Final Operational Global Analysis Research Articles

Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters

AbstractThis study consists of three main sections. The first section delves into a performance analysis centered around modeling PM10, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.

Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China

The meteorological reanalysis field is one of the key inputs to drive mesoscale meteorology simulations. To investigate the impacts of different reanalysis data on predictions of the meteorology parameters, as well as predictions of air quality, this study utilized the Weather Research and Forecasting/Community Multiscale Air Quality modeling system (WRF/CMAQ) with two sets of meteorological reanalysis inputs, the NCEP Final Operational Global Analysis (FNL) and the ECMWF Reanalysis v5.0 (ERA5) to predict the concentrations of ozone (O3) and fine particulate matter (PM2.5) in the Yangtze River Delta (YRD) in 2018. The results showed that ERA5 outperformed FNL in simulating 2-m temperature and relative humidity, while FNL demonstrated better accuracy in predicting wind fields. Both FNL and ERA5 underestimated the PM2.5 in spring and summer while overestimating it in autumn and winter. Furthermore, the Normalized mean bias (NMB) value for ERA5 exceeded the standard in winter (0.34), and the Normalized mean error (NME) value exceeded the standard in autumn (0.54). Both FNL and ERA5 underestimated the PM2.5 concentration during the pollution episodes, and FNL's simulations of PM2.5 concentrations were closer to observations than ERA5's as pollution levels intensified. Regarding O3 concentrations, ERA5 exhibited better performance than FNL, with the NMB (0.30, 0.27) and NME (0.39, 0.37) of FNL and ERA5 both exceeding the standard only in wintertime.

How Well Does Weather Research and Forecasting (WRF) Model Simulate Storm Rashmi (2008) Itself and Its Associated Extreme Precipitation over the Tibetan Plateau at the Same Time?

Northward tropical cyclones over the Bay of Bengal (BoB TCs) often interact with atmospheric circulation, transporting large amounts of water vapor to the Tibetan Plateau (TP), causing extreme precipitation. The BoB surrounded by land on three sides and the complex topography of the TP bring challenges to implementing numerical simulation in these regions. However, the scarcity of data in the two areas makes it necessary to find a technological process to perform practicable numerical simulations on the BoB TC and its induced extreme precipitation to carry out further research. In this study, the WRF 3.9.1 is used to perform many simulation experiments on a northward BoB TC Rashmi (2008) from 24 to 27 October 2008 associated with a record-breaking extreme precipitation on the TP, indicating that the selection of the simulation region, the source of initial-boundary conditions, and the cumulus convection schemes are three important factors influencing the results. We examined and compared the simulation of Rashmi with 10 experiments that were generated by combining The Final Operational Global Analysis (FNL) reanalysis data and the European Centre for Medium-Range Weather Forecasting 5(th) generation reanalysis (ERA5) data as initial-boundary conditions with five cumulus convection schemes. Most of the experiments can predict Rashmi and precipitation in the TP to a certain degree, but present different characteristics. Compared with FNL, the ERA5 performs well regarding Rashmi’s intensity and thermal structure but overestimates Rashmi’s moving speed. For the extreme precipitation in the TP, experiments suffice to reproduce the heavy rainfall (>25 mm/day) in the TP, with TS and ETS scores above 0.3 and most HSS scores greater than 0.4. The optimal experiments of three stations with extreme precipitation deviated from the actual precipitation by less than 15%. The ERA5 TDK scheme is recommended as the optimal solution for balancing the simulation of Rashmi and its extreme precipitation in the TP.

