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Heavy rainfall events prediction at the local scale imposes a big challenge for meteorological agencies over the complex terrain areas in India such as Assam, Uttarakhand, and Himachal Pradesh and causes flash floods with severe consequences throughout the area causing a huge socio-economical loss over these regions. Assam is currently experiencing severe flooding in June 2023. Due to the limits of deterministic numerical weather models in accurately forecasting these events, this work investigates the incorporation of deep learning (DL) models, particularly spatial attention-based U-Net, using simulated daily collected rainfall outputs from various parametrization schemes. This is a pioneering effort to improve district-scale rainfall using the spatio-attention U-Net DL method, particularly over the orographically complex region such as Assam. The proposed model outperformed individual and ensemble Weather Research and Forecasting (WRF) model outputs over four days in June 2022, demonstrating greater abilities to forecast rainfall at the district scale with a mean absolute error of less than 10 mm. Additionally, the proposed model considerably outperformed WRF models by 51.3% in categorical rainfall prediction, achieving a high prediction accuracy of 91.9%. Furthermore, the proposed model has demonstrated improved spatial variation as compared to the WRF model by correctly predicting severe rainfall occurrences at the district scale, including Barpeta, Kamrup, Kokrajhar, and Nalbari. The WRF projections regularly underestimated rainfall intensity (< 100 mm), whereas the DL model's estimates matched actual rainfall readings from the India Meteorological Department (> 150 mm). On the quantitative estimation of rainfall thresholds using different skill scores, Equitable threat score values are more than 0.5 for all categories for the proposed model. In a nutshell, the findings of the study have direct implications for improving early warning systems and associated follow-up action in terms of developing efficient strategies toward better preparedness, mitigation, and adaptation measures over complex hilly regions to reduce loss of lives and properties. [ABSTRACT FROM AUTHOR]

According to the U.S. Drought Monitor, drought coverage in the Lower 48 States decreased slightly from February 27 to April 2. March storms helped replenish soil moisture in the Plains and Midwest, while the middle and northern Atlantic states experienced their wettest March on record. California received above-average rainfall and snowpack, ending its drought. The eastern half of the country experienced warmer temperatures, while the Pacific Coast and High Plains had near- or below-normal temperatures. Notable weather events included tornado outbreaks, heavy rainfall, and wildfires in certain areas. In April, Alaska had near to above normal snowpack, and the Bering Sea had slightly higher sea ice extent than the previous year. The month also saw severe thunderstorms, a high number of tornadoes, and a mix of extreme temperatures across the United States. Texas faced heavy rainfall and flooding, while Alaska experienced above-average precipitation and significant snowfall. [Extracted from the article]

This article provides an enhanced parallelization of the WSM7 microphysics scheme for the Weather Research and Forecasting Model (WRF). The parallelization is designed to maximize the utilization of a heterogeneous computing system consisting of CPUs, GPUs or both. Therefore the reference implementation of the WSM7 scheme is re-implemented for the heterogeneous execution model. For each time step, a dynamic load distribution is introduced which balances the computational load between the two components aiming for an overall minimum execution time. The evaluation of the parallelized implementation is done for a specific weather situation. Specifically, the precipitation of the low-pressure zone "Bernd" from July 2021 is simulated using an Intel Core i7-7700 CPU and a NVIDIA GTX 1070 GPU. The results show a speedup of up to 28.51 for the GPU version in comparison with the reference implementation. The heterogeneous dynamic load balancing increases the speedup achieved even further by introducing a distribution factor that is updated for each time step. [ABSTRACT FROM AUTHOR]

Many traditional societies forecast weather using observations of phenological changes. In Lacanja Chansayab, Chiapas, Mexico, Indigenous farmers use animal behavior, plant flowering, and solar and lunar halos, to predict rainfall. These indicators were observed and weather patterns were measured in Lacanja Chansayab. Interviews with Lacandon Maya revealed a diverse system of indicators that overlapped with systems used by other cultures. Due to indicator diversity, it is possible that this traditional system will remain useful as climate change alters local weather; cultural knowledge is nonetheless threatened by societal and economic changes. [ABSTRACT FROM AUTHOR]

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

Modeling the nonlinear interactions between flow, sediment, and vegetation is essential for improving our understanding and prediction of river system dynamics. Using simple numerical models, we simulate the key flow‐sediment‐vegetation interaction where the disturbance is intrinsically generated by the presence of vegetation. In this case, biomass growth modifies the flow field, induces bed scour, and thus potentially causes vegetation uprooting when erosion exceeds root depth. Our results show that this nonlinear feedback produces deterministic chaos under a wide range of conditions, with complex aperiodic dynamics generated by a period‐doubling route to chaos. Moreover, our results suggest relatively small values of Lyapunov time, spanning 2–4 growth‐flood cycles, which significantly restrict the predictability of riverbed evolution. Although further spatial and temporal processes may add complexity to the system, these results call for the use of ensemble methods and associated uncertainty estimates in ecomorphodynamic models. Plain Language Summary: Simple models that mimic the interplay between river dynamics and vegetation show that changes in the shape of riverbeds can be unpredictable. Our research suggests that we can make skillful predictions on time scales of the order of a few decades for many typical rivers, but we cannot accurately predict what riverbeds will look like in the long term. This has important implications for how we manage and restore rivers. It means that we need to run multiple numerical simulations to generate different scenarios, similar to the methods used in weather forecasting, to more accurately represent the uncertainty in our predictions. The chaotic behavior we found is not unique to rivers; it may be important in other systems where the interaction of flow, bed, and vegetation is key to how they change over time. Key Points: Coevolution of riverbed and vegetation, mediated by intrinisic biogeomorphic feedbacks, exhibits nonlinear dynamics and chaosFor chaos to be possible, the erosion during a flood must be comparable to the root depthThe time scale on which the riverbed/vegetation evolution becomes unpredictable is short, ranging between 2 and 4 growth‐flood periods [ABSTRACT FROM AUTHOR]

Sunspots play a crucial role in both weather forecasting and the monitoring of solar storms. In this work, we propose a novel combined model for sunspot prediction using improved gated recurrent units (GRU) guided by pinball loss for probabilistic forecasts. Specifically, we optimize the GRU parameters using the slime mould algorithm and employ a seasonal-trend decomposition procedure based on loess to tackle challenges related to sequence prediction, such as self-correlations and non-stationarity. To address prediction uncertainty, we replace the traditional l 2 -norm loss with pinball loss. This modification extends the conventional GRU-based point forecasting to a probabilistic framework expressed as quantiles. We apply our proposed model to analyze a well-established historical sunspot dataset for both single- and multi-step ahead forecasting. The results demonstrate the effectiveness of our combined model in predicting sunspot values, surpassing the performance of other existing methods. [ABSTRACT FROM AUTHOR]

Precipitable water vapour (PWV) available in the lower atmosphere plays a very crucial role in the determination of potential rainfall over an area and its early detection is very important for predicting heavy rainfall events. Hence, precise estimation of PWV is necessary for weather forecasting and other atmospheric analysis. In the present study, global positioning system (GPS) derived PWV is used for the prediction of short-term rainfall. The GPS observations acquired from the HYDE (Hyderabad) and IISc (Bengaluru) IGS stations and meteorological observations obtained from weather stations are used for the retrieval of PWV. Thereafter, estimated PWV values are used in a rain prediction algorithm. A relationship is established between hourly estimated PWV values and ERA5 reanalysis rainfall data to forecast the rainfall event. The validation of estimated results is carried out at different levels. Estimated zenith tropospheric delay (ZTD) results are compared with IGS results which show a good agreement with RMS errors close to 5 mm in different cases. Further estimated PWVs are compared with the ERA5 PWVs, which show a difference of 2–4 mm considering all the datasets. Subsequently, this validated PWV is used in rainfall detection analysis and results show that 56.6% of the rain events can be forecasted with a false alarm rate of 42.3% in the region around IISc and 52.2% of the rain events can be forecasted with a false alarm rate of 43.9% in the region around HYDE. A good prediction is observed for IISc as compared to HYDE. Considering different threshold values of different years, it is observed that 70% and 60% rain events can be forecasted for IISc and HYDE, respectively. The analysis shows that by using GPS PWV, an accurate prediction of rainfall is possible and this can be used further for weather prediction analyses over the region. [ABSTRACT FROM AUTHOR]

