scholarly journals Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution

2007 ◽  
Vol 46 (11) ◽  
pp. 1967-1980 ◽  
Author(s):  
Jason E. Nachamkin ◽  
John Cook ◽  
Mike Frost ◽  
Daniel Martinez ◽  
Gary Sprung
Keyword(s):  
Atmospheric Model ◽  
Prediction System ◽  
Grid Spacing ◽  
Atmospheric Input ◽  
Atmospheric Data ◽  
Atmospheric Observations ◽  
Nested Grids ◽  
Fine Resolution ◽  
Horizontal Grid

Abstract Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.

Multiresolution Ensemble Forecasts of an Observed Tornadic Thunderstorm System. Part I: Comparsion of Coarse- and Fine-Grid Experiments

2006 ◽  
Vol 134 (3) ◽  
pp. 807-833 ◽  
Author(s):  
Fanyou Kong ◽  
Kelvin K. Droegemeier ◽  
Nicki L. Hickmon
Keyword(s):  
Initial Conditions ◽  
Deep Convection ◽  
Value Added ◽  
Sufficient Information ◽  
Grid Spacing ◽  
Ensemble Forecasts ◽  
Fine Grid ◽  
Ensemble Techniques ◽  
Nested Grids ◽  
Horizontal Grid

Abstract Using a nonhydrostatic numerical model with horizontal grid spacing of 24 km and nested grids of 6- and 3-km spacing, the authors employ the scaled lagged average forecasting (SLAF) technique, developed originally for global and synoptic-scale prediction, to generate ensemble forecasts of a tornadic thunderstorm complex that occurred in north-central Texas on 28–29 March 2000. This is the first attempt, to their knowledge, in applying ensemble techniques to a cloud-resolving model using radar and other observations assimilated within nonhorizontally uniform initial conditions and full model physics. The principal goal of this study is to investigate the viability of ensemble forecasting in the context of explicitly resolved deep convective storms, with particular emphasis on the potential value added by fine grid spacing and probabilistic versus deterministic forecasts. Further, the authors focus on the structure and growth of errors as well as the application of suitable quantitative metrics to assess forecast skill for highly intermittent phenomena at fine scales. Because numerous strategies exist for linking multiple nested grids in an ensemble framework with none obviously superior, several are examined, particularly in light of how they impact the structure and growth of perturbations. Not surprisingly, forecast results are sensitive to the strategy chosen, and owing to the rapid growth of errors on the convective scale, the traditional SLAF methodology of age-based scaling is replaced by scaling predicated solely upon error magnitude. This modification improves forecast spread and skill, though the authors believe errors grow more slowly than is desirable. For all three horizontal grid spacings utilized, ensembles show both qualitative and quantitative improvement relative to their respective deterministic control forecasts. Nonetheless, the evolution of convection at 24- and 6-km spacings is vastly different from, and arguably inferior to, that at 3 km because at 24-km spacing, the model cannot explicitly resolve deep convection while at 6 km, the deep convection closure problem is ill posed and clouds are neither implicitly nor explicitly represented (even at 3-km spacing, updrafts and downdrafts only are marginally resolved). Despite their greater spatial fidelity, the 3-km grid spacing experiments are limited in that the ensemble mean reflectivity tends to be much weaker in intensity, and much broader in aerial extent, than that of any single 3-km spacing forecast owing to amplitude reduction and spatial smearing that occur when averaging is applied to spatially intermittent phenomena. The ensemble means of accumulated precipitation, on the other hand, preserve peak intensity quite well. Although a single case study obviously does not provide sufficient information with which to draw general conclusions, the results presented here, as well as those in Part II (which focuses solely on 3-km grid spacing experiments), suggest that even a small ensemble of cloud-resolving forecasts may provide greater skill, and greater practical value, than a single deterministic forecast using either the same or coarser grid spacing.

