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

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

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

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

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

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

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

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

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

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

This study evaluates the representation of the composite-mean surface response to sudden stratospheric warmings (SSWs) in 28 CMIP6 models. Most models can reproduce the magnitude of the SLP response over the Arctic, although the simulated Arctic SLP response varies from model to model. Regarding the structure of the SLP response, most models exhibit a basin-symmetric negative Northern Annular Mode (NAM)-like response with a cyclonic Pacific SLP response, whereas the reanalysis shows a highly basin-asymmetric negative NAO-like response without a robust Pacific center. We then explore the drivers of these model biases and spread by applying a multiple linear regression (MLR). The results show that the polar cap temperature anomalies at 100 hPa (ΔT100) modulate the magnitude of both the Arctic SLP response and the cyclonic Pacific SLP response. Apart from ΔT100, the intensity and latitudinal location of the climatological eddy-driven jet in the troposphere also affect the magnitude of the Arctic SLP response. The compensation of model biases in these two tropospheric metrics and the good model representation of ΔT100 explain the good agreement between the ensemble mean and the reanalysis on the magnitude of the Arctic SLP response, as indicated by the fact that the ensemble mean lies well within the reanalysis uncertainty range and that the reanalysis mean sits well within the model distribution. The Niño-3.4 SST anomalies and North Pacific SST dipole anomalies together with ΔT100 modulate the cyclonic Pacific SLP response. In this case, biases in both oceanic drivers work in the same direction and lead to the cyclonic Pacific SLP response in models that are not present in the reanalysis. Significance Statement: Sudden stratospheric warmings (SSWs) represent an important source of skill for forecasting winter weather on subseasonal-to-seasonal time scales. To what extent SSWs could be used to improve the prediction of surface weather depends on how well stratosphere–troposphere coupling associated with SSWs is represented in climate models. Therefore, we evaluate the representation of stratosphere–troposphere coupling associated with SSWs in 28 state-of-the-art climate models. The representation is found to diverge widely among climate models, and some are biased noticeably from the reanalysis. The models' spread and bias are largely driven by five major factors and can be reduced substantially by making bias corrections to these factors. [ABSTRACT FROM AUTHOR]

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

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

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

Complex terrain features such as mountains and hills can obstruct the airflow and force upward motion, thereby altering local atmospheric circulation patterns. During the rainy season, these terrain characteristics are more prone to causing intense local precipitation, leading to geological hazards such as floods and debris flows. These phenomena are closely linked to the intricate influence of terrain on wind fields, highlighting the necessity for in-depth research into wind field characteristics under complex terrain conditions. To address this, we propose a neural-network-based model leveraging terrain data and horizontal wind speed data to predict atmospheric motion characteristics and terrain uplift effects in specific terrain conditions. To enhance the generalization ability of the model, we innovatively extract key physical information from the horizontal wind vector data as training parameters. By comparing with the results of the Fluent model, we validate the model's capability in dynamic downscaling and flow field modeling. Experimental outcomes demonstrate that our model can generate terrain-adapted convective warning data with a high accuracy, even when terrain features are altered. Under unoptimized conditions, the results at a maximum resolution of 50 m require only 26 s, and the computation time can be further reduced with algorithmic improvements. This research on adaptive wind field modeling under complex terrain conditions holds significant implications for local wind field simulation and severe convective weather forecasting. [ABSTRACT FROM AUTHOR]

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

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

Hurricane Elsa was the first hurricane to impact the island of Barbados in more than 60 years. Global warming is expected to increase the number of intense hurricanes in the Atlantic Ocean, which present a greater risk of future devastating hurricane impacts on the island. This study investigates the meteorological conditions and rapid intensification of Elsa between June 27 and July 3, 2021, by using meteorological data sets and results from the Weather Research and Forecasting model. The study also uses damage assessment data to analyze wind damage caused to residential homes as Elsa passed over Barbados on July 2. Unusually warm sea surface temperatures for June/early July, and a strong North Atlantic Subtropical High that was positioned anomalously close to the Caribbean islands, contributed to Elsa's rapid intensification and its track across the Atlantic, respectively. It was also found that most reported damages on the island involved the complete or partial removal of roofs and were concentrated in and around the capital city Bridgetown, which is most likely due to the high concentration of poorly constructed houses in this area. Therefore, there is a need to improve the building codes of houses to ensure that they withstand strong hurricane winds. It was recommended that the implementation of a mandatory building code in addition to the provision of subsidies for low-income persons to improve their homes could aid with this issue. Furthermore, the paper highlights deficiencies in weather models in predicting the genesis and rapid intensification of Elsa, which highlights a need for improvements. Practical Applications: After more than 60 years without hurricanes in Barbados, Hurricane Elsa passed over Barbados on July 2, 2021, causing damage to the built environment on the island. Elsa was rated as a Category 1 hurricane. Unusually warm sea surface temperatures for June/early July and a strong high-pressure system that was positioned anomalously close to the Caribbean islands were responsible for Elsa's formation and trajectory over Barbados. Elsa exhibited rapid intensification—an increase in 1-min sustained wind speeds of more than 15 m s−1 in 24 h—but this intensification was not properly forecasted by weather prediction models. Most reported damages on the island involved the complete or partial removal of roofs. The highest concentration of damages was in and around Bridgetown. Barbados has a building code, but adherence to it is not mandatory. The enforcement of a mandatory building code would increase resilience of Barbados to hurricanes, but it would also require government subsidies for persons who cannot afford to build their houses up to code. It is imperative that the government and population take early action to prepare themselves before any devastating hurricane disasters in the future. [ABSTRACT FROM AUTHOR]

