The impact of atmosphere-ocean-wave coupling on extreme surface wind forecasts

Accurate modelling of air-sea surface exchanges is crucial for reliable extreme surface wind forecasts.&#160; While atmosphere-only weather forecast models represent ocean and wave effects through sea-state independent parametrizations, coupled multi-model systems capture sea-state dynamics by integrating feedbacks between atmosphere, ocean and wave model components.Here, we present the results of studying the sensitivity of extreme surface wind speeds to air-sea exchanges at kilometre scale using coupled and uncoupled configurations of the Met Office's UK Regional Coupled Environmental Prediction (UKC4) system. The case period includes the passage of extra-tropical cyclones Helen, Ali, and Bronagh, which brought maximum gusts of 36 ms-1 over the UK.Compared to the atmosphere-only results, coupling to ocean decreases the domain-average sea surface temperature by up to 0.5 K. Inclusion of coupling to waves decreases the 98th percentile 10-m wind speed by up to 2 ms-1 as young, growing wind waves decrease wind speed by increasing the sea aerodynamic roughness. Impacts on gusts are more modest, with local reductions of up to 1ms -1, due to enhanced boundary-layer turbulence which partially offsets air-sea momentum transfer.Using a new drag parametrization based on the COARE~4.0 scheme, with a cap on the neutral drag coefficient and decrease for wind speeds exceeding 27 ms-1 , the atmosphere-only model achieves equivalent impacts on 10-m wind speeds and gusts as from coupling to waves. Overall, the new drag parametrization achieves the same 20% improvement in forecast 10-m wind skill as coupling to waves, with&#160; the&#160; advantage&#160; of saving the computational cost of the ocean and wave models.&#160;

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The Impact of Atmosphere–Ocean–Wave Coupling on the Near-Surface Wind Speed in Forecasts of Extratropical Cyclones

Boundary-Layer Meteorology ◽

10.1007/s10546-021-00614-4 ◽

2021 ◽

Author(s):

Emanuele S. Gentile ◽

Suzanne L. Gray ◽

Janet F. Barlow ◽

Huw W. Lewis ◽

John M. Edwards

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Model Systems ◽

Sea State ◽

Near Surface ◽

Extreme Surface ◽

Wind Speeds ◽

The Impact

AbstractAccurate modelling of air–sea surface exchanges is crucial for reliable extreme surface wind-speed forecasts. While atmosphere-only weather forecast models represent ocean and wave effects through sea-state independent parametrizations, coupled multi-model systems capture sea-state dynamics by integrating feedbacks between the atmosphere, ocean and wave model components. Here, we investigate the sensitivity of extreme surface wind speeds to air–sea exchanges at the kilometre scale using coupled and uncoupled configurations of the Met Office’s UK Regional Coupled Environmental Prediction system. The case period includes the passage of extra-tropical cyclones Helen, Ali, and Bronagh, which brought maximum gusts of 36 m s$$^{-1}$$ - 1 over the UK. Compared with the atmosphere-only results, coupling to the ocean decreases the domain-average sea-surface temperature by up to 0.5 K. Inclusion of coupling to waves reduce the 98th percentile 10-m wind speed by up to 2 m s$$^{-1}$$ - 1 as young, growing wind waves reduce the wind speed by increasing the sea-surface aerodynamic roughness. Impacts on gusts are more modest, with local reductions of up to 1 m s$$^{-1}$$ - 1 , due to enhanced boundary-layer turbulence which partially offsets air–sea momentum transfer. Using a new drag parametrization based on the Coupled Ocean–Atmosphere Response Experiment 4.0 parametrization, with a cap on the neutral drag coefficient and reduction for wind speeds exceeding 27 m s$$^{-1}$$ - 1 , the atmosphere-only model achieves equivalent impacts on 10-m wind speeds and gusts as from coupling to waves. Overall, the new drag parametrization achieves the same 20% improvement in forecast 10-m wind-speed skill as coupling to waves, with the advantage of saving the computational cost of the ocean and wave models.