The diurnal cycle of total hydrometeor availability and its associated patterns of atmospheric circulation is studied over a connected Andes–Amazon (A–A) system in the central region of Peru during the summer season. Surface precipitation depends on the amount of hydrometeors that occur in the atmosphere and its atmospheric dynamics. Hydrometeors and the precipitation efficiency index were estimated using radar of the core satellite of the GPM system (N-GPM) for the period 2014–2022. The atmospheric dynamics were analyzed using the regional Weather Research and Forecasting (WRF) model. According to the results, the Andes mountain range produces precipitation at a surface level more efficiently during the afternoon and early evening hours (12–19 LT) due to the convergence of the thermal mesoscale circulations transporting moisture fluxes from the east and west. Both generate convective multicells along the Andes mountain range. The circulation from the west intensifies during the day, causing the displacement of the chain of convective multicells towards the east and producing hydrometeors and intense precipitations in the inter-Andean valleys. The A–A transition zone is more efficient in producing precipitation during the early hours of the day (00–07 LT) due to an increase in the northern circulation associated with the low-level jets and a change in the magnitude of the horizontal winds. Northerly winds enter the A–A transition zone with increased intensity and leave with reduced intensity. This mechanism is driven by the effect of the topographical barrier and the masses of cold air located in high areas on the eastern flank of the Andes. These factors generate significant updrafts and, therefore, the formation of storm clouds with high concentrations of hydrometeors and precipitation on the surface. [ABSTRACT FROM AUTHOR]

Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game changer for the incremental progress in traditional numerical weather prediction (NWP) known as the "quiet revolution" of weather forecasting. The computational cost of running a forecast with standard NWP systems greatly hinders the improvements that can be made by increasing model resolution and ensemble sizes. An emerging new generation of ML models, developed using high-quality reanalysis datasets like ERA5 for training, allows forecasts that require much lower computational costs and that are highly competitive in terms of accuracy. Here, we compare for the first time ML-generated forecasts with standard NWP-based forecasts in an operational-like context, initialized from the same initial conditions. Focusing on deterministic forecasts, we apply common forecast verification tools to assess to what extent a data-driven forecast produced with one of the recently developed ML models (PanguWeather) matches the quality and attributes of a forecast from one of the leading global NWP systems (the ECMWF IFS). The results are very promising, with comparable accuracy for both global metrics and extreme events, when verified against both the operational IFS analysis and synoptic observations. Overly smooth forecasts, increasing bias with forecast lead time, and poor performance in predicting tropical cyclone intensity are identified as current drawbacks of ML-based forecasts. A new NWP paradigm is emerging relying on inference from ML models and state-of-the-art analysis and reanalysis datasets for forecast initialization and model training. SIGNIFICANCE STATEMENT: We compare the traditional approach of generating weather forecasts with a new approach based on machine learning (ML). The traditional approach is based on resolving physical equations with numerical methods and can be quite expensive to run on supercomputers. ML models are trained on a very large dataset that combines observations and physically based models to derive our best estimate of the state of the atmosphere hour after hour, over the past decades. In this work, we assess the forecast performance with statistical tools and a focus on extreme events. Our results suggest that ML models, which are much less expensive to run than standard methods, could have a promising future in numerical weather prediction. Our study also points out weaknesses and limitations that would require further investigations and future ML model improvements. [ABSTRACT FROM AUTHOR]

The Hurricane Weather Research and Forecasting Model (HWRF) was the flagship hurricane model at NOAA's National Centers for Environmental Prediction for 16 years and a state-of-the-art tool for tropical cyclone (TC) intensity prediction at the National Weather Service and across the globe. HWRF was a joint development between NOAA research and operations, specifically the Environmental Modeling Center and the Atlantic Oceanographic and Meteorological Laboratory. Significant support also came from the National Hurricane Center, Developmental Testbed Center, University Corporation for Atmospheric Research, universities, cooperative institutes, and the TC community. In the North Atlantic basin, where most improvement efforts focused, HWRF intensity forecast errors decreased by 45%–50% at many lead times between 2007 and 2022. These large improvements resulted from increases in horizontal and vertical resolution, as well as advances in model physics and data assimilation. HWRF intensity forecasts performed particularly well over the Gulf of Mexico in recent years, providing useful guidance for a large number of impactful landfalling hurricanes. Such advances were made possible not only by significant gains in computing but also through substantial investment from the Hurricane Forecast Improvement Program. SIGNIFICANCE STATEMENT: HWRF is considered one of the most important and successful ocean-coupled, regional numerical weather prediction models. This work documents HWRF forecast performance for TCs in the North Atlantic since its operational inception (2007–22). Track, intensity, and storm size (wind radii) forecasts all showed significant error reductions and skill improvements, including 45%–50% lower intensity errors. HWRF intensity forecasts showed marked improvement for rapid intensification events, especially in the Gulf of Mexico, where numerous impactful TCs made landfall throughout HWRF's lifetime. HWRF's impressive performance led to its expanded use as guidance for official TC forecasts and as a research tool across the TC community. These successes paved the way for NOAA's next-generation hurricane model, the Hurricane Analysis and Forecast System. [ABSTRACT FROM AUTHOR]

This study investigates the effect of the initial tropical cyclone (TC) vortex structure on the intensity change during the eyewall replacement cycle (ERC) of TCs based on two idealized simulations using the Weather Research and Forecasting (WRF) model. Results show that an initially smaller TC with weaker outer winds experienced a much more drastic intensity change during the ERC than an initially larger TC with stronger outer winds. It is found that an initially larger TC vortex with stronger outer winds favored the development of more active spiral rainbands outside the outer eyewall, which slowed down the contraction and intensification of the outer eyewall and thus prolonged the duration of the concentric eyewall and slow intensity evolution. In contrast, the initially smaller TC with weaker outer winds corresponded to higher inertial stability in the inner core and weaker inertial stability but stronger filamentation outside the outer eyewall. These led to stronger boundary layer inflow, stronger updraft and convection in the outer eyewall, and suppressed convective activity outside the outer eyewall. These resulted in the rapid weakening during the formation of the outer eyewall, followed by a rapid re-intensification of the TC during the ERC. Our study demonstrates that accurate initialization of the TC structure in numerical models is crucial for predicting changes in TC intensity during the ERC. Additionally, monitoring the activity of spiral rainbands outside the outer eyewall can help to improve short-term intensity forecasts for TCs experiencing ERCs. [ABSTRACT FROM AUTHOR]

