The active tropical cyclone season of 2005–2006 over Northwest Australia: Operational model performance and high resolution case studies

2007 ◽  
Vol 97 (1-4) ◽  
pp. 69-91 ◽  
Author(s):  
B. W. Buckley ◽  
L. M. Leslie ◽  
M. Leplastrier ◽  
L. Qi
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Case Studies ◽  
Model Performance ◽  
Operational Model ◽  
Tropical Cyclone Season ◽  
Northwest Australia

Evaluation of Recent NCEP Operational Model Upgrades for Cool-Season Precipitation Forecasting over the Western Conterminous United States

2020 ◽  
Vol 35 (3) ◽  
pp. 857-877 ◽  
Author(s):  
Marcel Caron ◽  
W. James Steenburgh
Keyword(s):  
United States ◽  
High Resolution ◽  
Model Performance ◽  
Cool Season ◽  
Forecast System ◽  
Operational Model ◽  
Precipitation Forecasting ◽  
Modeling Systems ◽  
Forecasting System ◽  
Terrain Representation

Abstract In August 2018 and June 2019, NCEP upgraded the operational versions of the High-Resolution Rapid Refresh (HRRR) and Global Forecast System (GFS), respectively. To inform forecasters and model developers about changes in the capabilities and biases of these modeling systems over the western conterminous United States (CONUS), we validate and compare precipitation forecasts produced by the experimental, preoperational HRRRv3 and GFSv15.0 with the then operational HRRRv2 and GFSv14 during the 2017/18 October–March cool season. We also compare the GFSv14 and GFSv15.0 with the operational, high-resolution configuration of the ECMWF Integrated Forecasting System (HRES). We validate using observations from Automated Surface and Weather Observing System (ASOS/AWOS) stations, which are located primarily in the lowlands, and observations from Snowpack Telemetry (SNOTEL) stations, which are located primarily in the uplands. Changes in bias and skill from HRRRv2 to HRRRv3 are small, with HRRRv3 exhibiting slightly higher (but statistically indistinguishable at a 95% confidence level) equitable threat scores. The GFSv14, GFSv15.0, and HRES all exhibit a wet bias at lower elevations and neutral or dry bias at upper elevations, reflecting insufficient terrain representation. GFSv15.0 performance is comparable to GFSv14 at day 1 and superior at day 3, but lags HRES. These results establish a baseline for current operational HRRR and GFS precipitation capabilities over the western CONUS and are consistent with steady or improving NCEP model performance.

SEM-Based Nanoprobing on 40, 32 and 28 nm CMOS Devices Challenges for Semiconductor Failure Analysis

2013 ◽  
Author(s):  
Erik Paul ◽  
Holger Herzog ◽  
Sören Jansen ◽  
Christian Hobert ◽  
Eckhard Langer
Keyword(s):  
Electron Microscope ◽  
High Resolution ◽  
Scanning Electron Microscope ◽  
Failure Analysis ◽  
Case Studies ◽  
State Of The Art ◽  
Root Cause ◽  
Highly Efficient ◽  
Technology Nodes ◽  
Scanning Electron

Abstract This paper presents an effective device-level failure analysis (FA) method which uses a high-resolution low-kV Scanning Electron Microscope (SEM) in combination with an integrated state-of-the-art nanomanipulator to locate and characterize single defects in failing CMOS devices. The presented case studies utilize several FA-techniques in combination with SEM-based nanoprobing for nanometer node technologies and demonstrate how these methods are used to investigate the root cause of IC device failures. The methodology represents a highly-efficient physical failure analysis flow for 28nm and larger technology nodes.

Progress toward Cloud-Cleared Infrared radiance assimilation in a global modeling framework: Application to the 2017 Atlantic Tropical Cyclone Season.

2021 ◽  
Author(s):  
Niama Boukachaba ◽  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Manisha Ganeshan ◽  
Will McCarty ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Strong Impact ◽  
Modeling Framework ◽  
Atmospheric Infrared Sounder ◽  
Clear Sky ◽  
Earth Observing System ◽  
Infrared Radiance ◽  
Radiance Assimilation ◽  
The Impact ◽  
Tropical Cyclone Season

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>

scholarly journals Future Changes in Tropical Cyclone Activity in High‐Resolution Large‐Ensemble Simulations

2017 ◽  
Vol 44 (19) ◽  
pp. 9910-9917 ◽  
Author(s):  
Kohei Yoshida ◽  
Masato Sugi ◽  
Ryo Mizuta ◽  
Hiroyuki Murakami ◽  
Masayoshi Ishii
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Tropical Cyclone Activity ◽  
Cyclone Activity ◽  
Large Ensemble ◽  
Ensemble Simulations

scholarly journals Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 204
Author(s):  
Chamay Kruger ◽  
Willem Daniel Schutte ◽  
Tanja Verster
Keyword(s):  
Case Studies ◽  
Model Development ◽  
Model Performance ◽  
Data Set ◽  
Pooled Data ◽  
External Data ◽  
First Case ◽  
Multivariate Prediction ◽  
Credit Data

This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable.

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