Four-dimensional Data Assimilation Research Articles

The physical oceanographic environment during the CCE-LTER Years: Changes in climate and concepts

The California Current System (CCS) has been studied by the California Cooperative Oceanic Fisheries Investigations program for many decades. Since 2004, the Southern California Bight (SCB) and the oceanic region offshore has also been the site for the California Current Ecosystem (CCE) Long-Term Ecological Research (LTER) program, which has established long-term observational time series and executed several Process Cruises to better understand physical–biological variations, fluxes and interactions. Since the inception of the CCE-LTER, many new ideas have emerged about what physical processes are the key controls on CCS dynamics. These new perspectives include obtaining a better understanding of what climate patterns exert influences on CCS physical variations and what physical controls are most important in driving CCE ecological changes.Physical oceanographic and climatological conditions in the CCS varied widely since the inception of the CCE-LTER observational time series, including unusual climate events and persistently anomalous states. Although the CCE-LTER project commenced in 2004 in the midst of normal ocean conditions near the climatological means, over the following decade, El Nino/Southern Oscillation conditions flickered weakly from warm to cold, with the Pacific Decadal Oscillation (PDO) generally tracking that behavior, while the North Pacific Gyre Oscillation (NPGO) evolved to persistent and strong positive conditions after 2007, indicative of enhanced upwelling from 2007 to 2012. Together the combined impact of the negative PDO state (La Nina conditions) and positive NPGO state (increased upwelling conditions) yielded remarkably persistent cool conditions in the CCS from late 2007 to early 2009 and from mid-2010 through 2012.The broad-scale climate variations that occurred over the North Pacific and CCS during this time period are discussed here to provide physical context for the CCE-LTER time series observations and the CCE-LTER Process Cruises. Data assimilation fits, using the Regional Ocean Modeling System four-dimensional data assimilation framework, were successfully executed for the 1-month time period surrounding each of the Process Cruises. The fits provide additional information about how the physical flows evolve during the course of the multi-week Process Cruises. Relating these physical states to the numerous biological measurements gathered by the CCE-LTER time series observations and during the Process Cruises will yield vital long-term perspective of how changing climate conditions control the ocean ecosystem in this region and information on how this important ecosystem can be expected to evolve over the coming decades.

Expanded four-dimensional variatiaonal data assimilation method to optimize model physical parameters

A critical error in numerical simulation stems from the physical parameters in the model. To better assess the accuracy of the numerical stimulation, a mthod of impoving physical parameters is urgently desired. By modifying the four-dimensional variatiaonal data assimilation 4DVAR technique, in this paper a new method is proposed based on the use of observational data to optimize initial field and subsequent physical model. Ekman boundary layer model and Lorenz model are taken for example to conduct numerical experiment. The results show that through the variations in observational data, physical parameters and initial field are improved, thus effectively enhancing the accuracy of the model. This method improves the numerical model and physical parameters.

Urban Emissions of CO2 from Davos, Switzerland: The First Real-Time Monitoring System Using an Atmospheric Inversion Technique

AbstractAnthropogenic emissions from urban areas represent 70% of the fossil fuel carbon emitted globally according to carbon emission inventories. The authors present here the first operational system able to monitor in near–real time daily emission estimates, using a mesoscale atmospheric inversion framework over the city of Davos, Switzerland, before, during, and after the World Economic Forum 2012 Meeting (WEF-2012). Two instruments that continuously measured atmospheric mixing ratios of greenhouse gases (GHGs) were deployed at two locations from 23 December 2011 to 3 March 2012: one site was located in the urban area and the other was out of the valley in the surrounding mountains. Carbon dioxide, methane, and carbon monoxide were measured continuously at both sites. The Weather Research and Forecasting mesoscale atmospheric model (WRF), in four-dimensional data assimilation mode, was used to simulate the transport of GHGs over the valley of Davos at 1.3-km resolution. Wintertime emissions prior to the WEF-2012 were about 40% higher than the initial annual inventory estimate, corresponding to the use of heating fuel in the winter. Daily inverse fluxes were highly correlated with the local climate, especially during the severe cold wave that affected most of Europe in early February 2012. During the WEF-2012, emissions dropped by 35% relative to the first month of the deployment, despite similar temperatures and the presence of several thousand participants at the meeting. On the basis of composite diurnal cycles of hourly CO/CO2 ratios, the absence of traffic peaks during the WEF-2012 meeting indicated that change in road emissions is potentially responsible for the observed decrease in the city emissions during the meeting.