In this study, the effects of assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) observations from existing and recently added commercial cube satellites on analyses and forecasts over the Arctic region were investigated by conducting observing system experiments (OSEs). Profiles of refractivity were assimilated with a local observation operator using the three-dimensional variational method. The analyses and forecasts from the OSEs were verified against ERA5 reanalysis, radiosonde observations, and buoy observations. In addition to the averaged impact on forecast skill, the impact of GNSS RO observations was further examined for an individual Arctic cyclone case, focusing on the added value of the cube satellite data. The effects of GNSS RO observations from existing satellites on analyses and forecasts over the Arctic region are positive, and the assimilation of GNSS RO observations from cube satellites leads to additional improvements, particularly for temperature in the upper troposphere and lower stratosphere (UTLS). Temperature biases in the UTLS are significantly reduced in the analyses, and the improved analyses result in better forecasts of upper-level potential vorticity and cyclone development when GNSS RO observations from cube satellites are assimilated. This result demonstrates the potential of GNSS RO data from cube satellites to enhance forecasts over the Arctic region. [ABSTRACT FROM AUTHOR]

Experts, Social Scientists, and Techniques of Prognosis in Cold War America convincingly demonstrates the importance of RAND's Delphi method and political gaming as techniques of prognosis that were central to the construction of Cold War futures expertise. The book begins from the premise that although expertise in various forms of prognosis, prediction, and forecasting predated the creation of RAND, different modes of interaction among experts at RAND defined a new form of future-oriented expertise during the Cold War. This visual essay will use this premise as an entry point into other, earlier historical examples of interaction, albeit in different forms, that also produced expert knowledge about the future: 1.) nineteenth-century meteorological knowledge infrastructures and what Aitor Anduaga has described as "epistemic networks," 2.) agricultural statistics and cooperative bureaucracy, and 3.) early twentieth-century business forecasting and what Seth Rockman has identified as the "paper technologies of capitalism." This visual essay examines three images that illuminate earlier historical examples of expert interaction in predictive knowledge infrastructures in the late nineteenth- and early twentieth-century United States. The essay employs historian Paul Edwards's definition of knowledge infrastructures as "robust networks of people, artifacts, and institutions that generate, share, and maintain specific knowledge about the human and natural worlds." An infrastructural framework, as Edwards observes, shifts away from "thinking about knowledge as pure facts, theories, and ideas" and instead "views knowledge as an enduring, widely shared sociotechnical system." The visual essay format makes visible the material components of a knowledge infrastructure as well as its symbolic meanings, and it also reminds us to ask what is not pictured and why. This essay focuses on how these knowledge infrastructures have combined data, information, judgment, and opinion to produce expert knowledge about the future, and it asks us to consider how future-oriented expertise has emerged historically and who counts as an expert. It will explore three main questions: How has interaction among experts historically taken place at a distance and in person? How did knowledge infrastructures, bureaucracies, and paperwork shape the production of expert knowledge about the future before the Cold War? How has expert foreknowledge historically relied on various forms of "futurework" as labor? [ABSTRACT FROM AUTHOR]

In an era dominated by Internet of Things (IoT) devices, software-as-a-service (SaaS) platforms, and rapid advances in cloud and edge computing, the demand for efficient and lightweight models suitable for resource-constrained devices such as data processing units (DPUs) has surged. Traditional deep learning models, such as convolutional neural networks (CNNs), pose significant computational and memory challenges, limiting their use in resource-constrained environments. Echo State Networks (ESNs), based on reservoir computing principles, offer a promising alternative with reduced computational complexity and shorter training times. This study explores the applicability of ESN-based architectures in image classification and weather forecasting tasks, using benchmarks such as the MNIST, FashionMnist, and CloudCast datasets. Through comprehensive evaluations, the Multi-Reservoir ESN (MRESN) architecture emerges as a standout performer, demonstrating its potential for deployment on DPUs or home stations. In exploiting the dynamic adaptability of MRESN to changing input signals, such as weather forecasts, continuous on-device training becomes feasible, eliminating the need for static pre-trained models. Our results highlight the importance of lightweight models such as MRESN in cloud and edge computing applications where efficiency and sustainability are paramount. This study contributes to the advancement of efficient computing practices by providing novel insights into the performance and versatility of MRESN architectures. By facilitating the adoption of lightweight models in resource-constrained environments, our research provides a viable alternative for improved efficiency and scalability in modern computing paradigms. [ABSTRACT FROM AUTHOR]

As modern technologies, particularly home assistant devices and sensors, become more integrated into our daily lives, they are also making their way into the domain of energy management within our homes. Homeowners, now acting as prosumers, have access to detailed information at 15-min or even 5-min intervals, including weather forecasts, outputs from renewable energy source (RES)-based systems, appliance schedules and the current energy balance, which details any deficits or surpluses along with their quantities and the predicted prices on the local energy market (LEM). The goal for these prosumers is to reduce costs while ensuring their home's comfort levels are maintained. However, given the complexity and the rapid decision-making required in managing this information, the need for a supportive system is evident. This is particularly true given the routine nature of these decisions, highlighting the potential for a system that provides personalized recommendations to optimize energy consumption, whether that involves adjusting the load or engaging in transactions with the LEM. In this context, we propose a recommendation system powered by large language models (LLMs), Scikit-llm and zero-shot classifiers, designed to evaluate specific scenarios and offer tailored advice for prosumers based on the available data at any given moment. Two scenarios for a prosumer of 5.9 kW are assessed using candidate labels, such as Decrease, Increase, Sell and Buy. A comparison with a content-based filtering system is provided considering the performance metrics that are relevant for prosumers. [ABSTRACT FROM AUTHOR]

In 1950 the first successful numerical weather forecast was obtained by solving the barotropic vorticity equation using the Electronic Numerical Integrator and Computer (ENIAC), which marked the beginning of the age of numerical weather prediction. Here, we ask the question of how these numerical forecasts would have turned out, if machine learning based solvers had been used instead of standard numerical discretizations. Specifically, we recreate these numerical forecasts using physics‐informed neural networks. We show that physics‐informed neural networks provide an easier and more accurate methodology for solving meteorological equations on the sphere, as compared to the ENIAC solver. Plain Language Summary: In 1950, scientists made a groundbreaking advance in weather forecasting using a machine called the Electronic Numerical Integrator and Computer (ENIAC). Instead of traditional methods, we investigate how machine learning techniques could have been used for these forecasts. Our study focuses on using physics‐informed neural networks to simulate the weather predictions made by the ENIAC. We find that these neural networks, which blend physics principles with machine learning, offer a simpler and more accurate way to predict weather on Earth compared to the methods used by the ENIAC. Key Points: Recreation of ENIAC weather forecast using machine learningUsing physics informed neural networks for real meteorological data [ABSTRACT FROM AUTHOR]

Here we explore the relative contribution of the Madden‐Julian Oscillation (MJO) and El Niño Southern Oscillation (ENSO) to midlatitude subseasonal predictive skill of upper atmospheric circulation over the North Pacific, using an inherently interpretable neural network applied to pre‐industrial control runs of the Community Earth System Model version 2. We find that this interpretable network generally favors the state of ENSO, rather than the MJO, to make correct predictions on a range of subseasonal lead times and predictand averaging windows. Moreover, the predictability of positive circulation anomalies over the North Pacific is comparatively lower than that of their negative counterparts, especially evident when the ENSO state is important. However, when ENSO is in a neutral state, our findings indicate that the MJO provides some predictive information, particularly for positive anomalies. We identify three distinct evolutions of these MJO states, offering fresh insights into opportune forecasting windows for MJO teleconnections. Plain Language Summary: Weather is hard to predict with longer forecast leads. Here, we use a data‐driven statistical model to dissect tropical sources of predictability of North Pacific upper‐level variability on subseasonal (2 weeks to 2 months) timescales. This model was constructed so that we can identify the relative contributions of two tropical phenomena important for predictability on these timescales. Namely, we use the Madden‐Jullian Oscillation (MJO) and the El Niño Southern Oscillation (ENSO) as predictor variables, two phenomena that provide a teleconnecting signal from the tropics to North Pacific variability. We find that the ENSO signal alone consistently provides more forecast predictability than the MJO. However, when ENSO is not active, the MJO provides distinct windows of forecast opportunity, particularly for anomalously anticyclonic events. We identify three evolutions of the MJO which offer new insights into forecasting weather at long forecast leads. Key Points: An interpretable neural network is used to decompose contributions of MJO and ENSO to North Pacific subseasonal circulation predictabilityENSO alone is overall more useful than the MJO for subseasonal predictions across various lead times and predictand averaging windowsUnique MJO events, that provide enhanced subseasonal predictability during ENSO neutral conditions, are identified [ABSTRACT FROM AUTHOR]