Active and break spells of summer monsoon over Bangladesh

This study examines the intraseasonal variability of the southwest summer monsoon over Bangladesh using rainfall data from rain gauge stations of the Bangladesh Meteorological Department (BMD) collected over 30 years (1988–2017). In this paper, active and break spells are defined as periods during the peak monsoon months of July and August, in which the daily precipitation lasts for three or more days at 0.5 above and below the daily climatological cycle. The active and break phases of the monsoon over a period of 10 years (2008–2017) were also analyzed by The Weather Research and Forecasting (WRF) model simulation for analysing synoptic conditions. The model simulations for each year were done for the period of 1 May to 30 September with 1 May as the initial condition with a single domain of 30 km resolution and 19 vertical levels. The final operational global analysis data from the Global Forecasting System of National Centers for Environment Prediction (NCEP-FNL) with resolution 1° × 1° is used for model simulation. The model-simulated daily rainfall, Sea Level Pressure (SLP), wind pattern at 850 hPa, 200 hPa, and Outgoing Longwave Radiation (OLR) are compared with the observations from Tropical Rainfall Measuring Mission (TRMM), ERA5 (ERA5 is the European Centre for Medium-Range Weather Forecasts fifth-gen global atmospheric reanalysis data), and Kalpana-1. This study also finds that the increase in rainfall is concurrent with a southwesterly wind and the decrease of rainfall simultaneously occurs with a southeasterly wind. Active days were found to have lower OLR values and lower SLP than break days.

Scale Analysis of Typhoon In-Fa (2021) Based on FY-4A Geostationary Interferometric Infrared Sounder (GIIRS) Observed and All-Sky-Simulated Brightness Temperature

Based on the Final Operational Global Analysis (FNL) data from the National Centers for Environmental Prediction and the ERA5 reanalysis data from the European Centre for Medium-Range Weather Forecasts, the all-sky brightness temperatures of the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the Fengyun-4A satellite (FY-4A) are simulated, which are then used to investigate Typhoon In-Fa (2021). The results show that the all-sky simulations based on ERA5 and FNL for FY-4A GIIRS channels 27 (716.25 cm−1), 90 (755.625 cm−1), and 417 (960 cm−1) can reproduce well the structure and intensity of Typhoon In-Fa. But the FNL simulations overestimate the typhoon intensity with more high ice clouds around the typhoon center. Fourier analysis of Typhoon In-Fa at severe tropical storm (STS) and typhoon (TY) stages is conducted. The results reveal that the dominant features of Typhoon In-Fa are primarily large-scale, with a relatively small proportion of observations and simulations dedicated to small-scale features at the STS stage. However, the proportion of large-scale features decreases while the amount of small-scale information increases during the TY stage. The purpose of this study is to assess the performance of FY-4A GIIRS all-sky simulations based on the ERA5 and FNL data, as well as to prepare for future all-sky data assimilations.

Analysis of Interaction Between Atmosphere and Sea Using The Delft3D Hydrodynamics Model for Mapping Coastal Flood Zone at Belawan Port and Coastal

Belawan port and coastal areas were also not spared from the impact of the tidal flood. This study aims to determine the performance of the Delft3D hydrodynamic model in simulating sea level and waves in tidal floods at Belawan port and coastal area. Final operational global analysis data, MSLP data from NOAA/NCEP, and tidal data from ECMWF were used to run the Delft3D model. The model output was verified by using tide gauge observation data from BIG (Geospatial Information Agency). This research resulted in a mapping of areas affected by tidal flooding in the Belawan port and coastal area by analyzing the interaction between atmosphere, consisting of wind speed and direction parameters and sea parameters in the form of significant wave height. Based on the results of the Delft3D verification with observation data, the average error value is 23.5 cm and the coefficient of correlation is 0.93. This shows that the Delft3D model is quite good at simulating tidal flood heights in the Belawan port and coastal area. Based on atmospheric analysis, it does not really affect the increasing wave height. The influence is given by significant sea wave height, which can increase the height of tidal floods.

High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.

