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Difficulty in using observations to isolate the impacts of aerosols from meteorology on deep convection often stems from the inability to resolve the spatiotemporal variations in the environment serving as the storm's inflow region. During the U.S. Department of Energy (DOE) Tracking Aerosol Convection interactions Experiment (TRACER) in June-September 2022, a Texas A&M University (TAMU) team conducted a mobile field campaign to characterize the meteorological and aerosol variability in air masses that serve as inflow to convection across the ubiquitous mesoscale boundaries associated with the sea and bay breezes in the Houston, Texas, region. These boundaries propagate inland over the fixed DOE Atmospheric Radiation Measurement (ARM) sites. However, convection occurs on either or both the continental or maritime sides or along the boundary. The maritime and continental air masses serving as convection inflow may be quite distinct, with different meteorological and aerosol characteristics that fixed- site measurements cannot simultaneously sample. Thus, a primary objective of TAMU TRACER was to provide mobile measurements similar to those at the fixed sites, but in the opposite air mass across these moving mesoscale boundaries. TAMU TRACER collected radiosonde, lidar, aerosol, cloud condensation nuclei (CCN), and ice nucleating particle (INP) measurements on 29 enhanced operations days covering a variety of maritime, continental, outflow, and prefrontal air masses. This paper summarizes the TAMU TRACER deployment and measurement strategy, instruments, and available datasets and provides sample cases highlighting differences between these mobile measurements and those made at the ARM sites. We also highlight the exceptional TAMU TRACER undergraduate student participation in high-impact learning activities through forecasting and field deployment opportunities. SIGNIFICANCE STATEMENT: The environment influencing storms often varies across scales that are not always adequately captured by measurements collected at fixed locations. This paper describes our strategy for collecting mobile measurements of the aerosols and meteorology that influenced convection initiated by the sea breeze across the Houston, Texas, region. We show several examples of the local variations in aerosols and meteorology influencing storms that were captured by our mobile platform that were different from those sampled at fixed observation sites. We also highlight potential future studies and science questions that could be addressed using our dataset. KEYWORDS: Deep convection; Sea breezes; Aerosols; Cloud microphysics; Soundings; Aerosol-cloud interaction, 1. Motivation and goals Deep convective systems play a significant role in a number of critical components of the climate system through their large contribution to the hydrological cycle, feedback [...]

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. KEYWORDS: Social Science; Extreme events; Precipitation; Communications/ decision making, 1. Introduction Skillful prediction of extreme precipitation periods, defined in Dickinson et al. (2021), is a topic of significance for a variety of sectors, including civil protection (e.g., Curriero et [...]

Over the last decade, supercell simulations and observations with ever-increasing resolution have provided new insights into the vortex-scale processes of tornado formation. This article incorporates these and other recent findings into the existing three-step model by adding an additional fourth stage. The goal is to provide an updated and clear picture of the physical processes occurring during tornadogenesis. Specifically, we emphasize the importance of the low-level wind shear and mesocyclone for tornado potential, the organization and interaction of relatively small-scale pretornadic vertical vorticity maxima, and the transition to a tornado-characteristic flow. Based on these insights, guiding research questions are formulated for the decade ahead. SIGNIFICANCE STATEMENT: This article provides a nontechnical overview of how tornadoes form. Sequentially, the most important processes include the initial creation of rotating updrafts, the development of disorganized patches of rotation at the surface, the organization of these patches into a more defined, symmetric vortex, and the final transition into a fully developed tornado in which air turns abruptly upward very near the surface. Based on this proposed conceptual model, guiding research questions are formulated for the decade ahead. KEYWORDS: Tornadogenesis; Severe storms; Tornadoes; Radars/Radar observations; Supercells, 1. How does a tornado form? This is perhaps one of the most elementary yet enduring questions in meteorology. Even though severe-storm research has focused on this problem for over [...]

Given inconsistencies in reporting methods and general lack of documentation, the creation of a unified tornado database across the world has been an elusive target for severe weather climatology purposes and historical interest. Previous online tornado documentation has also often been inconsistent or is now defunct. Many individual countries or continents maintain tornado information through either government-sponsored or independent organizations. The Tornado Archive was developed to create a first-of-its-kind digitized synthesis of worldwide tornado documentation, using the most complete sources of information available for regions known to be tornadically active. Spatial and temporal trends in tornado occurrence and reporting can be visualized through an interactive user interface with a variety of filtering methods and environmental reanalysis datasets, such as the fifth major global reanalysis produced by ECMWF (ERA5). The additional data introduced using Thomas Grazulis' Significant Tornadoes may be beneficial for tornado climatology studies over the United States. The Tornado Archive is also intended to be a collaborative exercise, with clear data attribution and open avenues for augmentation, and the creation of a common data model to store the tornado information will assist in maintaining and updating the database. In this work, we document the methods necessary for creating the Tornado Archive database, provide broader climatological analysis of spatiotemporal patterns in tornado occurrence, and outline potential use cases for the data. We also highlight its key limitations and emphasize the need for further international standardization of tornado documentation. SIGNIFICANCE STATEMENT: Historical records of tornado occurrence across the world are dispersed among various sources and are sometimes difficult to access. We combined data from a wide variety of sources into a standardized database of over 100 000 tornadoes. This includes all recorded United States significant tornadoes from 1680 to 1949, which we digitized from a print volume. We hope our database will allow for analyses of tornado climatology to be more easily performed. We also developed an online interface which will allow researchers and laypersons alike to explore the data. KEYWORDS: Tornadoes; Climatology; Databases, 1. Introduction Records of tornado occurrence are currently spread among individual countries and regional research groups, with varying quality, standards, and temporal coverage. Thus, it is difficult to draw conclusions [...]

This study addresses the critical need for accurate lightning prediction in northeastern India, a region frequently impacted by severe convective storms. Despite advancements in forecasting, there remains a gap in predicting lightning activity, especially during extreme weather. This research evaluates the Weather Research and Forecasting (WRF) model's performance in simulating lightning events on June 16 and 17, 2022. The study employs the Morrison double-moment 6-class microphysics scheme and the Price and Rind A simple lightning parameterization for calculating global lightning distributions lightning parameterization method to model cloud-to-ground (CG) and intra-cloud (IC) lightning. Model predictions are validated against data from the Indian Institute of Tropical Meteorology Lightning Location Network (IITM-LLN) and the World-Wide Lightning Location Network (WWLLN), alongside INSAT-3D cloud brightness temperature and NASA-IMERG rainfall data. The research findings show that the lightning parameterization scheme with a 20 dBZ top threshold provides accurate predictions, with spatial autocorrelation metrics of 67.41% and 85.75% for June 16 and 17, respectively. Further analysis through skill scores, such as the Equitable Threat Score and Probability of Detection, supports the model's reliability. Hotspot analysis identifies regions of heightened lightning activity, offering valuable insights into areas prone to frequent strikes. This research emphasizes the importance of advanced modeling techniques and comprehensive data integration in enhancing weather forecasts, contributing to improved safety and mitigation strategies for extreme weather events in northeastern India. [ABSTRACT FROM AUTHOR]

