scholarly journals Evaluating the Impact of the 2020 Iowa Derecho on Corn and Soybean Fields Using Synthetic Aperture Radar

2020 ◽  
Vol 12 (23) ◽  
pp. 3878
Author(s):  
Mehdi Hosseini ◽  
Hannah R. Kerner ◽  
Ritvik Sahajpal ◽  
Estefania Puricelli ◽  
Yu-Hsiang Lu ◽  
...  
Keyword(s):  
Crop Damage ◽  
Insurance Companies ◽  
Large Area ◽  
Weather Event ◽  
Crop Type ◽  
Agricultural Regions ◽  
The Impact ◽  
Severe Weather Event ◽  
Damage Map ◽  
Sentinel 2

On 10 August 2020, a series of intense and fast-moving windstorms known as a derecho caused widespread damage across Iowa’s (the top US corn-producing state) agricultural regions. This severe weather event bent and flattened crops over approximately one-third of the state. Immediate evaluation of the disaster’s impact on agricultural lands, including maps of crop damage, was critical to enabling a rapid response by government agencies, insurance companies, and the agricultural supply chain. Given the very large area impacted by the disaster, satellite imagery stands out as the most efficient means of estimating the disaster impact. In this study, we used time-series of Sentinel-1 data to detect the impacted fields. We developed an in-season crop type map using Harmonized Landsat and Sentinel-2 data to assess the impact on important commodity crops. We intersected a SAR-based damage map with an in-season crop type map to create damaged area maps for corn and soybean fields. In total, we identified 2.59 million acres as damaged by the derecho, consisting of 1.99 million acres of corn and 0.6 million acres of soybean fields. Also, we categorized the impacted fields to three classes of mild impacts, medium impacts and high impacts. In total, 1.087 million acres of corn and 0.206 million acres of soybean were categorized as high impacted fields.

A comparison between 4D-VAR and cycling 3D-VAR methods for the simulation of a severe weather event in Central Italy. Preliminary results.

2020 ◽  
Author(s):  
Vincenzo Mazzarella ◽  
Rossella Ferretti
Keyword(s):  
Positive Impact ◽  
Central Italy ◽  
Wrf Model ◽  
Severe Weather ◽  
Precipitation Forecast ◽  
Mountain Range ◽  
Weather Event ◽  
Maximum Rainfall ◽  
The Impact ◽  
Severe Weather Event

<p>Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).</p><p>The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.</p>

scholarly journals Splitting of the middle layer of LPW SAFNWC/MSG satellite product in order to improve the monitoring of pre-convective environments

2008 ◽  
Vol 2 (1) ◽  
pp. 71-75
Author(s):  
G. Cuevas ◽  
M. A. Martinez ◽  
M. Velazquez ◽  
J. Ruiz ◽  
M. Manso
Keyword(s):  
Neural Networks ◽  
Precipitable Water ◽  
Middle Layer ◽  
Horizontal Resolution ◽  
Severe Weather ◽  
Convective System ◽  
Weather Event ◽  
Radar Images ◽  
The Impact ◽  
Severe Weather Event

Abstract. Seven of the infrared channels from the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) instrument, on board the Meteosat Second Generation (MSG), are used to retrieve Layer Precipitable Water (LPW) and Stability Analysis Imagery (SAI) in the SAFNWC framework. Both products are retrieved using a statistical retrieval based on neural networks; they are routinely generated every fifteen minutes at a satellite horizontal resolution of 3 km in NADIR only in cloud-free areas. Many factors are involved in the development of severe weather and these parameters are only some of the indicators. However, due to the high resolution of these products, the use of them in conjunction with satellite and radar images can help to identify mesoscale features related to convection. The MSG moisture and parcel instability time trend fields are especially useful during the period previous to convection. Once the outbreak of convection occurs, the products calculated in the clear air pixels surrounding the convective system can give us hints to anticipate its evolution. SAFNWC LPW and SAI were analyzed for a severe weather event during August 2004. A thunderstorm over Teruel (Spain) produced intense precipitation and hail; a tornado developed while this thunderstorm was moving towards SE. The pre-convective parcel potential buoyancy and moisture SAFNWC products changed in a way that was consistent with the observed intense convective activity. In previous studies, the atmospheric moisture in medium levels, which has been proven to be relevant in some cases, was represented by only one level parameter (ML: middle layer LPW). However, it was observed that this layer is too thick to do an adequate analysis of moisture available for convection. Hence, an improvement on the LPW algorithm has been carried out by splitting the middle layer into two new sub-layers (approximately separated at 700 hPa) and training two new neural networks. The impact of monitoring moisture in the new sub-layers separately in this severe weather event has been tested, and the improvements achieved have been evaluated.

