Estimating Maximum Surface Winds from Hurricane Reconnaissance Measurements

2009 ◽  
Vol 24 (3) ◽  
pp. 868-883 ◽  
Author(s):  
Mark D. Powell ◽  
Eric W. Uhlhorn ◽  
Jeffrey D. Kepert
Keyword(s):  
Wind Speed ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Reduction Factor ◽  
Surface Winds ◽  
Wind Maximum ◽  
Maximum Surface ◽  
Radial Profiles ◽  
Flight Level ◽  
Reduction Factors

Abstract Radial profiles of surface winds measured by the Stepped Frequency Microwave Radiometer (SFMR) are compared to radial profiles of flight-level winds to determine the slant ratio of the maximum surface wind speed to the maximum flight-level wind speed, for flight altitude ranges of 2–4 km. The radius of maximum surface wind is found on average to be 0.875 of the radius of the maximum flight-level wind, and very few cases have a surface wind maximum at greater radius than the flight-level maximum. The mean slant reduction factor is 0.84 with a standard deviation of 0.09 and varies with storm-relative azimuth from a maximum of 0.89 on the left side of the storm to a minimum of 0.79 on the right side. Larger slant reduction factors are found in small storms with large values of inertial stability and small values of relative angular momentum at the flight-level radius of maximum wind, which is consistent with Kepert’s recent boundary layer theories. The global positioning system (GPS) dropwindsonde-based reduction factors that are assessed using this new dataset have a high bias and substantially larger RMS errors than the new technique. A new regression model for the slant reduction factor based upon SFMR data is presented, and used to make retrospective estimates of maximum surface wind speeds for significant Atlantic basin storms, including Hurricanes Allen (1980), Gilbert (1988), Hugo (1989), Andrew (1992), and Mitch (1998).

Satellite and Aircraft Observations of the Eyewall Replacement Cycle in Typhoon Sinlaku (2008)

2015 ◽  
Vol 143 (9) ◽  
pp. 3406-3420 ◽  
Author(s):  
Elizabeth R. Sanabia ◽  
Bradford S. Barrett ◽  
Nicholas P. Celone ◽  
Zachary D. Cornelius
Keyword(s):  
Inner Core ◽  
Surface Wind ◽  
Surface Winds ◽  
Wind Speeds ◽  
Radial Profiles ◽  
Aircraft Observations ◽  
Eyewall Replacement Cycle ◽  
Flight Level ◽  
Cloud Tops ◽  
Replacement Cycle

Abstract Satellite and aircraft observations of the concurrent evolution of cloud-top brightness temperatures (BTs) and the surface and flight-level wind fields were examined before and during an eyewall replacement cycle (ERC) in Typhoon Sinlaku (2008) as part of The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) and the Tropical Cyclone Structure 2008 (TCS08) field campaign. The structural evolution of deep convection through the life cycle of the ERC was clearly evident in the radial variation of positive water vapor (WV) minus infrared (IR) brightness temperature differences over the 96-h period. Within this framework, the ERC was divided into six broadly defined stages, wherein convective processes (including eyewall development and decay) were analyzed and then validated using microwave data. Dual maxima in aircraft wind speeds and geostationary satellite BTs along flight transects through Sinlaku were used to document the temporal evolution of the ERC within the TC inner core. Negative correlations were found between IR BTs and surface wind speeds, indicating that colder cloud tops were associated with stronger surface winds. Spatial lags indicated that the strongest surface winds were located radially inward of both the flight-level winds and coldest cloud tops. Finally, timing of the ERC was observed equally in IR and WV minus IR (WVIR) BTs with one exception. Decay of the inner eyewall was detected earlier in the WVIR data. These findings highlight the potential utility of WVIR and IR BT radial profiles, particularly so for basins without active aircraft weather reconnaissance programs such as the western North Pacific.

scholarly journals Satellite Microwave Surface Observations in Tropical Cyclones

2010 ◽  
Vol 138 (2) ◽  
pp. 421-437 ◽  
Author(s):  
Yves Quilfen ◽  
Bertrand Chapron ◽  
Jean Tournadre
Keyword(s):  
Wind Speed ◽  
Tropical Cyclones ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Rain ◽  
Surface Wind Speed ◽  
Microwave Measurements ◽  
Dual Frequency

