Performance of 2020 Real-Time Atlantic Hurricane Forecasts from High-Resolution Global-Nested Hurricane Models: HAFS-globalnest and GFDL T-SHiELD

2021 ◽  
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Real Time ◽  
Experimental Models ◽  
Lead Times ◽  
Track Forecast ◽  
Positive Bias ◽  
Forecast System ◽  
Study Results ◽  
Boundary Layer Scheme

Abstract The global-nested Hurricane Analysis and Forecast System (HAFS-globalnest) is one piece of NOAA’s Unified Forecast System (UFS) application for hurricanes. In this study, results are analyzed from 2020 real-time forecasts by HAFS-globalnest and a similar global-nested model, the Tropical Atlantic version of GFDL’s System for High-resolution prediction on Earth- to- Local Domains (T-SHiELD). HAFS-globalnest produced the highest track forecast skill compared to several operational and experimental models, while T-SHiELD showed promising track skill as well. The intensity forecasts from HAFS-globalnest generally had a positive bias at longer lead times primarily due to the lack of ocean coupling, while T-SHiELD had a much smaller intensity bias particularly at longer forecast lead times. With the introduction of a modified planetary boundary layer scheme and an increased number of vertical levels, particularly in the boundary layer, HAFS forecasts of storm size had a smaller positive bias than occurred in the 2019 version of HAFS-globalnest. Despite track forecasts that were comparable to the operational GFS and HWRF, both HAFS-globalnest and T-SHiELD suffered from a persistent right-of-track bias in several cases at the 4-5 day forecast lead times. The reasons for this bias were related to the strength of the subtropical ridge over the western North Atlantic and are continuing to be investigated and diagnosed. A few key case studies from this very active hurricane season, including Hurricanes Laura and Delta, were examined.

Impacts of Assimilating High-Resolution Atmospheric Motion Vectors Derived from Himawari-8 on Tropical Cyclone Forecast in HWRF

2019 ◽  
Vol 147 (10) ◽  
pp. 3721-3740 ◽  
Author(s):  
Masahiro Sawada ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Vijay Tallapragada ◽  
Ryo Oyama ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Cyclone ◽  
Ensemble Forecast ◽  
Lead Times ◽  
Track Forecast ◽  
Background Error ◽  
Steering Flow ◽  
Error Covariance ◽  
Atmospheric Motion ◽  
Atmospheric Motion Vectors

Abstract The impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.

Real-Time Track Prediction of Tropical Cyclones over the North Indian Ocean Using the ARW Model

2013 ◽  
Vol 52 (11) ◽  
pp. 2476-2492 ◽  
Author(s):  
Krishna K. Osuri ◽  
U. C. Mohanty ◽  
A. Routray ◽  
M. Mohapatra ◽  
Dev Niyogi
Keyword(s):  
High Resolution ◽  
Indian Ocean ◽  
Real Time ◽  
Tropical Cyclones ◽  
North Indian Ocean ◽  
Track Forecast ◽  
Forecast Errors ◽  
The North ◽  
Arw Model ◽  
The Impact

AbstractThe performance of the Advanced Research version of the Weather Research and Forecasting (ARW) model in real-time prediction of tropical cyclones (TCs) over the north Indian Ocean (NIO) at 27-km resolution is evaluated on the basis of 100 forecasts for 17 TCs during 2007–11. The analyses are carried out with respect to 1) basins of formation, 2) straight-moving and recurving TCs, 3) TC intensity at model initialization, and 4) season of occurrence. The impact of high resolution (18 and 9 km) on TC prediction is also studied. Model results at 27-km resolution indicate that the mean track forecast errors (skill with reference to persistence track) over the NIO were found to vary from 113 to 375 km (7%–51%) for a 12–72-h forecast. The model showed a right/eastward and slow bias in TC movement. The model is more skillful in track prediction when initialized at the intensity stage of severe cyclone or greater than at the intensity stage of cyclone or lower. The model is more efficient in predicting landfall location than landfall time. The higher-resolution (18 and 9 km) predictions yield an improvement in mean track error for the NIO Basin by about 4%–10% and 8%–24%, respectively. The 9-km predictions were found to be more accurate for recurving TC track predictions by ~13%–28% and 5%–15% when compared with the 27- and 18-km runs, respectively. The 9-km runs improve the intensity prediction by 15%–40% over the 18-km predictions. This study highlights the capabilities of the operational ARW model over the Indian monsoon region and the continued need for operational forecasts from high-resolution models.

