Wind Errors Research Articles

Influence of Winding Accuracy on Magnetic Field Distribution in YBCO Pancake Coil for Cyclotron Application

In recent years, YBCO high-temperature superconducting (HTS) tapes have been expected to be applied to devices in power systems or in other industrial applications, owing to their rapidly improving quality and productivity. Our objective is to develop an HTS cyclotron with a compact body, high efficiency of output power, and high performance in heavy particle radiotherapy. In such an application, extremely high precision is required in the range 0.01% to 0.1% of magnetic field, both spatially and temporally. An isochronous field along with an azimuthally varying field is generated by an air-core pancake coil system during construction. Therefore, developing a technology for extremely high coil-winding accuracy is very important. In this study, we constructed an experimental model of a YBCO pancake coil using a high-precision coil winding machine. Then, the winding error in the radial and axial directions was experimentally evaluated using a pair of laser displacement meters and a surface roughness tester. We also developed a numerical simulation model to analyze the influence of winding error on the magnetic field distribution. The relationship between the winding error and the magnetic field of the meter-class YBCO coil assuming a real system was also evaluated by numerical simulation.

Interannual variation of the southern limit in the Yellow Sea Bottom Cold Water and its causes

The Yellow Sea Bottom Cold Water (YSBCW) occupies a wide region below the Yellow Sea (YS) thermocline in summer. The southern limit of the YSBCW shows pronounced interannual variability. A regional ocean model with realistic forcing was used to identify the structure of the YSBCW and to investigate the causes of its interannual variability from 1981 to 2010. Sea surface temperature (SST) in winter is strongly correlated with the southern limit of the YSBCW in summer. The correlation coefficient between the August southern limit and the February SST is −0.884. This result suggests that cold SST is associated with the increased southern limit in the following summer. Linear regression suggests that the southern limit increases by about 55km when the SST in February decreases by 1°C. The southern limits are more extended to the south in August than in June in some years despite surface heating. The difference in southern limits between June and August is positively correlated with the summer southerly wind stress with a correlation coefficient of 0.529. The contribution of SST in winter on the southern limit of the YSBCW in summer is larger than the wind stress in summer. The SST in winter is mainly determined by the air temperature and wind speed in winter. The other factor affecting winter SST is the previous year's bottom water temperature. The winter SST is significantly correlated with the bottom water temperature in previous year. The southern limit of the YSBCW in the observed data in the limited area has relatively weak correlation with the winter SST and summer southerly wind stress possibly due to observation error and uncertainty of the reanalysis wind.

Wind extraction potential from 4D-Var assimilation of stratospheric O<sub>3</sub>, N<sub>2</sub>O, and H<sub>2</sub>O using a global shallow water model

Abstract. The wind extraction due to assimilation of stratospheric trace gas (tracer) data is examined using a 4D-Var (four-dimensional variational) data assimilation system based on the shallow water equations coupled to the tracer continuity equation. The procedure is outlined as follows. First, a nature run is created, simulating middle stratospheric winter conditions. Second, ozone (O3), nitrous oxide (N2O), and water vapor (H2O) (treated in this study as passive tracers) are initialized using Aura Microwave Limb Sounder (MLS) mixing ratios at 850 K potential temperature and are advected by the nature run winds. Third, the initial dynamical conditions are perturbed by using a 6 h offset. Fourth, simulated hourly tracer observations on the full model grid are assimilated with a 4D-Var system in which tracer and winds are coupled via the adjoint of the tracer continuity equation. Multiple assimilation experiments are performed by varying the amount of random observation error added to the simulated measurements. Finally, the wind extraction potential (WEP) is calculated as the reduction of the vector wind root mean square error (RMSE) relative to the maximum possible reduction. For a single 6 h assimilation cycle with the smallest observation error, WEP values are ~60% for all three tracers, while 10-day multi-cycle simulations result in WEP of ~90%, wind errors of ~0.3 m s−1, and height errors of ~13 m. There is therefore sufficient information in the tracer fields to almost completely constrain the dynamics, even without direct assimilation of dynamical information. When realistic observation error is added (based on MLS precisions at 10 hPa), the WEP after 10 days is 90% for O3, 87% for N2O, and 72% for H2O. O3 and N2O provide more wind information than H2O due to stronger background gradients relative to the MLS precisions. The RMSE for wind reach a minimum level of ~0.3–0.9 m s−1 for the MLS precisions, suggesting a limit to which realistic tracers could constrain the winds, given complete global cover age. With higher observation noise levels, the WEP values decrease, but the impact on the winds is still positive up to noise levels of 100% (relative to the global mean value) when compared to the case of no data assimilation.