A Numerical Simulation of the Development Process of a Mesoscale Convection Complex Causing Severe Rainstorm in the Yangtze River Delta Region behind a Northward Moving Typhoon

In recent years, due to the influence of global warming, extreme weather events occur frequently, such as the continuous heavy precipitation, regional high temperature, super typhoon, etc. Tropical cyclones make frequent landfall, heavy rains and flood disasters caused by landfall typhoons have a huge impact, and typhoon rainstorms are often closely related to mesoscale and small-scale system activities. The application 2020 NCEP (National Centers for Environmental Prediction) final operational global analysis data and WRF (Weather Research and Forecasting model, version 3.9) mesoscale numerical prediction model successfully simulates the evolution characteristics of the mesoscale convective complex (MCC) that caused an extreme rainstorm in the Yangtze River delta region behind a northwards typhoon in this article. The results show that a meso-β-scale vortex existed in the mid- to upper troposphere in the region where the MCC occurred; accompanied by the occurrence of the meso-β-scale vortex, the convective cloud clusters developed violently, and its shape is a typical vortex structure. The simulation-sensitive experiment shows that the development of the meso-β-scale cyclonic vortex is the main reason for the enhancement of MCC. The occurrence and development of the MCC is manifested as a vertical positive vorticity column and a strong vertical ascending motion region in the dynamic field. In the development and maturity stage of the MCC, the vorticity and vertical rising velocity in the MCC area are significantly greater than those in the weakened typhoon circulation, which shows significant mesoscale convective system characteristics. The diagnostic analysis of the vorticity equation shows that the positive vorticity advection caused by the meso-β-scale cyclonic vortex in the mid- to upper troposphere plays important roles in the development of the MCC. Enhanced low-level convergence enhances vertical ascending motion. The convective latent heat release also plays an important role on the development of the MCC, changes the atmospheric instability by heating, enhances the upward movement, and delivers positive vorticity to the upper level, making the convection develop higher, forming a positive feedback mechanism between low-level convergence and high-level divergence. The simulation-sensitive experiment also shows that the meso-β-scale cyclonic vortex formation in this process is related to convective latent heat release.

Effects of Topography and Latent Heat on the Evolution of a Mesoscale Dual-Core Southwest Vortex Over Sichuan Basin, China

The southwest vortex (SWV), a low-pressure system bringing severe rainfall in southwest China, is one of the most important synoptic systems in China. Using both the National Centers for Environmental Prediction Final (NCEP-FNL) operational global analysis dataset and the Weather Research and Forecasting (WRF) model simulation, a sophisticated SWV with dual-core structure (DCSWV) over the Sichuan Basin in 2010 was studied. The DCSWV system consisted of two cores, one near Leshan City (named “C1”) and another near Langzhong City (named “C2”). The high-resolution WRF model reproduced the life cycle of the DCSWV well. The diagnostic analysis of the vorticity budget indicated that the stretching and tilting terms played important roles in the development stage of “C1”, while the stretching and vertical advection of vorticity were the major contributors to the formation and development stage of “C2”, which implied the importance of moisture convergence and ascending motion. Sensitivity experiments showed that the DCSWV was closely associated with the release in latent heat as well as the effect of topography. The great release in latent heat provided significantly positive feedback to the DCSWV system, which was decisive to the formation and development stages of “C2”. The topography of the Tibetan Plateau and the Yun-Gui Plateau affected the location and duration of the DCSWV.

Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation

In radio occultation (RO) data processing and data assimilation, the forward model (FM) is used to calculate bending angle (BA) from refractivity (N). The accuracy and precision of forward modeled BA are affected by refractivity profiles and FM methods, including Abel integral algorithms (direct, exp, exp_T, linear) and methods of interpolating refractivity during integral (log-cubic spline and log-linear). Experiment 1 compares these forward model methods by comparing the difference and relative difference (RD) of the experimental value (forward modeled ECMWF analysis) and the true value (BA of FY3D RO data). Results suggested that the exp with log-cubic spline (log-cubic) interpolation is the most accurate FM because it has better integral accuracy (less than 2%) to inputs, especially when the input is lower than an order of magnitude of 1 × 10−2 (that is, above 60 km). By contrast, the direct induced a 10% error, and the improvement of exp T to exp is limited. Experiment 2 simulated the exact errors of an FM (exp) based on inputs on different vertical resolutions. The inputs are refractivity profiles on model levels of three widely used analyses, including ECMWF 4Dvar analysis, final operational global analysis data (FNL), and ERA5. Results demonstrated that based on exp and log-cubic interpolation, BA on model level of ECMWF 4Dvar has the highest accuracy, whose RD is 0.5% between 0–35 km, 4% between 35–58 km, and 1.8% between 58–80 km. By contrast, the other two analyses have low accuracy. This paper paves the way to better understanding the FM, and simulation errors on model levels of three analyses can be a helpful FM error reference.

