KALMAN FILTER RETRIEVAL OF SEA SKIN TEMPERATURE FROM SEVIRI: A COMPARISON CASE STUDY

The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allow for the retrieval of a valuable source of information about geophysical parameters. To exploit this information we have developed a Kalman filter methodology for the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. The application of the retrieval methodology to SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared channels shows that we can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ± 0.2 K, respectively. This performance is exemplified in this paper with a case study, which considers the retrieval of sea skin temperature for a target area of the Naples Gulf. Retrieval for temperature has been intercompared with similar products derived from AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensors

Using the full IASI spectrum for the physical retrieval of temperature, H2O, HDO, O3, minor and trace gases

IASI (Infrared Atmospheric Sounder Interferometer) is flying on the European MetOp series of weather satellites. Besides acquiring temperature and humidity data, IASI also observes the infrared emission of the main minor and trace atmospheric components with high precision. The retrieval of these gases would be highly beneficial to the efforts of scientists monitoring Earths climate. IASI retrieval capability and algorithms have been mostly driven by Numerical Weather Prediction centers, whose limited resources for data transmission and computing is hampering the full exploitation of IASI information content. The quest for real or nearly real time processing has affected the precision of the estimation of minor and trace gases, which are normally retrieved on a very coarse spatial grid. The paper presents the very first retrieval of the complete suite of IASI target parameters by exploiting all its 8461 channels. The analysis has been exemplified for sea surface and the target parameters will include sea surface temperature, temperature profile, water vapour and HDO profiles, ozone profile, total column amount of CO, CO2, CH4, N2O, SO2, HNO3, NH3, OCS and CF4. Concerning CO2, CH4 and N2O, it will be shown that their colum amount can be obtained for each single IASI IFOV (Instantaneous Field of View) with a precision better than 1-2%, which opens the possibility to analyze, e.g., the formation of regional patterns of greenhouse gases. To assess the quality of the retrieval, a case study has been set up which considers two years of IASI soundings over the Hawaii, Manua Loa validation station

Hyper fast radiative transfer for the physical retrieval of surface parameters from SEVIRI observations

This paper describes the theoretical aspects of a fast scheme for the physical retrieval of surface temperature and emissivity from SEVIRI data, their implementation and some sample results obtained. The scheme is based on a Kalman Filter approach, which effectively exploits the temporal continuity in the observations of the geostationary Meteosat Second Generation (MSG) platform, on which SEVIRI (Spinning Enhanced Visible and InfraRed Imager) operates. Such scheme embodies in its core a physical retrieval algorithm, which employs an hyper fast radiative transfer code highly customized for this retrieval task. Radiative transfer and its customizations are described in detail. Fastness, accuracy and stability of the code are fully documented for a variety of surface features, showing a peculiar application to the massive Greek forest fires in August 2007

The very first multi-temporal and multi-spectral Level-2 SEVIRI processor for the simultaneous physical retrieval of surface temperature and emissivity

The estimation of surface parameters yields important information in several applications on regional and global scale. Because of their high temporal resolution, infrared instruments on board geostationary platforms are capable to provide time sequences of observations, which fully resolve the diurnal cycle. To exploit multi-temporal information, a Kalman filter (KF) methodology has been implemented in order to retrieve simultaneously surface temperature and emissivity from SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared data. Because of its sequential nature, the Kalman filter methodology yields a very fast software implementation, which can be applied to the SEVIRI full disk for off-line analysis. The software can run in real-time at the regional scale, which makes it very attractive for different applications such as land surveillance, natural hazards, risk management, and so on. The paper will show the basic methodology and applications at regional and global scale

All-sky radiative transfer calculations for IASI and IASI-NG: The σ-IASI-as code

In the context of the development by EUMETSAT of a new generation of meteorological satellites, we have built the new σ-IASI-as (where "as" stands for "all sky") radiative transfer code. Unlike its predecessor σ-IASI, the code is able to calculate both clear and cloudy sky radiances, as well as their Jacobians with respect to any desired geophysical parameter. In addition, σ-IASI-as can perform calculations to simulate the extinction effect of the most common types of atmospheric aerosols and of clouds via ab-initio Mie calculations. We briefly describe the analytical scheme on which the model is based, and have a glance to its potentialities illustrating some sample calculations. Overall, the new model is a complete and fast radiative transfer tool for IASI, and already available for IASI-NG and MTG-IRS