Planetary boundary layer errors in mesoscale inversions of column‐integrated CO2 measurements

Observing platforms of greenhouse gas column mole fractions using remote sensing instruments have enhanced the capability of carbon data assimilation systems at large scales and helped improve our understanding of the underlying processes involved in the exchange of carbon at the surface of the globe. In this study, we quantify the additional information carried by these measurements at finer scales and consider the impact of vertical transport errors in current modeling systems, one of the main sources of uncertainties in regional inverse flux estimates. Surface‐based column‐integrated sensors are shown to be a significant source of information to constrain local surface fluxes at fine scales. Gain and error reduction are only about 20% lower than for in situ instruments. Column measurements show less dependence on near‐field surface fluxes compared to in situ, with the error reduction being more homogeneously distributed. However, vertical transport errors still impact the flux retrievals, as with in situ measurements but to a lesser extent. Inverse fluxes from both types of measurements were affected by errors in vertical mixing and mean horizontal winds, with a larger impact on the inverse carbon balance using in situ measurements. The use of remote sensing measurements also appeared to constrain significantly the boundary concentrations, a critical limitation in current regional inversions. We finally performed a pseudo‐data experiment combining both types of instruments, creating an optimal observing network with a lower impact of planetary boundary layer transport errors on the surface fluxes and the boundary concentrations, and a more widely distributed reduction of the errors over the boundaries.

Simulation of frequency discrimination for spaceborne Doppler wind lidar (I)：Study on the retrieval of atmospheric wind speed for Mie channel based on Fizeau interferometer

System structure of spaceborne Doppler wind lidar is investigated. Simulation system of frequency discrimination is built based on Fizeau interferometer. The wind retrieval algorithm of Mie channel is simulated and studied. The horizontal line-of-sight (HLOS) wind error is analysed using the simulated result of the radiosonde dataset. The result shows that the wind speed of lower troposphere can be retrieved in Mie channel based on Fizeau interferometer. The average values of HLOS wind error deviation and standard deviation in lower troposphere are less than 1 m·s-1 and 2 m·s-1, respectively. Distributions of aerosol and cloud have an influence on wind error for spaceborne Doppler wind lidar, and the maximum wind deviation will increase twice.

Simulation of frequency discrimination for spaceborne Doppler wind lidar (Ⅱ)：Study on the retrieval of atmospheric wind speed for Rayleigh channel based on Fabry-Perot interferometer

Based on the principle of spaceborne Doppler wind lidar, a simulation system of frequency discrimination is built based on the double sequential Fabry-Perot (F-P) interferometer. The wind retrieval algorithm of Rayleigh channel is simulated and studied. The influence on the retrieved atmospheric line-of-sight (LOS) wind speed in Rayleigh channel by the Rayleigh-Brillouin effect and Mie contamination is systematically analyzed. The horizontal line-of-sight (HLOS) wind error is analysed using the simulated result of the radiosonde dataset. The results show that the wind speeds of the middle and upper atmosphere can be retrieved in Rayleigh channel based on the double sequential F-P interferometer; the Rayleigh-Brillouin effect and Mie contamination influence the accuracy of LOS wind speed retrieval in Rayleigh channel; the Rayleigh channel requires more accurate temperature; Mie contamination can be ignored in clear atmosphere; when Brillouin effect is not considered, below 2 km, the HLOS wind speed cannot be retrieved in Rayleigh channel, and above 2 km, the HLOS wind speed error in Rayleigh channel is less than 0.4 m/s and its standard deviation is 1-4 m/s. Just as the Mie channel, distributions of aerosol and cloud have an influence on wind error for spaceborne Doppler wind lidar in Rayleigh channel. The research results have an important reference value for the development of spaceborne lidar wind technology.

