High-Resolution Polar Low Winds Obtained from Unsupervised SAR Wind Retrieval

High-resolution sea surface observations by spaceborne synthetic aperture radar (SAR) instruments are sorely neglected resources for meteorological applications in polar regions. Such radar observations provide information about wind speed and direction based on wind-induced roughness of the sea surface. The increasing coverage of SAR observations in polar regions calls for the development of SAR-specific applications that make use of the full information content of this valuable resource. Here we provide examples of the potential of SAR observations to provide details of the complex, mesoscale wind structure during polar low events, and examine the performance of two current wind retrieval methods. Furthermore, we suggest a new approach towards accurate wind vector retrieval of complex wind fields from SAR observations that does not require a priori wind direction input that the most common retrieval methods are dependent on. This approach has the potential to be particularly beneficial for numerical forecasting of weather systems with strong wind gradients, such as polar lows.

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A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

10.5194/gmd-2018-238 ◽

2018 ◽

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Alexander Kann ◽

Heimo Truhetz

Keyword(s):

High Resolution ◽

Wind Speed ◽

Climate Model ◽

Strong Wind ◽

Wind Fields ◽

Dynamical Modeling ◽

Thermally Induced ◽

Wind Speeds ◽

Error Measures ◽

Wind Events

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with dynamical climate model wind field data from the non-hydrostatic climate model COSMO-CLM. The INCA analysis fields are available at a horizontal grid spacing of 1 km x 1 km, whereas the COSMO model fields are from simulations at a 3 km x 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields at a high resolution grid of 100 m x 100 m, using observations from two dense meteorological station networks: The WegenerNet Feldbach Region (FBR) and its alpine sister network, the WegenerNet Johnsbachtal (JBT). The high-density WegenerNet FBR is located in southeastern Styria, Austria, a region predominated by a hilly terrain and small differences in altitude. The network consists of more than 150 meteorological stations. The WegenerNet JBT contains eleven meteorological stations at elevations ranging from about 600 m to 2200 m in a mountainous region in northern Styria. The wind fields of these different empirical/dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill socres (FSS) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between COSMO and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, COSMO-CLM clearly underperforms in case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes and in general large biases of wind vectors occur between the dynamical (COSMO) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, gridpoint-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions show again a better agreement for the WegenerNet FBR than for the WegenerNet JBT region. In general, the difference between modeled and observed wind directions is smaller for strong wind speed events than for thermally induced ones. A combined examination of all spatial and gridpoint-based error measures shows that COSMO-CLM with its limited horizontal resolution of 3 km x 3 km and hence, a too smoothed orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate that the INCA analysis fields generally overestimate wind speeds in the summit regions. For strong wind speed events the wind speed in the valleys is underestimated by INCA, however. Regarding the WegenerNet diagnostic wind fields, the statistics show decent performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

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Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

10.5194/amt-2018-31 ◽

2018 ◽

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger

Keyword(s):

High Resolution ◽

Wind Field ◽

Climate Models ◽

Meteorological Station ◽

Strong Wind ◽

Climate Data ◽

Wind Fields ◽

Station Network ◽

Weather And Climate ◽

Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real time, recently developed by the authors (Schlager et al., 2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains eleven meteorological stations at elevations from about 600 m to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 minutes from the WegenerNet JBT, mean wind and wind gust fields at 10 m and 50 m height levels with a high spatial resolution of 100 × 100 m and a temporal resolution of 30 minutes. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. A main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance with somewhat better values for the strong wind conditions. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from five-year climate data of the WegenerNet JBT in comparison to nine-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al., (2017)In general, the five-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with and evaluation of wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

Atmospheric Measurement Techniques ◽

10.5194/amt-11-5607-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 5607-5627 ◽

Cited By ~ 1

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger

Keyword(s):

High Resolution ◽

Climate Models ◽

Meteorological Station ◽

Strong Wind ◽

Mountainous Region ◽

Climate Data ◽

Wind Fields ◽

Station Network ◽

Weather And Climate ◽

Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real-time, recently developed by the authors Schlager et al. (2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains 11 meteorological stations at elevations from about 600 to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 min from the WegenerNet JBT, mean wind and wind gust fields at 10 and 50 m height levels with a high spatial resolution of 100 m × 100 m and a temporal resolution of 30 min. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. The main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance. Due to the spatially more homogeneous wind speeds and directions for strong wind events in this mountainous region, the results show somewhat better performance for these events. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from 5-year climate data of the WegenerNet JBT in comparison to 9-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al. (2017). In general, the 5-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with, and evaluation of, wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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DEVELOPMENT OF AN INVERSE ESTIMATION METHOD OF SEA SURFACE DRAG COEFFICIENT UNDER STRONG WIND CONDITIONS

