Weather Prediction Research Articles

Real-Time Operational Trial of Atmosphere–Ocean–Wave Coupled Model for Selected Tropical Cyclones in 2024

An atmosphere–ocean–wave coupled regional model, the UWIN-CM, began its operational trial in real time at the Hong Kong Observatory (HKO) in the second half of 2024. Its performance in the analysis of three selected tropical cyclones, Severe Tropical Storm Prapiroon, Super Typhoon Gaemi, and Super Typhoon Yagi, are studied in this paper. The forecast track and intensity of the tropical cyclones were verified against the operational analysis. It is shown that the track error of the UWIN-CM was lower than other regional numerical weather prediction (NWP) models in operation at the HKO, with a reduction in mean direct positional error of up to 50% for the first 48 forecast hours. For cyclone intensity, the performance of the UWIN-CM was the best out of the available global and regional models at HKO for Yagi at forecast hours T + 36 to T + 84 h. The model captured the rapid intensification of Yagi over the SCS with a lead time of 24 h or more. The forecast winds were compared with the in situ measurements of buoy and with the wind field analysis obtained from synthetic-aperture radar (SAR). The correlation of forecast winds with measurements from buoy and SAR ranged between 65–95% and 50–70%, respectively. The model was found to perform generally satisfactorily in the above comparisons.

Application of forecast‐informed reservoir operations at US Army Corps of Engineers dams in California

AbstractThe US Army Corps of Engineers (USACE) prescribes flood control operations for reservoirs it regulates in watershed‐specific water control manuals (WCMs), which can be decades‐old and may not capture changed conditions in the watersheds or include the benefit of state‐of‐the‐science weather and streamflow prediction. Considering the specific characteristics of a reservoir, forecast‐informed reservoir operations (FIRO) may be used to enhance flood risk reduction, improve water availability, and achieve other benefits. The first FIRO pilot project at Lake Mendocino in California focused on determining if water supply reliability could be improved using FIRO without increasing flood risk. The final report concluded that FIRO concepts could indeed improve water supply reliability while enhancing flood risk reduction. Subsequently, USACE chose additional reservoir systems in California with different characteristics as additional pilot study locations to further investigate FIRO concepts. These successful FIRO efforts have provided justification to continue its expansion beyond the initial pilot sites. The lessons learned from the FIRO pilot projects are being used to inform the development of the FIRO Screening Process, a screening level framework intended to scale up the implementation of FIRO. The lessons learned could support FIRO implementation at suitable USACE reservoirs by updating WCMs.

Enhanced Cyclone Intensity Estimation Using Deep Learning Models

Every year in India, tropical cyclones pose one of the greatest threats to life and property. They include a variety of hazards, each of which can have a significant impact on life and property, such as storm surge, flooding, extreme winds, tornadoes, and lighting. These hazards interact with one another, substantially increasing the risk of loss of life and material damage. According to a study on extreme weather events, as many as 117 cyclones hit India in 50 years from 1970 to 2019, claiming over 40,000 lives. Due to advancements in technology, detection of cyclones has become much easier. Cyclones are categorized into various categories based on their intensity which is calculated by various methods and sources. Estimation of the intensity of cyclone helps them to categorize. In this paper we are trying to estimate the intensity of cyclones by using the state- of-the-art deep learning models as they are pre-trained on large data sets. Keywords: Tropical cyclones, Hazards, Intensity estimation, Deep learning models, Detection, Data sets, Cyclone intensity, Weather prediction, Disaster management.

