Spin-up Time Research Articles

The Improvement of Short-Term Quantitative Precipitation Forecast in Mountainous Areas by the Assimilation of Meteorological State Variables Retrieved by Multiple Doppler Radar Data

Abstract This study presents a sequential procedure formulated by combining a multiple-Doppler radar wind synthesis technique with a thermodynamic retrieval method, which can be applied to retrieve the three-dimensional wind, pressure, temperature, rainwater mixing ratio, and moisture over complex terrain. The retrieved meteorological state variables are utilized to reinitialize a high-resolution numerical model, which then carries out time integration using four different microphysical (MP) schemes, including the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. It is found that through this procedure, the short-term quantitative precipitation forecast (QPF) skill of a numerical model over mountainous areas can be significantly improved up to 6 h. The moisture field plays a crucial role in producing the correct rainfall forecast. Since no specific microphysical scheme outperforms the others, a combination of various rainfall scenarios forecasted by different MP schemes is suggested in order to provide a stable and reliable rainfall forecast. This work also demonstrates that, with the proposed approach, radar data from only two volume scans are sufficient to improve the rainfall forecasts. This is because the unobserved meteorological state variables are instantaneously retrieved and directly used to reinitialize the model, thereby the model spinup time can be effectively shortened.

Fluid dynamics and mixing in a novel intermittently rotating bioreactor for CAR-T cell therapy: Spin-down from incomplete spin-up

This work explores the fluid flow and mixing within a new type of intermittently rotating bioreactor used for the automated manufacturing of CAR-T cell therapy products; CAR-T is a novel cell-based gene therapy which offers a single-dose cure for several forms of advanced blood cancer. The rotating cylinder bioreactor has a free surface and a liquid height to radius ratio of 0.5. Agitation is achieved via intermittent rotation of the entire vessel around its central vertical axis. No comprehensive engineering characterisation of the fluid dynamics in a bioprocessing context exists to date for this type of bioreactor. The present study examines the fluid dynamics and mixing during spin-down to rest following incomplete spin-up (i.e. the starting state of the fluid is not solid body rotation). Novel Particle Image Velocimetry (PIV) and Planar Laser-Induced Fluorescence (PLIF) data is presented, shedding light on the different transient flow regions inside the intermittently rotating bioreactor, and on the impact spin-up time has on the mixing efficiency of the subsequent spin-down stage. The results presented can be used to inform the design of a custom intermittent agitation pattern for any resembling cylinder bioreactors, the adoption of which could lead to higher yields and reduced costs for the cell expansion step of the manufacturing process.

Taking the Milky Way for a spin: disc formation in the artemis simulations

ABSTRACT We investigate the formation (spin-up) of galactic discs in the artemis simulations of Milky Way (MW)-mass galaxies. In almost all galaxies, discs spin up at higher [Fe/H] than the MW. Those galaxies that contain an analogue of the Gaia Sausage-Enceladus (GSE) spin up at a lower average metallicity than those without. We identify six galaxies with spin-up metallicity similar to that of the MW, which formed their discs ∼8–11 Gyr ago. Five of these experience a merger similar to the GSE. The spin-up times correlate with the halo masses at early times: galaxies with early spin-up have larger virial masses at a lookback time tL = 12 Gyr. The fraction of stars accreted from outside the host galaxy is smaller in galaxies with earlier spin-ups. Accreted fractions small enough to be comparable to the MW are only found in galaxies with the earliest disc formation and large initial virial masses (M200c ≈ 2 × 1011 M⊙ at tL = 12 Gyr). We find that discs form when the halo’s virial mass reaches a threshold of M200c ≈ (6 ± 3) × 1011 M⊙, independent of the spin-up time. However, the failure to form a disc in other galaxies appears to be instead related to mergers at early times. We also find that discs form when the central potential is not particularly steep. Our results indicate that the MW assembled its mass and formed its disc earlier than the average galaxy of a similar mass.

