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Downward long-wave irradiance (DnLWI) is a variable that directly influences the surface net radiation, which in turn affects weather and climate. Due to the lack of abundant DnLWI observations, numerical weather prediction (NWP) models provide a very effective way to yield the DnLWI. Nevertheless, the reliability of the DnLWI predicted by the NWP models needs the evaluation based on the observations or accurate radiative transfer models. In this study, the DnLWI product of the China Meteorological Administration mesoscale model (CMA-MESO) was extensively validated using long-term high time-resolution (1 min) DnLWI measurements carried out at 42 sites in China. The results showed that the DnLWIs predicted by the CMA-MESO model generally agreed well with the observations, i.e., with a relative mean bias error (rMBE) of –2.0 %, but overestimated them under overcast (3.1 %) and underestimated them under dry (–5.3 %) and cloudless (–5.2 %) conditions. It is also found that the discrepancies in the DnLWI predicted by the CMA-MESO model exhibited spatial and diurnal variations, e.g., the discrepancies were significantly smaller at night than those during the day due to the stable nocturnal boundary layer. According to the results of the partial least squares analysis, the high cloud cover, medium cloud cover, planetary boundary layer height, and integrated cloud ice were the most important factors affecting the accuracy of the DnLWIs predicted by the CMA-MESO model under all sky conditions. By comparing the outputs of the CMA-MESO model and the MODerate resolution atmospheric TRANsmission (MODTRAN) model, it was found that the uncertainties in the DnLWI predicted by the CMA-MESO model mainly arose from an inappropriate consideration of the high and medium clouds under all sky conditions as well as shortcomings in the radiation scheme under cold dry cloudless conditions. [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]

Both unresolved physics in numerical models and limited theoretical understanding of the small-scale diffusion processes occurring near the ocean surface hamper predictability of tropical cyclone (TC) wind changes. An analytical model is here developed to diagnose the short-term evolution of the TC wind profile. An effective frictional parameter is introduced to control the unknown diffusion effects. When this frictional parameter is adjusted to match the TC intensity change, solutions are consistent with observed high-resolution ocean surface wind speeds from satellite synthetic aperture radar (SAR). The initial high-resolution estimate of the near-core wind structure is then found to strongly modulate the wind profile evolution. The frictional parameter can, unfortunately, not efficiently be calibrated using outer-core wind speed changes. Low-resolution observations or standard numerical weather predictions may thus not be directly used to reinterpret and anticipate short-term TC wind changes. The expected accumulation of orbiting SAR sensors as well as improved measurements of the ocean-atmosphere boundary layer characteristics shall then become essential to more precisely monitor TC dynamics. [ABSTRACT FROM AUTHOR]

We explore the domain-specific Python library GT4Py (GridTools for Python) for implementing a representative physical parametrization scheme and the related tangent-linear and adjoint algorithms from the Integrated Forecasting System (IFS) of ECMWF. GT4Py encodes stencil operators in an abstract and hardware-agnostic fashion, thus enabling more concise, readable and maintainable scientific applications. The library achieves high performance by translating the application into targeted low-level coding implementations. Here, the main goal is to study the correctness and performance-portability of the Python rewrites with GT4Py against the reference Fortran code and a number of automatically and manually ported variants created by ECMWF. The present work is part of a larger cross-institutional effort to port weather and climate models to Python with GT4Py. The focus of the current work is the IFS prognostic cloud microphysics scheme, a core physical parametrization represented by a comprehensive code that takes a significant share of the total forecast model execution time. In order to verify GT4Py for Numerical Weather Prediction (NWP) systems, we put additional emphasis on the implementation and validation of the tangent-linear and adjoint model versions which are employed in data assimilation. We benchmark all prototype codes on three European supercomputers characterized by diverse GPU and CPU hardware, node designs, software stacks and compiler suites. Once the application is ported to Python with GT4Py, we find excellent portability, competitive performance, and robust execution in all tested scenarios including with reduced precision. [ABSTRACT FROM AUTHOR]