SEVIRI Cloud mask by Cumulative Discriminant Analysis

In the context of cloud detection for satellite observations we want to use the method of Cumulative Discriminant Analysis (CDA) as a tool to distinguish between clear and cloudy sky applied to Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The methodology is based on the choice of several statistics related to the cloud properties, whose correlation has been analyzed by Principal Component Analysis (PCA). Results have been compared with the SEVIRI reference cloud mask provided by the European Centre for the Exploitation of Meteorological Satellite (EUMETSAT), in order to find suitable thresholds able to discriminate between clear or cloudy conditions. We trained the statistics on a selected region, the Basilicata area, located in the south of Italy, in different periods of the year 2012, in order to take into account the seasonal variability. Moreover we separated land and sea surface and distinguished between day-time or night-time. The validation of thresholds, obtained through SEVIRI observations analysis, shows a good agreement with the reference cloud mask

Surface parameters from seviri observations through a kalman filter approach: Application and evaluation of the scheme to the southern Italy

Geostationary satellites are capable to resolve the diurnal cycle by providing time sequence of observations with a very high temporal resolution. A Kalman filter methodology was developed to exploit such time continuity in order to simultaneously retrieve surface temperature and emissivity from SEVIRI (Spinning Enhanced Visible and Infrared Imager) data. The methodology has been applied and tested over a geographic region in the Southern Italy characterized by different surface features: arid, cultivated, vegetated and urban areas, and sea water. The objective is to implement a real-time continuous monitoring of surface parameters, which could be used for the various purposes of tourism and agronomy, land surveillance, natural hazards and risk assessment analysis. Retrieval of surface parameters has been performed for the whole year 2013 and the results have been compared to other similar satellite observations such as those derived from AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Reso-lution Imaging Spectroradiometer). Comparisons with ECMWF (European Centre for Medium range Weather Forecasts) analyses for sea surface are provided as well

Action tremor in Parkinson's disease: frequency and relationship to motor and non-motor signs

BACKGROUND AND PURPOSE: Action tremor may occur in patients with Parkinson's disease and cause misdiagnosis with other movement disorders such as essential tremor and dystonia. Data on the frequency of action tremor in Parkinson's disease and on the relationships with other motor and non-motor signs are limited. METHODS: A cross-sectional study of 237 patients with Parkinson's disease staging 1-2 on the Hoehn-Yahr scale was conducted. Data on action tremor and other motor and non-motor signs were collected using the Unified Parkinson's Disease Rating Scale part III and the Non-Motor Symptoms Scale. RESULTS: Action tremor was found in 46% of patients and was associated with both severity of rest tremor (adjusted odds ratio 3.0, P < 0.001) and severity of rigidity (adjusted odds ratio 1.5, P = 0.004). No association was found between action tremor and severity of bradykinesia (adjusted odds ratio 0.97, P = 0.4) or axial symptoms (adjusted odds ratio 0.9, P = 0.3). Moreover, patients who had action tremor reported a significant lower mean number of non-motor symptoms than those who had not (2.1 ± 1.3 vs. 2.4 ± 1.3; P = 0.04). CONCLUSIONS: Action tremor is a relatively frequent motor sign in patients with Parkinson's disease staging 1-2 on the Hoehn-Yahr scale. Action tremor correlates with rest tremor and rigidity and may be associated with a lower burden of non-motor symptoms. These findings suggest a contribution of non-dopaminergic mechanisms to action tremor pathophysiology

Neuro-Axonal Damage and Alteration of Blood–Brain Barrier Integrity in COVID-19 Patients

