Global Ocean Surface Wind Research Articles

WSPTGAN for Global Ocean Surface Wind Speed Generation With High Temporal Resolution and Spatial Coverage

WSPTGAN for Global Ocean Surface Wind Speed Generation With High Temporal Resolution and Spatial Coverage

Estimation of global ocean surface winds blending reanalysis, satellite and buoy datasets

Data derived from reanalysis projects and satellites are often used to increase the ocean surface wind data's spatiotemporal resolution available at buoy stations. All available datasets present variable accuracies and different performances on wind estimations due to: (i) the proximity to the coast; (ii) the wind strength; (iii) the geographical location; and (iv) the assumption of near-neutral atmospheric stability. An assessment of the effect of these aspects on estimating winds was carried out. To partly reduce the observed inaccuracies, a methodology is proposed to correct available spatiotemporal gridded datasets of ocean surface winds. It resulted in a hybrid model blending reanalysis and satellite, as usual, but additionally enriched with information from buoy measurements. This model shows potential to correct reanalysis datasets even for cases when reanalysis outperforms satellite wind accuracy. This model led to accuracy improvements ranging from 9% to 161% compared with wind estimations from newer and older generations of reanalysis models. The blended model was calibrated and validated between 2011 and 2016 using observations at 155 ocean-moored buoys located in 6 oceanic regions. The estimated wind fields presented the highest accuracy for mid-to-high winds (5–20 m/s) with an overall Mean Absolute Error (MAE) of 0.96 m/s and 11.6° for wind speed and wind direction, respectively.

Assimilation of GNSS reflectometry delay‐Doppler maps with a two‐dimensional variational analysis of global ocean surface winds

AbstractDirect remote‐sensing observations (e.g., radar backscatter, radiometer brightness temperature, or radio occultation bending angle) are often more effective for use in data assimilation (DA) than the corresponding geophysical retrievals (e.g., ocean surface winds, soil moisture, or atmospheric water vapor). In the particular case of Global Navigation Satellite System Reflectometry (GNSS‐R), the lower‐level delay‐Doppler map (DDM) observable shows a complicated relationship with the ocean surface wind field. Prior studies have demonstrated DA using GNSS‐R wind retrievals inferred from DDMs. The complexity of the DDM dependence on winds, however, suggests that the alternative approach of ingesting DDM observables directly into DA systems, without performing a wind retrieval, may be beneficial. We demonstrate assimilation of DDM observables from the NASA Cyclone Global Navigation Satellite System (CYGNSS) mission into global ocean surface wind analyses using a two‐dimensional variational analysis method. Bias correction and quality‐control methods are described. Several models for the required observation‐error covariance matrix are developed and evaluated, with the conclusion that a diagonal matrix performs as well as a fully populated matrix empirically tuned to a large ensemble of CYGNSS observation data. The 10‐m surface winds from the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational forecast are used as the background (i.e., prior in the variational analysis). Results are compared with independent scatterometer (the advanced scatterometer (ASCAT), the oceansat‐2 Scatterometer (OSCAT)) winds. For one month (June 2017) of data, the root‐mean‐square difference (RMSD) was reduced from 1.17 to 1.07 m·s−1 and bias from −0.14 to −0.08 m·s−1 for the wind speed at the specular point. Within a 150‐km wide swath along the specular point track, the RMSD was reduced from 1.20 to 1.13 m·s−1. These RMSD and bias statistics are smaller than other CYGNSS wind products available at this time.

On the Surface Current Measurement Capabilities of Spaceborne Doppler Scatterometry

AbstractWide‐swath spaceborne Doppler scatterometry is a promising technique for the simultaneous measurement of global ocean surface winds and currents. The technique has been proven from airborne platforms, and here we use the lessons learned to examine a range of implications for a spaceborne system. We use a Doppler scatterometer simulator and a state‐of‐the‐art global circulation model to generate surface current measurements and their random errors. We find that a feasible instrument could measure 5 km gridded surface currents with typical random errors between 10 and 25 cm/s. For higher wind speeds, the random error in surface current decreases logarithmically. This level of accuracy allows for the computation of surface current relative vorticity and horizontal divergence with typical wavelength resolutions of 15–30 and 25–60 km, respectively. Unlike previous studies, we find that these measurements do not require multiday averaging, opening up new avenues for monitoring global ocean circulation.

