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Accurately simulating snow cover dynamics and the snow–atmosphere coupling is of major importance for topics as wide-ranging as water resources, natural hazards, and climate change impacts with consequences for sea level rise. We present a new modelling framework for atmospheric flow simulations for cryospheric regions called CRYOWRF. CRYOWRF couples the state-of-the-art and widely used atmospheric model WRF (the Weather Research and Forecasting model) with the detailed snow cover model SNOWPACK. CRYOWRF makes it feasible to simulate the dynamics of a large number of snow layers governed by grain-scale prognostic variables with online coupling to the atmosphere for multiscale simulations from the synoptic to the turbulent scales. Additionally, a new blowing snow scheme is introduced in CRYOWRF and is discussed in detail. CRYOWRF's technical design goals and model capabilities are described, and the performance costs are shown to compare favourably with existing land surface schemes. Three case studies showcasing envisaged use cases for CRYOWRF for polar ice sheets and alpine snowpacks are provided to equip potential users with templates for their research. Finally, the future roadmap for CRYOWRF's development and usage is discussed.

During daylight hours, the mean radiant temperature Tmrt$T_{\text{mrt}}$ is one of the most important meteorological parameters to analyse heat stress for humans. This study conducts a spatio-temporal analysis of Tmrt$T_{\text{mrt}}$ for a summer period in 2018 for the city of Berlin, Germany. To this end, the mesoscale climate model COSMO-CLM (CCLM) is coupled with the urban Double Canyon Effect Parameterization scheme with a building energy model (DCEP–BEM) to derive Tmrt$T_{\text{mrt}}$. This coupled model system CCLM/DCEP–BEM enables a dynamic calculation of Tmrt$T_{\text{mrt}}$ for the microscale urban street canyons using a mesoscale model. To bring a more accurate comparison, a two-step approach is applied to assess the radiative fluxes and Tmrt$T_{\text{mrt}}$ from CCLM/DCEP–BEM. The radiation model SOLWEIG is first validated against measurement and then used to evaluate the DCEP–BEM model. Overall good agreement in Tmrt$T_{\text{mrt}}$ is found between CCLM/DCEP–BEM and SOLWEIG (R2=0.96$\text{R}^2 =\nobreak 0.96$). Nighttime Tmrt$T_{\text{mrt}}$ simulated with CCLM/DCEP–BEM is higher than that with SOLWEIG (MBE=2.9K$\text{MBE}=\nobreak 2.9\,\text{K}$), yet closer to measurements. Tmrt$T_{\text{mrt}}$ during the afternoon hours modeled with CCLM/DCEP–BEM is underestimated compared to SOLWEIG (MBE=-3.1K$\text{MBE}=\nobreak -3.1\,\text{K}$). Further, excluding vegetation, higher values for nighttime Tmrt$T_{\text{mrt}}$ are found in the densely built-up city center than in the suburbs with more open structures, while the city center has lower values for Tmrt$T_{\text{mrt}}$ during midday. This study provides a reliable representation of Tmrt$T_{\text{mrt}}$ in a mesoscale model and would be beneficial for future implementation of human-biometeorological variables such as the Universal Thermal Climate Index or Physiological Equivalent Temperature. These quantities are calculated using Tmrt$T_{\text{mrt}}$.

Top-of-atmosphere (TOA) albedo is a significant factor of earth energy budget, climate change, and environmental change. As tremendous regional and global changes are happening over ocean, more details are needed to monitor the ocean environment. However, there were still no high-spatial resolution TOA albedo products over ocean. In this study, a new algorithm for clear-sky TOA albedo estimation over ocean was proposed, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. Instead of building angular distribution models, direct retrieval models between TOA reflectance and TOA albedo were developed based on extensive radiative transfer (RT) simulations, covering thousands of ocean and atmosphere types. Three-component ocean water albedo model was involved to take account for the ocean surface anisotropy at different wind speed, wind direction, and chlorophyll concentration, while Modtran 5 was utilized to simulate different atmospheric conditions. Our results showed good agreement with the Clouds and the Earth's Radiant Energy System (CERES) based on a global comparison on August 4, 2011, with RMSE = 0.015 and bias = 0.002. And our MODIS-based products provide more spatial details due to higher spatial resolution (1 km), which will be a good data source for regional environmental and climatic research and will also enhance the understanding of Earth's radiation budget.

Offshore wind energy has seen a steady increase in the latest years as it serves as an ideal alternative energy source to meet our renewable energy goals. Due to this increase in interest and in installations of offshore wind farms a better understanding of the impact of waves on the power production of wind farms in necessary. This wave-wind farm power production interaction has scarcely been examined, however, some exceptions of small scale large-eddy-simulations exist. Unfortunately, these studies are not able to take multiple wind farms under real weather conditions into account. Here, we show the power production and wake lengths of 1250 offshore wind turbines located in the German Bight simulated by a stand-alone atmospheric (WRF) model and a coupled atmosphere-wave (WRF-SWAN) model. The coupled atmosphere-wave model estimates larger (smaller) power production in case of waves and wind traveling in the same (opposed) direction compared to the stand-alone atmospheric model. The relative difference between the two models can be as much as 20% for the two-week averaged grid power production, while the difference in total power over this two-week period is equal to 9%. Moreover, the wind farm wake lengths of waves and wind traveling in the same (opposed) direction are longer (shorter) for the coupled atmosphere-wave model compared to the stand-alone atmospheric model. Here, the relative difference of the mean wake length between the two models can be up to 25%. This shows the importance of waves as a part of the offshore wind environment and can be an important factor in future wind assessment or power production estimation studies.

In order to solve the problem that urban spatial pattern causes by gradually deteriorating the urban microclimate,directly affecting the comfort of the living environment and to clarify the influence of the urban spatial configuration on residential microclimate, a microclimate detection system based on wireless sensor network is proposed to detect the microclimate of urban residential area,and a mathematical modeling method of the relationship between different parameters such as: urban spatial pattern parameters (building density, average height of building, horizontal layout and vertical layout), residential microclimate parameters (mean radiant temperature(MRT), and Physiological Equivalent Temperature (PET)) based on surrogate model is investigated to analyze the correlation between spatial pattern change of urban residential area and microclimate. The Kriging model and response surface model are used to establish the mathematical model between the spatial shape and the microclimate. The results showed that the kriging model has higher precision than the response surface model, so the kriging model was used to model the relationship between urban spatial model parameters and residential microclimate parameters. The results of the influence of spatial morphology parameters on microclimate indicate that the building density and average height of building are positively correlated with the PET and MRT parameters,and horizontal layout and vertical layout are negatively correlated with the PET and MRT parameters. Thus through reasonable building layout, we can optimize and improve the microclimate environment of residential area, which is helpful in guiding the design of building scheme.