GEOS-MITgcm coupled atmosphere-ocean simulation for DYAMOND

2021 ◽  
Author(s):  
Ehud Strobach ◽  
Andrea Molod ◽  
Atanas Trayanov ◽  
William Putman ◽  
Dimitris Menemenlis ◽  
...  
Keyword(s):  
General Circulation ◽  
Circulation Model ◽  
Atmospheric Model ◽  
Ocean Model ◽  
Second Phase ◽  
Massachusetts Institute Of Technology ◽  
Grid Spacing ◽  
Institute Of Technology ◽  
Horizontal Grid ◽  
Ocean Simulation

<p>During the past few years, the Goddard Earth Observing System (GEOS) and Massachusetts Institute of Technology general circulation model (MITgcm) groups have produced, respectively, global atmosphere-only and ocean-only simulations with km-scale grid spacing. These simulations have proved invaluable for process studies and the development of satellite and in-situ sampling strategies. Nevertheless, a key limitation of these simulations is the lack of feedback between the ocean and the atmosphere, limiting their usefulness for studying air-sea interactions and designing observing missions to study these interactions. To remove this limitation, we have coupled the km-scale GEOS atmospheric model with the km-scale MITgcm ocean model. We will present preliminary results from the GEOS-MITgcm contribution to the second phase of the DYAMOND (DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains) initiative.</p><p>The coupled atmosphere-ocean simulation was integrated using a cubed-sphere-1440 (~6-7 km horizontal grid spacing) configuration of GEOS and a lat-lon-cap-2160 (2–5-km horizontal grid spacing) configuration of MITgcm. We will show results from a preliminary analysis of air-sea interactions between Sea Surface Temperature (SST) and surface winds. In particular, we will discuss non-local atmospheric overturning circulation formed above the Gulf Stream SST front with characteristic sub-mesoscale width. This formation of a secondary circulation above the front suggests that capturing such air-sea interaction phenomena requires high-resolution capabilities in both the models' oceanic and atmospheric components.</p>

Evaluating performances of one-year simulation by using 3.5 km mesh global nonhydrostatic model

2021 ◽  
Author(s):  
Yohei Yamada ◽  
Chihiro Kodama ◽  
Akira Noda ◽  
Masaki Satoh ◽  
Masuo Nakano ◽  
...  
Keyword(s):  
Atmospheric Model ◽  
Global Precipitation Climatology Project ◽  
Horizontal Resolution ◽  
Global Model ◽  
South Pacific Convergence Zone ◽  
Climate Simulation ◽  
Grid Spacing ◽  
Test Feasibility ◽  
One Year ◽  
Horizontal Grid

<p>Recent advancement of supercomputing enables us to conduct a climate simulation by using a global model with horizontal grid spacing of a few kilometers. We may need to tune the model in order to conduct a reliable simulation. In order to test feasibility of a few kilometer climate simulation in near future, we conducted one-year simulation from June 2004 to May 2005 by using Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with horizontal grid spacing of 28 km, 14 km, 7 km, and 3.5 km, and evaluated their simulation performances. In general, global models have shown weak wind speed of tropical cyclones compared to its central sea level pressure due to insufficient horizontal resolution. As expected, the 3.5 km simulation showed improvement of this bias. As for simulated mean state, globally annual mean precipitation tended to be decreased with finer horizontal resolution in NICAM. Compared with observation (Global Precipitation Climatology Project V2.2; 2.71 mm day<sup>-1</sup>), 7 km and 3.5 km simulations underestimated the global mean precipitation (2.54 mm day<sup>-1</sup> and 2.67 mm day<sup>-1</sup>), while 14 km and 28 km simulations overestimated (2.84 mm day<sup>-1</sup> and 2.78 mm day<sup>-1</sup>). The 3.5 km simulation showed the best performance for reproducing globally annual mean precipitation. However, the 3.5 simulation showed underestimation of the South Pacific Convergence Zone. In order to conduct a reliable simulation, we need to improve performance of the 3.5 km global model. This demands extensive computing resources. The supercomputer Fugaku will give us extensive computing resources for addressing this issue.</p>

scholarly journals Spatiotemporal Fusion of Formosat-2 and Landsat-8 Satellite Images: A Comparison of “Super Resolution-Then-Blend” and “Blend-Then-Super Resolution” Approaches