A mesoscale convective system (MCS) occurred over the East China coastal provinces and the East China Sea on 30 April 2021, producing damaging surface winds near the coastal city Nantong with observed speeds reaching 45 m s−1. A simulation using the Weather Research and Forecasting model with a 1.5-km grid spacing generally reproduces the development and subsequent organization of this convective system into an MCS, with an eastward protruding bow segment over the sea. In the simulation, an east-west-oriented high wind swath is generated behind the gust front of the MCS. Descending dry rear-to-front inflows behind the bow and trailing gust front are found to feed the downdrafts in the main precipitation regions. The inflows help to establish spreading cold outflows and enhance the downdrafts through evaporative cooling. Meanwhile, front-to-rear inflows from the south are present, associated with severely rearward-tilted updrafts initially forming over the gust front. Such inflows descend behind (north of) the gust front, significantly enhancing downdrafts and near-surface winds within the cold pool. Consistently, calculated trajectories show that these parcels that contribute to the derecho originate primarily from the region ahead (south) of the east-west-oriented gust front, and dry southwesterly flows in the low-to-middle levels contribute to strong downdrafts within the MCS. Moreover, momentum budget analyses reveal that a large westward-directed horizontal pressure gradient force within the simulated cold pool produced rapid flow acceleration towards Nantong. The analyses enrich the understanding of damaging wind characteristics over coastal East China and will prove helpful to operational forecasters. [ABSTRACT FROM AUTHOR]

Large‐eddy simulation (LES) has been adopted to study wind turbine wakes because it can capture fine‐scale details of turbulent wind flows and interactions with wind turbines. Here, we use the LES version of the Weather Research and Forecasting (WRF) model with an actuator disk model to gain insights on several wake effects that have been traditionally difficult to measure. The first finding is that the wake has a "dual nature," meaning that the wind speed deficit behaves differently from the added turbulent kinetic energy (TKE) and the two are not co‐located in space. For example, the wind speed deficit peaks at hub height and reaches the ground within 8D (D is the rotor diameter), but added TKE peaks near the rotor tip and generally remains aloft. Second, temperature changes near the ground are driven by the added TKE in the rotor area and by atmospheric stability. The combination of these two factors determines the sign and intensity of the vertical heat flux divergence below the rotor, with convergence and warming associated with stable conditions and weak divergence and modest cooling with unstable conditions. Third, wakes do not expand indefinitely, as suggested by similarity theory applied to the wind speed deficit, but eventually stop expanding and actually contract, at different rates depending on atmospheric stability. The implication of these findings is that, in order to study wakes, it is not sufficient to focus on wind speed deficit alone, because TKE is also important and yet behaves differently from the wind speed deficit. [ABSTRACT FROM AUTHOR]

Aims Methods and results Conclusion Long‐term fine particulate matter (PM2.5) exposure has been linked to incident heart failure (HF), but the impacts of its constituents remain unknown. We aimed to investigate the associations of PM2.5 constituents with incident HF, and further evaluate the modification effects of genetic susceptibility.PM2.5 and its constituents, including elemental carbon (EC), organic matter (OM), ammonium (NH4+), nitrate (NO3−), and sulfate (SO42−), were estimated using the European Monitoring and Evaluation Programme model applied to the UK (EMEP4UK) driven by Weather and Research Forecast model meteorology. A polygenic risk score (PRS) was calculated to represent genetic susceptibility to HF. We employed Cox models to evaluate the associations of PM2.5 constituents with incident HF. Quantile‐based g‐computation model was used to identify the main contributor of PM2.5 constituents. Among 411 807 individuals in the UK Biobank, 7554 participants developed HF during a median follow‐up of 12.05 years. The adjusted hazard ratios of HF for each interquartile range increase in PM2.5, EC, OM, NH4+, NO3−, and SO42− were 1.50 (1.46–1.54), 1.31 (1.27–1.34), 1.12 (1.09–1.15), 1.42 (1.41–1.44), 1.26 (1.23–1.29), and 1.25 (1.24–1.26), respectively. EC (43%) played the most important role, followed by NH4+ and SO42−. Moreover, synergistic additive interactions accounted for 9–16% of the HF events in individuals exposed to both PM2.5, NH4+, NO3−, and SO42− and PRS.Long‐term exposure to PM2.5 constituents may elevate HF risk, and EC was the major contributor. Additive effects of PM2.5 constituents and PRS on HF risk were revealed. [ABSTRACT FROM AUTHOR]

Interplanetary coronal mass ejections (ICMEs), characterized by their magnetic flux ropes, could potentially trigger geomagnetic disturbances. They have been attracting extensive investigations for decades. Despite numerous ICME models proposed in the past, few account for the curvature of the flux rope axis. In this study, we use conjunction observations from ACE, STEREO A and B, Juno and Solar Orbiter to analyze the evolution of the rope orientation of ICME flux ropes. Our findings indicate that the orientation of these ropes changes independently of the scale of the ropes or the distance they travel between spacecrafts. Furthermore, we estimate and compare the major radii of these flux ropes, uncovering a diverse range of distributions that do not seem to depend on the flux rope's width. These results provide fresh insights and constraints for global ICME models, thereby contributing to the advancement of space weather research. [ABSTRACT FROM AUTHOR]

The horizontal turbulence parameterization is vital for the intensity and structure forecasting of tropical cyclone (TC) in numerical weather prediction (NWP) models. The default two‐dimensional (2D) standard Smagorinsky model with a single universal constant in Weather and Research Forecasting (WRF) model has been proven to be over dissipative for TC, leading to underprediction of TC intensity. This study provides the first attempt to implement the physically based 2D dynamic Smagorinsky model (DSM) for horizontal turbulence parameterization in WRF model for TC forecasts. The DSM dynamically computes the Smagorinsky coefficient as a function of the resolved flow during the simulation, avoiding the need to prescribe the coefficient a prior. The test results of the DSM in a TC NWP model show that the DSM can significantly improve the wind intensity forecasts compared to the standard Smagorinsky model. Plain Language Summary: The representation of horizontal turbulent mixing in numerical models is important for the tropical cyclone (TC) forecasting. However, existing horizontal turbulence models (i.e., traditional Smagorinsky model with a constant coefficient) in numerical models underpredict the observed maximum surface wind speed. A dynamic Smagorinsky model with dynamically calculated coefficient according to the state of flow avoids the need for case‐by‐case tuning of the coefficient. The simulations of five TC cases using the dynamic Smagorinsky model present the improved wind intensity forecasts compared to the traditional Smagorinsky model. Key Points: Standard Smagorinsky model with default constant coefficient is overly dissipative for tropical cyclone (TC), underpredicting TC intensityWe are the first to attempt to implement the dynamic Smagorinsky model for horizontal turbulence parameterization in TC mesoscale numerical weather prediction modelDynamic Smagorinsky model significantly reduces the bias in TC intensity forecasts [ABSTRACT FROM AUTHOR]

The present study evaluated the performance of the Weather Research and Forecasting (WRF) model in the forecast of a very severe cyclonic storm (VSCS) Vardah that developed over the Bay of Bengal (BoB) and make landfall over the Tamil Nadu coast on 12 December 2016. The study examined the impact of microwave humidity sounder (MHS) satellite radiance data assimilation to forecast of structure, intensity and rainfall of Vardah cyclone near to the coast using two different land surface model (LSM; Noah and Noah-MP). The mean track error is about 33 and 38 km with data assimilation using Noah and Noah-MP LSM respectively and this error is about 41 km in without data assimilation using Noah and Noah-MP LSM. The predicted intensity of the storm in terms of the maximum surface wind is also slightly better predicted in data assimilation experiment with Noah LSM, this is due to the improved initial condition. Accumulated rainfall and the maximum reflectivity in terms of spatial distribution and magnitude of the VSCS Vardah is well simulated in all the experiments but slightly better in data assimilation experiments. Hovmoller diagram is also presented to see the shifting pattern of accumulated rainfall across the region during simulation period. [ABSTRACT FROM AUTHOR]