Computer simulation study about the dependence of amorphous silicon photonic waveguides efficiency on the material quality

2020 ◽  
Vol 90 (3) ◽  
pp. 30502
Author(s):  
Alessandro Fantoni ◽  
João Costa ◽  
Paulo Lourenço ◽  
Manuela Vieira
Keyword(s):  
Amorphous Silicon ◽  
High Throughput Screening ◽  
Simulation Analysis ◽  
Low Cost ◽  
Deposition Process ◽  
Large Area ◽  
Sidewall Roughness ◽  
Sensing Applications ◽  
Fabrication Defects ◽  
The Impact

Amorphous silicon PECVD photonic integrated devices are promising candidates for low cost sensing applications. This manuscript reports a simulation analysis about the impact on the overall efficiency caused by the lithography imperfections in the deposition process. The tolerance to the fabrication defects of a photonic sensor based on surface plasmonic resonance is analysed. The simulations are performed with FDTD and BPM algorithms. The device is a plasmonic interferometer composed by an a-Si:H waveguide covered by a thin gold layer. The sensing analysis is performed by equally splitting the input light into two arms, allowing the sensor to be calibrated by its reference arm. Two different 1 × 2 power splitter configurations are presented: a directional coupler and a multimode interference splitter. The waveguide sidewall roughness is considered as the major negative effect caused by deposition imperfections. The simulation results show that plasmonic effects can be excited in the interferometric waveguide structure, allowing a sensing device with enough sensitivity to support the functioning of a bio sensor for high throughput screening. In addition, the good tolerance to the waveguide wall roughness, points out the PECVD deposition technique as reliable method for the overall sensor system to be produced in a low-cost system. The large area deposition of photonics structures, allowed by the PECVD method, can be explored to design a multiplexed system for analysis of multiple biomarkers to further increase the tolerance to fabrication defects.

ANALISIS RISIKO OPERASIONAL MENGGUNAKAN PENDEKATAN DISTRIBUSI KERUGIAN DENGAN METODE AGREGAT

2011 ◽  
Vol 10 (2) ◽  
pp. 1
Author(s):  
Y. ARBI ◽  
R. BUDIARTI ◽  
I G. P. PURNABA
Keyword(s):  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Financial Institution ◽  
Expected Value ◽  
Insurance Companies ◽  
Limited Data ◽  
Loss Distribution Approach ◽  
Potential Loss ◽  
The Impact

Operational risk is defined as the risk of loss resulting from inadequate or failed internal processes or external problems. Insurance companies as financial institution that also faced at risk. Recording of operating losses in insurance companies, were not properly conducted so that the impact on the limited data for operational losses. In this work, the data of operational loss observed from the payment of the claim. In general, the number of insurance claims can be modelled using the Poisson distribution, where the expected value of the claims is similar with variance, while the negative binomial distribution, the expected value was bound to be less than the variance.Analysis tools are used in the measurement of the potential loss is the loss distribution approach with the aggregate method. In the aggregate method, loss data grouped in a frequency distribution and severity distribution. After doing 10.000 times simulation are resulted total loss of claim value, which is total from individual claim every simulation. Then from the result was set the value of potential loss (OpVar) at a certain level confidence.

scholarly journals Immunization Strategies for Funding Multiple Inflation-Linked Retirement Income Benefits

Risks ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 60
Author(s):  
Cláudia Simões ◽  
Luís Oliveira ◽  
Jorge M. Bravo
Keyword(s):  
Interest Rate ◽  
Yield Curve ◽  
Risk Measure ◽  
Management Strategies ◽  
Interest Rate Risk ◽  
Insurance Companies ◽  
Retirement Income ◽  
Investment Strategies ◽  
Immunization Strategies ◽  
The Impact

Protecting against unexpected yield curve, inflation, and longevity shifts are some of the most critical issues institutional and private investors must solve when managing post-retirement income benefits. This paper empirically investigates the performance of alternative immunization strategies for funding targeted multiple liabilities that are fixed in timing but random in size (inflation-linked), i.e., that change stochastically according to consumer price or wage level indexes. The immunization procedure is based on a targeted minimax strategy considering the M-Absolute as the interest rate risk measure. We investigate to what extent the inflation-hedging properties of ILBs in asset liability management strategies targeted to immunize multiple liabilities of random size are superior to that of nominal bonds. We use two alternative datasets comprising daily closing prices for U.S. Treasuries and U.S. inflation-linked bonds from 2000 to 2018. The immunization performance is tested over 3-year and 5-year investment horizons, uses real and not simulated bond data and takes into consideration the impact of transaction costs in the performance of immunization strategies and in the selection of optimal investment strategies. The results show that the multiple liability immunization strategy using inflation-linked bonds outperforms the equivalent strategy using nominal bonds and is robust even in a nearly zero interest rate scenario. These results have important implications in the design and structuring of ALM liability-driven investment strategies, particularly for retirement income providers such as pension schemes or life insurance companies.

scholarly journals An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping

2021 ◽  
Vol 3 ◽  
pp. 100018 ◽  
Author(s):  
Xiao-Peng Song ◽  
Wenli Huang ◽  
Matthew C. Hansen ◽  
Peter Potapov
Keyword(s):  
Modis Data ◽  
Type Mapping ◽  
Crop Type ◽  
Sentinel 2

scholarly journals Monitoring the Impact of the Large Building Investments on the Neighborhood