Performances of six reanalysis profile products in the atmospheric correction of passive microwave data for estimating land surface temperature under cloudy-sky conditions

ABSTRACT Passive microwave (PMW) observations from the Advanced Microwave Scanning Radiometer 2 provide a way to obtain cloudy land surface temperatures (LSTs). However, atmospheric corrections must be performed on cloudy LSTs due to the cloud effect at higher frequencies. In this paper, six reanalyzed profiles, including the fifth-generation European Centre for Medium-range Weather Forecasts Reanalysis (ERA5), Interim Reanalysis (ERA-Interim), Japanese 55-year Reanalysis Data (JRA-55), Modern-Era Retrospective analysis for Research and Application V2 (MERRA2), National Centers for Environmental Prediction (NCEP) /Final Operational Global Analysis (FNL), and NCEP/Global Forecasting System (GFS), were compared with 2829 radiosonde profiles derived from the University of Wyoming. Then, their performances in correcting the atmospheric effects of LSTs at cloudy skies were investigated. Results showed that the ERA5 had the best accuracy in revealing the actual atmospheric conditions, and the RMSEs of transmittance, downward radiance, and upward radiance were about 0.007, 2.01, and 1.89 K, respectively. The RMSEs between the estimated LSTs and referenced LSTs varied from 3.15 K of the ERA5 to 6.12 K of the NCEP/FNL, indicating the ERA5 can be recommended for the atmospheric correction of PMW-based LST retrievals. Additionally, transmittance accuracy plays an essential role in impacting the LST retrievals in any weather.

Can the ERA5 Reanalysis Product Improve the Atmospheric Correction Accuracy of Landsat Series Thermal Infrared Data?

Atmospheric correction is a key step toward estimating land surface temperature from the sensor with only one thermal infrared (TIR) channel. We use ground radiosounding profiles collected from 163 radiosonde observations to provide insights on how well the ERA5 reanalysis product performs in the atmospheric correction of Landsat series TIR data. Despite the poor performance of the ERA5 product for estimating atmospheric upward radiance, downward radiance, and transmittance of Landsat series TIR data in the Americas and Africa, the performance of the ERA5 product was superior to that of the M2I6NPANA (inst6_3d_ana_Np) dataset (MERRA2) and (Final) Operational Global Analysis data (FNL) products in Asia and Europe. The vertical distribution of air temperature and relative humidity profiles may explain the poor performance of ERA5 in the Americas and Africa. This letter shows the advantages and weaknesses of the ERA5 reanalysis product in the atmospheric correction of Landsat series TIR data and will benefit research fields that require an atmospheric profile as input.

Role of changing land use and land cover (LULC) on the 2018 megafloods over Kerala, India

The state of Kerala, India, experienced massive flooding in August 2018. Unprecedented extreme rainfall left the region with huge losses of infrastructure. Several studies reported the role of improper dam operations and climate change in this region. However, changing land use/land cover (LULC) is an important driver of flood modulation; we therefore studied regional LULC changes (over 4 decades) and their impacts on flooding during this event using the Weather Research Forecasting (WRF)-Hydro model. We downscaled the NCEP final operational global analysis data using WRF to provide meteorological inputs for the gridded WRF-Hydro model. WRF downscaled forcing, along with observed discharge, were used to calibrate WRF-Hydro, which was run with the meteorological forcing of the August 2018 floods using LULC corresponding to 1985, 1995, 2005, and 2018. Analysis of LULC change indicated a significant loss of evergreen forest and reductions in shrubland during 1995-2005 and considerable loss of mixed forest during 2005-2018. The most extensive changes in flooding attributes (discharge, inundation area, and flood surface height) were found for LULC changes from 1995 to 2005. Specifically, comparing 2005 to 1995, we found that high flows (estimated by Q10) increased by >10% at many stations (up to ~50% for several locations). The increase in surface water head was ~40% over some parts with increased inundation. During 2005-2018, afforestation measures reduced the steep decline in LC observed during 1995-2005. Thus, the contribution of LULC changes to the August 2018 flooding is mainly due to the deforestation-related changes during 1995-2005. This substantial role of LC in enhancing flood hazards highlights the need to better manage LC in watersheds.

