scholarly journals The Evaluation of Real-Time Hurricane Analysis and Forecast System (HAFS) Stand-Alone Regional (SAR) Model Performance for the 2019 Atlantic Hurricane Season

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 617 ◽  
Author(s):  
Jili Dong ◽  
Bin Liu ◽  
Zhan Zhang ◽  
Weiguo Wang ◽  
Avichal Mehra ◽  
...  
Keyword(s):  
Real Time ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Forecast System ◽  
Improvement Project ◽  
Multi Scale ◽  
The North ◽  
Sar Model ◽  
Hydrostatic Model ◽  
Cubed Sphere

The next generation Hurricane Analysis and Forecast System (HAFS) has been developed recently in the National Oceanic and Atmospheric Administration (NOAA) to accelerate the improvement of tropical cyclone (TC) forecasts within the Unified Forecast System (UFS) framework. The finite-volume cubed sphere (FV3) based convection-allowing HAFS Stand-Alone Regional model (HAFS-SAR) was successfully implemented during Hurricane Forecast Improvement Project (HFIP) real-time experiments for the 2019 Atlantic TC season. HAFS-SAR has a single large 3-km horizontal resolution regional domain covering the North Atlantic basin. A total of 273 cases during the 2019 TC season are systematically evaluated against the best track and compared with three operational forecasting systems: Global Forecast System (GFS), Hurricane Weather Research and Forecasting model (HWRF), and Hurricanes in a Multi-scale Ocean-coupled Non-hydrostatic model (HMON). HAFS-SAR has the best performance in track forecasts among the models presented in this study. The intensity forecasts are improved over GFS, but show less skill compared to HWRF and HMON. The radius of gale force wind is over-predicted in HAFS-SAR, while the hurricane force wind radius has lower error than other models.

scholarly journals Simulations of Monsoon Intraseasonal Oscillation Using Climate Forecast System Version 2: Insight for Horizontal Resolution and Moist Processes Parameterization

Atmosphere ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 429 ◽  
Author(s):  
Snehlata Tirkey ◽  
P. Mukhopadhyay ◽  
R. Phani Murali Krishna ◽  
Ashish Dhakate ◽  
Kiran Salunke
Keyword(s):  
Deep Convection ◽  
Intraseasonal Oscillation ◽  
Horizontal Resolution ◽  
Specific Humidity ◽  
Climate Forecast ◽  
Forecast System ◽  
Climate Forecast System ◽  
The North ◽  
Northward Propagation ◽  
Moist Processes

In the present study, we analyze the Climate Forecast System version 2 (CFSv2) model in three resolutions, T62, T126, and T382. We evaluated the performance of all three resolutions of CFSv2 in simulating the Monsoon Intraseasonal Oscillation (MISO) of the Indian summer monsoon (ISM) by analyzing a suite of dynamic and thermodynamic parameters. Results reveal a slower northward propagation of MISO in all models with the characteristic northwest–southeast tilted rain band missing over India. The anomalous moisture convergence and vorticity were collocated with the convection center instead of being northwards. This affected the northward propagation of MISO. The easterly shear to the north of the equator was better simulated by the coarser resolution models than CFS T382. The low level specific humidity showed improvement only in CFS T382 until ~15° N. The analyses of the vertical profiles of moisture and its relation to rainfall revealed that all CFSv2 resolutions had a lower level of moisture in the lower level (< 850 hPa) and a drier level above. This eventually hampered the growth of deep convection in the model. These model shortcomings indicate a possible need of improvement in moist process parameterization in the model in tune with the increase in resolution.

scholarly journals Verifying Forecast Precipitation Type with mPING*

2015 ◽  
Vol 30 (3) ◽  
pp. 656-667 ◽  
Author(s):  
Kimberly L. Elmore ◽  
Heather M. Grams ◽  
Deanna Apps ◽  
Heather D. Reeves
Keyword(s):  
Critical Infrastructure ◽  
Current Model ◽  
Model Performance ◽  
Transportation Systems ◽  
Forecast System ◽  
Freezing Rain ◽  
The North ◽  
Skill Scores ◽  
Precipitation Type ◽  
Surface Observations

