scholarly journals Calibration and Validation of Reprocessed HY-2A Altimeter Wave Height Measurements Using Data from Buoys, Jason-2, Cryosat-2, and SARAL/AltiKa

2018 ◽  
Vol 35 (6) ◽  
pp. 1331-1352 ◽  
Author(s):  
Maofei Jiang ◽  
Ke Xu ◽  
Yalong Liu
Keyword(s):  
Wave Height ◽  
Dynamic Environment ◽  
Radar Altimeter ◽  
National Data ◽  
Collection System ◽  
Application Service ◽  
Data Record ◽  
Using Data ◽  
Geophysical Data Record

AbstractThe Haiyang-2A (HY-2A) satellite is China’s first ocean dynamic environment satellite, and the radar altimeter is one of its main payloads. In this study the HY-2A altimeter sensor interim geophysical dataset records (SIGDR) data are reprocessed to obtain better significant wave height (Hs) measurements over a period of more than four years (from 1 October 2011 to 15 March 2016). The reprocessed HY-2A Hs measurements are calibrated and validated using National Data Buoy Center (NDBC) buoys and several operating altimeters: Joint Altimetry Satellite Oceanography Network-2 (Jason-2), CryoSat-2, and Satellite with Argos Data Collection System and Ka-Band Altimeter (SARAL/ALtiKa) The final results of buoys and cross-altimeter comparisons show that the accuracy of the reprocessed HY-2A Hs measurements is significantly improved with respect to the Hs measurements in the operational HY-2A interim geophysical data record (IGDR) publicly distributed by the National Satellite Ocean Application Service (NSOAS), State Oceanic Administration (SOA) of China. Compared with the NDBC Hs measurements, the reprocessed HY-2A Hs measurements show a root-mean-square error (RMSE) of 0.215 m with a positive bias of 0.117 m. After calibrating with the two-branched corrections, the RMSE for the reprocessed HY-2A Hs measurements is reduced to 0.173 m, which is lower than those for the calibrated HY-2A IGDR, Jason-2, Cryosat-2, and SARAL measurements with an RMSE of 0.278, 0.233, 0.239, and 0.184 m, respectively. Long-term validation of the altimeter Hs measurements shows that the reprocessed HY-2A Hs measurements after calibration are stable with respect to the buoys and three other altimeters over the entire period. The reprocessed HY-2A Hs measurements are expected to improve the practical applicability of HY-2A Hs measurements significantly.

Performance of a mobile infrasound station in the framework of CEEIN 

2021 ◽  
Author(s):  
Ulrike Mitterbauer ◽  
Daniela Ghica
Keyword(s):  
Military Training ◽  
Armed Forces ◽  
Eastern European ◽  
National Data ◽  
Private Company ◽  
Seismic Sensors ◽  
Global Events ◽  
Using Data ◽  
The Military

<p>The project ABC-MAUS is undertaken by a collaboration of the Austrian Ministry of Defense, Joanneum Research, the Austrian national weather and geophysical service Zentralanstalt für Meteorologie und Geodynamik (ZAMG), including the Austrian National Data Center (NDC), as well as the private company GIHMM. The aim is to develop a strategy of protection for chemical, biological, radiological and nuclear threads (CBRN) for the Austrian armed forces.</p><p>In the frame of the project, a mobile infrasound array was deployed together with seismic sensors to monitor the military training ground Allentsteig in Lower Austria. During one week a series of controlled explosions was recorded. Infrasound data was processed and analyzed by using a duo of infrasound detection-oriented software (DTK-GPMCC and DTK-DIVA, packaged into NDC-in-a-Box). The dataset contained not only local and regional data, but revealed as well long term sources and – after comparing the data with data from stations of the CEEIN (Central Eastern European Infrasound Network) – some global events. Those events were localized using data of the temporary deployed array and by observations collected by other stations of the CEEIN.</p>

On Sampling Between Data of Significant Wave Height for Long-Term Analysis With Equivalent Triangular Storm Model

2013 ◽  
Author(s):  
Felice Arena ◽  
Valentina Laface
Keyword(s):  
Wave Height ◽  
Return Period ◽  
Significant Wave Height ◽  
Significant Wave ◽  
Storm Duration ◽  
The Mean ◽  
Using Data ◽  
Two Parameters ◽  
Mean Time