Exploring the role of different data types and timescales in the quality of marine biogeochemical model calibration

Abstract. Global biogeochemical ocean models help to investigate the present and potential future state of the ocean, its productivity and cascading effects on higher trophic levels such as fish. They are often subjectively tuned against data sets of inorganic tracers and surface chlorophyll and only very rarely against organic components such as particulate organic carbon or zooplankton. The resulting uncertainty in biogeochemical model parameters (and parameterisations) associated with these components can explain some of the large spread of global model solutions with regard to the cycling of organic matter and its impacts on biogeochemical tracer distributions, such as oxygen minimum zones (OMZs). A second source of uncertainty arises from differences in the model spin-up length as, so far, there seems to be no agreement on the required simulation time that should elapse before a global model is assessed against observations. We investigated these two sources of uncertainty by optimising a global biogeochemical ocean model against the root-mean-squared error (RMSE) of six different combinations of data sets and different spin-up times. Besides nutrients and oxygen, the observational data sets also included phyto- and zooplankton, as well as dissolved and particulate organic phosphorus (DOP and POP, respectively). We further analysed the optimised model performance with regard to global biogeochemical fluxes, oxygen inventory and OMZ volume. Following the optimisation procedure, we evaluated the RMSE for all tracers located in the upper 100 m (except for POP, for which we considered the entire vertical domain), regardless of their consideration during optimisation. For the different optimal model solutions, we find a narrow range of the RMSE, between 14 % of the average RMSE after 10 years and 24 % after 3000 years of simulation. Global biogeochemical fluxes, global oxygen bias and OMZ volume showed a much stronger divergence among the models and over time than RMSE, indicating that even models that are similar with regard to local surface tracer concentrations can perform very differently when assessed against the global diagnostics for oxygen. Considering organic tracers in the optimisation had a strong impact on the particle flux exponent (Martin b) and may reduce much of the uncertainty in this parameter and the resulting deep particle flux. Independent of the optimisation setup, the OMZ volume showed a particularly sensitive response with strong trends over time, even after 3000 years of simulation time (despite the constant physical forcing); a high sensitivity to simulation time; and the highest sensitivity to model parameters arising from the tuning strategy setup (variation of almost 80 % of the ensemble mean). In conclusion, calibration against observations of organic tracers can help to improve global biogeochemical models even after short spin-up times; here especially, observations of deep particle flux could provide a powerful constraint. However, a large uncertainty remains with regard to global OMZ volume and its evolution over time, which can show very dynamic behaviour during the model spin-up, which renders temporal extrapolation to a final equilibrium state difficult if not impossible. Given that the real ocean shows variations on many timescales, the assumption of observations representing a steady-state ocean may require some reconsideration.

Exploring the Real-Time WRF Forecast Skill for Four Tropical Storms, Isaias, Henri, Elsa and Irene, as They Impacted the Northeast United States

Tropical storm Isaias (2020) moved quickly northeast after its landfall in North Carolina and caused extensive damage to the east coast of the United States, with electric power distribution disruptions, infrastructure losses and significant economic and societal impacts. Improving the real-time prediction of tropical storms like Isaias can enable accurate disaster preparedness and strategy. We have explored the configuration, initialization and physics options of the Weather Research and Forecasting (WRF) model to improve the deterministic forecast for Isaias. The model performance has been evaluated based on the forecast of the storm track, intensity, wind and precipitation, with the support from in situ measurements and stage IV remote sensing products. Our results indicate that the Global Forecasting System (GFS) provides overall better initial and boundary conditions compared to the North American Model (NAM) for wind, mean sea level pressure and precipitation. The combination of tropical suite physics options and GFS initialization provided the best forecast improvement, with error reduction of 36% and an increase of the correlation by 11%. The choices for model spin-up time and forecast cycle did not affect the forecast of the storm significantly. In order to check the consistency of the result found from the investigation related to TS Isaias, Irene (2011), Henri (2021) and Elsa (2021), three other tropical storms, were also investigated. Similar to Isaias, these storms are simulated with NAM and GFS initialization and different physics options. The overall results for Henri and Elsa indicate that the models with GFS initialization and tropical suite physics reduced error by 44% and 57%, respectively, which resonates with the findings from the TS Isaias investigation. For Irene, the initialization used an older GFS version and showed increases in error, but applying the tropical physics option decreased the error by 20%. Our recommendation is to consider GFS for the initialization of the WRF model and the tropical physics suite in a future tropical storm forecast for the NE US.

