In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.

In this paper, the precipitable water vapor (PWV) data from GNSS (Global Navigation Satellite System) is introduced into the ITU-R cloud attenuation model for higher temporal and spatial resolution for the tropical region. The revised model incorporates PWV value for estimation of the cloud integrated liquid water content (ILWC), and then determine the cloud attenuation. In this study, ILWC along the propagation path is obtained by processing radiosonde vertical profile using the combination of water vapor pressure cloud detection model and the Salonen and Uppala liquid water density model. From the analysis of 2-years radiosonde data from 8 sites within the tropical region, the results show that the ILWC along the path can be approximated by a power function relationship with the PWV. The estimated cloud attenuation using the improved model is compared with the values calculated using the ITU-R model and the cloud attenuation derived from a Ka-band beacon data. The results show that the proposed model is in good agreement with the ITU-R model at high percentage of time exceedance for the thin layer of stratus clouds and matches well with cloud attenuation suffered from beacon signal at low percentage of time exceedance for the thick layer of cumulonimbus clouds.

In the tropical region, convective rain is a dominant rain event. However, very little information is known about the convective rain melting layer. In this paper, S-band dual-polarized radar data is studied in order to identify both the stratiform and convective rain melting layers in the tropical region, with a focus on the convective events. By studying and analyzing the above-mentioned two types of rain events, amongst three radar measurements of reflectivity ( $\mathrm{Z}$ ), differential reflectivity ( ${\mathrm{Z}}_{\mathrm{DR}}$ ), and cross correlation coefficient ( ${\mathsf{\rho }}_{\mathrm{HV}}$ ), the latter one is the best indicator for convective rain melting layer detection. From two years (2014 and 2015) of radar and radiosonde observations, 13 convective rain melting layers are identified with available 0 °C isothermal heights which are derived from radiosonde vertical profiles. By comparing the melting layer top heights with the corresponding 0 °C isothermal heights, it is found that for convective rain events, the threshold to detect melting layer should be modified to ${\mathsf{\rho }}_{\mathrm{HV}}=0.95$ for the tropical region. The melting layer top and bottom heights are then estimated using the proposed threshold, and it is observed from this study that the thickness of convective rain melting layer is around 2 times that of stratiform rain melting layer which is detected by using the conventional ${\mathsf{\rho }}_{\mathrm{HV}}=0.97$ .

Searching for residue in the glaze of porcelain or stoneware is a difficult task because these glazes are high-fired, well vitrified, and nonporous. This paper analyzes the chemical composition of residue observed in glaze cracks of porcelain via SEM-EDS to determine how the crackle effect was produced, in particular, if it was intentionally created during production or the result of post-depositional processes. This study offers insights to a specific type of ancient Chinese porcelain called “Ge-type ware”, which has two different types of cracks, and whose origin has been debated for nearly 60 years because it has never been found at any kiln site. This paper analyzes the chemical composition of the two crack types, first using elemental mapping to ascertain the different mechanisms that produced these two crack types of the Heirloom Ge ware, and second using residue analysis and chemical fingerprinting to determine the provenance of this puzzling type of porcelain. In doing so, this paper demonstrates how the residue in the glaze of porcelain can be observed and analyzed via microchemical approaches and hopes to inspire more research using this technique in future.