Observational Nudging Research Articles

Sensitivity Analysis of the WRF Model according to the Impact of Nudging for Improvement of Ozone Prediction

Sensitivity analysis of the WRF model according to the impact of nudging (e.g., nudging techniques and application domains) was conducted during high nocturnal ozone episode to improve the prediction of the regional ozone concentration in the southeastern coastal area of the Korean peninsula. The analysis was performed by six simulation experiments: (1) without nudging (e.g., CNTL case), (2) with observation nudging (ONE case) to all domains (domain 1~4), (3) with grid nudging (GNE case) to all domains, (4)~(6) with grid nudging to domain 1, domain 1~2 and domain 1~3, respectively (GNE-1, GNE-2, GNE-3 case). The results for nudging techniques showed that the GNE case was in very good agreement with those observed during all analysis periods (e.g., daytime, nighttime, and total), as compared to the ONE case. In particular, the large effect of grid nudging on the near-surface meteorological factors (temperature, relative humidity, and wind fields) was predicted at the coastline and nearby sea during daytime. The results for application domains showed that the effects of nudging were distinguished between the meteorological factors and between the time periods. When applied grid nudging until subdomain, the improvement effects of temperature and relative humidity had differential tendencies. Temperature was increased for all time, but relative humidity was increased in daytime and was decreased in nighttime. Thus, GNE case showed better result than other cases.

Modeling studies on the formation of Hurricane Helene: the impact of GPS dropwindsondes from the NAMMA 2006 field campaign

Numerical simulations, using the weather research and forecasting (WRF) model in concert with GPS dropwindsondes released during the NASA African Monsoon Multidisciplinary Analyses 2006 Field Campaign, were conducted to provide additional insight on SAL-TC interaction. Using NCEP Final analysis datasets to initialize the WRF, a sensitivity test was performed on the assimilated (i.e., observation nudging) GPS dropwindsondes to understand the effects of individual variables (i.e., moisture, temperature, and winds) on the simulation and determine the extent of improvement when compared to available observations. The results suggested that GPS dropwindsonde temperature data provided the most significant difference in the simulated storm organization, storm strength, and synoptic environment, but all of the variables assimilated at the same time give a more representative mesoscale and synoptic picture.

Kilometre‐scale ensemble data assimilation for the COSMO model (KENDA)

An ensemble Kalman filter for convective‐scale data assimilation (KENDA) has been developed for the COnsortium for Small‐scale MOdelling (COSMO) model. The KENDA system comprises a local ensemble transform Kalman filter (LETKF) and a deterministic analysis based on the Kalman gain for the analysis ensemble mean. The KENDA software suite includes tools for adaptive localization, multiplicative covariance inflation, relaxation to prior perturbations and adaptive observation errors. In the version introduced here, conventional data (radiosonde, aircraft, wind profiler, surface station data) are assimilated. Latent heat nudging of radar precipitation has also been added to the KENDA system to be applied to the deterministic analysis only or additionally to all ensemble members. The performance of different system components is investigated in a quasi‐operational setting using a basic cycling environment (BACY) for a period of six days with 24 h forecasts. For this period and an additional 28 day period, deterministic KENDA forecasts are compared with forecasts based on the observation nudging data assimilation scheme, which is currently operational at the German Weather Service (Deutscher Wetterdienst, DWD). For our experiments, lateral boundary conditions for the regional model are given by a global ensemble Kalman filter for the ICOsahedral Nonhydrostatic (ICON) model. The performance of the KENDA system proves overall to be superior to the forecast quality of the operational nudging scheme, in particular with regard to precipitation. Latent heat nudging improves precipitation forecasts in both systems and has slightly more benefit in combination with the LETKF than with observation nudging.