Neurofilament light chain (NfL) is a specific biomarker of neuro-axonal damage. Matrix metalloproteinases (MMPs) are zinc-dependent enzymes involved in blood-brain barrier (BBB) integrity. We explored neuro-axonal damage, alteration of BBB integrity and SARS-CoV-2 RNA presence in COVID-19 patients with severe neurological symptoms (neuro-COVID) as well as neuro-axonal damage in COVID-19 patients without severe neurological symptoms according to disease severity and after recovery, comparing the obtained findings with healthy donors (HD). Overall, COVID-19 patients (n = 55) showed higher plasma NfL levels compared to HD (n = 31) (p < 0.0001), especially those who developed ARDS (n = 28) (p = 0.0005). After recovery, plasma NfL levels were still higher in ARDS patients compared to HD (p = 0.0037). In neuro-COVID patients (n = 12), higher CSF and plasma NfL, and CSF MMP-2 levels in ARDS than non-ARDS group were observed (p = 0.0357, p = 0.0346 and p = 0.0303, respectively). SARS-CoV-2 RNA was detected in four CSF and two plasma samples. SARS-CoV-2 RNA detection was not associated to increased CSF NfL and MMP levels. During COVID-19, ARDS could be associated to CNS damage and alteration of BBB integrity in the absence of SARS-CoV-2 RNA detection in CSF or blood. CNS damage was still detectable after discharge in blood of COVID-19 patients who developed ARDS during hospitalization

Development and validation of a prediction model for severe respiratory failure in hospitalized patients with SARS-CoV-2 infection: a multicentre cohort study (PREDI-CO study)

Objectives: We aimed to develop and validate a risk score to predict severe respiratory failure (SRF) among patients hospitalized with coronavirus disease-2019 (COVID-19).Methods: We performed a multicentre cohort study among hospitalized (>24 hours) patients diagnosed with COVID-19 from 22 February to 3 April 2020, at 11 Italian hospitals. Patients were divided into derivation and validation cohorts according to random sorting of hospitals. SRF was assessed from admission to hospital discharge and was defined as: SpO(2) <93% with 100% FiO(2), respiratory rate >30 breaths/min or respiratory distress. Multivariable logistic regression models were built to identify predictors of SRF, beta-coefficients were used to develop a risk score. Trial Registration NCT04316949.Results: We analysed 1113 patients (644 derivation, 469 validation cohort). Mean (+/- SD) age was 65.7 (+/- 15) years, 704 (63.3%) were male. SRF occurred in 189/644 (29%) and 187/469 (40%) patients in the derivation and validation cohorts, respectively. At multivariate analysis, risk factors for SRF in the derivation cohort assessed at hospitalization were age >= 70 years (OR 2.74; 95% CI 1.66-4.50), obesity (OR 4.62; 95% CI 2.78-7.70), body temperature >= 38 degrees C (OR 1.73; 95% CI 1.30-2.29), respiratory rate >= 22 breaths/min (OR 3.75; 95% CI 2.01-7.01), lymphocytes <= 900 cells/mm(3) (OR 2.69; 95% CI 1.60-4.51), creatinine >= 1 mg/dL (OR 2.38; 95% CI 1.59-3.56), C-reactive protein >= 10 mg/dL (OR 5.91; 95% CI 4.88 -7.17) and lactate dehydrogenase >= 350 IU/L (OR 2.39; 95% CI 1.11-5.11). Assigning points to each variable, an individual risk score (PREDI-CO score) was obtained. Area under the receiver-operator curve was 0.89 (0.86-0.92). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 71.6% (65%-79%), 89.1% (86%-92%), 74% (67%-80%) and 89% (85%-91%), respectively. PREDI-CO score showed similar prognostic ability in the validation cohort: area under the receiver-operator curve 0.85 (0.81e0.88). At a score of >3, sensitivity, specificity, and positive and negative predictive values were 80% (73%-85%), 76% (70%-81%), 69% (60%-74%) and 85% (80%-89%), respectively.Conclusion: PREDI-CO score can be useful to allocate resources and prioritize treatments during the COVID-19 pandemic. (c) 2020 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved