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The Weather Research and Forecasting (WRF) model is a numerical weather prediction system designed to serve both atmospheric research and operational forecasting needs. The WRF development is a done in collaboration around the globe. Further-more, the WRF is used by academic atmospheric scientists, weather forecasters at the operational centers and so on. The WRF contains several physics components. The most time consuming one is the microphysics. One microphysics scheme is the Goddard cloud microphysics scheme. It is a sophisticated cloud microphysics scheme in the Weather Research and Forecasting (WRF) model. The Goddard microphysics scheme is very suitable for massively parallel computation as there are no interactions among horizontal grid points. Compared to the earlier microphysics schemes, the Goddard scheme incorporates a large number of improvements. Thus, we have optimized the Goddard scheme code. In this paper, we present our results of optimizing the Goddard microphysics scheme on Intel Many Integrated Core Architecture (MIC) hardware. The Intel Xeon Phi coprocessor is the first product based on Intel MIC architecture, and it consists of up to 61 cores connected by a high performance on-die bidirectional interconnect. The Intel MIC is capable of executing a full operating system and entire programs rather than just kernels as the GPU does. The MIC coprocessor supports all important Intel development tools. Thus, the development environment is one familiar to a vast number of CPU developers. Although, getting a maximum performance out of MICs will require using some novel optimization techniques. Those optimization techniques are discussed in this paper. The results show that the optimizations improved performance of Goddard microphysics scheme on Xeon Phi 7120P by a factor of 4.7×. In addition, the optimizations reduced the Goddard microphysics scheme's share of the total WRF processing time from 20.0 to 7.5%. Furthermore, the same optimizations improved performance on Intel Xeon E5-2670 by a factor of 2.8× compared to the original code. [ABSTRACT FROM AUTHOR]

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

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The online coupled Weather Research and Forecasting-Chemistry (WRF-Chem) model was applied to simulate a haze event that happened in January 2010 in the North China Plain (NCP), and was validated against various types of measurements. The evaluations indicate that WRF-Chem provides reliable simulations for the 2010 haze event in the NCP. This haze event is mainly caused by high emissions of air pollutants in the NCP and stable weather conditions in winter. Secondary inorganic aerosols also played an important role and cloud chemistry had important contributions. Air pollutants outside Beijing contributed about 47.8% to the PM2.5 levels in Beijing during this haze event, and most of them are from south Hebei, Shandong and Henan provinces. In addition, aerosol feedback has important impacts on surface temperature, Relative Humidity (RH) and wind speeds, and these meteorological variables affect aerosol distribution and formation in turn. In Shijiazhuang, Planetary Boundary Layer (PBL) decreased about 300m and PM2.5 increased more than 20 μgm-3 due to aerosol feed back. Feedbacks associated to Black Carbon (BC) account for about 50% of the PM2.5 increases and 50% of the PBL decreases in Shijiazhuang, indicating more attention should be paid to BC from both air pollution control and climate change perspectives. [ABSTRACT FROM AUTHOR]

The article presents research on the simulations of tropospheric ozone over South Asia using the weather research and forecasting model with chemistry (WRF-Chem) model for 2008. The study suggests that ozone production in the area is mostly mono-nitrogen oxides (NOx-limited). Furthermore, it indicates the efficiency of WRF-Chem model in capturing many important features of the observations as well as provides an efficient use for understanding spatio-temporal variability of ozone.

Simulating surface wind over complex terrain is a challenge in regional climate modelling. Therefore, this study aims at identifying a setup of the WRF model that minimizes systematic errors of surface winds in hindcast simulations. Major factors of the model configuration are tested to find a suitable setup: the horizontal resolution, the PBL parameterization scheme and the way WRF is nested to the driving dataset. Hence, a number of sensitivity simulations at a spatial resolution of 2 km are carried out and compared to observations. Given the importance of wind storms, the analysis is based on case studies of 24 historical wind storms that caused great economic damage in Switzerland. Each of these events is downscaled using eight different model setups, but sharing the same driving dataset. The results show that the unresolved topography leads to a general overestimation of wind speed in WRF. However, this bias can be substantially reduced by using a PBL scheme that explicitly considers the effects of non-resolved topography, which also improves the spatial structure of wind speed over Switzerland. The wind direction, although generally well reproduced, is not very sensitive to the PBL scheme. Further sensitivity tests include four types of nesting methods: nesting only at the boundaries of the outermost domain, analysis and spectral nudging, and the so-called re-forecast method, where the simulation is frequently restarted. These simulations show that restricting the freedom of the model to develop large-scale disturbances slightly increases the temporal agreement with the observations, at the same time that it further reduces the overestimation of wind speed, especially for maximum wind peaks. The model skill is also evaluated in the outermost domains, where the resolution is coarser. The results demonstrate the important role of horizontal resolution, where the step from 6 to 2 km significantly improves model performance. In summary, the combination of a grid size of 2 km, the non-local PBL scheme modified to explicitly account for non-resolved orography, as well as analysis or spectral nudging, is a superior combination when dynamical downscaling is aimed at reproducing real wind fields. [ABSTRACT FROM AUTHOR]

To investigate the susceptibility of drifting snow along the Ya'an–Qamdo section of the railway, which is located in a high-altitude and cold plateau in Southwest China with scarce meteorological information, the Weather Research and Forecasting Model (WRF) is used in this paper to simulate the spatio-temporal distribution of meteorological data. According to the varying terrain, the railway section from Ya'an to Qamdo is divided into two regions along 100.8° E for double-layer nested simulation. The original land use data of the WRF model are used in region 1. Due to the increased number of mountains in region 2, the original data are replaced by the MCD12Q1v006 land use data, and the vertical direction layers are densified near the ground to increase simulation accuracy. The simulated results are compared with the observation data. It is found that after densification, the results have been significantly improved. The results obtained by the WRF model can accurately simulate the change trends of temperature, rainfall, and wind speed, and the correlation coefficients are relatively high, which verifies the accuracy of WRF for simulating complex terrain regions. The simulation results further indicate that approximately 300 km of the Ya'an–Qamdo railway may experience drifting snow. Among them, no drifting snow events occur in Ya'an County, and the areas with higher probability are located at the border between Luding County and Tianquan County, followed by Kangding area. The remaining areas have a probability of less than 10%. The WRF model demonstrates its capability in the drifting snow protection of railways with limited meteorological data. [ABSTRACT FROM AUTHOR]

Methane is the second-most important greenhouse gas after carbon dioxide and accounts for around 10 % of total European Union greenhouse gas emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modelling techniques appear as powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modelling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modelling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modelling component will be based on the WRF model coupled to a Lagrangian particle dispersion module. A description of the modelling tools, input data sets, and 1-year forward modelling evaluation from 1 April 2018 to 31 March 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modelling for Denmark, will be provided in a second paper. A good general agreement is found between the modelling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model–observation discrepancies for the summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 % to 2 %. Domain-wide correlation coefficients and root-mean-square errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. On the other hand, model–observation discrepancies for winter months show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 % to 3 %. Domain-wide correlation coefficients and root-mean-square errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modelling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total-column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe and demonstrate a huge potential for methane inverse modelling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

