European Centre For Medium-Range Weather Forecasts Winds Research Articles

ECMWF and UK Met Office Offshore Blowing Winds: Impact of Horizontal Resolution and Coastal Orography

AbstractWe analyze the performance of the European Centre for Medium‐Range Weather Forecasts (ECMWF) and UK Met Office (UKMO) meteorological models in predicting offshore blowing wind in coastal areas. Our attention is mainly on the Mediterranean coast, up to 200 km distance from the shore. We compare forecast neutral winds with Advanced Scatterometer measurements. The results indicate that the ECMWF forecasts systematically underestimate wind speed with respect to scatterometer data, while the UKMO model tends to overestimate. A cross‐analysis suggests that, better than fetch, model biases are a function of the model horizontal discretization, hence of the number of grid steps the wind runs over the sea. The steepness and roughness of the land orography before entering the sea, together with the related drag parameters, appear to have a strong role in determining the coastal offshore wind values. Surface drag over land tends to reduce the related wind speed, and it takes a few grid points to adjust to the smooth sea surface conditions. This is supported by a detailed study with a high resolution grid, but different orography resolutions. In these simulations, practically identical coastal wind speed distributions are found when the subgrid orography schemes are switched off. Vertical cross‐sections of potential temperature and wind in bora and mistral conditions illustrate the strong role of gravity waves, wind channeling and turbulent diffusion in coastal numerical weather prediction. Besides local wave modeling, this may be relevant for offshore wind farms, typically situated within 5–50 km from the coast.

Improved CYGNSS Wind Speed Retrieval Using Significant Wave Height Correction

This article presents the methodology for an improved estimation of the sea surface wind speed measured by the cyclone global navigation satellite system (CYGNSS) constellation of satellites using significant wave height (SWH) information as external reference data. The methodology consists of a correcting 2D look-up table (LUT) with inputs: (1) the CYGNSS wind speed given by the geophysical model function (GMF); and (2) the collocated reference SWH given by the WW3 model, which is forced by winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) organization. In particular, the analyzed CYGNSS wind speeds are the fully developed seas (FDS) obtained with the GMF 3.0, and the forcing winds are the ECMWF forecast winds. Results show an increase in sensitivity to large winds speeds and an overall reduction in the root mean square difference (RMSD) with respect to the ECMWF winds from 2.05 m/s to 1.74 m/s. The possible influence of the ECWMF winds on the corrected winds (due to their use in the WW3 model) is analyzed by considering the correlation between: (1) the difference between the ECMWF winds and those from another reference; and (2) the difference between the corrected CYGNSS winds and those from the same reference. Results using ASCAT, WindSat, Jason, and AltiKa as references show no significant influence.

Retrieval of Ocean Wave Parameters Using Envisat Advanced Synthetic Aperture Radar Data and Its Verification Analysis

Based on MPI iterative retrieve algorithm, we retrieve the 2-D (two-dimensional) ocean wave spectrum by using Envisat ASAR wave mode Single Look Complex (SLC) imageries. The first guess spectrum is a necessary input item to run this MPI algorithm. In this paper, the first guess spectrum was calculated with reanalyzing wind data of ECMWF (European Centre for Medium-Range Weather Forecasts) and NCEP (National Centers for Environmental Prediction) as input wind stress of WAM Model. The Significant Wave Heights (SWHs) retrieved from the first guess spectrum as well as the best fit spectrum corresponding to ECMWF and NCEP wind field respectively are compared with the measurement data from buoys all over the world ocean. The comparison shows that the SWHs retrieved by ECMWF wind are less than field measurement buoy data in general, while the SWHs retrieved by NCEP wind are greater than those buoy data, but the mean SWHs of them fit with the field buoy data are better than whichever pro. However, the MPI algorithm makes the retrieved SWHs more accurate in comparison with the first guess spectrum derived from either ECMWF or NCEP wind field. Moreover, the relative error of the MPI method is improved by ∼12 percent with respect to the first guess spectrum.