The risk of more frequent and intensified extreme events is expected to increase with climate change. In semi-arid regions, particularly, both increased flooding and prolonged droughts pose a threat as the water resources system must be prepared for both types of extremes. To understand how future extremes may be altered, however, past extremes must be understood. For this reason, this study employs three physically based models to simulate the atmospheric, snow, and hydrologic conditions in the American River Watershed: the Weather Research and Forecasting Model and the Watershed Environmental Hydrology Hydro-Climate Model with the addition of a SNOW Module. Each model is individually calibrated and validated against either gauge data or data provided by the Parameter-Elevation Regressions on Independent Slopes Model for two 10-year periods before a full 169-year daily reconstruction is performed for the period from 1852 through 2020. To analyze the model performance under hydrologic extremes, three flood years (1997, 2006, and 2017) and two drought periods (1987-1992 and 2012-2016) are further analyzed. The reconstruction outputs are found to successfully simulate historical conditions at both daily and monthly scales. Results highlight the importance of fully accounting for the role of temperature in snow processes and the subsequent flow conditions. By understanding how the models perform under historical extreme conditions, future extreme events can be compared, and necessary adaptation plans can be conceived to ensure that the watershed is prepared for future wet and dry conditions. Additionally, the created reconstruction dataset is comprehensive, simulating conditions across the hydroclimate, at a finer spatial and temporal scale than is typically available from observation data alone. The dataset can be further analyzed to assess changes in the trends and the potential trajectory of extreme events within the American River Watershed. [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]

To comprehensively characterize convective precipitation in the central Amazon region, we utilize the Python FLEXible object TRacKeR (PyFLEXTRKR) to track mesoscale convective systems (MCSs) observed through satellite measurements and simulated by the Weather Research and Forecasting model at a convection‐permitting resolution. This study spans a 2‐month period during the wet seasons of 2014 and 2015. We observe a strong correlation between the MCS track density and accumulated precipitation in the Amazon basin. Key factors contributing to precipitation, such as MCS properties (number, size, rainfall intensity, and movement), are thoroughly examined. Our analysis reveals that while the overall model produces fewer MCSs with smaller mean sizes compared to observations, it tends to overpredict total precipitation due to excessive rainfall intensity for heavy rainfall events (≥10 mm hr−1). These biases in simulated MCS properties could vary with the constraints on the convective background environment. Moreover, while the wet bias from heavy (convective) rainfall outweighs the dry bias in light (stratiform) rainfall, the latter can be crucial, particularly when MCS cloud cover is significantly underestimated. A case study for 1 April 2014 highlights the influence of environmental conditions on the MCS lifecycle and identifies an unrealistic model representation in both stratiform and convective precipitation features. Plain Language Summary: We tracked large‐size rain storms called mesoscale convective systems (MCSs) in the central Amazon during the wet seasons of 2014 and 2015. Data generated from the MCS tracking helps us understand how rainfall is produced as a function of the number of storms, as well as their size, rain intensity, and motion, and how those can be better simulated by weather and climate models. We found that the model produces fewer and smaller MCSs than in reality, but the total rainfall amount is often overestimated. This is because simulated MCSs produce unrealistically intense heavy rainfall events. On the other hand, light rainfall events are mostly underrepresented by the model. Thus, the model error in total precipitation is determined by how these two counterbalance each other. Our analysis also suggests model representation of the background environment can be critical for simulating realistic MCS properties. Key Points: Simulated and observed mesoscale convective system (MCS) clouds and precipitation are tracked during the 2014/15 wet seasons in the central AmazonExcessive heavy rain intensity (≥10 mm hr−1) of simulated MCS lead to an overprediction of precipitationDry bias in stratiform rainfall can also drive MCS precipitation bias when stratiform cloud cover is substantially underpredicted [ABSTRACT FROM AUTHOR]

Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions. [ABSTRACT FROM AUTHOR]

Satellite cloud image sequences contain rich spatial and temporal information, and forecasting future cloud image sequences is of great significance for meteorological research. Traditional satellite cloud image prediction methods usually ignore nonlinear variations in cloud masses, leading to large errors in prediction results and low prediction efficiency. The use of existing video prediction methods for satellite cloud image sequence prediction also suffers from problems of blurred prediction images and the accumulation of sequence errors. To address these issues, we propose a Multi-feature Extraction and High-resolution Generative Network (MEHGNet) for the prediction of satellite cloud image sequences, which consists of an encoder, a translator, a decoder, and a generator. To learn the spatial features and spatiotemporal dependencies of cloud images, 2D convolution multi-head attention mechanisms and local residue connections are introduced to the encoder and decoder. The generator preserves detailed features and improves the resolution of the predicted images using the generative ability of generative adversarial networks. In addition, a motion-aware loss function is proposed to learn high-level features of motion variations among cloud image sequences. Experiments on satellite datasets demonstrate that the proposed method is superior compared to other prediction methods. [ABSTRACT FROM AUTHOR]

In this study, high‐resolution CO2 concentration data were generated for East Asia to analyse long‐term changes in atmospheric CO2 concentrations, as East Asia is an important region for understanding the global carbon cycle. Using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem), atmospheric CO2 concentrations were simulated in East Asia at a resolution of 9 km for a period of 10 years (2009–2018). The generated CO2 concentration data include CO2 concentrations, biogenic CO2 concentrations, anthropogenic CO2 concentrations, oceanic CO2 concentrations, biospheric CO2 uptake, biospheric CO2 release and meteorological variables at 3‐h intervals. The simulated high‐resolution CO2 concentrations, biogenic CO2 concentrations and anthropogenic CO2 concentrations are stored in NetCDF‐4 (Network Common Data Form, version 4) format and are available for download at https://doi.org/10.7910/DVN/PJTBF3. The simulated annual mean surface CO2 concentrations in East Asia were 391.027 ppm in 2009 and 412.949 ppm in 2018, indicating an increase of 21.922 ppm over the 10‐year period with appropriate seasonal variabilities. The monthly mean CO2 concentrations in East Asia were verified using surface CO2 observations and satellite column‐averaged CO2 mole fraction (XCO2) from Orbiting Carbon Observatory 2 (OCO‐2). Based on surface CO2 observations and OCO‐2 XCO2 concentrations, the average root‐mean‐square error (RMSE) of the simulated CO2 concentrations in WRF‐Chem was 2.474 and 0.374 ppm, respectively, which is smaller than the average RMSE of the low‐resolution CarbonTracker 2019B (CT2019B) simulation. Therefore, the simulated high‐resolution atmospheric CO2 concentrations in East Asia in WRF‐Chem over 10 years are reliable data that resemble the observed values and could be highly valuable in understanding the carbon cycle in East Asia. [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]

The regional climate of New Zealand's South Island is shaped by the interaction of the Southern Hemisphere westerlies with the complex orography of the Southern Alps. Due to its isolated geographical setting in the south‐west Pacific, the influence of the surrounding oceans on the atmospheric circulation is strong. Therefore, variations in sea surface temperature (SST) impact various spatial and temporal scales and are statistically detectable down to temperature anomalies and glacier mass changes in the high mountains of the Southern Alps. To enable future studies on the processes that govern the link between large‐scale SST and local‐scale high‐mountain climate, we utilized dynamical downscaling with the Weather Research and Forecasting (WRF) model to produce a regional atmospheric modelling dataset for the South Island of New Zealand over a 16‐year period between 2005 and 2020. The 2 km horizontal resolution ensures realistic representation of high‐mountain topography and glaciers, as well as explicit simulation of convection. The dataset is extensively evaluated against observations, including weather station and satellite data, on both regional (in the inner domain) and local (on Brewster Glacier in the Southern Alps) scales. Variability in both atmospheric water content and near‐surface meteorological conditions is well captured, with minor seasonal and spatial biases. The local high‐mountain climate at Brewster Glacier, where land use and topographic model settings have been optimized, yields remarkable accuracy on both monthly and daily time scales. The data provide a valuable resource to researchers from various disciplines studying the local and regional impacts of climate variability on society, economies and ecosystems in New Zealand. The model output from the highest resolution model domain is available for download in daily temporal resolution from a public repository at the German Climate Computation Center (DKRZ) in Hamburg, Germany (Kropač et al., 2023; 16‐year WRF simulation for the Southern Alps of New Zealand, World Data Center for Climate (WDCC) at DKRZ [data set]. https://doi.org/10.26050/WDCC/NZ‐PROXY_16yrWRF). [ABSTRACT FROM AUTHOR]