The creation of features makes the difference in improving the photovoltaic forecast (PVF) for on‐grid, hybrid and off‐grid PV systems. The importance of the PVF is tremendous, and it can be essential in optimizing the home appliances to maximize the Renewable Energy Sources (RES) usage or to create performant bids for the electricity market. Several use cases are considered from the connectivity point of view. Therefore, in this paper, we propose a Weather Prediction Error (WPE)‐based method that uses a Stacking Regressor (SR) for various PV systems that coexist in the emerging Energy Communities (EC) landscape. The novelty of the research we conduct consists in proposing several features and determining the coefficients to adjust the PVF based on WPE. The forecast results of four types of PV systems from size and connectivity point of view are investigated. Compared with individual Machine Learning (ML) models, R2 increases with more than 3% with the SR and with more than 6% after applying the adjustment coefficients. Nevertheless, the major improvement is recorded for the off‐grid inverter and for the large industrial PV power plant, demonstrating that the proposed model is suitable for these types of systems. The other metrics improved as well, especially Mean Average Error (MAE) that decreases between 10% and 23%. A significant decrease is in the case of the industrial on‐grid PV, from 130 kW to 123 kW using the SR and to 107 kW after adjustments. This represents around 18% from the initial MAE. The ratio of daily deviations is also improved using the SR. For all the PV systems, the ratio stabilizes in a shorter interval, from daily values between 0.78 and 1.33 obtained with the ML models to values between 0.83 and 1.24 obtained with the SR. After the final adjustments, the interval becomes shorter, having daily values between 0.90 and 1.10. [ABSTRACT FROM AUTHOR]

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary: Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points: Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regimeGreenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern EuropeWeak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature [ABSTRACT FROM AUTHOR]

To enhance our understanding of cloud simulations over land, this study provides the first assessment of coupling between cloud and land surface in the Large‐Eddy Simulation (LES) Atmospheric Radiation Measurement Symbiotic Simulation and Observation (LASSO) activity for the shallow convection scenario. The analysis of observation data reveals a diurnal cycle of cloud‐land coupling, which co‐varies with surface fluxes. However, coupled (or decoupled) cumulus clouds are inadequately simulated, manifesting as a too‐high (or low) occurrence frequency during the afternoon. This discrepancy is mirrored by the overestimated cloud liquid water path and cloud‐top height. These overestimations are linked to the overpredicted boundary‐layer development and the easier trigger of shallow convection misrepresented in LES runs. Our study underscores the need to improve the representations of boundary‐layer processes and cloud‐land interactions within LES to better simulate shallow clouds in the future. Plain Language Summary: Cloud‐surface coupling is crucial in weather and climate predictions, yet its processes, particularly over the land surface, remain poorly understood. This study examined the evolution of coupled cloud occurrence frequency and cloud macro‐physical properties [e.g., cloud fraction, liquid water path (LWP), and cloud‐top height (CTH)] in LASSO Large‐Eddy Simulations (LESs) for 77 shallow convection case days against observations. We found that LES runs erroneously simulate a too‐high occurrence frequency of coupled cumulus clouds during the afternoon. In accord with that, shallow convection development is also markedly overestimated during the same period, manifested by overestimated cloud LWP and CTH. These errors are possibly linked to the misrepresentation of a key physical process, namely, the shallow convective trigger, which needs to be improved to achieve more accurate cloud simulations. Key Points: Observed cloud‐land coupling has notable diurnal variations but is inadequately simulated by Large‐Eddy Simulations (LESs)LESs overestimate the frequency and vertical extent of coupled cumuli in the afternoonThese overestimations by LESs are related to the misrepresentation of triggering shallow convection [ABSTRACT FROM AUTHOR]

Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events. Plain Language Summary: During June 2021, an extraordinarily strong heat wave occurred over parts of Western North America (WNA). It obliterated the high temperature record there and caused severe societal and ecological impacts, and is believed to be exacerbated by climate change. Low‐resolution Global Climate Models (GCMs) are currently used to quantify the atmospheric responses to climate change, even though they exhibit significant biases in their simulated climate. Their ability to predict extraordinarily strong heat wave events remains unassessed. In this study, low‐ and high‐resolution GCM simulations are shown to be able to reproduce extraordinarily strong heat wave events in the near future, but face challenges in reproducing the events as extreme as the observed one. This highlights the role of climate change in such events. A suite of short GCM simulations initialized from observations is used to show that even low‐resolution GCMs can forecast observed extreme strong heat wave events and their high‐pressure ridge over WNA at short lead times. We further show that in these short‐term forecasts, drier atmospheric moisture initial condition can lead to significantly higher near‐surface temperature. Key Points: Global climate models (GCMs) climate simulations can reproduce extreme heat events, but face challenges in capturing the observed extremityLow‐ and high‐resolution GCMs predict the 2021 Western North America heat wave similarly within a 1‐week forecast lead timeDry atmospheric conditions in GCMs can lead to significantly higher near‐surface temperatures in the simulated heat waves [ABSTRACT FROM AUTHOR]