Response of Surface Ocean Conditions to Typhoon Rammasun (2014)

ABSTRACT Xu, Y. and Wang, Z., 2017. Response of surface ocean conditions to Typhoon Rammasun (2014). In: Zhi, Y. and Guido Aldana, P.A. (eds.), Sustainability of Water Resources and the Development of Coastal Environments: Select Proceedings from the 2016 International Conference on Water Resource and Environment (WRE2016). Journal of Coastal Research, Special Issue No. 80, pp. 92–97. Coconut Creek (Florida), ISSN 0749-0208. The response of wind and wave field during typhoon Rammasun (2014) is studied in this paper. The circular symmetric typhoon wind model Jelesnianski-II is used in this paper for typhoon wind simulation. Then, our simulated wind and the ECMWF (European Centre for Medium-Range Weather Forecasts) wind were assimilated, as our newly developed reanalysis wind data, to provide wind fields for wave simulation. The new wind data is compared with the observation data, which shows a good agreement. The distributions of wind speed and wind direction are given in this paper. The wave is simulated ...

On mesoscale analysis and ASCAT ambiguity removal

The two‐dimensional variational ambiguity removal (2DVAR) method provides a spatial analysis of the sampled ocean vector winds to resolve the local Advanced Scatterometer (ASCAT) dual wind vector ambiguity. Like other variational meteorological data assimilation systems in numerical weather prediction (NWP), 2DVAR combines ASCAT observations with prior NWP background information, in this case from the European Centre for Medium‐range Weather Forecasts (ECMWF). Although 2DVAR is generally effective, it may select the wrong ambiguity under certain conditions, e.g. when the background mislocates frontal areas or low‐pressure centres, or when it misses convective systems. The relative influence of the ASCAT and ECMWF wind fields in the resulting 2DVAR analysis field can be controlled by adjusting the background error spatial correlation structure, and the background and/or observation error variances. In this article an adaptive 2DVAR approach is proposed to improve ASCAT ambiguity removal, using background error spatial correlations estimated from the autocorrelation of observed scatterometer wind components minus ECMWF forecasts, and using observation and background errors estimated from triple collocation analysis on collocated buoy, ASCAT and ECMWF data. The triple collocations are segregated into several categories according to the ASCAT‐derived parameters that have proven to be effective in detecting the correct position of frontal lines and low‐pressure centres. Verification using a typical cyclone case and collocated ASCAT and buoy winds shows that the 2DVAR analysis as well as the ASCAT ambiguity removal is improved significantly by putting more weight on the ASCAT observations using empirically determined spatial background error structure functions and situation‐dependent observation/background error variances.

Estimation of mean and extreme waves in the East China Seas

Accurately estimating the mean and extreme wave statistics and better understanding their directional and seasonal variations are of great importance in the planning and designing of ocean and coastal engineering works. Due to the lack of long-term wave measurement data, the analysis of extreme waves is often based on the numerical wave hind-casting results. In this study, the wave climate in the East China Seas (including the Bohai Sea, the Yellow Sea and the East China Sea) for the past 35 years (1979–2013) is hind-casted using a third generation wave model – WAMC4 (Cycle 4 version of WAM model). Two sets of reanalysis wind data from NCEP (National Centers for Environmental Prediction, USA) and ECMWF (European Centre for Medium-range Weather Forecasts) are used to drive the wave model to generate the long-term wave climate. The hind-casted waves are then analysed to study the mean and extreme wave statistics in the study area. The results show that the mean wave heights decrease from south to north and from sea to land in general. The extreme wave heights with return periods of 50 and 100 years in the summer and autumn seasons are significantly higher than those in the other two seasons, mainly due to the effect of typhoon events. The mean wave heights in the winter season have the highest values, mainly due to the effect of winter monsoon winds. The comparison of extreme wave statistics from both wind fields with the field measurements at several nearshore wave observation stations shows that the extreme waves generated by the ECMWF winds are better than those generated by the NCEP winds. The comparison also shows the extreme waves in deep waters are better reproduced than those in shallow waters, which is partly attributed to the limitations of the wave model used. The results presented in this paper provide useful insight into the wave climate in the area of the East China Seas, as well as the effect of wind data resolution on the simulation of long-term waves.