Dust and air pollution events are increasingly occurring around the Taklimakan Desert in southern Xinjiang and in the urban areas of northern Xinjiang. Predicting such events is crucial for the advancement, growth, and prosperity of communities. This study evaluated a dust and air pollution forecasting system based on the Weather Research and Forecasting model coupled with the China Meteorological Administration Chemistry Environment (WRF-CUACE) model using ground and satellite observations. The results showed that the forecasting system accurately predicted the formation, development, and termination of dust events. It demonstrated good capability for predicting the evolution and spatial distribution of dust storms, although it overestimated dust intensity. Specifically, the correlation coefficient (R) between simulated and observed PM10 was up to 0.85 with a mean absolute error (MAE) of 721.36 µg·m−3 during dust storm periods. During air pollution events, the forecasting system displayed notable variations in predictive accuracy across various urban areas. The simulated trends of PM2.5 and the Air Quality Index (AQI) closely aligned with the actual observations in Ürümqi. The R for simulated and observed PM2.5 concentrations at 24 and 48 h intervals were 0.60 and 0.54, respectively, with MAEs of 28.92 µg·m−3 and 29.10 µg·m−3, respectively. The correlation coefficients for simulated and observed AQIs at 24 and 48 h intervals were 0.79 and 0.70, respectively, with MAEs of 24.21 and 27.56, respectively. The evolution of the simulated PM10 was consistent with observations despite relatively high concentrations. The simulated PM2.5 concentrations in Changji and Shihezi were notably lower than those observed, resulting in a lower AQI. For PM10, the simulation–observation error was relatively small; however, the trends were inconsistent. Future research should focus on optimizing model parameterization schemes and emission source data. [ABSTRACT FROM AUTHOR]

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

In this study, eleven different cumulus parameterization schemes (CPSs) in Weather Research and Forecasting (WRF) model version 4.0 were used to test the sensitivity of Madden–Julian oscillation (MJO) eastward propagation to different CPSs and to explore the influences of different CPSs on MJO eastward propagation. The propagation and structures of the MJO-filtered outgoing longwave radiation (OLR) for the MJO active phase composites during the December–January–February (DJF) period of 2007–2016 over the Indian Ocean (IO) were diagnosed in this study. The results show that the simulated MJO propagation is sensitive to the selection of CPSs. And CPS5 (Tiedtke scheme) and CPS8 (New Tiedtke scheme) are selected for the good models composite while the CPS4 (Grell 3D ensemble scheme) and CPS9 (Grell-Devenyi ensemble scheme) are selected for the poor models composite. The good models composite provides similar MJO structures and MJO eastward propagation patterns as those observed, while poor models composite fails. The MJO propagation is highly correlated with boundary layer moisture convergence (BLMC) propagation pattern correlation coefficient (PCC), the U850 asymmetry index, the convective instability index and the new index reconstructed by the three main contributing factors, and BLMC propagation plays the most important role in simulating MJO propagation. Besides, different CPSs may result in different interactions between convective heating and moisture feedback, thus simulating different structures. The results support the trio‑interaction theory and provide a reference for the influence of CPS on the MJO eastward propagation. [ABSTRACT FROM AUTHOR]

The Laurentian Great Lakes significantly influence the climate of the Midwest and Northeast United States, due to their vast thermal inertia, moisture source potential, and unique heat and moisture flux dynamics. This study presents a newly developed coupled lake-ice-atmosphere (CLIAv1) modeling system for the Great Lakes by coupling the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) regional climate model (RCM) with the three-dimensional (3D) Finite Volume Community Ocean Model (FVCOM) and investigates the impact of coupled dynamics on simulating the Great Lakes' winter climate. By integrating 3D lake hydrodynamics, CLIAv1 addresses the limitations of traditional one-dimensional (1D) lake and demonstrates superior performance in reproducing observed LSTs, ice cover distribution, and the vertical thermal structure of the Great Lakes compared to the NU-WRF model coupled with the default 1D Lake Ice Snow and Sediment Simulator (LISSS). CLIAv1 also enhances simulation of over-lake atmospheric conditions, including air temperature, wind speed, and sensible and latent heat fluxes, underscoring the importance of resolving complex lake dynamics for reliable climate projections. More importantly, this study addresses the crucial question about what are the key processes influencing lake thermal structure and ice cover that are missed by 1D lake models but effectively captured by 3D lake models. Through process-oriented numerical experiments, we identify key 3D hydrodynamic processes – ice transport, heat advection, and shear production in turbulence – that explain the superiority of 3D lake models over 1D lake models, particularly in cold season performance and lake-atmosphere interactions. Properly resolving these processes using 3D hydrodynamic model is crucial for successfully simulating the lake-ice-atmosphere coupled Great Lakes winter system. This research underscores the necessity of incorporating 3D hydrodynamic models in RCMs to improve our predictive understanding of the Great Lakes' response to climate change. The findings advocate for a shift towards high-resolution, physics-based modeling approaches to ensure accurate future climate and limnological projections for large freshwater systems. [ABSTRACT FROM AUTHOR]

This research paper discusses a case study of Kelvin-Helmholtz (KH) waves in the atmosphere, specifically focusing on a KH wave event observed above Dubrovnik, Croatia on October 9, 2015. The authors conducted a numerical simulation using the Weather Research and Forecasting (WRF) model to analyze the atmospheric conditions that led to this particular KH event. The simulation suggested the presence of KH waves at altitudes corresponding to the observed KH clouds, and revealed a sheared layer in the middle troposphere and a saturated layer at a specific altitude, providing further evidence of the association between the KH clouds and the favorable conditions for KH waves. The article concludes that the WRF model has the potential to simulate even shorter wavelengths of KH waves with a finer grid resolution. [Extracted from the article]

Flood early warning requires rainfall data with a high temporal and spatial resolution for flood risk analysis to simulate flood dynamics in all small and large basins. However, such high-quality data are still very scarce in many developing countries. In this research, in order to identify the best and most up-to-date rainfall estimation tools for early flood forecasting in arid and semi-arid regions, the northeastern region of Iran with 17 meteorological stations and four rainfall events was investigated. The rainfall products of satellites (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, Global Satellite Mapping of Precipitation, Climate Hazards Group InfraRed Precipitation with Station, European Reanalysis (ERA5), Global Precipitation Measurement) along with the most widely used microphysical schemes of Weather Research and Forecasting (WRF) model (Purdue-Lin (Lin), WRF Single-Moment class 3, 6, and WRF Double-Moment class 6. were used for rainfall modeling. The efficiency of each of these models to forecasting the amount of rainfall was verified by four methods: Threat Scores (TS), False Alarm Ratio, Hit Rate (H), and False Alarm (F). Analysis of research findings showed that the WRF meteorological model has better accuracy in rainfall modeling for the next 24 h. In this model, Lin's microphysical scheme has the highest accuracy, and its threat score (TS) quantity is up to 98% efficient in some stations. The best accuracy of satellite products for estimating the amount of rainfall is up to 50%. This accuracy value is related to the satellite product (ERA5). In this method, an 18 km distance from the ground station is the best distance for setting up the space station, which is used for input to hydrological/hydraulic models. Based on the results of this research, by using the connection of the WRF model with hydrology/hydraulic models, it is possible to predict and simulate rainfall-runoff up to 72 h before its occurrence. Also, by using these space stations, the amount of rainfall is estimated for the entire area of the basin and an early flood warning is issued. [ABSTRACT FROM AUTHOR]