A significant share of our data are timeseries. Time can occur as the single dimension, or further (such as spatial) dimensions can be present as well, leading to multidimensional data such as 3D x/y/t satellite image timeseries or 4D x/y/z/t atmospheric simulations like weather forecasts. However, no user would like to count “seconds since epoch,” rather we all prefer calendar notation. Libraries, SQL, etc. support calendar arithmetics, yet more advanced functionality is needed. Instead of demoting advanced calendar functionality to low‐level coding, it seems desirable to have semantic support for calendars at high‐level, declarative query level. We present a conceptual framework for temporal modeling which allows the expression of a wide range of temporal queries. It smoothly enhances existing calendar handling by adding two new data and query description parameters, period of validity, and granularity. While our work is embedded in spatio‐temporal geo services, it can be incorporated in a non‐breaking manner in any environment needing calendar addressing. The concepts are implemented in an Array DBMS operational on several Petabytes of Earth datacubes. [ABSTRACT FROM AUTHOR]

As the climate warms, the consequent moistening of the atmosphere increases extreme precipitation. Precipitation variability should also increase, producing larger wet-dry swings, but that is yet to be confirmed observationally. Here we show that precipitation variability has already grown globally (over 75% of land area) over the past century, as a result of accumulated anthropogenic warming. The increased variability is seen across daily to intra seasonal timescales, with daily variability increased by 1.2% per 10 years globally, and is particularly prominent over Europe, Australia, and eastern North America. Increased precipitation variability is driven mainly by thermodynamics linked to atmospheric moistening, modulated at decadal timescales by circulation changes. Amplified precipitation variability poses new challenges for weather and climate predictions, as well as for resilience and adaptation by societies and ecosystems. [ABSTRACT FROM AUTHOR]

Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research. This paper introduces FuXi-S2S, a machine-learning model that outperforms conventional numerical weather prediction models at subseasonal timescales globally, extending the skillful Madden–Julian Oscillation prediction form 30 days to 36 days. [ABSTRACT FROM AUTHOR]

In ensemble weather forecast, tropical cyclone (TC) tracks sometimes group together into trajectories parting away from each other. The goal of this study is to propose an objective method, based on a robust clustering approach, to detect such separation scenarios in the Japan Meteorological Agency Meso‐scale Ensemble Prediction System (MEPS) for three TCs: "Dolphin" (2020), "Nepartak" (2021), and "Meari" (2022). Taking advantage of the independence of the density‐based spatial clustering of applications with noise algorithm to the prior choice of the number of clusters, we first describe an objective way to calculate the aggregation distance, by searching the most frequent Euclidean distance between all the tracks. The clustering is then applied to the forecasted tracks, for each initialization time of MEPS (every 6 hr). Separation scenarios exist when the number of clusters is greater than one. Plain Language Summary: To predict the weather, meteorologists use numerical models to simulate the future condition of the atmosphere, that is, forecast, from an initial condition. By slightly modifying this initial condition, an ensemble of future conditions is available, which provides the likelihood of an event occurring. In the case of a tropical cyclone (TC), it is particularly important to estimate its future path and strength, to prepare for disasters. Sometimes, the forecasted paths visually separate into distinct directions. We propose a straightforward way to numerically detect this separation in the forecasts. The method is illustrated by the three cases studies: TCs "Dolphin" (2020), "Nepartak" (2021), and "Meari" (2022). Key Points: Detecting separation scenarios in forecasted tropical cyclone (TC) trajectories to help preparedness and mitigation measuresA clustering analysis is used as a simple but effective tool to detect separations in tracks, by grouping similar trajectoriesA separation scenario exists when the number of clusters for a given initialization time is greater than 1 [ABSTRACT FROM AUTHOR]

