scholarly journals Ensemble-Tailored Pattern Analysis of High-Resolution Dynamically Downscaled Precipitation Fields: Example for Climate Sensitive Regions of South America

2021 ◽  
Vol 9 ◽  
Author(s):  
Tanja C. Portele ◽  
Patrick Laux ◽  
Christof Lorenz ◽  
Annelie Janner ◽  
Natalia Horna ◽  
...  
Keyword(s):  
High Resolution ◽  
Climate Adaptation ◽  
Risk Mitigation ◽  
Wrf Model ◽  
Region Of Interest ◽  
Horizontal Resolution ◽  
General Purpose ◽  
Spatial And Temporal Patterns ◽  
Radiation Physics ◽  
Structure Amplitude

For climate adaptation and risk mitigation, decision makers in water management or agriculture increasingly demand for regionalized weather and climate information. To provide these, regional atmospheric models, such as the Weather Research and Forecasting (WRF) model, need to be optimized in their physical setup to the region of interest. The objective of this study is to evaluate four cumulus physics (CU), two microphysics (MP), two planetary boundary layer physics (PBL), and two radiation physics (RA) schemes in WRF according to their performance in dynamically downscaling the precipitation over two typical South American regions: one orographically complex area in Ecuador/Peru (horizontal resolution up to 9 and 3 km), and one area of rolling hills in Northeast Brazil (up to 9 km). For this, an extensive ensemble of 32 simulations over two continuous years was conducted. Including the reference uncertainty of three high-resolution global datasets (CHIRPS, MSWEP, ERA5-Land), we show that different parameterization setups can produce up to four times the monthly reference precipitation. This underscores the urgent need to conduct parameterization sensitivity studies before weather forecasts or input for impact modeling can be produced. Contrarily to usual studies, we focus on distributional, temporal and spatial precipitation patterns and evaluate these in an ensemble-tailored approach. These ensemble characteristics such as ensemble Structure-, Amplitude-, and Location-error, allow us to generalize the impacts of combining one parameterization scheme with others. We find that varying the CU and RA schemes stronger affects the WRF performance than varying the MP or PBL schemes. This effect is even present in the convection-resolving 3-km-domain over Ecuador/Peru where CU schemes are only used in the parent domain of the one-way nesting approach. The G3D CU physics ensemble best represents the CHIRPS probability distribution in the 9-km-domains. However, spatial and temporal patterns of CHIRPS are best captured by Tiedtke or BMJ CU schemes. Ecuadorian station data in the 3-km-domain is best simulated by the ensemble whose parent domains use the KF CU scheme. Accounting for all evaluation metrics, no general-purpose setup could be identified, but suited parameterizations can be narrowed down according to final application needs.

scholarly journals Ensemble Sensitivity Analysis Applied to a Southern Plains Convective Event

2015 ◽  
Vol 143 (1) ◽  
pp. 230-249 ◽  
Author(s):  
Christopher N. Bednarczyk ◽  
Brian C. Ancell
Keyword(s):  
Sensitivity Analysis ◽  
High Resolution ◽  
Wrf Model ◽  
Region Of Interest ◽  
Thermodynamic Characteristics ◽  
Forecast Errors ◽  
Synoptic Scale ◽  
Southern Plains ◽  
Synoptic Features ◽  
Upper Level

Abstract Forecast sensitivity of an April 2012 severe convection event in northern Texas is investigated with a high-resolution Weather Research and Forecasting (WRF) Model–based ensemble Kalman filter (EnKF). Through ensemble sensitivity analysis (ESA), which relates a forecast metric to initial and early forecast errors by linear regression, features of the flow are revealed that reflect dynamical relationships with the forecast convection. Results indicate that ESA can be successfully applied to high-resolution forecasts of convection, and the most important features are related to the synoptic-scale flow such as positioning of an upper-level low and lower-level thermodynamic characteristics of air masses. Comparisons of the maximum and minimum convectively active members in the region of interest show that the fields generated by ESA are consistent with the actual evolution of the event: members with more eastward progression of the synoptic-scale system produced a stronger convection forecast. The forecast metric of interest is modified in several ways to further evaluate the strength of the results of the sensitivity analysis. Three different variables acting as convection proxies (reflectivity, vertical velocity, and precipitation) are tested along with changing the location of the forecast metric and its spatial size. These additional tests highlight the same synoptic features of the flow with the only major differences reflecting the importance of magnitude versus position of the convective forecast.