This work proposes an analysis of storms in Pacific and Atlantic Ocean, which is carried out by applying the Boccotti’s Equivalent Triangular Storm (ETS) model. The ETS model represents any actual storm by means of two parameters. The former gives the storm intensity, which is equal to the maximum significant wave height during the actual storm; the latter represents the storm duration and it is such that the maximum expected wave height is the same in the actual storm and in the equivalent triangular storm. Data from buoys of the NOAA-NDBC (National Data Buoy Center, USA) are used in the applications, by considering different sampling Δt between two consecutive records, which varies between 1 and 6 hours. The sensitivity of the ETS model with the variation of Δt is investigated for the long-term modeling of severe storms. The results show that the structure of storms is strongly modified as Δt increases: both the intensity and the duration may change significantly. The effects of this results for long term statistics are investigated by means of the return period R(Hs > h) of a storm in which the maximum significant wave height exceeds the threshold h, which is evaluated by using data with different sampling Δt between two consecutive records. Finally for different values of the return period R, the return value of significant wave height and the mean persistence Dm(h), giving the mean time during which the significant wave height is greater than fixed threshold (in the storms where the threshold is exceeded), are calculated.

Clinical impact of myositis-specific autoantibodies on long-term prognosis of juvenile idiopathic inflammatory myopathies: multicentre study

Rheumatology ◽  
2021 ◽  
Author(s):  
Yuichi Yamasaki ◽  
Norimoto Kobayashi ◽  
Shinji Akioka ◽  
Kazuko Yamazaki ◽  
Shunichiro Takezaki ◽  
...  
Keyword(s):  
Matrix Protein ◽  
Multiple Drug ◽  
Idiopathic Inflammatory Myopathies ◽  
Inflammatory Myopathies ◽  
Gamma Subunits ◽  
Specific Autoantibody ◽  
Long Term Prognosis ◽  
Using Data ◽  
Drug Free

Abstract Objectives This study aimed to investigate the clinical characteristics, treatment and prognosis of juvenile idiopathic inflammatory myopathies (JIIM) in Japan for each myositis-specific autoantibody (MSA) profile. Methods A multicentre, retrospective study was conducted using data of patients with JIIM at nine paediatric rheumatology centres in Japan. Patients with MSA profiles, determined by immunoprecipitation using stored serum from the active stage, were included. Results MSA were detected in 85 of 96 cases eligible for the analyses. Over 90% of the patients in this study had one of the following three MSA types: anti-melanoma differentiation-associated protein 5 (MDA5) (n = 31), anti-transcriptional intermediary factor 1 alpha and/or gamma subunits (TIF1γ) (n = 25) and anti-nuclear matrix protein 2 (NXP2) (n = 25) antibodies. Gottron papules and periungual capillary abnormalities were the most common signs of every MSA group in the initial phase. The presence of interstitial lung disease (ILD) was the highest risk factor for patients with anti-MDA5 antibodies. Most patients were administered multiple drug therapies: glucocorticoids and MTX were administered to patients with anti-TIF1γ or anti-NXP2 antibodies. Half of the patients with anti-MDA5 antibodies received more than three medications including i.v. CYC, especially patients with ILD. Patients with anti-MDA5 antibodies were more likely to achieve drug-free remission (29 vs 21%) and less likely to relapse (26 vs 44%) than others. Conclusion Anti-MDA5 antibodies are the most common MSA type in Japan, and patients with this antibody are characterized by ILD at onset, multiple medications including i.v. CYC, drug-free remission, and a lower frequency of relapse. New therapeutic strategies are required for other MSA types.

Increase associated risk of gynaecological cancer due to long-term exposure to high concentration of atmospheric SO2 industrial pollutant

2021 ◽  
pp. 1420326X2110036
Author(s):  
Qian Xu ◽  
Chan Lu ◽  
Rachael Gakii Murithi ◽  
Lanqin Cao
Keyword(s):  
Time Windows ◽  
Air Pollutant ◽  
Gynaecological Cancer ◽  
High Concentration ◽  
The Past ◽  
Short And Long Term ◽  
Using Data ◽  
Air Quality Monitoring Stations ◽  
Control Study

