Artificial neural-network technique for precipitation nowcasting from satellite imagery

The term nowcasting reflects the need of timely and accurate predictions of risky situations related to the development of severe meteorological events. In this work the objective is the very short term prediction of the rainfall field from geostationary satellite imagery entirely based on neural network approach. The very short-time prediction (or nowcasting) process consists of two steps: first, the infrared radiance field measured from geostationary satellite (Meteosat 7) is projected ahead in time (30 min or 1 h); secondly, the projected radiances are used to estimate the rainfall field by means of a calibrated microwave-based combined algorithm. The methodology is discussed and its accuracy is quantified by means of error indicators. An application to a satellite observation of a rainfall event over Central Italy is finally shown and evaluated

Rainfall rate retrieval in presence of path attenuation using C-band polarimetric weather radars

Weather radar systems are very suitable tools for the monitoring of extreme rainfall events providing measurements with high spatial and temporal resolution over a wide geographical area. Nevertheless, radar rainfall retrieval at C-band is prone to several error sources, such as rain path attenuation which affects the accuracy of inversion algorithms. In this paper, the so-called rain profiling techniques (namely the surface reference method FV and the polarimetric method ZPHI) are applied to correct rain path attenuation and a new neural network algorithm is proposed to estimate the rain rate from the corrected measurements of reflectivity and differential reflectivity. A stochastic model, based on disdrometer measurements, is used to generate realistic range profiles of raindrop size distribution parameters while a T-matrix solution technique is adopted to compute the corresponding polarimetric variables. A sensitivity analysis is performed in order to evaluate the expected errors of these methods. It has been found that the ZPHI method is more reliable than FV, being less sensitive to calibration errors. Moreover, the proposed neural network algorithm has shown more accurate rain rate estimates than the corresponding parametric algorithm, especially in presence of calibration errors

Relation between weather radar equation and first-order backscattering theory

International audienceAim of this work is to provide a new insight into the physical basis of the meteorological-radar theory in attenuating media. Starting form the general integral form of the weather radar equation, a modified form of the classical weather radar equation at attenuating wavelength is derived. This modified radar equation includes a new parameter, called the range-bin extinction factor, taking into account the rainfall path attenuation within each range bin. It is shown that, only in the case of low-to-moderate attenuating media, the classical radar equation at attenuating wavelength can be used. These theoretical results are corroborated by using the radiative transfer theory where multiple scattering phenomena can be quantified. From a new definition of the radar reflectivity, in terms of backscattered specific intensity, a generalised radar equation is deduced. Within the assumption of first-order backscattering, the generalised radar equation is reduced to the modified radar equation, previously obtained. This analysis supports the conclusion that the description of radar observations at attenuating wavelength should include, in principle, first-order scattering effects. Numerical simulations are performed by using statistical relationships among the radar reflectivity, rain rate and specific attenuation, derived from literature. Results confirm that the effect of the range-bin extinction factor, depending on the considered frequency and range resolution, can be significant at X band for intense rain, while at Ka band and above it can become appreciable even for moderate rain. A discussion on the impact of these theoretical and numerical results is finally included

Satellite radiometric remote sensing of rainfall fields: multi-sensor retrieval techniques at geostationary scale

International audienceThe Microwave Infrared Combined Rainfall Algorithm (MICRA) consists in a statistical integration method using the satellite microwave-based rain-rate estimates, assumed to be accurate enough, to calibrate spaceborne infrared measurements on limited sub-regions and time windows. Rainfall retrieval is pursued at the space-time scale of typical geostationary observations, that is at a spatial resolution of few kilometers and a repetition period of few tens of minutes. The actual implementation is explained, although the basic concepts of MICRA are very general and the method is easy to be extended for considering innovative statistical techniques or measurements from additional space-borne platforms. In order to demonstrate the potentiality of MICRA, case studies over central Italy are also discussed. Finally, preliminary results of MICRA validation by ground based remote and in situ measurements are shown and a comparison with a Neural Network (NN) based technique is briefly illustrated

Evaluation of radiative transfer schemes for mesoscale model data assimilation: a case study