The impact of observation nudging on simulated meteorology and ozone concentrations during DISCOVER-AQ 2013 Texas campaign

Abstract. Accurate meteorological fields are imperative for correct chemical transport modeling. Observation nudging, along with objective analysis, is generally considered a low-cost and effective technique to improve meteorological simulations. However, the meteorological impact of observation nudging on chemistry has not been well characterized. This study involved two simulations to analyze the impact of observation nudging on simulated meteorology and ozone concentrations during the 2013 Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Texas campaign period, using the Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models. The results showed improved correlations between observed and simulated parameters. For example, the index of agreement (IOA) improved by about 9 % for surface temperature and 6–11 % for surface zonal (U-WIND) and meridional (V-WIND) winds when observation nudging was employed. Analysis of a cold front event indicated that nudging improved the timing of wind transition during the front passage. Observation nudging also reduced the model biases for the planetary boundary layer height predictions. Additionally, the IOA for CMAQ simulated surface ozone improved by 6 % during the simulation period. The high-ozone episode on 25 September was a post-front ozone event in Houston. The small-scale morning wind shifts near the Houston Ship Channel combined with higher aloft ozone early morning likely caused the day's ozone exceedance. While observation nudging did not recreate the wind shifts on that day and failed to reproduce the observed high ozone, analyses of surface and aircraft data found that observation nudging helped the model yield improved ozone predictions. In a 2 h period during the event, substantially better winds in the sensitivity case noticeably improved the ozone. The average IOA for ozone in the period increased from just over 0.4 to near 0.7. Further work on improving the capability of nudging to reproduce local meteorological events such as stagnations and wind reversals could enhance a chemical transport model's skill for predicting high-ozone events.

An information exchange framework between physical modeling and numerical simulation to advance tropical cyclone boundary layer predictions

Our previous findings (Tse et al., 2014) suggest that both the physical and numerical simulations of a tropical cyclone boundary layer can be improved by information exchanges. In the present study, the improvements are quantitatively measured. For one thing, the approaching wind flow adopted in a wind-tunnel topographic study is adjusted according to the mean wind profile extracted from a meso-scale meteorological simulation of Typhoon Fengshen (2008) for a particular period, during which the observed wind direction indicated the sea-fetch wind flow. For another thing, the measurements obtained in a wind-tunnel topographic study are processed and used to improve the meso-scale meteorological simulation through observation nudging. Comparisons with the field-measurements taken at a main weather station and several auxiliary weather stations reveal that both the numerical and the physical simulations are improved. Specifically, the super-gradient flow—observed at the weather stations but not in the conventional wind-tunnel topographic study—is successfully simulated in the improved wind-tunnel test. The wind tunnel measurements, which more accurately reflect the influence of the underlying terrain, are incorporated into the meso-scale meteorological simulation of Typhoon Fengshen (2008). The simulation yields the results in better agreement with the field-measurements of the relative strength of wind speeds, with the exception of the wind strengths at mountain-summit points.

Meteorological Modeling Using the WRF-ARW Model for Grand Bay Intensive Studies of Atmospheric Mercury

Measurements at the Grand Bay National Estuarine Research Reserve support a range of research activities aimed at improving the understanding of the atmospheric fate and transport of mercury. Routine monitoring was enhanced by two intensive measurement periods conducted at the site in summer 2010 and spring 2011. Detailed meteorological data are required to properly represent the weather conditions, to determine the transport and dispersion of plumes and to understand the wet and dry deposition of mercury. To describe the mesoscale features that might influence future plume calculations for mercury episodes during the Grand Bay Intensive campaigns, fine-resolution meteorological simulations using the Weather Research and Forecasting (WRF) model were conducted with various initialization and nudging configurations. The WRF simulations with nudging generated reasonable results in comparison with conventional observations in the region and measurements obtained at the Grand Bay site, including surface and sounding data. The grid nudging, together with observational nudging, had a positive effect on wind prediction. However, the nudging of mass fields (temperature and moisture) led to overestimates of precipitation, which may introduce significant inaccuracies if the data were to be used for subsequent atmospheric mercury modeling. The regional flow prediction was also influenced by the reanalysis data used to initialize the WRF simulations. Even with observational nudging, the summer case simulation results in the fine resolution domain inherited features of the reanalysis data, resulting in different regional wind patterns. By contrast, the spring intensive period showed less influence from the reanalysis data.

