Accurate quantitative precipitation estimation (QPE) methods are essential for weather forecasting and for prevention of hydrogeological risk. QPE becomes even more important when facing severe precipitation events. In this letter, a comparison among different rainfall estimation methods is presented, using a severe event that occurred in Italy as a case study. In particular, the focus is on a merging method based on the dynamic adaptation of the Z–R relationship according to the spatiotemporal evolution of the observed phenomenon. Through a cross-validation analysis, we quantitatively assess the effectiveness of such a method: compared with the others, it performs better on the average, while it can outperform them in critical rainfall conditions, confirming its potential for localizing and monitoring areas with greatest risks.

Physical characteristics of precipitation, like temporal and spatial variability, jointly with coverage and costs of conventional meteorological devices for quantitative rainfall estimation (i.e., rain gauges, disdrometers, weather radars) make the precipitation monitoring a complex task. However, real time rainfall maps are an important tool for many applications, dealing with environment, social activities, and business. Recently, the use of “opportunistic” methods to estimate rainfall has been investigated, highlighting the possibility to exploit inexpensive opportunities to augment information about precipitation. This paper deals with SmartLNBs (Smart Low-Noise Block converters), which are commercially available interactive digital video broadcasting (DVB) receivers designed to be used as bidirectional modems for commercial interactive TV applications. In the last few years an algorithm that converts the SmartLNB raw data into attenuation values, from which the rainfall rate is obtained, has been developed and evaluated. The aim of this paper is to describe the improvements of the rainfall estimation from SmartLNBs brought by significant changes in the data acquisition from SmartLNB and by algorithms’ update. One year of data collected in Rome and Tuscany (Italy) are analyzed to test the performance of SmartLNB in estimating rainfall accumulation with respect to co-located rain gauges and disdrometer in the new configuration. Comparing SmartLNB and disdrometer data in Rome we obtained Root Mean Square Error equal to 7.7 mm, Normalized Mean Absolute Error equal to 44%, with a correlation coefficient of 0.91, that can point out the maturity of the technique.

The Normalized Differential Spectral Attenuation technique (NDSA) has been proposed as a method for measuring Integrated Water Vapor (IWV) by means of attenuation measurements in the Ku-/K-band along tropospheric microwave links. After a first measurement campaign aimed at demonstrating the NDSA method, a second field experiment has been carried out from August 1 to November 30, 2022. The transmitter was placed on the top of Monte Gomito (44.1277° lat, 10.6434° lon, 1892 meters a.s.l.) and the receiver on the roof of the Department of Information Engineering of the University of Florence (43.7985° lat, 11.2528° lon, 50 meters a.s.l.). The resulting radio link length was 61.15 km. Four ground weather stations of the regional weather service were selected among those available. The purpose is comparing the IWV estimates provided by NDSA measurements with the ground point data of air temperature, air humidity, barometric pressure, and rainfall.

Measurement of water vapor (WV) in the lower troposphere on a continuous temporal basis would improve our knowledge of the atmospheric dynamics and the performance of numerical weather prediction models. In recent studies, a new measurement concept, the normalized differential spectral attenuation (NDSA) approach, was proposed. It is based on measurements of differential attenuation at 18.8 and 19.2 GHz performed along a tropospheric radio link. While NDSA measurement at a fixed elevation angle provides information on integrated WV (IWV), measurements at different elevation angles allow to retrieve the vertical WV content profile. A prototype NDSA demonstrator, which consists of two units, a synthesized transmitter and a software-defined radio receiver, has been designed and implemented. The system was accurately characterized through several laboratory tests, and then a first experimental campaign was conducted at fixed elevation angle along a ground-to-ground radio link. Obtained results confirm the sensitivity of the NDSA measurements to the IWV along such link with a good agreement with the existing ground-based and satellite data products.

This paper illustrates the first results of the analysis carried out to check the possibility to provide an estimate of the Integrated Liquid Water (ILW) content along a microwave link established between the multifrequency transmitter and the receiver on board a couple of Low Earth Orbiting (LEO) satellites. The ILW estimate is provided through the power ratio of the received signals at 32 and 17 GHz. The results have been obtained using a simulation tool developed for LEO satellites orbiting in the same plane and along the same angular direction (co-rotating satellites), assuming some high-resolution atmospheric scenarios generated by using the WRF (Weather Research and Forecasting) numerical weather prediction model.