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

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

This study examines how different microphysical parameterization schemes influence orographically-induced precipitation and the distributions of hydrometeors and water vapour for mid-latitude summer conditions in the Weather Research and Forecasting (WRF) model. A high-resolution two-dimensional idealized simulation is used to assess the differences between the schemes in which a moist air flow is interacting with a bell-shaped 2 km high mountain. Periodic lateral boundary conditions are chosen to recirculate atmospheric water in the domain. It is found that the 13 selected microphysical schemes conserve the water in the model domain. The gain or loss of water is less than 0.81% over a simulation time interval of 61 days. The differences of the microphysical schemes in terms of the distributions of water vapour, hydrometeors and accumulated precipitation are presented and discussed. The Kessler scheme, the only scheme without ice-phase processes, shows final values of cloud liquid water 14 times greater than the other schemes. The differences among the other schemes are not as extreme, but still they differ up to 79% in water vapour, up to 10 times in hydrometeors and up to 64% in accumulated precipitation at the end of the simulation. The microphysical schemes also differ in the surface evaporation rate. The WRF singlemoment 3-class scheme has the highest surface evaporation rate compensated by the highest precipitation rate. The different distributions of hydrometeors and water vapour of the microphysical schemes induce differences up to 49Wm-2 in the downwelling shortwave radiation and up to 33Wm-2 in the downwelling longwave radiation. [ABSTRACT FROM AUTHOR]

The Weather Research & Forecasting meteorological (WRF) model has been coupled to the Soil Plant Atmosphere (SPA) terrestrial ecosystem model, to produce WRF-SPA. SPA generates realistic land-atmosphere exchanges through fully coupled hydrologi- cal, carbon and energy cycles. The addition of a land surface model (SPA) capable of modelling biospheric CO2 exchange allows WRF-SPA to be used for investigating the feedbacks between biosphere carbon balance, meteorology and land management/ land use change. We have extensively validated WRF-SPA using multi-annual observations of air temperature, turbulent fluxes, net radiation and net ecosystem ex change of CO2 at three sites, representing the dominant vegetation types in Scotland (forest, managed grassland and arable agriculture). WRF-SPA generates more realistic seasonal behaviour at the site level compared to an unmodified version of WRF, and produces realistic CO2 exchanges. WRF-SPA is also able to realistically model atmospheric profiles of CO2 over Scotland, spanning a 3 yr period (2004-2006), capturing both profile structure, indicating realistic transport, and magnitude indicating appropriate source sink distribution and CO2 exchange. WRF-SPA makes use of CO2 tracer pools and can therefore identify and quantify land surface contributions to the modelled atmospheric CO2 signal at a specified location. [ABSTRACT FROM AUTHOR]

The article presents a study that analyzes the functionality of the Weather Research and Forecasting model with coupled Chemistry (WRF-Chem) that simulates emission, transport, and sedimentation of pollutants during volcanic activities. It describes the method of the study that analyzes the additional information needed to establish three-dimensional cloud umbrella/vertical distribution. The result indicates that both models show good coincidence between WRF-Chem observations.

Rotation in hurricane flows can significantly impact the dynamics and structure of the turbulent boundary layer. Despite this unique feature of hurricane boundary layers, the current planetary boundary layer (PBL) schemes in weather models are neither specifically designed nor comprehensively tested for intensifying hurricane flows. The objective of this paper is to bridge this knowledge gap by characterizing the role of vertical diffusion depth and magnitude in simulated hurricane intensity, size, and track. To this end, five major hurricane cases undergoing an intensification period are simulated using two widely used local and non-local PBL schemes in Weather Research and Forecasting (WRF) model. In total, eighty WRF simulations are conducted by varying the grid resolution, PBL scheme, eddy diffusivity depth and magnitude, and PBL height. By decreasing the existing vertical diffusion depth and magnitude, on average, ~ 38 and ~ 24% improvements in hurricane intensity forecasts were obtained compared to the default models. Hence, the results indicate that the current PBL schemes in WRF are overly diffusive for simulating major hurricanes since they do not account for turbulence suppression effects in rotating hurricane flows. The paper yields new insights into the role of vertical diffusion in simulated hurricane dynamics and provides some guidance to enhance the PBL schemes of NWPs for improved hurricane forecasts. [ABSTRACT FROM AUTHOR]

Accurate predictions of wind and other weather phenomena are essential for making informed strategic and tactical decisions in sailing. Sailors worldwide utilize current state-of-the-art forecasts, yet such forecasts are often insufficient because they do not offer the high temporal and geographic resolution required by sailors. This paper examines wind forecasting in competitive sailing and demonstrates that traditional wind forecasts can be improved for sailing events by using an integration of traditional numerical modeling and machine learning (ML) methods. Our primary objective is to provide practical and more precise wind forecasts that will give sailors a competitive edge. As a case study, we demonstrate the capabilities of our proposed methods to improve wind forecasting at Lake Kinneret, a popular sailing site. The lake wind pattern is highly influenced by the area's topographic features and is characterized by unique local and mesoscale phenomena at different times of the day. In this research, we simulate the Kinneret wind during the summers of 2015–2021 in up to one-kilometer resolution using the Weather Research and Forecasting (WRF) atmospheric model. The results are used as input for convolutional neural network (CNN) and multilayer perceptron (MLP) ML models to postprocess and improve the WRF model accuracy. These advanced ML models are trained using training datasets based on the WRF data as well as real data measured by the meteorological service, and subsequently, a validation process of the trained ML model is performed on unseen datasets against site-specific meteorological service observations. Through our experimental analysis, we demonstrate the limitations of the WRF model. It uncovers notable biases in wind direction and velocity, particularly a persistent northern bias in direction and an overestimation of wind strength. Despite its inherent limitations, this study demonstrates that the integration of ML models can potentially improve wind forecasting due to the remarkable prediction accuracy rate achieved by the CNN model, surpassing 95%, while achieving partial success for the MLP model. Furthermore, a successful CNN-based preliminary forecast was effectively generated, suggesting its potential contribution to the future development of a user-friendly tool for sailors. [ABSTRACT FROM AUTHOR]

With increasing number and proximity of wind farms, it becomes crucial to consider wind farm effects (WFEs) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFEs are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE–AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting (WRF) model using case studies. The case studies include a case for WFEs above a wind farm as well as two cases for WFEs at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFEs on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations, and WFEs on TKE are particularly well captured by HARMONIE–FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series. [ABSTRACT FROM AUTHOR]