Exploring extreme rainfall-triggered landslides using 3D unsaturated flow, antecedent moisture and spatially distributed soil depth

The susceptibility to shallow landslides is affected by several factors. Rainfall intensity, soil thickness distribution, and antecedent moisture conditions are components that influence the spatio-temporal prediction of landslides at the watershed scale. Yet, the combined impact of these factors on the susceptibility to landslides has been overlooked. The purpose of this paper is to investigate landslides triggered by extreme precipitation in the Tijuca Massif, Rio de Janeiro, southeastern Brazil. Our approach couples a 3D variably saturated flow solver with the infinite slope stability method to calculate the statistical distributions of the safety factor and the pore pressure at the soil–bedrock interface. Numerical simulations were performed for 6 scenarios considering 1-year spin-up time for soil moisture and different spatially distributed soil depths. The results show that during extreme precipitation events, soil depth and initial moisture conditions may not have much influence on the safety factor, as the intensity and duration of rainfall will be the triggering agents. Comparison of simulated unstable zones and field data from mapped landslide scars further supported this conclusion. We conclude that simple soil depth models are feasible options for regional studies of landslide susceptibility. Our findings are relevant to understanding shallow landslides induced by extreme rainfall in Rio de Janeiro and other regions with similar geological and climate settings.

Developing spin-up time framework for WRF extreme precipitation simulations

Despite the wide application of the Weather Research and Forecasting (WRF) model in extreme precipitation simulations, there is a lack of consensus and clear guidance on identifying the suitable length of spin-up time. In this study, the WRF model was used to simulate the extreme precipitation events that happened on the 4 November 2015 at Alexandria of the Nile Delta. According to the observation from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), 21 spin-up time experiments of the 3-level-nested domain scenario and 21 spin-up time experiments of the 2-level-nested domain scenario were designed to explore the relationship between model required spin-up time and initial conditions. The simulation performances were evaluated by seven verification metrics and one overall performance score. Here we try to provide guidelines on how to determine the optimal spin-up time through satellite data without too many trial-and-error tests. An Optimal Spin-up Time Identifying (OSTI) framework with possible weather situations and spin-up time determining steps is proposed to help future work. It is found that the occurrences of disturbing weather events strengthen the influence of initial conditions on simulation outputs and increase model requirements for spin-up time lengths. Moreover, this framework is more useful for precipitation events with strong synoptic backgrounds because their simulation performances depend largely on the development of appropriate atmospheric circulations and physical equilibrium states in the model.

A Multivariate Additive Inflation Approach to Improve Storm‐Scale Ensemble‐Based Data Assimilation and Forecasts: Methodology and Experiment With a Tornadic Supercell

AbstractEnsemble‐based convective‐scale radar data assimilation commonly suffers from an underdispersive background ensemble. This study introduces a multivariate additive‐inflation method to address such deficiency. The multivariate additive inflation (AI) approach generates coherent random perturbations drawn from a newly constructed convective‐scale static background error covariance matrix for all state variables including hydrometeors and vertical velocity. This method is compared with a previously proposed univariate AI approach, which perturbs each variable individually without cross‐variable coherency. Comparisons are performed on the analyses and forecasts of the 8 May 2003 Oklahoma City tornadic supercell. Within assimilation cycles, the multivariate approach is more efficient in increasing reflectivity spread and thus has a reduced spinup time than the univariate approach; the additional inclusion of hydrometeors and vertical velocity results in more background spread for both reflectivity and radial velocity. Significant differences among AI experiments also exist in the subsequent forecasts and are more pronounced for the forecasts initialized from the earlier assimilation cycles. The multivariate approach yields better forecasts of low‐level rotation, reflectivity distributions, and storm maintenance for most lead times. The additional inclusion of hydrometeor and vertical velocity in the multivariate method is beneficial in forecasts. Conversely, the additional inclusion of hydrometeor and vertical velocity in the univariate method poses negative impacts for the majority of forecast lead times.