The volume and complexity of weather data, along with missing values and high correlation between collected variables, make it challenging to develop efficient deep learning frameworks that can handle data with more features. This leads to a lack of accurate and predictable weather forecasts. To develop a hybrid deep learning framework for weather forecast with rainfall prediction using weather big data analytics to ensure high detection rates. A modified planet optimization (MPO) algorithm is used for data preprocessing to remove unwanted artifacts. An improved Tuna optimization (ITO) algorithm is presented to select optimal features to avoid data dimensionality issues. A hybrid memory-augmented artificial neural network (MA-ANN) classifier is developed to improve weather early forecast detection rates. The proposed framework is validated against standard benchmark datasets such as weather underground and climate forecast system reanalysis (CFSR). The simulation results are compared with other existing state-of-the-art frameworks based on error measures (RMSE, MAPE, BIAS, R) and quality measures (accuracy, sensitivity, specificity, precision, F1-measure).The MA-ANN classifier accuracy obtained 97.65% for wunderground.com Delhi and 98.88% for Tamilnadu. The hybrid deep learning framework with rainfall prediction using weather big data analytics has shown promising results for accurate and predictable weather forecasts. The proposed framework outperforms other existing state-of-the-art frameworks, and the MA-ANN classifier has improved weather early forecast detection rates. The study demonstrates the potential of utilizing big data techniques in weather forecasting and highlights the importance of developing efficient deep learning frameworks to handle complex and high-dimensional weather data. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) in forecasting a mega Asian Dust Storm (ADS) event that occurred over South Korea on March 28–29, 2021. We specifically evaluated a combination of five dust emission schemes and four land surface schemes, which are crucial for predicting ADSs. Using in-situ and remote sensing data, we assessed surface meteorological and air quality variables, including 2 m temperature, 2 m relative humidity, 10 m wind speed, particulate matter 10 (PM10), and aerosol optical depth (AOD) over South Korea. Our results indicate that prediction of surface meteorological variables is more influenced by the land surface scheme than by the dust emission scheme—generally showing good performance when dust emission schemes are combined with the Noah land surface model with Multiple Parameterization options (Noah-MP). In contrast, prediction of air quality variables, including PM10 and AOD, is strongly affected by the dust emission schemes, which is directly related to the generation and amount of dust through interaction with surface properties. Among the total of 20 available scheme combinations, the University of Cologne 2004 combined with the Community Land Model version 4.0 (UoC04-CLM4) showed the best performance, closely followed by the University of Cologne 2001 combined with CLM4 (UoC01-CLM4). UoC04-CLM4 outperformed the other scheme combinations by reducing the root mean square errors of PM10 up to 29.6 %. However, both UoC04-CLM4 and UoC01-CLM4 simulated values closest to the MODIS AOD but tended to overestimate the AOD in some regions during the origination and transportation processes. In contrast, other scheme combinations significantly underestimated the AOD throughout the entire simulation process of ADSs. [ABSTRACT FROM AUTHOR]