Four-dimensional ensemble-variational data assimilation for global deterministic weather prediction

Abstract. The goal of this study is to evaluate a version of the ensemble-variational data assimilation approach (EnVar) for possible replacement of 4D-Var at Environment Canada for global deterministic weather prediction. This implementation of EnVar relies on 4-D ensemble covariances, obtained from an ensemble Kalman filter, that are combined in a vertically dependent weighted average with simple static covariances. Verification results are presented from a set of data assimilation experiments over two separate 6-week periods that used assimilated observations and model configuration very similar to the currently operational system. To help interpret the comparison of EnVar versus 4D-Var, additional experiments using 3D-Var and a version of EnVar with only 3-D ensemble covariances are also evaluated. To improve the rate of convergence for all approaches evaluated (including EnVar), an estimate of the cost function Hessian generated by the quasi-Newton minimization algorithm is cycled from one analysis to the next. Analyses from EnVar (with 4-D ensemble covariances) nearly always produce improved, and never degraded, forecasts when compared with 3D-Var. Comparisons with 4D-Var show that forecasts from EnVar analyses have either similar or better scores in the troposphere of the tropics and the winter extra-tropical region. However, in the summer extra-tropical region the medium-range forecasts from EnVar have either similar or worse scores than 4D-Var in the troposphere. In contrast, the 6 h forecasts from EnVar are significantly better than 4D-Var relative to radiosonde observations for both periods and in all regions. The use of 4-D versus 3-D ensemble covariances only results in small improvements in forecast quality. By contrast, the improvements from using 4D-Var versus 3D-Var are much larger. Measurement of the fit of the background and analyzed states to the observations suggests that EnVar and 4D-Var can both make better use of observations distributed over time than 3D-Var. In summary, the results from this study suggest that the EnVar approach is a viable alternative to 4D-Var, especially when the simplicity and computational efficiency of EnVar are considered. Additional research is required to understand the seasonal dependence of the difference in forecast quality between EnVar and 4D-Var in the extra-tropics.

Analysis of the surface temperature and wind forecast errors of the NCAR-AirDat operational CONUS 4-km WRF forecasting system

Investigating the characteristics of model-forecast errors using various statistical and object-oriented methods is necessary for providing useful guidance to end-users and model developers as well. To this end, the random and systematic errors (i.e., biases) of the 2-m temperature and 10-m wind predictions of the NCAR-AirDat weather research and forecasting (WRF)-based real-time four-dimensional data assimilation (RTFDDA) and forecasting system are analyzed. This system has been running operationally over a contiguous United States (CONUS) domain at a 4-km grid spacing with four forecast cycles daily from June 2009 to September 2010. In the result an exceptionally useful forecast dataset was generated and used for studying the error properties of the model forecasts, in terms of both a longer time period and a broader coverage of geographic regions than previously studied. Spatiotemporal characteristics of the errors are investigated based on the 24-h forecasts between June 2009 and April 2010, and the 72-h forecasts between May and September 2010. It was found that the biases of both wind and temperature forecasts vary greatly seasonally and diurnally, with dependency on the forecast length, station elevation, geographical location, and meteorological conditions. The temperature showed systematic cold biases during the daytime at all station elevations and warm biases during the nighttime above 1,000 m above sea level (ASL), while below 600 m ASL cold biases occurred during the nighttime. The forecasts of surface wind speed exhibited strong positive biases during the nighttime, while the negative biases were observed in the spring and summer afternoons. The surface wind speed was mostly over-predicted except for the stations located between 1,000 and 2,100 m ASL, for which negative biases were identified for most forecast cycles. The highest wind-speed errors were found over the high terrain and near sea-level stations. The wind-direction errors were relatively large at the high-terrain elevation in the Rocky and Appalachian mountain ranges and the western coastal areas and the error structure exhibited notable diurnal variability.

