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Using the Weather Research and Forecasting model with two planetary boundary layer schemes, ACM2 and MYNN, convection‐permitting model (CPM) regional climate simulations were conducted for a 6‐year period, including a one‐year spin‐up period, at a 15‐km grid spacing covering entire South America and a nested convection‐permitting 3‐km grid spacing covering the Peruvian central Andes region. These two CPM simulations along with a 4‐km simulation covering South America produced by National Center for Atmospheric Research (NCAR), three gridded precipitation products, and rain gauge data in Peru and Brazil, are used to document the characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian central Andes region. Results show that all km‐scale simulations generally capture the spatiotemporal patterns of precipitation and MCSs at both seasonal and diurnal scales, although biases exist in aspects such as precipitation intensity and MCS frequency, size, propagation speed, and associated precipitation intensity. The 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin, based on the comparison to the gridded precipitation products and gauge data. Dynamic factors, primarily low‐level jet and terrain‐induced uplift, are the key drivers for precipitation and MCS genesis along the east slope of the Andes, while thermodynamic factors control the precipitation and MCS activity in the western Amazon Basin and over elevated mountainous regions. The study suggests model improvements and better model configurations for future regional climate projections. Plain Language Summary: We ran high‐resolution model simulations at a 3‐km grid spacing with ACM2 and MYNN planetary boundary layer schemes for a 6‐year period, including a 1‐year spin‐up, to investigate precipitation and storm patterns in the Peruvian central Andes region. Other data sets including a 4‐km simulation produced by National Center for Atmospheric Research, three gridded precipitation products, and rain gauge data in Peru and Brazil were collected for comparison and evaluation. We found that all km‐scale simulations capture overall patterns of precipitation and storms at both seasonal and sub‐daily time scales, although some discrepancies exist in precipitation intensity and storm details. Compared to the gridded precipitation products and gauge data, the 3‐km simulation using MYNN scheme generally outperforms the other simulations in capturing seasonal and diurnal precipitation over the mountain, while both it and the 4‐km simulation demonstrate superior performance in the western Amazon Basin. Low‐level wind and terrain‐induced uplift are the key drivers for precipitation and storm genesis along the Andes' eastern slopes, while factors associated with vertical structures of temperature and humidity control the precipitation and storm activity in the western Amazon Basin and mountain regions. The study suggests model improvements and better model configurations for future regional climate projections. Key Points: Characteristics of precipitation and mesoscale convective systems (MCSs) in the Peruvian Central Andes are investigated based on convection‐permitting simulationsWRF3km_MYNN outperforms in simulating mountain precipitation; both it and WRF4km_SAAG show superior performance in western AmazonDynamic factors dominate precipitation and MCSs on the Andean east slope, while thermodynamic factors are dominant in western Amazon Basin [ABSTRACT FROM AUTHOR]

Long time series forecasting (LTSF), which involves modeling relationships within long time series to predict future values, has extensive applications in domains such as weather forecasting, financial analysis, and traffic prediction. Recently, numerous transformer-based models have been developed to address the challenges in LTSF. These models employ methods such as sparse attention to alleviate the inefficiencies associated with the attention mechanism and utilize decomposition architecture to enhance the predictability of the series. However, these complexity reduction methods necessitate additional calculations, and the series decomposition architecture overlooks the random components. To overcome these limitations, this paper proposes the Frequency Enhanced Decomposed Attention Free Transformer (FEDAF). FEDAF introduces two variants of the Frequency Enhanced Attention Free Mechanism (FEAFM), namely FEAFM-s and FEAFM-c, which seamlessly replace self-attention and cross-attention. Both variants perform calculations in the frequency domain without incurring additional costs, with the time and space complexity of FEAFM-s being O (L log L) . Additionally, FEDAF incorporates a time series decomposition architecture that considers random components. Unlike other models that solely decompose the series into trend and seasonal components, FEDAF also eliminates random terms by applying Fourier denoising. Our study quantifies data drift and validates that the proposed decomposition structure can mitigate the adverse effects caused by data shift. Overall, FEDAF demonstrates superior forecasting performance compared to state-of-the-art models across various domains, achieving a remarkable improvement of 19.49% for Traffic in particular. Furthermore, an efficiency analysis reveals that FEAFM enhances space efficiency by 12.8% compared to the vanilla attention mechanism and improves time efficiency by 43.63% compared to other attention mechanism variants. [ABSTRACT FROM AUTHOR]

Drought is one of the most devastating threats to the livelihoods of the southern African population, who mainly rely on rain‐fed agriculture for income. Previous studies have highlighted that the Botswana High influences drought over the region; however, its influence on the spatial modes of drought remains unknown. This study examines the spatiotemporal structures of drought modes (DMs) over southern Africa and their link with the Botswana High in observation, reanalysis and Model for Prediction Across Scales (MPAS). To characterize droughts, the study uses the 3‐month scale standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). Spatiotemporal characteristics of the DMs are identified using empirical orthogonal function (EOF) analysis on SPI and SPEI. EOF analysis is also used to identify the spatiotemporal characteristics of the Botswana High. The relationship between each DM and the Botswana High is quantified using correlation and R2 analysis. In all the datasets (Climate Research Unit (CRU), European Centre for Medium‐Range Weather Forecasts version 5 (ERA5), 20th Century reanalysis II (20C) and MPAS), the most dominant five DMs (hereafter DM1–DM5) over southern Africa jointly explain more than 60% of the interannual variability in the 3‐month scale summer droughts for SPEI and SPI. CRU, ERA5 and MPAS agree that the Botswana High correlates with the interannual variability of DM1, with a stronger correlation in ERA5 (r = −0.85) compared to MPAS (r = −0.42) and CRU (r = −0.35). Additionally, wet years (+ve SPEI and SPI) are characterized by a weak Botswana High and drought years (−ve SPEI and SPI) by a strong Botswana High. The wet and dry years correspond to the −ve and +ve phases of El Niño–Southern Oscillation (ENSO), respectively. Given this, the results of this study suggest that the Botswana High might be a teleconnection pattern through which ENSO signals influence DM1 over the region. [ABSTRACT FROM AUTHOR]

Black ice is responsible for dangerous road-related incidents that can cause collisions and harm vehicle drivers and pedestrians. Visual examination and weather forecasts are two standard traditional methods for detecting black ice on roads, but they are often inaccurate and may not deliver the vehicle driver with up-to-date information on road conditions. The evolution of Industry 4.0 enabling technologies such as wireless sensor network (WSN), Internet of Things (IoT), cloud computing, and machine learning (ML) has been capable of detecting events in real time. This study aims to analyse the integration of the WSN, IoT, ML, and image processing for black ice detection. The qualitative research method is followed in this study, where the problems of black ice detection are studied. Following this, the role of Industry 4.0 enabling technologies is analyzed in detail for black ice detection. According to the study, we can detect black ice using different methods, but some methods need to be refined if we talk about the prediction. By merging different technologies, we can improve the overall architecture and create an algorithm that works with images and physical variables like temperature, humidity, due point, and road temperature, which were responsible for black ice formation, and predict the chances of black ice formation by training the system. [ABSTRACT FROM AUTHOR]