With hyperspectral instruments measuring radiation emitted by Earth and its atmosphere in the thermal infrared range in multiple channels, several studies were made to select a subset of channels in order to reduce the number of channels to be used in a data assimilation system. An optimal selection of channels based on the information content depends on several factors related to observation and background error statistics and the assimilation system itself. An optimal channel selection for the Cross-track Infrared Sounder (CrIS) was obtained and then compared to selections made for different NWP systems. For instance, the channel selection of Carminati has 224 channels also present in our optimal selection, which includes 455 channels. However, in terms of analysis error variance, the difference between the two selections is small. Integrated over the whole profile, the relative difference is equal to 15.3% and 4.5% for temperature and humidity, respectively. Also, different observation error covariance matrices were considered to evaluate the impact of this matrix on channel selection. Even though the channels selected optimally were different in terms of which channels were selected for the various R matrices, the results in terms of analysis error are similar. Significance Statement: Satellites measure radiation from Earth and its atmosphere in the thermal infrared. Those radiance data contain thousands of measurements, called channels, and thus, a selection needs to be done retaining most of the information content since the large number of individual pieces of information is not usable for numerical weather prediction systems. The goal of this paper is to find an optimal selection for the instrument CrIS and to compare this selection with selections made for different numerical weather prediction systems. It was found that even though the channels selected optimally were different in terms of which channels were selected compared to other selections, the results in terms of precision of the analysis are similar and the results in terms of analysis error are similar due to the nature of hyperspectral instruments, which have multiple Jacobians overlapping. [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]

Short-term precipitation forecasting methods are mainly divided into statistical forecasting, numerical model-based forecasting, and radar image extrapolation techniques. The two methods based on statistical prediction and numerical model have the disadvantages of being unstable and generating large errors. Therefore, this study proposes the use of deep learning for radar image extrapolation for precipitation forecasting, in particular by developing algorithms for ConvLSTM and SmaAT-UNet. The ConvLSTM model is a fusion of a CNN (Convolutional Neural Network) and LSTM (Long Short-Term Memory network), which solves the challenge of processing spatial sequence data, which is a task that traditional LSTM models cannot accomplish. At the same time, SmaAT-UNet enhances the traditional UNet structure by incorporating the CBAM (Convolutional Block Attention Module) attention mechanism and replacing the standard convolutional layer with depthwise separable convolution. This innovative approach aims to improve the efficiency and accuracy of short-term precipitation forecasting by improving feature extraction and data processing techniques. Evaluation and analysis of experimental data show that both models exhibit good predictive ability, with the SmaAT-UNet model outperforming ConvLSTM in terms of accuracy. The results show that the performance indicators of precipitation prediction, especially detection probability (POD) and the Critical Success index (CSI), show a downward trend with the extension of the prediction time. This trend highlights the inherent challenges of maintaining predictive accuracy over longer periods of time and highlights the superior performance and resilience of the SmaAT-UNet model under these conditions. Compared with the statistical forecasting method and numerical model forecasting method, its accuracy in short-term rainfall forecasting is improved. [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]

The article discusses the importance of accurate monitoring and early warning of heavy rainfall induced by strong convection for preventing natural disasters such as floods and landslides. It highlights the use of weather radars as the most powerful technique for monitoring natural hazards caused by heavy rainstorms. The article also covers various topics related to radar signal processing methods, error characterization, new sensing techniques, and the application of radar data in weather analysis and forecasting. It emphasizes the advancements in weather radar technology and its potential for revolutionizing meteorological forecasting and disaster preparedness. [Extracted from the article]

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]

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]

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]

Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits. SIGNIFICANCE STATEMENT: The results of this paper are significant because they inform decision-makers about the future configuration of the NOAA's environmental satellite constellation, which serves millions of diverse users. [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]