Snow is a key component of the cryosphere and has significant impacts on surface energy balance, hydrology, atmospheric circulation, and etc. In addition, numerous studies have indicated that snow impurities, especially nitrate, are sensitive to sunlight and can be photolyzed to emit reactive species including NO2 and HONO, which serve as precursors of O3 and radicals and disturb the overlying atmospheric chemistry. This makes snow a reservoir of reactive species, and this reservoir is particularly important in remote and pristine regions with limited anthropogenic emissions. The magnitude of snow chemical emissions is also influenced by snow physical properties, including snow depth, density and concentrations of light-absorbing impurities (e.g., BC and dust). Exploring and elucidating the emissions and atmospheric consequences of the snow-sourced reactive species require a global or regional model with a snow module. Here, we parameterized atmospheric nitrate deposition and its distributions in snow using a regional chemical transport model, i.e., the WRF-Chem (the Weather Research and Forecasting Model coupled with Chemistry) model, and evaluated the performance of the WRF-Chem model in simulating snow cover, snow depth, and BC, dust and nitrate concentrations with field observations in northern China which is one of the regions with dense and prolong snow cover. The model reasonably reproduces the observed snow cover and depth in northeastern and northwestern China, and the observed snow dust and nitrate concentrations are also reasonably reproduced. These results illustrate the ability of WRF-Chem in simulating snow properties including concentrations of reservoir species in northern China, and in the future, we will incorporate snow nitrate photolysis in the model, exploring the emissions of snow NOx from nitrate photolysis and the impacts on local to regional atmospheric chemistry and air pollutant transformations. [ABSTRACT FROM AUTHOR]

During the Water Vapor Lidar Network Assimilation (WaLiNeAs) campaign, 8 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 spatio-temporal 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. This paper focuses on the WVMR profiling datasets obtained from three of the lidars managed by the French component of the WaLiNeAs team. These lidars were deployed in the towns of Coursan, Grau du Roi and Cannes. This measurement setup enabled monitoring of the water vapor content within the low troposphere along a period of three months over autumn – winter 2022 and four months in summer 2023. The lidars measured the WVMR profiles from the surface up to approximately 6–10 km at night, and 1–2 km during daytime; with a vertical resolution of 100 m and a time sampling between 15 – 30 min, 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, as well as the datasets given in NETcdf format. The final dataset is divided in two 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. [ABSTRACT FROM AUTHOR]

The accurate prediction of heavy precipitation in convective environments is crucial because such events, often occurring in Italy during the summer and fall seasons, can be a threat for people and properties. In this paper, we analyse the impact of satellite-derived surface-rainfall-rate data assimilation on the Weather Research and Forecasting (WRF) model's precipitation prediction, considering 15 days in summer 2022 and 17 days in fall 2022, where moderate to intense precipitation was observed over Italy. A 3DVar realised at CNR-ISAC (National Research Council of Italy, Institute of Atmospheric Sciences and Climate) is used to assimilate two different satellite-derived rain rate products, both exploiting geostationary (GEO), infrared (IR), and low-Earth-orbit (LEO) microwave (MW) measurements: One is based on an artificial neural network (NN), and the other one is the operational P-IN-SEVIRI-PMW product (H60), delivered in near-real time by the EUMETSAT HSAF (Satellite Application Facility in Support of Operational Hydrology and Water Management). The forecast is verified in two periods: the hours from 1 to 4 (1–4 h phase) and the hours from 3 to 6 (3–6 h phase) after the assimilation. The results show that the rain rate assimilation improves the precipitation forecast in both seasons and for both forecast phases, even if the improvement in the 3–6 h phase is found mainly in summer. The assimilation of H60 produces a high number of false alarms, which has a negative impact on the forecast, especially for intense events (30 mm/3 h). The assimilation of the NN rain rate gives more balanced predictions, improving the control forecast without significantly increasing false alarms. [ABSTRACT FROM AUTHOR]

The advancement of Numerical Weather Prediction (NWP) is pivotal for enhancing high-impact weather forecasting and warning systems. However, due to the high spatial and temporal inhomogeneity, the moisture field is difficult to describe by initial conditions in NWP models, which is the essential thermodynamic variable in the simulation of various physical processes. Data Assimilation techniques are central to addressing these challenges, integrating observational data with background fields to refine initial conditions and improve forecasting accuracy. This study evaluates the effectiveness of integrating observations from the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) and ground-based microwave radiometer (MWR) in forecasts and mechanism analysis of a heavy rainfall event in the Kaifeng region of central China. Our findings reveal that jointly assimilating AGRI radiance and MWR data significantly enhances the model's humidity profile accuracy across all atmospheric layers, resulting in improved heavy rainfall predictions. Analysis of the moisture sources indicates that the storm's water vapor predominantly originates from westward air movement ahead of a high-altitude trough, with sustained channeling towards the rainfall zone, ensuring a continuous supply of moisture. The storm's development is further facilitated by a series of atmospheric processes, including the interplay of high and low-level vorticity and divergence, vertical updrafts, the formation of a low-level jet, and the generation of unstable atmospheric energy. Additionally, this study examines the influence of Tai-hang Mountain's terrain on precipitation patterns in the Kaifeng area. Our experiments, comparing a control setup (CTL) with varied terrain heights, demonstrate that reducing terrain height by 50–60% significantly decreases precipitation coverage and intensity. In contrast, increasing terrain height enhances precipitation, although this effect plateaus when the elevation increase exceeds 100%, closely mirroring the precipitation changes observed with a 75% terrain height increment. [ABSTRACT FROM AUTHOR]

Entrainment at the top of the planetary boundary layer (PBL) is of significant importance because it controls the upward growth of the PBL height. An option called ysu_topdown_pblmix, which provides a parameterization of fog-top entrainment, has been proposed for valley fog modeling and introduced into the YSU (Yonsei University) PBL scheme in the Weather Research and Forecasting (WRF) model. However, enabling this option in simulations of sea fog over the Yellow Sea typically results in unrealistic dissipation near the fog bottom and even within the entire fog layer. In this study, we theoretically examine the composition of the option ysu_topdown_pblmix, and then argue that one term in this option might be redundant for sea-fog modeling. The fog-top variables are employed in this term to determine the basic entrainment in the dry PBL, which is already parameterized by the surface variables in the original YSU PBL scheme. This term likely leads to an overestimation of the fog-top entrainment rate, so we refer to it as redundant. To explore the connection between the redundant term and unrealistic dissipation, a widespread sea-fog episode over the Yellow Sea is employed as a case study based on the WRF model. The simulation results clearly attribute the unrealistic dissipation to the extra entrainment rate that the redundant term induces. Fog-top entrainment is unexpectedly overestimated due to this extra entrainment rate, resulting in a significantly drier and warmer bias within the interior of sea fog. When sea fog develops and reaches a temperature lower than the sea surface, the sea surface functions as a warming source to heat the fog bottom jointly with the downward heat flux brought by the fog-top entrainment, leading the dissipation to initially occur near the fog bottom and then gradually expand upwards. We suggest a straightforward method to modify the option ysu_topdown_pblmix for sea-fog modeling that eliminates the redundant term. The improvement effect of this method was supported by the results of sensitivity tests. However, more sea-fog cases are required to validate the modification method. [ABSTRACT FROM AUTHOR]

