scholarly journals Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: a case study

2021 ◽  
Vol 56 ◽  
pp. 77-87
Author(s):  
Marc Imberger ◽  
Xiaoli Guo Larsén ◽  
Neil Davis
Keyword(s):  
Sea Level ◽  
Local Minimum ◽  
Mesh Refinement ◽  
Sea Level Pressure ◽  
Variable Resolution ◽  
Level Pressure ◽  
Storm Duration ◽  
Buoy Measurements ◽  
Minimum Sea Level Pressure

Abstract. With the rising share of renewable energy sources like wind energy in the energy mix, high-impact weather events like mid-latitude storms increasingly affect energy production, grid stability and safety and reliable forecasting becomes very relevant for e.g. transmission system operators to allow for actions to reduce imbalances. Traditionally, meteorological forecasts are provided by limited-area weather prediction models (LAMs), which can use high enough model resolution to represent the range of atmospheric scales of motions associated with such storm structures. While generally satisfactory, deterioration and insufficient deepening of large-scale storm structures are observed when they are introduced near the lateral boundaries of the LAM due to inadequate spatial and temporal interpolation. Global models with regional mesh refinement capabilities like the Model for Prediction Across Scales (MPAS) have the potential to provide an alternative, while avoiding sharp resolution jumps and lateral boundaries. In this study, MPAS' capabilities of simulating key evaluation metrics like storm intensity, storm location and storm duration are investigated based on a case study and assessed in comparison with buoy measurements, forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) LAM. Quasi-uniform and variable-resolution MPAS mesh configurations with different model physics settings are designed to analyze the impact of the mesh refinement and model physics on the model performance. MPAS shows good performance in predicting storm intensity based on the local minimum sea level pressure, while time of local minimum sea level pressure (storm duration) was generally estimated too late (too long) in comparison with the buoy measurements in part due to an early west-wards shift of the storm center in MPAS. The variable-resolution configurations showed a combination of an additional south-westwards shift and deviations in the sea level pressure field south-west of the storm center that introduced additional bias to the time of local minimum sea level pressure at some locations. The study highlights the need for a more detailed analysis of applied mesh refinements for particular applications and emphasizes the importance of methods like data assimilation techniques to prevent model drifts.

scholarly journals Strength and Challenges of global model MPAS with regional mesh refinement for mid-latitude storm forecasting: A case study

2021 ◽  
Author(s):  
Marc Imberger ◽  
Xiaoli Guo Larsén ◽  
Neil Davis
Keyword(s):  
Sea Level ◽  
Transition Zone ◽  
Large Scale ◽  
Arrival Time ◽  
Mesh Refinement ◽  
Sea Level Pressure ◽  
Forecast Performance ◽  
Variable Resolution ◽  
Storm Duration

<p>Mid-latitude storms are large-scale weather patterns. They involve a large range of spatial and temporal atmospheric scales of motion. Their characteristic extreme precipitation, wind gusts and high surface winds can significantly impact wind farms (e.g. shutdowns of turbines due to exceedance of cut-off wind speed) <span><span>affecting </span></span>grid performance and safety. Adequate storm forecasting, which relies on high spatial model resolution, is crucial. Traditional methods usually involve the use of limited area models (LAMs). While the performance of LAMs is generally satisfactory, challenges arise when large-scale storm structures enter near the the lateral boundaries of the LAM. In this case, insufficient update intervals of the forcing data at the lateral boundaries <!-- What does this mean exactly -->and spatial and temporal interpolation can deteriorate the storm structure that cause insufficient storm deepening. The global Model for Prediction Across Scales (MPAS) with regional mesh refinement avoids lateral boundary conditions and allows refinement with smooth transition zones. Based on a case study of storm “Christian”, MPAS’ capabilities in simulating key storm characteristics are explored in this work. Buoy measurements of sea level pressure, reanalysis and forecast products from the Climate Forecast System (CFSv2) and simulations with the Weather Research and Forecasting (WRF) model are used to evaluate the forecast performance with respect to storm intensity, storm arrival time and storm duration. A mesh configuration with refinement from 54-km to 18-km (further referred to as variable-resolution mesh) is compared with quasi-uniform mesh configurations to examine the impact of transition zone and mesh refinement on the storm structure and forecast performance. It is found that MPAS is generally able to predict the storm intensity based on the local minimum sea level pressure, while the estimation of storm arrival time and storm duration have been negatively influenced by model drifts in MPAS and by impacts of the transition zone on the storm development in the variable-resolution configuration. <!-- Can be shortened. -->An additional low pressure system emerged in the variable-resolution mesh <!-- May need to explain this to the readers. -->whereby its presence is sensitive to model physics. The investigation highlights the importance of the transition zone design in MPAS and the need for additional strategies like data assimilation techniques to prevent model drifts for storm forecasting.</p>

scholarly journals The Application of Barnes Filter to Positioning the Center of Landed Tropical Cyclone in Numerical Models

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Haibo Zou ◽  
Shanshan Wu ◽  
Xueting Yi ◽  
Nan Wu
Keyword(s):  
Numerical Model ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Numerical Models ◽  
Small Scale ◽  
Model Output ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Distance Difference

After a tropical cyclone (TC) making landfall, the numerical model output sea level pressure (SLP) presents many small-scale perturbations which significantly influence the positioning of the TC center. To fix the problem, Barnes filter with weighting parameters C=2500 and G=0.35 is used to remove these perturbations. A case study of TC Fung-Wong which landed China in 2008 shows that Barnes filter not only cleanly removes these perturbations, but also well preserves the TC signals. Meanwhile, the centers (track) obtained from SLP processed with Barnes filter are much closer to the observations than that from SLP without Barnes filter. Based on the distance difference (DD) between the TC center determined by SLP with/without Barnes filter and observation, statistics analysis of 12 TCs which landed China during 2005–2015 shows that in most cases (about 85%) the DDs are small (between −30 km and 30 km), while in a few cases (about 15%) the DDs are large (greater than 30 km even 70 km). This further verifies that the TC centers identified from SLP with Barnes filter are more accurate compared to that directly obtained from model output SLP. Moreover, the TC track identified with Barnes filter is much smoother than that without Barnes filter.

A Record Minimum Sea Level Pressure Observed in Hurricane Gilbert

1989 ◽  
Vol 117 (12) ◽  
pp. 2824-2828 ◽  
Author(s):  
H. E. Willoughby ◽  
J. M. Masters ◽  
C. W. Landsea
Keyword(s):  
Sea Level ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Minimum Sea Level Pressure

Assimilation of Tropical Cyclone Advisory Minimum Sea Level Pressure in the NCEP Global Data Assimilation System

2011 ◽  
Vol 26 (6) ◽  
pp. 1085-1091 ◽  
Author(s):  
Daryl T. Kleist
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Sea Level Pressure ◽  
Level Pressure ◽  
Global Data Assimilation System ◽  
Data Assimilation System ◽  
Global Data ◽  
Minimum Sea Level Pressure ◽  
Assimilation System ◽  
Tropical Systems

Abstract The assimilation of official advisory minimum sea level pressure observations has been developed and tested in the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) to address forecaster concerns regarding some tropical systems being far too weak in operational Global Forecast System (GFS) analyses. The assimilation of these observations has been operational in the GFS since December 2009. Using the T574 version of the NCEP GFS model, it is demonstrated that the assimilation of these observations results in a substantial reduction in the initial intensity bias for tropical systems, resulting in improved track and intensity guidance for lead times out to 5 days.

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