The air traffic management automation imposes stringent requirements on the weather models, in such a way that they should be able to provide reliable short-time forecasts in digital formats in almost real time. The atmospheric boundary layer is one of the regions where aircraft operation and coordination are critical and therefore atmospheric model performance is also vital. This paper presents conventional and innovative techniques to improve the accuracy in the forecasting of winds in the lower atmospheric layer, proposing mechanisms to develop better models including deterministic and stochastic simulations. Accuracy is improved by optimizing the grid, assimilating observations in cycling simulations and managing a number of ensemble members. An operation-driven post-processing stage helps to incorporate detailed terrain definitions and real-time observations without re-running the model. The improvements are checked against mesoscale weather simulations at different scales and a dedicated flight campaign. The results show good performance of the model without sensitively increasing the required throughput.

Rainfall event forecasting is prominently done using climate models (CMs) to produce multiple forecasts for the same rainfall event. The best forecast is complicated to find and hence has not yet been explored in the CMs. Recent advances in deep learning methods have provided an exceptional ability to investigate intricate weather patterns from big climate data. In this article, a hybrid climate learning model (HCLM) is proposed that utilizes both the CM and the deep learning models for improving the rainfall forecast. More specifically, a probabilistic multilayer perceptron (PMLP) network evaluates multiple forecasts from the CM-generated forecasts and selects the best one. The selected forecast is next passed onto a hybrid deep long short-term memory (HD-LSTM) network, which looks back and learns the relationship of the selected forecast with corresponding rainfall and temperature observations to produce the next-day rainfall forecast. The experimental results from various climate zones in Australia show that the HCLM outperforms existing state-of-the-art climate and deep learning models.

To assess the impact of four-dimensional variational (4D-Var) data assimilation on the performance of a land–atmosphere coupled model, the satellite precipitation of the Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) was assimilated into the Weather Research Forecast (WRF) Model, and the WRF was coupled to the hydrological model TOPX. Precipitation and evaporation were both investigated as connecting elements in the coupled model WRF–TOPX. Differing in whether the 4D-Var data assimilation and evaporation were applied, one control experiment and four experiments were performed to simulate a historical flood event that happened in the Wangjiaba watershed in eastern China. The key hydrological variables of precipitation, potential evaporation, soil moisture, and discharge in the studied flood process were evaluated. The results showed that 1) the 4D-Var data assimilation with the IMERG could reduce both the overestimations of the WRF-predicted precipitation and potential evaporation; 2) the applied 4D-Var data assimilation could improve considerably the accuracy of the soil moisture and discharges from the coupled model WRF–TOPX; and 3) evaporation was also an important factor to influence the net precipitation to affect the performance of the coupled land–atmosphere model. With the two connecting elements of precipitation and evaporation, the 4D-Var assimilation based on IMERG could improve the Nash–Sutcliffe coefficient of the coupled model WRF–TOPX from 0.483 to 0.521 at the hourly scale. These investigations can provide important implications for the land–atmosphere coupling with both the precipitation and evaporation and using the 4D-Var data assimilation with IMERG for flood simulation at a large scale.

The Nonhydrostatic ICosahedral Atmospheric Model (NICAM), a global model with an icosahedral grid system, has been under development for nearly two decades. This paper describes NICAM16-S, the latest stable version of NICAM (NICAM.16), modified for the Coupled Model Intercomparison Project Phase 6, High Resolution Model Intercomparison Project (HighResMIP). Major updates of NICAM.12, a previous version used for climate simulations, included updates of the cloud microphysics scheme and land surface model, introduction of natural and anthropogenic aerosols and a subgrid-scale orographic gravity wave drag scheme, and improvement of the coupling between the cloud microphysics and the radiation schemes. External forcings were updated to follow the protocol of the HighResMIP. A series of short-term sensitivity experiments were performed to determine and understand the impacts of these various model updates on the simulated mean states. The NICAM16-S simulations demonstrated improvements in the ice water content, high cloud amount, surface air temperature over the Arctic region, location and strength of zonal mean subtropical jet, and shortwave radiation over Africa and South Asia. Some long-standing biases, such as the double intertropical convergence zone and smaller low cloud amount, still exist or are even worse in some cases, suggesting further necessity for understanding their mechanisms, upgrading schemes and parameter settings, and enhancing horizontal and vertical resolutions.

Various uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

Prediction of incidences of thunderstorms using different techniques is quite well established. To add to this knowledge, in this paper, a heuristic equation is proposed which relates the correlation coefficient of atmospheric parameters with the number of hourly incidences of thunderstorms. There are four ways to compute the indices values from the proposed heuristic equation. These indices values are used in the classification of the hourly incidences of thunderstorms. The proposed equation and indices work well, as tested on pre-monsoon hourly atmospheric data from 2018 and validated with hourly data from April 2019 and 2020. From the four indices, the first index value is computed with normalized average values of parameters of only hourly incidence of thunderstorms data of 2016-2017 in the month-wise method. The other three indices use optimization techniques namely, the Teaching Learning Based Optimization (TLBO) technique, Differential Evaluation (DE), and Simulated Annealing (SA). TLBO shows better classification of hourly incidences of thunderstorms for 2018 atmospheric data. TLBO is also precisely validating the hourly incidences of thunderstorms for April 2019 and April 2020 hourly atmospheric data. It performed better with 2020 data by 88%. The variations of atmospheric parameters before, after or, during the incidences of thunderstorms are also depicted.

Real-time heatwave risk management with fine-grained spatial resolution is important for analysis of urban heat island (UHI) effects and local heatwaves. This study analyzed the spatio-temporal behavior of ground temperatures and developed methods for modeling them. The developed models consider two higher-order stochastic spatial properties (skewness and kurtosis), which are key to understanding and describing local temperature fluctuations and UHI effects. Application of the developed models to the greater Tokyo metropolitan area demonstrated the feasibility of statistically incorporating a variety of real datasets. Remotely sensed imagery and data from a variety of ground-based monitoring sites were used to build models linking urban covariates to air temperature. Air temperature models were used to capture high-resolution spatial emulator outputs for modeling ground surface temperatures. The main processes studied were the Tukey g-and-h processes for capturing spatial and temporal aspects of heat processes in urban environments. The main finding is that consideration of not only the mean temperature but also the variance, skewness, and kurtosis parameters can reveal hidden heatwave structures.