In this study, we present the development of a Simple Actuator Disk model for Large-Eddy Simulation (SADLES), implemented within the Weather Research and Forecasting (WRF) model, which is widely used in atmospheric research. The WRF-SADLES model supports both idealized studies and realistic applications through downscaling from realistic data, with a focus on resolutions of tens of meters. Through comparative analysis with the Parallelized Large-eddy Simulation Model (PALM) at resolutions of 10 and 30 m, we validate the effectiveness of WRF-SADLES in simulating the wake characteristics of a 5 MW wind turbine. Results indicate good agreement between WRF-SADLES at 30 m resolution and 10 m resolution and the PALM model. Additionally, we demonstrate a practical case study of WRF-SADLES by downscaling ERA5 reanalysis data using a nesting method to simulate turbine wakes at the Alpha Ventus wind farm in the south of the North Sea. The meso-to-micro downscaling simulation reveals that the wake effect simulated by WRF-SADLES at the FINO1 offshore meteorological mast station aligns well with the cup anemometer and lidar measurements. Furthermore, we investigate an event of farm-to-farm interaction, observing a 16 % reduction in ambient wind speed and a 38 % decrease in average turbine power at Alpha Ventus due to the presence of a wind farm to the southwest. WRF-SADLES offers a promising balance between computational efficiency and accuracy for wind turbine wake simulations, making it valuable for wind energy assessments and wind farm planning. [ABSTRACT FROM AUTHOR]

Ground-level ozone (O 3) pollution is a persistent environmental concern, even in regions that have made efforts to reduce emissions. This study focuses on the state of Arizona, which has experienced elevated O 3 concentrations over past decades and contains two non-attainment areas as designated by the U.S. Environmental Protection Agency. Using the Weather Research and Forecasting with Chemistry (WRF-Chem) model, we examine O 3 levels in the semi-arid and arid regions of Arizona. Our analysis focuses on the month of June between 2017 and 2021, a period characterized by high O 3 levels before the onset of the North American Monsoon (NAM). Our evaluation of the WRF-Chem model against surface Air Quality System (AQS) observations reveals that the model adeptly captures the diurnal variation of hourly O 3 levels and the episodes of O 3 exceedance through the maximum daily 8 h average (MDA8) O 3 concentrations. However, the model tends to overestimate surface NO 2 concentrations, particularly during nighttime hours. Among the three cities studied, Phoenix (PHX) and Tucson (TUS) exhibit a negative bias in both hourly and MDA8 O 3 levels, while Yuma demonstrates a relatively large positive bias. The simulated mean hourly and MDA8 O 3 concentrations in Phoenix are 44.6 and 64.7 parts per billion (ppb), respectively, compared to observed values of 47.5 and 65.7 ppb, resulting in mean negative biases of -2.9 and -1.0 ppb, respectively. Furthermore, the analysis of the simulated ratio of formaldehyde (HCHO) to NO 2 (HCHO/NO2 ; FNR), reveals interesting insights of the sensitivity of O 3 to its precursors. In Phoenix, the FNR varies from a VOC (volatile organic compound)-limited regime in the most populated areas to a transition between VOC-limited and NO x -limited regimes throughout the metro area, with an average FNR of 1.15. In conclusion, this study sheds light on the persistent challenge of ground-level O 3 pollution in semi-arid and arid regions, using the state of Arizona as a case study. [ABSTRACT FROM AUTHOR]

NARCliM2.0 comprises two Weather Research and Forecasting (WRF) regional climate models (RCMs) downscaling five CMIP6 global climate models contributing to the Coordinated Regional Downscaling Experiment over Australasia at 20 km resolution, and south-east Australia at 4 km convection-permitting resolution. We first describe NARCliM2.0's design, including selecting two, definitive RCMs via testing seventy-eight RCMs using different parameterisations for planetary boundary layer, microphysics, cumulus, radiation, and land surface model (LSM). We then assess NARCliM2.0's skill in simulating the historical climate versus CMIP3-forced NARCliM1.0 and CMIP5-forced NARCliM1.5 RCMs and compare differences in future climate projections. RCMs using the new Noah-MP LSM in WRF with default settings confer substantial improvements in simulating temperature variables versus RCMs using Noah-Unified. Noah-MP confers smaller improvements in simulating precipitation, except for large improvements over Australia's southeast coast. Activating Noah-MP's dynamic vegetation cover and/or runoff options primarily improve simulation of minimum temperature. NARCliM2.0 confers large reductions in maximum temperature bias versus NARCliM1.0 and 1.5 (1.x), with small absolute biases of ~0.5 K over many regions versus over ~2 K for NARCliM1.x. NARCliM2.0 reduces wet biases versus NARCliM1.x by as much as 50 %, but retains dry biases over Australia's north. NARCliM2.0 is biased warmer for minimum temperature versus NARCliM1.5 which is partly inherited from stronger warm biases in CMIP6 versus CMIP5 GCMs. Under shared socioeconomic pathway (SSP)3-7.0, NARCliM2.0 projects ~3 K warming by 2060–79 over inland regions versus ~2.5 K over coastal regions. NARCliM2.0-SSP3-7.0 projects dry futures over most of Australia, except for wet futures over Australia's north and parts of western Australia which are largest in summer. NARCliM2.0-SSP1-2.6 projects dry changes over Australia with only few exceptions. NARCliM2.0 is a valuable resource for assessing climate change impacts on societies and natural systems and informing resilience planning by reducing model biases versus earlier NARCliM generations and providing more up-to-date future climate projections utilising CMIP6. [ABSTRACT FROM AUTHOR]

Numerical Weather Prediction (NWP) models generate enormous noises due to imbalances in the mass and wind fields at the initial stage, such as bogus gravity waves. Therefore, Initialization techniques have been developed to eliminate spurious gravity waves from the analysis. In this work, a digital filter initialization (DFI) method within the weather research and forecasting (WRF) model, known as twice diabatic filter initialization, is used to reduce the noises from the model's initial and boundary conditions. The conventional observations and satellite radiances have been assimilated using the 4DVar method in the WRF model. The performance of the optimized model framework has been evaluated for the simulation of four very severe cyclonic storms, namely Lehar (2013), Vardah (2016), Titli (2018), and Bulbul (2019) over the North Indian Ocean. A successful 6-hourly cycling data assimilation produced superior initial and boundary conditions, and the free forecasts, based on the final cycling assimilation, showed a better simulation for selected cyclones. The DFI with 4DVar substantially enhanced the simulation of cyclone characteristics, including track, intensity, and induced rainfall. The along-track and cross-track errors in the cyclones' simulations were reduced by approximately 20–25% in the 4DVar_DFI experiments. Thus, the results provide reasonable confidence in using DFI with the 4DVar method for the NIO cyclone's real-time prediction. [ABSTRACT FROM AUTHOR]

