnetometer constant and the data stability evaluation after installation. The thermostat was installed at the Icheon Geomagnetic Observatory operated by the Korean Space Weather Center of National Radio Research Agency. It was designed in consideration of stability of temperature control against safety incident, potential effects on magnetic field measurement, and the temperature control efficiency. After the temperature control device was installed, it was confirmed that the temperature was constantly maintained at the level of 20℃. Delta F and baseline values were used to evaluate geomagnetic data stability, and it was confirmed that delta F and baseline fluctuations were reduced after installation of the thermostat.

Medium-term prediction of the fluence of relativistic electrons in geostationary orbit using solar wind streams forecast based on solar observations

The new approaches to the modelling of the Sun-solar wind-magnetosphere-radiation belts chain and their implementation at MSU's Space weather center (Space Monitoring Data Center, SMDC) are presented. The main idea is to use data from medium-term (3–5 days) forecasts of the speed of quasi-stationary solar wind streams based on data obtained from solar images in the UV wavelength range to improve the quality of forecasts of the magnetospheric factors of the space environment. An operational model for predicting the daily fluence of relativistic electrons (>2 MeV) in the Earth's outer radiation belt has been developed, with the extended set of input parameters that include the forecast of the solar wind velocity. To validate the model, the period from October 11, 2016 to February 18, 2017 was considered, during which the Sun made several revolutions and it was possible to observe the evolution of coronal holes and the development of solar sporadic phenomena. The arrival of high-speed solar wind streams from coronal holes to the Earth's magnetosphere, as well as, as impact of coronal mass ejections, provoked geomagnetic disturbances of various intensity, and caused significant variations of the electron fluxes of the Earth's outer radiation belt. SMDC website implements solar wind model based on the data of solar observations by the AIA instrument of the SDO space observatory at wavelengths of 19.3 and 21.1 nm that forecasts the daily fluence of relativistic electrons (E > 2 MeV) in geostationary orbit. It is shown that the use of the results of medium-term forecasting of the solar wind speed as an input parameter in the daily electron fluence forecast model can significantly improve its quality with a forecast horizon of up to 4 days.

The Impact of Data Latency on Operational Global Weather Forecasting

Abstract The impact of low data latency is assessed using observations assimilated into the NCEP Finite-Volume Cubed-Sphere Global Forecast System (FV3GFS). Operationally, a full dataset is used to generate short-term (9-h) forecasts used as the background state for the next cycle, and a limited dataset with fewer observations is used for long-term (16-day) forecasts due to time constraints that exist in an operational setting. In this study, the sensitivity of the global weather forecast skill to the use of the full and limited datasets in both the short- and long-term forecasts (out to 10 days only) is evaluated. The results show that using the full dataset for long-term forecasts yields a slight improvement in forecast skill, while using the limited dataset for short-term forecasts yields a significant degradation. This degradation is primarily attributed to a decrease of in situ observations rather than remotely sensed observations, though no individual observation type captures the amount of degradation noted when all observations are limited. Furthermore, limiting individual types of in situ observations (aircraft, marine, rawinsonde) does not result in the level of degradation noted when limiting all in situ observations, demonstrating the importance of data redundancy in an operational observational system. Significance Statement Millions of observations are used in global models every day to understand the state of the atmosphere. These observations rely on quick transmission from observation source to weather centers for inclusion in operational models. For this study, we test how different groups of observations, which arrive at the model center at different times, impact the model forecast. We find that by not using the observations that take longer to arrive at the weather centers, the forecast is much worse, showing the importance of quick transmission of observations. Direct observations (those measured within the atmosphere) have a greater impact than remote observations (those viewed from afar, such as by satellites). However, no single observation type by itself causes a poor forecast by being limited, showing the importance of using different types of observations to capture the state of the atmosphere.

