scholarly journals Comparisons of Shipboard Infrared Sea Surface Skin Temperature Measurements from the CIRIMS and the M-AERI

2008 ◽  
Vol 25 (4) ◽  
pp. 598-606 ◽  
Author(s):  
R. Branch ◽  
A. T. Jessup ◽  
P. J. Minnett ◽  
E. L. Key
Keyword(s):  
Wind Speed ◽  
Skin Temperature ◽  
Environmental Conditions ◽  
Coast Guard ◽  
Sea Surface ◽  
Wide Range ◽  
Surface Skin Temperature ◽  
The Mean ◽  
Deployment Time ◽  
Lower Temperature

Abstract Extensive comparisons are made of the infrared sea surface skin temperature Tskin measured by the Calibrated Infrared In situ Measurement System (CIRIMS) and the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI). Data were collected from four separate deployments on the NOAA research vessel (R/V) Ronald H. Brown and the U.S. Coast Guard (USCG) Polar Sea over a wide range of latitudes and environmental conditions. The deployment time totaled roughly 6 months over a 4-yr period and resulted in over 7000 comparison values. The mean offset between the two instruments showed that CIRIMS consistently measured a lower temperature than the M-AERI, but by less than 0.10°C. This mean offset was found to be dependent upon sky condition, wind speed, and ship roll, which implies the offset is likely due to uncertainty in the emissivity. The CIRIMS Tskin was recomputed using two alterative emissivity values, one based on emissivity measured by the M-AERI and the other based on a wind-speed-dependent model. In both cases, the recomputation of the CIRIMS Tskin significantly reduced the mean offset. The overall standard deviation between the M-AERI and CIRIMS Tskin was 0.16°C, did not significantly depend on environmental conditions, and was within the expected values of instrument and comparison uncertainties. These comparisons demonstrate the success of CIRIMS in achieving good agreement with the M-AERI over a wide range of conditions. The results also highlight the importance of the sea surface emissivity when measuring the ocean surface skin temperature.

An Investigation of the Stability Dependence of SST-Induced Vertical Mixing over the Ocean in the Operational Met Office Model

2017 ◽  
Vol 30 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Qingtao Song ◽  
Dudley B. Chelton ◽  
Steven K. Esbensen ◽  
Andrew R. Brown
Keyword(s):  
Wind Speed ◽  
Weather Prediction ◽  
Surface Wind ◽  
Vertical Mixing ◽  
Surface Wind Speed ◽  
Satellite Observations ◽  
Sea Surface ◽  
Stable Conditions ◽  
Wide Range ◽  
The Impact

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [Formula: see text] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

scholarly journals Toward Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research

2002 ◽  
Vol 15 (4) ◽  
pp. 353-369 ◽  
Author(s):  
C. J. Donlon ◽  
P. J. Minnett ◽  
C. Gentemann ◽  
T. J. Nightingale ◽  
I. J. Barton ◽  
...  
Keyword(s):  
Skin Temperature ◽  
Sea Surface ◽  
Temperature Measurements ◽  
Climate Research ◽  
Surface Skin Temperature

scholarly journals Wind Speed Retrieval Based on Sea Surface Roughness Measurements from Spaceborne Microwave Radiometers

2013 ◽  
Vol 52 (2) ◽  
pp. 507-516 ◽  
Author(s):  
Sungwook Hong ◽  
Inchul Shin
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Data Assimilation ◽  
Thermal Emission ◽  
Surface Wind ◽  
Sea Surface ◽  
Retrieval Algorithm ◽  
Surface Emissivity ◽  
Wide Range ◽  
Global Data

AbstractWind speed is the main factor responsible for the increase in ocean thermal emission because sea surface emissivity strongly depends on surface roughness. An alternative approach to estimate the surface wind speed (SWS) as a function of surface roughness is developed in this study. For the sea surface emissivity, the state-of-the-art forward Fast Microwave Emissivity Model, version 3 (FASTEM-3), which is applicable for a wide range of microwave frequencies at incidence angles of less than 60°, is used. Special Sensor Microwave Imager and Advanced Microwave Scanning Radiometer (AMSR-E) observations are simulated using FASTEM-3 and the Global Data Assimilation and Prediction System operated by the Korea Meteorological Administration. The performance of the SWS retrieval algorithm is assessed by comparing its SWS output to that of the Global Data Assimilation System operated by the National Centers for Environmental Prediction. The surface roughness is computed using the Hong approximation and characteristics of the polarization ratio. When compared with the Tropical Atmosphere–Ocean data, the bias and root-mean-square error (RMSE) of the SWS outputs from the proposed wind speed retrieval algorithm were found to be 0.32 m s−1 (bias) and 0.37 m s−1 (RMSE) for the AMSR-E 18.7-GHz channel, 0.38 m s−1 (bias) and 0.42 m s−1 (RMSE) for the AMSR-E 23.8-GHz channel, and 0.45 m s−1 (bias) and 0.49 m s−1 (RMSE) for the AMSR-E 36.5-GHz channel. Consequently, this research provides an alternative method to retrieve the SWS with minimal a priori information on the sea surface.

Study on Model of the Sea Surface Skin Temperature and Bulk Sea Surface Temperature in Northern Region of South China Sea

2018 ◽  
Vol 47 (11) ◽  
pp. 1101001
Author(s):  
张建 ZHANG Jian ◽  
郝三峰 HAO San-feng ◽  
宋庆君 SONG Qing-jun ◽  
赵俍骁 ZHAO Liang-xiao ◽  
安飞 AN Fei
Keyword(s):  
South China Sea ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Skin Temperature ◽  
South China ◽  
Northern Region ◽  
Sea Surface ◽  
China Sea ◽  
Surface Skin Temperature
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