The rising temperature is one of the key indicators of a warming climate, capable of causing extensive stress to biological systems as well as built structures.Ambient temperature collected at ground level can have higher variability than regional weather forecasts, which fail to capture local dynamics. There remains a clear need for accurate air temperature prediction at the suburban scale at high temporal and spatial resolutions. This research proposed a framework based on a long short-term memory (LSTM) deep learning network to generate day-ahead hourly temperature forecasts with high spatial resolution. Air temperature observations are collected at a very fine scale (~150m) along major roads of New York City (NYC) through the Internet of Things (IoT) data for 2019-2020. The network is a stacked two layer LSTM network, which is able to process the measurements from all sensor locations at the same time and is able to produce predictions for multiple future time steps simultaneously. Experiments showed that the LSTM network outperformed other traditional time series forecasting techniques, such as the persistence model, historical average, AutoRegressive Integrated Moving Average (ARIMA), and feedforward neural networks (FNN). In addition, historical weather observations are collected from in situ weather sensors (i.e., Weather Underground, WU) within the region for the past five years. Experiments were conducted to compare the performance of the LSTM network with different training datasets: 1) IoT data alone, or 2) IoT data with the historical five years of WU data. By leveraging the historical air temperature from WU, the LSTM model achieved a generally increased accuracy by being exposed to more historical patterns that might not be present in the IoT observations. Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.

Global pressure and temperature (GPT) series models can provide the underlying meteorological parameters for tropospheric corrections without any other meteorological observations, which allows them to be widely used for a series of geodetic as well as meteorological and climatological purposes. Due to the height difference between the empirical model height and user location, a vertical correction of meteorological parameters is inevitable, particularly for airborne users. Unfortunately, the GPT series models have limitations on the vertical correction. We explored the temperature lapse rate for the vertical adjustment using 10 years of reanalysis data provided by the National Centers for Environmental Prediction (NCEP), and extended the GPT models to improved global pressure and temperature (IGPT) series models by introducing a new temperature lapse rate model and a new formulation of pressure reduction. An evaluation of the IGPT models expression determines that the IGPT models have better accuracy than the GPT models, particularly under large height differences, which is attributed to their ability to consider the real behavior of temperature in the atmosphere and adiabatic effects on air pressure. The performance of the IGPT models in zenith tropospheric delay (ZTD) estimations was also evaluated by comparison with the fifth-generation European Centers for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and International GNSS Service (IGS) data. The results confirm that our new models can effectively improve the accuracy of ZTDs, particularly at larger altitude differences between the target height and the corresponding four grid points of the model, not only enhancing the performance of the model in complex terrain but also extending the feasibility of the IGPT models from the Earth’s surface to higher altitudes.

Near-surface air temperature (NSAT) is a key parameter in climate changes, environmental ecosystem monitoring, and human settlement issues. As it is difficult for in situ observations to capture the spatial distribution characteristics of NSAT in great detail, various methods have been developed to use remotely sensed land surface temperature and other auxiliary variables to estimate the NSAT. Among them, machine learning turns out to be an exhilarating choice due to its superior performance. However, for long-term dataset estimation, when using machine learning methods with abundant related variables, computation loads and data storage cannot be ignored. Fortunately, Google Earth Engine (GEE) provides a wealth of geospatial datasets and powerful parallel computation capability. In this article, GEE has been utilized to demonstrate the feasibility of estimating long-term 1-km NSAT with two machine-learning models (random forest and deep neural network). After testing the effects of the input variables and the model portability, the 20-year monthly mean 1 km × 1 km NSAT over the Yellow River basin was generated and analyzed. Compared with in situ observations, the overall RMSE, MAE, R 2, and R of the NSAT product are 0.429, 0.302, 0.998, and 0.999, respectively. Specifically, for each observation station, R and R 2 are greater than 0.997, and RMSE and MAE are smaller than 0.5 and 0.4, respectively. The comparisons with three existing NSAT products show that our product outperforms the other three products. Once again, it is demonstrated in this study that GEE is a powerful platform to generate valuable products with ready-to-use datasets and computation resources.

The tropospheric delay is a major error source for Global Navigation Satellite System (GNSS) positioning and navigation, and usually can be corrected by using an empirical model. Due to the small number of parameters and simplified algorithm, the UNB3m model has been widely hardwired in GNSS receivers for real-time applications. However, many studies have noted that the UNB3m model has significant systematic errors in the correction of tropospheric delays, mainly due to the assumption of north-south symmetry. Therefore, considering the realistic atmospheric behavior, we proposed a new tabular zenith tropospheric delay (TZTD) model using 10 years of NCEP (National Centre for Environmental Prediction) data. The performance of the TZTD model was assessed along with the GPT3 and UNB3m models by comparison with ZTDs derived from the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA5) data and GNSS observations. The results show that the TZTD model (with a RMSE of 41mm) significantly outperforms the UNB3m model (with a RMSE of 49 mm). Furthermore, the accuracy of the new model is better than that of the GPT3 model in terms of the zenith tropospheric hydrostatic delay (ZHD) prediction, particularly at high altitudes. The TZTD model characterized by simplicity and accuracy, is expected to be a substitute for the UNB3m model in real-time GNSS applications.

At the rise of the fourth industrial revolution, artificial intelligence (AI), along with key enabling technologies such as millimeter waves (mm-waves) can be used to launch the fifth-generation (5G) and beyond communication links. However, the quality of radio links at higher frequency bands is limited by atmospheric elements. Among others, rainfall is the major propagation impairment at millimetric wave bands, which needs to be considered during the link budget planning. In this study, we investigated the rain attenuation results obtained from experimental data, existing models, and proposed supervised artificial neural network (SANN) at $K$ , $Ka$ , and $E$ -bands, respectively, for terrestrial links in South Korea. The measurement campaigns were between Incheon, National Radio Research Agency (RRA) tower station, to the EMS Dongyoksang tower station operating at 75 GHz over a 100-m path length, and between Incheon, RRA tower station to Khumdang, Korea Telecom (KT) tower station, operating at 18 and 38 GHz over a 3.2-km path length. The three-year rainfall and received signal level data measurements over these paths were used to determine rain attenuation distributions at different percentages of exceedance time distribution. Additionally, three existing attenuation models, ITU-R 530.17, Lin, and Revised Silva Mello (RSM) models were compared with measured rain attenuation. Our results indicate that these models did not correspond with measured results. Therefore, in this research, we proposed a supervised learning-based attenuation prediction method, which provides better performance than existing models. Furthermore, we validated our proposed model with measured received-signal level and rainfall data at the above-mentioned operating frequencies.

Cloud macrophysics schemes are unique parameterizations for general circulation models. We propose an approach based on a probability density function (PDF) that utilizes cloud condensates and saturation ratios to replace the assumption of critical relative humidity (RH). We test this approach, called the Global Forecast System (GFS) – Taiwan Earth System Model (TaiESM) – Sundqvist (GTS) scheme, using the macrophysics scheme within the Community Atmosphere Model version 5.3 (CAM5.3) framework. Via single-column model results, the new approach simulates the cloud fraction (CF)–RH distributions closer to those of the observations when compared to those of the default CAM5.3 scheme. We also validate the impact of the GTS scheme on global climate simulations with satellite observations. The simulated CF is comparable to CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. Comparisons of the vertical distributions of CF and cloud water content (CWC), as functions of large-scale dynamic and thermodynamic parameters, with the CloudSat/CALIPSO data suggest that the GTS scheme can closely simulate observations. This is particularly noticeable for thermodynamic parameters, such as RH, upper-tropospheric temperature, and total precipitable water, implying that our scheme can simulate variation in CF associated with RH more reliably than the default scheme. Changes in CF and CWC would affect climatic fields and large-scale circulation via cloud–radiation interaction. Both climatological means and annual cycles of many of the GTS-simulated variables are improved compared with the default scheme, particularly with respect to water vapor and RH fields. Different PDF shapes in the GTS scheme also significantly affect global simulations.