ASCAT Wind Quality Control Near Rain

In this paper, anomalous spatial gradients are investigated by an image processing method, known as singularity analysis, which is proposed to complement the current Advanced Scatterometer (ASCAT) quality control (QC) by using the singularity exponent (SE). The quality of ASCAT winds is known to be generally degraded, with increasing values of the inversion residual or maximum-likelihood estimator (MLE). In the current ASCAT Wind Data Processor (AWDP), an MLE-based QC is adopted to filter poor-quality winds, which has proven to be effective in screening artifacts in the ASCAT winds, associated with increased subcell wind variability and other phenomena such as confused sea state. However, some poorly verifying winds, which appear in areas with moist convection, are not screened by the operational QC. The extension of the QC procedure with SEs is investigated, based on a comprehensive analysis of quality-sensitive parameters, using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference, taking into account their spatial and temporal representation. The validation results show that the proposed method indeed effectively removes ASCAT winds in spatially variable conditions. It filters three times as many wind vectors as the operational QC, while preserving verification statistics with local buoys. We find that not the rain itself, but the extreme local wind variability associated with rain appears to generally decrease the consistency between ASCAT, buoy, and ECMWF winds.

Observation of horizontal winds in the middle-atmosphere between 30° S and 55° N during the northern winter 2009–2010

Abstract. Although the links between stratospheric dynamics, climate and weather have been demonstrated, direct observations of stratospheric winds are lacking, in particular at altitudes above 30 km. We report observations of winds between 8 and 0.01 hPa (~35–80 km) from October 2009 to April 2010 by the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the International Space Station. The altitude range covers the region between 35–60 km where previous space-borne wind instruments show a lack of sensitivity. Both zonal and meridional wind components were obtained, though not simultaneously, in the latitude range from 30° S to 55° N and with a single profile precision of 7–9 m s–1 between 8 and 0.6 hPa and better than 20 m s–1 at altitudes above. The vertical resolution is 5–7 km except in the upper part of the retrieval range (10 km at 0.01 hPa). In the region between 1–0.05 hPa, an absolute value of the mean difference < 2 m s–1 is found between SMILES profiles retrieved from different spectroscopic lines and instrumental settings. Good agreement (absolute value of the mean difference of ~2 m s–1) is also found with the European Centre for Medium-Range Weather Forecasts (ECMWF) analysis in most of the stratosphere except for the zonal winds over the equator (difference > 5 m s−1). In the mesosphere, SMILES and ECMWF zonal winds exhibit large differences (> 20 m s–1), especially in the tropics. We illustrate our results by showing daily and monthly zonal wind variations, namely the semi-annual oscillation in the tropics and reversals of the flow direction between 50–55° N during sudden stratospheric warmings. The daily comparison with ECMWF winds reveals that in the beginning of February, a significantly stronger zonal westward flow is measured in the tropics at 2 hPa compared to the flow computed in the analysis (difference of ~20 m s–1). The results show that the comparison between SMILES and ECMWF winds is not only relevant for the quality assessment of the new SMILES winds, but it also provides insights on the quality of the ECMWF winds themselves. Although the instrument was not specifically designed for measuring winds, the results demonstrate that space-borne sub-mm wave radiometers have the potential to provide good quality data for improving the stratospheric winds in atmospheric models.