One of the lesser-known regional international organizations in Europe is the European Centre for Medium-Range Weather Forecasts (ECMWF). It has a mandate to undertake tasks such as the production of weather forecasts, conduct scientific research to improve meteorological projections, maintain extensive datasets, and to better coordinate European meteorological infrastructure. The Centre has three sites across three European states where it has over 400 staff, and given it is immune from suit, its staff members only have an internal justice mechanism to resort to in the event of employment/pensions disputes. Little is known about the ECMWF Appeals Board given its untransparent nature. As uncovered in this article, it has undergone partial reform, and simultaneously, there has been an uptick in the number of cases. This article analyses these developments and highlights that more must be done to align the ECMWF Appeals Boards with the features of other international administrative tribunals. [ABSTRACT FROM AUTHOR]

In the final paper in this series, we review how remarkable advances in weather forecasting over successive decades have changed the role of the UK guidance forecaster. We then look at what might happen to this role in the future by examining how the continued growth of artificial intelligence might impact upon weather forecasting and whether forecasts will continue to improve. By contrast we also consider aspects such as risks to the observing network, which could potentially have an adverse effect. We also ask "will the weather forecaster still have a role in the future?" [ABSTRACT FROM AUTHOR]

Tropical cyclones (TC) are common high-impact weather events in the southeastern United States. One of the most intense TCs to hit north-central Florida in recent decades was Hurricane Frances (2004), which resulted in over $10B in damages. Given the growing concern about the relationship between anthropogenic climate change, precipitation, and TCs, we investigated how a Hurricane Frances-like event would affect north-central Florida in the late 21st century under an RCP-8.5 scenario. Using a 'pseudo' global warming dataset based on the high-resolution Weather Research and Forecasting (WRF) model, we explore historical and future simulations of Hurricane Frances's path, precipitation rates, and wind speeds. We discover that, across north-central Florida sub-daily to daily rainfall totals in the future simulation exceed that of the historical, having return period events in some cases reaching 500-year thresholds. We also find more broadly across the spatial domain of the hurricane that rainfall rates in all temporal categories and total rainfall accumulation increased significantly. Results for wind speeds were mixed, with an evident expansion of tropical storm winds, but a slight contraction of the spatial extent of strongest winds at the surface. We discuss the implications of these findings for decision-makers in Florida. [ABSTRACT FROM AUTHOR]

Rainfall is one of the remarkable hydrologic variables that is directly connected to the sustainable environment for any region over the globe. The present study aims to review different research papers on rainfall forecasting using artificial intelligence (AI) models including a bibliographic assessment of the most popular AI models and a comparison of the results based on the accuracy parameters. 39 journal papers, published in renowned international journals from 2000 to 2023, were studied extensively to categorize modeling techniques, best models, characteristics of input data, the period for the input variables, data division, and so forth. Although certain drawbacks still exist, the results of reviewed studies suggest that AI models may help simulate rainfall in various geographic locations. In some cases, the data splitting mechanism was delivered to the model itself so that the model accuracy gets improved. The recommendations from the reviewed papers will help future researchers fill the research gaps, especially tuning the hyperparameters while building the training models. Hybrid models were advised in some cases to minimize the gap between the simulated and the observed data. All recommendations from reviewed papers aimed to achieve a resilient rainfall forecasting model in the era of climate change. [ABSTRACT FROM AUTHOR]

In order to quantify the influence of external forcings on the predictability limit using observational data, the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent (CNLLE) method. The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent (NLLE) and signal-to-noise ratio methods using a coupled Lorenz model. The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings, therefore, it can quantify the predictability limit induced by the external forcings. On this basis, a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields. The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method. This similarity supports ENSO as the major predictable signal for weather and climate prediction. In addition, a ratio of predictability limit (RPL) calculated by the CNLLE method to that calculated by the NLLE method was proposed. The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit. For instance, ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months, as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean. Moreover, the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere. [ABSTRACT FROM AUTHOR]

This study evaluated the simulation performance of mesoscale convective system (MCS)-induced precipitation, focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula. The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts (ECMWF) and National Centers for Environmental Prediction (NCEP) analysis data, as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model. Particularly, temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data. Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis, which exhibited a cold and dry bias in the lower levels below 850 hPa. The model, in terms of the precipitation simulations, particularly for high-intensity precipitation over the Yellow Sea, demonstrated higher accuracy when applying ECMWF analysis data as the initial condition. This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features (i.e., warm and humid lower-level atmosphere) over the Yellow Sea. In conclusion, this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data. Furthermore, it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future. [ABSTRACT FROM AUTHOR]

As precision agriculture (PA) advances, the demand for accurate and high-resolution weather forecasts becomes critical for optimizing agricultural management practices. Despite improvements in Numerical Weather Prediction (NWP) models, they lack the granularity and efficiency needed for PA. Data-driven models offer a promising alternative by integrating predictive capabilities closer to IoT edge data sources, but their efficacy requires evaluation. Here, this paper evaluates six models from three data-driven eras (statistical, machine learning, and deep learning) using agrometeorological data from an Automatic Weather Station (AWS) in Sidi Rahal, East Marrakech, central Morocco, covering 2013–2020 at half-hour intervals, including air temperature, solar radiation, and relative humidity. First, the data is quality-controlled through imputation using ERA5-Land. Then, the dataset was split into training (2013–2019) and evaluation (2020) sets, with validation horizons of 1 day, 3 days, and 1 week. Statistical models generally perform well in air temperature forecasting, occasionally surpassing other models. However, the Temporal Convolutional Neural Network (TCNN) consistently demonstrates superior performance for challenging variables, balancing low RMSE and high R2 across various horizons, with some exceptions. Specifically, for relative humidity, the linear regression model achieves slightly lower RMSE (3,96% and 6,05%) compared to TCNN (4,00% and 6,79%) for 1 day and 3 days, respectively. Additionally, CatBoost outperforms TCNN for 1-week forecasts. In terms of training time, the Transformer requires the longest, followed by AutoARIMA and CatBoost. Uncertainty analysis of stochastic models using solar radiation showed the stable performance of TCNN with 0,80 and 0,01 for the RMSE and R2 standard deviations, respectively. Considering the trade-off between performance, training time, and capturing complex relationships, TCNN emerges as the optimal choice. ANOVA, Tukey's HSD and Mann-Whitney U statistical tests also confirmed TCNN's performance. Finally, a comparison with the Global Forecast System (GFS) reveals TCNN's clear superiority in all metrics, particularly evident for the RMSE of 3 days air temperature forecasts (TCNN: 1,96 °C, GFS: 3,59 °C). [ABSTRACT FROM AUTHOR]

Near-surface ocean wind measurements are important for weather forecasting, determining surface transports, and estimating air-sea interactions; however, in-situ wind observations are often limited. Previous studies indicate that the equilibrium range of the surface gravity wave spectrum can be used to estimate surface wind velocity. This approach is tested using spectral wave measurements from the Coastal Data Information Program (CDIP) buoy array off Monterey, California. Quality controlled wind vectors inferred from wave spectra are statistically compared to measurements from a nearby National Data Buoy Center (NDBC) buoy, demonstrating strong agreement with the control observations with root mean square errors for speed and direction of 1.8 m/s for all wind speeds and 13.2° for wind speeds greater than 7 m/s. We expand this estimation method to account for biofouling, which causes high-frequency damping of the wave spectrum, and the effects of form factor, which impact the platform's dynamic response to high-frequency waves. The method produces wind-proxy measurements solely from wave spectra and wave-based drag parametrizations, making it useful for operational integration. This work demonstrates the ability to make robust wind velocity estimates using wave data from multiple sources, increasing the coverage of wind information over the coastal and open ocean. [ABSTRACT FROM AUTHOR]