A cohort case–control study was conducted in XiangYa Hospital, Changsha, China, which involved 305 patients and 399 healthy women, from June 2010 to December 2018, to evaluate the association between Chinese women’s short- and long-term exposure to industrial air pollutant, SO2 and gynaecological cancer (GC). We obtained personal and family information from the XiangYa Hospital electronic computer medical records. Using data obtained from the air quality monitoring stations in Changsha, we estimated each woman’s exposure to the industrial air pollutant, sulphur dioxide (SO2), for different time windows, including the past 1, 5, 10 and 15 years before diagnosis of the disease. A multiple logistic regression model was used to assess the association between GC and SO2 exposure. GC was significantly associated with long-term SO2 exposure, with adjusted odds ratio (95% confidence interval) = 1.56 (1.10–2.21) and 1.81 (1.07–3.06) for a per interquartile range increase in the past 10 and 15 years, respectively. Sensitivity analysis showed that different groups reacted in different ways to long-term SO2 exposure. We concluded that long-term exposure to high concentration of industrial pollutant, SO2 is associated with the development of GC. This finding has implications for the prevention and reduction of GC.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Assisting scalable diagnosis automatically via CT images in the combat against COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bohan Liu ◽  
Pan Liu ◽  
Lutao Dai ◽  
Yanlin Yang ◽  
Peng Xie ◽  
...  
Keyword(s):  
Large Scale ◽  
Area Under The Curve ◽  
Independent Set ◽  
Reference Method ◽  
Ct Images ◽  
Loss Of Life ◽  
Case Identification ◽  
Reverse Transcription Pcr ◽  
Using Data

AbstractThe pandemic of Coronavirus Disease 2019 (COVID-19) is causing enormous loss of life globally. Prompt case identification is critical. The reference method is the real-time reverse transcription PCR (RT-PCR) assay, whose limitations may curb its prompt large-scale application. COVID-19 manifests with chest computed tomography (CT) abnormalities, some even before the onset of symptoms. We tested the hypothesis that the application of deep learning (DL) to 3D CT images could help identify COVID-19 infections. Using data from 920 COVID-19 and 1,073 non-COVID-19 pneumonia patients, we developed a modified DenseNet-264 model, COVIDNet, to classify CT images to either class. When tested on an independent set of 233 COVID-19 and 289 non-COVID-19 pneumonia patients, COVIDNet achieved an accuracy rate of 94.3% and an area under the curve of 0.98. As of March 23, 2020, the COVIDNet system had been used 11,966 times with a sensitivity of 91.12% and a specificity of 88.50% in six hospitals with PCR confirmation. Application of DL to CT images may improve both efficiency and capacity of case detection and long-term surveillance.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Long-term effectiveness in patients previously treated with cladribine tablets: a real-world analysis of the Italian multiple sclerosis registry (CLARINET-MS)

2020 ◽  
Vol 13 ◽  
pp. 175628642092268 ◽  
Author(s):  
Francesco Patti ◽  
Andrea Visconti ◽  
Antonio Capacchione ◽  
Sanjeev Roy ◽  
Maria Trojano ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Real World ◽  
Event Analysis ◽  
Treatment Change ◽  
Real World Data ◽  
Indirect Measure ◽  
Disease Modifying Drugs ◽  
Disease Modifying ◽  
Using Data

Background: The CLARINET-MS study assessed the long-term effectiveness of cladribine tablets by following patients with multiple sclerosis (MS) in Italy, using data from the Italian MS Registry. Methods: Real-world data (RWD) from Italian MS patients who participated in cladribine tablets randomised clinical trials (RCTs; CLARITY, CLARITY Extension, ONWARD or ORACLE-MS) across 17 MS centres were obtained from the Italian MS Registry. RWD were collected during a set observation period, spanning from the last dose of cladribine tablets during the RCT (defined as baseline) to the last visit date in the registry, treatment switch to other disease-modifying drugs, date of last Expanded Disability Status Scale recording or date of the last relapse (whichever occurred last). Time-to-event analysis was completed using the Kaplan–Meier (KM) method. Median duration and associated 95% confidence intervals (CI) were estimated from the model. Results: Time span under observation in the Italian MS Registry was 1–137 (median 80.3) months. In the total Italian patient population ( n = 80), the KM estimates for the probability of being relapse-free at 12, 36 and 60 months after the last dose of cladribine tablets were 84.8%, 66.2% and 57.2%, respectively. The corresponding probability of being progression-free at 60 months after the last dose was 63.7%. The KM estimate for the probability of not initiating another disease-modifying treatment at 60 months after the last dose of cladribine tablets was 28.1%, and the median time-to-treatment change was 32.1 (95% CI 15.5–39.5) months. Conclusion: CLARINET-MS provides an indirect measure of the long-term effectiveness of cladribine tablets. Over half of MS patients analysed did not relapse or experience disability progression during 60 months of follow-up from the last dose, suggesting that cladribine tablets remain effective in years 3 and 4 after short courses at the beginning of years 1 and 2.