First middle-atmospheric zonal wind profile measurements with a new ground-based microwave Doppler-spectro-radiometer

Abstract. We report on the wind radiometer WIRA, a new ground-based microwave Doppler-spectro-radiometer specifically designed for the measurement of middle-atmospheric horizontal wind by observing ozone emission spectra at 142.17504 GHz. Currently, wind speeds in five levels between 30 and 79 km can be retrieved which makes WIRA the first instrument able to continuously measure horizontal wind in this altitude range. For an integration time of one day the measurement error on each level lies at around 25 m s−1. With a planned upgrade this value is expected to be reduced by a factor of 2 in the near future. On the altitude levels where our measurement can be compared to wind data from the European Centre for Medium-Range Weather Forecasts (ECMWF) very good agreement in the long-term statistics as well as in short time structures with a duration of a few days has been found. WIRA uses a passive double sideband heterodyne receiver together with a digital Fourier transform spectrometer for the data acquisition. A big advantage of the radiometric approach is that such instruments can also operate under adverse weather conditions and thus provide a continuous time series for the given location. The optics enables the instrument to scan a wide range of azimuth angles including the directions east, west, north, and south for zonal and meridional wind measurements. The design of the radiometer is fairly compact and its calibration does not rely on liquid nitrogen which makes it transportable and suitable for campaign use. WIRA is conceived in a way that it can be operated remotely and does hardly require any maintenance. In the present paper, a description of the instrument is given, and the techniques used for the wind retrieval based on the determination of the Doppler shift of the measured atmospheric ozone emission spectra are outlined. Their reliability was tested using Monte Carlo simulations. Finally, a time series of 11 months of zonal wind measurements over Bern (46°57′ N, 7°26′ E) is presented and compared to ECMWF wind data.

Rain Effects on ASCAT-Retrieved Winds: Toward an Improved Quality Control

The quality of the Ku-band scatterometer-derived winds is known to be degraded by the presence of rain. Little work has been done in characterizing the impact of rain on C-band scatterometer winds, such as those from the Advanced Scatterometer (ASCAT) onboard Metop-A. In this paper, the rain impact on the ASCAT operational level 2 quality control (QC) and retrieved winds is investigated using the European Centre for Medium-range Weather Forecasts (ECMWF) model winds, the Tropical Rainfall Measuring Mission's (TRMM) Microwave Imager (TMI) rain data, and tropical buoy wind and precipitation data as reference. In contrast to Ku-band, it is shown that C-band is much less affected by direct rain effects, such as ocean splash, but effects of increased wind variability appear to dominate ASCAT wind retrieval. ECMWF winds do not well resolve the airflow under rainy conditions. ASCAT winds do but also show artifacts in both the wind speed and wind direction distributions for high rain rates (RRs). The operational QC proves to be effective in screening these artifacts but at the expense of many valuable winds. An image-processing method, known as singularity analysis, is proposed in this paper to complement the current QC, and its potential is illustrated. QC at higher resolution is also expected to result in improved screening of high RRs.

Mapping Surface Oil Extent From the Deepwater Horizon Oil Spill Using ASCAT Backscatter

The 2010 Deepwater Horizon oil spill in the Gulf of Mexico covered a sufficiently large area to be observed by the European Space Agency Advanced Scatterometer (ASCAT) on MetOp-A. In this paper, ASCAT data and numerically computed winds from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used to map the spatial extent of oil on the ocean surface over the duration of the spill event. Surface oil alters the ocean radar scattering properties, resulting in a difference between the measured backscatter and the backscatter that would be measured if oil were not present. Wind scatterometers infer the near-surface wind speed and direction using the wind geophysical model function (GMF) in conjunction with measured radar backscatter. The oil-altered backscatter error propagates through the wind retrieval process to create a difference in ASCAT-inferred winds and actual winds. Numerically computed vector winds from ECMWF are compared against ASCAT-inferred vector winds. The GMF is applied to ECMWF winds to create a predicted backscatter value to compare against ASCAT-measured backscatter. Large differences in wind or backscatter indicate areas of the ocean surface affected by oil. An objective function is developed to choose an appropriate threshold level to flag the oil-contaminated regions. Data from other sensors corroborate the ASCAT oil extent mapping.

