scholarly journals Improved ocean wind forcing products

2020 ◽  
Author(s):  
Rianne Giesen ◽  
Ana Trindade ◽  
Marcos Portabella ◽  
Ad Stoffelen
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Ocean Model ◽  
Wind Forcing ◽  
Small Scale ◽  
Wind Fields ◽  
Ocean Surface Wind ◽  
Ocean Wind

<p>The ocean surface wind plays an essential role in the exchange of heat, gases and momentum at the atmosphere-ocean interface. It is therefore crucial to accurately represent this wind forcing in physical ocean model simulations. Scatterometers provide high-resolution ocean surface wind observations, but have limited spatial and temporal coverage. On the other hand, numerical weather prediction (NWP) model wind fields have better coverage in time and space, but do not resolve the small-scale variability in the air-sea fluxes. In addition, Belmonte Rivas and Stoffelen (2019) documented substantial systematic error in global NWP fields on both small and large scales, using scatterometer observations as a reference.</p><p>Trindade et al. (2019) combined the strong points of scatterometer observations and atmospheric model wind fields into ERA*, a new ocean wind forcing product. ERA* uses temporally-averaged differences between geolocated scatterometer wind data and European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis fields to correct for persistent local NWP wind vector biases. Verified against independent observations, ERA* reduced the variance of differences by 20% with respect to the uncorrected NWP fields. As ERA* has a high potential for improving ocean model forcing in the CMEMS Model Forecasting Centre (MFC) products, it is a candidate for a future CMEMS Level 4 (L4) wind product. We present the ongoing work to further improve the ERA* product and invite potential users to discuss their L4 product requirements.</p><p>References:</p><p>Belmonte Rivas, M. and A. Stoffelen (2019): <em>Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT</em>, Ocean Sci., 15, 831–852, doi: 10.5194/os-15-831-2019.</p><p>Trindade, A., M. Portabella, A. Stoffelen, W. Lin and A. Verhoef (2019), <em>ERAstar: A High-Resolution Ocean Forcing Product</em>, IEEE Trans. Geosci. Remote Sens., 1-11, doi: 10.1109/TGRS.2019.2946019.</p>

scholarly journals Wind Features Extracted from Weather Simulations for Wind-Wave Prediction Using High-Resolution Neural Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1257
Author(s):  
Chih-Chiang Wei
Keyword(s):  
High Resolution ◽  
Spatial Data ◽  
Prediction Models ◽  
Wind Wave ◽  
Weather Prediction ◽  
Time Series Prediction ◽  
Surface Wind ◽  
Wind Fields ◽  
Forecast Horizon ◽  
Wave Prediction

Nearshore wave forecasting is susceptible to changes in regional wind fields and environments. However, surface wind field changes are difficult to determine due to the lack of in situ observational data. Therefore, accurate wind and coastal wave forecasts during typhoon periods are necessary. The purpose of this study is to develop artificial intelligence (AI)-based techniques for forecasting wind–wave processes near coastal areas during typhoons. The proposed integrated models employ combined a numerical weather prediction (NWP) model and AI techniques, namely numerical (NUM)-AI-based wind–wave prediction models. This hybrid model comprising VGGNNet and High-Resolution Network (HRNet) was integrated with recurrent-based gated recurrent unit (GRU). Termed mVHR_GRU, this model was constructed using a convolutional layer for extracting features from spatial images with high-to-low resolution and a recurrent GRU model for time series prediction. To investigate the potential of mVHR_GRU for wind–wave prediction, VGGNet, HRNet, and Two-Step Wind-Wave Prediction (TSWP) were selected as benchmark models. The coastal waters in northeast Taiwan were the study area. The length of the forecast horizon was from 1 to 6 h. The mVHR_GRU model outperformed the HR_GRU, VGGNet, and TSWP models according to the error indicators. The coefficient of mVHR_GRU efficiency improved by 13% to 18% and by 13% to 15% at the Longdong and Guishandao buoys, respectively. In addition, in a comparison of the NUM–AI-based model and a numerical model simulating waves nearshore (SWAN), the SWAN model generated greater errors than the NUM–AI-based model. The results of the NUM–AI-based wind–wave prediction model were in favorable accordance with the observed results, indicating the feasibility of the established model in processing spatial data.

