A New Hurricane Wind Direction Retrieval Method for SAR Images without Hurricane Eye

2018 ◽  
Vol 35 (11) ◽  
pp. 2229-2239 ◽  
Author(s):  
Yuan Gao ◽  
Changlong Guan ◽  
Jian Sun ◽  
Lian Xie
Keyword(s):  
Wind Direction ◽  
Microwave Radiometer ◽  
Estimation Method ◽  
Band Model ◽  
Sar Images ◽  
Wind Speeds ◽  
Research Division ◽  
External Data ◽  
Hurricane Wind ◽  
Good Potential

AbstractThis study presents a new approach for hurricane wind direction retrieval utilizing rainband streaks contained in synthetic aperture radar (SAR) images without hurricane eye information, based on the hurricane inflow angle. To calculate the wind direction field, a method for estimating the location of the hurricane center is given. In this paper, four Sentinel-1A (S-1A) images with a hurricane eye are used to clarify the center estimation method. Three S-1A SAR images without a hurricane eye are studied to evaluate the accuracy of the new method. The estimated locations of hurricane centers show good agreement with hurricane track data provided by the National Oceanic and Atmospheric Administration (NOAA)’s Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Research Division (HRD), HurricaneCity, and the National Institute of Informatics (NII). To validate the estimated wind directions, the NOAA HRD dropwindsonde observations for Tropical Storm Karl are collected and compared. The wind directions retrieved by our approach are more consistent with visual inspection than the fast Fourier transform (FFT) method in subimages. Moreover, the retrieved wind speeds utilizing C-band model 5.N (CMOD5.N) are compared with wind speed estimations observed by Stepped Frequency Microwave Radiometer (SFMR). The results suggest that the proposed method has good potential to retrieve hurricane wind direction from SAR images without a hurricane eye and external data.

scholarly journals Topographic Speed-Up Effects and Observed Roof Damage on Bermuda following Hurricane Fabian (2003)

2013 ◽  
Vol 28 (1) ◽  
pp. 159-174 ◽  
Author(s):  
Craig Miller ◽  
Michael Gibbons ◽  
Kyle Beatty ◽  
Auguste Boissonnade
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Surface Wind ◽  
Open Water ◽  
Clear Trend ◽  
Wind Speeds ◽  
Research Division ◽  
Hurricane Wind ◽  
Layer Flow ◽  
Observed Damage

Abstract In this study the impacts of the topography of Bermuda on the damage patterns observed following the passage of Hurricane Fabian over the island on 5 September 2003 are considered. Using a linearized model of atmospheric boundary layer flow over low-slope topography that also incorporates a model for changes of surface roughness, sets of directionally dependent wind speed adjustment factors were calculated for the island of Bermuda. These factors were then used in combination with a time-stepping model for the open water wind field of Hurricane Fabian derived from the Hurricane Research Division Real-Time Hurricane Wind Analysis System (H*Wind) surface wind analyses to calculate the maximum 1-min mean wind speed at locations across the island for the following conditions: open water, roughness changes only, and topography and roughness changes combined. Comparison of the modeled 1-min mean wind speeds and directions with observations from a site on the southeast coast of Bermuda showed good agreement between the two sets of values. Maximum open water wind speeds across the entire island showed very little variation and were of category 2 strength on the Saffir–Simpson scale. While the effects of surface roughness changes on the modeled wind speeds showed very little correlation with the observed damage, the effect of the underlying topography led to maximum modeled wind speeds of category 4 strength being reached in highly localized areas on the island. Furthermore, the observed damage was found to be very well correlated with these regions of topographically enhanced wind speeds, with a very clear trend of increasing damage with increasing wind speeds.

scholarly journals Tropical Cyclone Wind Retrievals from the Advanced Microwave Sounding Unit: Application to Surface Wind Analysis