Wind Retrieval and Error Analysis of Ground-based Fabry-Perot Interferometer for the Middle and Upper Atmosphere

基于子午工程地基法布里-帕罗干涉仪（Fabry-Perot Interferometer，FPI）的气辉观测数据，结合地基独特的观测模式（天顶角为0° 的天顶方向和天顶角为45°的东西北南四个方向）对地基中高层大气风速进行反演，包括数据预处理、干涉环圆心确定、干涉环半径计算和风速反演. 将2010年5月6-13日8天十个环（十个干涉环同时参与反演）的反演结果与地基FPI风速实测数据进行比较，得到557.7nm，630.0nm，892.0nm三种谱线气辉的反演平均偏差分别为2.7m·s^-1，5.5m·s^-1，7.7m·s^-1. 此外，基于反演算法对上述反演精度影响因素进行了分析. 研究发现，气辉辐射强度对风速的反演精度影响较大，气辉辐射越强，外环的半径计算精度越高，可参与的反演环数越多，则最终的风速反演精度越高. 而圆心偏差± 2pixel（五个环）和± 1pixel（十个环）及焦距变化（±10mm）对风速反演精度的影响相对较小，但当超出这一偏差范围，风速反演偏差会迅速增大.

Stratospheric Analysis and Forecast Errors Using Hybrid and Sigma Coordinates

Abstract Past investigations have documented large divergent wind anomalies in stratospheric reanalyses over steep terrain, which were attributed to discretization errors produced by the terrain-following (sigma) vertical coordinate in the forecast model. However, forecasting experiments have reported negligible differences in skill between sigma- and hybrid-coordinate models. This leads to the paradoxical conclusion that discretization errors in the forecast model yield significant stratospheric analysis errors, but insignificant stratospheric forecast errors. The authors reexamine this issue by performing two forecast-assimilation experiments that are identical except for the vertical coordinate: one uses a sigma coordinate and the other uses a hybrid coordinate. The sigma-coordinate analyses exhibit large divergent wind anomalies over terrain that extend from the surface to the model top and distort explicitly resolved orographic gravity waves. Above the tropopause, divergent wind errors are suppressed by an order of magnitude or more in the hybrid-coordinate analyses. Over a 3-month period, stratospheric skill scores in the hybrid experiment show statistically significant improvements relative to the sigma experiment. Previous studies, which found no such differences, all used forecasts initialized from a common archived analysis. The results show that the dominant pathway for error growth and net skill impacts is via 0–9-h forecast backgrounds cycling successively through the data assimilation phase without significant observational correction. The skill impacts noted here should further motivate weather and climate models to adopt a hybrid coordinate with the best error suppression characteristics for a given modeling application.

Price Determinants in the German Intraday Market for Electricity: An Empirical Analysis

This paper presents a first investigation of hourly price determinants in the German intraday market for electricity. The influence of power plant outages, forecast errors of wind and solar power production, load forecast errors and foreign demand and supply on intraday prices are explained from a theoretical perspective. Furthermore the influences of the non-linear merit-order shape, ramping costs and strategic market behavior are discussed. The empirical results from different regression analysis with data from 2010 and 2011 show that most price determinants increase and decrease intraday prices as expected. Nevertheless, only a minor share of power plant outages and solar power forecast errors are traded on the electronic intraday trading platform, thus influencing prices not as strongly as expected. Furthermore the price determinants influence intraday prices differently over the course of the day which may be explained by an alternating liquidity provision.

Changes in the economic value of wind energy and flexible resources at increasing penetration levels in the Rocky Mountain Power Area

ABSTRACTWe use a consistent economic framework to estimate the long‐run economic value of wind while including operational constraints for conventional generation and hourly variation in wind and load. Day‐ahead forecast errors in wind are corrected in the real‐time, after commitment decisions for many thermal generators have already been made. The framework is used to estimate the change in the marginal economic value of wind with increasing penetration in the Rocky Mountain Power Area of the USA. We also evaluate the marginal economic benefit to wind energy of implementing several strategies to manage wind energy variability and uncertainty: more flexible conventional generation, real‐time pricing, low cost bulk energy storage, and increased geographic diversity of wind plant siting. Without mitigation, the marginal economic value of wind is found to decrease by $21 MWh (37% of the marginal value of wind at 0% penetration) as wind penetration increases from 0% to 30%. The decline is largely because of the hourly profile of wind output and day‐ahead wind energy forecast errors; factors whose impact is reduced by the mitigation strategies. With mitigation, the marginal value of wind at the 30% penetration level is $6‐$11 MWh greater than the value without the measures (17‐31% increase in value). Although the marginal value of wind energy decreases with increasing penetration in this region, several different types of mitigation strategies are available and should be investigated in more detail. Copyright © 2013 John Wiley & Sons, Ltd.