FORMOSAT-7/COSMIC-2 was launched in June 2019 and provides high spatial resolution radio occultation (RO) profile data. The data can be beneficial for weather and climate studies and increase the accuracy of weather forecasts. This study aims to assess the two-year COSMIC-2 RO-PWV (precipitable water vapor) accuracy. Ten years of GNSS-PWV data inverted from PPP (precise point positioning) are better than those from the DD (double difference) method when taking radiosonde-observed data as the true reference values in Taiwan. Hence, in this study, GNSS-PWV from PPP is compared with COSMIC-2 RO-PWV to verify the accuracy of RO-PWV. Removing the elevation of the lowest point of an RO event above 5 km can prevent an RO event with an error exceeding 40 mm. The mean GNSS-PWV is higher than the mean RO-PWV, which are 45.7 mm and 39.9 mm, respectively, from 2020 to 2021. RO-PWV derived from COSMIC-2 in 2020 and 2021 is underestimated when taking GNSS-PWV as a reference. The mean RO error is 6.8 mm, and the mean error on land is higher than that on the ocean, with magnitudes of 8.0 mm and 6.6 mm, respectively. The higher RO-PWV accuracy on the ocean than on land is one of the reasons for the higher RO-PWV accuracy for RO events matched at the Japan GEONET stations (99.2 % on the ocean; mean error of 6.1 mm) than that at the Taiwan CWB stations (68.2 % on the ocean; mean error of 7.7 mm). In the monthly mean RO-PWV data from 2020 to 2021, the RO-PWV and its error show seasonal variations, with high values in summer (51.0 ± 9.2 mm) and low values in winter (31.9 ± 5.0 mm). The seasonal trend of RO-PWV is consistent with that of temperature. Here, we verify that the accuracy of COSMIC-2 RO-PWV is comparable to that of GNSS-PWV. [ABSTRACT FROM AUTHOR]

In this study, we present geomagnetic storms (GSs) selected from three solar cycles, spanning the years 1995 to 2022. We studied the development of the main phase of storms with in disturbance storm time (Dst) amplitudes ranging from Dst = - 64 nT to Dst = - 422 nT. In order to determine the solar wind (SW) parameters that mainly influence the main phase development of a GS, which can best describes the SW-magnetosphere coupling, we divided our selected GSs into four groups based on main phase duration. Superposed epoch analysis was performed on the selected geomagnetic indices, SW plasma and field parameters, and their derivatives separately for each group. To that end, the dynamics of GS main phase development is mainly guided by interplanetary driver's magnetic field southward component, (-Bz). It has been determined that there is a temporal difference between the peak values of Bz and Dst. As a result, Dst is delayed from Bz by 1 – 4 h, which is crucial for space weather forecasting. Our findings support the theory that the arrival of high-speed magnetic field structures in front of the Earth's magnetosphere is responsible for the development of GS, which begins a few hours later. The peak of Dst has a direct relationship with the amplitude of storm sudden commencement (SSC) and an inverse relationship with the duration of SSC. The inter-relationship between the peaks of the three indices (Dst, AE, and ap) during GS, is also obtained. Dst is found to be more closely related to ap than AE. To determine the best fit SW parameter to the geomagnetic activity indices, we used a linear correlation between the peak values of individual geomagnetic indices and SW plasma and field parameters and their derivatives. An electric field related function involving speed and IMF ( v 4 3 Bz) when coupled with a viscous term ( ρ 1 2 ) ( v 4 3 Bz ρ 1 2 ) correlates very well with the intensity of the GS (Dst min or Δ Dst) and the magnitude of (ap max) and (AE max) during storms. However, a related function ( v 4 3 B ρ 1 2 ) represents slightly better the peak of AE max during the storms. We strongly encourage higher-resolution observations to confirm and reach a thorough conclusion. [ABSTRACT FROM AUTHOR]

Microphysical observations of precipitating particles are critical data sources for numerical weather prediction models and remote sensing retrieval algorithms. However, obtaining coherent data sets of particle microphysics is challenging as they are often unindexed, distributed across disparate institutions, and have not undergone a uniform quality control process. This work introduces a unified, comprehensive Northern Hemisphere particle microphysical data set from the National Aeronautics and Space Administration precipitation imaging package (PIP), accessible in a standardized data format and stored in a centralized, public repository. Data is collected from 10 measurement sites spanning 34° latitude (37°N–71°N) over 10 years (2014–2023), which comprise a set of 1,070,000 precipitating minutes. The provided data set includes measurements of a suite of microphysical attributes for both rain and snow, including distributions of particle size, vertical velocity, and effective density, along with higher‐order products including an approximation of volume‐weighted equivalent particle densities, liquid equivalent snowfall, and rainfall rate estimates. The data underwent a rigorous standardization and quality assurance process to filter out erroneous observations to produce a self‐describing, scalable, and achievable data set. Case study analyses demonstrate the capabilities of the data set in identifying physical processes like precipitation phase‐changes at high temporal resolution. Bulk precipitation characteristics from a multi‐site intercomparison also highlight distinct microphysical properties unique to each location. This curated PIP data set is a robust database of high‐quality particle microphysical observations for constraining future precipitation retrieval algorithms, and offers new insights toward better understanding regional and seasonal differences in bulk precipitation characteristics. Plain Language Summary: This work introduces a new particle microphysics data set that is useful for improving weather prediction models and in enhancing precipitation estimation techniques. The data set, produced from National Aeronautics and Space Administration's precipitation imaging package, is comprehensive, well documented, and easy to access. It includes observations from 10 locations across the Northern Hemisphere over 10 years, providing information on both rain and snow. This information includes details like particle size, speed, and density, as well as estimates of rainfall and snowfall rates. The data has been standardized and checked for quality, making it reliable and easy to use. This product is a valuable resource for refining methods to measure precipitation, and offers new insights into regional and seasonal precipitation patterns. Key Points: This data set contains high temporal resolution, disdrometer‐derived precipitation microphysics observations from 10 sites over 10 yearsRigorous quality control practices yield a scalable, self‐describing data set packaged into a common, standardized NetCDF formatThe data's diverse geographic and environmental coverage offers new insights into regional and seasonal precipitation processes and patterns [ABSTRACT FROM AUTHOR]

The dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere‐Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case‐I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case‐II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large‐scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case‐II, while higher spectral width (>2 m s−1) is associated to Case‐I. The microphysical processes assessed by the WRF model pointed out that Case‐I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region. Plain Language Summary: This study examined two different weather events in the Himalayan region. The first event occurred during the monsoon season and mostly involved liquid precipitation, while the second event took place in winter and had both solid and liquid precipitation. To understand how these weather patterns formed in the context of their dynamical and microphysical processes, we analyzed the data obtained from ASTRad technology and the WRF model. The radar data played a crucial role in observing and analyzing the vertical structure and temporal evolution of the precipitation systems. The modeling data provided valuable insights of the orographic effects and microphysical processes behind the formation of solid and liquid phase precipitation. Understanding these processes is essential for predicting weather in the Himalayan region, especially the influence of mountains on liquid and solid precipitation. Key Points: Two distinct precipitation systems, one associated with monsoon and the other with western disturbance are investigated over the central HimalayaThese systems show distinct characteristics in terms of synoptic meteorological conditions, cloud processes, and spatio‐temporal scalesIntegrated radar and model data emphasize the importance of understanding the precipitation evolution influenced by orography over the region [ABSTRACT FROM AUTHOR]