Operational Meteorologists (OMs) in the Met Office have a perception that elevated convection is not well represented in kilometre‐scale models, which are generally associated with an improved representation of convection. Here, we consider why there may be a problem with representing elevated convection and consider how OMs judge the model to be poor so often. Three OMs have subjectively scored and classified observed elevated convection cases over the UK from 2017 to 2020. Continental plumes (warm, moist, air coming from the near continent or Africa) account for 73% of the cases. The most frequent errors are associated with (i) location, (ii) organisation, (iii) timing and (iv) intensity of the convection. Thus, OMs perceive that the biggest problem with predicting elevated convection is constraining the location of the convective events. The location errors are particularly prevalent for events coming to the UK from the near continent. The location errors are most frequently identified for flow regimes coming from the near continent in weakly forced synoptic conditions. The identification of this problem enables the specific targeting of research into continental plumes (for UK elevated convection) but also raises questions around the role of lateral boundary conditions in the forecasts of elevated convection. [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]

The West African Monsoon (WAM) system plays a crucial role in the West African climate system because it transports moisture from the Atlantic Ocean into the subcontinent in summer. This study evaluates the capability of the Model for Prediction Across Scales-Atmosphere (MPAS-A) to simulate the characteristic reproduce the WAM system and the associated rainfall-producing features. The MPAS model was used to perform a 30-year global climate simulation (1981–2010) at a regular grid (uniform resolution of 60 km). The simulation was initialized with the Climate Forecast System Reanalysis (CSFR) dataset. The results showed that MPAS simulate well the rainfall pattern over West Africa and reproduces the different phases of the monsoon dynamics system (i.e., the northward progression, the peak period, and the southward retreat). The model also reasonably replicates the pattern of the zonal components of wind and the vertical velocity. However, MPAS underestimates the orographic rainfall over the Guinea Coast, Jos Plateau, and Mount Cameroon. It also underestimates the vertical velocity and zonal wind magnitudes over the region. In addition, the model features a weaker temperature gradient than in the reanalysis. Understanding and correcting the sources of these model biases will enhance the suitability of MPAS for weather and seasonal forecasts over West Africa. Research highlights: In this study, we investigated the ability of the Model for Prediction Across Scales (MPAS) to simulate the West African monsoon rainfall, the associated atmospheric features controlling the variability of the seasonal and annual cycle, and the thermal wind conditions. First, the model is initialized using the CSFR reanalysis. Then, we run the model simulation for 30 years, from 1981 to 2010, using a regular grid (uniform resolution of 60 km). We showed that the model realistically simulated the spatial pattern of rainfall over West Africa. However, it has some problems when it comes to capturing the monsoon features. [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]

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]

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]

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]

Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5. [ABSTRACT FROM AUTHOR]

Accurate estimation of global winds is crucial for various scientific and practical applications, such as global chemical transport modeling and numerical weather prediction. One valuable source of wind measurements is atmospheric motion vectors (AMVs), which play a vital role in the global observing system and numerical weather prediction models. However, errors in AMV retrievals need to be addressed before their assimilation into data assimilation systems, as they can affect the accuracy of outputs. An assessment of the bias and uncertainty in passive-sensor AMVs can be done by comparing them with information from independent sources such as active-sensor winds. In this paper, we examine the benefit and performance of a colocation scheme using independent and sparse lidar wind observations as a dependent variable in a supervised machine learning model. We demonstrate the feasibility and performance of this approach in an observing system simulation experiment (OSSE) framework, with reference geophysical state data obtained from high-resolution Weather Research and Forecasting (WRF) model simulations of three different weather events. Lidar wind data are typically available in only one direction, and our study demonstrates that this single component of wind in high-precision active-sensor data can be leveraged (via a machine learning algorithm to model the conditional mean) to reduce the bias in the passive-sensor winds. Further, this active-sensor wind information can be leveraged through an algorithm that models the conditional quantiles to produce stable estimates of the prediction intervals, which are helpful in the design and application of error analysis, such as quality filters. [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]

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]

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]