2019 Atlantic Hurricane Forecasts from The Global-Nested Hurricane Analysis and Forecast System: Composite Statistics and Key Events

2021 ◽  
Author(s):  
Andrew Hazelton ◽  
Zhan Zhang ◽  
Bin Liu ◽  
Jili Dong ◽  
Ghassan Alaka ◽  
...  
Keyword(s):  
Real Time ◽  
Structure Prediction ◽  
Large Scale ◽  
Doppler Radar ◽  
Structural Features ◽  
Experimental Models ◽  
Forecast System ◽  
Improvement Program ◽  
The Bahamas ◽  
Large Scale Flow

AbstractNOAA’s Hurricane Analysis and Forecast System (HAFS) is an evolving FV3-based hurricane modeling system that is expected to replace the operational hurricane models at the National Weather Service. Supported by the Hurricane Forecast Improvement Program (HFIP), global-nested and regional versions of HAFS were run in real-time in 2019 to create the first baseline for the HAFS advancement. In this study, forecasts from the global-nested configuration of HAFS (HAFS-globalnest) are evaluated and compared with other operational and experimental models. The forecasts by HAFS-globalnest covered the period from July through October during the 2019 hurricane season. Tropical cyclone (TC) track, intensity, and structure forecast verifications are examined. HAFS-globalnest showed track skill superior to several operational hurricane models and comparable intensity and structure skill, although the skill in predicting rapid intensification was slightly inferior to the operational model skill. HAFS-globalnest correctly predicted that Hurricane Dorian would slow and turn north in the Bahamas and also correctly predicted structural features in other TCs such as a sting jet in Hurricane Humberto during extratropical transition. Humberto was also a case where HAFS-globalnest had better track forecasts than a regional version of HAFS (HAFS-SAR) due to a better representation of the large-scale flow. These examples and others are examined through comparisons with airborne tail Doppler radar from the NOAA WP-3D to provide a more detailed evaluation of TC structure prediction. The results from this real-time experiment motivates several future model improvements, and highlights the promise of HAFS-globalnest for improved TC prediction.

Electrical Probing and Surface Imaging of Deep Sub-Micron Integrated Circuits

1999 ◽  
Author(s):  
Kenneth Krieg ◽  
Richard Qi ◽  
Douglas Thomson ◽  
Greg Bridges
Keyword(s):  
High Resolution ◽  
Integrated Circuits ◽  
Real Time ◽  
High Speed ◽  
Time Signal ◽  
Surface Imaging ◽  
Atomic Force ◽  
Submicron Structures ◽  
High Resolution Images ◽  
Capacitive Loading

Abstract A contact probing system for surface imaging and real-time signal measurement of deep sub-micron integrated circuits is discussed. The probe fits on a standard probe-station and utilizes a conductive atomic force microscope tip to rapidly measure the surface topography and acquire real-time highfrequency signals from features as small as 0.18 micron. The micromachined probe structure minimizes parasitic coupling and the probe achieves a bandwidth greater than 3 GHz, with a capacitive loading of less than 120 fF. High-resolution images of submicron structures and waveforms acquired from high-speed devices are presented.

High-Resolution S-Band Profiling of the Atmospheric Boundary Layer

2001 ◽  
Author(s):  
Stephen J. Frasier ◽  
Andrew L. Pazmany ◽  
Turker Ince
Keyword(s):  
Boundary Layer ◽  
High Resolution ◽  
Atmospheric Boundary Layer
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