Between the 4th and the 6th of November 1994, Piedmont and the western part of Liguria (two regions in north-western Italy) were hit by heavy rainfalls that caused the flooding of the Po, the Tanaro rivers and several of their tributaries, causing 70 victims and the displacement of over 2000 people. At the time of the event, no early warning system was in place and the concept of hydro-meteorological forecasting chain was in its infancy, since it was still limited to a reduced number of research applications, strongly constrained by coarse-resolution modelling capabilities both on the meteorological and the hydrological sides. In this study, the skills of the high-resolution CIMA Research Foundation operational hydro-meteorological forecasting chain are tested in the Piedmont 1994 event. The chain includes a cloud-resolving numerical weather prediction (NWP) model, a stochastic rainfall downscaling model, and a continuous distributed hydrological model. This hydro-meteorological chain is tested in a set of operational configurations, meaning that forecast products are used to initialise and force the atmospheric model at the boundaries. The set consists of four experiments with different options of the microphysical scheme, which is known to be a critical parameterisation in this kind of phenomena. Results show that all the configurations produce an adequate and timely forecast (about 2 days ahead) with realistic rainfall fields and, consequently, very good peak flow discharge curves. The added value of the high resolution of the NWP model emerges, in particular, when looking at the location of the convective part of the event, which hit the Liguria region.

The article presents the prognostic capabilities of the SOCOL global climate model. A correction of the trajectory is proposed by supplementing the algorithms of the nominal control of the return vehicle with the atmospheric parameters at altitudes of 40–80 km obtained as a result of prognostic calculations.

This study evaluates the performance of the Grid-point Atmospheric Model of IAP LASG, version 3 (GAMIL3), in simulating the Madden–Julian Oscillation (MJO), based on the CMIP6 (phase 6 of the Coupled Model Intercomparison Project) AMIP (Atmospheric Model Intercomparison Project) simulation. Results show that GAMIL3 reasonably captures the main features of the MJO, such as the eastward-propagating signal in the MJO frequency band, the symmetric and asymmetric structures of the MJO, several convectively coupled equatorial waves, and the MJO life cycle. However, GAMIL3 underestimates the MJO amplitude, especially for outgoing longwave radiation, as do most CMIP5 models, and simulates slow eastward propagation.

In the present work, we analyzed different types of clouds observed at the limb as well as over the surface of the red planet by MCC and estimated the atmospheric parameter associated with it. Our analysis based on the data collected by Mars Colour Camera (MCC) on board Indian Mars Orbiter Mission during orbit number 44,45,48,49, 50,51,179,187,190,191,466 and 474. Estimated height of the high altitude cloud varies from 40 to 76 km and horizontal spreading of 400 to 1100 km. We used ARC-GIS to detect the area over which the high altitude clouds are observed. These detached layers of clouds are found to be formed of Dust and water-ice particle. MRO-MCS and MARCI confirms few dust events during the appearance of the high altitude cloud. Lee-wave cloud images are captured during the MY33 and MY34. Estimated wavelength of the Lee wave cloud varies from 38 to 44 km while wind speed varies from 54 to 64 m/sec. Aerosol Optical Depth (AOD) as a function of altitude varies from 0.6 to 2.4 for red channel and 0.8 to 1.8 for blue channel for the captured events. We consulted Global Circulation Model (GCM) as well as MOLA-DEM to frame the AOD output. Estimated wind speed profile from MCC and MARCI-MGDM suggested the circulation of strong wind across the globe and delivered input to formation of clouds through different processes related to the atmosphere dynamics viz. Deep Convection, Orographic lift, Thermal updraft, etc. In our present study we tried to estimate the parameter related to these processes and tried to correlate them with the observed events.

One of the most challenging tasks in atmospheric modeling is accurate spatio-temporal forecasting of extremely severe cyclonic storms (ESCSs). This study critically examined the numerical simulation of ESCS Hudhud, which formed over the North Indian Ocean basin in October 2014 on a medium-range (7-day) scale using six different cloud microphysical schemes of the Weather Research and Forecasting model. NCEP PREBUFR observation was used to improve the initial condition using a three-dimensional variational data assimilation system. A 7-day simulated track, intensity, and rapid intensification of ESCS were validated against the India Meteorological Department best-fit track observations. The least track error was about 42 km, 67 km, 87 km, and 192 km from Day 4 to Day 7 by using the Goddard Microphysical Scheme (MPS). Mean track error for 7 days was about 129 km, followed by the Lin MPS (~ 136 km) and WSM6 MPS (~ 139 km). The least mean absolute error in maximum surface winds is ~ 4 m/s using the Goddard, Lin, and WSM19 MPSs with a correlation coefficient > 0.93. Probability of detection for rapid intensification/dissipation was about 70% using WSM6 and Goddard MPSs, which was relatively better compared to other MPSs. Results clearly demonstrate that latent heating between 500 and 200 hPa was very important and contributed to intensification, and the maximum magnitude of latent heating correlated well with MSW of ESCS. Simulated maximum reflectivity was compared with the Doppler Weather Radar (DWR) observation from Visakhapatnam, and results indicate that reflectivity is well represented. However, the magnitude was slightly under-predicted compared to DWR in the Goddard MPS. The vertical distribution of hydrometeors and microphysical latent heating is found to play an important role in the initiation and development of cyclonic systems.

This is the first study concerning the assimilation of the INSAT-3D/3DR radiance in the Hurricane Weather Research and Forecasting (HWRF) model and assesses its credibility to improve track, intensity, and precipitation forecasts of tropical cyclone (TC) Titli that occurred over the Bay of Bengal (BoB), which showed rapid intensification (RI) and weakening through its lifetime. The inbuilt Gridpoint Statistical Interpolation (GSI) method is used with a 3-D variational (3DVAR) configuration. Three sets of numerical experiments such as control (CNTL) (no assimilation), Global Telecommunication System (GTS) (observations from GTS network), and INSAT-3D/3DR (INSAT-3D/3DR sounder radiance data and GTS observations) were carried out with seven different initializations. The radiance analysis reproduced the initial vortex and the prominent synoptic scale features associated with TC Titli. The average root-mean-square errors (RMSE) of the analysis were relatively lower in the INSAT-3D/3DR compared to the CNTL and GTS. The HWRF performance is enhanced for track simulation, with improvements in mean landfall position errors by 40%–70% and 26%–52% for the INSAT-3D/3DR and GTS runs, respectively. The assimilation of radiance data has a positive impact on the simulation of warm core and thermodynamic structures, which has led to a more accurate intensity prediction (by 30–47%) over the CNTL. The assimilation run could realistically simulate the RI and weakening phases of the TC. A cold dry air intrusion is also observed when associated with the weakening. The study highlights the need to incorporate INSAT-3D/3DR radiances for improved TC predictions over the BoB basin.

