Abstract
Satellite data are the main source of information for operational data assimilation systems, and Advanced Microwave Sounding Unit-A (AMSU-A) data are one of the types of satellite data that contribute most to the reduction of numerical forecast errors. However, the assimilation of AMSU-A data over land lags behind that over the ocean. In this respect, the accuracy of cloud detection over land is one of the factors affecting the assimilation of AMSU-A data, especially for the window and low-peaking channel (23–53.59 GHz and 89 GHz) data. Strong surface emissivity and high spatial and temporal variability make it difficult to distinguish between the radiative contributions of clouds and the atmosphere. Based on the differences in the response characteristics of different channels to clouds, five AMSU-A window and low-peaking channels (channels 1–4 and 15) were selected to develop a new index for cloud detection over land. Case studies showed that the AMSU-A cloud index can detect most of the convective clouds; additionally, by further matching the MHS (Microwave Humidity Sounder) cloud detection index, we can effectively distinguish between cloudy and clear-sky observations. Batch test results also verified the accuracy and stability of the new cloud detection method. By referring to the MODIS (Moderate Resolution Imaging Spectroradiometer) cloud product, the POD (probability of detection) of the cloud fields of view with the new method was nearly 84%. By using the new cloud detection method to remove the cloudy data, the bias and standard deviation of the observation-minus-simulated brightness temperature (O−B) were significantly reduced, with the bias of O−B for channels 2–4 being below 1.0 K and the standard deviation of channels 5 and 6 being nearly 1.0 K.
Highlights
By the end of the 20th century, direct assimilation of satellite radiance data into variational assimilation systems had begun to help with the problem of insufficient observational data, greatly improving the accuracy of numerical forecasts
In the clear-sky area, the Advanced Microwave Sounding Unit-A (AMSU-A) observed radiance is mainly dependent on the radiance emitted from the surwhere c is the speed of light and k is the Boltzmann constant, such that the brightness temperature (BT) is proportional to the quadratic of the frequency
Two examples of AMSU-A cloud index results are given in Figure 7, where the black circles are the cloudy FOVs detected by the AMSU-A cloud index
Summary
By the end of the 20th century, direct assimilation of satellite radiance data into variational assimilation systems had begun to help with the problem of insufficient observational data, greatly improving the accuracy of numerical forecasts. To improve the accuracy of model-simulated BT over land, various surface temperatures and surface emissivity estimation methods have been proposed, and these methods have yielded significant improvements in the clear-sky assimilation of AMSU-A terrestrial observations [26,27,28]. In this work, we attempted to develop a new AMSU-A terrestrial cloud detection method and, based on it, we evaluated the bias characteristics of different channels affected by clouds and different surface types under clear-sky conditions, and prepared for assimilating the observations of AMSU-A mid- and low-peaking channels over land in GRAPES.
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