During the late 19th and early 20th centuries, weather observers used a cipher code to transmit weather observations via telegraph. The code, developed by Cleveland Abbe, contained nearly 3,000 words that corresponded to specific weather elements and conditions. The code evolved over time and was used by the national weather service, but its arbitrary selection of code words made it challenging to decipher. In 1887, a new code was introduced to simplify the process and reduce costs, using consonants and vowels to represent measurements. The word-based Weather Code was eventually replaced with a numeric code in 1939, but gained renewed attention when coded messages were discovered in a silk dress from the 1880s. [Extracted from the article]

High-resolution temperature forecasting plays a crucial role in various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. The existing low-resolution forecast data may not accurately capture the fine-grained temperature patterns required for localized predictions. These forecasts may contain biases that need to be corrected for accurate results. Therefore, there is a need for an effective framework that can downscale low-resolution forecast data and correct biases to generate high-resolution temperature forecasts. The paper proposes a machine learning-based spatial downscaling and bias-correction framework for high-resolution temperature forecasting. The framework utilizes low-resolution forecast data from the European Centre for Medium-Range Weather Forecasts and real-time 1km analysis product data from the National Meteorological Administration, to generate high-resolution 1km forecast temperature data. The framework consists of four modules: data acquisition module, data preprocessing module, downscaling and correction model module, and post-processing and visualization module. Through experiments, it demonstrated that the framework has superior performance and potential in meteorological data downscaling and correction and can be used to achieve real-time high-resolution temperature forecasting, which has important significance for various applications such as climate impact assessment, hydrological modeling, and localized weather forecasting. [ABSTRACT FROM AUTHOR]

There is an increasing need for simulating the evolution of wildland fires. The realism of the simulation increases by accounting for feedbacks between the fire and the atmosphere. These coupled models combine a fire behavior model with a regional numerical weather prediction model and have been used for fire research during the last decades. This is the case, for instance, of the state-of-the-art Weather Research and Forecasting model with fire extensions (WRF-Fire). Typically, the coupling includes specific code for the particular models being coupled such as interpolation procedures to pass variables from the atmospheric grid to the fire grid, and vice versa. However, having a fire modeling framework that can be coupled to different atmospheric models is advantageous to foster collaborations and joint developments. With this aim, we have created, for the first time, a fire behavior model that can be connected to other atmospheric models without the need of developing specific low-level procedures for the particular atmospheric model being used. The fire behavior model, referred to as the Community Fire Behavior model (CFBM), closely follows WRF-Fire version 4.3.3 methods in its version 0.2.0, and makes use of the Earth System Modeling Framework library to communicate information between the fire and the atmosphere. The CFBM can be also run offline using an existing WRF simulation in what we refer to as the standalone model. Herein we describe the fire modeling framework and its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak Fire performed with UFS and WRF-Fire are presented to verify our implementation. Results from both models, as well as with the standalone version, are consistent indicating a proper development of the CFBM and its coupling to the UFS-Atmosphere. These results, and the possibility of using the fire behavior model with other atmospheric models, provide an attractive collaborative framework to further improve the realism of the model in order to meet the growing demand for accurate wildland fire simulations. [ABSTRACT FROM AUTHOR]

The following article is committed to diachronic text linguistics. It aims to discuss the transformation processes of (media) text genres using the example of the weather forecast. The focus is on the development of a specific programme over the last 40 years on the German television channel ARD. The study examines the changes of the TV weather forecast over the course of the period under investigation and as a consequence how widely its programme has varied and to what extent it has been standardized. Based on the standard features of the weather forecast, I attempt to clarify the differences in its text-genre-specific and stylistic design using the selected corpus. The processes of change are thus described from a text-linguistic point of view, whereby the changes at the level of the communication situation, text function, text structure, topic or sub-topics, as well as the multimodal constitution of the text are analysed. The aim of the study is to determine which features can be observed over the years in the established text genre of the selected television weather forecast, and how they can be explained. In this way, understanding of the processes of change in media texts will hopefully be furthered. [ABSTRACT FROM AUTHOR]

Monthly and seasonal precipitation forecasts can potentially assist disaster risk reduction and water resource management. The aim of this study is to assess the skill of an ensemble framework for monthly and seasonal precipitation forecasts over Iran by focusing on system design and model performance evaluation. The ensemble framework presented in this paper is based on a one‐way double‐nested model that uses Weather Research and Forecasting (WRF) modelling system to downscale the second version of the NCEP Climate Forecast System (CFSv2). The performance is evaluated for October–April period at 1‐, 2‐ and 3‐month lead time. Multiple initial conditions, model parameters and physics are used to construct ensemble members. Using quantile mapping (QM) method, the outputs of the model are bias corrected. This methodology is applied for two periods: (i) climatology from 2000 to 2019 to evaluate the model's ability to precipitation forecast on a monthly and seasonal time scale; (ii) the forecast for 2020 to evaluate the model's performance operationally. The model evaluation is performed using the continuous (e.g., RMSE, r, MBE, NSE) and categorical (e.g., POD, FAR, PC, Heidke skill score) assessment metrics. We conclude that model outputs were improved by the QM bias correction method. According to results, the proposed ensemble framework can accurately predict amount of monthly and seasonal precipitation in Iran with an accuracy of 58 to 45% for lead‐1 to 3. For all three lead times, the averaged NSE, CC, MBE, and RMSE were 0.4, 0.56, −15.5, and 41.6, indicating that the framework has reasonable performance. Our results suggest that precipitation forecast accuracy varies with lead time, so the accuracy for lead‐1 is higher than lead‐2 and lead‐3. Additionally, the model's accuracy differs in various regions of the country and decreases in the spring. Using the approach for an operational case, it was found that the spatial features of precipitation predicted by the framework were close to those observed. [ABSTRACT FROM AUTHOR]

Studies have implicated the importance of longwave (LW) cloud‐radiative forcing (CRF) in facilitating or accelerating the upscale development of tropical moist convection. While different cloud types are known to have distinct CRF, their individual roles in driving upscale development through radiative feedback is largely unexplored. Here we examine the hypothesis that CRF from stratiform regions has the greatest positive effect on upscale development of tropical convection. We do so through numerical model experiments using convection‐permitting ensemble WRF (Weather Research and Forecasting) simulations of tropical cyclone formation. Using a new column‐by‐column cloud classification scheme, we identify the contributions of five cloud types (shallow, congestus, and deep convective; and stratiform and anvil clouds). We examine their relative impacts on longwave radiation moist static energy (MSE) variance feedback and test the removal of this forcing in additional mechanism‐denial simulations. Our results indicate the importance stratiform and anvil regions in accelerating convective upscale development. Plain Language Summary: Infrared or longwave radiation and its interaction with clouds is important in the formation of tropical storms. Given the different shapes and distributions of distinct cloud types, we hypothesize that they interact with longwave radiation differently, and therefore exert different impacts on the organization of tropical convection. This issue has largely been unexplored. To address this gap, we tested our hypothesis by analyzing numerical model simulations of the formation of two tropical cyclones. Further, we developed a new cloud classification scheme based on cloud properties that identifies five distinct cloud types. Using this classification, we examined the impact of radiative interactions with different cloud types on the development of tropical storms by turning off this feedback in specific cloud types. Our results indicate that light‐raining regions, such as stratiform and anvil clouds, contribute dominantly to longwave cloud‐radiative trapping and the moistening of convective regions. This is due to both these cloud types' strong greenhouse trapping effect and their extensive areal coverage, which spreads this effect over large regions of a developing storm. Key Points: A new column‐by‐column cloud microphysical classification scheme is developed for application with numerical modelsRadiative feedback due to stratiform and anvil clouds is a leading driver of tropical convective upscale developmentThe local radiative forcing by deep convective regions is similar in magnitude to stratiform but its impact is limited by its smaller area [ABSTRACT FROM AUTHOR]