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds ($$rhminl)$$ r h m i n l ) and the auto-conversion-size-threshold-for-ice-to-snow $$\left( {dcs} \right).$$ dcs . The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on $$dcs$$ dcs and $$rhminl$$ rhminl . The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in $$dcs$$ dcs . Overall, we find that $$rhminl$$ rhminl , $$dcs$$ dcs , $$ai,$$ a i , and $$as$$ as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

On 29 October 2018 a windsurfer's mast broke about 1 km offshore from Istria during a severe scirocco storm in the northern Adriatic Sea. He drifted in severe marine conditions until he eventually beached alive and well in Sistiana (Italy) 24 h later. We conducted an interview with the survivor to reconstruct his trajectory and to gain insight into his swimming and paddling strategy. Part of survivor's trajectory was verified using high-frequency radar surface current observations as inputs for Lagrangian temporal back-propagation from the beaching site. Back-propagation simulations were found to be largely consistent with the survivor's reconstruction. We then attempted a Lagrangian forward-propagation simulation of his trajectory by performing a leeway simulation using the OpenDrift tracking code using two object types: (i) person in water in unknown state and (ii) person with a surfboard. In both cases a high-resolution (1 km) setup of the NEMO v3.6 circulation model was employed for the surface current component, and a 4.4 km operational setup of the ALADIN atmospheric model was used for wind forcing. The best performance is obtained using the person-with-a-surfboard object type, giving the highest percentage of particles stranded within 5 km of the beaching site. Accumulation of particles stranded within 5 km of the beaching site saturates 6 h after the actual beaching time for all drifting-particle types. This time lag most likely occurs due to poor NEMO model representation of surface currents, especially in the final hours of the drift. A control run of wind-only forcing shows the poorest performance of all simulations. This indicates the importance of topographically constrained ocean currents in semi-enclosed basins even in seemingly wind-dominated situations for determining the trajectory of a person lost at sea.

This paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. The SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

Using information from the A-Train satellites, the properties and radiative effects of eastern Pacific Ocean boundary layer clouds are evaluated in the Community Atmosphere Model, version 5 (CAM5), from the summer of 2007 and 2008. The cloud microphysical properties are inferred using measurements from CloudSat and CALIPSO (CC) that are then used to calculate the broadband radiative flux profiles. Accounting appropriately for sampling differences between the measurements and the simulation, evidence of the “too few, too bright” low cloud bias is found in CAM5. Single-layer low clouds have a frequency of occurrence of 42% from CC, as compared with just 29% in CAM5, and the averaged cloud radiative kernel (CRK) for the model shows stronger cooling. For stratocumulus in particular, the cooling in the model CRK is larger by a factor of 2 relative to the observations, implying an overly sensitive tropical low cloud feedback. Differences in the day/night occurrence of stratocumulus help to explain some of the difference in the CRK. The cloud-type microphysics for liquid clouds is represented reasonably well by the model, with a tendency for smaller water paths and smaller effective radii. Overall, the occurrence and CRK have partially compensating errors such that the net cooling at the top of the atmosphere for eastern Pacific low clouds is −43 W m−2 in CAM5, as compared with −32 W m−2 from CC. The cooling effect in the model is accomplished by fewer low clouds with a narrower range of properties, as compared with more clouds with a broader range of properties in the observation-based dataset.

Except for its known aspects of positioning, navigation, and timing (PNT), the Global Navigation Satellite System (GNSS) has extended its application to the rainfall forecasting. GNSS-derived zenith total delay (ZTD) or precipitable water vapor (PWV) has been used as a single factor to predict the occurrence of rainfall; however, the rainfall is highly correlated with myriad atmospheric parameters, which cannot be perfectly reflected by a single predictor. In this article, an improved rainfall forecasting model (IRFM) is proposed to forecast the rainfall. The IRFM considers five predictors: monthly PWV value, seasonal PWV/ZTD variations, and their first derivatives: it can forecast rainfall using a single predicator or an arbitrary combination of those predicators. The merit of IRFM is reducing the false forecasted rainfall (FFR) events and missed detected rainfall (MDR) events as much as possible while guaranteeing the true detected rainfall (TDR) events. An optimized selecting principle of predictors’ threshold has been determined using the percentile method. The test experiment has been performed using five GNSS stations derived from continuously operating reference system (CORS) network of Zhejiang province, China. The analysis reveals that the IRFM considering five predictors provides a better performance than that only using a single predictor or a combination of arbitrary predictors. The statistical result shows the average TDR value of more than 95%, FFR value of less than 30%, and MDR of less than 5%, respectively. Compared to the existing rainfall forecasting methods using ZTD or PWV, the IRFM reduces the FFR and MDR, respectively, with the lowest values, while the TDR value is the highest.

Wildfires and their emissions reduce air quality in many regions of the world, contributing to thousands of premature deaths each year. Smoke forecasting systems have the potential to improve health outcomes by providing future estimates of surface aerosol concentrations (and health hazards) over a period of several days. In most operational smoke forecasting systems, fire emissions are assumed to remain constant during the duration of the weather forecast and are initialized using satellite observations. Recent work suggests that it may be possible to improve these models by predicting the temporal evolution of emissions. Here, we develop statistical models to predict fire activity one to five days into the future using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite fire counts and weather data from ERA-interim reanalysis. Our predictive framework consists of two-Poisson regression models that separately represent new ignitions and the dynamics of existing fires on a coarse resolution spatial grid. We use ten years of active fire detections in Alaska to develop the model and use a cross-validation approach to evaluate model performance. Our results show that regression methods are significantly more accurate in predicting daily fire activity than persistence-based models (which suffer from an overestimation of fire counts by not accounting for fire extinction), with vapor pressure deficit being particularly effective as a single weather-based predictor in the regression approach.