Spin-up time and internal variability analysis for overlapping time slices in a regional climate model

Long-term regional climate simulations are computationally very costly. One way to improve their computational efficiency is to split them into overlapping time slices, which can then be run in parallel. Although this procedure reduces the cost, sufficient spin-up must be left at the start of each slice. In any case, discontinuities will occur due to internal variability where two different slices join. In this study, we explore the relative role of spin-up time and internal variability in the discontinuities of overlapping time slice simulations and their effect on the simulated climate. This analysis has implications also for non-overlapping time slices, commonly used in very high resolution climate modelling, where long transient simulations cannot be afforded. We show that discontinuities are negligible for surface and upper-air variables, but they are noticeable in variables with long response times, such as soil moisture or snow depth. For these variables, differences between the slices are mainly attributed to internal variability, but also to insufficient spin-up time, depending on the region. In general, the results show that the overlapping time slice approach is valid to accomplish long term regional climate simulations.

The Impact of Assimilating Winds Observed during a Tropical Cyclone on a Forecasting Model

The accurate and timely depiction of the state of severe weather is critical for enhancing forecaster situational awareness. This study attempted to develop a hurricane forecasting model with a warm-start run and investigated the impact of winds observed during a tropical cyclone on long-term lead times. The Hurricane Research System initialized with the Hurricane Local Analysis Prediction System (HRS/HLAPS) was applied to Hurricanes Katrina and Dennis (2005). The forecasting model used a warm-start run with 7% improved wind data and cloud initialization using the HLAPS. The simulated cyclones were more intense and realistic structures, although the performance varied slightly according to the lead time and cyclone characteristics. The results show that the tropical cyclone development (track and intensity) was significantly affected by initial forcing up to 6–12 h, as well as by the forcing of the limit condition after 6 h. The well-organized spiral bands of convective precipitations were also captured, particularly within the 6 h spin-up time due to vertical wind shear and water vapor trapped in the lower atmosphere. This study demonstrates that aircraft-observed winds and convective initialization can be useful for numerical modeling and operational forecasting.

A systematic exploration of reservoir computing for forecasting complex spatiotemporal dynamics

A reservoir computer (RC) is a type of recurrent neural network architecture with demonstrated success in the prediction of spatiotemporally chaotic dynamical systems. A further advantage of RC is that it reproduces intrinsic dynamical quantities essential for its incorporation into numerical forecasting routines such as the ensemble Kalman filter—used in numerical weather prediction to compensate for sparse and noisy data. We explore here the architecture and design choices for a “best in class” RC for a number of characteristic dynamical systems. Our analysis points to the importance of large scale parameter optimization. We also note in particular the importance of including input bias in the RC design, which has a significant impact on the forecast skill of the trained RC model. In our tests, the use of a nonlinear readout operator does not affect the forecast time or the stability of the forecast. The effects of the reservoir dimension, spinup time, amount of training data, normalization, noise, and the RC time step are also investigated. Finally, we detail how our investigation leads to optimal design choices for a parallel RC scheme applied to the 40 dimensional spatiotemporally chaotic Lorenz 1996 dynamics.

Quantifying Temperature and Precipitation Change Caused by Land Cover Change: A Case Study of India Using the WRF Model

The large-scale Land-Uses and Land-Cover Changes (LULCC) in India in the past several decades is primarily driven by anthropogenic factors that influence the climate from regional to global scales. Therefore, to understand the LULCC over the Indian region from 2002 to 2015 and its implications on temperature and precipitation, we performed Weather Research Forecast (WRF) model simulation using the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data for the period 2009 to 2015 as a boundary condition with 2009 as spin-up time. The results showed moderate forest cover loss in major parts of northeast India, and the Himalayan region during 2002–2015. Such large LULC changes, primarily significant alteration of grassland and agriculture from the forest, led to increased precipitation due to increasing evapotranspiration (ET) similar to the forest-dominated regions. An increase in the precipitation patterns (>300 mm) was observed in the parts of eastern and western Himalayas, western Ghats, and the northwestern part of central India, while most parts of northeast Himalayas have an exceptional increase in precipitation (∼100–150 mm), which shows similar agreement with an increase of leaf area index (LAI) by ∼15%. The overall phenomenon leads to a greening-induced ET enhancement that increases atmospheric water vapor content and promotes downwind precipitation. In the case of temperature, warming was observed in the central to eastern parts of India, while cooling was observed in the central and western parts. The increase in vegetated areas over northwest India led to an increase in ET, which ultimately resulted in decreased temperature and increased precipitation. The study highlights the changes in temperature and precipitation in recent decades because of large LULCC and necessitates the formulation of sustainable land use-based strategies to control meteorological variability and augment ecological sustainability.

