Estimation of sea surface temperature, wind speed and water vapor with microwave radiometer data based on simulated annealing

2006 ◽  
Vol 37 (12) ◽  
pp. 2202-2207 ◽  
Author(s):  
K. Arai ◽  
J. Sakakibara
Keyword(s):  
Simulated Annealing ◽  
Water Vapor ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Microwave Radiometer ◽  
Sea Surface

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 Variability of the Great Whirl and Its Impacts on Atmospheric Processes

2019 ◽  
Vol 11 (3) ◽  
pp. 322 ◽  
Author(s):  
Sen Wang ◽  
Weijun Zhu ◽  
Jing Ma ◽  
Jinlin Ji ◽  
Jingsong Yang ◽  
...  
Keyword(s):  
Water Vapor ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Vertical Velocity ◽  
Vertical Direction ◽  
Early Summer ◽  
Sea Surface ◽  
Velocity Anomalies ◽  
Zonal Momentum

Using satellite remote sensing data and re-analysis products for the period of 1993–2015, the variation of a large anticyclonic eddy, the so-called Great Whirl (GW), located in the Northwest Indian Ocean off the coast of Somali, and its impacts on atmosphere were investigated. The GW is generated in early summer and decays in late fall every year. The center of the GW is located at 7.73°N, 53.20°E. The mean lifetime, sea level anomaly (SLA) difference, sea surface temperature anomaly (SSTA), radius, normalized vorticity, eddy kinetic energy (EKE), and deformation rate are 169 days, 0.07 m, 0.83°C, 116.86 km, −0.53, 0.08 m2∙s-2, and 0.58, respectively. All these variables exhibit interannual variations. Composite analyses show that the maximum values of sea surface temperature (SST), wind, and water vapor anomalies occur in the northwest of the GW center. The fitting coefficient between the SST and wind speed anomaly is 1.1, indicating that, corresponding to 1 °C increases of the SST, the wind speed increases by about 1.1m∙s-1, and the fitting coefficient between the SST and water vapor anomaly is 0.45, indicating that water vapor increases by about 0.45 mm in response to 1 °C increases in the SST. In the vertical direction, the maximum and minimum values of vertical velocity anomalies and vertical transport of transient zonal momentum occur over the GW at about 900 hPa, and wind speed anomalies occur at about 950 hPa. Both the positive transport anomalies of transient zonal momentum and the positive vertical velocity anomalies on the west side of the GW can accelerate the wind speed in the lower level.

The Behavior of the Bulk – Skin Sea Surface Temperature Difference under Varying Wind Speed and Heat Flux

1996 ◽  
Vol 26 (10) ◽  
pp. 1969-1988 ◽  
Author(s):  
Gary A. Wick ◽  
William J. Emery ◽  
Lakshmi H. Kantha ◽  
Peter Schlüssel
Keyword(s):  
Heat Flux ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Temperature Difference ◽  
Sea Surface

Modeling the Atmospheric Boundary Layer Wind Response to Mesoscale Sea Surface Temperature Perturbations

2014 ◽  
Vol 142 (11) ◽  
pp. 4284-4307 ◽  
Author(s):  
Natalie Perlin ◽  
Simon P. de Szoeke ◽  
Dudley B. Chelton ◽  
Roger M. Samelson ◽  
Eric D. Skyllingstad ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Coupling Coefficient ◽  
Eddy Viscosity ◽  
Sea Surface ◽  
Coupling Coefficients ◽  
Wind Response ◽  
Speed Response