Forecasting rainfall is essential for warning of issues and mitigating natural disasters. For this purpose, employing numerical weather models, even with their uncertainties, can generate reliable forecasts and guide decision-makers. The accuracy of a numerical model can be verified using statistical tools, and it is an essential procedure that needs to be made operationally, aiming to increase the forecasts' reliability. Numerical precipitation forecasts for the mountainous region of Rio de Janeiro, Brazil, were performed using the Weather Research & Forecasting model, configured with three spatial resolution grids of 9, 3, and 1 km, and combining different parameterizations for five physical processes: cloud microphysics, cumulus, planetary boundary layer, surface layer, and land surface. The period of interest was January 11th–12th, 2011, when significant rainfall accumulations originated the fatal natural hazards in Brazil. Analyses of the spatial distribution of rainfall and its temporal evolution were performed to evaluate the predictions from the quantitative and qualitative approaches. The results showed that the Kessler (cloud microphysics), MYNN3 (planetary boundary layer), Grell-Freitas, Betts-Miller-Janjic (cumulus) parameterizations, and the two highest resolution grids (at times, one was better than the other) had predicted the highest rainfall accumulations. From the initial results, this work reinforces the importance of forecast verification, especially considering different physical parameterizations and spatial resolutions since they can strongly influence the results. Also, it corroborates the importance of local numerical forecasts studies aiming to identify the best numerical configurations to forecast heavy rainfall events to alert decision-makers to the possibility of a natural hazard. [ABSTRACT FROM AUTHOR]

Downslope windstorms are common leeward of the Rocky Mountains across the High Plains of Colorado during October through March and can cause property damage and travel disruptions. This region is renowned for two primary types of strong downslope winds: (1) chinooks that feature warm, dry winds and are often associated with the development of mountain waves, and (2) boras that are colder and often associated with the passage of a mid- and upper-level trough. The National Blend of Models (NBM) is regularly utilized by forecasters at the National Weather Service Forecast Office (WFO) in Boulder and is one tool used by the WFO to forecast downslope windstorms. No study, however, has quantified the performance of the NBM during downslope windstorms along the Colorado Front Range. In this exploratory study, downslope windstorms from October 2020 through May 2021 were identified using ten observation sites along the Colorado Front Range. Windstorms were subsequently analyzed to quantify forecast statistics of wind speeds and gusts during windstorms to provide a better understanding of NBM v4.0 performance. On average, we found that the overall maximum magnitudes of wind speeds and gusts are on average 38% and 35% too low in the NBM output, respectively, when averaged across 24 to 72-h lead times. Weather and Research Forecasting (WRF) model simulations of varying vertical resolution were also completed to determine if WRF, a constituent model of the NBM, could provide a robust representation of select downslope windstorms across the Colorado Front Range. [ABSTRACT FROM AUTHOR]

A study for atmospheric transport is essential for the consequence assessment of severe nuclear accidents since radionuclides could be released from the nuclear facility into the atmosphere and cause radioactive pollution in the environment. Atmospheric transport behaviors are strongly related with meteorological conditions, which can obviously influence the transport and diffusion characteristics of radioactive materials in the atmosphere; thus, it is meaningful to investigate the coupling effects between meteorological processes and transport behaviors of radioactive materials. To evaluate the influence of meteorological conditions on atmospheric transport, meteorological parameters for different seasons were first acquired by the weather research forecast model. Furthermore, atmospheric transport behaviors of radioactive materials were simulated by the meso-scale numerical model under different meteorological conditions, and numerical analyses were conducted toward transport and deposition behaviors of radioactive materials. In addition, the influence of FDDA (four-dimensional data assimilation) on meteorological parameters and atmospheric transport behaviors was researched. The present study is important for strengthening consequence assessment for severe nuclear accident and made it possible to apply the data assimilation technology in further research works. [ABSTRACT FROM AUTHOR]

The planned expansion of wind energy in the German Bight is creating much more densely staggered wind farms and wind farm clusters. This results in a significantly greater influence of the generated wakes on energy production of neighboring wind farms. The Dornier‐128 research aircraft operated by the Technische Universität of Braunschweig was used to measure the wind field in the lee of single and multiple wind farm clusters in the German Bight on 4 days during July 2020 and July 2021. The data at 120 m aMSL (above mean sea level) were analyzed to identify wake areas and the wind speed decrease behind the wind farm clusters. The observations were then compared to a range of numerical data including the mesoscale model Weather Research and Forecasting (WRF) applying a wind farm parameterization (WRF with wind farm parameterization [WRF‐WF]) to model wake effects and an engineering model with different setups. A model calibrated on a single wind farm is established as the baseline. A modification with a lower wake recovery, the TurbOPark model, and a WRF‐coupled model make up the three additional declinations considered. Overall, the models compared well to the measurement data in the direct vicinity of the wind farms and up to 20–30 km downstream of the wind farm clusters. The accuracy in wind speed prediction of the model results decreased with distance to the wind farms, where the mesoscale model (WRF‐WF) exhibited a more consistent performance across varying distances. [ABSTRACT FROM AUTHOR]

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models' representation of PCI. Moreover, the models' poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB. [ABSTRACT FROM AUTHOR]

The Beiji Pavilion Observatory, also referred to as the National Research Institute of Meteorology, Academia Sinica, is a meteorological observatory in China. Research indicates that as early as the 1930s, the surface synoptic maps drawn up by the National Research Institute of Meteorology of Academia Sinica in the identification of fronts and air masses reflect the polar front theory, including some synoptic analysis principles, had been introduced to China. It offers a different perspective to traditional ‘Western’ theory. China assimilated many ideologies from the West and internalised them within Chinese meteorology. [ABSTRACT FROM AUTHOR]

The mechanism of convective cell initiation and regeneration in the Bandung Basin ( 107. 35 ∘ E - 107. 95 ∘ E ; 6. 75 ∘ S - 7. 25 ∘ S ), Indonesia, is investigated using a Weather Research and Forecasting model. Based on the Cumulative Distribution Function and Regeneration Index, model simulations were conducted using case studies in February–March 2019. Upslope wind flows to the mountain peak in north and south of the basin due to solar heating, carrying water vapor from the bottom of the basin. Therefore, low-level convergence is formed due to the convergence of winds from the bottom of the basin and from outside the basin. The low-level convergence causes the developing updraft, makes atmosphere unstable, and generates convective initiation. The convective system will be developed in the mountains region, produced precipitation, and formed cold pools on the surface. The cold pool will fall down the slopes and convective activity from the mountains will continue to the bottom of the basin. The convective system's outflow causes the cold pool to move in the west–east direction. The cold pool will collide with warmer air from the opposite direction, resulting in an updraft at the cold pool leading edge, and convection will be re-initiated (convective regeneration) in the Bandung Basin, with the new convective system tending to move eastward. [ABSTRACT FROM AUTHOR]

