CloudSat Bias on Falling Snow Estimates Over the Daylight Only Operational Period (2012-2019)

Falling snow is a key component for the global atmospheric, hydrological and energy cycles, and its retrieval from space-based observations represents the best current capability to evaluate it globally. The Global Precipitation Measurement (GPM) Mission Core Observatory, launched in 2014, together with its constellation sensors, can provide quasi-global precipitation estimates every 30 minutes (for level 3 products). Evaluation and validation efforts for such products are crucial, and for global evaluations, one of the most suitable instruments is the Cloud Profiling Radar (CPR) on board CloudSat, which is sensitive to light rain and falling snow. However, due to a battery anomaly in 2011, during its period of overlapping observations with GPM the CPR has operated in a Daylight Only Operations mode (DO-Op) in which it makes measurements primarily during only the daylit portion of its orbit. The goal of this work is to estimate biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat's snowfall measurements during its Full Operations (Full-Op) period from 2006 to 2010 to evaluate the impact DO-Op mode would have had during this period. Results indicate that omitting the nocturnal component of the diurnal cycle of snowfall has nonnegligible impact on snowfall amounts in some regions. The lack of nighttime data during DO-Op biases the latitudinally averaged mean snowfall rates as well as some regional values. Hemispheric differences in bias may be due to more pronounced diurnal variability in the northern hemisphere owing to more prevalent land surface versus the southern hemisphere. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products

Multi-sensor Satellite Precipitation Estimate for Hydrogeological Hazard Mitigation

High-impact meteorological events have in the last decade received increasing interest and considerable efforts are constantly undertaken to mitigate their effects on human activities and environment. Several projects addressing different aspects of the risk mitigation strategy have been financed in Europe, and PROSA (Prodotti di Osservazione Satellitare per l'Allerta Meteorologica - Satellite products for meteorological alert), funded by the Italian Space Agency (ASI), represents the Italian attempt to solve the meteorological side of the hazard mitigation scheme. It is devoted to design, develop, test and demonstrate a prototype system dedicated to the innovative dynamic characterization of meteorological parameters at the ground by means of satellite data. This work is part of PROSA and the main objective is the implementation and optimization of three di�erent satellite precipitation estimation algorithms. The algorithms are based on Artificial Neural Networks and correlate multi-sensors satellite data, in the Visible, Infrared (from the European\ud Geostationary satellite Meteosat) and Microwave bands (from polar orbiting satellites), to the precipitation rate at ground. The ANNs are set up as classification problem and use rain-gauges data as true values of precipitation at the ground for the training, testing an validation of the techniques. The work is divided in three main steps: the first version of the algorithm gives a binary classification of satellite pixel as rain and no-rain classes, with seasonal and day-time characterization of the precipitation maps. The second version gives a quantitative estimate, classifying the rain-rate in five precipitation intervals. Finally, the last version provides precipitation maps with quantitative values expressed in mmh^-1, and also explicitly uses microwave data. To reach the main objective several sensitivity studies and intermediate goals have been pursued, in order to refine and tune the technique. The sensitivity to precipitation of the infrared channels with respect to the seasonal cycle and the impact of the visible channels on the estimates have been assessed. The relationship between the probability of precipitation, output of the neural network, and the rain-rate, as measured by rain-gauges, has been established for warm and cold months, and the optimal way to ingest in the algorithm the microwave estimates has been defined by analyzing the di�erent performances of microwave and visible-infrared techniques. Finally, the results have been critically discussed in comparison with other algorithms taking part of the PROSA system

DeepPrecip: A deep neural network for precipitation retrievals

Remotely-sensed precipitation retrievals are critical for advancing our understanding of global energy and hydrologic cycles in remote regions. Radar reflectivity profiles of the lower atmosphere are commonly linked to precipitation through empirical power laws, but these relationships are tightly coupled to particle microphysical assumptions that do not generalize well to different regional climates. Here, we develop a robust, highly generalized precipitation retrieval from a deep convolutional neural network (DeepPrecip) to estimate 20-minute average surface precipitation accumulation using near-surface radar data inputs. DeepPrecip displays high retrieval skill and can accurately model total precipitation accumulation, with a mean square error (MSE) 99 % lower, on average, than current methods. DeepPrecip also outperforms a less complex machine learning retrieval algorithm, demonstrating the value of deep learning when applied to precipitation retrievals. Predictor importance analyses suggest that a combination of both near-surface (below 1 km) and higher-altitude (1.5 &ndash; 2 km) radar measurements are the primary features contributing to retrieval accuracy. Further, DeepPrecip closely captures total precipitation accumulation magnitudes and variability across nine distinct locations without requiring any explicit descriptions of particle microphysics or geospatial covariates. This research reveals the important role for deep learning in extracting relevant information about precipitation from atmospheric radar retrievals.</p

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

Renal vein obstruction and orthostatic proteinuria: a review

Objectives. The cause of orthostatic proteinuria is not clear but may often relate to obstruction of the left renal vein in the fork between the aorta and the superior mesenteric artery (= renal nutcracker). However, reports dealing with proteinuria only marginally refer to this possible cause of orthostatic proteinuria. We analysed the corresponding literature. Results. Five reports addressed the frequency of renal nutcracker in 229 subjects with orthostatic proteinuria. Their age ranged between 5.2 and 17years (female-to-male ratio: 0.96:1.00). Imaging studies demonstrated renal nutcracker in 156 (68%) subjects. Renal nutcracker was also demonstrated in 9 anecdotal reports for a total of 53 subjects with postural proteinuria. Very recently, 13 Italian subjects with orthostatic proteinuria associated with renal nutcracker were reassessed 6years after the initial diagnosis: in nine subjects, both orthostatic proteinuria and renal nutcracker had disappeared; in three, both orthostatic proteinuria and renal nutcracker had persisted; and in one, orthostatic proteinuria had persisted unassociated with renal nutcracker. Conclusions. These data provide substantial support for renal nutcracker as a common cause of orthostatic proteinuri

Myositis and acute kidney injury in bacterial atypical pneumonia: Systematic literature review.