Application of the Weather Research and Forecasting Model for Air Quality Modeling in the San Francisco Bay Area

AbstractThe Weather Research and Forecasting (WRF) model is evaluated by conducting various sensitivity experiments over central California including the San Francisco Bay Area (SFBA), with the goal of establishing a WRF model configuration to be used by the Bay Area Air Quality Management District (BAAQMD) for its air quality applications. For the two selected cases, a winter particulate matter case and a summer ozone case, WRF solutions are evaluated both quantitatively by comparing the error statistics and qualitatively by analyzing the model-simulated mesoscale features. Model evaluation is also performed for the SFBA, Sacramento Valley, and San Joaquin Valley subregions. The recommended WRF configuration includes use of the Rapid Radiative Transfer Model/Dudhia (or RRTMG) radiation schemes and the Pleim–Xiu land surface physics, combined with a multiscale four-dimensional data assimilation strategy throughout the simulation period to assimilate the available observations, including standard observations from the World Meteorological Organization and local special observations. With the recommended model configuration, WRF is able to simulate the meteorological variables with reasonable error, with the added value, although relatively small, of assimilating the additional BAAQMD local special observations. Mesoscale features, simulated reasonably well for both cases, include the upslope and downslope flows that occur along the mountains that surround the Central Valley of California, as well as the mesoscale eddies that develop within the valley.

An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part III: Antarctic Applications with Advanced Research WRF Using Real Data

Abstract A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.

Impact of satellite-retrieved atmospheric temperature profiles assimilation on Asian summer monsoon 2010 simulation

The impact of assimilation of satellite-retrieved atmospheric temperature profiles data in simulating mean monsoon circulation and rainfall of summer monsoon 2010 is examined in this study using the regional climate model, Weather Research and Forecasting model (WRF). Two experiments are performed; the first one is the control experiment (WRF-CTL) where no assimilation is carried out, and the second one is similar to the first one, but satellite-retrieved atmospheric temperature profiles are assimilated using a four-dimensional data assimilation method (WRF-AIRS). Mean monsoon features such as the low level jet, monsoon trough, tropical easterly jet, meridional pressure gradient and the spatial distribution of rainfall are better simulated in WRF-AIRS. Correlation coefficients between the observed and WRF-AIRS (WRF-CTL) daily zonal wind shear, meridional wind shear and rainfall indices over the Indian summer monsoon region are 0.98, 0.96 and 0.67 (0.32, 0.35 and 0.23), respectively. The zonal and meridional wind indices over the western Pacific and East Asia are 0.9 and 0.8 (0.6 and 0.5), respectively. Spatial distribution of rainfall displays double ITCZ like rainfall pattern over the tropical Indian Ocean in WRF-CTL, whereas WRF-AIRS display a single ITCZ pattern, which is similar to the observed one. The temporal evolution of the vertical structure of temperature (associated with rainfall activity over the monsoon core region) shows warming in the midtroposphere to upper troposphere (by 0.5 to 1.5 °C) and cooling in the midtroposphere to lower troposphere (by 0.5 to 1.0 °C). This atmospheric temperature distribution associated with rainfall is well simulated by WRF-AIRS. Under heavy rainfall conditions, WRF-AIRS produces strong vertical motion consistent with the observations but is absent (or weak) in WRF-CTL. This study deduces that the assimilation of temperature profiles in the regional climate model can significantly improve the dynamical and thermodynamical features of monsoon by representing the vertical distribution of temperature more realistically. Our analysis reiterates that the Asian summer monsoon circulation is mainly controlled by thermal forcing. Our study suggests that it is essential to improve the existing parameterization schemes for better simulation of summer monsoon.