Abstract Sea surface estimates of local winds, waves, and rain-rate conditions are crucial to complement infrared/visible satellite images in estimating the strength of tropical cyclones (TCs). Satellite measurements at microwave frequencies are thus key elements of present and future observing systems. Available for more than 20 years, passive microwave measurements are very valuable but still suffer from insufficient resolution and poor wind vector retrievals in the rainy conditions encountered in and around tropical cyclones. Scatterometer and synthetic aperture radar active microwave measurements performed at the C and Ku band on board the European Remote Sensing (ERS), the Meteorological Operational (MetOp), the Quick Scatterometer (QuikSCAT), the Environmental Satellite (Envisat), and RadarSat satellites can also be used to map the surface wind field in storms. Their accuracy is limited in the case of heavy rain and possible saturation of the microwave signals is reported. Altimeter dual-frequency measurements have also been shown to provide along-track information related to surface wind speed, wave height, and vertically integrated rain rate at about 6-km resolution. Although limited for operational use by their dimensional sampling, the dual-frequency capability makes altimeters a unique satellite-borne sensor to perform measurements of key surface parameters in a consistent way. To illustrate this capability two Jason-1 altimeter passes over Hurricanes Isabel and Wilma are examined. The area of maximum TC intensity, as described by the National Hurricane Center and by the altimeter, is compared for these two cases. Altimeter surface wind speed and rainfall-rate observations are further compared with measurements performed by other remote sensors, namely, the Tropical Rainfall Measuring Mission instruments and the airborne Stepped Frequency Microwave Radiometer.

Comparison of AI-based approaches for statistical downscaling of surface wind fields in the North Atlantic

2021 ◽  
Author(s):  
Vadim Rezvov ◽  
Mikhail Krinitskiy ◽  
Alexander Gavrikov ◽  
Sergey Gulev
Keyword(s):  
Wind Speed ◽  
Statistical Downscaling ◽  
North Atlantic ◽  
Surface Wind ◽  
Surface Winds ◽  
Wind Fields ◽  
The North ◽  
The North Atlantic ◽  
Vector Wind ◽  
Resolution Data

<p>Surface winds — both wind speed and vector wind components — are fields of fundamental climatic importance. The character of surface winds greatly influences (and is influenced by) surface exchanges of momentum, energy, and matter. These wind fields are of interest in their own right, particularly concerning the characterization of wind power density and wind extremes. Surface winds are influenced by small-scale features such as local topography and thermal contrasts. That is why accurate high-resolution prediction of near‐surface wind fields is a topic of central interest in various fields of science and industry. Statistical downscaling is the way for inferring information on physical quantities at a local scale from available low‐resolution data. It is one of the ways to avoid costly high‐resolution simulations. Statistical downscaling connects variability of various scales using statistical prediction models. This approach is fundamentally data-driven and can only be applied in locations where observations have been taken for a sufficiently long time to establish the statistical relationship. Our study considered statistical downscaling of surface winds (both wind speed and vector wind components) in the North Atlantic. Deep learning methods are among the most outstanding examples of state‐of‐the‐art machine learning techniques that allow approximating sophisticated nonlinear functions. In our study, we applied various approaches involving artificial neural networks for statistical downscaling of near‐surface wind vector fields. We used ERA-Interim reanalysis as low-resolution data and RAS-NAAD dynamical downscaling product (14km grid resolution) as a high-resolution target. We compared statistical downscaling results to those obtained with bilinear/bicubic interpolation with respect to downscaling quality. We investigated how network complexity affects downscaling performance. We will demonstrate the preliminary results of the comparison and propose the outlook for further development of our methods.</p><p>This work was undertaken with financial support by the Russian Science Foundation grant № 17-77-20112-P.</p>

A Coupled-Pixel Model (CPM) Atmospheric Retrieval Algorithm for High-Resolution Imagers

2015 ◽  
Vol 32 (10) ◽  
pp. 1866-1879 ◽  
Author(s):  
Mary Morris ◽  
Christopher S. Ruf
Keyword(s):  
Wind Speed ◽  
Rain Rate ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Low Frequency ◽  
Surface Wind Speed ◽  
Retrieval Algorithm ◽  
Single Column ◽  
Simplifying Assumptions ◽  
Cross Track

