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Gang Luo

Abstract

The dependence of pixel-scale cloud-cover frequency distribution on cloud size and pixel size is examined through Monte Carlo simulations. A shape parameter, which describes the shape of the frequency distribution, is found to be a simple function of the ratio of cloud size to pixel size. The form of the frequency distribution changes from U shape to bell shape when the ratio decreases. It becomes uniform when the ratio is about 0.8–0.9 for the cases where the regional-scale cloud cover is 0.5. It is shown that on average the cloud cover in partially cloudy pixels increases with increasing regional-scale cloud cover when the ratio of cloud size to pixel size is small. It becomes insensitive to regional-scale cloud cover when the ratio becomes large. It is also shown that, in comparison with the results from Monte Carlo simulations, the grid-scale cloud-cover frequency distribution obtained using a threshold method tends to be more U shaped, and that obtained using a method that assigns 50% cloudiness to partially cloudy pixels tends to be less U shaped, particularly for subgrid cloudiness. A possible way of retrieving cloud size is suggested. It is found that the difference between a simulated cloud field, where clouds are uniformly distributed, and a real cloud field, where clouds may not be uniformly distributed, biases cloud size retrieval. Investigations on how clouds are distributed in a real cloud field are recommended.

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Yi Luo, Xudong Liang, Gang Wang, and Zheng Cao

Abstract

In this study, we propose a new way to obtain motion vectors using the integrating velocity–azimuth process (IVAP) method for extrapolation nowcasting. Traditional tracking methods rely on tracking radar echoes of a few time slices. In contrast, the IVAP method does not depend on the past variation of radar echoes; it only needs the radar echo and radial velocity observations at the latest time. To demonstrate it is practical to use IVAP-retrieved winds to extrapolate radar echoes, we carried out nowcasting experiments using the IVAP method, and compared these results with the results using a traditional method, namely, the tracking radar echoes by correlation (TREC) method. Comparison based on a series of large-scale mature rainfall cases showed that the IVAP method has similar accuracy to that of the TREC method. In addition, the IVAP method provides the vertical wind profile that can be used to anticipate storm type and motion deviations.

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Lisuo Hu, Jing-Jia Luo, Gang Huang, and Matthew C. Wheeler

Abstract

Central Africa (CA) is identified as a location of a large positive trend of the occurrence of heat waves (HWs) during 1979–2016, appearing to result mostly from a regime shift around the year 2000. Therefore, we study the evolution of synoptic features associated with the occurrence of HW events in CA. It is found that the HW-related circulation is typically characterized by an anomalous convergence in the upper troposphere but there are important differences for HW events occurring in the south region of CA (CA_S) versus the north region (CA_N). For the occurrence of the HW events in CA_S, the anomalous subsidence associated with upper troposphere anomalous convergence is the dominant factor for their occurrence and magnitude: the strong subsidence leads to warming through greater solar insolation. The HW events in CA_S are also accompanied by an anomalous surface anticyclone in the north with anomalous northerly flow transporting heat into the CA_S region. In contrast, although the HW events in CA_N are also associated with upper troposphere anomalous convergence, the intensity of the convergence is weak with small solar insolation. Instead, the anomalous warm advection is the main factor for determining the magnitude of the HW events in CA_N, induced by the prevailing northerly winds acting on the anomalous temperature gradient. Thus, the synoptic features associated with HW events in the CA_N and CA_S are quite different despite their nearby locations. The discovered dominant factors for the HW events in CA can be used to improve the forecast skill.

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Gang Luo, Paul A. Davis, Larry L. Stowe, and E. Paul McClain

Abstract

An automated pixel-scale algorithm has been developed to retrieve cloud type, related cloud layer(s), and the fractional coverages for all cloud layers in each AVHRR (Advanced Very High Resolution Radiometer) pixel at night. In the algorithm, cloud-contaminated pixels are separated from cloud-free pixels and grouped into three generic cloud types. Cloud layers in each cloud type are obtained through a cloud-type uniformity check, a thermal uniformity check, and a channel 4 ( 11 μm) brightness temperature histogram analysis, within a grid area. The algorithm allows for pixels to be mixed among different cloud layers of different cloud types, as well as between cloud layers and the ocean or land surface. A “neighbor-cheek” method is developed to identify the cloud layers associated with each mixed pixel and to calculate the coverages of each of the cloud layers in the pixel. Digital color images are generated based on information on the location, cloud type, cloud layer, and cloud amount of each individual pixel. Visualization comparisons show good agreement between color-coded images and the standard black and white satellite images. The results of the pixel-scale algorithm also show good agreements with the spatial coherence analysis and with National Weather Service surface and radiosonde observations. The pixel-scale algorithm has been developed for use in validation of output from CLAYR (clouds from AVHRR) project algorithms.

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