In this study we present a series of sensitivity experiments conducted for the Last Glacial Maximum (LGM, ∼21 ka) over Europe using the regional climate Weather Research and Forecasting model (WRF). Using a four-step two-way nesting approach, we are able to reach a convection-permitting horizontal resolution over the inner part of the study area, covering central Europe and the Alpine region. The main objective of the paper is to evaluate a model version including a series of new developments better suitable for the simulation of paleo-glacial time slices with respect to the ones employed in former studies. The evaluation of the model is conducted against newly available pollen-based reconstructions of the LGM European climate and takes into account the effect of two main sources of model uncertainty: a different height of continental glaciers at higher latitudes of the Northern Hemisphere and different land cover. Model results are in good agreement with evidence from the proxies, in particular for temperatures. Importantly, the consideration of different ensemble members for characterizing model uncertainty allows for increasing the agreement of the model against the proxy reconstructions that would be obtained when considering a single model realization. The spread of the produced ensemble is relatively small for temperature, besides areas surrounding glaciers in summer. On the other hand, differences between the different ensemble members are very pronounced for precipitation, in particular in winter over areas highly affected by moisture advection from the Atlantic. This highlights the importance of the considered sources of uncertainty for the study of European LGM climate and allows for determining where the results of a regional climate model (RCM) are more likely to be uncertain for the considered case study. Finally, the results are also used to assess the effect of convection-permitting resolutions, at both local and regional scales, under glacial conditions. [ABSTRACT FROM AUTHOR]

Increased computing power has made it possible to run simulations of the Weather Research and Forecasting (WRF) numerical model in high spatial resolution. However, running high-resolution simulations requires a higher-detail mapping of landforms, land use, and land cover. Often, higher-resolution data have limited coverage or availability. This paper presents the feasibility of using CORINE Land Cover (CLC) land use and land cover data and alternative high-resolution global coverage land use/land cover (LULC) data from Copernicus Global Land Service Land Cover Map (CGLS-LC100) V2.0 in high-resolution WRF simulations (100 × 100 m). Global LULC data with a resolution of 100 m are particularly relevant for areas not covered by CLC. This paper presents the method developed by the authors for reclassifying land cover data from CGLS-LC100 to MODIS land use classes with defined parameters in the WRF model and describes the procedure for their implementation into the model. The obtained simulation results of the basic meteorological parameters from the WRF simulation using CLC, CGLS-LC100 and default geographical data from MODIS were compared to observations from 13 meteorological stations in the Warsaw area. The research has indicated noticeable changes in the forecasts of temperature, relative humidity wind speed, and direction after using higher-resolution LULC data. The verification results show a significant difference in weather predictions in terms of CLC and CGLS-LC100 LULC data implementation. Due to the fact that better results were obtained for CLC simulations than for CGLS-LC100, it is suggested that CLC data are first used for simulations in numerical weather prediction models and to use CGLS-LC100 data when the area is outside of CLC coverage. [ABSTRACT FROM AUTHOR]

The performance of the Weather Research and Forecasting regional model with chemistry (WRF-Chem) in simulating the spatial and temporal variations in aerosol mass, composition, and size over California is quantified using measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010. The extensive meteorological, trace gas, and aerosol measurements collected at surface sites and along aircraft and ship transects during CalNex and CARES were combined with operational monitoring network measurements to create a single dataset that was used to evaluate the one configuration of the model. Simulations were performed that examined the sensitivity of regional variations in aerosol concentrations to anthropogenic emissions and to long-range transport of aerosols into the domain obtained from a global model. The configuration of WRF-Chem used in this study is shown to reproduce the overall synoptic conditions, thermally-driven circulations, and boundary layer structure observed in region that controls the transport and mixing of trace gases and aerosols. However, sub-grid scale variability in the meteorology and emissions as well as uncertainties in the treatment of secondary organic aerosol chemistry likely contribute to errors at a primary surface sampling site located at the edge of the Los Angeles basin. Differences among the sensitivity simulations demonstrate that the aerosol layers over the central valley detected by lidar measurements likely resulted from lofting and recirculation of local anthropogenic emissions along the Sierra Nevada. Reducing the default emissions inventory by 50% led to an overall improvement in many simulated trace gases and black carbon aerosol at most sites and along most aircraft flight paths; however, simulated organic aerosol was closer to observed when there were no adjustments to the primary organic aerosol emissions. The model performance for some aerosol species was not uniform over the region, and we found that sulfate was better simulated over northern California whereas nitrate was better simulated over southern California. While the overall spatial and temporal variability of aerosols and their precursors were simulated reasonably well, we show cases where the local transport of some aerosol plumes were either too slow or too fast, which adversely affects the statistics regarding the differences between observed and simulated quantities. Comparisons with lidar and in-situ measurements indicate that long-range transport of aerosols from the global model was likely too high in the free troposphere even though their concentrations were relatively low. This bias led to an over-prediction in aerosol optical depth by as much as a factor of two that offset the under-predictions of boundary-layer extinction resulting primarily from local emissions. Lowering the boundary conditions of aerosol concentrations by 50% greatly reduced the bias in simulated aerosol optical depth for all regions of California. This study shows that quantifying regional-scale variations in aerosol radiative forcing and determining the relative role of emissions from local and distant sources is challenging during "clean" conditions and that a wide array of measurements are needed to ensure model predictions are correct for the right reasons. In this regard, the combined CalNex and CARES datasets are an ideal testbed that can be used to evaluate aerosol models in great detail and develop improved treatments for aerosol processes. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a testbed for evaluating and comparing climate modeling systems at cloud resolving scales using hindcasts of the June 2012 North American derecho. To demonstrate its utility for model intercomparison, the testbed is applied to two models: the regionally‐refined Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) at 6.5, 3.25 and 1.625 km grid spacing and the Weather Research and Forecasting (WRF) model with 3.2 and 1.6 km grid spacing. We find the simulation results to be highly sensitive to the initial conditions (ICs), initialization time, and model configurations, with ICs from the Rapid Refresh producing the best simulation. Significant improvement is identified in both models as horizontal grid spacing is refined. While a propagation delay of approximately 2 hr is found in both models, SCREAM at 1.625 km simulates the observed bow echo structure of the derecho well and predicts strong surface gusts that exceed 30 m/s. In comparison, WRF has difficulty producing surface wind over 25 m/s, with wind gusts in WRF 42%–46% lower than in SCREAM. However, WRF has a lower bias in simulating cloud top temperature and extent, but overestimates precipitation intensity. Both models reproduce the observed outgoing longwave radiation spatial patterns well (Pearson correlation >0.88), but, compared with NEXRAD observations, simulate generally larger areas of composite radar reflectivity >40 dBZ and underestimate the precipitating area by ∼47%. Plain Language Summary: This paper describes a testbed for evaluating model performance on a particular high‐impact weather event—the June 2012 North American derecho, a storm event associated with extreme winds and precipitation. The testbed is applied to evaluate the Simple Cloud‐Resolving E3SM Atmosphere Model (SCREAM) and the Weather Research and Forecasting (WRF) model at resolutions that resolve cloud systems. The performance of both models is shown to be sensitive to the data set used for model initialization. Finer grid resolution generally leads to better model performance. All simulations show a 2‐hr delay in predicting the evolution of the derecho and produce more intense rainfall. SCREAM generates a more realistic convective front than WRF and produces stronger surface winds. The evaluation protocol can be used to better understand the credibility of model simulations of extreme events and guide model development. Key Points: A testbed of observational products, diagnostics, and metrics is constructed to evaluate hindcasts of the June 2012 North American derechoHindcast results are sensitive to initial conditions, initialization time, horizontal resolutions, and convective and microphysics schemesThe Simple Cloud‐Resolving E3SM Atmosphere Model at 1.625 km successfully reproduces severe surface gusts with wind speeds above 30 m/s [ABSTRACT FROM AUTHOR]