AbstractLow-frequency passive microwave observations allow for oceanic remote sensing of surface wind speed and rain rate from spaceborne and airborne platforms. For most instruments, the modeling of contributions of rain absorption and reemission in a particular field of view is simplified by the observing geometry. However, the simplifying assumptions that can be applied in most applications are not always valid for the scenes that the airborne Hurricane Imaging Radiometer (HIRAD) regularly observes. Collocated Stepped Frequency Microwave Radiometer (SFMR) and HIRAD observations of Hurricane Earl (2010) indicate that retrieval algorithms based on the usual simplified model, referred to here as the decoupled-pixel model (DPM), are not able to resolve two neighboring rainbands at the edge of HIRAD’s swath. The DPM does not allow for the possibility that a single column of atmosphere can affect the observations at multiple cross-track positions. This motivates the development of a coupled-pixel model (CPM) that is developed and tested in this paper. Simulated observations as well as HIRAD’s observations of Hurricane Earl (2010) are used to test the CPM algorithm. Key to the performance of the CPM algorithm is its ability to deconvolve the cross-track scene, as well as unscramble the signatures of surface wind speed and rain rate in HIRAD’s observations. While the CPM approach was developed specifically for HIRAD, other sensors could employ this method in similar complicated observing scenarios.

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

2007 ◽  
Vol 24 (6) ◽  
pp. 1131-1142 ◽  
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.

scholarly journals Extreme Wind Speeds Retrieval Using Sentinel-1 IW Mode SAR Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1867
Author(s):  
Yuan Gao ◽  
Jian Sun ◽  
Jie Zhang ◽  
Changlong Guan
Keyword(s):  
Wind Speed ◽  
Mean Squared Error ◽  
Incident Angle ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
European Space Agency ◽  
Noise Removal ◽  
Wind Retrieval ◽  
Wind Speeds ◽  
Microwave Radar

With the improvement in microwave radar technology, spaceborne synthetic aperture radar (SAR) is widely used to observe the tropical cyclone (TC) wind field. Based on European Space Agency Sentinel-1 Interferometric Wide swath (IW) mode imagery, this paper evaluates the correlation between vertical transmitting–horizontal receiving (VH) polarization signals and extreme ocean surface wind speeds (>40 m/s) under strong TC conditions. A geophysical model function (GMF) Sentinel-1 IW mode wind retrieval model after noise removal (S1IW.NR) was proposed, according to the SAR images of nine TCs and collocated stepped frequency microwave radiometer (SFMR) and soil moisture active passive (SMAP) radiometer wind speed measurements. Through curve fitting and regression correction, the new GMF exploits the relationships between VH-polarization normalized radar cross section, incident angle, and wind speed in each sub-swath and covers wind speeds up to 74 m/s. Based on collocated SAR and SFMR measurements of four TCs, the new GMF was validated in the wind speed range from 2 to 53 m/s. Results show that the correlation coefficient, bias, and root mean squared error were 0.89, −0.89 m/s, and 4.13 m/s, respectively, indicating that extreme winds can be retrieved accurately by the new model. In addition, we investigated the relationship between the S1IW.NR wind retrieval bias and the SFMR-measured rain rate. The S1IW.NR model tended to overestimate wind speeds under high rain rates.

scholarly journals Whitecap and Wind Stress Observations by Microwave Radiometers: Global Coverage and Extreme Conditions

2019 ◽  
Vol 49 (9) ◽  
pp. 2291-2307 ◽  
Author(s):  
Paul A. Hwang ◽  
Nicolas Reul ◽  
Thomas Meissner ◽  
Simon H. Yueh
Keyword(s):  
Surface Wave ◽  
Wind Speed ◽  
Wind Stress ◽  
Thermal Emission ◽  
Microwave Radiometer ◽  
Wave Spectrum ◽  
Surface Wind ◽  
Unit Surface Area ◽  
Whitecap Coverage ◽  
Surface Wind Stress