We analyzed two historical extreme heat events in Los Angeles to explore the potential of increasing vegetative cover and surface solar reflectance (albedo) to reduce total exposure (indoor and outdoor) to dangerously hot conditions. We focus on three population subgroups, the elderly, office workers, and outdoor workers, and explore the extreme case where each subgroup does not have functioning air conditioning in their residences. For each heat event, we conducted atmospheric model simulations for a control case and four mitigation cases with varying levels of increased albedo and vegetation cover. Simultaneously, we conducted building simulations of representative residential buildings that lacked mechanical air conditioning. These simulations factored in both the indirect cooling effects associated with neighborhood implementation of mitigation strategies and the direct effects of high albedo roofing on the individual buildings. From both the atmospheric and building models, we exported hourly values of air temperature and dew point temperature, and used this information in combination with various scenarios of occupant behavior to create profiles of individual heat exposure. We also gathered heat-mortality data for the two heat events and developed a synoptic climatology-based relationship between exposure and excess mortality. This relationship was then applied to the scenarios in which albedo and canopy cover were increased. The results suggest that improvements in indoor thermal conditions are responsible for a sizable portion of the health benefit of large-scale implementation of heat mitigation strategies.

In order to improve the predictability of winter storm waves in the East Sea, this article explores the use of the ensemble Kalman filter technique for data assimilation in wave modeling. The nested wave model has been established using SWAN along the east coast of Korea to simulate wave transformation and wave dissipation in coastal areas to obtain a better modeling performance with regard to wind waves and swells in the East Sea. The regional atmospheric model is used to provide high-resolution forcing winds. These are adjusted by directly assimilating measurements of offshore wave heights into the wave model state. The model setup, data assimilation parameters, and validation of prediction are described with optimal conditions during the stormy periods in 2015. The ensemble Kalman filter data assimilation has shown itself to be very efficient, leading to large reductions of up to 40% in the root-mean- square error of the signification wave height compared to the results with and without data assimilation at locations other than those of the observations used. It shows that the wave modeling with ensemble Kalman filter data assimilation is very feasible to predict coastal waves, in particular storm events in the East Sea. kw]Keywords -winter storm waves, wave modeling, data assimilation, wind forcing, East Sea

Tropical cyclones are devastating hazards and have been a major problem for the coastal population of Bangladesh. Among the advancements in atmospheric and oceanic prediction, accurate forecasting of storm surges is of specific interest due to their great potential to inflict loss of life and property. For decades, the numerical model based storm surge prediction systems have been an important tool to reduce the loss of human lives and property damage. In order to improve the accuracy in predicting storm surge and coastal inundation, recent model development efforts tended to include more modeling components, such as meteorology model and surface wave model in storm surge modeling. In this study, we used the outputs of an atmospheric model to force the ocean model for simulating storm surges in the Bay of Bengal with particular focus on the Bangladesh coast. The ability of the modeling system was investigated simulating water levels in the Bangladesh coast of two tropical cyclones Sidr (2007) and Aila (2009). The effectiveness of the model was verified through comparing the obtained computational outputs against tide gauge data. The cyclone tracks and intensities reproduced by the atmospheric model were reasonable, though the model had a tendency to overestimate the cyclone intensity during peaks and also close to coast. The water levels are reproduced fairly well by the ocean model, although errors still exist. The root mean square errors in water level at different gauges range from 0.277 to 0.419 m with coefficient of correlation (R2) between 0.64 and 0.97 in case of Sidr and 0.209–0.581 m with R2 0.62 to 0.98 for Aila. The overall coupled modeling system is found to be useful with reasonable accuracy and precision, though there are spaces for improvement. Higher-resolution modeling approaches are recommended to gain more skills.

This data assimilation study exploits infrasound from explosions to probe an atmospheric wind component from the ground up to stratospheric altitudes. Planned explosions of old ammunition in Finland generate transient infrasound waves that travel through the atmosphere. These waves are partially reflected back towards the ground from stratospheric levels, and are detected at a receiver station located in northern Norway at 178 km almost due North from the explosion site. The difference between the true horizontal direction towards the source and the backazimuth direction (the horizontal direction of arrival) of the incoming infrasound wave-fronts, in combination with the pulse propagation time, are exploited to provide an estimate of the average cross-wind component in the penetrated atmosphere. We perform offline assimilation experiments with an ensemble Kalman filter and these observations, using the ERA5 ensemble reanalysis atmospheric product as background (prior) for the wind at different vertical levels. We demonstrate that information from both sources can be combined to obtain analysis (posterior) estimates of cross-winds at different vertical levels of the atmospheric slice between the explosion site and the recording station. The assimilation makes greatest impact at the 12-60 km levels, with some changes with respect to the prior of the order of 0.1-1.0 m/s, which is a magnitude larger than the typical standard deviation of the ERA5 background. The reduction of background variance in the higher levels often reached 2-5%. This is the first published study demonstrating techniques to implement assimilation of infrasound data into atmospheric models. It paves the way for further exploration in the use of infrasound observations - especially natural and continuous sources - to probe the middle atmospheric dynamics and to assimilate these data into atmospheric model products., Comment: Manuscript submitted to the Quarterly Journal of the Royal Meteorological Society. The current document is an e-print typeset by the authors

We revisit the concept of the cloud vertical structure (CVS) classes we have previously employed to classify the planet's cloudiness (Oreopoulos et al., 2017). The CVS classification reflects simple combinations of simultaneous cloud occurrence in the three standard layers traditionally used to separate low, middle, and high clouds and was applied to a dataset derived from active lidar and cloud radar observations. This classification is now introduced in an atmospheric global climate model, specifically a version of NASA's GEOS-5, in order to evaluate the realism of its cloudiness and of the radiative effects associated with the various CVS classes. Such classes can be defined in GEOS-5 thanks to a subcolumn cloud generator paired with the model's radiative transfer algorithm, and their associated radiative effects can be evaluated against observations. We find that the model produces 50 % more clear skies than observations in relative terms and produces isolated high clouds that are slightly less frequent than in observations, but optically thicker, yielding excessive planetary and surface cooling. Low clouds are also brighter than in observations, but underestimates of the frequency of occurrence (by ∼20 % in relative terms) help restore radiative agreement with observations. Overall the model better reproduces the longwave radiative effects of the various CVS classes because cloud vertical location is substantially constrained in the CVS framework.

Wind speed and direction are fundamental data for many application fields, such as power generation and hydrological modelling. Wind measurements are usually few and sparse; hence, spatial interpolation of wind data is required. However, in mountainous areas with complex orography, accurate interpolation of wind data should consider topographic effects. Due to computational constraints, fully physically based methods that solve thermodynamic and mass conservation equations in three dimensions cannot be applied for long-time simulations or very large areas, while fast empirical methods seem more suitable. The aim of this work is to compare fast empirical methods to interpolate wind speed against a physically based full atmospheric model in order to assess the impact of the introduced approximation in estimating the wind field and the potential evapotranspiration. Comparison is carried out over the area of the upper Po River basin, a predominantly alpine region located in northern Italy. Results show that empirical topographic correction can increase accuracy of interpolated wind speed in areas with complex topography, but it requires about 50% more computational time than simpler empirical methods that do not consider topography.