2006 ◽  
Vol 45 (3) ◽  
pp. 399-415 ◽  
Author(s):  
Kotaro Bessho ◽  
Mark DeMaria ◽  
John A. Knaff
Keyword(s):  
Tropical Cyclone ◽  
Wind Direction ◽  
Surface Wind ◽  
Wind Fields ◽  
Directional Bias ◽  
Wind Speeds ◽  
Research Division ◽  
Microwave Sounding Unit ◽  
Microwave Sounding ◽  
Advanced Microwave Sounding Unit

Abstract Horizontal winds at 850 hPa from tropical cyclones retrieved using the nonlinear balance equation, where the mass field was determined from Advanced Microwave Sounding Unit (AMSU) temperature soundings, are compared with the surface wind fields derived from NASA's Quick Scatterometer (QuikSCAT) and Hurricane Research Division H*Wind analyses. It was found that the AMSU-derived wind speeds at 850 hPa have linear relations with the surface wind speeds from QuikSCAT or H*Wind. There are also characteristic biases of wind direction between AMSU and QuikSCAT or H*Wind. Using this information to adjust the speed and correct for the directional bias, a new algorithm was developed for estimation of the tropical cyclone surface wind field from the AMSU-derived 850-hPa winds. The algorithm was evaluated in two independent cases from Hurricanes Floyd (1999) and Michelle (2001), which were observed simultaneously by AMSU, QuikSCAT, and H*Wind. In this evaluation the AMSU adjustment algorithm for wind speed worked well. Results also showed that the bias correction algorithm for wind direction has room for improvement.

scholarly journals Uncertainty and Intercalibration Analysis of H*Wind

2012 ◽  
Vol 29 (6) ◽  
pp. 822-833 ◽  
Author(s):  
Steven M. DiNapoli ◽  
Mark A. Bourassa ◽  
Mark D. Powell
Keyword(s):  
Final Analysis ◽  
Total Uncertainty ◽  
Software Application ◽  
Wind Speeds ◽  
Research Division ◽  
Uncertainty Sources ◽  
Hurricane Wind ◽  
Wind Analysis ◽  
Wide Range ◽  
Storm Center

Abstract The Hurricane Research Division (HRD) Real-time Hurricane Wind Analysis System (H*Wind) is a software application used by NOAA’s HRD to create a gridded tropical cyclone wind analysis based on a wide range of observations. These analyses are used in both forecasting and research applications. Although mean bias and RMS errors are listed, H*Wind lacks robust uncertainty information that considers the contributions of random observation errors, relative biases between observation types, temporal drift resulting from combining nonsimultaneous measurements into a single analysis, and smoothing and interpolation errors introduced by the H*Wind analysis. This investigation seeks to estimate the total contributions of these sources, and thereby provide an overall uncertainty estimate for the H*Wind product. A series of statistical analyses show that in general, the total uncertainty in the H*Wind product in hurricanes is approximately 6% near the storm center, increasing to nearly 13% near the tropical storm force wind radius. The H*Wind analysis algorithm is found to introduce a positive bias to the wind speeds near the storm center, where the analyzed wind speeds are enhanced to match the highest observations. In addition, spectral analyses are performed to ensure that the filter wavelength of the final analysis product matches user specifications. With increased knowledge of bias and uncertainty sources and their effects, researchers will have a better understanding of the uncertainty in the H*Wind product and can then judge the suitability of H*Wind for various research applications.

Satellite retrieval of hurricane wind speeds using the AMSR2 microwave radiometer

2015 ◽  
Vol 33 (5) ◽  
pp. 1104-1114 ◽  
Author(s):  
Panpan Yao ◽  
Jianhua Wan ◽  
Jin Wang ◽  
Jie Zhang
Keyword(s):  
Microwave Radiometer ◽  
Wind Speeds ◽  
Satellite Retrieval ◽  
Hurricane Wind

scholarly journals Relationship between SAR-Derived Wind Vectors and Wind at 10-m Height Represented by a Mesoscale Model

2006 ◽  
Vol 134 (5) ◽  
pp. 1505-1517 ◽  
Author(s):  
Wolfgang Koch ◽  
Frauke Feser
Keyword(s):  
Wind Speed ◽  
Mesoscale Model ◽  
Numerical Models ◽  
Synthetic Aperture ◽  
Band Model ◽  
Strong Decrease ◽  
Wind Fields ◽  
Sar Images ◽  
Wind Speeds ◽  
Sar Imagery

