Development and Evaluation of a New Method for AMSU-A Cloud Detection over Land

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.

Development and Evaluation of AMSU-A Cloud Detection over the Tibetan Plateau

“…As shown in Figure 3a-e, in the cloudy area, the observed BTs of AMSU-A channels showed obvious low-value centers. Based on the different responses of channel 15 and channel 3 to clouds (channel 15 responds more strongly than channel 3), Wu et al [39] selected five low-peaking channels (channels 1-4 and 15) of AMSU-A to construct an AMSU-A cloud index, and the AMSU-A cloud index was defined as follows: (…”

“…Comparing Figure 3a,e, it can be seen that the observed BT of AMSU-A channel 15 was significantly lower than that of channel 3 over the cloudy region, indicating that the sensitivity of channel 15 to clouds was also stronger than that of channel 3 over the Tibetan Plateau region, meaning this AMSU-A cloud index is also applicable to the Tibetan Plateau region. Wu et al [39] showed that the AMSU-A cloud index can detect most of the deep convective clouds but misses cirrus and some cirrostratus clouds; however, after adding the matched MHS cloud index, most of the clouds missed by the AMSU-A cloud index could be compensated for, and most of the cloudy observations could be eliminated. The MHS cloud index is not easily affected by the low temperature of the plateau but reflects more of the water vapor change.…”

“…Thus, the MHS cloud index can still play an important role over the Tibetan Plateau region. Referring to the study of Wu et al [39], this paper attempted to use the AMSU-A cloud index and MHS cloud index for cloud detection over the Tibetan Plateau region. The MHS cloud index is defined as follows:…”

“…Moreover, short-term convective systems are commonplace in the Tibetan Plateau region, which requires the development of fast cloud detection algorithms that only depend on satellite observations. Wu et al [39] developed an AMSU-A cloud index for the plain area based on the differences in the response characteristics of different channels of AMSU-A to clouds and selected five AMSU-A windows and low-peaking channels (channels 15 and 1-4). The results showed that the AMSU-A cloud index can detect most convective clouds but misses cirrus and some cirrostratus clouds; however, after adding the matched MHS cloud index, most of the clouds missed by the AMSU-A index could be compensated for.…”

The Evolution of Meteorological Satellite Cloud Detection Methodologies

“…For example, channel 4 of AMSU-A is the window corresponding to channels 6 and 7 of AMSU-A. In [217,218] the authors proposed an AMSU-A cloud detection based on 5 channels, 4 windows from the first to the fourth, and the fifteenth (a low-peaking), exploiting their different responses to the clouds. In [219] AMSU-A observations were considered as cloud-contaminated if the liquid or ice water path retrieved from co-located AMSU-A and MHS were greater than 0.02 g/kg.…”