Non-reasonable but efficient use of schemes in current model to improve realistic explicit convection modelling

2020 ◽  
Author(s):  
christophe messager ◽  
marc honnorat
Keyword(s):  
High Resolution ◽  
Current Model ◽  
Wrf Model ◽  
Region Of Interest ◽  
Limited Area ◽  
Operational Mode ◽  
Model Framework ◽  
Large Eddy Simulation Model ◽  
Weather Model ◽  
Daily Forecast

<p>There is actually no limitation of current high-resolution weather model for producing simulation and forecast of convection at kilometer and infra-kilometer horizontal resolutions. However, the disappointing results as well as the associated huge amount of computer resources required may lead to focus on Large Eddy Simulation model instead. However, the use of LES is not trivial and required a long and non-portable adjustment over the region of interest. Also, it is difficult to use in operational mode for daily forecast since they require specific inputs.</p><p>In the other side, pushing the current regional or Limited Area Model towards very high resolution is a convenient way to reach explicit resolution of convective process for instance. However, an explicit simulation is not a guarantee of a realistic result mainly due to the fact that initial condition is crucial as well as all other descriptions of the environment (soil, vegetation, sst, etc) and use of correct parameterization schemes.</p><p>For instance, within the WRF model framework, one can identify more than 4000 set of parameterizations plus all the scheme adjustments and threshold associated to.</p><p>However, a physically based analyze of what it is necessary for a realistic and explicit convection simulation may conduct a physicist user to define its “ideal” physics with what it already exists in the model. It may conduct to so-called unrealistic model requests in term of computation requirement regarding the radiative, the turbulence and the microphysics schemes but it does works with HPC systems. This kind of parameterization will be presented here and used with a very realistic vertical circulation into convective systems with convective updraft and downdraft modelling, from few meters up to several kilometers height.</p>

scholarly journals Evaluation of WRF model seasonal forecasts for tropical region of Singapore

2015 ◽  
Vol 12 (1) ◽  
pp. 69-72 ◽  
Author(s):  
J. Singh ◽  
K. Yeo ◽  
X. Liu ◽  
R. Hosseini ◽  
J. R. Kalagnanam
Keyword(s):  
High Resolution ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Tropical Region ◽  
Seasonal Forecasts ◽  
Near Surface ◽  
High Resolution Data ◽  
Model Configuration ◽  
And Performance ◽  
Resolution Data

Abstract. The Weather and Research Forecast (WRF) model is evaluated for the monsoon and inter-monsoon seasons over the tropical region of Singapore. The model configuration, physical parameterizations and performance results are described in this paper. In addition to the ready-to-use data available with the WRF model, the model configuration includes high resolution MODIS land use (500 m horizontal resolution) and JPL-NASA sea surface temperature (1 km horizontal resolution) data. The model evaluation is performed against near surface observations for temperature, relative humidity, wind speed and direction, available from a dense network of weather monitoring stations across Singapore. It is found that the high resolution data sets bring significant improvement in the model forecasts. The results also indicate that the model forecasts are more accurate in the monsoon seasons compared to the inter-monsoon seasons.

scholarly journals Intercomparisons of some dust models over West Asia

2019 ◽  
Vol 99 ◽  
pp. 01012
Author(s):  
Amirhossein Nikfal ◽  
Abbas Ranjbar Saadatabadi ◽  
Mehdi Rahnama ◽  
Sahar Tajbakhsh ◽  
Mohammad Moradi
Keyword(s):  
High Resolution ◽  
Region Of Interest ◽  
Horizontal Resolution ◽  
Soil Erodibility ◽  
The West ◽  
Dust Sources ◽  
Model Set ◽  
Set Up ◽  
Dust Modeling ◽  
Dust Surface

Evaluation and assessment of dust model results is of primary importance to get a better understanding of the models' performance, and therefore, enhancing the models' set up and structure. Besides some SDS-WAS dust models, two other high resolution WRF-Chem runs have been carried out for two dust episodes over the West Asia with alterations in the soil erodibility fields as one of the primary criteria of dust sources. The main aim of this article was to investigate the high resolution WRF-Chem modeling with the default and altered soil erosion, against the WMO SDS-WAS models. In this paper we investigated the application of WRF-Chem dust modeling for the region of interest (Iran), which cannot be seen entirely by the SDS-WAS models' domains. Comparisons of modelled dust surface concentrations with ground based measurements on 8 air quality stations show that the high resolution WRF-Chem could more or less lead to better predictions. For some cases, the results of the high resolution WRF-Chem unexpectedly presented a declined performance, which indicate that the improvements in the horizontal resolution and soil erodibility could not always lead to improved dust predictions, and more factors such as the model set-up and structure should be considered.