Impacts of Chemical Initial Conditions in the WRF-CMAQ Model on the Ozone Forecasts in Eastern China

Ozone (O3) has become the major factor for exceeding air pollution standards in many Chinese cities, especially in the more economically developed and densely populated regions, such as eastern China. In this study, we applied the Weather Research and Forecasting/Community Multiscale Air Quality (WRF/CMAQ) model to predict the air quality, and evaluated the influences of different chemical initial conditions on the O3 forecasts with observations in Tai’an and other 13 cities in eastern China in June 2021. The influences of different chemical initial conditions on the O3 forecasts are presented by using two sets of meteorological data (NCEP Final Operational Global Analysis [FNL] and Global Forecast System [GFS]) as initial conditions (IC) and boundary conditions (BC) to drive the WRF/CMAQ model. It was found that the O3 concentrations forecasted by FNL-GFS, in which the chemical IC derived from the CMAQ simulation results by using the FNL data as IC and BC, were closer to observations in all cities than GFS-GFS, in which the chemical IC derived from the CMAQ simulation results by using the GFS data as IC and BC. The normalized mean bias (NMB) values of FNL-GFS for O3 met the benchmark (±15%), while the NMB values of GFS-GFS in Hangzhou and Shijiazhuang did not meet the benchmark. The model performances in Tai’an city were similar to those in 13 cities with better results for FNL-GFS than GFS-GFS. The comparisons of contributions of source regions to O3 in the receptor Tai’an city indicate that different episodes had different relative contributions of source regions and that the simulations of FNL-GFS were more similar to the retrospective simulations than GFS-GFS. The comparisons of contributions of different source sectors to O3 in Tai’an city show that industry emissions are the largest contributor, followed by transportation, power plants and residential emissions.

Stage characteristics and causes of a mixed severe convective weather process

Based on the conventional observation data, the intensive automatic weather station data, the 1°×1° Final Operational Global Analysis data and the Doppler radar data, this study analyzes the stage characteristics and causes of a mixed severe convective weather process in Hunan Province from April 23 to 24, 2019. The results show that this severe convective weather mainly has two stages. In the first stage, the disastrous weather is mainly the short-term heavy precipitation. The convective weather in this stage is a severe mixed convective weather process forced by warm advection. The short-term heavy precipitation is mainly caused by the continuous influence of multi-cell storms. The disastrous weather in the second stage is mainly the thunderstorm gale. It is a squall line process forced by upper-level cold advection. Besides, there is apparent mid-altitude radial convergence and high wind core on the radial velocity map. The surface convergence line is the primary dynamic uplift condition triggering the mixed convection in the first stage, and there are several abnormal low centers of three-hour pressure change in the western and southern Hunan. The severe convective weather process in the second stage is mainly triggered by the forward-tilted trough. The dynamic conditions and energy conditions are favourable for the convections at both stages. Compared with the second stage, water vapor is more abundant in the first stage, which is more favorable for the occurrence of short-term heavy precipitation. The three-body scatter spike of hail is not obvious before the formation of the squall line. The early warning signals are mainly the height of the strong echo and vertical integrated liquid water content, both of which increase significantly before the formation of the squall line. The early warning of thunderstorm gale after the formation of the squall line can be issued in advance by monitoring the low-level wind in the downstream areas of the squall line.