The present study assesses the simulated precipitation and cloud properties using three microphysics schemes (Morrison, Thompson and MY) implemented in the Weather Research and Forecasting model. The precipitation, differential reflectivity (ZDR), specific differential phase (KDP) and mass-weighted mean diameter of raindrops (Dm) are compared with measurements from a heavy rainfall event that occurred on 27 June 2020 during the Integrative Monsoon Frontal Rainfall Experiment (IMFRE). The results indicate that all three microphysics schemes generally capture the characteristics of rainfall, ZDR, KDP and Dm, but tend to overestimate their intensity. To enhance the model performance, adjustments are made based on the MY scheme, which exhibited the best performance. Specifically, the overall coalescence and collision parameter (Ec) is reduced, which effectively decreases Dm and makes it more consistent with observations. Generally, reducing Ec leads to an increase in the simulated content (Qr) and number concentration (Nr) of raindrops across most time steps and altitudes. With a smaller Ec, the impact of microphysical processes on Nr and Qr varies with time and altitude. Generally, the autoconversion of droplets to raindrops primarily contributes to Nr, while the accretion of cloud droplets by raindrops plays a more significant role in increasing Qr. In this study, it is emphasized that even if the precipitation characteristics could be adequately reproduced, accurately simulating microphysical characteristics remains challenging and it still needs adjustments in the most physically based parameterizations to achieve more accurate simulation. [ABSTRACT FROM AUTHOR]

The current paper evaluates the weather research and forecasting (WRF) model sensitivity to five different combinations of cumulus, microphysics, radiation, and planetary boundary layer (PBL) schemes over Loess Plateau for the period 2015, in terms of 2 m temperature and precipitation. The WRF configuration consists of a 10 km resolution domain nested in a coarser domain driven by European Center for Medium-Range Weather Forecasts Reanalysis (ERA-Interim) data. The model simulated 2 m temperature and precipitation have been evaluated at daily and monthly scales with gridded observational dataset. The analysis shows that all experiments reproduce well the daily 2 m temperature, with overestimation particularly in the low-temperature range. Precipitation is less well simulated, with underestimation in all range, especially for intense rainfall. Comparing with ERA-Interim, WRF shows no clear benefit in simulating daily 2 m temperature while prominent improvement in simulating daily precipitation. WRF simulations capture the annual cycle of monthly 2 m temperature and precipitation with a warm bias and wet bias for most experiments in summer. Some reasonable configurations are identified. The "best" configuration depends on the criteria. [ABSTRACT FROM AUTHOR]

High‐resolution models have brought about capabilities in forecasting that could barely have been envisaged decades ago. The same advances in computer power and atmospheric science that enabled successful high‐resolution modelling have also led to useful forecasts further into the medium range and beyond. However, there remain problems forecasting at very short range, in particular concerning convection, for which new products and techniques are being trialled at the Met Office. We examine how these latest developments present the forecaster not only with exciting opportunities, but also fresh challenges, in particular, interpreting the plethora of output now available, whilst adhering to tight operational deadlines. [ABSTRACT FROM AUTHOR]

The scientific literature has many studies evaluating numerical weather prediction (NWP) models. However, many of those studies averaged across a myriad of different atmospheric conditions and surface forcings that can obfuscate the atmospheric conditions when NWP models perform well versus when they perform inadequately. To help isolate these different weather conditions, we used observations from the U.S. Climate Reference Network (USCRN) obtained between 1 January and 31 December 2021 to distinguish among different near-surface atmospheric conditions [i.e., different near-surface heating rates ( d T / d t ), incoming shortwave radiation (SWd) regimes, and 5-cm soil moisture (SM05)] to evaluate the High-Resolution Rapid Refresh (HRRR) Model, which is a 3-km model used for operational weather forecasting in the United States. On days with small (large) d T / d t , we found afternoon T biases of about 2°C (−1°C) and afternoon SWd biases of up to 170 W m−2 (100 W m−2), but negligible impacts on SM05 biases. On days with small (large) SWd, we found daytime temperature biases of about 3°C (−2.5°C) and daytime SWd biases of up to 190 W m−2 (80 W m−2). Whereas different SM05 had little impact on T and SWd biases, dry (wet) conditions had positive (negative) SM05 biases. We argue that the proper evaluation of weather forecasting models requires careful consideration of different near-surface atmospheric conditions and is critical to better identify model deficiencies in order to support improvements to the parameterization schemes used therein. A similar, regime-specific verification approach may also be used to help evaluate other geophysical models. Significance Statement: Improving weather forecasting models requires careful evaluations against high-quality observations. We used observations from the U.S. Climate Reference Network (USCRN) and found that the performance of the High-Resolution Rapid Refresh (HRRR) Model varies as a function of differences in near-surface heating and solar radiation. This finding indicates that model evaluations need to be conducted under varying near-surface weather conditions rather than averaging across multiple weather types. This new approach will allow for model developers to better identify model deficiencies and is a useful step to helping improve weather forecasts. [ABSTRACT FROM AUTHOR]

This study comparatively evaluates the skill of the (un)coupled Weather Research and Forecasting (WRF) – WRF-Hydro system supported with satellite-based soil moisture initiation in long-term simulations of hydrometeorological variables under humid and semi-arid climate conditions. Prior to the coupling, the WRF-Hydro model is calibrated using streamflow data at different data scales (i.e. hourly and daily) to discuss the influences of the changing temporal resolution of the reference dataset on the optimum parameter values. According to the results, fully coupled models correct the location and magnitude of the occurrence of hourly extreme precipitation. Changing the initial soil moisture data source affects the terrestrial water cycle elements in the semi-arid region (i.e. Mediterranean (MED)) more than in the humid area (i.e. Eastern Black Sea (EBS)), especially in spring and summer. The initiation of the coupled model with satellite-based soil moisture improves the streamflow outputs after a reasonable spin-up period for the MED region. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall along the south China coast poses significant forecasting challenges due to its localized nature and prolonged duration. To improve the prediction of such high-impact weather events, high-resolution numerical weather prediction (NWP) models are increasingly used to more accurately represent topographic effects. However, as these models' grid spacing approaches the scale of convective processes, they enter a "gray zone", where the models struggle to fully resolve the turbulent eddies within the atmospheric boundary layer, necessitating partial parameterization. The appropriateness of applying convection parameterization (CP) schemes within this gray zone remains controversial. To address this, scale-aware CP schemes have been developed to improve the representation of convective transport. Among these, the multi-scale Kain–Fritsch (MSKF) scheme enhances the traditional Kain–Fritsch (KF) scheme, incorporating modifications that facilitate its effective application at spatial resolutions as high as 2 km. In recent years, there has been an increase in the application of machine learning (ML) models across various domains of atmospheric sciences, including efforts to replace conventional physical parameterizations with ML models. This work introduces a multi-output bidirectional long short-term memory (Bi-LSTM) model intended to replace the scale-aware MSKF CP scheme. This multi-output Bi-LSTM model is capable of simultaneously predicting the convection trigger while also modeling the associated convective tendencies and precipitation rates with a high performance. Data for training and testing the model are generated using the Weather Research and Forecast (WRF) model over south China at a horizontal resolution of 5 km. Furthermore, this work evaluates the performance of the WRF model coupled with the ML-based CP scheme against simulations with the traditional MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the promising potential of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due to the statistical nature of ML algorithms and the high complexity of ESMs, these hybrid ML ESMs require careful validation. Simulation stability needs to be monitored in fully coupled simulations, and the plausibility of results needs to be evaluated in suitable experiments. We present the coupling of SuperdropNet, a machine learning model for emulating warm-rain processes in cloud microphysics, with ICON (Icosahedral Nonhydrostatic) model v2.6.5. SuperdropNet is trained on computationally expensive droplet-based simulations and can serve as an inexpensive proxy within weather prediction models. SuperdropNet emulates the collision–coalescence of rain and cloud droplets in a warm-rain scenario and replaces the collision–coalescence process in the two-moment cloud microphysics scheme. We address the technical challenge of integrating SuperdropNet, developed in Python and PyTorch, into ICON, written in Fortran, by implementing three different coupling strategies: embedded Python via the C foreign function interface (CFFI), pipes, and coupling of program components via Yet Another Coupler (YAC). We validate the emulator in the warm-bubble scenario and find that SuperdropNet runs stably within the experiment. By comparing experiment outcomes of the two-moment bulk scheme with SuperdropNet, we find that the results are physically consistent and discuss differences that are observed in several diagnostic variables. In addition, we provide a quantitative and qualitative computational benchmark for three different coupling strategies – embedded Python, coupler YAC, and pipes – and find that embedded Python is a useful software tool for validating hybrid ML ESMs. [ABSTRACT FROM AUTHOR]