Abstract In winter weather, precipitation type is a pivotal characteristic because it determines the nature of most preparations that need to be made. Decisions about how to protect critical infrastructure, such as power lines and transportation systems, and optimize how best to get aid to people are all fundamentally precipitation-type dependent. However, current understanding of the microphysical processes that govern precipitation type and how they interplay with physics-based numerical forecast models is incomplete, degrading precipitation-type forecasts, but by how much? This work demonstrates the utility of crowd-sourced surface observations of precipitation type from the Meteorological Phenomena Identification Near the Ground (mPING) project in estimating the skill of numerical model precipitation-type forecasts and, as an extension, assessing the current model performance regarding precipitation type in areas that are otherwise without surface observations. In general, forecast precipitation type is biased high for snow and rain and biased low for freezing rain and ice pellets. For both the North American Mesoscale Forecast System and Global Forecast System models, Gilbert skill scores are between 0.4 and 0.5 and from 0.35 to 0.45 for the Rapid Refresh model, depending on lead time. Peirce skill scores for individual precipitation types are 0.7–0.8 for both rain and snow, 0.2–0.4 for freezing rain and freezing rain, and 0.25 or less for ice pellets. The Rapid Refresh model displays somewhat lower scores except for ice pellets, which are severely underforecast, compared to the other models.

scholarly journals Seasonal range test run with Global Eta Framework

2017 ◽  
Vol 14 ◽  
pp. 247-251 ◽  
Author(s):  
Dragan Latinović ◽  
Sin Chan Chou ◽  
Miodrag Rančić
Keyword(s):  
Model Performance ◽  
Seasonal Prediction ◽  
Atmospheric Model ◽  
Horizontal Resolution ◽  
Curvilinear Coordinates ◽  
Smooth Transition ◽  
Precipitation Pattern ◽  
Global Atmospheric Model ◽  
Coordinate Lines ◽  
Cubed Sphere

Abstract. Global Eta Framework (GEF) is a global atmospheric model developed in general curvilinear coordinates and capable of running on arbitrary rectangular quasi-uniform spherical grids, using stepwise (Eta) representation of the terrain. In this study, the model is run on a cubed-sphere grid topology, in a version with uniform Jacobians (UJ), which provides equal-area grid cells, and a smooth transition of coordinate lines across the edges of the cubed-sphere. Within a project at the Brazilian Center for Weather Forecasts and Climate Studies (CPTEC), a nonhydrostatic version of this model is under development and will be applied for seasonal prediction studies. This note describes preliminary tests with the GEF on the UJ cubed-sphere in which model performance is evaluated in seasonal simulations at a horizontal resolution of approximately 25 km, running in the hydrostatic mode. Comparison of these simulations with the ERA-Interim reanalyses shows that the 850 hPa temperature is underestimated, while precipitation pattern is mostly underestimated in tropical continental regions and overestimated in tropical oceanic regions. Nevertheless, the model is still able to well capture the main seasonal climate characteristics. These results will be used as a control run in further tests with the nonhydrostatic version of the model.

scholarly journals Forecaster Use and Evaluation of Real-Time 3DVAR Analyses during Severe Thunderstorm and Tornado Warning Operations in the Hazardous Weather Testbed

2014 ◽  
Vol 29 (3) ◽  
pp. 601-613 ◽  
Author(s):  
Kristin M. Calhoun ◽  
Travis M. Smith ◽  
Darrel M. Kingfield ◽  
Jidong Gao ◽  
David J. Stensrud
Keyword(s):  
Data Assimilation ◽  
Real Time ◽  
Mesoscale Model ◽  
Three Dimensional ◽  
Processing System ◽  
The United States ◽  
Horizontal Resolution ◽  
Similar Data ◽  
The North ◽  
Hazardous Weather