The use of single precision in floating-point representation has become increasingly common in operational weather prediction. Meanwhile, climate simulations are still typically run in double precision. The reasons for this are likely manifold and range from concerns about compliance and conservation laws to the unknown effect of single precision on slow processes or simply the less frequent opportunity and higher computational costs of validation. Using an ensemble-based statistical methodology, could detect differences between double- and single-precision simulations from the regional weather and climate model COSMO. However, these differences are minimal and often only detectable during the first few hours or days of the simulation. To evaluate whether these differences are relevant for regional climate simulations, we have conducted 10-year-long ensemble simulations over the European domain of the Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) in single and double precision with 100 ensemble members. By applying the statistical testing at a grid-cell level for 47 output variables every 12 or 24 h, we only detected a marginally increased rejection rate for the single-precision climate simulations compared to the double-precision reference based on the differences in distribution for all tested variables. This increase in the rejection rate is much smaller than that arising from minor variations of the horizontal diffusion coefficient in the model. Therefore, we deem it negligible as it is masked by model uncertainty. To our knowledge, this study represents the most comprehensive analysis so far on the effects of reduced precision in a climate simulation for a realistic setting, namely with a fully fledged regional climate model in a configuration that has already been used for climate change impact and adaptation studies. The ensemble-based verification of model output at a grid-cell level and high temporal resolution is very sensitive and suitable for verifying climate models. Furthermore, the verification methodology is model-agnostic, meaning it can be applied to any model. Our findings encourage exploiting the reduction of computational costs (∼30% for COSMO) obtained from reduced precision for regional climate simulations. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting plays an important role in mitigating the damage caused by severe weather. The objective of precipitation nowcasting is to forecast the weather conditions 0–2 h ahead. Traditional models based on numerical weather prediction and radar echo extrapolation obtain relatively better results. In recent years, models based on deep learning have also been applied to precipitation nowcasting and have shown improvement. However, the forecast accuracy is decreased with longer forecast times and higher intensities. To mitigate the shortcomings of existing models for precipitation nowcasting, we propose a novel model that fuses spatiotemporal features for precipitation nowcasting. The proposed model uses an encoder–forecaster framework that is similar to U-Net. First, in the encoder, we propose a spatial and temporal multi-head squared attention module based on MaxPool and AveragePool to capture every independent sequence feature, as well as a global spatial and temporal feedforward network, to learn the global and long-distance relationships between whole spatiotemporal sequences. Second, we propose a cross-feature fusion strategy to enhance the interactions between features. This strategy is applied to the components of the forecaster. Based on the cross-feature fusion strategy, we constructed a novel multi-head squared cross-feature fusion attention module and cross-feature fusion feedforward network in the forecaster. Comprehensive experimental results demonstrated that the proposed model more effectively forecasted high-intensity levels than other models. These results prove the effectiveness of the proposed model in terms of predicting convective weather. This indicates that our proposed model provides a feasible solution for precipitation nowcasting. Extensive experiments also proved the effectiveness of the components of the proposed model. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of the joint assimilation of ground-based microwave radiometer (MWR) and FY-3D microwave sounder (MWTS-2/MWHS-2) observations on the analyses and forecasts for precipitation forecast. Based on the weather research and forecasting data assimilation (WRFDA) system, four experiments are conducted in this study, concerning a heavy precipitation event in Beijing on 2 July 2021, and 10-day batch experiments were also conducted. The key study findings include the following: (1) Both ground-based microwave radiometer and MWTS-2/MWHS-2 data contribute to improvements in the initial fields of the model, leading to appropriate adjustments in the thermal structure of the model. (2) The forecast fields of the experiments assimilating ground-based microwave radiometer and MWTS-2/MWHS-2 data show temperature and humidity performances closer to the true fields compared with the control experiment. (3) Separate assimilation of two types of microwave radiometer data can improve precipitation forecasts, while joint assimilation provides the most accurate forecasts among all the experiments. In the single-case, compared with the control experiment, the individual and combined assimilation of MWR and MWTS-2/MWHS-2 improves the six-hour cumulative precipitation threat score (TS) at the 25 mm level by 57.1%, 28.9%, and 38.2%, respectively. The combined assimilation also improves the scores at the 50 mm level by 54.4%, whereas individual assimilations show a decrease in performance. In the batch experiments, the MWR_FY experiment's TS of 24 h precipitation forecast improves 28.5% at 10 mm and 330% at 25 mm based on the CTRL. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of assimilating the Microwave Humidity Sounder II (MWHS-2) radiance data carried on the FY-3D and FY-3E satellites on the analyses and forecasts of Typhoon Muifa in 2022 under clear-sky conditions. Data assimilation experiments are conducted using the Weather Research and Forecasting (WRF) model coupled with the Three-Dimensional Variational (3D-Var) Data Assimilation method to compare the different behaviors of FY-3D and FY-3E radiances. Additionally, the data assimilation strategies are assessed in terms of the sequence of applying the conventional and MWHS-2 radiance data. The results show that assimilating MWHS-2 data is able to enhance the dynamic and thermal structures of the typhoon system. The experiment with FY-3E MWHS-2 assimilated demonstrated superior performance in terms of simulating the typhoon's structure and providing a prediction of the typhoon's intensity and track than the experiment with FY-3D MWHS-2 did. The two-step assimilation strategy that assimilates conventional observations before the radiance data has improved the track and intensity forecasts at certain times, particularly with the FY-3E MWHS-2 radiance. It appears that large-scale atmospheric conditions are more refined by initially assimilating the Global Telecommunication System (GTS) data, with subsequent satellite data assimilation further adjusting the model state. This strategy has also confirmed improvements in precipitation prediction as it enhances the dynamic and thermal structures of the typhoon system. [ABSTRACT FROM AUTHOR]

Wildfires have long been regarded as one chief culprit in regional air pollution, and pose great impacts on climate change. Although climate forcing of wildfire smoke has been widely investigated, its influence on synoptic systems remains unclear. Based on measurement and modeling analysis, the impact of wildfire smoke on the development of a mid‐latitude cyclone was revealed for Canadian wildfires in early June of 2023. The radiative forcing induced by smoke at surface and in the atmosphere reached up to −150 and 100 W m−2, posing opposite tendencies of atmospheric stratification over the land and ocean. Such perturbations contributed to the enhancement and stagnation of the cyclone, which favored the transport of smoke from the fire‐intensive region, indicated by nearly 40% increment of PM2.5 mass flux. With escalating wildfire risk in the future, the inclusion of smoke aerosols' impacts on meteorology in weather forecast models is of great importance. Plain Language Summary: Wildfires are uncontrolled fires that burn in the wildland vegetation, posing great challenges to regional air quality and global climate. Wildfire smoke has been known to exert great climate forcing via aerosol‐radiation interaction yet its impact on synoptic scales needs further investigation. Here, based on comprehensive observations and modeling analysis for the extreme Canadian wildfires in early June of 2023, smoke aerosol is revealed to induce significant radiative forcing and yield opposite modifications of temperature stratification over the land and ocean, resulting in intensified and stagnant mid‐latitude cyclone. Such perturbations favored the transport of smoke from fire‐intensive region to downwind cities in Canada and United States, and the subsequent long‐range transport dominated by the cyclone. Key Points: Smoke from intensive Canadian wildfires in early June of 2023 degraded the air quality of cities in eastern Canada and United StatesThe wildfire smoke enhanced the development of the mid‐latitude cyclone via smoke aerosol‐radiation interactionThe intensification and stagnation of the cyclone facilitated the transport of smoke to downwind cities in northeastern United States [ABSTRACT FROM AUTHOR]