Enhanced radiance bias correction in the National Centers for Environmental Prediction's Gridpoint Statistical Interpolation data assimilation system

AbstractRadiance bias correction is an important and necessary step in the proper use of satellite observations in a data assimilation system. The original radiance bias‐correction scheme used in the Gridpoint Statistical Interpolation (GSI) data assimilation system consists of two components: a variational air‐mass dependent component and a scan‐angle component. The air‐mass component is updated within the GSI, while the scan‐angle component is updated outside the GSI.This study examines and enhances several aspects of the radiance bias‐correction problem. First, a modified pre‐conditioning is applied to the bias‐correction coefficients and the analysis variables to speed up convergence of the minimization process. A new procedure for applying the modified pre‐conditioning in the GSI is utilized. Second, capabilities for detecting any new/missing/recovering radiance data and initializing the bias correction for new radiance data are implemented. A new scheme is proposed and employed to adjust the background‐error variances for the bias‐correction coefficients automatically, using an approximation of the analysis‐error variances from the previous cycle, and to remove the pre‐specified predictor scaling parameters. Finally, the capability to perform bias correction for passive channels within the GSI is developed with a new approach.The two‐step bias‐correction procedure originally used is replaced with a one‐step variational bias‐correction scheme within the GSI. Experiment results with the GSI‐based hybrid ensemble‐variational system show that using the modified pre‐conditioning leads to a better convergence rate. Moreover, with the one‐step scheme, the anomaly correlation of geopotential height at 500 mb is neutral in the Northern Hemisphere but improved in the Southern Hemisphere. The root‐mean‐square (RMS) error of wind is comparable to that of the two‐step scheme and the biases of the global temperature 24 h and 48 h forecasts fitted to the rawinsonde are reduced.

Application of data assimilation in the Whole Atmosphere Community Climate Model to the study of day‐to‐day variability in the middle and upper atmosphere

The Data Assimilation Research Testbed (DART) ensemble adjustment Kalman filter (EAKF) is employed to perform data assimilation in the Whole Atmosphere Community Climate Model (WACCM). To demonstrate the potential of the WACCM+DART for studying short‐term variability in the mesosphere and lower thermosphere (MLT), results are presented based on the assimilation of synthetic observations that are sampled from a known model truth. We assimilate temperature and wind from radiosondes and aircraft, satellite drift winds, and COSMIC refractivity in the lower atmosphere, and SABER temperature observations in the middle/upper atmosphere. Relative to an unconstrained WACCM simulation, the assimilation of only lower atmosphere observations reduces the global root mean square error (RMSE) in zonal wind by up to 40% at MLT altitudes. Using data assimilation to constrain the lower atmosphere can therefore provide significant insight into MLT variability. The RMSE in the MLT is reduced by an additional 10–15% when SABER observations are also assimilated. The WACCM+DART is shown to be able to reproduce the large‐scale features of the day‐to‐day variability in the zonal mean, migrating, and nonmigrating tides in the MLT. Though our simulation results are based on idealized conditions, they demonstrate that the WACCM+DART can reproduce the day‐to‐day variability in the MLT. Assimilation of real observations in the WACCM+DART will therefore enable significant insight into the real day‐to‐day dynamical variability from the surface to the lower thermosphere.

Assessment of the ASCAT wind error characteristics by global dropwindsonde observations

This research focuses on assessing the accuracy of the advanced scatterometer (ASCAT) wind vector around tropical cyclones by using data from dropwindsondes deployed by the surveillance and reconnaissance flights during 2007–2010. There are 987 matching samples for the comparisons, with wind speed up to 50 m s−1. The bias and root‐mean‐square differences of wind speed between ASCAT and dropwindsonde data are −1.7 and 5.3 m s−1, respectively. Further analyses also indicate that large wind direction differences occur in the low wind speed regime, while large wind speed differences occur in the high wind speed regime. The accuracy of wind vector in weak wind speed and high wind speed regimes are significantly reduced in saturated regions, implying that the rain contamination issue still affects the accuracy of ASCAT wind retrieval. The wind vector in the medium wind speed regime has much better quality, in good agreement with the satellite's designed specification. The ASCAT wind data obviously contain negative wind speed bias, which grows larger as wind speed increases. A regression fit between ASCAT and dropwindsonde wind speed is adopted to correct the bias of the ASCAT wind speed. The error characteristics are largely determined by the magnitude of wind speed and moisture saturation which are highly variable around the storm. Nevertheless, results from this study suggest that ASCAT wind data of velocities measuring between 12 and 18 m s−1 are more reliable and can be applied to determine the radius of 34 knot winds, a critical parameter in operational tropical cyclone analysis.