Quantitative accuracy of serotonergic neurotrans-mission imaging with high-resolution 123I SPECT

2004 ◽  
Vol 43 (06) ◽  
pp. 185-189 ◽  
Author(s):  
J. T. Kuikka
Keyword(s):  
High Resolution ◽  
Scatter Correction ◽  
Region Of Interest ◽  
Binding Potential ◽  
Partial Volume ◽  
Therapeutic Drugs ◽  
Quantitative Accuracy ◽  
Critical Error ◽  
Statistical Noise

Summary Aim: Serotonin transporter (SERT) imaging can be used to study the role of regional abnormalities of neurotransmitter release in various mental disorders and to study the mechanism of action of therapeutic drugs or drugs’ abuse. We examine the quantitative accuracy and reproducibility that can be achieved with high-resolution SPECT of serotonergic neurotransmission. Method: Binding potential (BP) of 123I labeled tracer specific for midbrain SERT was assessed in 20 healthy persons. The effects of scatter, attenuation, partial volume, mis-registration and statistical noise were estimated using phantom and human studies. Results: Without any correction, BP was underestimated by 73%. The partial volume error was the major component in this underestimation whereas the most critical error for the reproducibility was misplacement of region of interest (ROI). Conclusion: The proper ROI registration, the use of the multiple head gamma camera with transmission based scatter correction introduce more relevant results. However, due to the small dimensions of the midbrain SERT structures and poor spatial resolution of SPECT, the improvement without the partial volume correction is not great enough to restore the estimate of BP to that of the true one.

scholarly journals Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1965
Author(s):  
Raul-Ronald Galea ◽  
Laura Diosan ◽  
Anca Andreica ◽  
Loredana Popa ◽  
Simona Manole ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Diagnostic System ◽  
Region Of Interest ◽  
General Purpose ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Learning Methods ◽  
Study Results ◽  
Whole Heart

Despite the promising results obtained by deep learning methods in the field of medical image segmentation, lack of sufficient data always hinders performance to a certain degree. In this work, we explore the feasibility of applying deep learning methods on a pilot dataset. We present a simple and practical approach to perform segmentation in a 2D, slice-by-slice manner, based on region of interest (ROI) localization, applying an optimized training regime to improve segmentation performance from regions of interest. We start from two popular segmentation networks, the preferred model for medical segmentation, U-Net, and a general-purpose model, DeepLabV3+. Furthermore, we show that ensembling of these two fundamentally different architectures brings constant benefits by testing our approach on two different datasets, the publicly available ACDC challenge, and the imATFIB dataset from our in-house conducted clinical study. Results on the imATFIB dataset show that the proposed approach performs well with the provided training volumes, achieving an average Dice Similarity Coefficient of the whole heart of 89.89% on the validation set. Moreover, our algorithm achieved a mean Dice value of 91.87% on the ACDC validation, being comparable to the second best-performing approach on the challenge. Our approach provides an opportunity to serve as a building block of a computer-aided diagnostic system in a clinical setting.

An Efficient Dual-Resolution Approach for Ensemble Data Assimilation and Tests with Simulated Doppler Radar Data

2008 ◽  
Vol 136 (3) ◽  
pp. 945-963 ◽  
Author(s):  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Computational Cost ◽  
Radar Data ◽  
Horizontal Resolution ◽  
Velocity Data ◽  
Background Error ◽  
Cross Covariance ◽  
Radial Velocity Data

Abstract A new efficient dual-resolution (DR) data assimilation algorithm is developed based on the ensemble Kalman filter (EnKF) method and tested using simulated radar radial velocity data for a supercell storm. Radar observations are assimilated on both high-resolution and lower-resolution grids using the EnKF algorithm with flow-dependent background error covariances estimated from the lower-resolution ensemble. It is shown that the flow-dependent and dynamically evolved background error covariances thus estimated are effective in producing quality analyses on the high-resolution grid. The DR method has the advantage of being able to significantly reduce the computational cost of the EnKF analysis. In the system, the lower-resolution ensemble provides the flow-dependent background error covariance, while the single-high-resolution forecast and analysis provides the benefit of higher resolution, which is important for resolving the internal structures of thunderstorms. The relative smoothness of the covariance obtained from the lower 4-km-resolution ensemble does not appear to significantly degrade the quality of analysis. This is because the cross covariance among different variables is of first-order importance for “retrieving” unobserved variables from the radar radial velocity data. For the DR analysis, an ensemble size of 40 appears to be a reasonable choice with the use of a 4-km horizontal resolution in the ensemble and a 1-km resolution in the high-resolution analysis. Several sensitivity tests show that the DR EnKF system is quite robust to different observation errors. A 4-km thinned data resolution is a compromise that is acceptable under the constraint of real-time applications. A data density of 8 km leads to a significant degradation in the analysis.

Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation

2021 ◽  
Author(s):  
Luke J. LeBel ◽  
Brian H. Tang ◽  
Ross A. Lazear
Keyword(s):  
New York ◽  
High Resolution ◽  
Wind Shear ◽  
Model Simulation ◽  
Wrf Model ◽  
Severe Weather ◽  
Streamwise Vorticity ◽  
Low Level ◽  
Severe Convection ◽  
The Impact

AbstractThe complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York.The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

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