Regional adjustment of emission strengths via four dimensional data assimilation

The Four-Dimensional Data Assimilation was performed to evaluate source emission strengths over the United States. The USEPA Models-3 system (CMAQ/MM5/SMOKE) and ridge regression are used as the forward and inverse models, respectively. The continental US is divided into six regions, and data assimilation is performed for each region in July 2001 and January 2002. In addition, two separate scaling factors are calculated for weekdays and weekends. Results show that base emissions for CO and SO2 sources are relatively accurate. Base emissions for PEC source are overestimated 100%, but those for POA source are underestimated up to 70% when compared with the adjusted emissions. Emissions for NH3, NOx, and PMFINE sources are relatively accurate in July 2001, but those in January 2002 are around 100% higher than the adjusted emissions. Base VOC emissions in July 2001 are similar to the adjusted emissions but those in January 2002 are underestimated up to 70% when compared with the adjusted emissions. Though the emission adjustment itself improves the overall air quality model performance, a better improvement is expected with the modification of speciation profiles and temporal allocations in the Models-3 system, as well.

Role of Four-Dimensional Data Assimilation on Track and Intensity of Severe Cyclonic Storms

To examine the role of four dimensional data assimilation (FDDA) on prediction of two Severe Cyclonic Storm (Aila (May 23–26, 2009) andJal (Nov 4–8, 2010)), four numerical experiments are performed using WRF-ARW model with three nested domains having two-way interaction and physical parameterization schemes as CPS-BMJ, MP-WSM6, and PBL-YSU. In each experiment, the model integration is started prior to the formation of depression and continued till the observed landfall. For the experiment without FDDA, NCEP-FNL data alone is used as initial and boundary conditions and for the experiments with FDDA, additional observations are used. In all the experiments, FDDA is considered only in the outer domain upto 24 hrs of integration and then the inner domain is introduced. The results are examined in terms of surface circulation, vorticity, CSLP, MSW, and surface track error. FDDA-produced surface circulation and vorticity showed well-organized structure. For the case of Aila, the surface track (maximum track error: 281 km) and landfall position (88°E/21.73°N) in FDDA experiment are predicted better than experiment without FDDA (track error: 445 km and landfall position 87.13°E/20.37°N) whereas the landfall time experiment without FDDA is closer to observations (between 09 and 12 UTC of May 25) than that of experiment with FDDA(06 UTC of May 25). When CSLP and MSW are examined, the overall intensity is well predicted with FDDA experiment except near to the landfall time. For Jal cyclone, FDDA played significant role to improve the landfall position (80.16°E/13.67°N) with a time lead of ~10 hrs but other parameters remain more or less unchanged.

Effect of different meteorological fields on the regional air quality modelling over Pearl River Delta, China

This study evaluates the performances of three Community Multiscale Air Quality (CMAQ) model v5.0 simulations that utilise meteorological input data from the 5th-generation Mesoscale Model (MM5), the Weather Research and Forecasting (WRF) model and WRF with Four-Dimensional Data Assimilation (WRF/FDDA). Three sets of CMAQ model simulations were performed for a high air pollution episode from October 9 to October 12, 2004 over the Pearl River Delta (PRD) region, which was caused by a high-pressure system. The observations at Guangzhou urban site (113.26°E, 23.13°N) were used to evaluate the results of meteorological models and air quality models. In all three experiments, the simulated pollutants were distributed mainly in the western part of PRD, but there was still a difference of horizontal distribution among them. The predicted wind fields, relative humidity and planetary boundary layer (PBL) height in meteorological simulations were the major factors contributing to this difference.

Improving High-Resolution Numerical Weather Simulations by Assimilating Data from an Unmanned Aerial System

Abstract In this study, it is demonstrated how temperature, humidity, and wind profile data from the lower troposphere obtained with a lightweight unmanned aerial system (UAS) can be used to improve high-resolution numerical weather simulations by four-dimensional data assimilation (FDDA). The combined UAS and FDDA system is applied to two case studies of northeasterly flow situations in southwest Iceland from the international Moso field campaign on 19 and 20 July 2009. Both situations were characterized by high diurnal boundary layer temperature variation leading to thermally driven flow, predominantly in the form of sea-breeze circulation along the coast. The data assimilation leads to an improvement in the simulation of the horizontal and vertical extension of the sea breeze as well as of the local background flow. Erroneously simulated fog over the Reykjanes peninsula on 19 July, which leads to a local temperature underestimation of 8 K, is also corrected by the data assimilation. Sensitivity experiments show that both the assimilation of wind data and temperature and humidity data are important for the assimilation results. UAS represents a novel instrument platform with a large potential within the atmospheric sciences. The presented method of using UAS data for assimilation into high-resolution numerical weather simulations is likely to have a wide range of future applications such as wind energy and improvements of targeted weather forecasts for search and rescue missions.