Validation and Calibration of ASCAT Using CMOD5.n

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> The Advanced Scatterometer (ASCAT) onboard the Metop-A satellite became operational shortly after launch in 2006, and an absolute calibration using three transponders was achieved in November 2008. In this paper, we describe how the CMOD5.n ocean backscatter geophysical model function (GMF), which was derived using data from previous scatterometers onboard the European Remote Sensing 1 and 2 satellites (ERS-1 and ERS-2), was used to derive backscatter bias correction factors. The purpose is to remove the bias between ASCAT backscatter data and the CMOD5.n GMF output which allows these data to be used in place of ERS data in existing wind processing algorithms. The ASCAT Wind Data Processor, developed at the Royal Netherlands Meteorological Institute (KNMI), applies the bias correction factors to ASCAT data and uses CMOD5.n to retrieve wind vectors in order to produce an operational wind product. This resulted in a stable and high-quality ASCAT wind product since February 2007. We validate this product by comparing it to the European Centre for Medium-range Weather Forecasts (ECMWF) winds and buoy measurements. The bias correction factors indicate that ASCAT data and the GMF differ by roughly 0.3 dB below 55 <formula formulatype="inline"><tex Notation="TeX">$^{\circ}$</tex></formula> and up to 0.8 dB above 55<formula formulatype="inline"> <tex Notation="TeX">$^{\circ}$</tex></formula>. A possible explanation lies in CMOD5.n which has been poorly validated in this incidence angle regime. Validation of ASCAT data using the ocean calibration method confirms this result and also indicates that bias-corrected data are everywhere within 0.3 dB of CMOD5.n. The wind product validation shows an rms error of 1.3 <formula formulatype="inline"><tex Notation="TeX">$\hbox{m} \cdot \hbox{s}^{-1}$</tex></formula> in wind speed and 16 <formula formulatype="inline"><tex Notation="TeX">$^{\circ}$</tex></formula> in wind direction when compared to ECMWF winds. This is better than the results achieved using ERS scatterometer data. Against buoy winds, we find an rms error wind component error of approximately 1.8 <formula formulatype="inline"><tex Notation="TeX">$\hbox{m} \cdot \hbox{s}^{-1}$</tex> </formula>. These results show that the ASCAT wind product is of high quality and satisfies its wind component accuracy requirement of 2 <formula formulatype="inline"><tex Notation="TeX">$\hbox{m} \cdot \hbox{s}^{-1}$</tex></formula>. </para>

Spatial and Temporal Behavior of Microwave Backscatter Directional Modulation Over the Saharan Ergs

Radar backscatter (sigmadeg) from ergs is modulated with view direction [incidence (thetas) and azimuth (phi) angles], where the modulation characteristics reflect the surface geometry. sigmadeg also varies spatially and reflects the spatial inhomogeneity of the sand surface. We use sigmadeg measurements at different thetas and phi angles from the NASA, European Remote Sensing satellite, and SeaWinds scatterometers to understand the relationship between wind and erg bedforms. A model incorporating the sigmadeg phi-modulation and spatial inhomogeneity is proposed. Surface slope variations are related to the sigmadeg spatial inhomogeneity. We compare the backscatter model results with numerically predicted wind direction data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) over the erg surfaces. We use the maxima of the phi-modulation at thetas=33deg to infer the orientation of the dominant slip-sides on the sand surface. These orientations are consistent with the ECMWF wind directions spatially and temporally

Scatterometer-Based Assessment of 10-m Wind Analyses from the Operational ECMWF and NCEP Numerical Weather Prediction Models

Abstract Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively. The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models. There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

Long-term lidar observations of polar stratospheric clouds at Esrange in northern Sweden