Sensitivity of movement and intensity of severe cyclone AILA to the physical processes

Accurate prediction of movement and intensity of tropical cyclone is still most challenging problem in numerical weather prediction. The positive progress in this field can be achieved by providing network of observations in the storm region and best representation of atmospheric physical processes in the model. In the present study later part was attempted to investigate the sensitivity of movement and intensity of the severe cyclonic storm AILA to different physical processes in the Weather Research and Forecasting model. Three sets of experiments were conducted for convection, microphysics (MP) and planetary boundary layer (PBL) processes. Model-simulated fields like minimum central surface pressure, maximum surface wind, track and vector displacement error are considered to test the sensitivity. The results indicate that the movement of the system is more sensitive to the cumulus physics and the intensity of the cyclone is sensitive to both PBL and cumulus physics. The combination of Betts Miller Janjic (BMJ) for convection, Yonsei University (YSU) for PBL and Purdue Lin (LIN) for microphysics is found to perform better than other combination schemes. The horizontal and vertical features of the system along with its special features like complete northward movement of the system throughout the travel period and the consistent cyclonic storm intensity until 15 hrs after the landfall could be well simulated by the model.

Height Correction of Atmospheric Motion Vectors Using Airborne Lidar Observations

AbstractUncertainties in the height assignment of atmospheric motion vectors (AMVs) are the main contributor to the total AMV wind error, and these uncertainties introduce errors that can be horizontally correlated over several hundred kilometers. As a consequence, only a small fraction of the available AMVs are currently used in numerical weather prediction systems. For this reason, alternative approaches for the height assignment of AMVs are investigated in this study: 1) using collocated airborne lidar observations and 2) treating AMVs as layer winds instead of winds at a discrete level. Airborne lidar observations from a field campaign in the western North Pacific Ocean region are used to demonstrate the potential of improving AMV heights in an experimental framework. On average, AMV wind errors are reduced by 10%–15% when AMV winds are assigned to a 100–150-hPa-deep layer beneath the cloud top derived from nearby lidar observations. In addition, the lidar–AMV height correction is expected to reduce the correlation of AMV errors as lidars provide independent cloud height information. This suggests that satellite lidars may be a valuable source of information for the AMV height assignment in the future. Furthermore, AMVs are compared with dropsonde and radiosonde winds averaged over vertical layers of different depth to investigate the optimal height assignment for AMVs in data assimilation. Consistent with previous studies, it is shown that AMV winds better match sounding winds vertically averaged over ~100 hPa than sounding winds at a discrete level. The comparison with deeper layers further reduces the RMS difference but introduces systematic differences of wind speeds.

An estimate of some analysis‐error statistics using the Global Modeling and Assimilation Office observing‐system simulation framework

AbstractThe observing‐system simulation experiment (OSSE) framework developed at the National Aeronautics and Space Administration (NASA), Global Modeling and Assimilation Office (GMAO) is used to estimate some characteristics of analysis error. This is possible because, within the framework, the true state of the simulated atmosphere is known and, after suitable interpolation to a common grid, can be simply differenced with the analysis to compute errors explicitly. The only assumption is that the OSSE is sufficiently realistic so that error statistics derived from it adequately describe those obtained when analyzing observations of the real atmosphere. Attention here is restricted to the troposphere during the months of July and August at 0000 and 1200 UTC using an observation data set from 2011.Analysis‐error statistics presented here include geographical distributions of temporal variances, power spectra of spherical harmonics and correlations in meridional, latitudinal and vertical directions. Our most noteworthy results include the revelation through spectral analysis that only synoptic scales are well analyzed and that horizontal correlation lengths in the extratropics are short, with little vertical variation, and the dominance of rotational wind errors in contrast to divergent ones.Also examined are fractional reductions in background‐error variances due to analysis of observations. These reveal that in most geographical regions throughout the troposphere, errors are reduced by analyzing observations, as intended. The reductions are typically small, however (−10% in most regions), as should be expected from consideration of the equilibration of errors implied by the Kalman‐filter equations.

Error features and their possible causes in simulated low‐level winds by WRF at a wind farm

ABSTRACTThe WRF model is applied to simulate the low‐level wind field for a wind farm located in a typical arid region in northwest China for February 2008. The selected region has complex terrain with sparse vegetation. Overall, the WRF model reproduced the variation features of wind speeds and wind directions. However, the model overestimated the observed low‐level wind speeds, and there were large discrepancies for the low wind velocity (i.e. the errors of simulated winds increase with height and will be larger when the observed wind speeds are lower than 2.5 m/s). The features of the simulated errors and the possible causes in the model were analysed. The simulated low‐level wind in the afternoon is more accurate than that in early morning, which is usually unstable. Copyright © 2013 John Wiley & Sons, Ltd.