Comparison of Ensemble Kalman Filter–Based Forecasts to Traditional Ensemble and Deterministic Forecasts for a Case Study of Banded Snow

Abstract The ensemble Kalman filter (EnKF) technique is compared to other modeling approaches for a case study of banded snow. The forecasts include a 12- and 3-km grid-spaced deterministic forecast (D12 and D3), a 12-km 30-member ensemble (E12), and a 12-km 30-member ensemble with EnKF-based four-dimensional data assimilation (EKF12). In D12 and D3, flow patterns are not ideal for banded snow, but they have similar precipitation accumulations in the correct location. The increased resolution did not improve the quantitative precipitation forecast. The E12 ensemble mean has a flow pattern favorable for banding and precipitation in the approximate correct location, although the magnitudes and probabilities of relevant features are quite low. Six members produced good forecasts of the flow patterns and the precipitation structure. The EKF12 ensemble mean has an ideal flow pattern for banded snow and the mean produces banded precipitation, but relevant features are about 100 km too far north. The EKF12 has a much lower spread than does E12, a consequence of their different initial conditions. Comparison of the initial ensemble means shows that EKF12 has a closed surface low and a region of high low- to midlevel humidity that are not present in E12. These features act in concert to produce a stronger ensemble-mean cyclonic system with heavier precipitation at the time of banding.

Modeling and surface observations of arsenic dispersion from a large Cu-smelter in southwestern Europe

Abstract Arsenic is a toxic element for human health. Consequently, a mean annual target level for arsenic at 6 ng m −3 in PM10 was established by the European Directive 2004/107/CE to take effect January 2013. Cu-smelters can contribute to one-third of total emissions of arsenic in the atmosphere. Surface observations taken near a large Cu-smelter in the city of Huelva (Spain) show hourly arsenic concentrations in the range of 0–20 ng m −3 . The arsenic peaks of 20 ng m −3 are higher than values normally observed in urban areas around Europe by a factor of 10. The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model has been employed to predict arsenic emissions, transport, and dispersion from the Cu-smelter. The model utilized outputs from different meteorological models and variations in the model physics options to simulate the uncertainty in the dispersion of the arsenic plume. Modeling outputs from the physics ensemble for each meteorological model driving HYSPLIT show the same number of arsenic peaks. HYSPLIT coupled with the Weather Research and Forecasting (WRF-ARW) meteorological output predicted the right number of peaks for arsenic concentration at the observation site. The best results were obtained when the WRF simulation used both four-dimensional data assimilation and surface analysis nudging. The prediction was good in local sea breeze circulations or when the flow was dominated by the synoptic scale prevailing winds. However, the predicted peak was delayed when the transport and dispersion was under the influence of an Atlantic cyclone. The calculated concentration map suggests that the plume from the Cu-smelter can cause arsenic pollution events in the city of Huelva as well as other cities and tourist areas in southwestern Spain.

Improving the horizontal transport in the lower troposphere with four dimensional data assimilation

The physical processes involved in air quality modeling are governed by dynamically-generated meteorological model fields. This research focuses on reducing the uncertainty in the horizontal transport in the lower troposphere by improving the four dimensional data assimilation (FDDA) strategy in retrospective meteorological modeling. In particular, characterization of winds in the nocturnal low-level jet and overlying residual layer is crucial to accurately model regional-scale ozone transport in the key airsheds of the US. Since model errors in wind speed and direction lead to spatial displacements of pollution plumes, observations not routinely used in previous retrospective modeling are introduced through FDDA in an effort to help reduce this transport uncertainty. Prior to the main modeling sensitivity, an observational uncertainty analysis was pursued to identify uncertainties in the wind speed and direction in the lower 1-km of the troposphere that are inherent in the observational data sets used in FDDA. Comparisons of observations among various platforms (radar wind profilers, radiosonde soundings and weather radar profiles) taken in close proximity revealed that an uncertainty of approximately 1.8 m s �1 for wind speed and about 20� for wind direction was intrinsic to the measurements. In the modeling sensitivities, some minimal improvement of modeled winds within the convective planetary boundary layer (PBL) was found when surface analysis nudging of wind was eliminated. Improvements in the nocturnal jet and residual layer winds at night are demonstrated as a reaction to the use of new observations in the data assimilation in layers above the stable PBL. There is also evidence that the assimilated observations above the convective PBL during the day led to improvements of winds within the PBL, which may relieve the need of nudging within the PBL, including surface analysis nudging. Published by Elsevier Ltd.