Coastal Engineering Proceedings ◽

10.9753/icce.v32.waves.32 ◽

2011 ◽

Vol 1 (32) ◽

pp. 32 ◽

Cited By ~ 1

Author(s):

Masaki Yokota ◽

Noriaki Hashimoto ◽

Koji Kawaguchi ◽

Hiroyasu Kawai

Keyword(s):

Drag Coefficient ◽

A Priori ◽

Estimation Method ◽

Sea Surface ◽

Observation Data ◽

Strong Wind ◽

High Wind ◽

Surface Drag ◽

Inverse Estimation ◽

The Given

For the purpose of clarifying the mechanism of energy transfer from high wind to waves, the ADWAM, a wave prediction model incorporating the data assimilation method, was modified to deduce the sea surface drag coefficients as its control variables. Validity of the model was verified through the identical twin experiment in deep sea conditions. Also, the behavior of the deduced parameter was examined through several experiments. As a result, it was confirmed that the drag coefficient deduced by the model is accurate enough when the number of the given observation data is sufficient compared with the number of the unknown parameter. It was also confirmed that the accuracy of the deduced coefficient can be improved by adding an a priori condition even if the number of the observation data is insufficient.

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SENSITIVITY OF UPPER OCEAN DYNAMICS IN HIGH-RESOLUTION TROPICAL INDIAN OCEAN MODEL TO DIFFERENT FLUX PARAMETERIZATION: CASE STUDY FOR THE BAY OF BENGAL (BOB)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-839-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 839-847 ◽

Cited By ~ 1

Author(s):

S. K. Mallick ◽

N. Agarwal ◽

R. Sharma ◽

K. V. S. R. Prasad

Keyword(s):

High Resolution ◽

Indian Ocean ◽

Bay Of Bengal ◽

Tropical Indian Ocean ◽

Ocean Dynamics ◽

Sea Surface ◽

Upper Ocean ◽

Wind Component ◽

Strong Wind ◽

Flux Parameterization

Abstract. Simulation experiments using a high-resolution ocean general circulation model (OGCM) of the tropical Indian Ocean (TIO) were carried out to assess the model’s sensitivity to different flux parameterization. The flux formulation proposed by Kara et al. (2000) is used in the control run (CR). One more experiment differing in the bulk fluxes formulation for the computation of momentum, freshwater and heat is carried out. In the first experiment (CR), actual wind is used for the computation of the exchange coefficient in air-sea bulk flux formulation. In the second experiment (E1), model surface current is used in the wind stress formulation to compute the turbulent air-sea fluxes for TIO region. The formulation used in E1 is the same as it is used in CR, instead of actual wind, relative wind component is used in flux formulas. Both experiments are carried out for the period 2014&ndash;2016. The OGCM is forced using the daily fields of winds, radiation and freshwater fluxes obtained from ERA-Interim Reanalysis. In this study, we examine and quantify the performance of the above-mentioned experiments with respect to observations from ARGO, satellite-based sea surface temperature (SST) and sea surface salinity (SSS) for the year 2015. We observe that the upper ocean dynamics is significantly modulated by different flux algorithms. The errors in simulated SST is reduced by &sim;8% to 10% in E1 compared to CR, respectively. The temperature errors in the top 20m depth are reduced by 8% in E1. It is found that this flux formulation using relative winds is effective in accurately simulating the upper ocean dynamics in strong wind regimes of the Bay of Bengal.

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A spatial evaluation of high-resolution wind fields from empirical and dynamical modeling in hilly and mountainous terrain

Geoscientific Model Development ◽

10.5194/gmd-12-2855-2019 ◽

2019 ◽

Vol 12 (7) ◽

pp. 2855-2873

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger ◽

Alexander Kann ◽

Heimo Truhetz

Keyword(s):