Modeling studies of landfalling atmospheric rivers and orographic precipitation over northern California

This study investigated a slow-moving long-wave trough that brought four Atmospheric Rivers (AR) “episodes” within a week to the U.S. West Coast from 28 November to 3 December 2012, bringing over 500 mm to some coastal locations. The highest 6- and 12-hourly rainfall rates (131 and 195 mm, respectively) over northern California occurred during Episode 2 along the windward slopes of the coastal Santa Lucia Mountains. Surface observations from NOAA’s Hydrometeorological Testbed sites in California, available GPS Radio Occultation (RO) vertical profiles from the Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) satellite mission were both assimilated into WRF-ARW via eight combinations of observation nudging, grid nudging, and 3DVAR to improve the upstream moisture characteristics and quantitative precipitation forecast (QPF) during this event. Results during the 6-hourly rainfall maximum period in Episode 2 revealed that the models underestimated the observed 6-hourly rainfall rate maximum on the windward slopes of the Santa Lucia mountain range. The grid-nudging experiments smoothed out finer mesoscale details in the inner domain that may affect the final QPFs. Overall, the experiments that did not use grid nudging were more accurate in terms of less mean absolute error. In the time evolution of the accumulated rainfall forecast, the observation nudging experiment that included RAOB and COSMIC GPS RO data demonstrated results with the least error for the north central Coastal Range and the 3DVAR cold-start experiment demonstrated the least error for the windward Sierra Nevada. The experiment that combined 3DVAR cold start, observation nudging, and grid nudging showed the most error in the rainfall forecasts. Results from this study further suggest that including surface observations at frequencies less than 3 h for observation nudging and having cycling intervals less than 3 h for 3DVAR cycling would be more beneficial for short-to-medium range mesoscale QPFs during high-impact AR events over northern California.

Impacts of using an ensemble Kalman filter on air quality simulations along the California–Mexico border region during Cal–Mex 2010 Field Campaign

The purpose of this study is to investigate the impact of using an ensemble Kalman filter (EnKF) on air quality simulations in the California–Mexico border region on two days (May 30 and June 04, 2010) during Cal–Mex 2010. The uncertainties in ozone (O3) and aerosol simulations in the border area due to the meteorological initial uncertainties were examined through ensemble simulations. The ensemble spread of surface O3 averaged over the coastal region was less than 10ppb. The spreads in the nitrate and ammonium aerosols are substantial on both days, mostly caused by the large uncertainties in the surface temperature and humidity simulations. In general, the forecast initialized with the EnKF analysis (EnKF) improved the simulation of meteorological fields to some degree in the border region compared to the reference forecast initialized with NCEP analysis data (FCST) and the simulation with observation nudging (FDDA), which in turn leading to reasonable air quality simulations. The simulated surface O3 distributions by EnKF were consistently better than FCST and FDDA on both days. EnKF usually produced more reasonable simulations of nitrate and ammonium aerosols compared to the observations, but still have difficulties in improving the simulations of organic and sulfate aerosols. However, discrepancies between the EnKF simulations and the measurements were still considerably large, particularly for sulfate and organic aerosols, indicating that there are still ample rooms for improvement in the present data assimilation and/or the modeling systems.

Evaluation of a seven-year air quality simulation using the Weather Research and Forecasting (WRF)/Community Multiscale Air Quality (CMAQ) models in the eastern United States

The performance of the Weather Research and Forecasting (WRF)/Community Multi-scale Air Quality (CMAQ) system in the eastern United States is analyzed based on results from a seven-year modeling study with a 4-km spatial resolution.For 2-m temperature, the monthly averaged mean bias (MB) and gross error (GE) values are generally within the recommended performance criteria, although temperature is over-predicted with MB values up to 2K. Water vapor at 2-m is well-predicted but significant biases (>2gkg−1) were observed in wintertime. Predictions for wind speed are satisfactory but biased towards over-prediction with 0<MB<0.5ms−1 and root mean square error (RMSE) around 1.5 to 2ms−1. Wind direction, predicted without observation nudging, is not well-reproduced with GE values as large as 50° in summertime. Performance in other months is better with RMSE around 20–30° and MB within ±10°.O3 performance meets the EPA criteria of mean normalized bias (MNB) within ±0.15 and accuracy of unpaired peak (AUP) within 0.2. Normalized gross error (NGE) is mostly below 0.25, lower than the criteria of 0.35. Performance of PM10 is satisfactory with mean fractional bias (MFB) within ±0.6, but a large under-prediction in springtime was frequently observed. Performance of PM2.5 and its components is mostly within performance goals except for organic carbon (OC), which is universally under-predicted with MFB values as large as −0.8. The predicted frequency distribution of PM2.5 generally agrees with observations although the predictions are slightly biased towards more frequent high concentrations in most areas. Elemental carbon (EC), nitrate and sulfate concentrations are also well reproduced. The other unresolved PM2.5 components (OTHER) are significantly overestimated by more than a factor of two. No conclusive explanations can be made regarding the possible cause of this universal overestimation, which warrants a follow-up study to better understand this problem.