Recently, inversion of Integral Water Vapor (IWV) measurements, obtained through the use of the Normalized Differential Spectral Absorption (NDSA) has been proposed to retrieve the tropospheric Water Vapor (WV) content. In this paper, the results of the application of the External Reconstruction Tomographic Algorithm (ERTA) to that problem are presented. The results have been obtained by considering a measurement scenario composed of LEO satellites orbiting in the same plane and along the same angular direction (co-rotating satellites).

In recent years, severe weather phenomena have occurred with increasing frequency throughout the Mediterranean area. Because of the extreme intensity of the phenomena and of their small spatio-temporal scales, an early warning of severe rainfall through a timely and accurate estimation is crucial for reducing the hydrological risk and for disaster mitigation. On the other hand, the rain-gauge networks are often not able to detect rainfall due to their limited sampling capability, as occurred in the two case studies presented in this work, characterized by severe weather conditions. For both case studies, we utilized a data-fusion procedure aimed at real-time estimation of cumulative rainfall fields, based on the reflectivity factor Z provided by ground weather radar and rain-gauge estimates of the rainfall intensity R. The use of Z-R relationships determined a priori, as typically done by operational weather services, is not appropriate for obtaining accurate rainfall estimates, as needed, for instance, to forecast flash flood events. Consequently, additional information is needed. The procedure utilized is based on an adaptive data-fusion technique, relying on the dynamic adjustment of the coefficients of the Z-R relationship to the observed phenomenon. Its application to two severe weather case studies demonstrated the capability of the methodology to correctly identify and monitor areas of high potential risk as well as to provide rainfall estimates in such areas.

The knowledge of the water vapor (WV) distribution in the Earth's atmosphere is of great importance for weather prediction. Meteorological models, in particular, the so-called limited area models, can assimilate humidity measurements, increasing the reliability of the simulated atmospheric dynamics. An important improvement can be achieved, for instance, if we are able to provide the total column with a sufficient precision and accuracy. In this paper, the novel normalized differential spectral attenuation (NDSA) approach is applied to retrieve the vertical profile of WV-and thus the total column-from measurements of differential attenuation signals at microwaves. A forward model (FM) has been used to simulate the ray-tracing of a microwave signal from a transmitter to a receiver in the atmosphere by using the 3-D atmospheric parameters as provided by a numerical weather prediction (NWP) model. From the NDSA measurement, the integrated WV (IWV) content can be directly derived. A further retrieval code is able to invert the measurements of IWV along the path length, providing the vertical humidity profile, which is directly related to the total vertical column assimilated by weather prediction models. In this paper, we show that the values of the total column can be retrieved with a precision and accuracy up to about 0.6% and 2.1%, respectively, which could have a positive impact on NWP models at short time scale.

In this paper, we propose a novel Bayesian procedure to update the probability distribution for a set of possible atmospheric states, once ground measures of temperature, pressure, humidity, and tropospheric delay of Global Navigation Satellite System (GNSS) signals are made. It is based on a representative dataset of matching pairs of reanalysis atmospheric states and ground measures. By applying the basic rules of probability theory and logic inference, a computable expression for the conditional probability of the states given the measures is found. This allows us to select the most plausible atmospheric conditions, consistent with ground observations. Compared with more conventional techniques, the proposed approach has the advantage of always giving a result, even if not all the measures are available. Moreover, it provides the probability distributions of the retrieved quantities, which collapse to the corresponding prior distributions in the worst case of no significant measures. In any case, the final uncertainties are fully quantified, as needed for many meteorological applications, including data assimilation and ensemble forecasts for a numerical weather model. In addition to the theoretical details, a practical example of operational application, using a ten-year dataset on a Mediterranean test site, is also presented. The most probable retrieved atmospheric profiles of water vapor and temperature, as well as the corresponding values of precipitable water, are compared with balloon measurements on such a test site, showing good agreement and a significant improvement when the GNSS delay measure is added. In particular, the precipitable water retrieval turns out at least as accurate as that obtained with conventional approaches.