The National Weather Service–North Carolina State University Internship Course: Impacts and Success over a Generation

AbstractNearly 100 North Carolina State University (NCSU) students have participated in a unique, highly structured internship course conducted by the National Weather Service (NWS) Weather Forecast Office (WFO) in Raleigh, North Carolina. Here, we explore the impact that this course has had on their professional development and career trajectories. As of this writing, the course has been running for 17 years; this paper provides an update on how the course has changed over time, and information concerning participant outcomes. Changes include a reduction in class size to allow for more individualized mentoring, and the addition of experiences outside of the WFO. The course serves as a compelling selling point in student recruiting for the Department of Marine, Earth, and Atmospheric Sciences (MEAS), and participation also helps to ensure that the curriculum is adequately preparing students for positions in the NWS. The NWS benefits from a pool of potential employees that will require less spin-up time if hired; additionally, some NCSU graduates have participated in similar student volunteer programs at their respective offices once hired.

Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.

Development of Convective-Scale Static Background Error Covariance within GSI-Based Hybrid EnVar System for Direct Radar Reflectivity Data Assimilation

AbstractA convective-scale static background-error covariance (BEC) matrix is further developed to include the capability of direct reflectivity assimilation and evaluated within the GSI-based three-dimensional variational (3DVar) and hybrid ensemble–variational (EnVar) methods. Specific developments are summarized as follows: 1) Control variables (CVs) are extended to include reflectivity, vertical velocity, and all hydrometeor types. Various horizontal momentum and moisture CV options are included. 2) Cross correlations between all CVs are established. 3) A storm intensity-dependent binning method is adopted to separately calculate static error matrices for clear-air and storms with varying intensities. The resultant static BEC matrices are simultaneously applied at proper locations guided by the observed reflectivity. 4) The EnVar is extended to adaptively incorporate static BECs based on the quality of ensemble covariances. Evaluation and examination of the new static BECs are first performed on the 8 May 2003 Oklahoma City supercell. Detailed diagnostics and 3DVar examinations suggest zonal/meridional winds and pseudo–relative humidity are selected as horizontal momentum and moisture CVs for direct reflectivity assimilation, respectively; inclusion of cross correlations favors spin up and maintains the analyzed storms; application of binning improves characteristics and persistence of the simulated storm. Relative to an experiment using the full ensemble BECs (Exp-PureEnVar), incorporating static BECs in hybrid EnVar reduces spinup time and better analyzes reflectivity distributions while the background ensemble is deficient in sampling errors. Compared to both pure 3DVar and Exp-PureEnVar, hybrid EnVar better predicts reflectivity distributions and better maintains a strong mesocyclone. Further examination through the 20 May 2013 Oklahoma supercells confirms these results and additionally demonstrates the effectiveness of adaptive hybridization.

The interaction of an urban heat island with a sea breeze front during moist convection over Tianjin, China

The interaction of an urban heat island with a sea breeze front during moist convection over <scp>Tianjin, China</scp>

PREDIKSI DEBIT SUNGAI BENGAWAN SOLO MENGGUNAKAN NUMERICAL WEATHER MODEL GLOBAL FORECAST SYSTEM DAN INTEGRATED FLOOD ANALYSIS SYSTEM (Prediction of discharge in Bengawan Solo River using Numerical Weather Model Global Forecast System and Integrated Flood Analysis System)

Flood is one of the most common hydro-meteorological disasters. Bengawan Solo is one of the watersheds in Indonesia that also hit by this disaster. This study discusses the flood disaster in the Bengawan Solo area in early March 2019. The purpose of this study is to conduct a discharge simulation using numerical weather model Global Forecast System (GFS) data through Integrated Flood Analysis System (IFAS) so it is possible to predict discharge in the future. There are three types of numerical weather model GFS data that have been downscale using weather research and forecasting model which differentiated based on spin-up time. The numerical weather model product is then used as rainfall data input for IFAS simulation. Based on the analysis, the flood discharge simulation using an 84-hour spin-up time has a satisfactory performance in describing the change in discharge with respect to time. This happens because numerical weather models will be better at quantifying processes that occur on a meso scale with spatial scale of 10 to 1000 km. The result of this research shows that it is possible to predict river discharge up to 84 hours before the disaster so this is can support the mitigation process for hydrometeorological disasters.