Forecasting precipitation accurately poses significant challenges due to various factors affecting its distribution and intensity, including but not limited to subgrid variability. Although higher resolution simulations are often considered to improve precipitation forecasts, it is crucial to note that simply increasing resolution may not suffice without appropriate adjustments to parameterization schemes or tuning. Traditionally, ensembles of simulations are used to generate uncertainty predictions associated with precipitation forecasts, but this approach can be computationally intensive. As an alternative, there is a growing trend towards leveraging neural networks for precipitation prediction, which offers potential computational advantages. We propose a new approach to generating ensemble weather predictions for high‐resolution precipitation without requiring high‐resolution training data. The method uses generative adversarial networks to learn the complex patterns of precipitation and produce diverse and realistic precipitation fields, allowing to generate realistic precipitation ensemble members using only the available control forecast. We demonstrate the feasibility of generating realistic precipitation ensemble members on unseen higher resolutions. We use evaluation metrics such as RMSE, CRPS, rank histogram and ROC curves to demonstrate that our generated ensemble is almost identical to the ECMWF IFS ensemble, on which our model was trained on. [ABSTRACT FROM AUTHOR]

Deep atmospheric convection is often observed over the Kuroshio in the East China Sea (ECSK). However, the mechanisms by which warm oceanic currents fuel transient deep convection are not fully understood. This study investigates an atmospheric cold front that brought heavy precipitation as it traversed the ECSK in April 2004. The southwesterlies ahead of the cold front advected moist and warm air, creating a zone with high convective available potential energy (CAPE) values. As the cold front approached the ECSK, the pre‐frontal high CAPE values coalesced with those over the warm current that substantially strengthened the deep convection, with precipitation rate increasing from 3 mm hr−1 to 10 mm hr−1. A numerical model well simulated the marked increase in precipitation over the ECSK, permitting the isolation of the ECSK's influence by contrasting the control (CTRL) run with an experiment with smoothed sea surface temperatures (SMTH run). Results show the ECSK contributed to 46% of the precipitation over the warm current. The ECSK was found to amplify ascending motion and elevate neutral buoyancy levels, extending its effect up to the tropopause. Furthermore, the strengthened deep convection significantly lowered the sea level pressure (SLP) over the ECSK and impressed upon the time‐mean SLP field. An additional experiment with lowered SST underscored the high SST's critical role in deep convection. This case study suggests a novel pathway by which the effects of warm oceanic currents influence the upper troposphere under extreme conditions with strong baroclinic instability. Plain Language Summary: Warm ocean currents like the Kuroshio in the East China Sea can shape the weather above, often causing towering clouds and heavy rainfalls. Understanding how this happens is key to better weather forecasts. Our research focused on an event in April 2004, when a cold front passing over the Kuroshio led to intense rain. We used satellite data and computer simulations to see how the warm current influenced the storm. We found that the Kuroshio, by warming and adding moisture to the surface air, strengthened the storm and increased its height, resulting in more rainfall. This effect also left a noticeable imprint on the sea‐level pressure field. These results are important because they help us understand and better predict the impact of warm ocean currents on severe weather events. Key Points: An atmospheric cold front swept over the East China Sea Kuroshio (ECSK) and produced heavy precipitation over the warm currentThe ECSK increased surface moist entropy, which intensified the precipitation from deep convectionThe cold‐frontal deep convection projected the ECSK's effect up to the tropopause [ABSTRACT FROM AUTHOR]

Atmosphere modelling applications are becoming increasingly memory-bound due to the inconsistent development rates between processor speeds and memory bandwidth. In this study, we mitigate memory bottlenecks and reduce the computational load of the Global–Regional Integrated Forecast System (GRIST) dynamical core by adopting a mixed-precision computing strategy. Guided by an application of the iterative development principle, we identify the coded equation terms that are precision insensitive and modify them from double to single precision. The results show that most precision-sensitive terms are predominantly linked to pressure gradient and gravity terms, while most precision-insensitive terms are advective terms. Without using more computing resources, computational time can be saved, and the physical performance of the model is largely kept. In the standard computational test, the reference runtime of the model's dry hydrostatic core, dry nonhydrostatic core, and the tracer transport module is reduced by 24 %, 27 %, and 44 %, respectively. A series of idealized tests, real-world weather and climate modelling tests, was performed to assess the optimized model performance qualitatively and quantitatively. In particular, in the long-term coarse-resolution climate simulation, the precision-induced sensitivity can manifest at the large scale, while in the kilometre-scale weather forecast simulation, the model's sensitivity to the precision level is mainly limited to small-scale features, and the wall-clock time is reduced by 25.5 % from the double- to mixed-precision full-model simulations. [ABSTRACT FROM AUTHOR]

Meteorological fronts are important due to their associated surface impacts, including extreme precipitation and extreme winds. Objective identification of fronts is therefore of interest in both operational weather prediction and research settings. The aim of this study is to produce a front identification algorithm based on earlier studies that is portable and scalable to different resolution datasets. We have made a number of changes to an earlier objective front identification algorithm, applied these to reanalysis datasets, and present the improvements associated with these changes. First, we show that a change in the order of operations yields smoother fronts with fewer breaks. Next, we propose the selection of the front identification thresholds in terms of climatological quantiles of the threshold fields. This allows for comparison between datasets of differing resolutions. Finally, we include a number of numerical improvements in the implementation of the algorithm and better handling of short fronts, which yield further benefits in the smoothness and number of breaks. This updated version of the algorithm has been made fully portable and scalable to different datasets in order to enable future climatological studies of fronts and their impacts. [ABSTRACT FROM AUTHOR]

Variational data assimilation theoretically assumes Gaussian-distributed observational errors, yet actual data often deviate from this assumption. Traditional quality control methods have limitations when dealing with nonlinear and non-Gaussian-distributed data. To address this issue, our study innovatively applies two advanced machine learning (ML)-based quality control (QC) methods, Minimum Covariance Determinant (MCD) and Isolation Forest, to process precipitable water (PW) data derived from satellite FengYun-2E (FY2E). We assimilated the ML QC-processed TPW data using the Gridpoint Statistical Interpolation (GSI) system and evaluated its impact on heavy precipitation forecasts with the Weather Research and Forecasting (WRF) v4.2 model. Both methods notably enhanced data quality, leading to more Gaussian-like distributions and marked improvements in the model's simulation of precipitation intensity, spatial distribution, and large-scale circulation structures. During key precipitation phases, the Fraction Skill Score (FSS) for moderate to heavy rainfall generally increased to above 0.4. Quantitative analysis showed that both methods substantially reduced Root Mean Square Error (RMSE) and bias in precipitation forecasting, with the MCD method achieving RMSE reductions of up to 58% in early forecast hours. Notably, the MCD method improved forecasts of heavy and extremely heavy rainfall, whereas the Isolation Forest method demonstrated a superior performance in predicting moderate to heavy rainfall intensities. This research not only provides a basis for method selection in forecasting various precipitation intensities but also offers an innovative solution for enhancing the accuracy of extreme weather event predictions. [ABSTRACT FROM AUTHOR]