Abstract A weather-adaptive three-dimensional variational data assimilation (3DVAR) system was included in the NOAA Hazardous Weather Testbed as a first step toward introducing warn-on-forecast initiatives into operations. NWS forecasters were asked to incorporate the data in conjunction with single-radar and multisensor products in the Advanced Weather Interactive Processing System (AWIPS) as part of their warning-decision process for real-time events across the United States. During the 2011 and 2012 experiments, forecasters examined more than 36 events, including tornadic supercells, severe squall lines, and multicell storms. Products from the 3DVAR analyses were available to forecasters at 1-km horizontal resolution every 5 min, with a 4–6-min latency, incorporating data from the national Weather Surveillance Radar-1988 Doppler (WSR-88D) network and the North American Mesoscale model. Forecasters found the updraft, vertical vorticity, and storm-top divergence products the most useful for storm interrogation and quickly visualizing storm trends, often using these tools to increase the confidence in a warning decision and/or issue the warning slightly earlier. The 3DVAR analyses were most consistent and reliable when the storm of interest was in close proximity to one of the assimilated WSR-88D, or data from multiple radars were incorporated into the analysis. The latter was extremely useful to forecasters in blending data rather than having to analyze multiple radars separately, especially when range folding obscured the data from one or more radars. The largest hurdle for the real-time use of 3DVAR or similar data assimilation products by forecasters is the data latency, as even 4–6 min reduces the utility of the products when new radar scans are available.

scholarly journals Convective-Permitting Hindcast Simulations during the North American Monsoon GPS Transect Experiment 2013: Establishing Baseline Model Performance without Data Assimilation

2018 ◽  
Vol 57 (8) ◽  
pp. 1683-1710 ◽  
Author(s):  
James M. Moker ◽  
Christopher L. Castro ◽  
Avelino F. Arellano ◽  
Yolande L. Serra ◽  
David K. Adams
Keyword(s):  
North American ◽  
Initial Conditions ◽  
Model Performance ◽  
Precipitable Water ◽  
Border Region ◽  
Precipitation Forecast ◽  
North American Monsoon ◽  
Forecast System ◽  
The North ◽  
Northwestern Mexico

AbstractDuring the North American monsoon global positioning system (GPS) Transect Experiment 2013, daily convective-permitting WRF simulations are performed in northwestern Mexico and the southern Arizona border region using the operational Global Forecast System (GFS) and North American Mesoscale Forecast System (NAM) models as lateral boundary forcing and initial conditions. Compared to GPS precipitable water vapor (PWV), the WRF simulations display a consistent moist bias in the initial specification of PWV leading to convection beginning 3–6 h early. Given appreciable observed rainfall, days are classified as strongly and weakly forced based only on the presence of an inverted trough (IV); gulf surges did not noticeably impact the development of mesoscale convective systems (MCSs) and related convection in northwestern Mexico. Strongly forced days display higher modeled precipitation forecast skill than weakly forced days in the slopes of the northern Sierra Madre Occidental (SMO) away from the crest, especially toward the west where MCSs account for the greatest proportion of all monsoon-related precipitation. A case study spanning 8–10 July 2013 illustrates two consecutive days when nearly identical MCSs evolved over northern Sonora. Although a salient MCS is simulated on the strongly forced day (9–10 July 2013) when an IV is approaching the core monsoon region, a simulated MCS is basically nonexistent on the weakly forced day (8–9 July 2013) when the IV is farther away. The greater sensitivity to the initial specification of PWV in the weakly forced day suggests that assimilation of GPS-derived PWV for these types of days may be of greatest value in improving model precipitation forecasts.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

The Debris Flow Disaster Faster-Than-Early Forecast System (DFS) with Multi-Sensors Integrated Wireless Sensor Networks

2013 ◽  
Vol 347-350 ◽  
pp. 975-979
Author(s):  
Rong Zhao ◽  
Cai Hong Li ◽  
Yun Jian Tan ◽  
Jun Shi ◽  
Fu Qiang Mu ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Debris Flow ◽  
Real Time ◽  
Debris Flows ◽  
Base Station ◽  
Base Stations ◽  
Wireless Sensor ◽  
Forecast System ◽  
Debris Flow Disaster

This paper presents a Debris Flow Disaster Faster-than-early Forecast System (DFS) with wireless sensor networks. Debris flows carrying saturated solid materials in water flowing downslope often cause severe damage to the lives and properties in their path. Faster-than-early or faster-than-real-time forecasts are imperative to save lives and reduce damage. This paper presents a novel multi-sensor networks for monitoring debris flows. The main idea is to let these sensors drift with the debris flow, to collect flow information as they move along, and to transmit the collected data to base stations in real time. The Raw data are sent to the cloud processing center from the base station. And the processed data and the video of the debris flow are display on the remote PC. The design of the system address many challenging issues, including cost, deployment efforts, and fast reaction.

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