Impact of Enhanced Satellite-Derived Atmospheric Motion Vector Observations on Numerical Tropical Cyclone Track Forecasts in the Western North Pacific during TPARC/TCS-08

AbstractEnhanced atmospheric motion vectors (AMVs) produced from the geostationary Multifunctional Transport Satellite (MTSAT) are assimilated into the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) to evaluate the impact of these observations on tropical cyclone track forecasts during the simultaneous western North Pacific Ocean Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (TPARC) and the Tropical Cyclone Structure—2008 (TCS-08) field experiments. Four-dimensional data assimilation is employed to take advantage of experimental high-resolution (space and time) AMVs produced for the field campaigns by the Cooperative Institute for Meteorological Satellite Studies. Two enhanced AMV datasets are considered: 1) extended periods produced at hourly intervals over a large western North Pacific domain using routinely available MTSAT imagery and 2) limited periods over a smaller storm-centered domain produced using special MTSAT rapid-scan imagery. Most of the locally impacted forecast cases involve Typhoons Sinlaku and Hagupit, although other storms are also examined. On average, the continuous assimilation of the hourly AMVs reduces the NOGAPS tropical cyclone track forecast errors—in particular, for forecasts longer than 72 h. It is shown that the AMVs can improve the environmental flow analyses that may be influencing the tropical cyclone tracks. Adding rapid-scan AMV observations further reduces the NOGAPS forecast errors. In addition to their benefit in traditional data assimilation, the enhanced AMVs show promise as a potential resource for advanced objective data-targeting methods.

The application of sea-surface wind detection with doppler lidar in Olympic sailing

The mobile incoherent Doppler lidar (MIDL), which was jointly developed by State Key Laboratory of Severe Weather (LaSW) of the Chinese Academy of Meteorological Sciences (CAMS) and Ocean University of China, provided meteorological services during the Olympic sailing events in Qingdao in 2008. In this study, two experiments were performed based on these measurements. First, the capabilities of MIDL detection of sea-surface winds were investigated by comparing its radial velocities with those from a sea buoy. MIDL radial velocity was almost consistent with sea-buoy data; both reflected the changes in wind with time. However, the MIDL data was 0.5 m s−1 lower on average than the sea-buoy data due to differences in detection principle, sample volume, sample interval, spatial and temporal resolution. Second, the wind fields during the Olympic sailing events were calculated using a four-dimensional variation data assimilation (4DVAR) algorithm and were evaluated by comparing them with data from a sea buoy. The results show that the calculations made with the 4DVAR wind retrieval method are able to simulate the fine retrieval of sea-surface wind data—the retrieved wind fields were consistent with those of sea-buoy data. Overall, the correlation coefficient of wind direction was 0.93, and the correlation coefficient of wind speed was 0.70. The distribution of retrieval wind fields was consistent with that of MIDL radial velocity; the root-mean-square error between them had an average of only 1.52 m s−1.