Weather Research and Forecasting Model simulations of extended warm-season heavy precipitation episode over the US Southern Great Plains: data assimilation and microphysics sensitivity experiments

This study examines eight microphysics schemes (Lin, WSM5, Eta, WSM6, Goddard, Thompson, WDM5, WDM6) in the Advanced Research Weather Research and Forecasting Model (WRF-ARW) for their reproduction of observed strong convection over the US Southern Great Plains (SGP) for three heavy precipitation events of 27–31 May 2001. It also assesses how observational analysis nudging (OBNUD), three-dimensional (3DVAR) and four-dimensional variational (4DVAR) data assimilation (DA) affect simulated cloud properties relative to simulations with no DA (CNTRL). Primary evaluation data were cloud radar reflectivity measurements by the millimetre cloud radar (MMCR) at the Central Facility (CF) of the SGP site of the ARM Climate Research Facility (ACRF). All WRF-ARW microphysics simulations reproduce the intensity and vertical structure of the first two major MMCR-observed storms, although the first simulated storm initiates a few hours earlier than observed. Of three organised convective events, the model best identifies the timing and vertical structure of the second storm more than 50 hours into the simulation. For this well-simulated cloud structure, simulated reflectivities are close to the observed counterparts in the mid- and upper troposphere, and only overestimate observed cloud radar reflectivity in the lower troposphere by less than 10 dBZ. Based on relative measures of skill, no single microphysics scheme excels in all aspects, although the WDM schemes show much-improved frequency bias scores (FBSs) in the lower troposphere for a range of reflectivity thresholds. The WDM6 scheme has improved FBSs and high simulated-observed reflectivity correlations in the lower troposphere, likely due to its large production of liquid water immediately below the melting level. Of all the DA experiments, 3DVAR has the lowest mean errors (MEs) and root mean-squared errors (RMSEs), although both the 3DVAR and 4DVAR simulations reduced noticeably the MEs for seven of eight microphysics schemes relative to CNTRL. Lower-tropospheric θe and convective available potential energy (CAPE) also are closer to the observations for the 4DVAR than CNTRL simulations.

Simulation of a persistent medium-term precipitation event over the western Iberian Peninsula

Abstract. This study evaluated the performance of the WRF-ARW (Weather Research and Forecasting with Advanced Research) weather prediction model in simulating the spatial and temporal patterns of an extreme rainfall period over a complex orographic region in north-central Portugal. The analysis was performed during the rainy season and, more specifically, the month of December 2009. In this period, the region of interest was under the influence of a sequential passage of low-pressure systems associated with frontal surfaces. These synoptic weather patterns were responsible for long periods of rainfall, resulting in a high monthly precipitation. The WRF model results during the study period were furthermore evaluated with the specific objective to complement gaps in the precipitation recordings of a reference meteorological station (located in Pousadas), the data of which are fundamental for hydrological studies in nearby experimental catchments. Three distinct WRF model runs were forced with initial fields and boundary conditions obtained from a global domain model: (1) a reference experiment with no nudging (RunRef); (2) observational nudging for a specific location, i.e. the above-mentioned Pousadas reference station (RunObsN); and (3) nudging to the analysed field (RunGridN). Model performance was evaluated, using several statistical parameters, against a dataset of 27 rainfall stations that were grouped by elevation. The three model runs had similar performances, even though RunGridN resulted in a slight improvement. Regarding the other two experiments, this improvement justifies its use for complementing the surface measurements at the Pousadas reference station. Overall model accuracy, expressed in root mean square error (RMSE), of the three runs was comparable for the stations of the different elevations classes. Even so, it was slightly better for stations in the lowlands than the highlands. Furthermore, model predictions tended to be less accurate for stations located in rough terrain and deep valleys.