E2E model from crop suggestion to harvesting time prediction in sugarcane plant utilizing machine learning and deep learning

AbstractThe automated system for enhancing plant growth presents an innovative approach to optimize quality of sugarcane cultivation for four main sugarcane growing zones. It includes issues like recommendation of crops based on soil nutrients, diagnosis of disease in the leaf and stem images of sugarcane, weed detection and harvesting time prediction. The research work proposed in the article presents an innovative two-stage approach for object detection and classification in agricultural imagery. Initially, YOLOv8 (You Only Look Once) is employed to accurately detect objects within images, delineating them with precise boundary boxes. Subsequently, the focus of hybrid model integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, known as Contextual Long Short-Term Memory (CLSTM), is employed. This dual-stage methodology harnesses the speed and accuracy of YOLOv8 for robust object localization, while the CLSTM model ensures nuanced classification, contributing to comprehensive and accurate approach for object detection and crop-weed differentiation in agricultural scenarios. The proposed approach is compared with the four DL algorithms for identifying weeds in sugarcane crops and subsequently assessed their accuracy and F1 score performance. At a learning rate of 0.002, the findings of CLSTM showcase superior precision at 98.5%, recall at 97.8%, F1 score at 98.1%, and an overall accuracy of 97.7%. The subsequent task is harvesting time prediction, which entails identifying the best time to harvest sugarcane based on the planting period, weather predictions, and sugarcane brix value. The implementation of this automated system not only enhances the productivity of sugarcane cultivation but also serves as a model for sustainable and resource-efficient agriculture.

Capturing the Variability of the Nocturnal Boundary Layer through Localized Perturbation Modeling

Abstract A single-column model is used to investigate regime transitions within the stable atmospheric boundary layer, focusing on the role of small-scale fluctuations in wind and temperature dynamics and of turbulence intermittency as triggers for these transitions. Previous studies revealed abrupt near-surface temperature inversion transitions within a limited wind speed range. However, representing these transitions in numerical weather prediction (NWP) and climate models is a known difficulty. To shed light on boundary layer processes that explain these abrupt transitions, the Ekman layer height and its correlation with regime shifts are analyzed. A sensitivity study is performed with several types of perturbations of the wind and temperature tendencies, as well as with the inclusion of intermittent turbulent mixing through a stochastic stability equation. The effect of small fluctuations of the dynamics on regime transitions is thereby quantified. The combined results for all tested perturbation types indicate that small-scale phenomena can drive persistent regime transitions from very to weakly stable regimes, but for the opposite direction, no evidence of persistent regime transitions was found. The inclusion of intermittency prevents the model from getting trapped in the very stable regime, thus preventing the so-called ”runaway cooling”, an issue for commonly used short-tail stability functions. The findings suggest that using stochastic parameterizations of boundary layer processes, either through stochastically perturbed tendencies or parameters, is an effective approach to represent sharp transitions in the boundary layer regimes and is, therefore, a promising avenue to improve the representation of stable boundary layers in NWP and climate models.

Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model.

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India's five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike's information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and SARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04-0.12), MAE (0.03-0.1), SMAPE (0.04-0.2), and MASE (0.5-.08).

Evaluating Numerical Weather Prediction Models in the Middle Atmosphere Using Coherent Oceanic Acoustic Noise Observations

AbstractWe develop a method to assess numerical weather prediction (NWP) model performances from the mid‐stratosphere to the mesosphere‐lower thermosphere (30–120 km), through comparisons between observed and simulated amplitude of oceanic infrasound known as microbaroms. We adapt a recently published array processing algorithm, the multichannel maximum‐likelihood (MCML), to the 360‐observations of microbaroms. We simulate infrasound propagation using a source model and atmospheric specifications prescribed by NWP models. As this study paves the way for the assimilation of microbarom observations in these models, we assess the different components of our method. The sensitivity of the NWP model assessments to the acoustic propagation is investigated. We demonstrate the limitations of a parametrized attenuation solely driven by atmospheric fields at the infrasound station (a method previously used due to its computational efficiency) by comparing it with an explicit simulation retaining the whole 3D atmospheric fields. Importantly, in the microbarom simulations, we account for the array response to allow one‐to‐one comparisons with observations. We also highlight an observed intermittent semi‐diurnal periodicity, whose occurrence depends on middle‐atmospheric conditions, pointing at arrivals from the mesosphere and lower thermosphere. Hence, its variability needs to be accounted for in the simulations. We use a circular optimal transport metric to quantify differences between simulated and observed microbarom azimuthal distributions in a systematic way. We present NWP models relative performances diagnostics over periods of interest, including a sudden stratospheric warming in January 2021. We discuss how our approach provides insights into the model performances in the middle atmosphere.

