Wind Retrieval Method Research Articles

Assimilation of GBVTD‐retrieved winds from single‐Doppler radar for short‐term forecasting of super typhoon Saomai (0608) at landfall

AbstractA single‐Doppler wind retrieval method called the Ground‐Based Velocity‐Track Display technique (GBVTD) has been developed in recent years to retrieve horizontal circulations of tropical cyclones. The technique is able to retrieve axisymmetric tangential and radial winds, asymmetric tangential winds for wave numbers 1–3, and along‐beam mean winds in tropical cyclones. It has been successfully applied to tropical cyclone monitoring and warning. This study explores, for the first time, the assimilation of GBVTD‐retrieved winds into a tropical cyclone prediction model, and examines its impact relative to that of directly assimilated radial velocity data. super typhoon Saomai (2006), the most intense landfalling typhoon ever recorded in China, is chosen as the test case, and data from the coastal operational radar at Wenzhou, China, are used. The ARPS 3DVAR system is used to assimilate either the radial velocity data directly or the GBVTD‐retrieved winds, at 30 min intervals for 2 hours.The assimilation of the GBVTD‐retrieved winds results in much improved structure and intensity analyses of Saomai compared to those in the Japan Meteorological Agency mesoscale reanalysis and compared to the analysis assimilating radial velocity (Vr) data directly. The ability of the GBVTD method in providing wind information covering the full circle of the inner‐core circulation is the primary reason for its superior performance over direct assimilation of Vr data; for the latter, the azimuthal data coverage is often incomplete. With the improved initial conditions, the subsequent forecasts of typhoon intensity, track and precipitation are also improved. The improvements to both track and intensity predictions persist over a 12‐hour forecast period, which is mostly after landfall. Subjective and quantitative evaluations of the precipitation and circulation patterns show consistent results. A further sensitivity experiment shows that the axisymmetric wind component in the GBVTD retrieval has the dominant impact on the prediction. Copyright © 2011 Royal Meteorological Society

The application of sea-surface wind detection with doppler lidar in Olympic sailing

The mobile incoherent Doppler lidar (MIDL), which was jointly developed by State Key Laboratory of Severe Weather (LaSW) of the Chinese Academy of Meteorological Sciences (CAMS) and Ocean University of China, provided meteorological services during the Olympic sailing events in Qingdao in 2008. In this study, two experiments were performed based on these measurements. First, the capabilities of MIDL detection of sea-surface winds were investigated by comparing its radial velocities with those from a sea buoy. MIDL radial velocity was almost consistent with sea-buoy data; both reflected the changes in wind with time. However, the MIDL data was 0.5 m s−1 lower on average than the sea-buoy data due to differences in detection principle, sample volume, sample interval, spatial and temporal resolution. Second, the wind fields during the Olympic sailing events were calculated using a four-dimensional variation data assimilation (4DVAR) algorithm and were evaluated by comparing them with data from a sea buoy. The results show that the calculations made with the 4DVAR wind retrieval method are able to simulate the fine retrieval of sea-surface wind data—the retrieved wind fields were consistent with those of sea-buoy data. Overall, the correlation coefficient of wind direction was 0.93, and the correlation coefficient of wind speed was 0.70. The distribution of retrieval wind fields was consistent with that of MIDL radial velocity; the root-mean-square error between them had an average of only 1.52 m s−1.

Near-surface wind speed retrieval from space-based, multi-angle imaging of ocean sun glint patterns

The relationship between the MISR multi-angle observations of sun glint pattern width over ocean and the near-surface wind speed is explored. With this relationship, we develop an algorithm that can constrain the standard, wind-driven glint/white cap model that defines the ocean–surface boundary condition in MISR aerosol retrievals. The key geometric factor determining wind retrieval quality is the angular distance between the reflection vector and the closest MISR multi-angle observation — the minimum Sun Glint Angle (SGA). MISR-retrieved winds are within 2.4 m/s of buoy-measured values for minimum SGA below about 15° for the 38 cases studied, and usually better than 1.1 m/s, except one for which the wind speed was extremely low. For minimum SGA above about 15°, MISR angular glint pattern reflectance sampling is not adequate to perform wind retrievals. An ambiguity in the retrieval that can occur for minimum SGA greater than 6° or 7° is resolved by the minimum observed absolute reflectance. We also confirm the high accuracy of the Cox–Munk ocean surface reflectance model, used to analyze the glint pattern dependence on wind speed. The wind-retrieval method could be used in an operational multi-angle aerosol retrieval algorithm to dynamically constrain the ocean surface boundary condition when glint pattern angular sampling is adequate.