High Resolution ◽

Wind Speed ◽

Climate Model ◽

Grid Point ◽

Regional Climate ◽

Small Scale ◽

Strong Wind ◽

Wind Fields ◽

Thermally Induced ◽

Wind Events

Abstract. Empirical high-resolution surface wind fields, automatically generated by a weather diagnostic application, the WegenerNet Wind Product Generator (WPG), were intercompared with wind field analysis data from the Integrated Nowcasting through Comprehensive Analysis (INCA) system and with regional climate model wind field data from the Consortium for Small Scale Modeling Model in Climate Mode (CCLM). The INCA analysis fields are available at a horizontal grid spacing of 1 km × 1 km, whereas the CCLM fields are from simulations at a 3 km × 3 km grid. The WPG, developed by Schlager et al. (2017, 2018), generates diagnostic fields on a high-resolution grid of 100 m × 100 m, using observations from two dense meteorological station networks: the WegenerNet Feldbach Region (FBR), located in a region predominated by a hilly terrain, and its Alpine sister network, the WegenerNet Johnsbachtal (JBT), located in a mountainous region. The wind fields of these different empirical–dynamical modeling approaches were intercompared for thermally induced and strong wind events, using hourly temporal resolutions as supplied by the WPG, with the focus on evaluating spatial differences and displacements between the different datasets. For this comparison, a novel neighborhood-based spatial wind verification methodology based on fractions skill scores (FSSs) is used to estimate the modeling performances. All comparisons show an increasing FSS with increasing neighborhood size. In general, the spatial verification indicates a better statistical agreement for the hilly WegenerNet FBR than for the mountainous WegenerNet JBT. The results for the WegenerNet FBR show a better agreement between INCA and WegenerNet than between CCLM and WegenerNet wind fields, especially for large scales (neighborhoods). In particular, CCLM clearly underperforms in the case of thermally induced wind events. For the JBT region, all spatial comparisons indicate little overlap at small neighborhood sizes, and in general large biases of wind vectors occur between the regional climate model (CCLM) and analysis (INCA) fields and the diagnostic (WegenerNet) reference dataset. Furthermore, grid-point-based error measures were calculated for the same evaluation cases. The statistical agreement, estimated for the vector-mean wind speed and wind directions again show better agreement for the WegenerNet FBR than for the WegenerNet JBT region. A combined examination of all spatial and grid-point-based error measures shows that CCLM with its limited horizontal resolution of 3 km × 3 km, and hence too smoothed an orography, is not able to represent small-scale wind patterns. The results for the JBT region indicate significant biases in the INCA analysis fields, especially for strong wind speed events. Regarding the WegenerNet diagnostic wind fields, the statistics show acceptable performance in the FBR and somewhat overestimated wind speeds for strong wind speed events in the Enns valley of the JBT region.

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Towards 1-Ångstrom-resolution STEM

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100150812 ◽

1993 ◽

Vol 51 ◽

pp. 996-997

Author(s):

H.S. von Harrach ◽

D.E. Jesson ◽

S.J. Pennycook

Keyword(s):

High Resolution ◽

Field Emission ◽

Phase Contrast ◽

Spherical Aberration ◽

A Priori ◽

Dark Field ◽

Image Resolution ◽

Objective Lens ◽

Annular Dark Field ◽

First Results

Phase contrast TEM has been the leading technique for high resolution imaging of materials for many years, whilst STEM has been the principal method for high-resolution microanalysis. However, it was demonstrated many years ago that low angle dark-field STEM imaging is a priori capable of almost 50% higher point resolution than coherent bright-field imaging (i.e. phase contrast TEM or STEM). This advantage was not exploited until Pennycook developed the high-angle annular dark-field (ADF) technique which can provide an incoherent image showing both high image resolution and atomic number contrast.This paper describes the design and first results of a 300kV field-emission STEM (VG Microscopes HB603U) which has improved ADF STEM image resolution towards the 1 angstrom target. The instrument uses a cold field-emission gun, generating a 300 kV beam of up to 1 μA from an 11-stage accelerator. The beam is focussed on to the specimen by two condensers and a condenser-objective lens with a spherical aberration coefficient of 1.0 mm.

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High-resolution Satellite-derived Wind Fields PE 0602435 (035-71)

10.21236/ada634456 ◽

1997 ◽

Author(s):

Jeffrey D. Hawkins

Keyword(s):

High Resolution ◽

Wind Fields

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Matchup Characteristics of Sea Surface Salinity Using a High-Resolution Ocean Model

Remote Sensing ◽

10.3390/rs13152995 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2995

Author(s):

Frederick M. Bingham ◽

Severine Fournier ◽

Susannah Brodnitz ◽

Karly Ulfsax ◽

Hong Zhang

Keyword(s):

High Resolution ◽

Time Window ◽

Single Point ◽

Ocean Model ◽

Sea Surface ◽

Sea Surface Salinity ◽

Surface Salinity ◽

Argo Floats ◽

Statistical Measures ◽

Salinity Difference

Sea surface salinity (SSS) satellite measurements are validated using in situ observations usually made by surfacing Argo floats. Validation statistics are computed using matched values of SSS from satellites and floats. This study explores how the matchup process is done using a high-resolution numerical ocean model, the MITgcm. One year of model output is sampled as if the Aquarius and Soil Moisture Active Passive (SMAP) satellites flew over it and Argo floats popped up into it. Statistical measures of mismatch between satellite and float are computed, RMS difference (RMSD) and bias. The bias is small, less than 0.002 in absolute value, but negative with float values being greater than satellites. RMSD is computed using an “all salinity difference” method that averages level 2 satellite observations within a given time and space window for comparison with Argo floats. RMSD values range from 0.08 to 0.18 depending on the space–time window and the satellite. This range gives an estimate of the representation error inherent in comparing single point Argo floats to area-average satellite values. The study has implications for future SSS satellite missions and the need to specify how errors are computed to gauge the total accuracy of retrieved SSS values.

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