On the Implementation of a regional X-bandweather radar network

In the last few years, the number of worldwide operational X-band weather radars has rapidly been growing, thanks to an established technology that offers reliability, high performance, and reduced efforts and costs for installation and maintenance, with respect to the more widespread C- and S-band systems. X-band radars are particularly suitable for nowcasting activities, as those operated by the LaMMA (Laboratory of Monitoring and Environmental Modelling for the sustainable development) Consortium in the framework of its institutional duties of operational meteorological surveillance. In fact, they have the capability to monitor precipitation, resolving very local scales, with good spatial and temporal details, although with a reduced scanning range. The Consortium has recently installed a small network of X-band weather radars that partially overlaps and completes the existing national radar network over the north Tyrrhenian area. This paper describes the implementation of this regional network, detailing the aspects related with the radar signal processing chain that provides the final reflectivity composite, starting from the acquisition of the signal power data. The network performances are then qualitatively assessed for three case studies characterised by different precipitation regimes and different seasons. Results are satisfactory especially during intense precipitations, particularly regarding what concerns their spatial and temporal characterisation

The Potential of Smartlnb Networks for Rainfall Estimation

NEFOCAST is a research project that aims at retrieving rainfall fields from channel attenuation measurements on satellite links. Rainfall estimation algorithms rely on the deviation of the measured Es/N0 from the clear-sky conditions. Unfortunately, clear-sky measurements exhibit signal fluctuations (due to a variety of causes) which could generate false rain detections and reduce estimation accuracy. In this paper we first review the main causes of random amplitude fluctuations in the received Es/N0, and then we present an adaptive tracking algorithm based on two Kalman filters: one that tracks slow changes in Es/N0 due to external causes and another which tracks fast Es/N0 variations due to rain. A comparison of the outputs of the two filters confirms the reliability of the rainfall rate estimate

Real-time rain rate evaluation via satellite downlink signal attenuation measurement

We present the NEFOCAST project (named by the contraction of "Nefeleâ", 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

Rainfall Field Reconstruction by Opportunistic Use of the Rain-Induced Attenuation on Microwave Satellite Signals: The July 2021 Extreme Rain Event in Germany as a Case Study

This paper presents a practical application of an opportunistic technique for the estimation of rainfall intensity and accumulated precipitation. The proposed technique is based upon signal strength measurements made by commercial-grade interactive satellite terminals. By applying some processing, the rain-induced attenuation on the microwave downlink from the satellite is first evaluated; then the rain attenuation is eventually mapped into a rainfall rate estimate via a tropospheric model. This methodology has been applied to a test area of 30×30 km2 around the city of Dortmund (North Rhine-Westphalia, upper basin of Ermscher river), for the heavy rain event that devastated western Germany in July, 2021. A rainfall map on this area is obtained from the measurements collected by a set of satellite terminals deployed in the region, and successfully compared with a map obtained with a conventional weather radar

Development and Calibration of a Low-Cost, Piezoelectric Rainfall Sensor through Machine Learning

In situ measurements of precipitation are typically obtained by tipping bucket or weighing rain gauges or by disdrometers using different measurement principles. One of the most critical aspects of their operational use is the calibration, which requires the characterization of instrument responses both in laboratory and in real conditions. Another important issue with in situ measurements is the coverage. Dense networks are desirable, but the installation and maintenance costs can be unaffordable with most of the commercial conventional devices. This work presents the development of a prototype of an impact rain gauge based on a very low-cost piezoelectric sensor. The sensor was developed by assembling off-the-shelf and reused components following an easy prototyping approach; the calibration of the relationship between the different properties of the voltage signal, as sampled by the rain drop impact, and rainfall intensity was established using machine-learning methods. The comparison with 1-minute rainfall obtained by a co-located commercial disdrometer highlights the fairly good performance of the low-cost sensor in monitoring and characterizing rainfall events

Radiative effects of simulated cirrus clouds on top of a deep convective storm in METEOSAT second generation SEVIRI channels

Cirrus clouds often form above intense convective storms due to several different mechanisms and affect the radiation field at the top of the atmosphere. Radiative transfer computations are performed to characterize these effects within the spectral bands of METEOSAT Second Generation’s (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI). Computations refer to five visible, near infrared and infrared MSG SEVIRI channels centered at 0.8, 1.6, 3.9, 10.8, and 12.0 µm. Reflectances and brightness temperatures are computed using the 1-D radiative transfer model STREAMER adopting simple parameterizations of the cloud layers and associated microphysical properties for the determination of the necessary optical properties. A sensitivity study is carried out by varying the cirrus ice crystal size and optical depth. The 1.6 and 3.9 µm channels reveal instrumental for the simultaneous detection of optical depth and crystal size of the cirrus layer. In particular, the results of the 3.9 µm channel show that the smaller the crystal size the higher the reflectance values. The computations provide interpretation clues on the phenomenon of ice crystal plumes on top of deep convective clouds, which are known to produce enhanced reflectivity signatures in the 3.7 µm channel of the Advanced Very High Resolution Radiometer (AVHRR). The sensitivity of the IR channels to cirrus cloud optical depth and ice crystal size is examined and the brightness temperature differences evaluated. Satellite observations and radiative transfer computations are at present the only way of studying such cloud features due to the unavailability of in situ aircraft measurements

Assimilating X- and S-Band Radar Data for a Heavy Precipitation Event in Italy

During the night between 9 and 10 September 2017, multiple flash floods associated with a heavy-precipitation event affected the town of Livorno, located in Tuscany, Italy. Accumulated precipitation exceeding 200 mm in two hours was recorded. This rainfall intensity is associated with a return period of higher than 200 years. As a consequence, all the largest streams of the Livorno municipality flooded several areas of the town. We used the limited-area weather research and forecasting (WRF) model, in a convection-permitting setup, to reconstruct the extreme event leading to the flash floods. We evaluated possible forecasting improvements emerging from the assimilation of local ground stations and X- and S-band radar data into the WRF, using the configuration operational at the meteorological center of Tuscany region (LaMMA) at the time of the event. Simulations were verified against weather station observations, through an innovative method aimed at disentangling the positioning and intensity errors of precipitation forecasts. A more accurate description of the low-level flows and a better assessment of the atmospheric water vapor field showed how the assimilation of radar data can improve quantitative precipitation forecasts

Aerosol characterization and optical thickness retrievals using GOME and METEOSAT satellite data

Retrievals of atmospheric aerosol optical thickness are highly dependent on the choice of the class describing the aerosol properties leading to significant errors while using classes available in the literature. High spectral resolution measurements from GOME (Global Ozone Monitoring Experiment) between the ultraviolet and the near infrared can be used for an accurate characterization of the aerosol optical properties. The radiometer MVIRI (METEOSAT Visible and Infrared Imager) on board the geostationary satellite METEOSAT, while being equipped only with broadband VIS channel, ensures an adequate half-hourly monitoring of the atmospheric conditions over a large portion of the Earth. The present algorithm is based on a combination of data from both sensors for the retrieval of the aerosol optical thickness at the reference wavelength of 0.55μm (AOT). A case of a desert dust outbreak from the African continent over the Atlantic Ocean is examined. AOT values obtained using a priori fixed classes taken from the literature are compared with those retrieved with this algorithm using the GOME-derived classes. Systematic differences of the order of a few tenths on average are found which remain significant also after considering the measurement errors. This represents a novelty introduced by the synergetic use of both sensors