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A Novel Sea Surface Roughness Parameterization Based on Wave State and Sea Foam

Journal of Marine Science and Engineering ◽

10.3390/jmse9030246 ◽

2021 ◽

Vol 9 (3) ◽

pp. 246

Author(s):

Difu Sun ◽

Junqiang Song ◽

Xiaoyong Li ◽

Kaijun Ren ◽

Hongze Leng

Keyword(s):

Surface Roughness ◽

Wind Speed ◽

Drag Coefficient ◽

Sea Surface ◽

High Wind Speed ◽

Wave State ◽

Wind Speeds ◽

Sea Surface Roughness ◽

Extreme Wind ◽

The Impact

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

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An Investigation of the Stability Dependence of SST-Induced Vertical Mixing over the Ocean in the Operational Met Office Model

Journal of Climate ◽

10.1175/jcli-d-16-0086.1 ◽

2017 ◽

Vol 30 (1) ◽

pp. 91-107 ◽

Cited By ~ 2

Author(s):

Qingtao Song ◽

Dudley B. Chelton ◽

Steven K. Esbensen ◽

Andrew R. Brown

Keyword(s):

Wind Speed ◽

Weather Prediction ◽

Surface Wind ◽

Vertical Mixing ◽

Surface Wind Speed ◽

Satellite Observations ◽

Sea Surface ◽

Stable Conditions ◽

Wide Range ◽

The Impact

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [Formula: see text] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

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A Wind Speed Retrieval Model for Sentinel-1A EW Mode Cross-Polarization Images

Remote Sensing ◽

10.3390/rs11020153 ◽

2019 ◽

Vol 11 (2) ◽

pp. 153 ◽

Cited By ~ 3

Author(s):

Yuan Gao ◽

Changlong Guan ◽

Jian Sun ◽

Lian Xie

Keyword(s):

Wind Speed ◽

Incidence Angle ◽

Surface Wind ◽

European Space Agency ◽

Sea Surface ◽

Cross Polarization ◽

Retrieval Model ◽

Sar Images ◽

Wind Speeds ◽

Sea Surface Wind

In contrast to co-polarization (VV or HH) synthetic aperture radar (SAR) images, cross-polarization (CP for VH or HV) SAR images can be used to retrieve sea surface wind speeds larger than 20 m/s without knowing the wind directions. In this paper, a new wind speed retrieval model is proposed for European Space Agency (ESA) Sentinel-1A (S-1A) Extra-Wide swath (EW) mode VH-polarized images. Nineteen S-1A images under tropical cyclone condition observed in the 2016 hurricane season and the matching data from the Soil Moisture Active Passive (SMAP) radiometer are collected and divided into two datasets. The relationships between normalized radar cross-section (NRCS), sea surface wind speed, wind direction and radar incidence angle are analyzed for each sub-band, and an empirical retrieval model is presented. To correct the large biases at the center and at the boundaries of each sub-band, a corrected model with an incidence angle factor is proposed. The new model is validated by comparing the wind speeds retrieved from S-1A images with the wind speeds measured by SMAP. The results suggest that the proposed model can be used to retrieve wind speeds up to 35 m/s for sub-bands 1 to 4 and 25 m/s for sub-band 5.

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Characteristics and Applications of the Ground-Based X Band Low Elevation Angle Brightness Temperatures under Low Sea State Based on Measured Data

Remote Sensing ◽

10.3390/rs12111736 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1736

Author(s):

Zhongqing Cao ◽

Lixin Guo ◽

Shifeng Kang ◽

Xianhai Cheng ◽

Qingliang Li ◽

...

Keyword(s):

