Andrea Antonini

Qualitative weather radar mosaic in a multisensor rainfall monitoring approach

Abstract. A method is presented for integrating the information available in a limited area (corresponding to Tuscany in Italy) coming from satellite sensors, point measurement stations and ground-based radars. The objective is the exploitation of the complementary information provided by the variety of methods and instruments nowadays existing for measuring precipitation or precipitation-related parameters, in order to upgrade the capability of reconstructing weather phenomena of main interest. Ground- and satellite-based measurements, working locally or remotely, are jointly analyzed to evaluate how heterogeneous data can amplify the effectiveness of the measurements, when synergically analyzed, and this holds also when some of the available instruments essentially give just qualitative information. A way to synthesize the different information provided by various instruments is presented, assessing the quality of all the available observations. Namely, steps are described for the achievement of a mosaic of qualitative weather radars, and it is shown how the joint analysis of satellite, rain gauge and lightning observations can support a correct interpretation of precipitation phenomena. Finally, a logical scheme for data integration is presented and discussed.

Recalibration of cumulative rainfall estimates by weather radar over a large area

Abstract. The real-time measurement of rainfall is a primary information source for many purposes, such as weather forecasting, flood risk assessment, and landslide prediction and prevention. In this perspective, remote sensing techniques to monitor rainfall fields by means of radar measurements are very useful. In this work, a technique is proposed for the estimation of cumulative rainfall fields averaged over a large area, applied on the Tuscany region using the Italian weather radar network. In order to assess the accuracy of radar-based rainfall estimates, they are compared with coincident spatial rain gauge measurements. Observations are compared with average rainfall over areas as large as a few tens of kilometers. An ordinary block kriging method is applied for rain gauge data spatialization. The comparison between the two types of estimates is used for recalibrating the radar measurements. As a main result, this paper proposes a recalibrated relationship for retrieving precipitation from radar data. The accuracy of the estimate increases when considering larger areas: an area of 900  km2 has a standard deviation of less than few millimeters. This is of interest in particular for extending recalibrated radar relationships over areas where rain gauges are not available. Many applications could benefit from it, from nowcasting for civil protection activities, to hydrogeological risk mitigation or agriculture.

Water Vapor Probabilistic Retrieval Using GNSS Signals

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.

Estimates of cumulative rainfall over a large area by weather radar

In this work we propose a technique for 15-minutes cumulative rainfall mapping, applied over Tuscany, using Italian weather radar networks together with the regional rain gauge network. In order to assess the accuracy of the radar-based rainfall estimates, we have compared them with spatial coincident rain gauge measurements. Precipitation at ground is our target observable: rain gauge measurements of such parameter have a so small error that we consider it negligible (especially if compared from what retrievable from radars). In order to make comparable the observations given from these two types of sensors, we have collected cumulative rainfall over areas a few tens of kilometres wide. The method used to spatialise rain gauges data has been the Ordinary Block Kriging. In this case the comparison results have shown a good correlation between the cumulative rainfall obtained from the rain gauges and those obtained by the radar measurements. Such results are encouraging in the perspective of using the radar observations for near real time cumulative rainfall nowcasting purposes. In addition the joint use of satellite instruments as SEVIRI sensors on board of MSG-3 satellite can add relevant information on the nature, spatial distribution and temporal evolution of cloudiness over the area under study. For this issue we will analyse several MSG-3 channel images, which are related to cloud physical characteristics or ground features in case of clear sky.

Real-Time Rain Rate Evaluation via Satellite Downlink Signal Attenuation Measurement

We present the NEFOCAST project (named by the contraction of “Nefele”, which is the Italian spelling for the mythological cloud nymph Nephele, and “forecast”), funded by the Tuscany Region, about the feasibility of a system for the detection and monitoring of precipitation fields over the regional territory based on the use of a widespread network of new-generation Eutelsat “SmartLNB” (smart low-noise block converter) domestic terminals. Though primarily intended for interactive satellite services, these devices can also be used as weather sensors, as they have the capability of measuring the rain-induced attenuation incurred by the downlink signal and relaying it on an auxiliary return channel. We illustrate the NEFOCAST system architecture, consisting of the network of ground sensor terminals, the space segment, and the service center, which has the task of processing the information relayed by the terminals for generating rain field maps. We discuss a few methods that allow the conversion of a rain attenuation measurement into an instantaneous rainfall rate. Specifically, we discuss an exponential model relating the specific rain attenuation to the rainfall rate, whose coefficients were obtained from extensive experimental data. The above model permits the inferring of the rainfall rate from the total signal attenuation provided by the SmartLNB and from the link geometry knowledge. Some preliminary results obtained from a SmartLNB installed in Pisa are presented and compared with the output of a conventional tipping bucket rain gauge. It is shown that the NEFOCAST sensor is able to track the fast-varying rainfall rate accurately with no delay, as opposed to a conventional gauge.

Joint use of weather radars, satellites, and rain gauge for precipitation monitoring

Intense precipitation phenomena occurring over the Tyrrhenian area between Tuscany, Corse Sardinia, and Liguria very often cause floods with considerable socio-economic damages. The need of monitoring such events has led to the implementation of an observing weather radar network: it firstly started with an S-band radar in Corse, three C-band radars in Liguria, Tuscany and Sardinia. Recently, the implementation of an X–band network of three radars in Tuscany and two further C-band radars in Sardinia completed the network. This work shows how this network can be used for the characterization of weather events, following their development and dynamics and providing some information about their possible evolution. Furthermore, the use of meteorological satellites observations can upscale the area of interest to the mesoscale level and provide an enlarged temporal overview. For instance, the Meteosat Second Generation satellites provide useful information about the air mass distribution, convective phenomena occurrence and microphysics in the observed scene, by combining different spectral channels. Finally, ground based observations are meaningful for assessing the observing capabilities of other instruments and for characterizing the effects on soil surface. For some selected case studies, the different observing instruments were compared and a methodology to integrate them synergically is presented and tested. Weather radars correctly detect the rainfall systems and their motion in all the case studies. Clearly, the higher spatial resolution of X-band radars allows detecting the different precipitation areas with great spatial details, while C- and S-band radars can detect phenomena at higher distances. Satellites images have lower spatial resolutions but especially thanks to the RSS (Rapid Scan Service) they can help to detect the growing or dissipating stage of the whole phenomena. Moreover the ground-based network confirms its relevance in improving the identification of the precipitation intensity and in reducing the number of false alarms.