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In 2021, a group of seven italian institutions decided to bring together their know-how, experience, and instruments for measuring the drop size distribution (DSD) of atmospheric precipitation giving birth to the Italian Group of Disdrometry (in Italian named: Gruppo Italiano Disdrometria, GID, https://www.gid-net.it/). The GID has made freely available a database of 1-minute records of DSD collected by the disdrometer network along the Italian peninsula. At the time of writing, the disdrometer network is composed of eight laser disdrometers belonging to six different Italian institutions (including research centers, universities and environmental regional agencies). This work aims to document the technical aspects of the italian DSD database consisting of 1-minute sampling data from 2012 to 2021 in a uniform standard format defined within GID. Although not all the disdrometers have the same length of the data record, the DSD data collection effort is the first of its kind in Italy, and from here onwards, it opens new opportunities in the surface characterization of microphysical properties of precipitation in the perspective of climate records. The GID database can be downloaded here https://doi.org/10.5281/zenodo.6875801 (Adirosi et al., 2022). [ABSTRACT FROM AUTHOR]

The transition from the Advanced Very High Resolution Radiometer (AVHRR)/2 to AVHRR/3 on NOAA polar orbiters was associated with a switching from daylight operations of the 3.7- to 1.6-[micro]m wave band, while retaining 3.7 [micro]m for nighttime operations. Investigations of the daylight applicability of the two channels suggest that the 1.6-[micro]m wave band for daylight operations does not prove to be the better choice, at least for cloud applications. The 3.7-[micro]m wave band is much less affected by surface contamination, and measures more faithfully and unambiguously the particle effective radius near cloud tops. The 1.6-[micro]m radiation penetrates deeper into the cloud, supplying an integrated signal throughout the inner portions of the cloud, including surface contribution. Therefore, a synergetic use of the two wave bands can provide an improved retrieval of cloud microstructure and precipitation than from any of the channels alone. However, when one channel must be selected for the AVHRR/3, 3.7 [micro]m performs much better for these applications. Both wave bands identify equally well microphysical features in the anvils of severe storms. For other applications, such as detection of ice and snow over vegetated surfaces and desert dust aerosols, the 1.6-[micro]m wave band does not present clear advantages with respect to 3.7 [micro]m, except that it can be used directly as is, whereas the 3.7-[micro]m wave band has to be corrected for the thermal emission and water vapor absorption. Anyway, the Moderate Resolution Imaging Spectroradiometer (MODIS) can be used instead for the applications to the relatively slowly changing surface properties, while prioritizing the AVHRR for the faster varying atmospheric applications. Finally, the 3.7-[micro]m wave band is more effective in detecting fog, fires, and hot spots. All these factors need to be considered by the operators of AVHRR/3 making a justifiable choice of the channels for the maximum benefit of the user community.

The reflectivity (Z)—rain rate (R) model has not been tested on single polarization radar for estimating monsoon rainfall in Southeast Asia, despite its widespread use for estimating heterogeneous rainfall. The artificial neural network (ANN) regression has been applied to the radar reflectivity data to estimate monsoon rainfall using parametric Z-R models. The 10-min reflectivity data recorded in Kota Bahru radar station (in Malaysia) and hourly rain record in nearby 58 gauge stations during 2013–2015 were used. The three-dimensional nearest neighbor interpolation with altitude correction was applied for pixel matching. The non-linear Levenberg Marquardt (LM) regression, integrated with ANN regression minimized the spatiotemporal variability of the proposed Z-R model. Results showed an improvement in the statistical indicator, when LM and ANN overestimated (6.6%) and underestimated (4.4%), respectively, the mean total rainfall. For all rainfall categories, the ANN model has a positive efficiency ratio of >0.2. [ABSTRACT FROM AUTHOR]