Abstract Wind vectors over the ocean were extracted from a large number of synthetic aperture radar (SAR) images from the European Remote Sensing Satellites (ERS-1 and ERS-2). The wind directions are inferred from the orientation of wind streaks that are imaged by the SAR, while the wind speeds are retrieved by inversion of the C-band model CMOD4. The derived wind directions and speeds were compared to wind vectors from the numerical Regional Model (REMO) that are available hourly on a 55-km grid. The large number of comparisons and independent weather situations allowed for an analysis of subsets that are classified by SAR-derived wind speed. A strong decrease of the standard deviation of directional differences with increasing wind speed was found. Biases of directional differences depend on SAR wind speed as well. Furthermore, the influence of the temporal difference between SAR overflight and model and an automatic image filtering on the directional error is demonstrated. Overall, reasonable fields of wind vectors were extracted from SAR imagery in 70 of 80 cases. These fields provide valuable information for validation of numerical models of the atmosphere and case studies of coastal wind fields.

scholarly journals Wind Speed Retrieval from Simulated RADARSAT Constellation Mission Compact Polarimetry SAR Data for Marine Application

2019 ◽  
Vol 11 (14) ◽  
pp. 1682 ◽  
Author(s):  
Torsten Geldsetzer ◽  
Shahid K. Khurshid ◽  
Kerri Warner ◽  
Filipe Botelho ◽  
Dean Flett
Keyword(s):  
Wind Speed ◽  
Low Noise ◽  
Quality Data ◽  
Band Model ◽  
Data Set ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Geophysical Model ◽  
The Right ◽  
Quality Threshold

RADARSAT Constellation Mission (RCM) compact polarimetry (CP) data were simulated using 504 RADARSAT-2 quad-pol SAR images. These images were used to samples CP data in three RCM modes to build a data set with co-located ocean wind vector observations from in situ buoys on the West and East coasts of Canada. Wind speeds up to 18 m/s were included. CP and linear polarization parameters were related to the C-band model (CMOD) geophysical model functions CMOD-IFR2 and CMOD5n. These were evaluated for their wind retrieval potential in each RCM mode. The CP parameter Conformity was investigated to establish a data-quality threshold (>0.2), to ensure high-quality data for model validation. An accuracy analysis shows that the first Stokes vector (SV0) and the right-transmit vertical-receive backscatter (RV) parameters were as good as the VV backscatter with CMOD inversion. SV0 produced wind speed retrieval accuracies between 2.13 m/s and 2.22 m/s, depending on the RCM mode. The RCM Medium Resolution 50 m mode produced the best results. The Low Resolution 100 m and Low Noise modes provided similar results. The efficacy of SV0 and RV imparts confidence in the continuity of robust wind speed retrieval with RCM CP data. Three image-based case studies illustrate the potential for the application of CP parameters and RCM modes in operational wind retrieval systems. The results of this study provide guidance to direct research objectives once RCM is launched. The results also provide guidance for operational RCM data implementation in Canada’s National SAR winds system, which provides near-real-time wind speed estimates to operational marine forecasters and meteorologists within Environment and Climate Change Canada.

scholarly journals Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

2017 ◽  
Vol 32 (6) ◽  
pp. 2217-2227 ◽  
Author(s):  
Siri Sofie Eide ◽  
John Bjørnar Bremnes ◽  
Ingelin Steinsland
Keyword(s):  
Wind Speed ◽  
Wind Direction ◽  
Bayesian Model Averaging ◽  
Weather Prediction ◽  
Model Averaging ◽  
Strong Wind ◽  
Ensemble Forecasts ◽  
Wind Speeds ◽  
Low Wind Speeds ◽  
The One