Integrated kinetic energy of Atlantic tropical cyclones in a global ocean surface wind analysis

The integrated kinetic energy (IKE) of a tropical cyclone (TC), a volume integration of the surface winds around the centre of the TC, is computed from a comprehensive surface wind (National Aeronautics and Space Administration’s (NASA) cross‐calibrated multi‐platform [CCMP]) analysis available over the global oceans to verify against IKE from wind radii estimates of extended best‐track data maintained by NOAA for the North Atlantic TCs. It is shown that CCMP surface wind analysis severely underestimates IKE largely from not resolving hurricane force winds for majority of the Atlantic TCs, under sampling short‐lived and small‐sized TCs. The seasonal cycle of the North Atlantic TC IKE also verifies poorly in the CCMP analysis. In this article we introduce proxy IKE (PIKE) based on the kinetic energy of the winds at the radius of the last closed isobar (ROCI), which shows promise for a wide range of TC sizes including the smaller‐sized TCs unresolved in the CCMP data set.

Use of an optimum interpolation method to construct a high-resolution global ocean surface vector wind dataset from active scatterometers and passive radiometers

ABSTRACTThis study presents a new 0.25° gridded 6-hourly global ocean surface wind vector dataset from 2000 to 2015 produced by blending satellite wind retrievals from five active scatterometers (QuikSCAT, ASCAT-A, ASCAT-B, OSCAT, and HY-2A), nine passive radiometers (four SSM/I sensors, two SSMIS sensors, TMI, AMSR-E, and AMSR2) and one polarimetric radiometer (WindSat) with reanalysis from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) employing an optimum interpolation method (OIM). The accuracy of this wind product is determined through various comparisons with buoy measurements, NCEP/NCAR reanalysis and the cross-calibrated multi-platform (CCMP) winds. The comparisons indicate that OIM winds agree well with buoys, showing a root-mean-squared difference of 1.32 m s−1 for wind speed and 24.73° for wind direction over 0–30 m s−1 wind speed range. And the quality of OIM winds is improved significantly relative to NCEP/NCAR reanalysis and can be comparable with CCMP winds. Furthermore, OIM winds can reveal abundant small-scale features that are not visible in reanalysis data. In addition, the wind speed and direction retrievals of most satellites are proved to play an important role in generating the high-quality product, but the procedure for including HY-2A winds and WindSat wind directions should be further explored.

Ocean Surface Wind Retrieval Using SMAP L-Band SAR

The soil moisture active passive (SMAP) L-band synthetic aperture radar (SAR) could continuously provide global km scale ocean surface wind observations, which had a better coverage than other SARs and a higher spatial resolution than scatterometers. This paper investigates SMAP normalized radar cross sections (NRCS) dependence on wind vectors using more than 5 million matchups consisting of Defense Meteorological Satellite Program F17 Special Sensor Microwave Image/Sounder wind speed, National Center for Environmental Predication wind direction and SMAP L-band NRCS. An L-band geophysical model function (GMF) is proposed for SMAP wind retrieval on the basis of these matchups, and it indicates wind speed and direction dependence of SMAP L-band NRCS for about 40° incidence angle and 0–25 m/s wind speed range in both HH and VV polarization. The wind speed dependence increases rapidly with wind speed, and HH-polarized one is greater than VV polarization. The upwind–downwind difference for HH polarization is greater than that for VV polarization. A negative upwind–crosswind asymmetry occurs for HH- and VV-polarized backscatter at lower wind speeds. The retrieved SMAP wind speed using the proposed GMF is validated by using National Data Buoy Center buoy winds. The root mean square differences and biases are 1.77 and 0.19 m/s, respectively. The accuracies of SMAP wind speeds at 0–10 m/s range are better than those at higher wind speed range. In addition, SMAP wind speeds in upwind and downwind directions are relatively more accurate than those in crosswind directions.

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m·s−1, respectively. The corresponding RMSEs of the wind direction were 46.63°, 43.11°, 39.93°, and 47.47°, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m·s−1 and 39.68° for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

Rain Identification in ASCAT Winds Using Singularity Analysis

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

Pattern forced geophysical vector field segmentation based on Clifford FFT

Vector field segmentation is gaining increasing importance in geophysics research. Existing vector field segmentation methods usually can only handle the statistical characteristics of the original data. It is hard to integrate the patterns forced by certain geophysical phenomena. In this paper, a template matching method is firstly constructed on the foundation of the Clifford Fourier Transformation (CFT). The geometric meanings of both inner and outer components can provide more attractive information about the similarities between original vector field and template data. A composed similarity field is constructed based on the coefficients fields. After that, a modified spatial consistency preserving K-Means cluster algorithm is proposed. This algorithm is applied to the similarity fields to extract the template forced spatial distribution pattern. The complete algorithm for the overall processing is given and the experiments of ENSO forced global ocean surface wind segmentation are configured to test our method. The results suggest that the pattern forced segmentation can extract more latent information that cannot be directly measured from the original data. And the spatial distribution of ENSO influence on the surface wind field is clearly given in the segmentation result. All the above suggest that the method we proposed provides powerful and new thoughts and tools for geophysical vector field data analysis.