Diagnostic evaluation of ozone production and horizontal transport in a regional photochemical air quality modeling system

Abstract A diagnostic model evaluation effort has been performed to focus on photochemical ozone formation and the horizontal transport process since they strongly impact the temporal evolution and spatial distribution of ozone (O3) within the lower troposphere. Results from the Community Multiscale Air Quality (CMAQ) modeling system are evaluated against surface and upper air measurements from field studies during summer 2002 when several high O3 episodes occurred in the eastern United States. Modeled O3 and winds are compared to research aircraft measurements and wind profiler data, respectively, to investigate whether model underestimates of daily maximum 8-h ozone concentrations during high O3 episodes might be attributable to discrepancies in either or both of these modeled processes. Comparisons of 10 AM surface O3 concentrations, which are representative of O3 levels in the residual layer aloft, revealed that model underestimation was greater at higher observed ozone levels. Mid-morning vertical ozone profiles corroborated this surface-level finding, as modeled concentrations tended to be lower than observed O3 aloft. Net ozone production efficiency (OPE) results suggested photochemical ozone formation was comparable between the model and observations with composite OPE values of 6.7 and 7.6, respectively, within the afternoon planetary boundary layer. Evaluation of wind profiles revealed modeled wind speeds with the base four-dimensional data assimilation (FDDA) approach underestimated observed speeds by more than 2 m s−1 and direction was biased by about 20° in the nocturnal residual layer aloft as coarse resolution analysis fields involved in FDDA were found to inhibit modeled winds. These differences could produce large spatial displacements in modeled and observed ozone patterns within the region. Although sensitivity simulation results with the WRF meteorological model with FDDA using all available upper air profile observations displayed improvements in capturing wind fields aloft, CMAQ maximum 8-h O3 results using the improved wind fields also underestimated observations.

Simultaneous nested modeling from the synoptic scale to the LES scale for wind energy applications

This paper describes an advanced multi-scale weather modeling system, WRF–RTFDDA–LES, designed to simulate synoptic scale (∼2000 km) to small- and micro-scale (∼100 m) circulations of real weather in wind farms on simultaneous nested grids. This modeling system is built upon the National Center for Atmospheric Research (NCAR) community Weather Research and Forecasting (WRF) model. WRF has been enhanced with the NCAR Real-Time Four-Dimensional Data Assimilation (RTFDDA) capability. FDDA is an effective data assimilation algorithm, which is capable of assimilating diverse weather measurements on model grids and seamlessly providing realistic mesoscale weather forcing to drive a large eddy simulation (LES) model within the WRF framework. The WRF based RTFDDA LES modeling capability is referred to as WRF–RTFDDA–LES. In this study, WRF–RTFDDA–LES is employed to simulate real weather in a major wind farm located in northern Colorado with six nested domains. The grid sizes of the nested domains are 30, 10, 3.3, 1.1, 0.370 and 0.123 km, respectively. The model results are compared with wind–farm anemometer measurements and are found to capture many intra-farm wind features and microscale flows. Additional experiments are conducted to investigate the impacts of subgrid scale (SGS) mixing parameters and nesting approaches. This study demonstrates that the WRF–RTFDDA–LES system is a valuable tool for simulating real world microscale weather flows and for development of future real-time forecasting system, although further LES modeling refinements, such as adaptive SGS mixing parameterization and wall-effect modeling, are highly desired.

Preliminary Meteorological Results of a Four-Dimensional Data Assimilation Technique in Southern Italy

A four-dimensional data assimilation (FDDA) scheme based on a Newtonian relaxation (or “nudging”) was tested using observational asynoptic data collected at a coastal site in the Central Mediterranean peninsula of Calabria, southern Italy. The study is referred to an experimental campaign carried out in summer 2008. For this period a wind profiler, a sodar and two surface meteorological stations were considered. The collected measurements were used for the FDDA scheme, and the technique was incorporated into a tailored version of the Regional Atmospheric Modeling System (RAMS). All instruments are installed and operated routinely at the experimental field of the CRATI-ISAC/CNR located at 600 m from the Tyrrhenian coastline. Several simulations were performed, and the results show that the assimilation of wind and/or temperature data, both throughout the simulation time (continuous FDDA) and for a 12 h time window (forecasting configuration), produces improvements of the model performance. Considering a whole single day, improvements are substantial in the case of continuous FDDA while they are smaller in the case of forecasting configuration. Enhancements, during the first six hours of each run, are generally higher. The resulting meteorological fields are finalised as input into air quality and agro-meteorological models, for short-term predictions of renewable energy production forecast, and for atmospheric model initialization.