Water vapor Raman lidar observations from multiple sites in the framework of WaLiNeAs

Abstract. During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, eight lidars specifically designed to measure water vapor mixing ratio (WVMR) profiles were deployed on the western Mediterranean coast. The main objectives were to investigate the water vapor content during case studies of heavy-precipitation events in the coastal western Mediterranean and assess the impact of high spatiotemporal WVMR data on numerical weather prediction forecasts by means of state-of-the-art assimilation techniques. Given the increasing occurrence of extreme events due to climate change, WaLiNeAs is the first program in Europe to provide network-like, simultaneous and continuous water vapor profile measurements over a period of 3–4 months. This paper focuses on the WVMR profiling datasets obtained from three of the lidars run by the French part of the WaLiNeAs team. These three lidars were deployed in the cities of Coursan, Le Grau-du-Roi and Cannes. This measurement setup enabled monitoring of the water vapor content of the lower troposphere over periods of 3 months in fall and winter 2022, with some interruptions, and 4 months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at nighttime and 1–2 km during daytime. They had a vertical resolution of 100 m and a time resolution between 15 and 30 min, and they were selected to meet the needs of weather forecasting with an uncertainty lower than 0.4 g kg−1. The paper presents details about the instruments, the experimental strategy and the datasets provided. The final dataset (https://doi.org/10.25326/537; Chazette et al., 2023) is divided into two sub-datasets: the first with a time resolution of 15 min, which contains a total of 26 423 WVMR vertical profiles, and the second with a time resolution of 30 min to improve the signal-to-noise ratio and signal altitude range.

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.

A Lightweight Transformer-Based Spatiotemporal Analysis Prediction Algorithm for High-Dimensional Meteorological Data

High-dimensional meteorological data offer a comprehensive overview of meteorological conditions. Nevertheless, predicting regional high-dimensional meteorological data poses challenges due to the vast scale and rapid changes. Apart from slow conventional numerical weather prediction methods, recently developed deep learning methods often fail to fully integrate spatial information of the high-dimensional data and require a significant amount of computational resources. This paper presents the spatiotemporal analysis fitting prediction algorithm (SA-Fit), an approximation algorithm for regional high-dimensional meteorological data prediction. SA-Fit proposes two key designs to achieve efficient prediction of the high-dimensional data. SA-Fit introduces a lightweight Transformer-based spatiotemporal analysis network to encode spatiotemporal information, which can integrate the interaction information between different coordinates in the data. Furthermore, SA-Fit introduces explicit functions with a lasso penalty to fit variations in high-dimensional meteorological data, achieving the prediction of a large amount of data with minimal prediction values. We performed experiments using the ERA5 dataset from the Shanghai and Xi’an regions. The experimental results show that SA-Fit is comparable to other advanced deep learning prediction methods in overall prediction performance. SA-Fit shortens training time and significantly reduces model parameters while using the Transformer structure to ensure prediction accuracy.

Assessment of reference evapotranspiration forecasting in the Mediterranean climate of Central Chile using the ASCE standardized Penman-Monteith equation, the Hargreaves-Samani equation, and weather predictions from the Global Forecast System model

Accurate estimation of crop evapotranspiration (ETc) is essential for effective agricultural water resource planning. To determine ETc it is common multiplying the reference evapotranspiration (ETref) by an appropriate crop coefficient (Kc). Forecasting ETref could be particularly beneficial for irrigation scheduling. In this work, daily and cumulative (7 days) ETref predictions were estimated with the Penman-Monteith (PM) and the Hargreaves-Samani (HS) models using environmental variables obtained from the Global Forecast System (GFS). Results were compared with ETref calculated with the ASCE (American Society of Civil Engineers) PM equation from measured daily values of solar radiation, air temperature, relative humidity and wind speed. Four weather stations located in Central Chile during 2020 to 2022 were used. The best results were obtained using the HS equation, for all weather stations, the statistical indicators for daily ETref predictions fall within the following ranges: Accuracy (defined as the percentage of daily ETref predictions with an absolute error within 1mmd−1) varies between 72.8% and 91.8%; the root mean square error (RMSE) ranges from 0.59mmd−1 to 0.98mmd−1; the normalized root mean square error (NRMSE) is between 16.1% and 24.1%; the mean bias error (MBE) is between 0.10mmd−1 and 0.36mmd−1; and the normalized mean bias error (NMBE) varies from 2.5% to 8.3%. On the other hand, the indicators for the 7-day cumulative ETref forecasts were: RMSE from 2.86mm to 5.08mm; NRMSE from 11.0% to 16.3%; MBE from 0.66mm to 2.57mm; and NMBE from 2.4% to 8.3%. These results suggest that HS ETref values predicted from GFS temperature forecasts could be effective for foreseeing near-future water demand and enhancing the accuracy of irrigation scheduling and management in Central Chile.