On 18 December 2022, Hawaiian Airlines flight HA35 encountered severe turbulence without warning in a cloud‐free height. We reproduced this incident using the Weather Research and Forecasting Model (WRF) at a convection‐permitting resolution. We found that this case of tropical upper‐level turbulence occurred primarily due to the fast‐growing convective tower in the unstable layer created by gravity wave breaking. At lower altitudes, a mesoscale convective system (MCS) caused a decrease in wind speed in both upstream and downstream regions. At upper levels, a large‐scale jet descended and accelerated after flowing over the top of the MCS, which acted like a barrier and produced a situation similar to a downslope windstorm due to mountain terrain. Upper‐level turbulence is 2–3 km higher than the top of the MCS. The critical level above the jet and the locally self‐induced critical level created the locally enhanced descending jet stream, which destabilized the flow through Kelvin–Helmholtz instabilities. The convective tower existed near the flight route and played an important role in triggering turbulence in the unstable environment through its convective gravity waves. Plain Language Summary: On 18 December 2022, Hawaiian Airlines flight HA35 encountered severe turbulence without warning, resulting in injuries to some passengers and damage to the equipment. This incident requires careful investigation to broaden our understanding of aviation turbulence. A numerical model at kilometer‐scale resolution was used to simulate this case and reveal the dynamics, and we successfully captured the occurrence of turbulence with the simulation. The cause of the turbulence was a previously non‐highlighted mechanism. Severe turbulence was generated by a fast‐growing convective tower at an unstable level. This level was caused by the interaction between the convective system that acted as a barrier and an upper‐level jet. Understanding the causes and spatial distribution of turbulence can help aircraft avoid potential areas with turbulence. Key Points: A regionally convection‐permitting model was used to reproduce a severe turbulence encounterDeep convection acted like terrain and interacted with a jet stream, causing an upper‐level windstorm downstream of the convection topFast‐growing convective tower and downslope windstorm induced turbulence at different locations of the jet stream [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) double-moment 6-class (WDM6) scheme was modified by incorporating predicted graupel density. Explicitly predicted graupel density, in turn, modifies graupel characteristics such as the fall velocity–diameter and mass–diameter relationships of graupel. The modified WDM6 has been evaluated based on a two-dimensional (2D) idealized squall line simulation and winter snowfall events that occurred during the International Collaborative Experiment for Pyeongchang Olympics and Paralympics (ICE-POP 2018) field campaign over the Korean Peninsula. From the 2D simulation, we confirmed that the modified WDM6 can simulate varying graupel densities, ranging from low values in an anvil cloud region to high values in the convective region at the mature stage of a squall line. Simulations with the modified WDM6 increased graupel amounts at the surface and decreased graupel aloft because of the faster sedimentation of graupel for two winter snowfall cases during the ICE-POP 2018 campaign, as simulated in the 2D idealized model. The altered graupel sedimentation in the modified WDM6 influenced the magnitude of the major microphysical processes of graupel and snow, subsequently reducing the surface snow amount and precipitation over the mountainous region. The reduced surface precipitation over the mountainous region mitigates the surface precipitation bias observed in the original WDM6, resulting in better statistical skill scores for the root mean square errors. Notably, the modified WDM6 reasonably captures the relationship between graupel density and its fall velocity, as retrieved from 2D video disdrometer measurements, thus emphasizing the necessity of including predicted graupel density to realistically represent the microphysical properties of graupel in models. [ABSTRACT FROM AUTHOR]

Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change. [ABSTRACT FROM AUTHOR]

Reducing methane (CH 4) emissions from the oil and gas (O&G) sector is crucial for mitigating climate change in the near term. MethaneSAT is an upcoming satellite mission designed to monitor basin-wide O&G emissions globally, providing estimates of emission rates and helping identify the underlying processes leading to methane release in the atmosphere. MethaneSAT data will support advocacy and policy efforts by helping to track methane reduction commitments and targets set by countries and industries. Here, we introduce a CH 4 retrieval algorithm for MethaneSAT based on the CO 2 proxy method. We apply the algorithm to observations from the maiden campaign of MethaneAIR, an airborne precursor to the satellite that has similar instrument specifications. The campaign was conducted during winter 2019 and summer 2021 over three major US oil and gas basins. Analysis of MethaneAIR data shows that measurement precision is typically better than 2 % at a 20×20 m 2 pixel resolution, exhibiting no strong dependence on geophysical variables, e.g., surface reflectance. We show that detector focus drifts over the course of each flight, likely due to thermal gradients that develop across the optical bench. The impacts of this drift on retrieved CH 4 can mostly be mitigated by including a parameter that squeezes the laboratory-derived, tabulated instrument spectral response function (ISRF) in the spectral fit. Validation against coincident EM27/SUN retrievals shows that MethaneAIR values are generally within 1 % of the retrievals. MethaneAIR retrievals were also intercompared with retrievals from the TROPOspheric Monitoring Instrument (TROPOMI). We estimate that the mean bias between the instruments is 2.5 ppb, and the latitudinal gradients for the two data sets are in good agreement. We evaluate the accuracy of MethaneAIR estimates of point-source emissions using observations recorded over the Permian Basin, an O&G basin, based on the integrated-mass-enhancement approach coupled with a plume-masking algorithm that uses total variational denoising. We estimate that the median point-source detection threshold is 100–150 kg h -1 at the aircraft's nominal above-surface observation altitude of 12 km. This estimate is based on an ensemble of Weather Research and Forecasting (WRF) large-eddy simulations used to mimic the campaign's conditions, with the threshold for quantification set at approximately twice the detection threshold. Retrievals from repeated basin surveys indicate the presence of both persistent and intermittent sources, and we highlight an example from each case. For the persistent source, we infer emissions from a large O&G processing facility and estimate a leak rate between 1.6 % and 2.1 %, higher than any previously reported emission levels from a facility of its size. We also identify a ruptured pipeline that could increase total basin emissions by 2 % if left unrepaired; this pipeline was discovered 2 weeks before it was found by its operator, highlighting the importance of regular monitoring by future satellite missions. The results showcase MethaneAIR's capability to make highly accurate, precise measurements of methane dry-air mole fractions in the atmosphere, with a fine spatial resolution (∼ 20×20 m 2) mapped over large swaths (∼ 100×100 km 2) in a single flight. The results provide confidence that MethaneSAT can make such measurements at unprecedentedly fine scales from space (∼ 130×400 m 2 pixel size over a target area measuring ∼ 200×200 km 2), thereby delivering quantitative data on basin-wide methane emissions. [ABSTRACT FROM AUTHOR]

The synergistic interactions between urban heat islands (UHI) and heat waves (HW) continue to be debated. Despite the expectations of UHI intensification during HW, several studies have demonstrated variations. Notably, there is a dearth of investigations concerning the UHI–HW synergy in tropical climate cities amidst the escalating trend of more frequent and severe HW in Southeast Asia. To address this gap, our study aimed to investigate the synergies between the UHI and HW phenomena in Greater Kuala Lumpur (GKL) and its surrounding areas. We employed the advanced research version 4.2.2 of the Weather Research and Forecasting (WRF) model, coupled with a single‐layer Urban Canopy Model (UCM), to examine the impact of UHI during two heat wave events in 2016 (Case 1) and 2020 (Case 2), against the periods immediately before and after these events, which we refer to as Pre‐Post HW (PPHW), in GKL. An elevated UHI intensity (UHII) was evident during the HW in both observations and simulations, with a noticeable distinction particularly observed in Case 1. During HW, observed data indicates average UHII peaks at 1.8°C (0100 LST (UTC+8)) and 1.7°C (1500 LST) in Cases 1 and 2, respectively. In contrast, those for PPHW days for Cases 1 and 2 are 1.5°C (0000 LST) and 1.2°C (0100 LST), respectively. The maximum observed heat loads are likely to occur at noon, reaching 2.3°C at 1600 LST in Case 1 and 3.7°C at 1500 LST in Case 2. LST stands for local standard time. Heat flux component analysis from the surface energy balance model confirmed the UHI–HW synergy. A notable difference in the Bowen Ratio between urban and rural areas highlights the effect of urbanisation on heat fluxes, potentially exacerbating urban discomfort during HW. Consistent across all measurement methods, the evidence indicates a clear and positive synergy between the UHI and HW in the GKL. This study can potentially deliver valuable insights, especially in urban planning, where the implications of weather events are substantial. [ABSTRACT FROM AUTHOR]