A hybrid nudging-ensemble Kalman filter approach to data assimilation. Part II: application in a shallow-water model

ABSTRACTA hybrid nudging-ensemble Kalman filter (HNEnKF) data assimilation approach, explored in the Lorenz three-variable system in Part I, is tested in a two-dimensional shallow-water model for dynamic analysis and numerical weather prediction. The HNEnKF effectively combines the advantages of the ensemble Kalman filter (EnKF) and the observation nudging to achieve more gradual and continuous data assimilation by computing the nudging coefficients from the flow-dependent, time-varying error covariances of the EnKF. It can also transform the gain matrix of the EnKF into additional terms in the model's predictive equations to assist the data assimilation process. The HNEnKF is tested for both a wave case and a vortex case with different observation frequencies and observation networks. The HNEnKF generally produces smaller root mean square (RMS) errors than either nudging or EnKF alone. It also has better temporal smoothness than the EnKF and lagged ensemble Kalman smoother (EnKS). The HNEnKF allows the gain matrix of the EnKF to be applied gradually in time, reducing the error spikes commonly found around the analysis times when using intermittent data assimilation methods. Therefore, the HNEnKF produces a seamless analysis with better inter-variable consistency and dynamic balance than the intermittent EnKF.

A hybrid nudging‐ensemble Kalman filter approach to data assimilation in WRF/DART

AbstractA hybrid nudging‐ensemble Kalman filter (HNEnKF), previously tested in the Lorenz three‐variable model and a two‐dimensional shallow‐water model, is now explored in WRF/DART with real observations using a Cross Appalachian Tracer Experiment case from September 1983 (CAPTEX‐83). The HNEnKF, effectively combining observation nudging and the ensemble Kalman filter (EnKF), achieves a more gradual data assimilation and greatly reduces the insertion noise compared to the EnKF.Three‐hourly surface observations and 12‐hourly rawinsonde observations from the World Meteorological Organization (WMO) are assimilated. These assimilated meteorological observations are used to verify the priors of the experiments. It is found that the HNEnKF generally obtains better priors than the EnKF. To independently verify the HNEnKF approach, the hourly WRF experiment results are also used to drive the Second‐Order Closure Integrated Puff (SCIPUFF) model to predict surface tracer concentrations that are verified against the observed surface concentration data. The HNEnKF analyses driving SCIPUFF produce better statistics of the independent tracer data than the EnKF. Thus there appear to be some advantages in the hourly dynamic analyses produced by the continuous HNEnKF compared to the intermittent EnKF. The analyses of surface pressure tendency demonstrate that the HNEnKF is able to provide better temporal smoothness and dynamic consistency in the hourly analyses than the EnKF. Copyright © 2012 Royal Meteorological Society

Effects of assimilating precipitation zones derived from satellite and lightning data on numerical simulations of tropical-like Mediterranean storms

Abstract. The scarcity of meteorological observations in maritime areas is a well-known problem that can be an important limitation in the study of different phenomena. Tropical-like storms or medicanes developed over the Mediterranean sea are intense storms with some similarities to the tropical ones. Although they do not reach the hurricane intensity, their potential for damage is very high, due to the densely populated Mediterranean coastal regions. In this study, the two notable cases of medicane development which occurred in the western Mediterranean basin in September 1996 and October 2003, are considered. The capability of mesoscale numerical models to simulate general aspects of such a phenomena has been previously shown. With the aim of improving the numerical results, an adjustment of the humidity vertical profiles in MM5 simulations is performed by means of satellite derived precipitation. Convective and stratiform precipitation types obtained from satellite images are used to individually adjust the profiles. Lightning hits are employed to identify convective grid points. The adjustment of the vertical humidity profiles is carried out in the European Centre for Medium-Range Weather Forecasts (ECMWF) analyses used as initial conditions for the simulations. Analyses nudging to ECMWF analyses and to the satellite-based humidity-corrected version of these analyses has also been applied using Four Dimensional Data Assimilation (FDDA). An additional adjustment is applied as observation nudging of satellite/lightning information at different time and spatial resolutions. Statistical parameters are proposed and tested as an objective way to intercompare satellite-derived and simulated trajectories. Simulations of medicanes exhibit a strong sensitivity to vertical humidity profiles. Trajectories of the storms are improved or worsened by using FDDA. A case dependence is obtained on the characteristics of the humidity-corrected medicanes. FDDA sensitivity on temporal and spatial resolution of the assimilated data also presents a case dependence. It also shows a significant sensitivity of the results of the observation nudging to the specific choice of the values of coefficient weight and vertical ratio of the ingested observations.