Polar stratospheric clouds (PSCs) play a key role in the depletion of polar ozone. The type of cloud and the length of time for which it exists are crucial for the amount of chlorine activation during the polar night. The Bonn University backscatter lidar at Esrange in northern Sweden (68°N, 21°E) is well equipped for long-term observation and classification of these clouds. Nearly continuous measurements through several winters are rare, in particular in wave-active regions like Esrange. Lidar measurements have been performed each winter since 1997—a total of more than 2000 h of observation time has been accumulated, including more than 300 h with PSCs. Analysis of this unique data set leads to a classification scheme with four different scattering characteristics which can be associated with four different cloud types: (1) supercooled ternary solution (STS), (2) nitric acid trihydrate (NAT), (3) ice and (4) mixtures of solid and liquid particles. The analysis of observations over seven winters gives an overview of the frequency of appearance of the individual PSC types. Most of the clouds contain layers of different PSC types. The analysis of these layers shows STS and mixed clouds to occur most frequently, with more than 39% and 37% of all PSC observations, respectively, whereas NAT (15%) and ice clouds (9%) are seen only rarely. The lidar is located close to the Scandinavian mountain ridge, which is a major source of orographically induced gravity waves that can rapidly cool the atmosphere below cloud formation temperatures. Comparing the individual existence temperature of the observed cloud type with the synoptic-scale temperature provided by the European Centre for Medium-range Weather Forecasts (ECMWF) gives information on the frequency of synoptically and wave-induced PSCs. Further, the analysis of ECMWF temperature and wind data gives an estimate of the transparency of the atmosphere to stationary gravity waves. During more than 80% of all PSC observations in synoptic-scale temperatures which were too warm the atmosphere was transparent for stationary gravity waves. Our measurements show that dynamically induced cooling is crucial for the existence of PSCs above Esrange. In particular ice PSCs are observed only in situations where there are gravity waves.

The influence of data assimilation on the age of air calculated with a global chemistry‐transport model using ECMWF wind fields

The age of air is a useful integrated quantity that represents transport processes. Most atmospheric models underestimate the age of air, especially when wind fields from data assimilation systems are used. Nonetheless data assimilation is necessary to provide realistic winds. The European Centre for Medium‐Range Weather Forecasts (ECMWF) uses different assimilation procedures with various forecast periods, where forecasting allows the model to recover from the non‐physical adjustments of the assimilation procedure. In this study we examine the impact of different assimilation procedures and forecast periods on ECWMF wind fields by performing simulations of the age of air. For all data sets the age of air is too young, and for the 45‐years re‐analysis data (ERA‐40) it is even anomalously young, which has to be attributed to the data assimilation procedure used in ERA‐40. Extending the forecast period partially remedies the low ages of air, but to an unsatisfactory degree.

A Practical, Hybrid Model for Predicting the Trajectories of Near-Surface Ocean Drifters

A hybrid Lagrangian–Eulerian model for calculating the trajectories of near-surface drifters in the ocean is developed in this study. The model employs climatological, near-surface currents computed from a spline fit of all available drifter velocities observed in the Pacific Ocean between 1988 and 1996. It also incorporates contemporaneous wind fields calculated by either the U.S. Navy [the Navy Operational Global Atmospheric Prediction System (NOGAPS)] or the European Centre for Medium-Range Weather Forecasts (ECMWF). The model was applied to 30 drifters launched in the tropical Pacific Ocean in three clusters during 1990, 1993, and 1994. For 10-day-long trajectories the forecasts computed by the hybrid model are up to 164% closer to the observed trajectories compared to the trajectories obtained by advecting the drifters with the climatological currents only. The best-fitting trajectories are computed with ECMWF fields that have a temporal resolution of 6 h. The average improvement over all 30 drifters of the hybrid model trajectories relative to advection by the climatological currents is 21%, but in the open-ocean clusters (1990 and 1993) the improvement is 42% with ECMWF winds (34% with NOGAPS winds). This difference between the open-ocean and coastal clusters is due to the fact that the model does not presently include the effect of horizontal boundaries (coastlines). For zero initial velocities the trajectories generated by the hybrid model are significantly more accurate than advection by the mean currents on time scales of 5–15 days. For 3-day-long trajectories significant improvement is achieved if the drifter's initial velocity is known, in which case the model-generated trajectories are about 2 times closer to observations than persistence. The model's success in providing more accurate trajectories indicates that drifters' motion can deviate significantly from the climatological current and that the instantaneous winds are more relevant to their trajectories than the mean surface currents. It also demonstrates the importance of an accurate initial velocity, especially for short trajectories on the order of 1–3 days. A possible interpretation of these results is that winds affect drifter motion more than the water velocity since drifters do not obey continuity.