scholarly journals Evaluation of Spaceborne GNSS-R Retrieved Ocean Surface Wind Speed with Multiple Datasets

2019 ◽  
Vol 11 (23) ◽  
pp. 2747 ◽  
Author(s):  
Zhounan Dong ◽  
Shuanggen Jin
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Navigation Satellite ◽  
Ocean Surface Wind ◽  
Global Navigation ◽  
Global Navigation Satellite

Spaceborne Global Navigation Satellite Systems-Reflectometry (GNSS-R) can estimate the geophysical parameters by receiving Earth’s surface reflected signals. The CYclone Global Navigation Satellite System (CYGNSS) mission with eight microsatellites launched by NASA in December 2016, which provides an unprecedented opportunity to rapidly acquire ocean surface wind speed globally. In this paper, a refined spaceborne GNSS-R sea surface wind speed retrieval algorithm is presented and validated with the ground surface reference wind speed from numerical weather prediction (NWP) and cross-calibrated multi-platform ocean surface wind vector analysis product (CCMP), respectively. The results show that when the wind speed was less than 20 m/s, the RMS of the GNSS-R retrieved wind could achieve 1.84 m/s in the case where the NWP winds were used as the ground truth winds, while the result was better than the NWP-based retrieved wind speed with an RMS of 1.68 m/s when the CCMP winds were used. The two sets of inversion results were further evaluated by the buoy winds, and the uncertainties from the NWP-derived and CCMP-derived model prediction wind speed were 1.91 m/s and 1.87 m/s, respectively. The accuracy of inversed wind speeds for different GNSS pseudo-random noise (PRN) satellites and types was also analyzed and presented, which showed similar for different PRN satellites and different types of satellites.

scholarly journals Decadal regime shift linkage between global marine fish landings and atmospheric planetary wave forcing

2015 ◽  
Vol 6 (1) ◽  
pp. 125-146 ◽  
Author(s):  
A. M. Powell ◽  
J. Xu
Keyword(s):  
Wind Stress ◽  
Planetary Wave ◽  
Surface Wind ◽  
Ocean Surface ◽  
Regime Shifts ◽  
Planetary Waves ◽  
Marine Life ◽  
Surface Wind Stress ◽  
Ocean Surface Wind ◽  
Ocean Wind

Abstract. This investigation focuses on a global forcing mechanism for decadal regime shifts and their subsequent impacts. The proposed global forcing mechanism is that the global atmospheric planetary waves can lead to changes in the global surface air–sea conditions and subsequently fishery changes. In this study, the five decadal regime shifts (1956–1957, 1964–1965, 1977–1978, 1988–1989, and 1998–1999) in the most recent 59-year period (1950–2008) have been identified based on Student t tests and their association with global marine ecosystem change has been discussed. Changes in the three major oceanic (Pacific, Atlantic, and Indian) ecosystems will be explored with the goal of demonstrating the linkage between stratospheric planetary waves and the ocean surface forcing that leads to fisheries impacts. The global forcing mechanism is described with a top-down approach to help the multidisciplinary audience follow the analysis. Following previous work, this analysis addresses how changes in the atmospheric planetary waves may influence the vertical wind structure, surface wind stress, and their connection with the global ocean ecosystems based on a coupling of the atmospheric regime shifts with the decadal regime shifts determined from marine life changes. The multiple decadal regime shifts related to changes in marine life are discussed using the United Nations Food and Agriculture Organization's (FAO) global fish capture data (catch/stock). Analyses are performed to demonstrate that examining the interactions between the atmosphere, ocean, and fisheries is a plausible approach to explaining decadal climate change in the global marine ecosystems and its impacts. The results show a consistent mechanism, ocean wind stress, responsible for marine shifts in the three major ocean basins. Changes in the planetary wave pattern affect the ocean wind stress patterns. A change in the ocean surface wind pattern from longwave (relatively smooth and less complex) to shorter-wave (more convoluted and more complex) ocean surface wind stress creates changes in global marine fisheries.

scholarly journals Decadal regime shift linkage between global marine fish landings and atmospheric planetary wave forcing

