Use of scatterometer based surface vorticity fields in forecasting genesis of tropical cyclones over the north Indian Ocean

. Utilizing surface vorticity fields computed with the ocean surface wind speed and direction dataobtained from QuikSCAT, a study has been undertaken to investigate the increase in surface vorticity during the genesisphase of tropical cyclones over the north Indian Ocean. Six named tropical cyclones; Agni, Hibaru, Mala, Akash, Nargisand Phyan which formed over the region during 2004-2009 have been selected for this purpose. It has been found thatthere was a steep rise in scatterometer based surface vorticity before the formation of a cyclone in the cyclogenesisregion. The peak surface vorticity in the genesis region was observed on the day of intensification of the vortex to thedepression stage or a day earlier. However, the rising trend in the genesis region begins a few days before the formationof the system. Thus, the surface vorticity fields derived on the basis of scatterometer data can provide predictiveindication of the genesis of tropical cyclones over the Bay of Bengal and Arabian Sea with a lead time of 2-3 days. Usingthis technique it is possible to increase the lead time of pre-cyclone watch period over the north Indian Ocean. No relationship was found between the peak surface vorticity anomaly during the genesis phase and the surfacevorticity anomaly at the time of peak intensity of the system during its life cycle. In other words, the peak surfacevorticity anomaly during genesis phase does not provide any indication of future maximum intensity of the cyclone.

Comparison of Multiple Surface Ocean Wind Products with Buoy Data over Blue Amazon (Brazilian Continental Margin)

Remote sensing data for space-time characterization of wind fields in extensive oceanic areas have been shown to be increasingly useful. Orbital sensors, such as radar scatterometers, provide data on ocean surface wind speed and direction with spatial and temporal resolutions suitable for multiple applications and air-sea studies. Even considering the relevant role of orbital scatterometers to estimate ocean surface wind vectors on a regional and global scale, the products must be validated regionally. Six different ocean surface wind datasets, including advanced scatterometer (ASCAT-A and ASCAT-B products) estimates, numerical modelling simulations (BRAMS), reanalysis (ERA5), and a blended product (CCMP), were compared statistically with in situ measurements obtained by anemometers installed in fifteen moored buoys in the Brazilian margin (8 buoys in oceanic and 7 in shelf waters) to analyze which dataset best represents the wind field in this region. The operational ASCAT wind products presented the lowest differences in wind speed and direction from the in situ data (0.77 ms−1 < RMSEspd < 1.59 ms−1, 0.75 < Rspd < 0.96, −0.68 ms−1 < biasspd < 0.38 ms−1, and 12.7° < RMSEdir < 46.8°). CCMP and ERA5 products also performed well in the statistical comparison with the in situ data (0.81 ms−1 < RMSEspd < 1.87 ms−1, 0.76 < Rspd < 0.91, −1.21 ms−1 < biasspd < 0.19 ms−1, and 13.7° < RMSEdir < 46.3°). The BRAMS model was the one with the worst performance (RMSEspd > 1.04 m·s−1, Rspd < 0.87). For regions with a higher wind variability, as in the southern Brazilian continental margin, wind direction estimation by the wind products is more susceptible to errors (RMSEdir > 42.4°). The results here presented can be used for climatological studies and for the estimation of the potential wind power generation in the Brazilian margin, especially considering the lack of availability or representativeness of regional data for this type of application.

Tropical Cyclone Wind Speeds from WindSat, AMSR and SMAP: Algorithm Development and Testing

The measurement of ocean surface wind speeds in precipitation from satellite microwave radiometers is a challenging task. Rain attenuates the signal that is emitted from the ocean surface.

Scaling Feature of the Ocean Surface Wind Field

&lt;p&gt;The ocean surface wind plays a crucial role in the air-sea exchanges of momentum, heat, and mass, consequently is vital to the controlling of weather and climate. Due to the extremely large range of scales of the motion of the wind field, e.g., flow structures from millimeters to thousands of kilometers, the multiscale dynamics are known to be relevant. In this work, with the help of a Wiener-Khinchine theorem-based Fourier power spectrum estimator, the scaling features of the wind field provided by several satellites, i.e., QuikSCAT, Metop-A, -B, and -C, Haiyang-2B, and China France Oceanography SATellite (CFOSAT), is examined. Power-law scaling behavior is evident in the ranges of 100 to 3000 km with a scaling exponent &amp;#946; varying from 5/3 to 3. The global distributions and seasonal variations of the scaling exponent &amp;#946; have also been considered. The results show that due to the energetic convective activities in the low-latitude zones, the scaling exponents &amp;#946; in these regions are closer to the value of 5/3. As for the mid-latitudes, the values of &amp;#946; are close to 2 and independent of the variation of longitude. Concerning the seasonal variations, for most regions, the scaling exponents measured in winter are larger than those in summer. Furthermore, the seasonal variations of &amp;#946; in low-latitudes are stronger than those in the mid-latitudes. Our preliminary results indicate that all satellites provide a consistent scaling feature of the ocean surface wind field.&lt;/p&gt;

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed From CALIPSO: A Neural Network Approach

A neural network nonlinear regression algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS (AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurements of 2015 are also compared with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission–Water “Shizuku” (GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind–surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from our previous studies, as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.