2021 ◽  
Vol 11 (14) ◽  
pp. 6537
Author(s):  
Marian Łupieżowiec
Keyword(s):  
Groundwater Level ◽  
Soil Improvement ◽  
Deep Excavation ◽  
Large Area ◽  
Safety Measures ◽  
High Rise ◽  
The Impact ◽  
Large Building ◽  
Additional Safety ◽  
Made In

The article presents the concept of monitoring buildings and infrastructure elements located near large construction investments (the construction of high-rise buildings of the Oak Terraces housing estate in Katowice and the construction of a tunnel under the roundabout in Katowice along the intercity express road DTŚ). The impacts include deep excavation, lowering of the groundwater level over a large area, and dynamic influences related to the use of impact methods of soil improvement. The presented monitoring includes observation of the groundwater level with the use of piezometers, geodetic measurements of settlement and inclinations, as well as the measurement of vibration amplitudes generated during the works involving shocks and vibrations. It was also important to observe the development of cracks on the basis of a previously made inventory of damage. The results of the monitoring allow corrections to be made in the technology of works (e.g., reduction of vibration amplitudes, application of additional protections at excavations, etc.) or the use additional safety measures. Currently, there are also monitoring systems used during the operation of completed facilities.

scholarly journals Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3982
Author(s):  
Giacomo Lazzeri ◽  
William Frodella ◽  
Guglielmo Rossi ◽  
Sandro Moretti
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Fire Severity ◽  
Experimental Methodology ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Remotely Sensed Data ◽  
The Impact ◽  
Sentinel 2

Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

scholarly journals Cyber operational risk scenarios for insurance companies

2019 ◽  
Vol 24 ◽  
Author(s):  
R. Egan ◽  
S. Cartagena ◽  
R. Mohamed ◽  
V. Gosrani ◽  
J. Grewal ◽  
...  
Keyword(s):  
Cyber Security ◽  
Operational Risk ◽  
Social Engineering ◽  
Insurance Companies ◽  
List Type ◽  
Operational Risks ◽  
Life Insurer ◽  
Cyber Risk ◽  
The Impact ◽  
Over Time

AbstractCyber Operational Risk: Cyber risk is routinely cited as one of the most important sources of operational risks facing organisations today, in various publications and surveys. Further, in recent years, cyber risk has entered the public conscience through highly publicised events involving affected UK organisations such as TalkTalk, Morrisons and the NHS. Regulators and legislators are increasing their focus on this topic, with General Data Protection Regulation (“GDPR”) a notable example of this. Risk actuaries and other risk management professionals at insurance companies therefore need to have a robust assessment of the potential losses stemming from cyber risk that their organisations may face. They should be able to do this as part of an overall risk management framework and be able to demonstrate this to stakeholders such as regulators and shareholders. Given that cyber risks are still very much new territory for insurers and there is no commonly accepted practice, this paper describes a proposed framework in which to perform such an assessment. As part of this, we leverage two existing frameworks – the Chief Risk Officer (“CRO”) Forum cyber incident taxonomy, and the National Institute of Standards and Technology (“NIST”) framework – to describe the taxonomy of a cyber incident, and the relevant cyber security and risk mitigation items for the incident in question, respectively.Summary of Results: Three detailed scenarios have been investigated by the working party:∙Employee leaks data at a general (non-life) insurer: Internal attack through social engineering, causing large compensation costs and regulatory fines, driving a 1 in 200 loss of £210.5m (c. 2% of annual revenue).∙Cyber extortion at a life insurer: External attack through social engineering, causing large business interruption and reputational damage, driving a 1 in 200 loss of £179.5m (c. 6% of annual revenue).∙Motor insurer telematics device hack: External attack through software vulnerabilities, causing large remediation / device replacement costs, driving a 1 in 200 loss of £70.0m (c. 18% of annual revenue).Limitations: The following sets out key limitations of the work set out in this paper:∙While the presented scenarios are deemed material at this point in time, the threat landscape moves fast and could render specific narratives and calibrations obsolete within a short-time frame.∙There is a lack of historical data to base certain scenarios on and therefore a high level of subjectivity is used to calibrate them.∙No attempt has been made to make an allowance for seasonality of renewals (a cyber event coinciding with peak renewal season could exacerbate cost impacts)∙No consideration has been given to the impact of the event on the share price of the company.∙Correlation with other risk types has not been explicitly considered.Conclusions: Cyber risk is a very real threat and should not be ignored or treated lightly in operational risk frameworks, as it has the potential to threaten the ongoing viability of an organisation. Risk managers and capital actuaries should be aware of the various sources of cyber risk and the potential impacts to ensure that the business is sufficiently prepared for such an event. When it comes to quantifying the impact of cyber risk on the operations of an insurer there are significant challenges. Not least that the threat landscape is ever changing and there is a lack of historical experience to base assumptions off. Given this uncertainty, this paper sets out a framework upon which readers can bring consistency to the way scenarios are developed over time. It provides a common taxonomy to ensure that key aspects of cyber risk are considered and sets out examples of how to implement the framework. It is critical that insurers endeavour to understand cyber risk better and look to refine assumptions over time as new information is received. In addition to ensuring that sufficient capital is being held for key operational risks, the investment in understanding cyber risk now will help to educate senior management and could have benefits through influencing internal cyber security capabilities.

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