Wind Speed ◽

Meteorological Data ◽

Surface Wind ◽

Elevation Angle ◽

Surface Wind Speed ◽

Sea Surface ◽

Sea State ◽

Sea Surface Wind Speed ◽

Sea Surface Wind ◽

X Band

In ground-based microwave radiometer remote sensing, low-elevation-angle (−3°~3°) radiation data are often discarded because they are considered to be of little value and are often difficult to model due to the complicated mechanism. Based on the observed X-band horizontal polarization low elevation angle microwave radiation data and the meteorological data at the same time, this study investigated the generation mechanism of low elevation angle brightness temperature (LEATB) and its relationship with meteorological data, i.e., temperature, humidity, and wind speed, under low sea state. As a result, one could find that the LEATB was sensitive to the atmosphere at the elevation angle between 1° to 3°, and a diurnal variation of the LEATB reached up to 10 K. This study also found a linear relationship between the LEATB and sea surface wind speed under low sea state at an elevation range from −3° to 0°, i.e., the brightness temperature decreased as the wind speed increased, which was inconsistent with the observations at the elevation angle from −10° to −5°. The variation of the LEATB difference according to the change in the over-the-horizon detection capability (OTHDC) of the shipborne microwave radar was examined to identify the reason for this phenomenon theoretically. The results showed that the LEATB difference was significantly influenced by a change in the OTHDC. Further, this study examined a remote sensing method to extract the sea surface wind speed data from experimental LEATB data under low sea state. The results demonstrated that the X-band horizontal polarization LEATBs were useful to retrieve the sea surface wind speed data at a reasonable accuracy—the root mean square error of 0.02408 m/s. Overall, this study proved the promising potential of the LEATB data for retrieving temperature profiles, humidity profiles, sea surface winds, and the OTHDC.

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Sea Surface Wind Correction Using HF Ocean Radar and Its Impact on Coastal Wave Prediction

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-16-0249.1 ◽

2017 ◽

Vol 34 (9) ◽

pp. 2001-2020 ◽

Cited By ~ 3

Author(s):

Yukiharu Hisaki

Keyword(s):

Wind Direction ◽

Surface Wind ◽

Hf Radar ◽

Sea Surface ◽

Wave Parameter ◽

Simple Method ◽

Wind Speeds ◽

Wave Prediction ◽

Sea Surface Wind ◽

The Impact

AbstractBoth wind speeds and wind directions are important for predicting wave heights near complex coastal areas, such as small islands, because the fetch is sensitive to the wind direction. High-frequency (HF) radar can be used to estimate sea surface wind directions from first-order scattering. A simple method is proposed to correct sea surface wind vectors from reanalysis data using the wind directions estimated from HF radar. The constraints for wind speed corrections are that the corrections are small and that the corrections of horizontal divergences are small. A simple algorithm for solving the solution that minimizes the weighted sum of the constraints is developed. Another simple method is proposed to correct sea surface wind vectors. The constraints of the method are that corrections of wind vectors and horizontal divergences from the reanalysis wind vectors are small and that the projection of the corrected wind vectors to the direction orthogonal to the HF radar–estimated wind direction is small. The impact of wind correction on wave parameter prediction is large in the area in which the fetch is sensitive to wind direction. The accuracy of the wave prediction is improved by correcting the wind in that area, where correction of wind direction is more important than correction of wind speeds for the improvement. This method could be used for near-real-time wave monitoring by correcting forecast winds using HF radar data.

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Validation of Sea Surface Wind Speeds from Satellite Altimeters and Relation to Sea State Bias

Journal of the Korean earth science society ◽

10.5467/jkess.2018.39.2.139 ◽

2018 ◽

Vol 39 (2) ◽

pp. 139-153 ◽

Cited By ~ 3

Author(s):

Do-Young Choi ◽

◽

Hye-Jin Woo ◽

Kyung-Ae Park ◽

Do-Seong Byun ◽

...

Keyword(s):

Surface Wind ◽

Sea Surface ◽

Sea State ◽

Wind Speeds ◽

Sea Surface Wind ◽

Sea State Bias

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A Comparative Assessment of Surface Wind Speed and Sea Surface Temperature over the Indian Ocean by TMI, MSMR, and ERA-40

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech2021.1 ◽

2007 ◽

Vol 24 (6) ◽

pp. 1131-1142 ◽

Cited By ~ 23

Author(s):

Anant Parekh ◽

Rashmi Sharma ◽

Abhijit Sarkar

Keyword(s):

Wind Speed ◽

Indian Ocean ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Microwave Radiometer ◽