The long-term atmospheric mixing layer height (MLH) information plays an important role in air quality and weather forecasting. However, it is not sufficient to study the characteristics of MLH using long-term high spatial and temporal resolution data in the desert. In this paper, over the southern edge of the Taklimakan Desert, the diurnal, monthly, and seasonal variations in the daytime MLH (retrieved by coherent Doppler wind lidar) and surface meteorological elements (provided by the local meteorological station) in a two-year period (from July 2021 to July 2023) were statistically analyzed, and the relationship between the two kinds of data was summarized. It was found that the diurnal average MLH exhibits a unimodal distribution, and the decrease rate in the MLH in the afternoon is much higher than the increase rate before noon. From the seasonal and monthly perspective, the most frequent deep mixing layer (>4 km) was formed in June, and the MLH is the highest in spring and summer. Finally, in terms of their mutual relationship, it was observed that the east-pathway wind has a greater impact on the formation of the deep mixing layer than the west-pathway wind; the dust weather with visibility of 1–10 km contributes significantly to the formation of the mixing layer; the temperature and relative humidity also exhibit a clear trend of a concentrated distribution at about the height of 3 km. The statistical analysis of the MLH deepens the understanding of the characteristics of dust pollution in this area, which is of great significance for the treatment of local dust pollution. [ABSTRACT FROM AUTHOR]

Globalization and industrialization have significantly disturbed the environmental ecosystem, leading to critical challenges such as global warming, extreme weather events, and water scarcity. Forecasting temperature trends is crucial for enhancing the resilience and quality of life in smart sustainable cities, enabling informed decision-making and proactive urban planning. This research specifically targeted Ahmedabad city in India and employed the seasonal autoregressive integrated moving average with exogenous factors (SARIMAX) model to forecast temperatures over a ten-year horizon using two decades of real-time temperature data. The stationarity of the dataset was confirmed using an augmented Dickey–Fuller test, and the Akaike information criterion (AIC) method helped identify the optimal seasonal parameters of the model, ensuring a balance between fidelity and prediction accuracy. The model achieved an RMSE of 1.0265, indicating a high accuracy within the typical range for urban temperature forecasting. This robust measure of error underscores the model's precision in predicting temperature deviations, which is particularly relevant for urban planning and environmental management. The findings provide city planners and policymakers with valuable insights and tools for preempting adverse environmental impacts, marking a significant step towards operational efficiency and enhanced governance in future smart urban ecosystems. Future work may extend the model's applicability to broader geographical areas and incorporate additional environmental variables to refine predictive accuracy further. [ABSTRACT FROM AUTHOR]

This study aims to address the challenges of climate change, which has led to extreme temperature events and reduced rainfall, using Internet of Things (IoT) technologies. Specifically, we monitored the effects of drought on maize crops in the Republic of Croatia. Our research involved analyzing an extensive dataset of 139,965 points of weather data collected during the summer of 2022 in different areas with 18 commercial sensor nodes using the Long-Range Wide Area Network (LoRaWAN) protocol. The measured parameters include temperature, humidity, solar irradiation, and air pressure. Newly developed maize-specific predictive models were created, taking into account the impact of urbanization on the agrometeorological parameters. We also categorized the data into urban, suburban, and rural segments to fill gaps in the existing literature. Our approach involved using 19 different regression models to analyze the data, resulting in four regional models per parameter and four general models that apply to all areas. This comprehensive analysis allowed us to select the most effective models for each area, improving the accuracy of our predictions of agrometeorological parameters and helping to optimize maize yields as weather patterns change. Our research contributes to the integration of machine learning and AI into the Internet of Things for agriculture and provides innovative solutions for predictive analytics in crop production. By focusing on solar irradiation in addition to traditional weather parameters and accounting for geographical differences, our models provide a tool to address the pressing issue of agricultural sustainability in the face of impending climate change. In addition, our results have practical implications for resource management and efficiency improvement in the agricultural sector. [ABSTRACT FROM AUTHOR]

Chl-a concentration is one of the key characteristics of marine areas related to photosynthesis, along with oxygen levels and water salinity. Most studies focus on estimating chl-a concentration in closed water bodies, rivers, and coastal areas of the tropical and temperate Earth belts and are therefore limited to specific regions and also require direct measurements and chemical analysis to obtain precise information about marine environmental conditions. Remote sensing techniques and spatial modeling aim to offer tools for rapid and global analysis of climate and ecological changes. In this study, we aim to develop a machine learning (ML)-based approach to estimate chlorophyll-a concentration when satellite data are unavailable. To provide physical parameters that may influence the predicted variable (chl-a concentration), we combined satellite observations from MODIS with geophysical Weather Research & Forecasting (WRF) and Nucleus for European Modelling of the Ocean (NEMO) models. Classical ML and deep learning (DL) algorithms were compared and analyzed for their ability to extract key biogeochemical patterns in the Barents Sea. The proposed approach allows us to forecast chl-a concentration for the next 8 days based on spatial features and measurements from preceding days. The best R² metric achieved was 0.578 using a LightGBM algorithm, confirming the applicability of the developed solution to map the northern marine region even in cases where MODIS observations are unavailable for the preceding period due to insufficient illumination and dense cloud cover. [ABSTRACT FROM AUTHOR]

The "23.7" event, an extreme rainstorm that affected North China from July 29 to 2 August 2023, was simulated using the Weather Research and Forecasting (WRF) model, version 4.2. We focus on dynamically diagnosing and analyzing the mass and latent heat budgets of rainwater during the extreme precipitation event on July 31 in the Beijing area, where the hourly rainfall reached an extraordinary 111.8 mm. Generally, the model effectively simulated the rainstorm, enabling further assessment of the extreme precipitation. Results indicated that under the combined influence of three major weather systems -- the residual circulation of Typhoon Doksuri (a low-pressure system after typhoon landfall), the embryonic stage of Typhoon Khanun, and the North China high-pressure dam -- a continuous influx of moisture and energy was transported to the North China region, promoting heavy precipitation. Application of vorticity equation diagnostics indicates that the horizontal transport term is the primary source term. Mass balance analysis reveals that the primary source of rainwater is the accretion of cloud droplets by rain, and the condensation of water vapor into cloud droplets is the main contributor to the latent heat. [ABSTRACT FROM AUTHOR]

Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km 2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from 10.5281/zenodo.11400539 (Jones et al., 2024) and 10.5281/zenodo.11420742 (Kelley et al., 2024a). [ABSTRACT FROM AUTHOR]

In the past few decades, the discrete dynamics of difference maps have attained the remarkable attention of researchers owing to their incredible applications in different domains, like cryptography, secure communications, weather forecasting, traffic flow models, neural network models, and population biology. In this article, a generalized chaotic system is proposed, and superior dynamics is disclosed through fixed point analysis, time-series evolution, cobweb representation, period-doubling, period-3 window, and Lyapunov exponent properties. The comparative bifurcation and Lyapunov plots report the superior stability and chaos performance of the generalized system. It is interesting to notice that the generalized system exhibits superior dynamics due to an additional control parameter β . Analytical and numerical simulations are used to explore the superior dynamical characteristics of the generalized system for some specific values of parameter β . Further, it is inferred that the superiority in dynamics of the generalized system may be efficiently used for better future applications. [ABSTRACT FROM AUTHOR]