In this study, we have incorporated tropospheric gradient observations from a Global Navigation Satellite Systems (GNSS) ground station network into the Weather Research and Forecasting (WRF) model through a newly developed observation operator. The experiments aim at testing the functionality of the developed observation operator and at analyzing the impact of tropospheric gradients on the sophisticated data assimilation (DA) system. The model was configured for a 0.1° mesh over Germany with 50 vertical levels up to 50 hPa. Our initial conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25° resolution, and conventional observations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), restricted to mainly surface stations and radiosondes. We selected approximately 100 GNSS stations with high data quality and availability covering Germany. We performed DA every 6 h for June and July 2021. Four experiments were conducted: (1) a control run assimilating only conventional observations; (2) an impact run assimilating zenith total delays (ZTDs) on top of the control run; (3) an impact gradient run assimilating ZTDs and gradients on top of the control run; and (4) a gradient run assimilating only gradients on top of the control run. The error for the impact run was reduced by 32 % and 10 % for ZTDs and gradients, whereas the error for the impact gradient run was reduced by 35 % and 18 %, respectively. The gradient errors for the gradient run were nearly equal to those of the impact gradient. Overall, the newly developed operator for the WRFDA system works as intended. In particular, the combined assimilation of gradients and the ZTDs led to a notable improvement in the humidity field at altitudes above 2.5 km. With the operator codes developed and freely available to the WRF users, we aim to trigger further GNSS tropospheric gradient assimilation studies. [ABSTRACT FROM AUTHOR]

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction. [ABSTRACT FROM AUTHOR]

The southwest vortices (SWVs) are a unique type of mesoscale vortex that frequently induce torrential rainfall in China. In this study, we focused a long‐lived quasi‐stationary SWV, which was the primary system for producing an extremely heavy rainstorm within/around Sichuan (the maximum hourly precipitation was ~103.8 mm) in Mid July 2021. After reproduced the SWV's formation by using Weather Research and Forecasting model, we conducted trajectory analyses and topography sensitivity simulations to understand the effects of complicated topography on the vortex's formation. It is found that, the regions south and southwest of the SWV acted as the most important source regions for the air clusters that formed the SWV (proportion ≥ 65%), and the air clusters originated from the upper layer contributed the most (≥60%). Of these, the air clusters sourced from the upper layer southwest and south of the SWV played the most important role in the SWV's formation, as their increase in cyclonic vorticity and their contributions to trajectory number and vorticity were all much larger than those of the others. Sensitivity simulations indicated that, detailed topography features around the Sichuan Basin were crucial in determining the structure, intensity and precipitation of the SWV, whereas, the topography features were not a decisive factor for the SWV's formation. In summary, our findings are useful to enrich the current understanding of the SWVs' formation, which would be helpful to improve the related forecasts. [ABSTRACT FROM AUTHOR]

Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect. [ABSTRACT FROM AUTHOR]

Since a trend towards aridification is expected in Algeria, as in the countries of the Maghreb and the Mediterranean basin, changes in precipitation and temperature can have a significant impact on various water and energy balance processes and, consequently, on surface soil moisture (SSM). The latter is an important measure for monitoring and even forecasting meteorological and hydrological droughts, as well as irrigation for water resource optimization. This study examines the spatiotemporal sensitivity of SSM to variations in precipitation and temperature in different climatic zones of the Cheliff basin. The monthly mean data from four datasets of SSM, extracted from two reanalysis products (ERA-Land and ERA5), active and passive microwave satellite observations (ESA CCI SM), GLEAM model, along with precipitation and temperature records over the period from 1980 to 2018 from 104 weather stations, were analyzed by using the Modified Mann–Kendall test (MMK) and canonical correlation; the study revealed that (1) SSM generally increased from September to December or March, depending on the dataset; (2) monthly precipitation increased from September to November; (3) SSM decreased from June to August, coinciding with rising temperatures; (4) the results of the canonical analysis revealed that monthly SSM is positively correlated with delayed precipitation from 1 to 3 months, depending on the climatic zone; (5) SSM is negatively correlated with temperatures over the previous 2 months for the stations in Csa and Bsk climate zones and with temperatures over the previous 1 and 2 months for the stations in Bsh climate zone from 1 to 2 months prior, also depending on the climatic zone. These results highlight the crucial role of SSM in monitoring and forecasting weather events such as droughts and provide insights for irrigation strategies aiming to optimize water resources. [ABSTRACT FROM AUTHOR]

Based on numerical weather prediction model Weather Research and Forecasting (WRF) and Hydrologic Modeling System (HEC-HMS), a coupling model is constructed in Taihang Piedmont basin. The WRF model parameter scheme combinations composed of microphysics, planetary boundary layers, and cumulus parameterizations suitable for the study area are optimized. In both time and space, we tested the effects of the WRF model by a multi-index evaluation system composed of relative error, root meantime square error, probability of detection, false alarm ratio, and critical success index and established this system in two stages. A multi-attribute decision-making model based on Technique for Order Preference by Similarity to an Ideal Solution and grey correlation degree is proposed to optimize each parameter scheme. Among 18 parameter scheme combinations, Mellor-Yamada-Janjic, Grell-Devinji, Purdue-Lin, Betts-Miller-Janjić, and Single-Moment6 are ideal choices according to the simulation performance in both time and space. Using the unidirectional coupling method, the rolling rainfall forecast results of the WRF model in the 24 h and 48 h forecast periods are input to HEC-HMS hydrological model to simulate three typical floods. The coupling simulation results are better than the traditional forecast method, and it prolongs the flood forecast period of the Taihang Piedmont basin. [ABSTRACT FROM AUTHOR]

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public's attention to weather during holidays versus regular days, assessing the public's attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected. [ABSTRACT FROM AUTHOR]

This study assesses the performance of the Weather Research and Forecasting (WRF) model using a high-resolution spatial grid (1 km) with various combinations of physical parameterization packages to simulate a severe event in August 2021 in the southeastern Brazilian coast. After determining the optimal set of physical parameterizations for representing wind patterns during this event, a year-long evaluation was conducted, covering forecast horizons of 24, 48, and 72 h. The simulation results were compared with observational wind data from four weather stations. The findings highlight variations in the efficacy of different physical parameterization sets, with certain sets encountering challenges in accurately depicting the peak of the severe event. The most favorable results were achieved using a combination of Tiedtke (cumulus), Thompson (microphysics), TKE (boundary layer), Monin-Obukhov (surface layer), Unified-NOAH (land surface), and RRTMG (shortwave and longwave radiation). Over the one-year forecasting period, the WRF model effectively represented the overall wind pattern, including forecasts up to three days in advance (72-h forecast horizon). Generally, the statistical metrics indicate robust model performance, even for the 72-h forecast horizon, with correlation coefficients consistently exceeding 0.60 at all analyzed points. While the model proficiently captured wind distribution, it tended to overestimate northeast wind speed and gust intensities. Notably, forecast accuracy decreased as stations approached the ocean, exemplified by the ATPM station. [ABSTRACT FROM AUTHOR]