Nonlinear Effects of Variable Winds on Ocean Stress Climatologies

Abstract Variability in surface winds at subseasonal time scales can affect the estimates of the mean and seasonal stress over the ocean, owing to the nonlinear dependence of stress on wind speed. A global ocean wind product that merges the European Centre for Medium-Range Weather Forecasts (ECMWF) fields with satellite and in situ data is used to assess the nonlinear effects of wind variability, in particular synoptic signals (taken here to include all periods of <6 days), on estimates of the mean and the seasonal cycle in zonal and meridional stress. Climatologies based on the period March 1988–February 1999 are considered. Synoptic effects are most pronounced at mid- and high latitudes, where they can amount up to 20% of the mean or seasonal stress. Uncertainties in stress values associated with synoptic wind errors are assessed by comparing estimates from merged-data winds to those from original ECMWF winds. Differences in synoptic winds contribute noticeably to the stress differences. Outside the Tro...

The accuracy of stratospheric analyses in the northern hemisphere inferred from long‐duration balloon flights

AbstractIn January and February 2002, six superpressure balloons (SPBs) were launched from Kiruna, Sweden (69°N,21°E). The balloons drifted in the polar lower stratosphere for up to 45 days. Temperature, wind and altitude observations were collected every 15 minutes during the flights. This dataset serves as a reference to assess the accuracy of the European Centre for Medium‐Range Weather Forecasts (ECMWF) analyses and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalyses in the lower stratosphere during the 2002 winter. In particular, it is found that both models succeeded in simulating the large temperature fluctuations observed during the SPB flights, which were induced by a geographical shift between the stratospheric vortex and the low temperature area. Furthermore, the model biases are found to be small for all variables (temperature, wind, height). Nevertheless, it is found that NCEP/NCAR reanalyses tend to be slightly warmer (0.8 K) than the observations, while the converse is true for ECMWF analyses (−0.3 K). The observations also exhibit small‐scale fluctuations that are presumably produced by (inertia‐)gravity waves and which induce some scatter in the analysis/observation comparisons. Finally, trajectory comparisons are performed. It is found that trajectories built with ECMWF winds are more accurate than those built with NCEP/NCAR winds. With both models, the trajectory errors increase with time upto about 15 days and then tend to fluctuate. Typical errors after 15 days are 1000 ± 1200 km for ECMWF and 2300 ± 1300 km for NCEP trajectories. Copyright © 2004 Royal Meteorological Society

A Model of the Near-Surface Circulation of the Santa Barbara Channel: Comparison with Observations and Dynamical Interpretations

Abstract Previous studies indicate the importance of wind, wind curl, and density differences in driving the near-surface circulation in the Santa Barbara Channel (SBC). Here model sensitivity experiments and dynamical analyses of the near-surface currents in the SBC are presented. Various approximations of the wind—from coarse-resolution European Centre for Medium-Range Weather Forecasts (ECMWF) archives to a high-resolution dataset that incorporates buoy, oil-platform, and land-based wind stations—are used. In some experiments, observed temperatures at 10 moorings are also assimilated into the model. Model solutions are sensitive to channel-scale [O(10 km)] wind distribution. Modeled currents forced by the ECMWF wind yield poor results when compared with observations. The simulation using the high-resolution wind (without assimilation) captures the observed spatial and seasonal patterns of the circulation, though the intensity is underestimated. With assimilation, the intensity is increased. In particul...