This paper selects the case of the Atlantic hurricane "Michael" in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious "flow-dependent" characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system. [ABSTRACT FROM AUTHOR]

This paper studies the simulation of Cyclone Nicholas that occurred close to the coastal area of Western Australia and fell on the mainland of Southwestern Australia. The simulation was conducted via a dynamical downscaling model, Weather Research and Forecasting (WRF), to obtain a higher resolution with reference to the regional climate data. The model simulation is generated using a global reanalysis of climate data for the initial and lateral boundary conditions. We investigated the response of the tropical storm to the model regarding the track and intensity using a modified Kyklop method that appears more appropriate for a landfall cyclone. Our study suggests that the regional climate data computed by the model deviates from the storm track of the global climate data forcing field. In this study, the track of the simulated storm is parallel to the satellite data, but it is shifted slightly to the east, closer to the mainland. Nevertheless, the model simulation can implement the intensity of the storm as strongly as the observation, while the forcing data delivers substantial underestimation. [ABSTRACT FROM AUTHOR]

Field research campaigns in 2019 and 2020 collected hourly atmospheric profiles via radiosonde surrounding the 2 July 2019 and 14 December 2020 total solar eclipses over South America from locations within the paths of eclipse totality. As part of these atmospheric data collection campaigns, the eclipse module of the Advanced Research Weather Research and Forecast (WRF-ARW) model was utilized to model meteorological conditions before, during, and after the eclipse events. The surface and upper-air observational datasets collected through these campaigns have enabled further assessment and validation of the WRF-ARW's eclipse module performance in simulating atmospheric responses to total solar eclipses. We provide descriptions of the field campaigns for both 2019 and 2020 and present results from comparisons of meteorological variables both at the surface and aloft using observational datasets obtained through the campaigns. The paper concludes by recommending further scientific analyses to be explored utilizing the unique datasets presented. [ABSTRACT FROM AUTHOR]

High-resolution (< 1 km) atmospheric modeling is increasingly used to study precipitation distributions in complex terrain and cryosphere–atmospheric processes. While this approach has yielded insightful results, studies over annual timescales or at the spatial extents of watersheds remain unrealistic due to the computational costs of running most atmospheric models. In this paper we introduce a high-resolution variant of the Intermediate Complexity Atmospheric Research (ICAR) model, HICAR. We detail the model development that enabled HICAR simulations at the hectometer scale, including changes to the advection scheme and the wind solver. The latter uses near-surface terrain parameters which allow HICAR to simulate complex topographic flow features. These model improvements clearly influence precipitation distributions at the ridge scale (50 m), suggesting that HICAR can approximate processes dependent on particle–flow interactions such as preferential deposition. A 250 m HICAR simulation over most of the Swiss Alps also shows monthly precipitation patterns similar to two different gridded precipitation products which assimilate available observations. Benchmarking runs show that HICAR uses 594 times fewer computational resources than the Weather Research and Forecasting (WRF) atmospheric model. This gain in efficiency makes dynamic downscaling accessible to ecohydrological research, where downscaled data are often required at hectometer resolution for whole basins at seasonal timescales. These results motivate further development of HICAR, including refinement of parameterizations used in the wind solver and coupling of the model with an intermediate-complexity snow model. [ABSTRACT FROM AUTHOR]

A unique, high-resolution, hydroclimate reanalysis, 40-plus-year (October 1979–September 2021), 4 km (named as CONUS404), has been created using the Weather Research and Forecasting Model by dynamically downscaling of the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate dataset (ERA5) over the conterminous United States. The paper describes the approach for generating the dataset, provides an initial evaluation, including biases, and indicates how interested users can access the data. The motivation for creating this National Center for Atmospheric Research (NCAR)–U.S. Geological Survey (USGS) collaborative dataset is to provide research and end-user communities with a high-resolution, self-consistent, long-term, continental-scale hydroclimate dataset appropriate for forcing hydrological models and conducting hydroclimate scientific analyses over the conterminous United States. The data are archived and accessible on the USGS Black Pearl tape system and on the NCAR supercomputer Campaign storage system. [ABSTRACT FROM AUTHOR]

Lightning is an important threat to life and properties and its forecast is important for several applications. In this paper, we show the performance of the "dynamic lightning scheme" for next-day total strokes forecast. The predictions were compared against strokes recorded by a ground observational network for a forecast period spanning one year. Specifically, a total of 162 case studies were selected between 1 March 2020 and 28 February 2021, characterized by at least 3000 observed strokes over Italy. The events span a broad range of lightning intensity from about 3000 to 600,000 strokes in one day: 69 cases occurred in summer, 46 in fall, 18 in winter, and 29 in spring. The meteorological driver was the Weather Research and Forecasting (WRF) model (version 4.1) and we focused on the next-day forecast. Strokes were simulated by adding three extra variables to WRF, namely, the potential energies for positive and negative cloud to ground flashes and intracloud strokes. Each potential energy is advected by WRF, it is built by the electrification processes occurring into the cloud, and it is dissipated by lightning. Observed strokes were remapped onto the WRF model grid with a 3 km horizontal resolution for comparison with the strokes forecast. Results are discussed for the whole year and for different seasons. Moreover, statistics are presented for the land and the sea. In general, the results of this study show that lightning forecast with the dynamic lightning scheme and WRF model was successful for Italy; nevertheless, a careful inspection of forecast performance is necessary for tuning the scheme. This tuning is dependent on the season. A numerical experiment changing the microphysics scheme used in WRF shows the sensitivity of the results according to the choice of the microphysics scheme. [ABSTRACT FROM AUTHOR]

