| Both sea fog and low stratus are important weather phenomenon in atmospheric boundary layer. They are significantly influencing the earth’s radiation balance. While sea fog is a more main obstruction for air, land and sea traffic, due to the low visibility on the surface, compared with low stratus. While rare observations over ocean, satellite are more widely utilized in sea fog detection. By now, sea fog and low stratus retrieval based on satellite is still a pending problem, the biggest reason is sea fog and low stratus share the similar physical structure and optical properties. Thus, in this paper two main results are observed through combining passive remote sensing satellite data and land-based/space borne active remote data:(1) We find the stratus and fog have distinction in the temperature difference between brightness temperature of MODIS 31 channel(～11μm) and SST could base on multi-years Radar sounding data and situ observation at Qingdao, specially the temperature difference have a monthly variation. Then, we propose a comprehensive dynamic threshold algorithm for daytime sea fog retrieval over the Chinese adjacent seas. Further, this paper provide a multi-year sea fog frequency in May and June with higher resolution, compared with the situ weather observation the retrieval accuracy is very encouraged (86.5%).(2) Aiming to improve sea fog retrieval accuracy, this paper supposes if the stratus base height could be retrieved, further sea fog would be detected from stratus through judging if the stratus base touching the surface. If do so, firstly, we should retrieve stratus top height and stratus thickness, and then make difference between them to get the stratus base height.The first, an accurate stratus top retrieval from geostationary and low earth orbit platforms is still a pending problem, specially an inversion layer generally cover the stratus top. In this paper, a novel method is presented to retrieve low stratus/fog top heights with special reference to the Yellow Sea and its surroundings using the infrared (IR) water vapor and split-window bands. Get an encouraging accuracy compared with the stratus top based on only temperature profile.The second, it is a little complicated to get the stratus thickness, in this step, this paper utilizes an iterative method. Firstly, a stratus thickness are supposed, then integrate every layer together to get a calculated liquid water path(LWP), which is then used to compare with retrieved LWP from satellite data, if both LWP are equal approximately, then we consider the supposed stratus thickness is the real thickness, while if not equal, it should make a little change to supposed a new thickness, then integrate layers to get a calculated LWP and keep running the calculations and comparison above again, quit the circle and output the stratus thickness until the retrieved LWP is equal with the calculated LWP. Before running the circle above, the cloud optical thickness(COT) and droplet effective radius(DER) should be retrieved to calculate the ’retrieved LWP’. Through compared with COT and DER from MODIS, we prefer MTSAT-2 as the source data. At the meantime, we rebuild the Yellow Sea in-cloud structure model to help integrate the LWP.It is a promising result that the retrieval of stratus based height based on above method could be used to distinct the sea fog from stratus. While there are still some inaccurate factor in our method. For example, lots of work still are needed to improve the stratus top height retrieval accuracy and more observation are also needed to optimize the cloud structure model. So, lots of assessment and modification towards to the detail method are necessary in the future. |
PDF Full Text Request