In Numerical Weather Prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, parameter uncertainty in physics parameterization schemes significantly impacts model output. Our study adopts a Bayesian probabilistic approach, building on prior research that identified temperature (T) and relative humidity (Rh) as sensitive to three key WRF parameters during southeast Australia's extreme heat events. Using Gaussian process regression‐based Bayesian Optimisation (G‐BO), we accurately estimated the optimal distributions for these parameters. Results show that the default values are outside their corresponding optimal distribution bounds for two of the three parameters, suggesting the need to reconsider these default values. Additionally, the robustness of the optimal parameter distributions is validated by their application to an independent extreme heat event, not included in the optimisation process. In this test, the optimized parameters substantially improved the simulation of T and Rh, highlighting their effectiveness in enhancing simulation accuracy during extreme heat conditions. Plain Language Summary: This study aims to enhance the accuracy of a numerical weather model called the Weather Research and Forecasting (WRF) model, especially for simulating extreme heat events in Southeast Australia. Typically, the accuracy of such models depends on specific settings, which are often set to default values. Our research used a method known as Gaussian process regression‐based Bayesian Optimisation (G‐BO) to determine the best range of values for these settings. We found that the default settings were not optimal. By applying G‐BO, we identified more effective values that substantially improved the model's simulations of temperature and humidity during heat extremes. This improvement was consistent even when tested on an independent extreme heat event. These advancements are vital for more accurate weather forecasting, which is essential for emergency services, electricity management, and agriculture planning during extreme heat conditions. Key Points: Our study optimizes WRF model parameters for Southeast Australia heat extremes, enhancing the accuracy of the model simulationG‐BO method finds optimal parameter ranges, substantially improving the simulation of temperature and humidityResults suggest updating WRF model's default settings for better extreme heat event simulations [ABSTRACT FROM AUTHOR]

China has long-term high PM2.5 levels, and its oxidative potential (OP) is worth studying as it may unravel the impacts of aerosol pollution on public health better than PM2.5 alone. OP refers to the ability of PM2.5 to induce oxidative stress (OS). OP and PM2.5 are influenced by meteorological factors, anthropogenic emission sources, and atmospheric aging. Although their impact on PM2.5 has been studied, OP measurements only recently became available and on a limited scale, as they require considerable technical expertise and resources. For this, the joint relationship between PM2.5 and OP for a wide range of meteorological conditions and emission profiles remain elusive. Towards this, we estimated PM2.5 and OP over China using the Danish Eulerian Hemispheric Model (DEHM) system with meteorological input from the Weather Research and Forecasting (WRF) model. It was found that higher values of PM2.5 and OP were primarily concentrated in urban agglomerations in the central and eastern regions of China, while lower values were found in the western and northeastern regions. Furthermore, the probability density function revealed that about 40 % of areas in China had annual average PM2.5 concentrations exceeding the Chinese concentration limit. For OP, 36 % of the regions have OP below 1 nmolmin-1m-3 , 41 % have OP between 1 and 2 nmolmin-1m-3 , and 23 % have OP above 2 nmolmin-1m-3 , which are in line with previous measurement studies. Analysis of the simulations indicates that meteorological conditions contributed 46 % and 65 % to PM2.5 concentrations and OP variability, respectively, while anthropogenic emissions contributed 54 % and 35 % to PM2.5 concentrations and OP variability, respectively. The emission sensitivity analysis also highlighted the fact that PM2.5 and OP levels are mostly determined by secondary aerosol formation and biomass burning. [ABSTRACT FROM AUTHOR]

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

Regular remote sensing of the magnetic field embedded within the million-degree solar corona is severely lacking. This reality impedes fundamental investigations of the nature of coronal heating, the generation of solar and stellar winds, and the impulsive release of energy into the solar system via flares and other eruptive phenomena. Resulting from advancements in large aperture solar coronagraphy, we report unprecedented maps of polarized spectra emitted at 1074 nm by Fe+12 atoms in the active corona. We detect clear signatures of the Zeeman effect that are produced by the coronal magnetic field along the optically thin path length of its formation. Our comparisons with global magnetohydrodynamic models highlight the valuable constraints that these measurements provide for coronal modeling efforts, which are anticipated to yield subsequent benefits for space weather research and forecasting. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting (WRF) model has been widely used for various applications, especially for solving mesoscale atmospheric dynamics. Its high-order numerical schemes and nesting capability enable high spatial resolution. However, a growing number of applications are demanding more realistic simulations through the incorporation of coupling with new model compartments and an increase in the complexity of the processes considered in the model. (e.g., ocean, surface gravity wave, land-surface, chemistry...). The present paper details the development and the functionalities of the coupling interface we implemented in WRF. It uses the Ocean-Atmosphere-Sea-Ice-Soil – Model Coupling Toolkit (OASIS3-MCT) coupler, which has the advantage of being non-intrusive, efficient, and very flexible to use. OASIS3-MCT has already been implemented in many climate and regional models. This coupling interface is designed with the following baselines: (1) it is structured with a 2-level design through 2 modules: a general coupling module, and a coupler-specific module, allowing to easily add other couplers if required, (2) variables exchange, coupling frequency, and any potential time and grid transformations are controlled through an external text file, offering great flexibility, (3) the concepts of "external domains" and "coupling mask" are introduced to facilitate the exchange of fields to/from multiple sources (different models, fields from different models/grids/zooms...). Finally, two examples of applications of ocean-atmosphere coupling are proposed. The first is related to the impact of ocean surface current feedback to the atmospheric boundary layer, and the second concerns the coupling of surface gravity waves with the atmospheric surface layer. [ABSTRACT FROM AUTHOR]

An accurate forecast of typhoon tracks is crucial for disaster warning and mitigation. However, existing numerical weather prediction models, such as the Weather Research and Forecasting (WRF) model, still exhibit significant errors in track forecasts. This study aims to improve forecast accuracy by correcting WRF-forecasted tracks using deep learning models, including Bidirectional Long Short-Term Memory (BiLSTM) + Convolutional Long Short-Term Memory (ConvLSTM) + Wide and Deep Learning (WDL), BiLSTM + Convolutional Gated Recurrent Unit (ConvGRU) + WDL, and BiLSTM + ConvLSTM + Extreme Deep Factorization Machine (xDeepFM), with a comparison to the Kalman Filter. The results demonstrate that the BiLSTM + ConvLSTM + WDL model reduces the 72 h track prediction error (TPE) from 255.18 km to 159.23 km, representing a 37.6% improvement over the original WRF model, and exhibits significant advantages across all evaluation metrics, particularly in key indicators such as Bias2, Mean Squared Error (MSE), and Sequence. The decomposition of MSE further validates the importance of the BiLSTM, ConvLSTM, WDL, and Temporal Normalization (TN) layers in enhancing the model's spatio-temporal feature-capturing ability. [ABSTRACT FROM AUTHOR]

To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM2.5, meteorological changes exert a significant impact on PM2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM2.5 pollution in the region (up 0~50 µg·m−3). Our findings also shed light on the potential sources and transport pathways of PM2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions. [ABSTRACT FROM AUTHOR]