Satellite retrievals strive to exploit the information contained in thousands of channels provided by hyperspectral sensors and show promise in providing a gain in computational efficiency over current radiance assimilation methods by transferring computationally expensive radiative transfer calculations to retrieval providers. This paper describes the implementation of a new approach based on the transformation proposed in 2008 by Migliorini et al., which reduces the impact of the a priori information in the retrievals and generates transformed retrievals (TRs) whose assimilation does not require knowledge of the hyperspectral instruments characteristics. Significantly, the results confirm both the viability of Migliorini's approach and the possibility of assimilating data from different hyperspectral satellite sensors regardless of the instrument characteristics. The Weather Research and Forecasting (WRF) Model's Data Assimilation (WRFDA) 3-h cycling system was tested over the central North Pacific Ocean, and the results show that the assimilation of TRs has a greater impact in the characterization of the water vapor distribution than on the temperature field. These results are consistent with the knowledge that temperature field is well constrained by the initial and boundary conditions of the Global Forecast System (GFS), whereas the water vapor distribution is less well constrained in the GFS. While some preliminary results on the comparison between the assimilation with and without TRs in the forecasting system are presented in this paper, additional work remains to explore the impact of the new assimilation approach on forecasts and will be provided in a follow-up publication. [ABSTRACT FROM AUTHOR]

Frost on agricultural products in spring imposes heavy financial losses to agriculture particularly in northwest of Iran's orchards. Frost early warning is an effective way in preventing frost risk in apple orchards. This paper aims to validate frost early warning system in apple orchards of northwestern Iran by predicting flowering date of apple tree and its combination with WRF (Weather Research and Forecasting) model simulations of the 2-m temperature. To this end, stepwise multiple linear regression model was used for predicting flowering date; therefore thermal variables data including mean temperature, mean maximum temperature, mean minimum temperature, last frost date, heat wave duration, heat wave intensity, GDD and maximum standard temperature data larger than 1 (Z > 1) for the period of 2007–2016 (from March 1st until flowering date) in Kahriz agrometeorological station in northwest of Iran were calculated and added to the model. Also, to evaluate the accuracy of WRF model simulations, the 72-h simulations of the 2-m air temperature for internal computational grid in northwestern Iran (West Azerbaijan Province) in 17 synoptic stations were compared to minimum air temperature observed in the stations. Results revealed that the proposed stepwise regression method is a relatively precise model with a mean absolute error of 2.7 days between the predicted and observed flowering dates. Findings were also indicative of an acceptable accuracy of 72-h minimum air temperature simulations of WRF model in the study area. [ABSTRACT FROM AUTHOR]

The Indian capital megacity of Delhi is reeling under deteriorating air quality, and control measures are not yielding any significant changes, mainly due to a poor understanding of sources of emissions; hence, priority option in mitigation planning is lacking. In this paper, we have made an attempt to develop a spatially resolved technological high-resolution gridded (∼0.4km×0.4km) emission inventory for eight major pollutants of the Delhi region where high-resolution activity data of all possible major and unattended minor sources are generated by organizing a mega-campaign involving hundreds of volunteers. It is for the first time that we are able to estimate the decadal growth in emissions of various pollutants by comparing newly developed 2020 emissions with SAFAR (System of Air Quality and Weather Forecasting and Research) emissions of 2010 using the identical methodology and quantum of activity data. The estimated annual emissions for PM 2.5 , PM 10 , CO, NOx , VOC, SO2 , BC, and OC over the Delhi National Capital Region (NCR) are estimated to be 123.8, 243.6, 799.0, 488.9, 730.0, 425.8, 33.6, and 20.3 Ggyr-1 , respectively, for the year 2020. The decadal growth (2010–2020) in PM 2.5 and PM 10 is found to be marginal at 31 % and 3 %, respectively. The maximum growth is found to be in the transport sector followed by the industrial and other sectors. Maximum decadal growth found for the pollutants BC, OC, and NOx is 57 %, 34 %, and 91 %, respectively. The decadal shift of sectorial emissions with changing policies is examined. The complete dataset is available on Zenodo at 10.5281/zenodo.7715595 (Sahu et al., 2023). [ABSTRACT FROM AUTHOR]

This paper reports analysis of eddy-covariance data collected during the WFIP2 field campaign in the complex-terrain of the US Pacific Northwest. A 31-day period representative of the region's dry season was used to address the following questions: (1) To what extent does the Constant-Flux Layer (CFL) assumption hold? (2) What is the spatial variability of turbulent and momentum fluxes over km scales? and (3) How skilful are the surface-layer parameterizations of mesoscale models? These questions are directly relevant to subgrid parameterization studies of mesoscale models. Results show that the efficacy of the CFL concept and the spatial variability of turbulent and momentum fluxes are dependent on: (i) the turbulent parameter being analysed, (ii) the measurement's location, (iii) the atmospheric stability regime (determined by the flow and vertical stratification), and (iv) the magnitude of the flux. Finally, the skill of the physics formulation of an often-used surface-layer parameterization scheme available in the Weather Research and Forecasting (WRF) model was also evaluated. Meteorological conditions associated with the highest and the lowest errors were identified. A metric to quantify (time-dependent) flow heterogeneity is proposed, which appears to be a good candidate to predict the skill of idealized surface-layer parameterization schemes in complex terrain. [ABSTRACT FROM AUTHOR]

Quantifying the microphysical process contributions to surface precipitation in numerical simulations can be challenging. This is due to the fact that many microphysical processes contribute to the formation and depletion of rain drops and there is almost always a spatial/temporal mismatch between where/when rain is formed and where/when it strikes the surface. In this work, we develop a tracing method that tracks the sources and sinks of raindrop mass and number as they are advected by the Weather Research and Forecasting model. Applying the method to an idealized squall line confirms that convective precipitation is dominated by warm rain processes (autoconversion and accretion) while stratiform precipitation is dominated by the melting of rimed and unrimed ice crystals. Sensitivity experiments in which the prescribed cloud drop number concentration is increased confirm the conventional wisdom that weakened autoconversion increases the fraction of raindrops originating from cold rain processes. The method also reveals that when applied to deep convection the Khairoutdinov and Kogan autoconversion scheme produces an excessive number of raindrops which are subsequently clipped in P3 microphysics to keep the rain size distribution within prescribed limits. This problem can mostly be mitigated by increasing the assumed radius for raindrops created by autoconversion. Plain Language Summary: Weather and climate models simulate precipitation by accounting for a multitude of processes that form and remove rain drops. Due to spatial and temporal lags between the processes and the consequent surface precipitation, it can be challenging to quantitatively link the former to the latter. This paper remedies the challenge with a tool that tracks processes contributions throughout the rain lifecycle. The tool's utility is demonstrated through an idealized squall line which produces precipitation through various microphysical pathways. Additionally, perturbations to model parameters and their consequent impact on the squall line are interpreted through the lens of the new tool. Key Points: A tool to trace the process contributions to surface precipitation is developedThe tool confirms dominance of warm processes in convective rain and cold processes in stratiform rainThe tool reveals that the autoconversion formulation produces an excessive amount of rain drops that the microphysics scheme clips [ABSTRACT FROM AUTHOR]