Wind resource assessment from C-band SAR

Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. First, wind speeds retrieved from a series of 91 ERS-2 SAR and Envisat ASAR images, at moderate wind speeds (2–15 m s − 1 ), were validated against in situ measurements from an offshore mast in the North Sea. The wind direction input, necessary for SAR wind speed retrievals, was obtained from the meteorological mast and from a local gradient analysis of wind streaks in the SAR images. A wind speed standard deviation of ∼ 1.1 m s − 1 was found when in situ wind directions were used. The use of local gradient wind directions yielded a standard deviation of ∼ 1.3 m s − 1 . Wind speeds retrieved from three geophysical model functions (CMOD-IFR2, CMOD4, and CMOD5) were compared. The best approximation to the in situ measurements of wind speed was found for CMOD-IFR2, despite a bias on the order of − 0.3 m s − 1 . CMOD4 retrievals also underestimated the wind speed, whereas the bias on CMOD5 retrievals was negligible. Then, wind resource assessments were made from the SAR-based wind observations to show how errors in wind speed from the different SAR wind retrievals were reflected in the wind statistics. The mean wind speed, obtained for all of the 91 SAR scenes, was linked closely to the bias of SAR wind retrievals. Agreement to ± 15% of the in situ measurements was found for all the wind retrieval methods tested. The accuracy of power density estimates for the entire data set was evaluated by the standard deviation of SAR wind retrievals relative to the in situ measurements. SAR wind fields retrieved with CMOD-IFR2, using in situ wind direction inputs, exactly yielded the power density predicted from in situ measurements alone. The SAR-based wind resource assessment also corresponded well to predictions from longer time series of in situ measurements. This indicates that a reliable wind resource assessment may be achieved from a series of randomly selected SAR images. The findings presented here could be useful in future wind resource assessment based on SAR images.

Simultaneous wind and rain retrieval using SeaWinds data

The SeaWinds scatterometers onboard the QuikSCAT and the Advanced Earth Observing Satellite 2 measure ocean winds on a global scale via the relationship between the normalized radar backscattering cross section of the ocean and the vector wind. The current wind retrieval method ignores scattering and attenuation of ocean rain, which alter backscatter measurements and corrupt retrieved winds. Using a simple rain backscatter and attenuation model, two methods of improving wind estimation in the presence of rain are evaluated. First, if no suitable prior knowledge of the rain rate is available, a maximum-likelihood estimation technique is used to simultaneously retrieve the wind velocity and rain rate. Second, when a suitable outside estimate of the rain rate is available, wind retrieval is performed by correcting the wind geophysical model function for the known rain via the rain backscatter model. The new retrieval techniques are evaluated via simulation and validation with data from the National Centers for Environmental Prediction and the Tropical Rainfall Measuring Mission Precipitation Radar. The simultaneous wind/rain estimation method yields most accurate winds in the sweet spot of SeaWinds' swath. On the outer-beam edges of the swath, simultaneous wind/rain estimation is not usable. Wind speeds from simultaneous wind/rain retrieval are nearly unbiased for all rain rates and wind speeds, while conventionally retrieved wind speeds become increasingly biased with rain rate. A synoptic example demonstrates that the new method is capable of reducing the rain-induced wind vector error while producing a consistent (yet noisy) estimate of the rain rate.

Toward an optimal inversion method for synthetic aperture radar wind retrieval

In recent years, particular efforts have been made to derive wind fields over the oceans from synthetic aperture radar (SAR) images. In contrast with the scatterometer, the SAR has a higher spatial resolution and therefore has the potential to provide higher resolution wind information. Since there are at least two geophysical parameters (wind speed and wind direction) modulating the single SAR backscatter measurements, the inversion of wind fields from SAR observations has an inherent problem of underdetermination. Moreover, this modulation is highly nonlinear, further complicating the inversion. Lorenc [1986] presented a general statistical approach to solve inversion problems (including underdetermined problems) in meteorological analysis. We propose a SAR wind retrieval method based on this general approach. This simplified method combines the SAR information with some background information coming from high‐resolution limited area model to retrieve the most probable wind vector, assuming that all sources of information contain errors and that these are well characterized. We then evaluate two different SAR wind retrieval methods. The first one is commonly used by the SAR community and is based on a combination of a wind streak detection algorithm (wind direction retrieval) and a C band model inversion (wind speed retrieval). The second one is the new method we propose, based on the general statistical approach. We show the potential problems and limitations of using any of these methods and show how the second method can potentially contribute to a significant improvement in SAR wind retrieval. The new method prepares the ground for the assimilation of SAR data in high‐resolution numerical weather prediction models.

Retrievals of Horizontal Winds from Single-Doppler Clear-Air Data by Methods of Cross Correlation and Variational Analysis

Abstract Four methods for retrieval of the horizontal wind field are described and compared using single-Doppler observations of a sea-breeze front measured during the Convective and Precipitation/Electrification Experiment. The first method examined is the TREC (tracking radar echoes by correlation) technique similar to the one proposed by Tuttle and Foote. Two other methods, similar to TREC, in which wind vectors are estimated by minimizing the difference between successive patterns of reflectivity, are then examined. These methods conceptually link the TREC method and the velocity volume processing (VVP) approach to the variational wind retrieval method described here. The variational formulation uses the conservation of reflectivity and the radial momentum equation as physical constraints and in this way it incorporates the concepts on which TREC and VVP are based. The performance of the methods is compared using the dual-Doppler wind analysis as ground truth. Results show that the variational method ...