Artificial intelligence-based weather prediction framework using neural networks

For humans, weather prediction is vital in making rational everyday choices and avoiding risk. Accurate weather forecasting is regarded as one of the world’s most difficult issues. New weather forecasting, unlike conventional techniques, relies on a mixture of computer simulations, observation (via balloons and satellites), and information of patterns and trends (via local weather analysts and weather stations). Predictions are rendered with fair precision using such techniques. Prediction algorithms based on complicated formulas run the majority of computational models used for prediction. This paper highlights the prediction of weather with the artificial neural networks (ANN) using the latest available smart computing devices. To assess the effectiveness of the model, comparison research is conducted with the other existing models in the same area. The result demonstrates that our approach is better in comparison to other similar research and products. The comparative analysis has been undergone which confirms the superiority of our proposed techniques with an accuracy of 90.4%.

Joint Short-term Power Forecasting of Hydro-Wind-Photovoltaic Considering Spatiotemporal Delay of Weather Processes

Regional integrated energy systems (RIES) represented by hydro, wind, and solar energy are crucial avenues for future clean energy development, fully leveraging their complementarity can facilitate the orderly supply of renewable energy, significantly enhance the level of new energy consumption, and make outstanding contributions to achieving carbon neutrality goals. However, run-of-river hydropower (RHP), wind power (WP), and photovoltaic (PV) are affected by the chaotic characteristics of the weather system, with significant random fluctuations, and their output characteristics have significant heterogeneity, which brings a big challenge for the complementary coordinated scheduling. The high-accuracy power prediction of RHP, WP, and PV in the region is one of the key means for solving the above problems. To meet this challenge, by analyzing the spatio-temporal correlation properties between weather processes and station output, a novel joint prediction framework for short-term power forecasting (STPF) of regional hydro-wind-PV clusters is proposed. Firstly, to solve the problem of significant heterogeneity of water, wind, and solar, a Differentiated Spatio-Temporal Mixture Network (DSTMN) is proposed, which differentially encodes the weather and power information at multiple locations, enabling the extraction of common features while preserving their specificities, which addresses the difficulty of representing the inherent correlation patterns between wide-area meteorological information and heterogeneous energy sources. Secondly, to further explore the spatio-temporal correlation between meteorological information and power information, to improve the forecasting performance of the model, a spatio-temporal-lagged correlation (STLC) that can quantify their spatio-temporal delays is proposed. By analyzing the correlation characteristics between regional grid numerical weather prediction (NWP) information and station output at different spatial and temporal scales, the optimal spatial location and delay time of NWP inputs under the current scenario are determined for each station. Based on this, the best NWP scene probabilistic transfer model is established based on the Rank Bayesian Ensemble (RBE) method. By fitting the conditional probability distribution of the current best NWP spatiotemporal location and the 2nd day’s best NWP spatiotemporal location, providing a reliable input for the STPF model. Finally, a case study with 164 hydro-wind-solar stations in China demonstrates the effectiveness of the proposed method. Specifically, we achieved an accuracy improvement of 0.46% — 11.03% on the 2nd day of forecasting compared to benchmark methods.