This review summarizes the general developments of the operational mesoscale model system based on the Global/Regional Assimilation and Prediction System-Tropical Monsoon Model (GRAPES-TMM) at the Guangzhou Regional Meteorological Center. GRAPES-TMM consists of the Tropical Regional Atmospheric Model System for the South China Sea (TRAMS, a typhoon model with a horizontal resolution of 9 km), the Mesoscale Atmospheric Regional Model System (MARS, 3km) and the fine-scale Rapid Update Cycling (RUC, 1km) forecasting system. The main advances of model dynamical core and physical processes are summarized, including the development of the 3D reference atmosphere scheme, the coupling scheme between dynamics and model physics, the calculation of nonlinear terms by fractional steps, the gravity wave drag scheme induced by sub-grid orography and a simplified model for landsurface scheme. The progress of model applications is reviewed and evaluated. The results show that the updated 9-3-1 forecasting system provides an overall improved performance on the weather forecasting in south China, especially for typhoon-genesis and typhoon-track forecasting as well as short-range weather forecasting. Capabilities and limitations as well as the future development of the forecasting system are also discussed.

Tropical cyclone (TCs) prediction has improved in the last decade, but studying their structure and dynamics remains a challenging task due to limited in situ observations. The global navigation satellite system (GNSS) technique enables retrieving the state of the troposphere with high accuracy in all weather conditions. In this article, we use GNSS slant total delays (STDs) to verify the quality of the weather models during the passage of the TC Meranti, which was the strongest TC in 2016. STDs observed from 28 GNSS stations in Taiwan are compared with the STDs reconstructed from three meteorological data sets: the weather research and forecasting (WRF) model, the global forecast system (GFS), and the ERA-Interim (ERA) using a 2-D ray-tracing technique. Furthermore, two strategies were tested—including and excluding the delays due to liquid and ice water content. The results reveal a good consistency between the GNSS and the ray-traced STDs. The best agreement was found for the WRF, with a mean difference of −0.5 mm and a standard deviation of 29.4 mm when the hydrometeor delay was included. The mean hydrometeor contribution reached up to 2.8 mm for the WRF, while for the ERA and GFS, the contribution to the total delay was negligible. The recorded absolute percent differences were greater for the northern HENC station (mostly within 0.8%) than for the southern GOLI station (on average below 0.5%) due to a closer location to the passing Meranti. The most significant biases were seen in the ERA (around 7%) and the GFS (exceeding −3%) for the PLIM station, located in the central mountain range valley.

Geological hazard monitoring plays a pivotal role in preventing geological hazards. The main challenge faced by many experiments is to quantitatively monitor and analyze geological hazards. This paper proposes a quantitative monitoring indicator, called the ground surface stability anomaly (GSSA), for the first time and gives the identification criterion of the GSSA based on three stability factors. The features of the three stability factors, including geodetic height, ground gravity, and vertical deviation, reflect the relationship between load-induced changes and geological hazards. To verify the effectiveness and applicability of the GSSA quantitative monitoring method, a regional GSSA model was constructed based on the continuously operating reference station (CORS) network data and load impact data in southeastern Zhejiang, China. The larger the value of the GSSA is, the more likely geological hazards are to occur, especially when an abnormal dynamic environment (such as heavy rainfall, sea level anomaly, atmospheric pressure anomaly, or spring tide) is encountered. By comparing the GSSA with the potential trouble points of geological hazards and 40 geological hazard events that have occurred, the results show that the method can quantitatively, accurately, and continuously monitor the GSSA in an area covered by the CORS network, and the method has the ability to capture the precursors to geological hazards.

Impacts from analysis and lateral boundary updates as well as the assimilation of observations are investigated using the forecast sensitivty to observation impact framework in a limited-area atmospheric model. High temporal frequency estimates of forecast error are produced using aircraft observations for validation. Using these estimates, forecast error reduction between background and analysis trajectories is shown to decrease through the first 24 h of forecast time. The increasing importance of lateral boundary updates in decreasing forecast error with forecast lead time is presented. However, the ability of the adjoint forecast model to attribute forecast error reduction to analysis and lateral boundary updates decreases as forecast length increases. The relative distributions of the largest observation impacts for different lead times are similar. This means that impacts for shorter forecast lengths are a good proxy for impacts on longer forecasts, thereby mitigating some of the problems in long adjoint model integrations. Finally, a metric that measures forecast error against radiosondes is introduced and produces different distributions of observation impact importance.

Short-term forecasting of solar irradiance at a targeted site with consideration of its time series and measurements at neighboring sites is studied in this paper. A data-driven framework for forecasting solar irradiance based on fusing spatial and temporal information is proposed. In the framework, data-driven approaches including boosted regression trees (BRT), artificial neural network, support vector machine, and least absolute shrinkage and selection operator are applied to model spatial dependence among solar irradiance time series thereby to generate forecasts of solar irradiance at the targeted site. A comprehensive comparison among data-driven forecasting models is performed using 30-min averaged data of recent two years. Moreover, benchmarking models including scaled persistence model, autoregressive model, and autoregressive exogenous model are employed to further validate the effectiveness of data-driven forecasting models. Computational results of multiple-steps ahead forecasting demonstrate that the BRT model gives the best performance with the lowest normalized root mean squared error of 18.4%, 24.3%, 27.9%, and 30.6% for forecasting horizons of 30, 60, 90, and 120-min, respectively.

We have investigated the predictability of precipitation using a new configuration of the superparameterized Community Atmosphere Model, SP-CAM. The new configuration, called the multiple-instance superparameterized-CAM, or MP-CAM, uses the average heating and drying rates from 10 independent two-dimensional cloud-permitting models (CPMs) in each grid column of the global model, instead of a single CPM. The 10 CPMs start from slightly different initial conditions, and simulate alternative realizations of the convective cloud systems. By analyzing the ensemble of possible realizations, we can study the predictability of the cloud systems, and identify the weather regimes and physical mechanisms associated with chaotic convection. We explore alternative methods for quantifying the predictability of precipitation. Our results show that unpredictable precipitation occurs when the simulated atmospheric state is close to critical points as defined by Peters and Neelin (2006, https://doi.org/10.1038/nphys314). The predictability of precipitation is also influenced by the convective available potential energy (CAPE) and the degree of mesoscale organization. It is strongly controlled by the large-scale circulation. A companion paper compares the global atmospheric circulations simulated by SP-CAM and MP-CAM.

The COSMO-Ru1Mp prototype of the numerical weather prediction system for the Moscow region with a grid spacing of 1 km and with the included and adapted TERRA_URB parameterization module for the urban areas is implemented. The module is provided with necessary information on the urban development and on the sources of anthropogenic heat based on the adaptation of open data. The module is interfaced with the COSMO-Ru operational system for the regional numerical weather prediction developed in the Hydrometcenter of Russia. The primary testing of the COSMO-Ru1Mp is performed, including daily forecasts based on operational data. The advantage of this forecast system and the prospects of its further development are revealed.