Due to various technical issues, existing numerical weather prediction (NWP) models often perform poorly at forecasting rainfall in the first several hours. To correct the bias of an NWP model and improve the accuracy of short-range precipitation forecasting, we propose a deep learning-based approach called UNetMask, which combines NWP forecasts with the output of a convolutional neural network called UNet. The UNetMask involves training the UNet on historical data from the NWP model and gridded rainfall observations for 6-hour precipitation forecasting. The overlap of the UNet output and the NWP forecasts at the same rainfall threshold yields a mask. The UNetMask blends the UNet output and the NWP forecasts by taking the maximum between them and passing through the mask, which provides the corrected 6-hour rainfall forecasts. We evaluated UNetMask on a test set and in real-time verification. The results showed that UNetMask outperforms the NWP model in 6-hour precipitation prediction by reducing the FAR and improving CSI scores. Sensitivity tests also showed that different small rainfall thresholds applied to the UNet and the NWP model have different effects on UNetMask's forecast performance. This study shows that UNetMask is a promising approach for improving rainfall forecasting of NWP models. [ABSTRACT FROM AUTHOR]

Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6. [ABSTRACT FROM AUTHOR]

Extreme precipitation over a 2-week period can cause significant impacts to life and property. Trustworthy and easy-to-understand forecasts of these extreme periods on the subseasonal-to-seasonal time frame may provide additional time for planning. The Prediction of Rainfall Extremes at Subseasonal to Seasonal Periods (PRES2iP) project team conducted three workshops over 6 years to engage with stakeholders to learn what is needed for decision-making for subseasonal precipitation. In this study, experimental subseasonal-to-seasonal (S2S) forecast products were designed, using knowledge gained from previous stakeholder workshops, and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Our stakeholders preferred a combination of products that covered the spatial extent, regional daily values, with associated uncertainty, and text narratives with anticipated impacts for planning within the S2S time frame. When targeting longer extremes, having information regarding timing of expected impacts was seen as crucial for planning. We found that there is increased uncertainty tolerance with stakeholders when using products at longer lead times that typical skill metrics, such as critical success index or anomaly correlation coefficient, do not capture. Therefore, the use of object-oriented verification that allows for more flexibility in spatial uncertainty might be beneficial for evaluating S2S forecasts. These results help to create a foundation for the design, verification, and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. SIGNIFICANCE STATEMENT: There has been an increase in demand for forecasts between the 10- and 14-day weather forecast time frame and 1–3-month seasonal time frame with respect to periods of extreme precipitation among decision-makers, yet this relies on trustworthy and usable forecasts. In this study, experimental forecast products were designed and shown to decision-makers to evaluate the products for two 14-day extreme precipitation period scenarios. Participants preferred a combination of map products, daily regional products, and text narratives with anticipated impacts when making decisions at long lead times. These results help to create a foundation for the design and implementation of future operational forecast products with longer lead times, while also providing an example for future workshops that engage both researchers and decision-makers. [ABSTRACT FROM AUTHOR]

The Heihe River Basin, located in Northwest China, serves as a major commodity grain base in China due to its state‐of‐the‐art irrigation system. The rapid increase in soil moisture caused by irrigation can alter the land–atmosphere energy fluxes and regulate regional climate. However, the effects and mechanisms of irrigation on wind speed related to thermodynamics remain unclear. Here, we carried out two 10‐year numerical simulations using the Weather Research and Forecast (WRF) model incorporating a real‐time irrigation scheme. By comparing the simulation differences (including and excluding irrigation), we found that irrigation significantly decreased the daily mean and maximum wind speed at 10 m above ground by 0.30 and 0.55 m·s−1, respectively, in the irrigated area during the growth season. Such surface wind slowdown could be explained by irrigation‐induced surface air cooling and, hence, intensifying atmospheric column stability, weakening turbulent momentum transport, as well as opposite‐to‐prevailing winds vector anomaly caused by increased southward pressure gradient. Meanwhile, we also found wind slowdown mainly occurs below 1100 m while acceleration effect above that level. It was highlighted that the surface wind slowdown effect of irrigation substantially improved the performance of the WRF model. Due to the surface wind slowdown effect of irrigation, the positive bias of daily mean and maximum wind speed simulated by the WRF model was reduced by 15.3% and 27.4%, respectively. Our findings implicate the potential importance of irrigation in improving the performance of climate models as well as in explaining the phenomenon of global stilling. [ABSTRACT FROM AUTHOR]

The first version operational Hurricane Analysis and Forecast System (HAFS) implemented the Vortex Initialization (VI) technique to optimize tropical cyclone structure and intensity, which was adopted from the Hurricane Weather Research and Forecasting system (HWRF) and does not initialize cloud hydrometeors and vertical velocity. This limitation in the VI caused the inconsistency issue between hurricane vortex and its cloud in the model initial condition. A new VI, which can relocate or cycle cloud hydrometeors and vertical velocity, has been developed to solve this issue. For the cold start, the VI simply relocates the cloud and vertical velocity fields of Global Forecasting System (GFS) analysis; for the warm start, the cloud and vertical velocity associated with a hurricane in the GFS analysis are replaced by the fields extracted from the 6 h HAFS forecast of a previous cycle. This new VI has been tested for the 2023 HAFS-A real-time experiment configuration, and another sensitivity experiment without relocating or cycling both cloud and vertical velocity is conducted to examine the effect of the new VI. A comparison of the results reveals that the new VI improves the intensity forecast and generates a very realistic initial cloud field in correct position. Validating the model initial conditions with observed radar data reveals that the new VI captures the secondary eyewall of major hurricanes and asymmetric convective structure of weak tropical storms. This improvement of the cloud field in the model initial condition through the new VI expects to provide a better background for further data assimilation. Additional sensitivity experiment that only relocates or cycles cloud hydrometeors without correcting the vertical velocity field results in poorer intensity forecasts, which highlights the importance of vertical velocity in the model initial condition. [ABSTRACT FROM AUTHOR]

To improve the forecast accuracy of heavy precipitation, re-forecasts are conducted for the Henan 21.7 rainstorm. The Intermediate Complexity Atmospheric Research Model (ICAR) and the Weather Research and Forecasting Model (WRF) with a 1 km horizontal grid spacing are used for the re-forecasts. The results indicate that heavy precipitation forecasted by ICAR primarily accumulates on the windward slopes of the mountains. In contrast, some severe precipitation forecasted by WRF is beyond the mountains. The main difference between ICAR and WRF is that ICAR excludes the "impacts of physical processes on winds and the nonlinear interactions between the small resolvable-scale disturbances" (briefed as the "physical–dynamical interactions"). Thus, heavy precipitation beyond the mountains is attributed to the "physical–dynamical interactions". Furthermore, severe precipitation on the windward slopes of the mountains typically aligns with the observations, whereas heavy rainfall beyond the mountains seldom matches the observations. Therefore, severe precipitation on the windward slopes of (beyond) the mountains is more (less) predictable. Based on these findings and theoretical thinking about the predictability of severe precipitation, a scheme of using the ICAR's prediction to adjust the WRF's prediction is proposed, thereby improving the forecast accuracy of heavy rainfall. [ABSTRACT FROM AUTHOR]

Through the application of cloud top temperature data and the extraction of supercooled cloud information in cloud-type data from the next-generation Himawari-8 geostationary satellite with high spatial–temporal resolution, a quantitative evaluation of the forecasting performance of the weather modification model named the Cloud and Precipitation Explicit Forecasting System (CPEFS) was conducted. The evaluation, based on selected forecast cases from 8 days in September and October 2018 initialized at 00 and 12 UTC every day, focused especially on the forecasting performance in supercooled clouds (vertical integrated supercooled liquid water, VISL > 0), including the comprehensive spatial distribution of cloud top temperature (CTT) and 3 h precipitation over 0.1 mm (R3 > 0.1). The results indicated that the forecasting performance for VISL > 0 was relatively good, with the Threat Score (TS) ranging from 0.46 to 0.67. The forecasts initialized at 12 UTC slightly outperformed the forecasts initialized at 00 UTC. Additionally, the corresponding spatial Anomaly Correlation Coefficient (ACC) of CTT between forecasts and observations was 0.23, and the TS for R3 > 0.1 reached as high as 0.87. For a mix of cold and warm cloud systems, there was a correlation between the forecasting performance of VISL > 0 and CTT. The trends in the TS for VISL > 0 and the ACC of CTT aligned with the forecast lead-time. [ABSTRACT FROM AUTHOR]

