1,721,025 research outputs found
Iterative Bayesian Retrieval of Hydrometeor Content From X-Band Polarimetric Weather Radar
No full textDual-polarized weather radars are capable to detect and identify different classes of hydrometeors, within stratiform and convective storms, exploiting polarimetric diversity. Among the various techniques, a model-supervised Bayesian method for hydrometeor classification, tuned for S- and X-band polarimetric weather radars, can be effectively applied. Once the hydrometeor class is estimated, the retrieval of their water content can also be statistically carried out. However, the critical issue of X-band radar data processing, and in general of any attenuating wavelength active system, is the intervening path attenuation, which is usually not negligible. Any approach aimed at estimating hydrometeor water content should be able to tackle, at the same time, path attenuation correction, hydrometeor classification uncertainty, and retrieval errors. An integrated iterative Bayesian radar algorithm (IBRA) scheme, based on the availability of the differential phase measurement, is presented in this paper and tested during the International H(2)O Project experiment in Oklahoma in 2002. During the latter campaign, two dual-polarized radars, at S- and X-bands, were deployed and jointly operated with closely matched scanning strategies, giving the opportunity to perform experimental comparisons between coincident measurements at different frequencies. Results of the IBRA technique at X-band are discussed, and the impact of path attenuation correction is quantitatively analyzed by comparing hydrometeor classifications and estimates with those obtained at S-band. The overall results in terms of error budget show a significant improvement with respect to the performance with no path attenuation correction
Evaluation of high-frequency channels for deep-space data transmission using radiometeorological model forecast
No full textThe aim of this paper is to investigate the usability of high-frequency channels for deep-space (DS) transmissions exploiting radiometeorological forecast modeling. A previously developed model chain for DS link-budget optimization, based on numerical weather forecasts (WFs), is adopted. The latter, already tested at Ka-band, exploits the combination of a high-resolution mesoscale forecast model and a radiative transfer model to predict the atmospheric scenario and optimize received data volume (DV) during DS transmissions. To shift available Ka-band results to other frequencies, we apply frequency-scaling laws to extrapolate forecast path attenuation, link parameters, and maximum allowed bit-rate for data transmission. Exploiting the available WF-based methodology, we compute DV return for DS missions operating at X -, K -, Ka-, Q -, and W -bands in order to make a comparative study of the behavior of DS transmission-channels at these frequencies. Results show that, in terms of received DV, an innovative WF-based approach is more convenient than traditional methodologies and exhibits a trend similar to the benchmark (ideal case). Increasing link frequency, received DV increases up to Q -band. From Q - to W -band, despite received DV does not increase significantly, lost data remain under reasonable values, thus making the W -band suitable if coupled with a WF-based technique
Bayesian statistical analysis of ground-clutter for the relative calibration of dual polarization weather radars
Full text linkA new data processing methodology, based on the statistical analysis of ground-clutter echoes and aimed at investigating the stability of the weather radar relative calibration, is presented. A Bayesian classification scheme has been used to identify meteorological and/or ground-clutter echoes. The outcome is evaluated on a training dataset using statistical score indexes through the comparison with a deterministic clutter map. After discriminating the ground clutter areas, we have focused on the spatial analysis of robust and stable returns by using an automated region-merging algorithm. The temporal series of the ground-clutter statistical parameters, extracted from the spatial analysis and expressed in terms of percentile and mean values, have been used to estimate the relative clutter calibration and its uncertainty for both co-polar and differential reflectivity. The proposed methodology has been applied to a dataset collected by a C-band weather radar in southern Italy
Inside Volcanic clouds: Remote Sensing of Ash Plumes Using Microwave Weather Radars
No full textAsh clouds due to volcanic eruptions can be detected in near–real time, quantitatively
retrieved, and microphysically characterized by using ground-based microwave weather
radars and their high-resolution spatial–temporal coverage.Ash clouds due to volcanic eruptions can be detected in near–real time, quantitatively
retrieved, and microphysically characterized by using ground-based microwave weather
radars and their high-resolution spatial–temporal coverage
Potential of weather radar in estimating volcanic eruption source parameters: case study of Eyjafjallajökull volcano eruption
No full textQuantitative estimation of precipitation on X-Band Synthetic Aperture Radar Imagery
No full textSpaceborne synthetic aperture radars (SARs) have become an important, in same extent fundamental, instrument for Earth observation and analysis. In particular, platforms operating at L-band and above have found a wide diffusion e.g. for flood areas detection and monitoring, earthquakes analysis, digital elevation model production, land use
monitoring and classification, while other application are under research, such as analysis of volcanic ashes. One of most interesting characteristic of these instruments is the ground spatial resolution (that can reach meters dimension). On the contrary, one of their traditional limitations is given by the reduced duty cycle and coverage: in this respect, recent space SAR missions, operational or near to completing deployment, have greatly reduced this limit. Other characteristic, traditionally allocated to SAR system, is the “all-weather” nature, which is insensitivity to meteorological phenomenon. Experience with simulated and observed data has indicated that this affirmation requires some refinements. Precipitations can significantly affect the signal backscattered from the ground surface (e.g. Ferrazzoli and Schiavon, 1997). Moreover meteorological phenomenon can directly alter the SAR received signal, both in amplitude and phase, as assessed by several authors in the last years (e.g. Marzano et al., 2010, Baldini et al., 2014) analyzing X-Band SAR data by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. Indeed the probability of matching a significant event is low (Danklmayer et al., 2009). If this sensitivity usually represents a problem to be addressed by SAR users, it could represent an interesting opportunity to detect and measure precipitations in wild areas. Moreover they offer the unique opportunity to ingest within flood forecasting model precipitation data at the catchment scale. In this work, we propose a processing framework aiming at producing precipitation maps and cloud masks by X-SARs data. Cloud masks are useful to SAR ground applications user to detect areas compromised by precipitations; in this