Plumes from the boreal spring biomass burning (BB) in northern peninsular Southeast Asia (nPSEA) are lifted into the subtropical jet stream and transported and deposited across nPSEA, South China, Taiwan and even the western North Pacific Ocean. This paper as part of the Seven SouthEast Asian Studies (7-SEAS) project effort attempts to improve the chemical weather prediction capability of the Weather Research and Forecasting coupled with the Community Multiscale for Air Quality (WRF–CMAQ) model over a vast region, from the mountainous near-source burning sites at nPSEA to its downwind region. Several sensitivity analyses of plume rise are compared in the paper, and it is discovered that the initial vertical allocation profile of BB plumes and the plume rise module (PLMRIM) are the main reasons causing the inaccuracies of the WRF–CMAQ simulations. The smoldering emission from the Western Regional Air Partnership (WRAP) empirical algorithm included has improved the accuracies of PM10 , O3 and CO at the source. The best performance at the downwind sites is achieved with the inline PLMRIM, which accounts for the atmospheric stratification at the mountainous source region with the FINN burning emission dataset. Such a setup greatly improves not only the BB aerosol concentration prediction over near-source and receptor ground-based measurement sites but also the aerosol vertical distribution and column aerosol optical depth of the BB aerosol along the transport route. The BB aerosols from nPSEA are carried by the subtropical westerlies in the free troposphere to the western North Pacific, while BB aerosol has been found to interact with the local pollutants in the Taiwan region through three conditions: (a) overpassing western Taiwan and entering the central mountain area, (b) mixing down to western Taiwan, (c) transport of local pollutants upwards and mixing with a BB plume on higher ground. The second condition, which involves the prevailing high-pressure system from Asian cold surge, is able to impact most of the population in Taiwan. [ABSTRACT FROM AUTHOR]

The amount of solar irradiation that reaches Earth's surface is a key quantity of solar energy research and is difficult to predict, because it is directly affected by the changing constituents of the atmosphere. The numerical weather prediction (NWP) model performs computational simulations of the evolution of the entire atmosphere to forecast the future state of the atmosphere based on the current state. The Weather Research and Forecasting (WRF) model is a mesoscale NWP. WRF solar is an augmented feature of WRF, which has been improved and configured specifically for solar energy applications. The aim of this paper is to evaluate the performance of the high resolution WRF solar model and compare the results with the low resolution WRF solar and Global Forecasting System (GFS) models. We investigate the performance of WRF solar for a high-resolution spatial domain of resolution 1 × 1 km and compare the results with a 3 × 3 km domain and GFS. The results show error metrices rMAE {23.14%, 24.51%, 27.75%} and rRMSE {35.69%, 36.04%, 37.32%} for high resolution WRF solar, coarse domain WRF solar and GFS, respectively. This confirms that high resolution WRF solar performs better than coarse domain and in general. WRF solar demonstrates statistically significant improvement over GFS. [ABSTRACT FROM AUTHOR]

An increase in the frequency of atmospheric hazards in a changing climate has attracted interest in the study of regional features of mesoscale convective systems and trends of lightning activity. Severe convective storms are the most destructive weather events causing substantial damage and fatalities. In this paper, we analyze general trends in the lightning activity in the Upper Volga region and identify the particular features of a severe thunderstorm on 13–14 July 2020. The analysis of the annual variability of the lightning activity in the Upper Volga region, carried out according to the World-Wide Lightning Location Network (WWLLN) data, has shown that the total number of discharges during the convective seasons of 2016–2021 increased. For the convective season of 2020, the daily number of discharges in the neighborhood of Nizhny Novgorod (the center of the region) is calculated according to the WWLLN data and the electric field mill (EFM) measurements. It is revealed that the most powerful (per convective season) thunderstorms have a number of similarities both according to observations and numerical simulation. The thunderstorm on 13–14 July was numerically simulated using the Weather Research and Forecasting (WRF) model in combination with calculations of the electric parameters (such as electric potential and intracloud electric field). The simulations were carried out using two parametrizations of microphysical processes to reveal the features associated with allowance for aerosol particles. The data from the meteorological radar and WWLLN were used to validate the radar reflectivity simulation results of the WRF model. Some features of the thunderstorm evolution characteristic for each of the microphysical parametrizations were identified. In general, the non-aerosol parametrization gives a more correct description of the development of severe thunderstorms in the Upper Volga region (with the exception of the final stage of the convective system development). For a convective event to have the required duration (more than 6 h), aerosol particles should be taken into account. [ABSTRACT FROM AUTHOR]

Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from 1 to 2%. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3%. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenicsources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications. [ABSTRACT FROM AUTHOR]

Observations from a hyperspectral infrared (IR) sounding interferometer such as the Infrared Atmospheric Sounding Interferometer (IASI) and the Cross-Track Infrared Sounder (CrIS) are crucial to numerical weather prediction (NWP). By measuring radiance at the top of the atmosphere using thousands of channels, these observations convey accurate atmospheric information to the initial condition through data assimilation (DA) schemes. The massive data volume has pushed the community to develop novel approaches to reduce the number of assimilated channels while retaining as much information content as possible. Thus, channel-selection schemes have become widely accepted in every NWP center. Two significant limitations of channel-selection schemes are (1) the deficiency in retaining the observational information content and (2) the higher cross-channel correlation in the observational error (R) matrix. This paper introduces a hyperspectral IR observation DA scheme in the principal component (PC) space. Four-month performance comparison case studies using the Weather Research and Forecasting model (WRF) as a forecast module between PC-score assimilation and the selected-channel assimilation experiment show that the PC-score assimilation scheme can reduce the initial condition's root-mean-squared error for temperature and water vapor compared to the channel-selection scheme and thus improve the forecasting of precipitation and high-impact weather. Case studies using the Unified Forecast System Short-Range Weather (UFS-SRW) application as forecast module also indicate that the positive impact can be retained among different NWP models. [ABSTRACT FROM AUTHOR]

Based on the Morrison Two-Moment Scheme coupled with the non-inductive electrification mechanism and the discharge parameterization scheme in the Weather Research and Forecasting (WRF) model, a thunderstorm process was simulated by using the WRF electrical coupling model in Sichuan Basin on 21 July 2019, in this paper. Through analysis and discussion of the macroscopic and microscopic characteristics of the thunderstorm activity and the microphysical and dynamic processes, respectively, the study shows that the simulation results of radar echo and lightning are well consistent with the meteorological observation which indicates the WRF model has a certain ability to reproduce the thunderstorm process in Sichuan Basin, there is a good correspondence between the main electrification area and the distribution position of the ice-phase particles in the thunderstorm. The simulated charge structure of the thunderstorm is that the graupel particles are mainly negatively charged, the ice crystals and snow particles are mainly positively charged, and the thunderstorm shows a dipole charge structure with an upper positive charge center and a lower negative charge center. It also shows that the updrafts greatly influence ice-graupel and snow-graupel collisions during the thunderstorm discharge process, the higher the updraft speed, the stronger the electrical activity, and, especially, the stronger the discharge process of ice-particle collisions and separation. [ABSTRACT FROM AUTHOR]