Site-specific weather forecasts are essential for accurate prediction of power demand and are consequently of great interest to energy operators. However, weather forecasts from current numerical weather prediction (NWP) models lack the fine-scale detail to capture all important characteristics of localised real-world sites. Instead, they provide weather information representing a rectangular gridbox (usually kilometres in size). Even after post-processing and bias correction, area-averaged information is usually not optimal for specific sites. Prior work on site-optimised forecasts has focused on linear methods, weighted consensus averaging, and time-series methods, among others. Recent developments in machine learning (ML) have prompted increasing interest in applying ML as a novel approach towards this problem. In this study, we investigate the feasibility of optimising forecasts at sites by adopting the popular machine learning model "gradient boosted decision tree", supported by the XGBoost package (v.1.7.3) in the Python language. Regression trees have been trained with historical NWP and site observations as training data, aimed at predicting temperature and dew point at multiple site locations across Australia. We developed a working ML framework, named "Multi-SiteBoost", and initial test results show a significant improvement compared with gridded values from bias-corrected NWP models. The improvement from XGBoost (0.1–0.6 °C, 4–27% improvement in temperature) is found to be comparable with non-ML methods reported in the literature. With the insights provided by SHapley Additive exPlanations (SHAP), this study also tests various approaches to understand the ML predictions and increase the reliability of the forecasts generated by ML. [ABSTRACT FROM AUTHOR]

Operational forecasters desire information about how their reservoir and riverine systems will evolve over monthly to seasonal timescales. Seasonal traces of hydrometeorological variables at a daily or sub-daily resolution are needed to drive hydrological models at this timescale. Operationally available models such as the Climate Forecast System (CFS) provide seasonal precipitation forecasts, but their coarse spatial scale requires further processing for use in local or regional hydrologic models. We focus on three methods to generate such forecasts: (1) a bias-adjustment method, in which the CFS forecasts are bias-corrected by ground-based observations; (2) a weather generator (WG) method, in which historical precipitation data, conditioned on an index of the El Niño–Southern Oscillation, are used to generate synthetic daily precipitation time series; and (3) a historical analog method, in which the CFS forecasts are used to condition the selection of historical satellite-based mean areal precipitation (MAP) traces. The Yaque del Norte River basin in the Dominican Republic is presented herein as a case study, using an independent dataset of rainfall and reservoir inflows to assess the relative performance of the methods. The methods showed seasonal variations in skill, with the MAP historical analog method having the strongest overall performance, but the CFS and WG methods also exhibited strong performance during certain seasons. These results indicate that the strengths of each method may be combined to produce an ensemble forecast product. [ABSTRACT FROM AUTHOR]

Throughout its history, Papua New Guinea (PNG) has faced recurrent agricultural droughts, imposing considerable strain on both livelihoods and the economy. Particularly severe droughts have been associated with El Niño climate patterns. During these episodes, PNG becomes especially vulnerable to extended periods of aridity and diminished precipitation. Historically, humanitarian assistance for these events has primarily focused on responding to emergencies after an agricultural drought has been declared and communities have already been impacted. Here, we developed a proactive agricultural drought-triggering method for anticipatory action (AA) in PNG to offer a more sustainable and cost-effective approach to address this hazard. Our AA uses weather forecasts and risk data to identify and implement mitigative actions before a disaster occurs. The research details a step-by-step guide from early warning to action implemented by the Food and Agricultural Organization of the United Nations and the Government of Papua New Guinea. This preemptive disaster risk management initiative integrates a combined drought index (CDI) with specific thresholds and tailored anticipatory actions based on crop calendars. Moreover, the developed CDI provides a 3-month lead time for implementing AA to reduce the impact of the agricultural drought. During the El Niño-induced drought event that began in 2023, the CDI was tested and the AA was piloted for the first time. [ABSTRACT FROM AUTHOR]

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations. [ABSTRACT FROM AUTHOR]

This study investigates the impact of dust on radiation over the Arabian Peninsula (AP) during the reported high, low, and normal dust seasons (March–August) of 2012, 2014, and 2015, respectively. Simulations were performed using the Weather Research and Forecasting model coupled to a Chemistry module (WRF‐Chem). The simulated seasonal horizontal and vertical dust concentrations, and their interannual distinctions, match well with those from two ground‐based AERONET observations, and measurements from MODIS and CALIOP satellites. The maximum dust concentrations over the dust‐source regions in the southern AP reach vertically upto 700 hPa during the high dust season, but only upto 900–950 hPa during the low/normal dust seasons. Stronger incoming low‐level winds along the southern Red Sea and those from Iraq bring in higher‐than‐normal dust during the high dust summers. We conducted a sensitivity experiment by switching‐off the dust module to assess the radiative perturbations due to dust. The results suggest that active dust‐module improved the fidelity of simulated radiation fluxes distributions at the surface and top of the atmosphere vis‐à‐vis Clouds and the Earth's Radiant Energy System (CERES) measurements. Dust results in a 26 Wm−2 short‐wave (SW) radiative forcing in the tropospheric‐column over the AP. The SW radiative forcing increases by another 6–8 Wm−2 during the high dust season due to the increased number of extreme dust days, which also amplifies atmospheric heating. During extreme dust days, the heating rate exhibits a dipolar structure, with cooling over the Iraq region and warming of 40%–60% over the southern‐AP. Plain Language Summary: Dust is the dominant tropospheric aerosol in the Arabian Peninsula (AP). High dust loading and prolonged episodes are crucial in dust‐radiation feedback mechanisms. WRF‐Chem successfully simulated the spatial distribution of dust aerosols and their underlying dust‐transport mechanisms over the study region. Model results reveal that dust aerosols influence surface and atmospheric radiation, resulting in cooling at the surface and warming in the overlying atmosphere. Extreme dust days exhibit significant heating in the south and cooling in the north. This study enhances our understanding of dust‐aerosol interactions with regional meteorology, aiding in better weather and climate prediction in dust‐laden regions like the AP. Key Points: This study explores the influence of dust aerosols on radiative fluxes over the Arabian Peninsula (AP)WRF‐Chem model effectively captures the physical mechanisms of dust transportExtreme dustiness significantly modulates the radiative fluxes, thereby exerting a notable influence on the atmospheric heating rates [ABSTRACT FROM AUTHOR]