Simulation of summer climate over Central Asia shows high sensitivity to different land surface schemes in WRF

Land surface processes are vital to the performance of regional climate models in dynamic downscaling application. In this study, we investigate the sensitivity of the simulation by using the weather research and forecasting (WRF) model at 10-km resolution to the land surface schemes over Central Asia. The WRF model was run for 19 summers from 2000 to 2018 configured with four different land surface schemes including CLM4, Noah-MP, Pleim-Xiu and SSiB, hereafter referred as Exp-CLM4, Exp-Noah-MP, Exp-PX and Exp-SSiB respectively. The initial and boundary conditions for the WRF model simulations were provided by the National Centers for Environmental Prediction Final (NCEP-FNL) Operational Global Analysis data. The ERA-Interim reanalysis (ERAI), the GHCN-CAMS and the CRU gridded data were used to comprehensively evaluate the WRF simulations. Compared with the reanalysis and observational data, the WRF model can reasonably reproduce the spatial patterns of summer mean 2-m temperature, precipitation, and large- scale atmospheric circulation. The simulations, however, are sensitive to the option of land surface scheme. The performance of Exp-CLM4 and Exp-SSiB are better than that of Exp-Noah-MP and Exp-PX assessed by Multivariable Integrated Evaluation (MVIE) method. To comprehensively understand the dynamic and physical mechanisms for the WRF model’s sensitivity to land surface schemes, the differences in the surface energy balance between Ave-CLM4-SSiB (the ensemble average of Exp-CLM4 and Exp-SSiB) and Ave-NoanMP-PX (the ensemble average of Exp-Noah-MP and Exp-PX) are analyzed in detail. The results demonstrate that the sensible and latent heat fluxes are respectively lower by 30.42 W·m−2 and higher by 14.86 W·m−2 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX. As a result, large differences in geopotential height occur over the simulation domain. The simulated wind fields are subsequently influenced by the geostrophic adjustment process, thus the simulations of 2-m temperature, surface skin temperature and precipitation are respectively lower by about 2.08 ℃, 2.23 ℃ and 18.56 mm·month−1 in Ave-CLM4-SSiB than that in Ave-NoahMP-PX over Central Asia continent.

The Impact of the Variation in Weather and Season on WRF Dynamical Downscaling in the Pearl River Delta Region

In this study, National Centers for Environmental Prediction (NCEP) Final (FNL) operational global analysis data and meteorological observation data from 2013 to 2017 were used to evaluate the impact of seasonal changes and different circulation classifications on the dynamical downscaling simulation results of Weather Research and Forecasting (WRF) in the Pearl River Delta (PRD) region. The results show that the dynamical downscaling method can accurately simulate the time variation characteristics of the near-surface meteorological field and the hit rates of a 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction are 92.66%, 93.98%, 26.78%, and 76.78%, respectively. The WRF model slightly underestimates the temperature and relative humidity, and overestimates the wind speed and precipitation. For precipitation, the WRF model can better simulate the variation characteristics of light rain and heavy rain, with the probability of detection are 0.59 and 0.69, respectively. For seasonal factors, the WRF model can conduct a perfect simulation in autumn and winter, followed by spring, while summer is vulnerable to extreme weather, so the result of the simulation is relatively poor. The circulation type is an important parameter of downscaling assessment. When the PRD is controlled by high pressure, the simulated results of WRF are good, and when the PRD is affected by low pressure or extreme weather, the simulation results are relatively poor.

Effectiveness of emission control in reducing PM<sub>2.5</sub> pollution in central China during winter haze episodes under various potential synoptic controls