2021 ◽  
Vol 13 (4) ◽  
pp. 606
Author(s):  
Tee-Ann Teo ◽  
Yu-Ju Fu
Keyword(s):  
Satellite Images ◽  
Traditional Approach ◽  
Spectral Band ◽  
Super Resolution ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Fusion Model ◽  
Image Blending ◽  
Fine Resolution

The spatiotemporal fusion technique has the advantages of generating time-series images with high-spatial and high-temporal resolution from coarse-resolution to fine-resolution images. A hybrid fusion method that integrates image blending (i.e., spatial and temporal adaptive reflectance fusion model, STARFM) and super-resolution (i.e., very deep super resolution, VDSR) techniques for the spatiotemporal fusion of 8 m Formosat-2 and 30 m Landsat-8 satellite images is proposed. Two different fusion approaches, namely Blend-then-Super-Resolution and Super-Resolution (SR)-then-Blend, were developed to improve the results of spatiotemporal fusion. The SR-then-Blend approach performs SR before image blending. The SR refines the image resampling stage on generating the same pixel-size of coarse- and fine-resolution images. The Blend-then-SR approach is aimed at refining the spatial details after image blending. Several quality indices were used to analyze the quality of the different fusion approaches. Experimental results showed that the performance of the hybrid method is slightly better than the traditional approach. Images obtained using SR-then-Blend are more similar to the real observed images compared with images acquired using Blend-then-SR. The overall mean bias of SR-then-Blend was 4% lower than Blend-then-SR, and nearly 3% improvement for overall standard deviation in SR-B. The VDSR technique reduces the systematic deviation in spectral band between Formosat-2 and Landsat-8 satellite images. The integration of STARFM and the VDSR model is useful for improving the quality of spatiotemporal fusion.

Warm Cores, Eyewall Slopes, and Intensities of Tropical Cyclones Simulated by a 7-km-Mesh Global Nonhydrostatic Model

2016 ◽  
Vol 73 (11) ◽  
pp. 4289-4309 ◽  
Author(s):  
Tomoki Ohno ◽  
Masaki Satoh ◽  
Yohei Yamada
Keyword(s):  
Tropical Cyclones ◽  
Inner Core ◽  
Grid Spacing ◽  
Satellite Measurements ◽  
Warm Core ◽  
Nonhydrostatic Model ◽  
Proposed Model ◽  
Tangential Wind ◽  
Balanced Model ◽  
Horizontal Grid

Abstract Based on the data of a 1-yr simulation by a global nonhydrostatic model with 7-km horizontal grid spacing, the relationships among warm-core structures, eyewall slopes, and the intensities of tropical cyclones (TCs) were investigated. The results showed that stronger TCs generally have warm-core maxima at higher levels as their intensities increase. It was also found that the height of a warm-core maximum ascends (descends) as the TC intensifies (decays). To clarify how the height and amplitude of warm-core maxima are related to TC intensity, the vortex structures of TCs were investigated. By gradually introducing simplifications of the thermal wind balance, it was established that warm-core structures can be reconstructed using only the tangential wind field within the inner-core region and the ambient temperature profile. A relationship between TC intensity and eyewall slope was investigated by introducing a parameter that characterizes the shape of eyewalls and can be evaluated from satellite measurements. The authors found that the eyewall slope becomes steeper (shallower) as the TC intensity increases (decreases). Based on a balanced model, the authors proposed a relationship between TC intensity and eyewall slope. The result of the proposed model is consistent with that of the analysis using the simulation data. Furthermore, for sufficiently strong TCs, the authors found that the height of the warm-core maximum increases as the slope becomes steeper, which is consistent with previous observational studies. These results suggest that eyewall slopes can be used to diagnose the intensities and structures of TCs.