The Statistical Hurricane Intensity Prediction Scheme (SHIPS) is a multiple linear regression model for predicting tropical cyclone (TC) intensity. It has been widely used in operational centers because of forecast stability, high accuracy, easy interpretation, and low computational cost. The Japan Meteorological Agency (JMA) version of SHIPS is called the Typhoon Intensity Forecasting scheme based on SHIPS (TIFS) and predicts both maximum wind speed and central pressure. Although the addition of new predictors to SHIPS and TIFS has improved its accuracy, predicting TC intensity with a single regression model has limitations. In this study, a new TIFSbased forecasting scheme is developed using data from 2000 to 2021, in which three TIFS regression models corresponding to the intensifying, steady-state, and weakening stages of TCs are introduced and in which the weighted mean of the three TIFS forecasts based on random forest (RF) decision trees is computed as a final intensity forecast. Compared to the conventional TIFS model, the new scheme (TIFS-RF) has better accuracy with improvement rates of up to 12 % at forecast times from 1 to 4 days. The improvement is particularly significant for steady-state TCs, tropical depressions, and TCs undergoing extratropical transition within five days. The accuracy of TIFS-RF forecasts is generally better than that of conventional TIFS forecasts for rapidly intensifying TCs, but much worse for rapidly weakening TCs. This study also confirms that a consensus forecast of the TIFSRF and Hurricane Weather Research and Forecasting models can overcome the weaknesses of each model used alone. [ABSTRACT FROM AUTHOR]

An investigation is undertaken to explore a sudden quasi-linear precipitation and gale event that transpired in the afternoon of 30 May 2024 over Beijing. It was situated at the southwestern periphery of a double-center low-vortex system, where a moisture-rich belt efficiently channeled abundant warm, humid air northward from the south. The interplay between dynamical lifting, convergent airflow-induced uplift, and the amplifying effects of the northern mountainous terrain's topography creates favorable conditions that support the development and persistence of quasi-linear convective precipitation, accompanied by gale-force winds at the surface. The study also analyzes the impacts of five microphysics schemes (Lin, WSM6, Goddard, Morrison, and WDM6) employed in a weather research and forecasting (WRF) numerical model, with which the simulated rainfall and radar reflectivity are compared against ground-based rain gauge network and weather radar observations, respectively. Simulations with the five microphysics schemes demonstrate commendable skills in replicating the macroscopic quasi-linear pattern of the event. Among the schemes assessed, the WSM6 scheme exhibits its superior agreement with radar observations. The Morrison scheme demonstrates superior performance in predicting cumulative rainfall. Nevertheless, five microphysics schemes exhibit limitations in predicting the rainfall amount, the rainfall duration, and the rainfall area, with a discernible lag of approximately 30 min in predicting precipitation onset, indicating a tendency to forecast peak rainfall events slightly posterior to their true occurrence. Furthermore, substantial disparities emerge in the simulation of the vertical distribution of hydrometeors, underscoring the intricacies of microphysical processes. [ABSTRACT FROM AUTHOR]

This study evaluates the capability of an unconstrained tomographic algorithm to capture 3D water vapor density variability throughout 2017 in Houston, U.S. The algorithm relies solely on Global Navigation Satellite System (GNSS) observations and does not require an initial guess or other specific constraints regarding water vapor density variability within the tomographic domain. The test domain, featuring 9 km horizontal, 500 m vertical, and 30 min temporal resolutions, yielded remarkable results when compared to data retrieved from the ECMWF Reanalysis v5 (ERA5), regional Weather Research and Forecasting Model (WRF) data, and GNSS-Radio Occultation (RO). For the first time, a time series of Precipitable Water Vapor maps derived from the Interferometric Synthetic Aperture Radar (InSAR) technique was used to validate the spatially integrated water vapor computed by GNSS tomography. Tomographic results clearly indicate the passage of Hurricane Harvey, with integrated water vapor peaking at 60 kg/m2 and increased humidity at altitudes up to 7.5 km. Our findings suggest that GNSS tomography holds promise as a reliable source of atmospheric water vapor data for various applications. Future enhancements may arise from denser and multi-constellation networks. [ABSTRACT FROM AUTHOR]

Applying deep learning to high-precision ionospheric parameter prediction is a significant and growing field within the realm of space weather research. This paper proposes an improved model, Mixed Convolutional Neural Network (CNN)—Bidirectional Long Short-Term Memory (BiLSTM), for predicting the Total Electron Content (TEC) in China. This model was trained using the longest available Global Ionospheric Maps (GIM)-TEC from 1998 to 2023 in China, and underwent an interpretability analysis and accuracy evaluation. The results indicate that historical TEC maps play the most critical role, followed by Kp, ap, AE, F10.7, and time factor. The contributions of Dst and Disturbance Index (DI) to improving accuracy are relatively small but still essential. In long-term predictions, the contributions of the geomagnetic index, solar activity index, and time factor are higher. In addition, the model performs well in short-term predictions, accurately capturing the occurrence, evolution, and classification of ionospheric storms. However, as the predicted length increases, the accuracy gradually decreases, and some erroneous predictions may occur. The northeast region exhibits lower accuracy but a higher F1 score, which may be attributed to the frequency of ionospheric storm occurrences in different locations. Overall, the model effectively predicts the trends and evolution processes of ionospheric storms. [ABSTRACT FROM AUTHOR]

In the present study, multimodel ensemble forecast experiments of the global horizontal irradiance (GHI) were conducted using the dynamic variable weight technique. The study was based on the forecasts of four numerical models, namely, the China Meteorological Administration Wind Energy and Solar Energy Prediction System, the Mesoscale Weather Numerical Prediction System of China Meteorological Administration, the China Meteorological Administration Regional Mesoscale Numerical Prediction System-Guangdong, and the Weather Research and Forecasting Model-Solar, and observational data from four photovoltaic (PV) power stations in Yangjiang City, Guangdong Province. The results show that compared with those of the monthly optimal numerical model forecasts, the dynamic variable weight-based ensemble forecasts exhibited 0.97%-15.96% smaller values of the mean absolute error and 3.31%-18.40% lower values of the root mean square error (RMSE). However, the increase in the correlation coefficient was not obvious. Specifically, the multimodel ensemble mainly improved the performance of GHI forecasts below 700 W m-2, particularly below 400 W m-2, with RMSE reductions as high as 7.56%-28.28%. In contrast, the RMSE increased at GHI levels above 700 W m-2. As for the key period of PV power station output (02:00-07:00), the accuracy of GHI forecasts could be improved by the multimodel ensemble: the multimodel ensemble could effectively decrease the daily maximum absolute error (AEmax) of GHI forecasts. Moreover, with increasing forecasting difficulty under cloudy conditions, the multimodel ensemble, which yields data closer to the actual observations, could simulate GHI fluctuations more accurately. [ABSTRACT FROM AUTHOR]

To investigate the stratosphere-troposphere exchange (STE) process induced by the gravity waves (GWs) caused by Typhoon Molave (2020) in the upper troposphere and lower stratosphere, we analyzed the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts and the CMA Tropical Cyclone Best Track Dataset. We also adopted the mesoscale forecast model Weather Research and Forecasting model V4.3 for numerical simulation. Most of the previous studies were about typhoon-induced STE and typhoon-induced GWs, while our research focused on the STE caused by typhoon-induced gravity waves. Our analysis shows that most of the time, the gravity wave signal of Typhoon Molave appeared below the tropopause. It was stronger on the east side of the typhoon center (10°-20°N, 110°-120°E) than on the west side, suggesting an eastward tilted structure with height increase. When the GWs in the upper troposphere and lower stratosphere region on the west side of the typhoon center broke up, it produced strong turbulence, resulting in stratosphere-troposphere exchange. At this time, the average potential vorticity vertical flux increased with the average ozone mass mixing ratio. The gravity wave events and STE process simulated by the WRF model were basically consistent with the results of ERA5 reanalysis data, but the time of gravity wave breaking was different. This study indicates that after the breaking of the GWs induced by typhoons, turbulent mixing will also be generated, and thus the STE. [ABSTRACT FROM AUTHOR]