Abstract In this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables in addition to the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably because of a lack of methods. A Bayesian modeling approach (BMA) is adopted, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-min wind speeds based on ensemble forecasts from the European Centre for Medium-Range Weather Forecasts at 204 locations in Norway for lead times from +12 to +108 h. An improvement in the continuous ranked probability score is seen for approximately 85% of the locations using the proposed method compared to standard BMA based on only wind speed forecasts. For moderate-to-strong wind the improvement is substantial, while for low wind speeds there is generally less or no improvement. On average, the improvement is 5%. The proposed methodology can be extended to include more NWP variables in the calibration and can also be applied to other variables.

scholarly journals The “Bag Breakup” Spume Droplet Generation Mechanism at High Winds. Part II: Contribution to Momentum and Enthalpy Transfer

2018 ◽  
Vol 48 (9) ◽  
pp. 2189-2207 ◽  
Author(s):  
Yu. Troitskaya ◽  
O. Druzhinin ◽  
D. Kozlov ◽  
S. Zilitinkevich
Keyword(s):  
High Speed ◽  
Aerodynamic Drag ◽  
Stable Stratification ◽  
Noticeable Increase ◽  
Wind Speeds ◽  
Breakup Mechanism ◽  
Hurricane Wind ◽  
Exchange Processes ◽  
Aerodynamic Drag Coefficient ◽  
High Winds

AbstractIn Part I of this study, we used high-speed video to identify “bag breakup” fragmentation as the dominant mechanism by which spume droplets are generated at gale-force and hurricane wind speeds. We also constructed a spray generation function (SGF) for the bag-breakup mechanism. The distinctive feature of this new SGF is the presence of giant (~1000 μm) droplets, which may significantly intensify the exchange between the atmosphere and the ocean. In this paper, Part II, we estimate the contribution of the bag-breakup mechanism to the momentum and enthalpy fluxes, which are known to strongly affect the development and maintenance of hurricanes. We consider three contributions to the spray-mediated aerodynamic drag: 1) “bags” as obstacles before fragmentation, 2) acceleration of droplets by the wind in the course of their production, and 3) stable stratification of the marine atmospheric boundary layer due to levitating droplets. Taking into account all of these contributions indicates a peaking dependence of the aerodynamic drag coefficient on the wind speed, which confirms the results of field and laboratory measurements. The contribution of the spray-mediated flux to the ocean-to-atmosphere moist enthalpy is also estimated using the concept of “reentrant spray,” and the equation for the enthalpy flux from a single droplet to the atmosphere is derived from microphysical equations. Our estimates show that a noticeable increase in the enthalpy exchange coefficient at winds exceeding 30–35 m s−1 is due to the enhancement of the exchange processes caused by the presence of giant droplets originating from bag-breakup fragmentation.

scholarly journals Wind Power potential in Kigali and Western provinces of Rwanda

2015 ◽  
Vol 2 (1) ◽  
pp. 25-36
Author(s):  
Otieno Fredrick Onyango ◽  
Sibomana Gaston ◽  
Elie Kabende ◽  
Felix Nkunda ◽  
Jared Hera Ndeda
Keyword(s):  
Wind Speed ◽  
Probability Density ◽  
Wind Power ◽  
Wind Direction ◽  
Small Scale ◽  
Energy Potential ◽  
Wind Speeds ◽  
Mean Wind Speed ◽  
Wind Energy Potential ◽  
Low Wind Speeds

Wind speed and wind direction are the most important characteristics for assessing wind energy potential of a location using suitable probability density functions. In this investigation, a hybrid-Weibull probability density function was used to analyze data from Kigali, Gisenyi, and Kamembe stations. Kigali is located in the Eastern side of Rwanda while Gisenyi and Kamembe are to the West. On-site hourly wind speed and wind direction data for the year 2007 were analyzed using Matlab programmes. The annual mean wind speed for Kigali, Gisenyi, and Kamembe sites were determined as 2.36m/s, 2.95m/s and 2.97m/s respectively, while corresponding dominant wind directions for the stations were ,  and  respectively. The annual wind power density of Kigali was found to be  while the power densities for Gisenyi and Kamembe were determined as and . It is clear, the investigated regions are dominated by low wind speeds thus are suitable for small-scale wind power generation especially at Kamembe site.

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