MM5 simulations for air quality modeling: An application to a coastal area with complex terrain

A series of modifications were implemented in MM5 simulation in order to account for wind along the Santa Clarita valley, a north–south running valley located in the north of Los Angeles. Due to high range mountains in the north and the east of the Los Angeles Air Basin, sea breeze entering Los Angeles exits into two directions. One branch moves toward the eastern part of the basin and the other to the north toward the Santa Clarita valley. However, the northward flow has not been examined thoroughly nor simulated successfully in the previous studies. In the present study, we proposed four modifications to trigger the flow separation. They were (1) increasing drag over the ocean, (2) increasing soil moisture content, (3) selective observational nudging, and (4) one-way nesting for the innermost domain. The Control run overpredicted near-surface wind speed over the ocean and sensible heat flux, in an urbanized area, which justifies the above 1st and 2nd modification. The Modified run provided an improvement in near-surface temperature, sensible heat flux and wind fields including southeasterly flow along the Santa Clarita valley. The improved MM5 wind field triggered a transport to the Santa Clarita valley generating a plume elongated from an urban center to the north, which did not exist in MM5 Control run. In all, the modified MM5 fields yielded better agreement in both CO and O 3 simulations especially in the Santa Clarita area.

Development of the Flux-Adjusting Surface Data Assimilation System for Mesoscale Models

Abstract The flux-adjusting surface data assimilation system (FASDAS) is developed to provide continuous adjustments for initial soil moisture and temperature and for surface air temperature and water vapor mixing ratio for mesoscale models. In the FASDAS approach, surface air temperature and water vapor mixing ratio are directly assimilated by using the analyzed surface observations. Then, the difference between the analyzed surface observations and model predictions of surface layer temperature and water vapor mixing ratio are converted into respective heat fluxes, referred to as adjustment heat fluxes of sensible and latent heat. These adjustment heat fluxes are then used in the prognostic equations for soil temperature and moisture via indirect assimilation in the form of several new adjustment evaporative fluxes. Thus, simulated surface fluxes for the subsequent model time step are affected such that the predicted surface air temperature and water vapor mixing ratio conform more closely to observations. The simultaneous application of indirect and direct data assimilation maintains greater consistency between the soil temperature–moisture and the surface layer mass-field variables. The FASDAS is coupled to a land surface submodel in a three-dimensional mesoscale model and tests are performed for a 10-day period with three one-way nested domains. The FASDAS is applied in the analysis nudging mode for two coarse-resolution nested domains and in the observational nudging mode for a fine-resolution nested domain. Further, the effects of FASDAS on two different initial specifications of a three-dimensional soil moisture field are also studied. Results indicate that the FASDAS consistently improved the accuracy of the model simulations.

Intercomparison of the Community Multiscale Air Quality Model and CALGRID Using Process Analysis

This study was designed to examine the similarities and differences between two advanced photochemical air quality modeling systems: EPA Models-3/CMAQ and CALGRID/CALMET. Both modeling systems were applied to an ozone episode that occurred along the I-5 urban corridor in western Washington and Oregon during July 11-14, 1996. Both models employed the same modeling domain and used the same detailed gridded emission inventory. The CMAQ model was run using both the CB-IV and RADM2 chemical mechanisms, while CALGRID was used with the SAPRC-97 chemical mechanism. Outputfrom the Mesoscale Meteorological Model (MM5) employed with observational nudging was used in both models. The two modeling systems, representing three chemical mechanisms and two sets of meteorological inputs, were evaluated in terms of statistical performance measures for both 1- and 8-h average observed ozone concentrations. The results showed that the different versions of the systems were more similar than different, and all versions performed well in the Portland region and downwind of Seattle but performed poorly in the more rural region north of Seattle. Improving the meteorological input into the CALGRID/CALMET system with planetary boundary layer (PBL) parameters from the Models-3/CMAQ meteorology preprocessor (MCIP) improved the performance of the CALGRID/CALMET system. The 8-h ensemble case was often the best performer of all the cases indicating that the models perform better over longer analysis periods. The 1-h ensemble case, derived from all runs, was not necessarily an improvement over the five individual cases, but the standard deviation about the mean provided a measure of overall modeling uncertainty. Process analysis was applied to examine the contribution of the individual processes to the species conservation equation. The process analysis results indicated that the two modeling systems arrive at similar solutions by very different means. Transport rates are faster and exhibit greater fluctuations in the CMAQ cases than in the CALGRID cases, which lead to different placement of the urban ozone plumes. The CALGRID cases, which rely on the SAPRC97 chemical mechanism, exhibited a greater diurnal production/loss cycle of ozone concentrations per hour compared to either the RADM2 or CBIV chemical mechanisms in the CMAQ cases. These results demonstrate the need for specialized process field measurements to confirm whether we are modeling ozone with valid processes.