To develop a progressive multimetric configuration optimisation method for WRF simulations of extreme rainfall events over Egypt

The Weather Research and Forecasting (WRF) model can help to improve our understanding of analysing and forecasting hydrometeorological disasters. Especially for some regions like the Nile Delta, which faces growing climate hazards but has inadequate in situ rainfall observations. However, identifying an optimal configuration to run the WRF model is often a challenge. In this study, the WRF model was used to simulate extreme rainfall events at high spatial and temporal resolutions centered around Alexandria, in northern Egypt. In particular, a progressive multimetric configuration optimisation (PMCO) method is proposed to identify the possible optimal configurations of WRF in the aspect of domain size, numbers of vertical levels, nesting ratio, spin-up times, and physical parameterization schemes (microphysics, planetary boundary layer, and cumulus), based on 48 specifically designed experiments. The simulation performances are quantified and sorted by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). All WRF simulations use the ERA5 reanalysis dataset as boundary conditions and the WRF results are verified against the Integrated Multi-satellitE Retrievals for GPM (IMERG). The results show that the rainfall distribution and magnitude are most sensitive to the spin-up time and physical parameterization schemes. It is also observed that the improvement of WRF's reproducibility of rainfall intensity is usually accompanied by a decrease in the reproducibility of rainfall distribution. The best model configuration for the study area comprises of three-level nesting (D01 80x80; D02 112x112; D03 88X88), 58 vertical levels, 1:3:3 downscaling ratio, 48h spin-up time, WRF Single-Moment 6-class microphysics scheme, Mellor-Yamada-Janjic planetary boundary layer scheme, and Grell-Freitas cumulus. The stability of this configuration is also verified with the other three extreme rainfall events over Egypt. The results show that there exists a common WRF configuration set in Egypt that produces the relatively good simulations for extreme rainfall events.

Stepwise transitions in spin-up of rotating Rayleigh–Bénard convection

Abstract

Numerical study of the effects of initial conditions and emissions on PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; concentration simulations with CAMx v6.1: a Xi'an case study

Abstract. A series of model sensitivity experiments was designed to explore the effects of different initial conditions and emissions in Xi'an in December 2016; Xi'an is a major city in the Fenwei Plain, which is a key area with respect to air pollution control in China. Three methods were applied for the initial condition tests: a clean initial simulation, a restart simulation, and a continuous simulation. In the clean initial simulation test, the C00, C06, C12, C18, and C24 sensitivity experiments were conducted to explore the effect of the intercepted time periods used. The results of these experiments showed that the fine particulate matter (PM2.5) model performance was better when the start time of the intercepted time periods was delayed. For experiments C00 to C24, the absolute mean bias (MB) decreased from 51.07 to 3.72 µg m−3, and the index of agreement (IOA) increased from 0.49 to 0.86, which illustrates that the model performance of C24 is much better than that of C00. The R1120 and R1124 sensitivity experiments were used to explore the restart simulation and, in turn, the effect of the date of the first day of the model simulation. While the start times of the simulations were different, the simulation results with different start times were nearly consistent after a spin-up time period, and the results revealed that the spin-up time was approximately 27 h. For the continuous simulation test, the CT12 and CT24 sensitivity experiments were conducted. The start times of the intercepted time periods for CT12 and R1120 were the same, and the simulation results were almost identical. Based on the simulation results, CT24 showed the best performance of all of the sensitivity experiments, with the correlation coefficient (R), MB, and IOA reaching 0.81, 6.29 µg m−3, and 0.90 respectively. For the emission tests, an updated local emission inventory with construction fugitive dust emissions was added and was compared with the simulation results from the original emission inventory. The simulation with the updated local emissions showed much better performance for PM2.5 modelling. Therefore, combining the CT24 method and the updated local emission inventory can satisfactorily improve the PM2.5 model performance in Xi'an: the absolute MB decreased from 35.16 to 6.29 µg m−3, and the IOA reached 0.90.