Numerical scattering simulations for interpreting simultaneous observations of clouds by a W-band spaceborne and a C-band ground radar

The spaceborne W-band (94 GHz) Cloud Profiling Radar (CPR) onboard the CloudSat (CS) satellite, which was launched in 2006, is providing valuable information about global cloud properties. This work aims at interpreting collocated time/space observations from CPR on CS and a ground C-band (5.6 GHz) Radar (GR), with the help of numerical simulations of electromagnetic scattering returns from populations of monodisperse spheres of ice and liquid water. Two cloud systems over Apulia region are investigated. CPR and GR images have been geo-referenced, then combined and displayed for analysis. The numerical simulations of the two radar reflectivities are used as a tool in the inversion procedure, aiming at identifying the hydrometeors, in their phase and size distribution, in the cloud volume simultaneously observed by the two radars. The possible vertical profiles of hydrometeors are presented

CloudSat-based assessment of GPM Microwave Imager snowfall observation capabilities

The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60◦N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water,and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166∆TB)and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW \u3e 3.6 kg·m−2, IWP \u3e 0.24 kg·m−2 over land, and SIC \u3e 57%, TPW \u3e 5.1 kg·m−2 over sea). The complex combined 166∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms

SLALOM: An all-surface snow water path retrieval algorithm for the GPM microwave imager

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI

Multi-Variable Classification Approach for the Detection of Lightning Activity Using a Low-Cost and Portable X Band Radar

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using “ad hoc„ classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG > 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters

Exploitation of GPM/CloudSat coincidence dataset for global snowfall retrieval

© 2018 IEEE. The assessment of the actual observational capabilities of snowfall by spaceborne microwave radiometers is crucial to develop and improve precipitation retrieval algorithms. Exploiting coincident spaceborne active and passive microwave sensor datasets can effectively enhance our understanding of high-frequency microwave channels sensitivity to snowfall. This study illustrates the results of the analysis of matched Global Precipitation Measurement (GPM) Microwave Imager (GMI) and CloudSat Cloud Profiling Radar (CPR) snowfall observations (mainly found at latitudes between 55° and 65°) providing insights on GMI multi-frequency signals associated with different snowfall types. These findings are used to develop a new algorithm to retrieve snow water path associated to surface snowfall from GMI multichannel measurements

Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR

© 2017 An important objective of the Global Precipitation Measurement (GPM) mission is the detection of falling snow, since it accounts for a significant fraction of precipitation in the mid-high latitudes. The GPM Core Observatory carries the first spaceborne Dual-frequency Precipitation Radar (DPR), designed with enhanced sensitivity to detect lighter liquid and solid precipitation. The primary goal of this study is to assess the DPR\u27s ability to identify snowfall using near-coincident CloudSat Cloud Profiling Radar (CPR) observations and products as an independent reference dataset. CloudSat near global coverage and high sensitivity of the W-band CPR make it very suitable for snowfall-related research. While DPR/CPR radar sensitivity disparities contribute substantially to snowfall detection differences, this study also analyzes other factors such as precipitation phase discriminators that produce snowfall identification discrepancies. Results show that even if the occurrence of snowfall events correctly detected by DPR products is quite small compared to CPR (around 5–7%), the fraction of snowfall mass is not negligible (29–34%). A direct comparison of CPR and DPR reflectivities illustrates that DPR misdetection is worsened by a noise-reducing DPR algorithm component that corrects the measured reflectivity. This procedure eliminates the receiver noise and side lobe clutter effects, but also removes radar signals related to snowfall events that are associated with relatively low reflectivity values. In an effort to increase DPR signal fidelity associated with snowfall, this paper proposes a simple algorithm using matched DPR Ku/Ka radar reflectivities producing an increase of the fraction of snowfall mass detected by DPR up to 59%

Antibacterial properties of bioactive materials functionalized with silver nanoparticles and plant-derived biomolecules to minimize implant infections

titanium and its alloys have developed into key materials for biomedical applications owing to their biocompatibility with human tissues and excellent mechanical properties. Unmodified titanium is susceptible to microbial infections that might lead to implant failure and devastating complications with high morbidity and treatment costs. An effective approach to minimize microbial contamination on implant surfaces is to modify the biomaterial surface chemistry in order to reduce adhesion and biofilm development by microorganisms. The aim of this research was to evaluate the antibacterial activities of bioactive titanium alloy Ti6Al4V, functionalized with silver nanoparticles or the innovative Mentha piperita (MP) essential oil, both recognized for strong antimicrobial propertie