GNSS radio occultation (RO) observations play an increasingly important role in monitoring climate changes and numerical weather forecasts in the upper troposphere and stratosphere. The magnitudes of the RO bending angle are small at these altitudes, and therefore residual ionospheric error (RIE) is critical to retrieve vertical profiles of atmospheric temperature and refractivity. The latter represent the state variables of the weather and climate models. RIEs remain poorly characterized in terms of the global geographical distribution and its variations with the local time and altitude influenced by the solar cycle and solar-geomagnetic disturbances. In this study we developed a new method to determine RIE from the RO excess phase measurement on a profile-by-profile basis. The method, called Φ ex -gradient method, is self-sufficient and based on the vertical derivative of the RO excess phase (Φ ex ) profile, which can be applied to individual RO bending angle observations for RIE correction. In addition to the RIE in bending angle measurements, RIEs are found in the RO Φ ex measurements in the upper atmosphere where an exponential dependence is expected and in small-scale temperature variance of the RO retrieval. We found that the RIE values derived from the Φ ex -gradient method can be both positive and negative, which is fundamentally different from the k -method that produces only the positive RIE values. The new algorithm reveals a latitude-dependent diurnal variation with a larger daytime negative RIE (up to ~3 μrad) in the tropics and subtropics. Based on the observed RIE climatology, a local-time dependent RIE representation is used to evaluate its impacts on reanalysis data. We examined these impacts by comparing the data from the Goddard Earth Observing System (GEOS) data assimilation (DA) system with and without the RIE. The RIF impact on GEOS DA temperature is mainly confined to the polar regions of stratosphere. Between 10 hPa and 1 hPa the temperature differences are ~1 K and exceed ~3–4 K in some cases. These results further highlight the need for RO RIE correction in the modern DA systems. [ABSTRACT FROM AUTHOR]

Accurate long-term daily cloud-gap-filled fractional snow cover products are essential for climate change and snow hydrological studies in the Asian Water Tower (AWT) region, but existing Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products are not sufficient. In this study, the multiple-endmember spectral mixture analysis algorithm based on automatic endmember extraction (MESMA-AGE) and the multistep spatiotemporal interpolation algorithm (MSTI) are used to produce the MODIS daily cloud-gap-filled fractional snow cover product over the AWT region (AWT MODIS FSC). The AWT MODIS FSC products have a spatial resolution of 0.005° and span from 2000 to 2022. The 2745 scenes of Landsat-8 images are used for the areal-scale accuracy assessment. The fractional snow cover accuracy metrics, including the coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), are 0.80, 0.16 and 0.10, respectively. The binarized identification accuracy metrics, including overall accuracy (OA), producer's accuracy (PA) and user's accuracy (UA), are 95.17 %, 97.34 % and 97.59 %, respectively. Snow depth data observed at 175 meteorological stations are used to evaluate accuracy at the point scale, yielding the following accuracy metrics: an OA of 93.26 %, a PA of 84.41 %, a UA of 82.14 % and a Cohen kappa (CK) value of 0.79. Snow depth observations from meteorological stations are also used to assess the fractional snow cover resulting from different weather conditions, with an OA of 95.36 % (88.96 %), a PA of 87.75 % (82.26 %), a UA of 86.86 % (78.86 %) and a CK of 0.84 (0.72) under the MODIS clear-sky observations (spatiotemporal reconstruction based on the MSTI algorithm). The AWT MODIS FSC product can provide quantitative spatial distribution information on snowpacks for mountain hydrological models, land surface models and numerical weather prediction in the Asian Water Tower region. This dataset is freely available from the National Tibetan Plateau Data Center at 10.11888/Cryos.tpdc.272503 (Jiang et al., 2022) or from the Zenodo platform at 10.5281/zenodo.10005826 (Jiang et al., 2023a). [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]