2014 ◽  
Vol 5 (2) ◽  
pp. 945-989
Author(s):  
A. M. Powell ◽  
J. Xu
Keyword(s):  
Wind Stress ◽  
Planetary Wave ◽  
Surface Wind ◽  
Ocean Surface ◽  
Regime Shifts ◽  
Planetary Waves ◽  
Marine Life ◽  
Surface Wind Stress ◽  
Ocean Surface Wind ◽  
Ocean Wind

Abstract. This investigation focuses on a global forcing mechanism for decadal regime shifts and their subsequent impacts. The proposed global forcing mechanism is the global atmospheric planetary waves that can lead to changes in the global surface air–sea conditions and subsequently fishery changes. In this study, the five decadal regime shifts (1956–1957, 1964–1965, 1977–1978, 1988–1989, and 1998–1999) in the recent 59 years (1950–2008) have been identified based on student t tests and their association with global marine ecosystem change has been discussed. Changes in the three major oceanic (Pacific, Atlantic and Indian) ecosystems will be explored with the goal of demonstrating the linkage between stratospheric planetary waves and the ocean surface forcing that leads to fisheries impacts. Due to the multidisciplinary audience, the global forcing mechanism is described from a top-down approach to help the multidisciplinary audience follow the analysis. Following previous work, this analysis addresses how changes in the atmospheric planetary waves may influence the vertical wind structure, surface wind stress, and their connection with the global ocean ecosystems based on a coupling of the atmospheric regime shifts with the decadal regime shifts determined from marine life changes. The multiple decadal regime shifts related to changes in marine life are discussed using the United Nations Food and Agriculture Organization's (FAO) global fish capture data (catch/stock). Analyses are performed to demonstrate the interactions between the atmosphere, ocean, and fisheries are a plausible approach to explaining decadal climate change in the global marine ecosystems and its impacts. The results show a consistent mechanism, ocean wind stress, responsible for marine shifts in the three major ocean basins. Changes in the planetary wave pattern affect the ocean wind stress patterns. A change in the ocean surface wind pattern from long wave (relatively smooth and less complex) to shorter wave (more convoluted and more complex) ocean surface wind stress creates changes in the ocean marine fisheries.

scholarly journals Higher Ocean Surface Wind Speeds During Marine Cold Air Outbreaks

2017 ◽  
Author(s):  
Erik W. Kolstad
Keyword(s):  
Large Scale ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Surface Wind ◽  
Ocean Surface ◽  
Strong Wind ◽  
Cold Air ◽  
Wind Speeds ◽  
Ocean Surface Wind ◽  
Cold Air Outbreaks

Marine cold air outbreaks (MCAOs) are large-scale phenomena in which cold air masses are advected over open ocean. It is well-known that these events are linked to the formation of polar lows and other mesoscale phenomena associated with high wind speeds, and that they therefore in some cases represent a hazard to maritime activities. However, it is still unknown whether MCAOs are generally conducive to higher wind speeds than normal. Here this is investigated by comparing the behaviour of ocean surface wind speeds during MCAOs in three atmospheric reanalysis products with different horizontal grid spacings, along with case studies using a convection-permitting numerical weather prediction model. The study regions are the Labrador Sea and the Greenland–Iceland–Norwegian (GIN) Seas, where MCAOs have been shown to be important for air–sea interaction and deep water formation. The main findings are: 1) Wind speeds during the most extreme MCAO events are stronger than normal and higher than wind speeds during less severe events; 2) The peak times of MCAO usually occur when baroclinic waves pass over the regions; and 3) Reanalyses with grid spacings of more than 50 km appear to underestimate winds driven by the large ocean–atmosphere energy fluxes during MCAOs. It is also shown that while the strong wind episodes during MCAOs generally last for just a few days, MCAOs can persist for up to 50 days. These findings demonstrate that it would be worthwhile to forecast MCAOs, and that it might be possible to do this beyond the standard weather forecasting range of up to 10 days.

Ocean surface wind fields estimated from satellite active and passive microwave instruments

1999 ◽  
Vol 37 (5) ◽  
pp. 2469-2486 ◽  
Author(s):  
A. Bentamy ◽  
P. Queffeulou ◽  
Y. Quilfen ◽  
K. Katsaros
Keyword(s):  
Surface Wind ◽  
Ocean Surface ◽  
Passive Microwave ◽  
Wind Fields ◽  
Ocean Surface Wind
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