Abstract. Currently, mitigating severe particle pollution in autumn and winter is the key to further improving the air quality of China. The source contributions and transboundary transport of fine particles (PM2.5) in pollution episodes are closely related to large-scale or synoptic-scale atmospheric circulation. How to effectively reduce emissions to control haze pollution under different synoptic conditions is rarely reported. In this study, we classify the synoptic conditions over central China from 2013 to 2018 by using Lamb–Jenkinson method and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Final (FNL) operational global analysis data. The effectiveness of emission control to reduce PM2.5 pollution during winter haze episodes under potential synoptic controls is simulated by GEOS-Chem model. Among the 10 identified synoptic patterns, four types account for 87 % of the total pollution days. Two typical synoptic modes are characterized by low surface wind speed and stable weather conditions or high relative humidity (A or C type) over central China due to a high-pressure system or a southwest trough and low-pressure system, blocking pollutants dispersion. Sensitivity simulations show that these two heavy pollution processes are mainly contributed by local emission sources with ∼82 % for A type and ∼85 % for C type, respectively. The other two patterns lead to pollution of transport characteristics affected by northerly or southerly winds (NW or SW type), carrying air pollution from northern or southern China to central China. The contribution of pollution transmission from northern and southern China is 36.9 % and 7.6 %, respectively of PM2.5, and local emission sources contribute 41 % and 69 %, respectively. We also estimate the effectiveness of emission reduction in these four typical severe pollution synoptic processes. By only reducing SO2 and NOx emission and not controlling NH3, the enhanced nitrate counteracts the effect of sulfate reduction on PM2.5 mitigation, with a less than 4 % decrease in PM2.5. In addition, to effectively mitigate haze pollution of NW- and SW-type synoptic-controlled episodes, local emission control actions should be in coordination with regional collaborative actions.

Investigating the WRF Temperature and Precipitation Performance Sensitivity to Spatial Resolution over Central Europe

The grid size resolution effect on the annual and seasonal simulated mean, maximum and minimum daily temperatures and precipitation is assessed using the Advanced Research Weather Research and Forecasting model (ARW-WRF, hereafter WRF) that dynamically downscales the National Centers for Environmental Prediction’s final (NCEP FNL) Operational Global Analysis data. Simulations were conducted over central Europe for the year 2015 using 36, 12 and 4 km grid resolutions. Evaluation is done using daily E-OBS data. Several performance metrics and the bias adjusted equitable threat score (BAETS) for precipitation are used. Results show that model performance for mean, maximum and minimum temperature improves when increasing the spatial resolution from 36 to 12 km, with no significant added value when further increasing it to 4 km. Model performance for precipitation is slightly worsened when increasing the spatial resolution from 36 to 12 km while further increasing it to 4 km has minor effect. However, simulated and observed precipitation data are in quite good agreement in areas with precipitation rates below 3 mm/day for all three grid resolutions. The annual mean fraction of observed and/or forecast events that were correctly predicted (BAETS), when increasing the grid size resolution from 36 to 12 and 4 km, suggests a slight modification on average over the domain. During summer the model presents significantly lower BAETS skill score compared to the rest of the seasons.

Analysis on the Evolution and Microphysical Characteristics of Two Consecutive Hailstorms in Spring in Yunnan, China

By using products of the cloud model, National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) reanalysis data, and Doppler weather radar data, the mesoscale characteristics, microphysical structure, and mechanism of two hail cloud systems which occurred successively within 24 h in southeastern Yunnan have been analyzed. The results show that under the influence of two southwest jets in front of the south branch trough (SBT) and the periphery of the western Pacific subtropical high (WPSH), the northeast-southwest banded echoes affect the southeastern Yunnan of China twice. Meanwhile, the local mesoscale radial wind convergence and uneven wind speed lead to the intense development of convective echoes and the occurrence of hail. The simulated convective cloud bands are similar to the observation. The high-level mesoscale convergence line leads to the development of convective cloud bands. The low-level wind direction or wind speed convergence and the high-level wind speed divergence form a deep tilted updraft, with the maximum velocity of 15 m·s−1 at the −40~−10 °C layer, resulting in the intense development of local convective clouds. The hail embryos form through the conversion or collision growth of cloud water and snowflakes and have little to do with rain and ice crystals. Abundant cloud water, especially the accumulation region of high supercooled water (cloud water) near the 0 °C layer, is the key to the formation of hail embryos, in which qc is up to 1.92 g·kg−1 at the −4~−2 °C layer. The hail embryos mainly grow by collision-coalescence (collision-freezing) with cloud water (supercooled cloud drops) and snow crystal riming.