Simulating Urban-Scale Air Pollutants and Their Predicting Capabilities over the Seoul Metropolitan Area

Urban-scale air pollutants for sulfur dioxide, nitrogen dioxide, particulate matter with aerodynamic diameter >10 μm, and ozone (O3) were simulated over the Seoul metropolitan area, Korea, during the period of July 2-11, 2002, and their predicting capabilities were discussed. The Air Pollution Model (TAPM) and the highly disaggregated anthropogenic and the biogenic gridded emissions (1 km × 1 km) recently prepared by the Korean Ministry of Environment were applied. Wind fields with observational nudging in the prognostic meteorological model TAPM are optionally adopted to comparatively examine the meteorological impact on the prediction capabilities of urban-scale air pollutants. The result shows that the simulated concentrations of secondary air pollutant largely agree with observed levels with an index of agreement (IOA) of >0.6, whereas IOAs of ∼0.4 are found for most primary pollutants in the major cities, reflecting the quality of emission data in the urban area. The observationally nudged wind fields with higher IOAs have little effect on the prediction for both primary and secondary air pollutants, implying that the detailed wind field does not consistently improve the urban air pollution model performance if emissions are not well specified. However, the robust highest concentrations are better described toward observations by imposing observational nudging, suggesting the importance of wind fields for the predictions of extreme concentrations such as robust highest concentrations, maximum levels, and >90th percentiles of concentrations for both primary and secondary urban-scale air pollutants.

MM5 simulations of a SCOS97-NARSTO episode

MM5 simulations of the 4–7 August SCOS97-NARSTO ozone episode in the SoCAB showed that a relatively rare easterly upper-level background flow influenced surface wind flow directions at in-land sites. An inland-moving surface convergence zone resulted where the offshore background flow met an inland moving westerly (sea breeze plus thermal upslope) onshore flow. Maximum inland penetration of the zone was to the San Gabriel Mountains, where it produced peak observed ozone concentrations. MM5 reproduced the main qualitative features of the evolution of the diurnal sea breeze cycle, as it reproduced the opposing easterly flow, inland movement of the sea breeze front, and retreat of the land breeze front. The accuracy of predicted surface winds and temperatures were improved by modifications of MM5 and/or its input parameters, e.g., by use of the PSU MBLI scheme, analysis nudging with NCAR/NCEP model output, accurate specification of deep-soil temperature, observational nudging with SCOS97-NARSTO surface and upper level winds and temperatures, and use of updated urban land-use patterns.

Improving ozone modeling in regions of complex terrain using observational nudging in a prognostic meteorological model

Three air quality simulations are presented for an ozone episode that occurred in the Cascadia region of the Pacific Northwest during 11–14 July 1996. These three scenarios were the result of three different fine-scale (5 km grid) wind field predictions that were input into the air quality model. Wind field modeling in the Cascadia region is difficult due to the complex terrain that exists within the domain. Wind fields were developed using the prognostic model MM5 and the diagnostic model CALMET. The first wind field was created by MM5 and did not employ observational nudging in the data assimilation process. Results were often poor, especially in mountainous regions. The second wind field combined the first MM5 solution with CALMET’s objective analysis feature to adjust the MM5 predictions toward the observed winds. This was an iterative approach that yielded acceptable results both in terms of the wind field prediction and subsequent air quality prediction, but was very time consuming to apply. The final wind field was an MM5 simulation that incorporated observational nudging, using hourly surface wind measurements. This approach yielded better wind field predictions relative to the MM5 simulation that did not employ observational nudging. In addition, ozone predictions were improved when this wind field was input into the air quality model, relative to the other two ozone simulations. These results illustrate the importance of applying observational nudging in a prognostic meteorological model when simulating air quality in regions of complex terrain.