Improvements to MISR stereo motion vectors

[1] This study evaluated the effects of recent major modifications to the Multiangle Imaging SpectroRadiometer (MISR) wind retrieval algorithm by repeating an earlier yearlong comparison with Meteosat-9 cloud motion vectors. Algorithm upgrades included an area-based stereo matcher, enhanced quality control, georegistration corrections to focal plane distortions, and increased retrieval resolution. The new winds had better quality at all levels, yielding a particularly large increase in coverage at middle and high levels. The upgrades had an overall neutral impact on the E-W wind comparison statistics, which were already quite good in the preceding MISR data set. The comparison statistics for the more error-prone N-S wind, however, improved significantly on all scales—global, zonal, and regional—and throughout the entire atmosphere. Both the negative N-S wind bias and root mean square difference decreased, and correlation increased substantially, with middle and high levels, and tropics and subtropics experiencing the largest improvements. Subpixel georegistration corrections reduced cross-swath variations in N-S wind and height by half. As the net effect of these improvements, the error characteristics of the previously more uncertain N-S wind component became comparable to those of the E-W wind component at low and middle levels. Despite the substantial reductions in N-S wind errors, which are highly correlated with stereo height errors, the MISR – Meteosat-9 height comparison did not generally improve, strongly suggesting Meteosat-9 height assignment errors as the primary driver of discrepancy.

Examination of Errors in Near-Surface Temperature and Wind from WRF Numerical Simulations in Regions of Complex Terrain

Abstract The performance of an advanced research version of the Weather Research and Forecasting Model (WRF) in predicting near-surface atmospheric temperature and wind conditions under various terrain and weather regimes is examined. Verification of 2-m temperature and 10-m wind speed and direction against surface Mesonet observations is conducted. Three individual events under strong synoptic forcings (i.e., a frontal system, a low-level jet, and a persistent inversion) are first evaluated. It is found that the WRF model is able to reproduce these weather phenomena reasonably well. Forecasts of near-surface variables in flat terrain generally agree well with observations, but errors also occur, depending on the predictability of the lower-atmospheric boundary layer. In complex terrain, forecasts not only suffer from the model's inability to reproduce accurate atmospheric conditions in the lower atmosphere but also struggle with representative issues due to mismatches between the model and the actual terrain. In addition, surface forecasts at finer resolutions do not always outperform those at coarser resolutions. Increasing the vertical resolution may not help predict the near-surface variables, although it does improve the forecasts of the structure of mesoscale weather phenomena. A statistical analysis is also performed for 120 forecasts during a 1-month period to further investigate forecast error characteristics in complex terrain. Results illustrate that forecast errors in near-surface variables depend strongly on the diurnal variation in surface conditions, especially when synoptic forcing is weak. Under strong synoptic forcing, the diurnal patterns in the errors break down, while the flow-dependent errors are clearly shown.

Experiments of the WRF three-/four-dimensional variational (3/4DVAR) data assimilation in the forecasting of Antarctic cyclones

The three-/four-dimensional variational data assimilation systems (3/4DVAR) of the Weather Research and Forecasting (WRF) model were explored in the forecasting of two Antarctic synoptic cyclones, which had large influence on the Ross Sea/Ross Ice Shelf region in October 2007. A suite of variational data assimilation experiments, including regular 3DVAR, high-resolution 3DVAR, and 4DVAR experiments, were designed to evaluate their performances in weather analysis and forecasting in Antarctica. In general, both 4DVAR and high-resolution 3DVAR experiments showed better forecasting skill than regular 3DVAR experiments. High-resolution 3DVAR experiments were the most efficient in reducing the analysis errors of surface winds and temperature, and had the best performance during the first 24 h of forecasting. However, during the following forecast period, 4DVAR experiments showed either better or about comparable performance to high-resolution 3DVAR experiments. These results indicate that increasing the spatial resolution during 3DVAR is an economical approach to improving the weather analysis and forecasting over Antarctica. At the same time, the 4DVAR approach had a longer impact on forecasting than the high-resolution 3DVAR approach. Understandably, both of the variational assimilation approaches are promising techniques toward improving the regional analysis and forecasting over Antarctica.