Comparison of Desroziers and Three-Cornered Hat Methods for Estimating COSMIC-2 Bending Angle Uncertainties

Abstract We compare two seemingly different methods of estimating random error statistics (uncertainties) of observations, the three-cornered hat (3CH) method and Desroziers method, and show several examples of estimated uncertainties of COSMIC-2 (C2) radio occultation (RO) observations. The two methods yield similar results, attesting to the validity of both. The small differences provide insight into the sensitivity of the methods to the assumptions and computational details. These estimates of RO error statistics differ considerably from several RO error models used by operational weather forecast centers, suggesting that the impact of RO observations on forecasts can be improved by adjusting the RO error models to agree more closely with the RO error statistics. Both methods show RO uncertainty estimates that vary with latitude. In the troposphere, uncertainties are higher in the tropics than in the subtropics and middle latitudes. In the upper stratosphere–lower mesosphere, we find the reverse, with tropical uncertainties slightly less than in the subtropics and higher latitudes. The uncertainty estimates from the two techniques also show similar variations between a 31-day period during Northern Hemisphere tropical cyclone season (16 August–15 September 2020) and a month near the vernal equinox (April 2021). Finally, we find a relationship between the vertical variation of the C2 estimated uncertainties and atmospheric variability, as measured by the standard deviation of the C2 sample. The convergence of the error estimates and the standard deviations above 40 km indicates a lessening impact of assimilating RO above this level. Significance Statement Uncertainties of observations are of general interest and their knowledge is important for assimilation in numerical weather prediction models. This paper compares two methods of estimating these uncertainties and shows that they give nearly identical results under certain conditions. The estimation of the COSMIC-2 bending angle uncertainties and how they compare to the assumed bending angle error models in several operational weather centers suggests that there is an opportunity for obtaining improved impact of RO observations in numerical model forecasts. Finally, the relationship between the COSMIC-2 bending angle errors and atmospheric variability provides insight into the sources of RO observational uncertainties.

Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations

This paper provides an overview of the contributions of the space-based global navigation satellite system (GNSS) radio occultation (RO) measurements from the FORMOSAT-7/COSMIC2 (F7/C2) mission in advancing our understanding of ionospheric plasma physics in the purview of space weather. The global positioning system (GPS) occultation experiment (GOX) onboard FORMOSAT-3/COSMIC (F3/C), with more than four and half million ionospheric RO soundings during April 2006–May 2020, offered a unique three-dimensional (3D) perspective to examine the global electron density distribution and unravel the underlying physical processes. The current F7/C2 carries TGRS (Tri-GNSS radio occultation system) has tracked more than 4000 RO profiles within ±35° latitudes per day since 25 June 2019. Taking advantage of the larger number of low-latitude soundings, the F7/C2 TGRS observations were used here to examine the 3D electron density structures and electrodynamics of the equatorial ionization anomaly, plasma depletion bays, and four-peaked patterns, as well as the S4 index of GNSS signal scintillations in the equatorial and low-latitude ionosphere, which have been previously investigated by using F3/C measurements. The results demonstrated that the denser low-latitude soundings enable the construction of monthly global electron density maps as well the altitude-latitude profiles with higher spatial and temporal resolution windows, and revealed longitudinal and seasonal characteristics in greater detail. The enhanced F7/C2 RO observations were further applied by the Central Weather Bureau/Space Weather Operation Office (CWB/SWOO) in Taiwan and the National Oceanic and Atmospheric Administration/Space Weather Prediction Center (NOAA/SWPC) in the United States to specify the ionospheric conditions for issuing alerts and warnings for positioning, navigation, and communication customers. A brief description of the two models is also provided.

Verifying operational intra-day solar forecasts from ECMWF and NOAA

Global horizontal irradiance (GHI) forecasting at intra-day horizons of up to 12-h ahead is vital to grid integration of solar photovoltaics, but has been fundamentally difficult for all methods that do not involve numerical weather prediction (NWP), since non-NWP methods are unable to extrapolate the data to a horizon beyond a fews hours. The European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) are the most representative weather centers in Europe and America, respectively. To understand their operational impact and value to grid integration, the ECMWF’s High Resolution (HRES) model and two models from NOAA, namely, Rapid Refresh (RAP) and High-Resolution Rapid Refresh (HRRR), are validated through the Murphy–Winkler distribution-oriented verification framework, over the year 2020, at seven locations. All forecasts are retrieved at native horizontal resolutions—9 km for HRES, 13 km for RAP, and 3 km for HRRR; it depicts the “off-the-shelf” scenario if these forecasts are to be utilized by end users. Three simple linear correction methods, each being statistically optimal in its own respect, are used to post-process the raw forecasts. It was found that 1–12-h-ahead ECMWF’s HRES forecasts have a significantly lower root mean square error (14.0–33.7%) as compared to NOAA’s HRRR (19.0–53.2%) and RAP (19.2–45.9%). Even after the large biases in HRRR and RAP forecasts are removed, those post-processed versions are still inferior to the raw HRES forecasts.