Analysis of Idealized Tropical Cyclone Simulations Using the Weather Research and Forecasting Model: Sensitivity to Turbulence Parameterization and Grid Spacing

2009 ◽  
Vol 137 (2) ◽  
pp. 745-765 ◽  
Author(s):  
Kevin A. Hill ◽  
Gary M. Lackmann
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Maximum Intensity ◽  
Weather Research And Forecasting ◽  
Grid Spacing ◽  
Exchange Coefficient ◽  
Model Sensitivity ◽  
Wind Speeds ◽  
Horizontal Grid ◽  
Weather Research

Abstract The Weather Research and Forecasting Advanced Research Model (WRF-ARW) was used to perform idealized tropical cyclone (TC) simulations, with domains of 36-, 12-, and 4-km horizontal grid spacing. Tests were conducted to determine the sensitivity of TC intensity to the available surface layer (SL) and planetary boundary layer (PBL) parameterizations, including the Yonsei University (YSU) and Mellor–Yamada–Janjic (MYJ) schemes, and to horizontal grid spacing. Simulations were run until a quasi-steady TC intensity was attained. Differences in minimum central pressure (Pmin) of up to 35 hPa and maximum 10-m wind (V10max) differences of up to 30 m s−1 were present between a convection-resolving nested domain with 4-km grid spacing and a parent domain with cumulus parameterization and 36-km grid spacing. Simulations using 4-km grid spacing are the most intense, with the maximum intensity falling close to empirical estimates of maximum TC intensity. Sensitivity to SL and PBL parameterization also exists, most notably in simulations with 4-km grid spacing, where the maximum intensity varied by up to ∼10 m s−1 (V10max) or ∼13 hPa (Pmin). Values of surface latent heat flux (LHFLX) are larger in MYJ than in YSU at the same wind speeds, and the differences increase with wind speed, approaching 1000 W m−2 at wind speeds in excess of 55 m s−1. This difference was traced to a larger exchange coefficient for moisture, CQ, in the MYJ scheme. The exchange coefficients for sensible heat (Cθ) and momentum (CD) varied by <7% between the SL schemes at the same wind speeds. The ratio Cθ/CD varied by <5% between the schemes, whereas CQ/CD was up to 100% larger in MYJ, and the latter is theorized to contribute to the differences in simulated maximum intensity. Differences in PBL scheme mixing also likely played a role in the model sensitivity. Observations of the exchange coefficients, published elsewhere and limited to wind speeds <30 m s−1, suggest that CQ is too large in the MYJ SL scheme, whereas YSU incorporates values more consistent with observations. The exchange coefficient for momentum increases linearly with wind speed in both schemes, whereas observations suggest that the value of CD becomes quasi-steady beyond some critical wind speed (∼30 m s−1).

Simulations of Föhn in Antarctica with WRF for the Antarctic Mesoscale Prediction System AMPS

2021 ◽  
Author(s):  
Amélie Kirchgaessner ◽  
John King ◽  
Alan Gadian ◽  
Phil Anderson
Keyword(s):  
Weather Research And Forecasting ◽  
Mountain Range ◽  
Prediction System ◽  
Atmospheric Conditions ◽  
Ice Shelf ◽  
Cloud Properties ◽  
Surface Melt ◽  
Mixed Phase Clouds ◽  
The Antarctic

<p>We examine the representation of Föhn events across the Antarctic Peninsula Mountains during 2011 as they were observed in measurements by an Automatic Weather Station, and in simulations with the Weather Research and Forecasting Model (WRF) as run for the Antarctic Mesoscale Prediction System (AMPS). On the Larsen Ice Shelf (LIS) in the lee of this mountain range Föhn winds are thought to provide the atmospheric conditions for significant warming over the ice shelf thus leading to the initial firn densification and subsequently providing the melt water for hydrofracturing. This process has led to the dramatic collapse of huge parts of the LIS in 1995 and 2002 respectively.</p><p>Measurements obtained at a crest AWS on the Avery Plateau (AV), and the analysis of conditions upstream using the Froude number help to put observations at CP into a wider context. We find that, while the model generally simulates meteorological parameters very well, and shows good skills in capturing the occurrence, frequency and duration of Föhn events realistically, it underestimates the temperature increase and the humidity decrease during the Föhn significantly, and may thus underestimate the contribution of Föhn to driving surface melt on the LIS.</p><p>Our results indicate that the misrepresentation of cloud properties and particularly the absence of mixed phase clouds in AMPS, affects the quality of weather simulation under normal conditions to some extent, and to a larger extent the model’s capability to simulate the strength of Föhn conditions - and thus their contribution to driving surface melt on the LIS - adequately. Most importantly our data show that Föhn conditions can raise the air temperature to above freezing, and thus trigger melt/sublimation even in winter.</p>