Comparison of oceanic winds measured by space-borne scatterometers and altimeters

Global ocean surface wind speeds from Quick Scatterometer (QuikSCAT) for 5 years (2005–2009) and from Oceansat-2 Scatterometer (OSCAT) for 1 year (2010) were compared with wind speeds estimated from JASON Altimeters for representative months to investigate the consistency in wind speeds between these sensors. The comparison was carried out through statistical analyses. The spatial window used for comparison with JASON was 0.25° for QuikSCAT and 0.5° for OSCAT, while the temporal window for both QuikSCAT and OSCAT was ±30 min. The results of the inter-comparison indicate that OSCAT wind speeds are almost as consistent with JASON as QuikSCAT wind speeds.

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

AbstractMeasurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA’s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation’s (ISRO’s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)’s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results.

The WindSat spaceborne polarimetric microwave radiometer: sensor description and early orbit performance

The global ocean surface wind vector is a key parameter for short-term weather forecasting, the issuing of timely weather warnings, and the gathering of general climatological data. In addition, it affects a broad range of naval missions, including strategic ship movement and positioning, aircraft carrier operations, aircraft deployment, effective weapons use, underway replenishment, and littoral operations. WindSat is a satellite-based multifrequency polarimetric microwave radiometer developed by the Naval Research Laboratory for the U.S. Navy and the National Polar-orbiting Operational Environmental Satellite System Integrated Program Office. It is designed to demonstrate the capability of polarimetric microwave radiometry to measure the ocean surface wind vector from space. The sensor provides risk reduction for the development of the Conical Microwave Imager Sounder, which is planned to provide wind vector data operationally starting in 2010. WindSat is the primary payload on the Department of Defense Coriolis satellite, which was launched on January 6, 2003. It is in an 840-km circular sun-synchronous orbit. The WindSat payload is performing well and is currently undergoing rigorous calibration and validation to verify mission success.

Monthly mean ocean surface winds observed by NASA Scatterometer (NSCAT)

Winds are the critical factor in determining regional weather patterns and global climate. NASA Scatterometer (NSCAT) aboard Japan's Advanced Earth Observing Satellite (ADEOS, Midori) continually observed the global ocean surface wind speed and direction from September 1996 to June 1997. In the present article, the monthly mean global ocean surface winds observed by NSCAT will be shown. This provides a better description of atmospheric circulation over the global oceans.

On Climatological Monthly Mean Wind Stress and Wind Stress Curl Fields over the World Ocean

Abstract Using a version of the global surface marine observation historical data set, a new 1° spatial resolution global ocean surface wind stress climatology has been evaluated using the Large and Pond surface drag coefficient formulation. These new results are compared, after spatial smoothing, with those of Hellerman and Rosenstein, who used a different drag coefficient form. It is found that the new stresses are almost everywhere smaller than those of Hellerman and Rosenstein, often by 20%–30%, which is greater than the formal error estimates from their calculations. The stress differences show large-scale spatial structure, as would he expected given the spatial variation of the surface stability parameter and the known different wind variability regions. Basin zonally averaged Ekman transports are computed to provide perspective on the significance of the stress differences; annual mean differences can exceed 10 Sv (Sv = 106 m3 s−1) equatorward of 20° lat, but are smaller poleward. Wind stress curl...

Central swath mapping by a future satellite-borne fan-beam microwave scatterometer for inferring global ocean wind fields

The SeaSat-A satellite scatterometer is a microwave sensor designed to provide a capability for mapping the global ocean surface wind speed and direction. Four fan beams whose major axes are oriented at \pm45 and \pm135\deg to the flight vector cover a swath width of 1900 km, but a central region remains that is inadequately mapped. In this paper, two additional fan beams are suggested for a future scatterometer that provide a more complete coverage of the central region.