Abstract The wind speed response to mesoscale SST variability is investigated over the Agulhas Return Current region of the Southern Ocean using the Weather Research and Forecasting (WRF) Model and the U.S. Navy Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) atmospheric model. The SST-induced wind response is assessed from eight simulations with different subgrid-scale vertical mixing parameterizations, validated using Quick Scatterometer (QuikSCAT) winds and satellite-based sea surface temperature (SST) observations on 0.25° grids. The satellite data produce a coupling coefficient of sU = 0.42 m s−1 °C−1 for wind to mesoscale SST perturbations. The eight model configurations produce coupling coefficients varying from 0.31 to 0.56 m s−1 °C−1. Most closely matching QuikSCAT are a WRF simulation with the Grenier–Bretherton–McCaa (GBM) boundary layer mixing scheme (sU = 0.40 m s−1 °C−1), and a COAMPS simulation with a form of Mellor–Yamada parameterization (sU = 0.38 m s−1 °C−1). Model rankings based on coupling coefficients for wind stress, or for curl and divergence of vector winds and wind stress, are similar to that based on sU. In all simulations, the atmospheric potential temperature response to local SST variations decreases gradually with height throughout the boundary layer (0–1.5 km). In contrast, the wind speed response to local SST perturbations decreases rapidly with height to near zero at 150–300 m. The simulated wind speed coupling coefficient is found to correlate well with the height-averaged turbulent eddy viscosity coefficient. The details of the vertical structure of the eddy viscosity depend on both the absolute magnitude of local SST perturbations, and the orientation of the surface wind to the SST gradient.

scholarly journals Sea surface wind perturbations over the Kashevarov Bank of the Okhotsk Sea: a satellite study

2011 ◽  
Vol 29 (2) ◽  
pp. 393-399
Author(s):  
T. I. Tarkhova ◽  
M. S. Permyakov ◽  
E. Yu. Potalova ◽  
V. I. Semykin
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Surface Wind ◽  
Sea Surface ◽  
Cold Spot ◽  
Okhotsk Sea ◽  
Autumn Period ◽  
Sea Surface Wind ◽  
Spot Center

Abstract. Sea surface wind perturbations over sea surface temperature (SST) cold anomalies over the Kashevarov Bank (KB) of the Okhotsk Sea are analyzed using satellite (AMSR-E and QuikSCAT) data during the summer-autumn period of 2006–2009. It is shown, that frequency of cases of wind speed decreasing over a cold spot in August–September reaches up to 67%. In the cold spot center SST cold anomalies reached 10.5 °C and wind speed lowered down to ~7 m s−1 relative its value on the periphery. The wind difference between a periphery and a centre of the cold spot is proportional to SST difference with the correlations 0.5 for daily satellite passes data, 0.66 for 3-day mean data and 0.9 for monthly ones. For all types of data the coefficient of proportionality consists of ~0.3 m s−1 on 1 °C.

Correcting infrared satellite estimates of sea surface temperature for atmospheric water vapor attenuation

1994 ◽  
Vol 99 (C3) ◽  
pp. 5219 ◽  
Author(s):  
William J. Emery ◽  
Yunyue Yu ◽  
Gary A. Wick ◽  
Peter Schluessel ◽  
Richard W. Reynolds
Keyword(s):  
Water Vapor ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Atmospheric Water Vapor ◽  
Atmospheric Water

scholarly journals CO2 flux behavior in the maritorium of San Andres Islands on 2019

Respuestas ◽  
2020 ◽  
Vol 25 (3) ◽  
Author(s):  
Juan Guillermo Popayán-Hernández ◽  
Orlando Zúñiga-Escobar
Keyword(s):  
Wind Speed ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Satellite Images ◽  
Co2 Flux ◽  
Sea Surface ◽  
Remote Sensors ◽  
Partial Pressures ◽  
Thermodynamic Relationship ◽  
San Andres

This document estimated the behavior of the CO2 flux in the San Andrés Islas maritime for the first half of 2019. This behavior was established based on the thermodynamic relationship between the sea surface temperature, the partial pressures of CO2 in the atmosphere and the water column, this from data derived from remote sensors. The satellite data were derived from the MODIS aqua sensors and the MERRA model for sea surface temperature and wind speed respectively. Satellite images were obtained from NASA databases, subsequently processed and specialized in ArcGis 10.1. Finally, the behavior of the CO2 flux is shown for the San Andrés Islas maritime, finding that it does not have a tendency to capture CO2, so acidification processes are discarded for the selected study period.

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