There is an urgent need for strategies to reduce the negative impacts of a warming climate on human health. Cooling urban neighborhoods by planting trees and vegetation and increasing albedo of roofs, pavements, and walls can mitigate urban heat. We used synoptic climatology to examine how different tree cover and albedo scenarios would affect heat-related morbidity in Los Angeles, CA, USA, as measured by emergency room (ER) visits. We classified daily meteorological data for historical summer heat events into discrete air mass types. We analyzed those classifications against historical ER visit data to determine both heat-related and excess morbidity. We used the Weather Research and Forecasting model to examine the impacts of varied tree cover and albedo scenarios on meteorological outcomes and used these results with standardized morbidity data algorithms to estimate potential reductions in ER visits. We tested three urban modification scenarios of low, medium, and high increases of tree cover and albedo and compared these against baseline conditions. We found that avoiding 25% to 50% of ER visits during heat events would be a common outcome if the urban environment had more tree cover and higher albedo, with the greatest benefits occurring under heat events that are moderate and those that are particularly hot and dry. We conducted these analyses at the county level and compared results to a heat-vulnerable, working-class Los Angeles community with a high concentration of people of color, and found that reductions in the rate of ER visits would be even greater at the community level compared to the county. [ABSTRACT FROM AUTHOR]

The Precipitating Change Project was a 5-year development, implementation, and research study of an innovative 4-week middle school curricular unit in computational weather forecasting that integrates students' learning and use of meteorology and computational thinking (CT) concepts and practices. The project produced a list of CT skills and definitions that students use to predict the weather, CT assessment instruments, and a CT classroom observation protocol. Data was collected from 306 eighth grade (ages 13–14) students in rural indigenous communities in the Artic and urban and suburban Northeast communities in the USA. The project met its goal of producing an intentional instructional sequence that integrates disciplinary science and CT practices to increase students' science knowledge and their ability to use CT skills and processes. The results indicate that teachers were able to use the curriculum to embed CT practices into the classroom. Students, in turn, had the opportunity to practice using these skills in class discussion as evidenced by the classroom observation data, and students' science knowledge of CT content and practices significantly increased as evidenced by their performance on the weather content and CT skills pre- and post-assessments. While statistically significant gains in science knowledge and CT skills and practices were evident in all settings (urban, suburban, and rural indigenous communities), there were noticeable differences in gains in students' CT skills and practices between the three settings and additional research is needed in a diversity of settings to understand this difference. [ABSTRACT FROM AUTHOR]

Pastoralism is a livelihood system for millions of people around the world and a great majority of them are found in Africa. The indigenous knowledge and strategies on pastoralism are not well understood and properly documented. Hence, this study sheds light on location-specific indigenous climate change adaptation strategies and explores the pastoralist and agro-pastoralist households' perceptions against the meteorological records. Data were collected from 191 sample households, 12 key informants, 32 focus group participants, and National Meteorological Services. The results reveal that there is a high climate variability (CV = 30), high rainfall intensity, and longer dry periods. Almost every year the Rainfall seasonality index (SI) value predicts a longer dry season. The community's perception matched with recorded climate data of the past 36 years and identified 10 major climate extremes orally recounted in history. Indigenous strategies include indigenous weather forecasts, mating calendar, destocking, herd mobility, herd diversification, traditional rotational grazing system ('Seri'), and also emerging adaptation strategies (farming, petty trade, handcraft, charcoal sale, and casual labor) utilized as a result of the severity of climate variability and extremes in the region. The results indicate that emerging adaptation strategies are replacing the preexisting pastoralist livelihood system and that indigenous strategies need support to withstand the current and predicted weather and climate variability in the sites. Pastoralists and agro-pastoralists will be in a better position to adapt to the consequences of climate variability and extremes if indigenous institutions are revitalized with innovations. [ABSTRACT FROM AUTHOR]

The growing global population demands an increase in agricultural production and the promotion of sustainable practices. Smart agriculture, driven by advanced technologies, is crucial to achieving these goals. These technologies provide real-time information for crop monitoring, yield prediction, and essential farming functions. However, adopting intelligent farming systems poses challenges, including learning new systems and dealing with installation costs. Robust support is crucial for integrating smart farming into practices. Understanding the current state of agriculture, technology trends, and the challenges in technology acceptance is essential for a smooth transition to Agriculture 4.0. This work reports on the pivotal synergy of IoT technology with other research trends, such as weather forecasting and robotics. It also presents the applications of smart agriculture worldwide, with an emphasis on government initiatives to support farmers and promote global adoption. The aim of this work is to provide a comprehensive review of smart technologies for precision agriculture and especially of their adoption level and results on the global scale; to this end, this review examines three important areas of smart agriculture, namely field, greenhouse, and livestock monitoring. [ABSTRACT FROM AUTHOR]

This study proposes deterministic and stochastic energy‐aware hybrid models that should enable simulations of idealized and primitive‐equations Geophysical Fluid Dynamics (GFD) models at low resolutions without compromising on quality compared with high‐resolution runs. Such hybrid models bridge the data‐driven and physics‐driven modeling paradigms by combining regional stability and classical GFD models at low resolution that cannot reproduce high‐resolution reference flow features (large‐scale flows and small‐scale vortices) which are, however, resolved. Hybrid models use an energy‐aware correction of advection velocity and extra forcing compensating for the drift of the low‐resolution model away from the reference phase space. The main advantages of hybrid models are that they allow for physics‐driven flow recombination within the reference energy band, reproduce resolved reference flow features, and produce more accurate ensemble forecasts than their classical GFD counterparts. Hybrid models offer appealing benefits and flexibility to the modeling and forecasting communities, as they are computationally cheap and can use both numerically‐computed flows and observations from different sources. All these suggest that the hybrid approach has the potential to exploit low‐resolution models for long‐term weather forecasts and climate projections thus offering a new cost effective way of GFD modeling. The proposed hybrid approach has been tested on a three‐layer quasi‐geostrophic model for a beta‐plane Gulf Stream flow configuration. The results show that the low‐resolution hybrid model reproduces the reference flow features that are resolved on the coarse grid and also gives a more accurate ensemble forecast than the physics‐driven model. Plain Language Summary: Reliable weather forecast and climate prediction are crucial for socio‐economic sectors, decision making, and strategic planning for mitigating risks and impacts of natural disasters. In order to forecast weather and climate, observations and numerical models are used. A necessary ingredient for these forecasts is the ocean‐atmospheric model that resolves mesoscale oceanic eddies and can use large ensembles (hundred of members). Supercomputers can run eddy‐resolving simulations but only for single runs or use large ensembles but in non‐eddy‐resolving regimes. Applying the large ensemble approach with eddy‐resolving simulations is far beyond what supercomputers will be able to compute for the next decades, while it is urgently needed. This study offers deterministic and stochastic energy‐aware hybrid models that enable simulations of Geophysical Fluid Dynamics models at low resolutions without compromising on quality compared with high‐resolution runs. The use of low resolutions makes hybrid models much faster than high‐resolution physics‐driven runs. This acceleration can be translated into the use of larger ensembles. Thus, the proposed hybrid approach has the potential to exploit large ensembles of high‐quality solutions for long‐term weather forecasts and climate projections for the very first time. Key Points: Deterministic and stochastic energy‐aware hybrid models have been proposedHybrid models allow low‐resolution simulations without compromising on quality compared with high‐resolution runsHybrid approach produce more accurate ensemble predictions than their classical Geophysical Fluid Dynamics counterparts [ABSTRACT FROM AUTHOR]