The assimilation of Special Sensor Microwave Imager (SSM/I) data into the Mesoscale Model 5 (MM5) allows for improving the weather forecast. However the results suggested an update the Radiative Transfer Equation (RTE) within the three-dimensional variational (3DVAR) algorithm which is tailored for non rainy conditions only. To this purpose, a new RTE algorithm is tested, in order to account for radiometric response in rainy regions. The new brightness temperatures (<i>T_B</i>) are estimated by using hydrometeor profiles from the MM5 mesoscale model, running with two different microphysical parameterizations. The goodness of the results is assessed by comparing the new <i>T_B</i> with those of the original RTE algorithm in the 3DVAR code and the SSM/I observed data. The results confirm a better reliability of the new RTE compared to the old one

X-band synthetic aperture radar methods

Spaceborne Synthetic Aperture Radars (SARs), operating at L-band and above, offer microwave observations of the Earth at very high spatial resolution in almost all-weather conditions. Nevertheless, precipitating clouds can significantly affect the signal backscattered from the ground surface in both amplitude and phase, especially at X band and beyond. This evidence has been assessed by numerous recent efforts analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions at X band. This sensitivity can be exploited to detect and quantify precipitations from SARs at the spatial resolution of a few hundred meters, a very appealing feature considering the current resolution of precipitation products from space. Forward models of SAR response in the presence of precipitation have been developed for analyzing SAR signature sensitivity and developing rainfall retrieval algorithms. Precipitation retrieval algorithms from SARs have also been proposed on a semi-empirical basis. This chapter will review experimental evidences, modelling approaches, retrieval methods and recent applications of X-band SAR data to rainfall estimation

Assessing radiative transfer models trained by numerical weather forecasts using sun-tracking radiometric measurements for satellite link characterization up to W band

Radio communications, and in particular Earth-to-satellite links, are worldwide used for delivering digital services. The bandwidth demand of such services is increasing accordingly to the advent of more advanced applications (e.g., multimedia services, deep-space explorations, etc.) thus pushing the scientific community toward the investigation of channel carriers at higher frequencies. When using carrier frequencies above X band, the main drawback is how to tackle the impact of tropospheric processes (i.e., rain, cloud, water vapor). This work assesses the joint use of weather forecast models, radiative transfer models and Sun-tracking radiometric measurements to explore their potential benefits in predicting path attenuation and sky noise temperature for slant paths at frequencies between K and W band, thus paving the way to the optimization of satellite link-budgets

Evaluation of radiative transfer schemes for mesoscale model data assimilation: a case study

International audienceThe assimilation of Special Sensor Microwave Imager (SSM/I) data into the Mesoscale Model 5 (MM5) allows for improving the weather forecast. However the results suggested an update the Radiative Transfer Equation (RTE) within the three-dimensional variational (3DVAR) algorithm which is tailored for non rainy conditions only. To this purpose, a new RTE algorithm is tested, in order to account for radiometric response in rainy regions. The new brightness temperatures (TB) are estimated by using hydrometeor profiles from the MM5 mesoscale model, running with two different microphysical parameterizations. The goodness of the results is assessed by comparing the new TB with those of the original RTE algorithm in the 3DVAR code and the SSM/I observed data. The results confirm a better reliability of the new RTE compared to the old one

Maximum-likelihood retrieval of volcanic ash concentration and particle size from ground-based scanning lidar

An inversion methodology, named maximum-likelihood (ML) volcanic ash light detection and ranging (Lidar) retrieval (VALR-ML), has been developed and applied to estimate volcanic ash particle size and ash mass concentration within volcanic plumes. Both estimations are based on the ML approach, trained by a polarimetric backscattering forward model coupled with a Monte Carlo ash microphysical model. The VALR-ML approach is applied to Lidar backscattering and depolarization profiles, measured at visible wavelength during two eruptions of Mt. Etna, Catania, Italy, in 2010 and 2011. The results are compared with those of ash products derived from other parametric retrieval algorithms. A detailed comparison among these different retrieval techniques highlights the potential of VALR-ML to determine, on the basis of a physically consistent approach, the ash cloud area that must be interdicted to flight operations. Moreover, the results confirm the usefulness of operating scanning Lidars near active volcanic vents