scholarly journals First-Line Atezolizumab Plus Bevacizumab versus Sorafenib in Hepatocellular Carcinoma: A Cost-Effectiveness Analysis

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 931
Author(s):  
Chi-Leung Chiang ◽  
Sik-Kwan Chan ◽  
Shing-Fung Lee ◽  
Horace Cheuk-Wai Choi
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cost Effectiveness ◽  
Lifetime Cost ◽  
Cost Effective ◽  
First Line ◽  
First Line Therapy ◽  
Payer Perspective ◽  
Life Years ◽  
Using Data

Background: The IMbrave 150 trial revealed that atezolizumab plus bevacizumab (atezo–bev) improves survival in patients with unresectable hepatocellular carcinoma (HCC) (1 year survival rate: 67.2% vs. 54.6%). We assessed the cost-effectiveness of atezo–bev vs. sorafenib as first-line therapy in patients with unresectable HCC from the US payer perspective. Methods: Using data from the IMbrave 150, we developed a Markov model to compare the lifetime cost and efficacy of atezo–bev as first-line systemic therapy in HCC with those of sorafenib. The main outcomes were life-years, quality-adjusted life-years (QALYs), lifetime costs, and incremental cost-effectiveness ratio (ICER). Results: Atezo–bev demonstrated a gain of 0.44 QALYs, with an additional cost of USD 79,074. The ICER of atezo–bev was USD 179,729 per QALY when compared with sorafenib. The model was most sensitive to the overall survival hazard ratio and body weight. If we assumed that all patients at the end of the IMbrave 150 trial were cured of HCC, atezo–bev was cost-effective (ICER USD 53,854 per QALY). However, if all patients followed the Surveillance, Epidemiology, and End Results data, the ICER of atezo–bev was USD 385,857 per QALY. Reducing the price of atezo–bev by 20% and 29% would satisfy the USD 150,000/QALY and 100,000/QALY willingness-to-pay threshold. Moreover, capping the duration of therapy to ≤12 months or reducing the dosage of bev to ≤10 mg/kg would render atezo–bev cost-effective. Conclusions: The long-term effectiveness of atezo–bev is a critical but uncertain determinant of its cost-effectiveness. Price reduction would favorably influence cost-effectiveness, even if long-term clinical outcomes were modest. Further studies to optimize the duration and dosage of therapy are warranted.

scholarly journals Long-Term Development of Urban Agriculture: Resilience and Sustainability of Farmers Facing the Covid-19 Pandemic in Japan

2021 ◽  
Vol 13 (8) ◽  
pp. 4316
Author(s):  
Shingo Yoshida ◽  
Hironori Yagi
Keyword(s):  
Urban Agriculture ◽  
Food Systems ◽  
Direct Marketing ◽  
Farming System ◽  
Long Term Effects ◽  
Short Term ◽  
Short Supply ◽  
Using Data ◽  
Urban People

The coronavirus disease 2019 (Covid-19) pandemic has forced global food systems to face unprecedented uncertain shocks even in terms of human health. Urban agriculture is expected to be more resilient because of its short supply chain for urban people and diversified farming activities. However, the short-and long-term effects of the Covid-19 pandemic on urban farms remain unclear. This study aims to reveal the conditions for farm resilience to the Covid-19 pandemic in 2020 and the relationship between short-term farm resilience and long-term farm development using data from a survey of 74 farms located in Tokyo. The results are as follows. First, more than half of the sample farms increased their farm sales during this period. This resilience can be called the “persistence” approach. Second, short-term farm resilience and other sustainable farm activities contributed to improving farmers’ intentions for long-term farm development and farmland preservation. Third, the most important resilience attributes were the direct marketing, entrepreneurship, and social networks of farmers. We discussed the necessity of building farmers’ transformative capabilities for a more resilient urban farming system. These results imply that support to enhance the short-term resilience of urban farms is worth more than the short-term profit of the farms.

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