We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3‐hr time resolution for up to 1‐year lead times on a 110‐km global mesh using the Hierarchical Equal Area isoLatitude Pixelization (HEALPix). In comparison to state‐of‐the‐art (SOTA) machine learning (ML) weather forecast models, such as Pangu‐Weather and GraphCast, our DLWP‐HPX model uses coarser resolution and far fewer prognostic variables. Yet, at 1‐week lead times, its skill is only about 1 day behind both SOTA ML forecast models and the SOTA numerical weather prediction model from the European Center for Medium‐Range Weather Forecasts. We report several improvements in model design, including switching from the cubed sphere to the HEALPix mesh, inverting the channel depth of the U‐Net, and introducing gated recurrent units (GRU) on each level of the U‐Net hierarchy. The consistent east‐west orientation of all cells on the HEALPix mesh facilitates the development of location‐invariant convolution kernels that successfully propagate weather patterns across the globe without requiring separate kernels for the polar and equatorial faces of the cube sphere. Without any loss of spectral power after the first 2 days, the model can be unrolled autoregressively for hundreds of steps into the future to generate realistic states of the atmosphere that respect seasonal trends, as showcased in 1‐year simulations. Plain Language Summary: Weather forecasting traditionally relies on numerical weather prediction models that solve physical equations to simulate the evolution of the atmosphere. Such numerical models are compute intensive, and their performance is increasingly challenged by less compute demanding but still highly sophisticated machine learning (ML) approaches. Yet, a downside for many of these new ML models is they tend to drift away from climatology while producing excessively smoothed fields if they are iteratively stepped forward for several months. Here, a parsimonious machine learning model is developed to forecast just seven atmospheric variables that can be stepped forward to give realistic weather patterns over a full year. Despite using at least a factor of 10 less variables than the 67–227 in the best ML models, our model generates 8‐day forecasts with errors that are only a day behind those from state‐of‐the‐art ML forecasts. Our model provides a path toward sub‐seasonal and seasonal forecasting that could potentially improve planning for agriculture, water resources, disaster preparedness, and energy production. Key Points: The model forecasts seven atmospheric variables, an order of magnitude less than that used in state‐of‐the‐art ML weather forecast modelsForecasts are generated on the HEALPix mesh, facilitating the development of location invariant convolution kernelsWithout converging to climatology, the model produces realistic atmospheric states in 365‐day iterative rollouts [ABSTRACT FROM AUTHOR]

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( RMSE ¯ = 11.90, MAE ¯ = 4.46) and Kalman Smoothing with structure time series ( RMSE ¯ = 11.37, MAE ¯ = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( RMSE ¯ = 16.57, MAE ¯ = 8.0) and Kalman Smoothing with structure time series ( RMSE ¯ = 16.84, MAE ¯ = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( RMSE ¯ = 15.45, MAE ¯ = 6.77), and very large (> 30 days) ( RMSE ¯ = 12.91, MAE ¯ = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research. [ABSTRACT FROM AUTHOR]

Coastal areas play a crucial role in regional development and the environment, but they are increasingly vulnerable to extreme weather events due to global climate change. Sea surface temperature (SST) is an important parameter in the ocean-atmosphere system and can help monitor and understand climate change. Changes in SST have significant impacts on extreme weather events, such as typhoons, and can threaten the safety of coastal cities. Advances in satellite remote sensing technology have improved the accuracy and resolution of SST data, providing new opportunities for studying the impact of SST on urban development in coastal cities. Future research should focus on the impact of SST on the ocean-atmosphere CO2 flux, climate change, typhoons, hazardous weather, the energy balance of coastal megacities, and sea-land breeze circulation. [Extracted from the article]

Accurate rain forecasting is crucial for optimizing agricultural practices and improving crop yields. This study presents a real-time rain forecasting model using a Bidirectional Long Short-Term Memory (BiLSTM) algorithm for an on-device AI platform. The model uses historical weather data to predict rainfall, enabling farmers to make data-driven decisions in irrigation, pest control, and field operations. This model enables farmers to optimize water use, conserve energy, and improve overall resource management. Real-time capabilities allow immediate adjustments to agricultural activities, mitigating risks associated with unexpected weather changes. The Bi-LSTM model achieved a mean accuracy of 92%, significantly outperforming the traditional LSTM (85%) and ARIMA (80%) models. This high accuracy is attributed to the model's bidirectional processing capability, which captures comprehensive temporal patterns in the weather data. Implementing this model can enhance decision-making processes for farmers, resulting in increased productivity and profitability in the agricultural sector. [ABSTRACT FROM AUTHOR]

Predicting the displacement of landslide is of utmost practical importance as the landslide can pose serious threats to both human life and property. However, traditional methods have the limitation of random selection in sliding window selection and seldom incorporate weather forecast data for displacement prediction, while a single structural model cannot handle input sequences of different lengths at the same time. In order to solve these limitations, in this study, a new approach is proposed that utilizes weather forecast data and incorporates the maximum information coefficient (MIC), long short-term memory network (LSTM), and attention mechanism to establish a teacher-student coupling model with parallel structure for short-term landslide displacement prediction. Through MIC, a suitable input sequence length is selected for the LSTM model. To investigate the influence of rainfall on landslides during different seasons, a parallel teacher-student coupling model is developed that is able to learn sequential information from various time series of different lengths. The teacher model learns sequence information from rainfall intensity time series while incorporating reliable short-term weather forecast data from platforms such as China Meteorological Administration (CMA) and Reliable Prognosis (https://rp5.ru) to improve the model's expression capability, and the student model learns sequence information from other time series. An attention module is then designed to integrate different sequence information to derive a context vector, representing seasonal temporal attention mode. Finally, the predicted displacement is obtained through a linear layer. The proposed method demonstrates superior prediction accuracies, surpassing those of the support vector machine (SVM), LSTM, recurrent neural network (RNN), temporal convolutional network (TCN), and LSTM-Attention models. It achieves a mean absolute error (MAE) of 0.072 mm, root mean square error (RMSE) of 0.096 mm, and pearson correlation coefficients (PCCS) of 0.85. Additionally, it exhibits enhanced prediction stability and interpretability, rendering it an indispensable tool for landslide disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

The accuracy of weather scene recognition is critical in a world where weather affects every aspect of our everyday lives, particularly in areas like intelligent transportation networks, autonomous vehicles, and outdoor vision systems. The importance of weather in many aspects of our life highlights the vital necessity for accurate information. Precise weather detection is especially crucial for industries like intelligent transportation, outside vision systems, and driverless cars. The outdated, unreliable, and time-consuming manual identification techniques are no longer adequate. Unmatched accuracy is required for local weather scene forecasting in real time. This work utilizes the capabilities of computer vision to address these important issues. Specifically, we employ the advanced Vision Transformer model to distinguish between 11 different weather scenarios. The development of this model results in a remarkable performance, achieving an accuracy rate of 93.54%, surpassing industry standards such as MobileNetV2 and VGG19. These findings advance computer vision techniques into new domains and pave the way for reliable weather scene recognition systems, promising extensive real-world applications across various industries. [ABSTRACT FROM AUTHOR]