Accurate representation of soil and snow state variables in the land surface model is crucial for good simulation results. In order to investigate the potential impacts of the overestimated snow cover over the Tibetan Plateau (TP) on precipitation over China, we designed comparative experiments over TP with and without updated soil texture data, soil hydrological parameters, and the fresh snow density scheme to address this issue with the Weather Research and Forecasting Model (WRF). The results show that snow cover fraction (SCF) and surface albedo in winter on the TP were overestimated in the WRF control experiment (CTL), with albedo bias exceeding 0.4 in the western TP. The SCF in the WRF improved experiment (WRFIM) is about 8% lower than that of the CTL, and the bias is decreased to 0.2–0.3 in the interior of the TP. The thermal and dynamic effects of the TP in winter have a significant impact on the weather and climate of East Asia during summer. Thus, the simulated summer rainfall bias is significantly reduced by WRFIM in North China, and the mid-lower reaches of the Yangtze River, along with the spatial correlation coefficient between the observed-based and WRF-simulated rainfall increased from 0.76 in CTL to 0.88 in WRFIM. The WRFIM-simulated reductions in snow cover and albedo cause a decrease in upward shortwave radiation and eventually increase net surface radiation. The horizontal temperature gradient of the atmosphere is increased in the WRFIM compared to the CTL, and the increased temperature in the lower and middle layers accommodates more water vapor, which is not conducive to precipitation formation. Correspondingly, the Western Pacific Subtropical High at 500 hPa in the middle troposphere weakened. The low-level anticyclonic circulation at 850 hPa also weakened to the east, affecting water vapor transport in eastern China. It leads to a decrease in the water vapor transported northward by southeasterly from the western Pacific Ocean and a decrease in the water vapor flux reaching North China. This work indicates that accurate characterization of soil properties and snow cover on the TP is crucial for improving the simulation bias of summer rainfall in China. [ABSTRACT FROM AUTHOR]

This study evaluates the decadal prediction skill of 13 forecast systems in predicting winter precipitation over Eurasia, contributing to the Decadal Climate Prediction Project of the Coupled Model Intercomparison Project Phase 6. Northeast Asia stands out as a region with improved decadal prediction skill for forecast years 2–5 due to the initialization. Observations show anticyclonic and cyclonic wind anomalies over the North Pacific and Northeast Asia, respectively, with southwesterly flow to the east of Northeast Asia. Ten forecast systems reproduce such circulation anomalies favoring abundant winter precipitation in Northeast Asia. The significant positive (negative) correlations between the detrended Northeast Asian precipitation (NEAP) and AMV (PDO-like) time series are reproduced by seven (nine) forecast systems. However, most forecast systems underestimate the correlation between the NEAP and the AMV, and have relatively low skill in predicting the PDO. Further improvements in these aspects will help to improve the decadal prediction skill of winter precipitation over Northeast Asia. The multi-model ensemble (MME) is able to reproduce both links of NEAP with AMV and PDO-like variability. The MME demonstrates significant skill and outperforms the individual forecast systems in predicting the NEAP for all 4-year averaged periods in the range of 1–8 years, demonstrating the benefits of using the ensemble mean of multiple models. [ABSTRACT FROM AUTHOR]

The Madden–Julian Oscillation (MJO) provides an important source of global subseasonal-to-seasonal (S2S) predictability, while its prediction remains great challenges. Based on an atmosphere–ocean coupled model and the widely-used nudging method, suitable initialization and ensemble schemes are explored toward an improved MJO prediction. Results show that the ensemble strategy with perturbed atmospheric nudging coefficients facilitates adequate ensemble spread and hence improves the prediction skill, particularly for weak cases. Finally, an 18-member ensemble subseasonal prediction system called NUIST CFS1.1 is developed. Skill evaluation indicates that the NUIST CFS1.1 can extend the MJO prediction to 24 days lead, which reaches the world-average level but is far from the estimated potential predictability (~ 45 days). The limited skill at longer lead times corresponds to forecast errors exhibiting slower propagation and weaker intensity, which are jointly caused by the initial errors and model's imperfections. The latter is associated with unrealistic representations of MJO-related physical processes. The model underestimates the diabatic heating (mostly contributed by the latent heat release) of enhanced convection and fails to reproduce the suppressed convection within the MJO structure, collaboratively weakening the Kelvin/Rossby waves. This causes weaker horizontal winds and ultimately reduces the horizontal moisture advection on the two flanks of MJO convection. Furthermore, the underestimated Kelvin wave induces insufficient planetary boundary layer (PBL) convergence and thereby results in poor simulation of PBL premoistening ahead of MJO convection. Interestingly, the commonly dry bias is absent in our model, and the bias in horizontal moisture gradients may have a minor impact on the MJO propagation. Further efforts are urged to improve the model physics involving cumulus and PBL processes. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [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 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]