Foehn winds have been a focus of research in mid‐latitude mountainous regions for more than 150 years, where their onset is typically associated with warm, dry, and gusty winds. This research has now extended into high latitude regions, yet research of foehn winds in subtropical and tropical regions remains scarce. Here we present results from the first investigation of foehn winds in the subtropics of Southeast Queensland (SEQ), Australia. Analysis of meteorological records found that foehn winds occur throughout the year with peak frequency and duration in late winter (August) associated with the passage of shortwave troughs over southern Australia. Modeling of wind fields and atmospheric boundary layer conditions for three case studies was conducted using the Weather Research and Forecasting (WRF) model. Results showed foehn events in SEQ can be associated with mountain waves and hydraulic jump features in the lee of topographic barriers. Over lee slopes, acceleration of wind speeds and topographic channeling of foehn winds was found to occur, along with substantial increases in air temperature, and decreases in relative humidity. Warming of the foehn airstream is believed to occur primarily through isentropic drawdown with a likely contribution from surface sensible heat flux. Recommendations for future research are made in light of the importance of foehn winds to wildfire management and mitigation in SEQ. Plain Language Summary: Foehn winds form as airflow associated with the passage of large‐scale weather systems descends over the lee slopes of mountains and escarpments. These warm, dry, and gusty winds significantly change the meteorology in the lee of mountains and escarpments causing increases in air temperature, wind speed and decreases in humidity. While foehn winds have been a focus of mountain meteorology in mid‐latitude regions for more than 150 years, research of foehn in other climate zones is scarce. Here we present results from the first investigation of foehn winds in the subtropics of Southeast Queensland (SEQ), Australia—a region where foehn winds significantly increase the threat of wildfires. Results show that foehn winds in this region are most common in late winter (August) with the passage of shortwave troughs over southern Australia. Using the Weather Research and Forecasting (WRF) model we find that foehn wind events in SEQ may be associated with mountain waves and other dynamical features in the lee of topography. We suggest avenues for future foehn wind research in SEQ given their importance to wildfire management and mitigation. Key Points: Subtropical foehn winds are accurately modeled using the Weather Research of Forecasting modelFoehn warming of +10 to +14°C was simulated and caused by isentropic drawdownModeling showed mountain wave and hydraulic jump features in trans‐barrier flow [ABSTRACT FROM AUTHOR]

Dust transported from rangelands of the Southwestern United States (US) to mountain snowpack in the Upper Colorado River Basin during spring (March‐May) forces earlier and faster snowmelt, which creates problems for water resources and agriculture. To better understand the drivers of dust events, we investigated large‐scale meteorology responsible for organizing two Southwest US dust events from two different dominant geographic locations: (a) the Colorado Plateau and (b) the northern Chihuahuan Desert. High‐resolution Weather Research and Forecasting coupled with Chemistry model (WRF‐Chem) simulations with the Air Force Weather Agency dust emission scheme incorporating a MODIS albedo‐based drag‐partition was used to explore land surface‐atmosphere interactions driving two dust events. We identified commonalities in their meteorological setups. The meteorological analyses revealed that Polar and Sub‐tropical jet stream interaction was a common upper‐level meteorological feature before each of the two dust events. When the two jet streams merged, a strong northeast‐directed pressure gradient upstream and over the source areas resulted in strong near‐surface winds, which lifted available dust into the atmosphere. Concurrently, a strong mid‐tropospheric flow developed over the dust source areas, which transported dust to the San Juan Mountains and southern Colorado snowpack. The WRF‐Chem simulations reproduced both dust events, indicating that the simulations represented the dust sources that contributed to dust‐on‐snow events reasonably well. The representativeness of the simulated dust emission and transport in different geographic and meteorological conditions with our use of albedo‐based drag partition provides a basis for additional dust‐on‐snow simulations to assess the hydrologic impact in the Southwest US. Plain Language Summary: Dust transported from rangelands of the Southwestern United States (US) to the mountain snowpack in the Upper Colorado River Basin during spring (March–May) is a growing problem for water resources and agriculture. Understanding the drivers of dust events is essential to better forecast the possible impact of dust on water resources. We investigated the weather conditions that led to two dust events originating from the Colorado Plateau and northern Chihuahuan Desert. We also performed computer simulations with an improved dust emission model to explore the influence of vegetation on the susceptibility of landscapes to wind erosion during dust events. Our meteorological analysis revealed commonalities in the upper‐level meteorology before dust event formation and provided valuable insights into where and when high‐impact dust events may occur. However, further investigation is needed to generalize the impact of jet stream interaction on dust events' intensity. Our simulations represented the dust sources reasonably well by using an improved dust emission model. The improved dust emission and transport simulation under different meteorological and geographical conditions provided a basis for future dust‐on‐snow simulations to assess the hydrological impact in the Southwestern US. Key Points: Large‐scale meteorological analysis and dust emission and transport simulation of Southwest US dust‐on‐snow eventsPolar and Sub‐tropical jet stream interaction was a common upper‐level meteorological feature to Southwest US dust eventsSimulated dust source areas on the Colorado Plateau and Chihuahuan Desert reflect observed dust emission hot spots [ABSTRACT FROM AUTHOR]