Short-Range Prediction of Squall-Line-Induced Heavy Rainfall: An Added Impact of GPS RO to FY-4A AGRI Data Assimilation

Abstract The GPS radio occultation technique has prompted a new era as GPS receivers are placed onboard traditional polar-orbiting operational environmental satellites and newly emerging commercial small satellite constellations. With high vertical resolutions of a few hundred meters, GPS radio occultation-observed refractivity profiles effectively complement the geostationary satellite observations of infrared brightness temperatures at high horizontal resolutions (∼2 km) for data assimilation in numerical weather prediction. In this study, numerical experiments are performed to assimilate radio occultation (RO) refractivity observations from FY-3D/FY-3E, MetOp-B/MetOp-C, COSMIC-2, SPIRE, and GeoOptics and brightness temperature observations at channels 9 (6.25 μm) and 10 (7.1 μm) from the Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A. The benefits of adding the refractivity to the brightness temperature observations for improving quantitative precipitation forecasts (QPFs) of two squall-line cases over eastern China are shown using the Weather Research and Forecasting Model and the Gridpoint Statistical Interpolation analysis system. In the first case, the squall line was caused by a superposition of cold and dry air advection behind an upper-level trough over low-level warm and humid air by a low-level jet. A combined assimilation of the GPS RO and FY-4A AGRI observations produced a skillful forecast of the squall-line distribution, characterized by a warm zone 100–300-km long ahead of a cold front near the ground; this led to more consistent improvements in the forecast of a narrow band of squall-line thunderstorms than those from two separate assimilations. The threat scores of 24-h QPFs associated with both squall-line cases are significantly improved, especially for heavier rainfall events.

Impact of boundary layer parameterizations on simulated seasonal meteorology over North-East India

This study evaluated the accuracy of six planetary boundary layer (PBL) parameterization schemes in simulating two different seasons of pre-monsoon and monsoon in India's North-East region through the Weather Research and Forecasting (WRF) model. Twelve one-month simulations were conducted with the PBL schemes, six each for April (pre-monsoon) and July (monsoon), and the model outputs were compared against observations. Three non-local schemes, Asymmetric Convective Model (ACM2), Yonsei University (YSU), Shin-Hong (HONG), and three local schemes, Quasi Normal Scale Elimination (QNSE), Mellor Yamada Janjic (MYJ) and Mellor Yamada Nakanishi Nino (MYNN3), were tested. The meteorological variables of temperature, relative humidity (RH), wind speed, wind direction, and rainfall were evaluated, and the performance of each scheme for each meteorological variable is reported. The 2m temperature (T2) variable was well simulated by ACM2, MYJ in April, and YSU in July, while MYNN3 best simulated the 2m RH (RH2) during both seasons. 10m wind speed (WS10) and directions (WD10) were better simulated by MYNN3, HONG and YSU. HONG also best-simulated rainfall in April and MYJ in July. April and July being rainfall periods, an analysis of the schemes’ simulated rainfall frequency was also carried out. Moreover, the PBL schemes were also ranked, considering their combined performance with all the above meteorological parameters. While considering both the seasons and all meteorological variables, the scale-aware scheme, HONG, was the best scheme and can be used to simulate both seasons. Additionally, an in-depth analysis of surface and atmospheric parameters was also carried out to reason the simulated meteorology. QNSE expends the highest amount of its surface energy through surface evaporation, leading to the lowest surface skin temperature and T2 predictions. In contrast, MYNN3 produced the lowest mixing, which caused the moistest boundary layer, highest RH, cloud cover, and highly overestimated rainfall. Besides evaluation, which will help to choose a suitable PBL scheme for weather predictions in this region, this study also identifies the characteristics and deficiencies of PBL and surface layer schemes for improvement.