AbstractWhitecaps manifest surface wave breaking that impacts many ocean processes, of which surface wind stress is the driving force. For close to a half century of quantitative whitecap reporting, only a small number of observations are obtained under conditions with wind speed exceeding 25 m s−1. Whitecap contribution is a critical component of ocean surface microwave thermal emission. In the forward solution of microwave thermal emission, the input forcing parameter is wind speed, which is used to generate the modeled surface wind stress, surface wave spectrum, and whitecap coverage necessary for the subsequent electromagnetic (EM) computation. In this respect, microwave radiometer data can be used to evaluate various formulations of the drag coefficient, whitecap coverage, and surface wave spectrum. In reverse, whitecap coverage and surface wind stress can be retrieved from microwave radiometer data by employing precalculated solutions of an analytical microwave thermal emission model that yields good agreement with field measurements. There are many published microwave radiometer datasets covering a wide range of frequency, incidence angle, and both vertical and horizontal polarizations, with maximum wind speed exceeding 90 m s−1. These datasets provide information of whitecap coverage and surface wind stress from global oceans and in extreme wind conditions. Breaking wave energy dissipation rate per unit surface area can be estimated also by making use of its linear relationship with whitecap coverage derived from earlier studies.

scholarly journals SFMR Surface Wind Undersampling over the Tropical Cyclone Life Cycle

2019 ◽  
Vol 147 (1) ◽  
pp. 247-268 ◽  
Author(s):  
Bradley W. Klotz ◽  
David S. Nolan
Keyword(s):  
Life Cycle ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Wind Field ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Real Data ◽  
Lookup Table ◽  
Wind Speeds ◽  
Observing System Simulation Experiment

Surface wind speeds in tropical cyclones are important for defining current intensity and intensification. Traditionally, airborne observations provide the best information about the surface wind speeds, with the Stepped Frequency Microwave Radiometer (SFMR) providing a key role in obtaining such data. However, the flight patterns conducted by hurricane hunter aircraft are limited in their azimuthal coverage of the surface wind field, resulting in an undersampling of the wind field and consequent underestimation of the peak 10-m wind speed. A previous study provided quantitative estimates of the average underestimate for a very strong hurricane. However, no broader guidance on applying a correction based on undersampling has been presented in detail. To accomplish this task, a modified observing system simulation experiment with five hurricane simulations is used to perform a statistical evaluation of the peak wind speed underestimate over different stages of the tropical cyclone life cycle. Analysis of numerous simulated flights highlights prominent relationships between wind speed undersampling and storm size, where size is defined by the radius of maximum wind speed (RMW). For example, an intense hurricane with small RMW needs negligible correction, while a large-RMW tropical storm requires a 16%–19% change. A lookup table of undersampling correction factors as a function of peak SFMR wind speed and RMW is provided to assist the tropical cyclone operations community. Implications for hurricane best track intensity estimates are also discussed using real data from past Atlantic hurricane seasons.

scholarly journals Examination of Surface Wind Asymmetry in Tropical Cyclones over the Northwest Pacific Ocean Using SMAP Observations

2019 ◽  
Vol 11 (22) ◽  
pp. 2604 ◽  
Author(s):  
Ziyao Sun ◽  
Biao Zhang ◽  
Jun A. Zhang ◽  
William Perrie
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Microwave Radiometer ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Relative Strength ◽  
Northwest Pacific ◽  
Translation Speed ◽  
Northwest Pacific Ocean ◽  
Maximum Wind

Tropical cyclone (TC) surface wind asymmetry is investigated by using wind data acquired from an L-band passive microwave radiometer onboard the NASA Soil Moisture Active Passive (SMAP) satellite between 2015 and 2017 over the Northwest Pacific (NWP) Ocean. The azimuthal asymmetry degree is defined as the factor by which the maximum surface wind speed is greater than the mean wind speed at the radius of the maximum wind (RMW). We examined storm motion and environmental wind shear effects on the degree of TC surface wind asymmetry under different intensity conditions. Results show that the surface wind asymmetry degree significantly decreases with increasing TC intensity, but increases with increasing TC translation speed, for tropical storm and super typhoon strength TCs; whereas no such relationship is found for typhoon and severe typhoon strength TCs. However, the degree of surface wind asymmetry increases with increasing wind shear magnitude for all TC intensity categories. The relative strength between the storm translation speed and the wind shear magnitude has the potential to affect the location of the maximum wind speed. Moreover, the maximum degree of wind asymmetry is found when the direction of the TC motion is nearly equal to the direction of the wind shear.

Sign in / Sign up

Export Citation Format

Share Document