Systematic Comparison of Convection-Allowing Models during the 2017 NOAA HWT Spring Forecasting Experiment

2019 ◽  
Vol 34 (5) ◽  
pp. 1395-1416 ◽  
Author(s):  
Corey K. Potvin ◽  
Jacob R. Carley ◽  
Adam J. Clark ◽  
Louis J. Wicker ◽  
Patrick S. Skinner ◽  
...  
Keyword(s):  
Model Design ◽  
Model Intercomparison ◽  
Grid Spacing ◽  
Single Model ◽  
Operational Model ◽  
Object Based ◽  
The One ◽  
Horizontal Grid ◽  
Cam Systems ◽  
Better Than

Abstract The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

Initial Conditions for Convection-Allowing Ensembles over the Conterminous United States

2020 ◽  
Vol 148 (7) ◽  
pp. 2645-2669
Author(s):  
Craig S. Schwartz ◽  
May Wong ◽  
Glen S. Romine ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell
Keyword(s):  
United States ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Limited Area ◽  
Grid Spacing ◽  
Forecast System ◽  
Skill Scores ◽  
Ensemble Mean ◽  
Horizontal Grid ◽  
Better Than

Abstract Five sets of 48-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were systematically evaluated over the conterminous United States with a focus on precipitation across 31 cases. The various CAEs solely differed by their initial condition perturbations (ICPs) and central initial states. CAEs initially centered about deterministic Global Forecast System (GFS) analyses were unequivocally better than those initially centered about ensemble mean analyses produced by a limited-area single-physics, single-dynamics 15-km continuously cycling ensemble Kalman filter (EnKF), strongly suggesting relative superiority of the GFS analyses. Additionally, CAEs with flow-dependent ICPs derived from either the EnKF or multimodel 3-h forecasts from the Short-Range Ensemble Forecast (SREF) system had higher fractions skill scores than CAEs with randomly generated mesoscale ICPs. Conversely, due to insufficient spread, CAEs with EnKF ICPs had worse reliability, discrimination, and dispersion than those with random and SREF ICPs. However, members in the CAE with SREF ICPs undesirably clustered by dynamic core represented in the ICPs, and CAEs with random ICPs had poor spinup characteristics. Collectively, these results indicate that continuously cycled EnKF mean analyses were suboptimal for CAE initialization purposes and suggest that further work to improve limited-area continuously cycling EnKFs over large regional domains is warranted. Additionally, the deleterious aspects of using both multimodel and random ICPs suggest efforts toward improving spread in CAEs with single-physics, single-dynamics, flow-dependent ICPs should continue.

scholarly journals Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 384
Author(s):  
John R. Lawson ◽  
William A. Gallus ◽  
Corey K. Potvin
Keyword(s):  
Mesoscale Convective System ◽  
Real Data ◽  
The United States ◽  
Convective System ◽  
Grid Spacing ◽  
Structure Amplitude ◽  
Cold Pools ◽  
Bow Echo ◽  
Mesoscale Convective ◽  
Horizontal Grid

The bow echo, a mesoscale convective system (MCS) responsible for much hail and wind damage across the United States, is associated with poor skill in convection-allowing numerical model forecasts. Given the decrease in convection-allowing grid spacings within many operational forecasting systems, we investigate the effect of finer resolution on the character of bowing-MCS development in a real-data numerical simulation. Two ensembles were generated: one with a single domain of 3-km horizontal grid spacing, and another nesting a 1-km domain with two-way feedback. Ensemble members were generated from their control member with a stochastic kinetic-energy backscatter scheme, with identical initial and lateral-boundary conditions. Results suggest that resolution reduces hindcast skill of this MCS, as measured with an adaptation of the object-based Structure–Amplitude–Location method. The nested 1-km ensemble produces a faster system than in both the 3-km ensemble and observations. The nested 1-km simulation also produced stronger cold pools, which could be enhanced by the increased (fractal) cloud surface area with higher resolution, allowing more entrainment of dry air and hence increased evaporative cooling.

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