On the Influence of the Solar Wind on the Propagation of Earth-impacting Coronal Mass Ejections

Coronal mass ejections (CMEs) are subject to changes in their direction of propagation, tilt, and other properties as they interact with the variable solar wind. We investigated the heliospheric propagation of 15 Earth-impacting CMEs observed during 2010 April to 2018 August in the field of view (FOV) of the Heliospheric Imager (HI) on board the STEREO. About half of the 15 events followed self-similar expansion up to 40 R ⊙. The remaining events showed deflection either in latitude, longitude, or a tilt change. Only 2 events showed significant rotation in the HI1 FOV. We also use toroidal and cylindrical flux rope fitting on the in situ observations of interplanetary magnetic field and solar wind parameters to estimate the tilt at L1 for these 2 events. Although the sample size is small, this study suggests that CME rotation is not very common in the heliosphere. We attributed the observed deflections and rotations of CMEs to a combination of factors, including their interaction with the ambient solar wind and the influence of the ambient magnetic field. These findings contribute to our understanding of the complex dynamics involved in CME propagation and highlight the need for comprehensive modeling and observational studies to improve space weather prediction. In particular, HI observations help us to connect observations near the Sun and near the Earth, improving our understanding of how CMEs move through the heliosphere.

Momentum Transport in Heterogeneous Forest Canopies

This study investigates the impact of spatial heterogeneity on momentum transport within forest canopies through wind tunnel experiments using 1:200 scale forest models. The models, crafted from 10 Pores Per Inch reticulated foam, emulate a leaf area index of 5.3 and include alternating patches and gaps of various sizes. Statistical results of the mean velocity profiles and velocity standard deviations show that the canopies develop a mixing layer. By employing lacunarity analysis to quantify spatial heterogeneity, we establish that the heterogeneity scale effectively represents variations in canopy height. The success of the lacunarity analysis as a metric is particularly noteworthy, providing a robust and practical measure of heterogeneity that can be easily applied in future research. Control volume analysis reveals that horizontal and vertical momentum advection terms rise as canopy heterogeneity increases, emphasizing its critical role in heterogeneous canopies and the possibility of describing this role using the lacunarity scale. The gaps also give rise to pressure terms through the local pressure gradient at each pattern. The study highlights the higher influence of gap size over heterogeneity scale on momentum flux. These insights contribute to improved parameterization of heterogeneous canopies in numerical weather prediction models, aiding in better representation of sub-grid scale processes and enhancing our understanding of canopy-atmosphere interactions.

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.

Insights from Nowcasting and Mesoscale Research Working Group Projects of the World Weather Research Programme

Abstract Insights from Forecast Demonstration Projects and Research Development Projects, training workshops and symposia, conducted between 2000 and 2024 are summarized. The projects were organized by the Nowcasting and Mesoscale Research Working Group of the World Weather Research Program of the World Meteorological Organization. The objective was to advance, promote and build capacity in nowcasting and very short-range forecasting. The projects were associated with the Olympic Games, emergency management and aviation services. They brought international experts together to work in a collaborative fashion. Extensive interaction with end-users and decision-makers expanded and extended the scope of services from traditional weather hazards (heavy rain, wind, hail, lightning) to include specific user needs (e.g., visibility in complex terrain or airport runways, periods of calm winds or light rain, heat stress). Substantial progress has been made in many areas including: advanced radar nowcasting algorithms, stochastic nowcasts, kilometric and hectometric numerical weather prediction models; blending of observations and models and multi-model systems. Verification was a key and valuable component of the projects quantifying the results. Also, the types of services have expanded to include both summer and winter services, complex terrain and urban environments, air transport, air quality, hydrology and health. Insights are presented in all aspects of nowcasting and very short-range forecasting from end-user decision-making, critical role of the forecaster, forecast systems (models, heuristics, observations), to science and knowledge gaps.

Prediction of Sunspot Activity Based on Differential Evolution Algorithm and BP Neural Network Model

In this research report, the prediction method of sunspot activity is deeply discussed, and a variety of statistical and data science models are used to make comprehensive prediction. Research focuses include the start time and duration of the solar maximum in the solar cycle, as well as the prediction of the number and area of sunspots. By capturing the periodicity of solar activity, Pearson correlation coefficient is used to analyze the relationship between the maximum solar activity and the number of sunspots, and the adaptive multivariate nonlinear regression-BP neural network model and particle swarm optimization BP neural network are combined to make high-precision prediction. The research results provide a scientific basis for the prediction of space weather, ionospheric state and communication system reliability.