title: [Radiance Simulation of BUV Hyperspectral Sensor on Multi Angle Observation, and Improvement to Initial Total Ozone Estimating Model of TOMS V8 Total Ozone Algorithm].
abstract: New hyperspectral sensor to detect total ozone is considered to be carried on geostationary orbit platform in the future, because local troposphere ozone pollution and diurnal variation of ozone receive more and more attention. Sensors carried on geostationary satellites frequently obtain images on the condition of larger observation angles so that it has higher requirements of total ozone retrieval on these observation geometries. TOMS V8 algorithm is developing and widely used in low orbit ozone detecting sensors, but it still lack of accuracy on big observation geometry, therefore, how to improve the accuracy of total ozone retrieval is still an urgent problem that demands immediate solution. Using moderate resolution atmospheric transmission, MODT-RAN, synthetic UV backscatter radiance in the spectra region from 305 to 360 nm is simulated, which refers to clear sky, multi angles (12 solar zenith angles and view zenith angles) and 26 standard profiles, moreover, the correlation and trends between atmospheric total ozone and backward scattering of the earth UV radiation are analyzed based on the result data. According to these result data, a new modified initial total ozone estimation model in TOMS V8 algorithm is considered to be constructed in order to improve the initial total ozone estimating accuracy on big observation geometries. The analysis results about total ozone and simulated UV backscatter radiance shows: Radiance in 317.5 nm (R₃₁₇.₅) decreased as the total ozone rise. Under the small solar zenith Angle (SZA) and the same total ozone, R₃₁₇.₅ decreased with the increase of view zenith Angle (VZA) but increased on the large SZA. Comparison of two fit models shows: without the condition that both SZA and VZA are large (> 80°), exponential fitting model and logarithm fitting model all show high fitting precision (R² > 0.90), and precision of the two decreased as the SZA and VZA rise. In most cases, the precision of logarithm fitting mode is about 0.9% higher than exponential fitting model. With the increasing of VZA or SZA, the fitting precision gradually lower, and the fall is more in the larger VZA or SZA. In addition, the precision of fitting mode exist a plateau in the small SZA range. The modified initial total ozone estimating model (ln(I) vs. Ω) is established based on logarithm fitting mode, and compared with traditional estimating model (I vs. ln(Ω)), that shows: the RMSE of ln(I) vs. Ω and I vs. ln(Ω) all have the down trend with the rise of total ozone. In the low region of total ozone (175-275 DU), the RMSE is obvious higher than high region (425-525 DU), moreover, a RMSE peak and a trough exist in 225 and 475 DU respectively. With the increase of VZA and SZA, the RMSE of two initial estimating models are overall rise, and the upraising degree is ln(I) vs. Ω obvious with the growing of SZA and VZA. The estimating result by modified model is better than traditional model on the whole total ozone range (RMSE is 0.087%-0.537% lower than traditional model), especially on lower total ozone region and large observation geometries. Traditional estimating model relies on the precision of exponential fitting model, and modified estimating model relies on the precision of logarithm fitting model. The improvement of the estimation accuracy by modified initial total ozone estimating model expand the application range of TOMS V8 algorithm. For sensor carried on geostationary orbit platform, there is no doubt that the modified estimating model can help improve the inversion accuracy on wide spatial and time range This modified model could give support and reference to TOMS algorithm update in the future.
authors.forename: Chun-guang
authors.initials: CG
authors.lastname: Lü
authors.forename: Wei-he
authors.initials: WH
authors.lastname: Wang
authors.forename: Wen-bo
authors.initials: WB
authors.lastname: Yang
authors.forename: Qing-iju
authors.initials: QI
authors.lastname: Tian
authors.forename: Shan
authors.initials: S
authors.lastname: Lu
authors.forename: Yun
authors.initials: Y
authors.lastname: Chen