Surface Wind ◽

Surface Wind Speed ◽

Sea Surface ◽

Wind Speeds ◽

Low Wind Speeds

A 2-yr (June 1999–June 2001) observation of ocean surface wind speed (SWS) and sea surface temperature (SST) derived from microwave radiometer measurements made by a multifrequency scanning microwave radiometer (MSMR) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) is compared with direct measurements by Indian Ocean buoys. Also, for the first time SWS and SST values of the same period obtained from 40-yr ECMWF Re-Analysis (ERA-40) have been evaluated with these buoy observations. The SWS and SST are shown to have standard deviations of 1.77 m s−1 and 0.60 K for TMI, 2.30 m s−1 and 2.0 K for MSMR, and 2.59 m s−1 and 0.68 K for ERA-40, respectively. Despite the fact that MSMR has a lower-frequency channel, larger values of bias and standard deviation (STD) are found compared to those of TMI. The performance of SST retrieval during the daytime is found to be better than that at nighttime. The analysis carried out for different seasons has raised an important question as to why one spaceborne instrument (TMI) yields retrievals with similar biases during both pre- and postmonsoon periods and the other (MSMR) yields drastically different results. The large bias at low wind speeds is believed to be due to the poorer sensitivity of microwave emissivity variations at low wind speeds. The extreme SWS case study (cyclonic condition) showed that satellite-retrieved SWS captured the trend and absolute magnitudes as reflected by in situ observations, while the model (ERA-40) failed to do so. This result has direct implications on the real-time application of satellite winds in monitoring extreme weather events.

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Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters

10.20944/preprints202002.0319.v1 ◽

2020 ◽

Author(s):

Haoyu Jiang ◽

Hao Zheng ◽

Lin Mu

Keyword(s):

Surface Wind ◽

Sea Surface ◽

Atmospheric Parameters ◽

Sea Temperature ◽

Wind Speeds ◽

Sea Surface Wind ◽

Wave Heights ◽

Data Source ◽

Significant Wave Heights ◽

The Impact

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are inter-correlated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error of the retrieved U10 was reduced from 1.42 m/s to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.

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On the Impact of Sea State on GNSS-R Polarimetric Observations

10.5194/egusphere-egu2020-21823 ◽

2020 ◽

Author(s):

Mostafa Hoseini ◽

Maximilian Semmling ◽

Erik Rennspiess ◽

Markus Ramatschi ◽

Rüdiger Haas ◽

...

Keyword(s):

Surface Roughness ◽

Tide Gauge ◽

Empirical Relation ◽

Sea Surface ◽

Sea State ◽

Sea Levels ◽

Wind Speeds ◽

Sea Surface Roughness ◽

Different Response ◽

The Impact

We investigate a long-term ground-based GNSS-R dataset to evaluate the effect of sea state on the polarization of the reflected signals. The dataset consists of one-year polarimetric observations recorded at Onsala space observatory in Sweden in 2016 using right- and left-handed circular polarization (RHCP and LHCP) antennas. One up-looking antenna to receive direct signal and two side-looking antennas to collect reflections are installed at about 3 meters above sea level. The data is collocated with the measurements from a nearby tide-gauge and meteorological station.We focus on precise power estimation using a polarimetric processor based on Lomb&#8211;Scargle periodogram at precisely observed sea levels. The processor converts 0.1 Hz coherent in-phase and quadrature correlation sums provided by a reflectometry receiver to power estimates of the direct and reflected signals. The power estimates are reduced to three power ratios, i.e. cross-, co-, and cross to co-polarization. A model, describing the elevation dependent power loss due to sea surface roughness, is then utilized to invert the calculated power ratios to the standard deviation of sea surface height.Analysis of about 14000 events found in the dataset (~40 continuous tracks per day) shows a fair agreement with the wind speeds as an indicator of the sea state. Although an increasing sensitivity to sea state is observed for all the power ratios at elevation angles above 10 degrees, the measurements from the co-polar link seem to be less affected by the surface roughness. The results reveal that the existing model cannot predict the effect of sea surface roughness in a comprehensive way. The different response of RHCP and LHCP observations to roughness is evident, however, the polarization dependence is not covered by the model. The deviations from the model are particularly clear at lowest elevations (<5 deg) where the roughness effect is expected to vanish. The results indicate that roughness also affect observations at lowest elevation angles. In this elevation range the expected dominance of the RHCP component above the LHCP component is not observed.&#160; A different approach is required to model the influence of sea state in GNSS-R. The increasing amount of reflectometry data may allow to retrieve an empirical relation between coherent reflection power and sea state in future investigation.

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