Weather phenomenon recognition notably affects many aspects of our daily lives, for example, weather forecast, road condition monitoring, transportation, agriculture, forestry management, and the detection of the natural environment. In contrast, few studies aim to classify actual weather phenomenon images, usually relying on visual observations from humans. To the best of our knowledge, the traditional artificial visual distinction between weather phenomena takes a lot of time and is prone to errors. Although some studies improved the recognition accuracy and efficiency of weather phenomenon by using machine learning, they identified fewer types of weather phenomena. In this paper, a novel deep convolutional neural network (CNN) named MeteCNN is proposed for weather phenomena classification. Meanwhile, we establish a data set called the weather phenomenon database (WEAPD) containing 6,877 images with 11 weather phenomena, which has more categories than the previous data set. The classification accuracy of MeteCNN on the WEAPD testing set is around 92%, and the experimental result demonstrates the superiority and effectiveness of the proposed MeteCNN model. Realizing the automatic and high‐quality classification of weather phenomena images can provide a reference for future research on weather image classification and weather forecasting. Key Points: A novel deep convolutional neural network (CNN) named MeteCNN was proposed for weather phenomena classificationWe established a data set called the weather phenomenon database (WEAPD) containing 6,877 images with 11 weather phenomena, which has more categories than the previous dataset [ABSTRACT FROM AUTHOR]

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. [ABSTRACT FROM AUTHOR]

Atmospheric gravity waves are essential meso-small-scale oscillations that facilitate material exchange within the atmosphere. These waves can significantly affect the ozone layer in the Upper Troposphere and Lower Stratosphere (UTLS) on the Qinghai-Tibetan Plateau. However, the influence of gravity wave dynamic processes on upper stratospheric ozone has rarely been studied. This paper identifies the gravity waves on the Tibetan Plateau based on ECMWF Reanalysis 5 (ERA5) data, analyzes the response of the upper stratospheric ozone to the event, and simulates the dynamic propagation mechanism of gravity waves by the Weather Research and Forecasting (WRF) model. This analysis reveals that between 15:00 and 21:00 UTC on July 29, 2015, gravity waves propagated from the surface of Tibetan Plateau (450 hPa) up to the upper stratosphere (20 − 3 hPa) in an arc-shaped structure and tilted to the east with height. The gravity wave signals started to weaken at 21:00 UTC on the same day. Influenced by the easterly rapids, gravity waves partially broke near 3 hPa at 02:30 UTC on July 30, but gravity wave signals were still present, and gravity waves completely broke and released energy at 04:00 UTC. During this process, ozone in the 20–3 hPa region (upper stratosphere) on the Qinghai-Tibetan Plateau responds well to gravity waves, and the ozone mixing ratio began to drop in ozone concentration 30 min after the partial breakup (03:00 UTC). The ozone dropped drastically by about 0.014 ppmv from 04:00 to 05:00 UTC. The WRF simulation results agree well with ERA5 and accurately capture the intricate characteristics of gravity waves. Furthermore, the breakup of gravity waves caused a total drop in ozone of 0.024 ppmv. [ABSTRACT FROM AUTHOR]

In this paper, the Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on the prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing a better simulation for the typhoon's warm core aloft, vortex wind structure, and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and, consequently, better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) plays an important role in the typhoon forecast. By comparison, satellite retrieval products can provide more accurate SST data than reanalysis data in typhoon simulations. However, the effect of SST data from Chinese Fengyun-3 (FY-3) satellites on typhoon simulation hasn't been evaluated yet. In this paper, the impact of FY-3D SST retrieval data with ascending and descending orbits on the forecast of typhoon In-Fa, 2021, is investigated with the Weather Research Forecast (WRF) model. Compared to the control experiments with SST data from Global Data Assimilation System (GDAS) data, the replacement experiments with FY-3D SST data significantly improve the forecast of typhoon central sea level pressure, track, and precipitation. Especially, the SST from the descending orbit satellite data provides the optimal track and intensity forecast, which are verified against the best track data from the Japan Meteorological Agency. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of the statistical errors of the meteorological parameters predicted by the WRF (Weather Research and Forecasting) model. The model is used for forecasting air pollution in the Silesian Voivodeship with an accuracy of about 70-80%. Thus, the main idea of the research was assessing to what extent the error in the forecast of meteorological parameters affects the error of the air quality forecast, which can be evaluated using the system InfoSMOG – MED for the inhabitants of the Silesian Voivodeship at www.slaskiesmogstop. It was also investigated how the reduction of the mesh size of the forecast of meteorological parameters affects the error of the forecast of these parameters, which was necessary for the next project SMART. The research was carried out for 3 computational domains: Poland, Silesian Voivodeship and Upper Silesia Metropolitan Region, each with a different grid resolution. The predicted temperature and wind speed data were compared with the measured data from the Polish synoptic stations and the stations of the METAR airports meteorological shield located in the respective domains. In addition, the error of the predicted parameters was investigated in the grid where the climatological station is located in the Śląskie Planetarium in Chorzów. [ABSTRACT FROM AUTHOR]

Data assimilation (DA) offers immense potential for uncertainty identification, improving the initial estimates for hydrological and atmospheric modelling. This paper reviews the studies in hydrological DA using Kalman filters. Recent applications of Kalman filters in hydrological and atmospheric DA are summarized. Existing challenges for DA studies are briefly described. In addition, three case study examples are presented highlighting the effects of: (a) soil moisture DA in the Noah land surface model; (b) variational assimilation for improving precipitation forecasts in the WRF (Weather Research Forecast) model; and (c) simulating AMSR-2 (Advanced Microwave Scanning Radiometer-2) radiances towards DA. Although there are many unresolved issues in DA that warrant further research, it has immense potential for predicting variables at a better lead time for hydrometeorology. [ABSTRACT FROM AUTHOR]

Throughout the years, wildfires have negatively impacted ecological systems and urban areas. Hence, reinforcing territorial risk management strategies against wildfires is essential. In this study, we built an early alert system (EAS) with two different Machine Learning (ML) techniques to calculate the meteorological conditions of two Colombian areas: (i) A 3D convolutional neural net capable of learning from satellite data and (ii) a convolutional network to bias-correct the Weather Research and Forecasting (WRF) model output. The results were used to quantify the daily Fire Weather Index and were coupled with the outcomes from a land cover analysis conducted through a Naïve-Bayes classifier to estimate the probability of wildfire occurrence. These results, combined with an assessment of global vulnerability in both locations, allow the construction of daily risk maps in both areas. On the other hand, a set of short-term preventive and corrective measures were suggested to public authorities to implement, after an early alert prediction of a possible future wildfire. Finally, Soil Management Practices are proposed to tackle the medium- and long-term causes of wildfire development, with the aim of reducing vulnerability and promoting soil protection. In conclusion, this paper creates an EAS for wildfires, based on novel ML techniques and risk maps. [ABSTRACT FROM AUTHOR]