This work aims to better understand how small scale disturbances that are generated at the air-sea interface propagate into the surrounding atmosphere under realistic environmental conditions. To that end, a one-way coupled atmosphere-ocean model is presented, in which predictions of sea surface currents and sea surface temperatures from a microscale ocean model are used as constant boundary conditions in a larger atmospheric model. The coupled model consists of an ocean component implemented while using the open source CFD software OpenFOAM, an atmospheric component solved using the Weather Research and Forecast (WRF) model, and a Python-based utility foamToWRF, which is responsible for mapping field data between the ocean and atmospheric domains. The results are presented for two demonstration cases, which indicate that the proposed coupled model is able to capture the propagation of small scale sea surface disturbances in the atmosphere, although a more thorough study is required in order to properly validate the model.

The two-way coupled Weather Research and Forecasting and Community Multiscale Air Quality (WRF-CMAQ) model has been developed to more realistically represent the atmosphere by accounting for complex chemistry–meteorology feedbacks. In this study, we present a comparative analysis of two-way (with consideration of both aerosol direct and indirect effects) and offline coupled WRF v3.4 and CMAQ v5.0.2 over the contiguous US. Long-term (5 years from 2008 to 2012) simulations using WRF-CMAQ with both offline and two-way coupling modes are carried out with anthropogenic emissions based on multiple years of the U.S. National Emission Inventory and chemical initial and boundary conditions derived from an advanced Earth system model (i.e., a modified version of the Community Earth System Model/Community Atmospheric Model). The comprehensive model evaluations show that both two-way WRF-CMAQ and WRF-only simulations perform well for major meteorological variables such as temperature at 2 m, relative humidity at 2 m, wind speed at 10 m, precipitation (except for against the National Climatic Data Center data), and shortwave and longwave radiation. Both two-way and offline CMAQ also show good performance for ozone (O3) and fine particulate matter (PM2.5). Due to the consideration of aerosol direct and indirect effects, two-way WRF-CMAQ shows improved performance over offline coupled WRF and CMAQ in terms of spatiotemporal distributions and statistics, especially for radiation, cloud forcing, O3, sulfate, nitrate, ammonium, elemental carbon, tropospheric O3 residual, and column nitrogen dioxide (NO2). For example, the mean biases have been reduced by more than 10 W m−2 for shortwave radiation and cloud radiative forcing and by more than 2 ppb for max 8 h O3. However, relatively large biases still exist for cloud predictions, some PM2.5 species, and PM10 that warrant follow-up studies to better understand those issues. The impacts of chemistry–meteorological feedbacks are found to play important roles in affecting regional air quality in the US by reducing domain-average concentrations of carbon monoxide (CO), O3, nitrogen oxide (NOx), volatile organic compounds (VOCs), and PM2.5 by 3.1 % (up to 27.8 %), 4.2 % (up to 16.2 %), 6.6 % (up to 50.9 %), 5.8 % (up to 46.6 %), and 8.6 % (up to 49.1 %), respectively, mainly due to reduced radiation, temperature, and wind speed. The overall performance of the two-way coupled WRF-CMAQ model achieved in this work is generally good or satisfactory and the improved performance for two-way coupled WRF-CMAQ should be considered along with other factors in developing future model applications to inform policy making.

The objective of this study was to assess feasibility of integrating a coupled fire-atmosphere model within an air-quality forecast system to create a multiscale air-quality modeling framework designed to simulate wildfire smoke. For this study, a coupled fire-atmosphere model, WRF-SFIRE, was integrated, one-way, with the AIRPACT air-quality modeling system. WRF-SFIRE resolved local meteorology, fire growth, the fire plume rise, and smoke dispersion, and provided AIRPACT with fire inputs. The WRF-SFIRE-forecasted fire area and the explicitly resolved vertical smoke distribution replaced the parameterized BlueSky fire inputs used by AIRPACT. The WRF-SFIRE/AIRPACT integrated framework was successfully tested for two separate wildfire events (2015 Cougar Creek and 2016 Pioneer fires). The execution time for the WRF-SFIRE simulations was 2.5 measurement sites to assess the performance of our multiscale smoke modeling framework. The largest improvements when coupling WRF-SFIRE with AIRPACT were observed for the Cougar Creek Fire where model errors were reduced by ∼50%. For the second case (Pioneer fire), the most notable change with WRF-SFIRE coupling was that the probability of detection increased from 16 to 52%.

Continuous spatial knowledge is required to control the regional ozone pollution. Measurements from ground-level sites are beneficial to this goal, but their number is limited due to the huge expenses of site establishment, operation, and maintenance. Remote sensing seems a promising data source, but its application is challenged by bad weather conditions. Always covered by thick clouds, Chongqing, a populated industrial city in west China, is facing serious ozone pollution, but relevant studies here are relatively insufficient. Another alternative is estimating ozone by models. Well-performed models degrade in Chongqing partially due to the very complex terrain. Modeled hourly ozone does not agree with ground-level measurements. Therefore, an optimization approach is proposed to improve model estimates for such regions. This approach integrates the ground-level information (e.g., measured ozone and meteorology) through the employment of ResNet (Residual Network). ResNet overcomes the notorious vanishing gradient issue in classic neural networks, and the ability of learning complex systems is largely boosted. Ozone distribution is like a gray image that varies every second, which is not the case usually learned by ResNet. A color-image alike data structure is raised to address this “nonstill image” problem; according to the Taylor Expansion, polynomials can describe a complex system, and the errors are acceptable. To facilitate the usage in business operations, this approach is designed to be robust, inexpensive, and easy to use. The scheme of control site selection is discussed in detail. In cross-validations, this approach performs well, averaged $R^{2}$ is higher than 0.9 and the error is less than $5 ~\mu \text {g/m}^{3}$ .

Episodes with high air pollution and large amounts of aeroallergens expose sensitive individuals to a health damaging cocktail of atmospheric particles. Particulate matter (PM) affects the radiative balance and atmospheric dynamics, hence affecting concentrations of pollutants. The aim of the study is to estimate feedback between meteorology and particles on concentrations of aeroallergens using an extended version of the atmospheric model WRF-Chem. The extension, originally designed for PM and dust, concerns common aeroallergens. We study a birch pollen episode coinciding with an air pollution event containing Saharan dust (late March to early April 2014), using the model results, pollen records from Southern UK and vertical profiles of meteorological observations. During the episode, increased concentrations of birch pollen were calculated over the European continent, causing plumes transported towards the UK. The arrival of these plumes matched well with observations. The lowest parts of the atmospheric boundary layer demonstrate a vertical profile that favours long distance transport, while the pollen record shows pollen types that typically flower at another time. The model calculations show that feedback between meteorology and particles changes pollen concentrations by ±30% and in some cases up to 100%. The atmospheric conditions favoured meteorological feedback mechanisms that changed long distance transport of air pollution and aeroallergens.