work, they are used also to improve the SAR precipitation product, using ancillary data. Precipitation maps, obtained at a very high ground resolution, as allowed in microwaves only by SAR systems, offer interesting opportunity not only in itself but also developing synergic uses with ground weather radar (WR), e.g. for WR calibration, development of improved WR precipitation retrieval algorithms or to improve cloud volume characterization, using the different operating frequency and observing geometry. In this respect, even if work has been done in the last years, several issues still need to be fully addressed. The developed procedure allows distinguishing flooded areas, precipitating clouds together with permanent water bodies, all appearing dark in the SAR image; this allows reducing the possibility of misinterpretations of the SAR data, which obviously have consequences on the precipitation map produced. Moreover, it allows estimating a cloud-free SAR image in order to retrieve the cloud attenuation. The following precipitation map procedure is based on the retrieval algorithm developed by Marzano et al. (2011), applied only to pixels where rain is known to be present. The developed procedure uses image segmentation techniques and fuzzy logic to perform the dark areas detection and recognition, while used ancillary data include local incident angle map and land cover (e.g. Pulvirenti et al. 2014 and Mori et al. 2012). The proposed methodology have been applied to 16 case study, acquired within TSX and CSK missions over Italy and United States, in order to analyzing both hurricane-like intense events and continental mid-latitude ones. Moreover this choice offer the possibility to establish the comparison with operational ground weather radar products, for both verify and validate the proposed methodology and to exploit the synergic use SAR-WR. We will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification. Produced precipitations map will be available online within the portal of the FP7 project EartH2Observe “Global Earth Observation for Integrated Water Resource Assessment” (http://www.earth2observe.eu
Spectral Downscaling of Integrated Water Vapor Fields From Satellite Infrared Observations
No full textAtmospheric water vapor is a crucial constituent affecting both climate change and hydrological cycle processes, whereas on the other hand, it has a significant impact on the electromagnetic signal propagation. Since the distribution of atmospheric water vapor strongly varies with time, location, and altitude, it is necessary to monitor it at high spatial and temporal resolution. Unfortunately, mapping its spatial distribution is difficult due to the lack of meteorological instrumentation at an adequate spatial and temporal observation scale. For many geophysical applications, there is also the need to reconstruct spatial details of integrated precipitable water vapor from information available only at coarser spatial scales. Spatial downscaling approaches can play a significant role when high-resolution water vapor retrievals from relatively new sensors, like synthetic aperture radars, or from conventional sensors, like the infrared radiometers MEdium Resolution Imaging Spectrometer (MERIS) or Moderate Resolution Imaging Spectroradiometer (MODIS), are used in synergy to enhance the accuracy of integrated water vapor retrievals. In this context, this paper introduces some new methodological aspects to increase the spatial resolution of integrated precipitable water vapor observations using a statistical downscaling spectral approach. To highlight the potential and the usefulness of the proposed downscaling estimation procedure, collocated 250-m MERIS and 1-km MODIS acquisitions are used. Results reveal the ability of spectral downscaling to reproduce quite well the second-order statistical variability of the water vapor field at small spatial scales with a root-mean-square error comparable with conventional interpolation techniques
Retrieval of tephra size spectra and mass flow rate from C-band radar during the 2010 Eyjafjallajökull eruption, Iceland
No full textThe eruption of the Eyjafjallajokull volcano in April-May 2010 was continuously monitored by the Keflavik C-band weather radar. The Keflavik radar is located at a distance of about 156 km from the volcano vent, and it has sensitivity of about -5 dBZ at 2-km range resolution over the volcanic area. The time series of radar volume data, which was available every 5 min, is quantitatively analyzed by using the Volcanic Ash Radar Retrieval (VARR) technique. The latter is a physically based methodology that is applied to estimate ash-fall rate and mass concentration within each radar volume. The VARR methodology is here extended, with respect to the previous formulation, to provide an approximate estimate of both mean particle diameter and airborne tephra particle size distribution under some assumptions. Deposited tephra at ground is also extrapolated together with an estimate of the magma mass flow rate (MFR) at the volcano vent, derived from the implementation of the mass continuity equation in the radar reference system. The VARR-based retrievals are compared with those derived from a direct tephra sampling at the ground, experimentally carried out in terms of ash grain size and loading during the Eyjafjallajokull eruption activity on May 5-7, 2010. VARR-based particle diameter estimates may suggest that a sorting of airborne particles during the downwind transport is taking place without observing aggregation processes during the ash fall. VARR-derived daily ash mass loadings in the period between April 14 and May 10 are also evaluated with respect to integrated ground and model-based data in the Eyjafjallajokull area. VARR-retrievedMFRs are finally compared with corresponding values obtained from analytical 1-D eruption models, using radar-estimated plume height and radio-sounding wind fields. A fairly good agreement is obtained, thus opening the exploitation of weather radar retrievals for volcanic eruption quantitative studies and ash dispersion model initialization
Exploiting microwave scanning radar for monitoring Icelandic volcanic eruption source parameters
No full textThe explosive eruption at the summit of sub-glacial Ejyafjallajökull volcano in April-May 2010 was of modest size, but with a erupted ash plume reaching a 6-10 km height above the volcano vent. The ash was widely dispersed over Iceland and Europe, causing a global interruption of main air traffic and causing large economic losses. The quality of the airborne ash particle forecast in the atmosphere depends on realistic description of erupted ash dispersion and then on erupted fine ash mass rate. In this primary work, we present the validity of the use of microwave radar data to estimate mass flows rate MFR through an interesting extension of the Volcanic Ash Radar Retrieval (VARR), which uses the observables of C-band radar in Keflavik
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