High‐resolution climate projections are valuable resources for understanding the regional impacts of climate change and developing appropriate adaptation/mitigation strategies. In this study, we developed a 10‐km gridded hydrometeorological dataset over India by dynamic downscaling of the bias‐corrected Community Earth System Model (CESMv1) climate projections under RCP8.5 scenario using the state‐of‐the‐art Weather Research and Forecasting (WRF) model. The downscaled CESM dataset (DSCESM) is archived in the World Data Center for Climate (WDCC) portal at three temporal resolutions (daily, monthly and monthly climatology) for current (2006–2015), mid‐century (2041–2050) and end‐century (2091–2100) periods. The dataset includes 2‐m air temperature, total accumulated precipitation, wind speed, relative humidity, sensible and latent heat fluxes, along with surface shortwave and outgoing longwave radiation. All the DSCESM variables were evaluated against reanalysis data and station observations for the period 2006–2015. This dataset can help us quantitatively understand regional climate change in India. It can also be used in conjunction with agricultural, hydrological, fire and other application models for climate change impact assessment on various sectors to help develop effective adaptation/mitigation strategies. [ABSTRACT FROM AUTHOR]

As cities face a changing climate, buildings will be subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under climate change is essential for maintaining sustainable and resilient communities. To better prepare building simulation climate data with urban effects, a computationally efficient approach is used to generate "urbanized" data, where the city's unique signature is obtained through the dynamic Weather Research and Forecasting model for the Ottawa, Canada region. We demonstrate this process using existing climate data and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. The data consists of several 31-year time series of climate variables such as temperature, humidity, wind speed and direction, pressure, cloud cover, and precipitation over different global warming thresholds. Such a dataset allows building practitioners to evaluate building performance under both historical and future climate conditions, as well as to evaluate the impacts of nature-based solutions to mitigate future climate change risks. [ABSTRACT FROM AUTHOR]

During the life cycle of a tropical cyclone from the homogeneous ocean-phase to the heterogeneous land-phase, estimating the catchment-scale evapotranspiration (ET) for agricultural water management is a challenging task. For assessing the catchment-scale ET and the causative weather variables associated with the land–atmosphere interactions during the Tropical Cyclone Phailin (TC Phailin), some popular parameterization scheme combinations in the Weather Research and Forecasting (WRF) model were selected. The suitability of different WRF parameterization scheme combinations (PSCs) were evaluated in the Brahmani River basin in eastern India to reproduce the observed weather variables of surface (2-m) air temperature, precipitation and atmospheric pressure at hourly and daily temporal resolutions during the pre-, at-, and post events of the TC Phailin. This study found that the 'Rapid Update Cycle' (RUC) LSM with 'Purdue Lin' microphysics and 'Arakawa convective' cumulus scheme performed the best. Overall, the PSCs could simulate the surface air temperature better than the precipitation during the short timeframe of the extreme event, whereas the overall regional pressure simulation showed a constant bias. The results reveal that WRF-LSM model that accounts for both local (clothesline) and global (oasis) advection effects could better simulate the ET flux compared to the corresponding MOD16A2 remote sensing product and the Food and Agricultural Organization (FAO)-56 Penmen-Monteith (PM) equation, especially during cloudy days. The local feedback of the TC Phailin over the land-surface ET flux and its climatic and land-surface drivers (soil moisture) during the pre-, at-, post-cyclone events reveal that: (a) the negative Bowen Ratio estimates during the heavy rainfall resulted in a reduced ET flux, (b) the negative sensible heat flux during this period facilitates for flow of heat from surface to atmosphere, cooling the soil of the river basin. Overall, this study aids in a better understanding of the moisture flux and energy transfer dynamics between the land–atmosphere system during the onset of a cyclone. [ABSTRACT FROM AUTHOR]