The Arctic is warming at more than twice the rate of the global average. This warming is influenced by clouds, which modulate the solar and terrestrial radiative fluxes and, thus, determine the surface energy budget. However, the interactions among clouds, aerosols, and radiative fluxes in the Arctic are still poorly understood. To address these uncertainties, the Ny-Ålesund Aerosol Cloud Experiment (NASCENT) study was conducted from September 2019 to August 2020 in Ny-Ålesund, Svalbard. The campaign's primary goal was to elucidate the life cycle of aerosols in the Arctic and to determine how they modulate cloud properties throughout the year. In situ and remote sensing observations were taken on the ground at sea level, at a mountaintop station, and with a tethered balloon system. An overview of the meteorological and the main aerosol seasonality encountered during the NASCENT year is introduced, followed by a presentation of first scientific highlights. In particular, we present new findings on aerosol physicochemical and molecular properties. Further, the role of cloud droplet activation and ice crystal nucleation in the formation and persistence of mixed-phase clouds, and the occurrence of secondary ice processes, are discussed and compared to the representation of cloud processes within the regional Weather Research and Forecasting Model. The paper concludes with research questions that are to be addressed in upcoming NASCENT publications. [ABSTRACT FROM AUTHOR]

A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale. [ABSTRACT FROM AUTHOR]

Due to global warming and urbanization, high-temperature events—which frequently occur in cities—are presenting an increasing threat to the daily lives of human beings. In coastal cities, sea breezes can cool the near surface and improve the urban environment to some extent. Understanding the cooling characteristics of sea breeze on the urban environment is informative for improving and mitigating the urban heat island (UHI) effect. In this paper, we analyze the basic characteristics of the timing of the cooling effect of sea breeze in urban summer based on the long-term multi-point measurements of air temperatures. Additionally, the Weather Research and Forecasting (WRF) model is used to show the influence of sea breeze on cities in terms of the cooling action time. The whole process is reproduced based on a time distribution map created using the results of the WRF simulation. The measured temperature and WRF simulation results are also evaluated with observations. The results show little difference between the two. The analysis of the distribution map shows that the sea breeze gradually penetrates inland from coastal areas. It can therefore be concluded that the sea breeze blows at different speeds in different areas. Our results show that the sea breeze is weak in places near the coast, while it is significantly stronger around inland rivers. Moreover, in urban areas that are far from inland rivers, the speed of the sea breeze is evenly distributed in space. The spatial pattern of sea breeze retreat time and arrival time is reversed: retreats happen earlier in inland areas. The duration of the sea breeze shows a significantly decreasing trend from the coast to inland, with the longest duration at the southern end of the urban area near the coast. [ABSTRACT FROM AUTHOR]

Wind is a renewable and green energy source that is vital for sustainable human development. Wind variability implies that wind power is random, intermittent, and volatile. For the reliable, stable, and secure operation of an electrical grid incorporating wind power systems, a multi-hour ahead wind power forecasting system comprising a physics-based model, a multi-criteria decision making scheme, and two artificial intelligence models was proposed. Specifically, a Weather Research and Forecasting (WRF) model was used to produce wind speed forecasts. A technique for order of preference by similarity to ideal solution (TOPSIS) scheme was employed to construct a 5-in-1 (ensemble) WRF model relying on 1334 initial ensemble members. Two adaptive neuro-fuzzy inference system (ANFIS) models were utilised to correct the wind speed forecasts and determine a power curve model converting the improved wind speed forecasts to wind power forecasts. Moreover, three common statistics-based forecasting models were chosen as references for comparing their predictive performance with that of the proposed WRF-TOPSIS-ANFIS model. Using a set of historical wind data obtained from a wind farm in China, the WRF-TOPSIS-ANFIS model was shown to provide good wind speed and power forecasts for 30-min to 24-h time horizons. This paper demonstrates that the novel forecasting system has excellent predictive performance and is of practical relevance. [ABSTRACT FROM AUTHOR]

The computational fluid dynamics of hurricane rapid intensification (RI) is examined through idealized simulations using two codes: a community‐based, finite‐difference/split‐explicit model (WRF) and a spectral‐element/semi‐implicit model (NUMA). The focus of the analysis is on the effects of implicit numerical dissipation (IND) in the energetics of the vortex response to heating, which embodies the fundamental dynamics in the hurricane RI process. The heating considered here is derived from observations: four‐dimensional, fully nonlinear, latent heating/cooling rates calculated from airborne Doppler radar measurements collected in a hurricane undergoing RI. The results continue to show significant IND in WRF relative to NUMA with a reduction in various intensity metrics: (a) time‐integrated, mean kinetic energy values in WRF are ∼20% lower than NUMA and (b) peak, localized wind speeds in WRF are ∼12 m/s lower than NUMA. Values of the eddy diffusivity in WRF need to be reduced by ∼50% from those in NUMA to produce a similar intensity time series. Kinetic energy budgets demonstrate that the pressure contribution is the main factor in the model differences with WRF producing smaller energy input to the vortex by ∼23%, on average. The low‐order spatial discretization of the pressure gradient in WRF is implicated in the IND. In addition, the eddy transport term is found to have a largely positive impact on the vortex intensification with a mean contribution of ∼20%. Overall, these results have important implications for the research and operational forecasting communities that use WRF and WRF‐like numerical models. Plain Language Summary: The intensity of a hurricane is primarily a balance between energy production and dissipation from various physical processes. Numerical models calculate this energy balance by solving complicated equations that attempt to capture these physical processes. Previous research has shown that the methods used to solve these equations can introduce additional dissipation into the system that affects the prediction of the storm intensity. In this paper, we examine this "numerical dissipation" idea more closely by conducting carefully designed comparisons between the community numerical model (WRF) and an advanced, research model (NUMA). Using observational estimates of heating in clouds, which feed the production of energy, we find that the WRF model produces significantly more numerical dissipation relative to NUMA that results in a reduced intensity of the storm. Our analysis indicates that the reason for the anomalous numerical dissipation in WRF is due to how the pressure gradient is computed. These results can have potentially important consequences for operational forecasts, especially the rapid intensification (RI) process. For example, the under‐prediction or low bias of RI forecasts may be partly due to excessive numerical dissipation. Key Points: The Weather Research and Forecasting (WRF) dynamic core dissipates ∼20% more kinetic energy than Non‐hydrostatic Unified Model of the Atmosphere (NUMA) for a dry vortex forced by four‐dimensional latent heating observationsValues of the eddy diffusivity in WRF need to be reduced by ∼50% from those in NUMA in order to produce a similar intensity time seriesBudgets and sensitivity tests indicate that the low‐order approximation of the pressure gradient is the source of the dissipation in WRF [ABSTRACT FROM AUTHOR]