Abstract Background Bacterial community-acquired atypical pneumonia is sometimes complicated by a myositis or by a renal parenchymal disease. Available reviews do not mention the concurrent occurrence of both myositis and acute kidney injury. Methods In order to characterize the link between bacterial community-acquired atypical pneumonia and both myositis and a renal parenchymal disease, we reviewed the literature (United States National Library of Medicine and Excerpta Medica databases). Results We identified 42 previously healthy subjects (35 males and 7 females aged from 2 to 76, median 42 years) with a bacterial atypical pneumonia associated both with myositis (muscle pain and creatine kinase ≥5 times the upper limit of normal) and acute kidney injury (increase in creatinine to ≥1.5 times baseline or increase by ≥27 μmol/L above the upper limit of normal). Thirty-six cases were caused by Legionella species (N = 27) and by Mycoplasma pneumoniae (N = 9). Further germs accounted for the remaining 6 cases. The vast majority of cases (N = 36) presented a diffuse myalgia. Only a minority of cases (N = 3) were affected by a calf myositis. The diagnosis of rhabdomyolysis-associated kidney injury was retained in 37 and that of acute interstitial nephritis in the remaining 5 cases. Conclusion Bacterial atypical pneumonia may occasionally induce myositis and secondary kidney damage

Development, Optimization, and Comparison of Different Sample Pre-Treatments for Simultaneous Determination of Vitamin E and Vitamin K in Vegetables

The absence of vitamin E from the diet can lead to cardiovascular disease, cancer, cataracts, and premature aging. Vitamin K deficiency can lead to bleeding disorders. These fat-soluble vitamins are important nutritional factors that can be determined in different methods in vegetables. In this work, the simultaneous determination of α-tocopherol, α-tocopheryl acetate, phylloquinone, and menaquinone-4 by gas chromatography–mass spectrometry (GC–MS) has been optimized using both direct injection and solid phase microextraction (SPME). Three different sample pre-treatment approaches based on: (A) solid–liquid–liquid–liquid extraction (SLE–LLE), (B) SLE, and (C) SPME were then applied to extract the target analytes from vegetables samples using menaquinone as internal standard. All the procedures allowed the determination of the target analytes in onion, carrot, celery, and curly kale samples. Similar results were obtained with the three different approaches, even if the one based on SPME offers the best performance, together with a reduced use of solvent, time consumption, and experimental complexity, which makes it the preferable option for industrial applications

Investigating the Role of Circulating miRNAs as Biomarkers in Colorectal Cancer: An Epidemiological Systematic Review

Colorectal cancer (CRC) is one of the most common cancers worldwide. Primary and secondary preventions are key to reducing the global burden. MicroRNAs (miRNAs) are a group of small non-coding RNA molecules, which seem to have a role either as tumor suppressor genes or oncogenes and to be related to cancer risk factors, such as obesity and inflammation. We conducted a systematic review and meta-analysis to identify circulating miRNAs related to CRC diagnosis that could be selected as biomarkers in a meet-in-the-middle analysis. Forty-four studies were included in the systematic review and nine studies in the meta-analysis. The pooled sensitivity and specificity of miR-21 for CRC diagnosis were 77% (95% CI: 69–84) and 82% (95% CI: 70–90), respectively, with an AUC of 0.86 (95% CI: 0.82–0.88). Several miRNAs were found to be dysregulated, distinguishing patients with CRC from healthy controls. However, little consistency was present across the included studies, making it challenging to identify specific miRNAs, which were consistently validated. Understanding the mechanisms by which miRNAs become biologically embedded in cancer initiation and promotion may help better understand cancer pathways to develop more effective prevention strategies and therapy approaches

An Epidemiological Systematic Review with Meta-Analysis on Biomarker Role of Circulating MicroRNAs in Breast Cancer Incidence

Breast cancer (BC) is a multifactorial disease caused by an interaction between genetic predisposition and environmental exposures. MicroRNAs are a group of small non-coding RNA molecules, which seem to have a role either as tumor suppressor genes or oncogenes and seem to be related to cancer risk factors. We conducted a systematic review and meta-analysis to identify circulating microRNAs related to BC diagnosis, paying special attention to methodological problems in this research field. A meta-analysis was performed for microRNAs analyzed in at least three independent studies where sufficient data to make analysis were presented. Seventy-five studies were included in the systematic review. A meta-analysis was performed for microRNAs analyzed in at least three independent studies where sufficient data to make analysis were presented. Seven studies were included in the MIR21 and MIR155 meta-analysis, while four studies were included in the MIR10b metanalysis. The pooled sensitivity and specificity of MIR21 for BC diagnosis were 0.86 (95%CI 0.76-0.93) and 0.84 (95%CI 0.71-0.92), 0.83 (95%CI 0.72-0.91) and 0.90 (95%CI 0.69-0.97) for MIR155, and 0.56 (95%CI 0.32-0.71) and 0.95 (95%CI 0.88-0.98) for MIR10b, respectively. Several other microRNAs were found to be dysregulated, distinguishing BC patients from healthy controls. However, there was little consistency between included studies, making it difficult to identify specific microRNAs useful for diagnosis