High-Resolution Atmospheric Modeling over the Philippine Archipelago

2007 ◽  
Author(s):  
James D. Doyle
Keyword(s):  
High Resolution ◽  
Atmospheric Modeling

scholarly journals Short-Range Precipitation Forecasts from Time-Lagged Multimodel Ensembles during the HMT-West-2006 Campaign

2008 ◽  
Vol 9 (3) ◽  
pp. 477-491 ◽  
Author(s):  
Huiling Yuan ◽  
John A. McGinley ◽  
Paul J. Schultz ◽  
Christopher J. Anderson ◽  
Chungu Lu
Keyword(s):  
High Resolution ◽  
Bias Correction ◽  
Short Range ◽  
Mesoscale Model ◽  
Atmospheric Modeling ◽  
Training Data ◽  
Local Analysis ◽  
Calibration Methods ◽  
Time Lagged ◽  
Multimodel Ensembles

Abstract High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.

High-Resolution Atmospheric Modeling of Fluorotelomer Alcohols and Perfluorocarboxylic Acids in the North American Troposphere

2007 ◽  
Vol 41 (16) ◽  
pp. 5756-5762 ◽  
Author(s):  
Greg Yarwood ◽  
Susan Kemball-Cook ◽  
Michael Keinath ◽  
Robert L. Waterland ◽  
Stephen H. Korzeniowski ◽  
...  
Keyword(s):  
High Resolution ◽  
North American ◽  
Atmospheric Modeling ◽  
The North ◽  
Perfluorocarboxylic Acids ◽  
Fluorotelomer Alcohols

scholarly journals Numerical analysis of the impact of agricultural emissions on PM<sub>2.5</sub> in China using a high-resolution ammonia emissions inventory

2020 ◽  
Author(s):  
Xiao Han ◽  
Lingyun Zhu ◽  
Mingxu Liu ◽  
Yu Song ◽  
Meigen Zhang
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Source Apportionment ◽  
Mass Concentration ◽  
Atmospheric Modeling ◽  
Central China ◽  
High Mass ◽  
Emissions Inventory ◽  
Aerosol Mass Concentration ◽  
The Impact

Abstract. China is one of the largest agricultural countries in the world. The NH3 emissions from agricultural activities in China significantly affect regional air quality and horizontal visibility. To reliably estimate the influence of NH3 on agriculture, a high-resolution agricultural NH3 emissions inventory, compiled with a 1 km × 1 km horizontal resolution, was applied to calculate the NH3 mass burden in China. The key emission factors of this inventory were enhanced by considering the results of many native experiments, and the activity data of spatial and temporal information were updated using statistical data from 2015. Fertilizer and husbandry, as well as farmland ecosystems, livestock waste, crop residue burning, fuel wood combustion, and other NH3 emission sources were included in the inventory. Furthermore, a source apportionment tool, ISAM (Integrated Source Apportionment Method), coupled with the air quality modeling system RAMS-CMAQ (Regional Atmospheric Modeling System and Community Multiscale Air Quality), was applied to capture the contribution of NH3 emitted from total agriculture (Tagr) in China. The aerosol mass concentration in 2015 was simulated, and the results showed that a high mass concentration of NH3, which exceeded 10 μg m−3, appeared mainly in the North China Plain (NCP), Central China (CNC), the Yangtz River Delta (YRD), and the Sichan Basin (SCB), and the annual average contribution of Tagr NH3 to PM2.5 mass burden in China was 14–18 %. Specific to the PM2.5 components, Tagr NH3 provided a major contribution to ammonium formation (87.6 %) but a tiny contribution to sulfate (2.2 %). In addition, several brute-force sensitivity tests were conducted to estimate the impact of Tagr NH3 emissions reduction on the PM2.5 mass burden. Compared with the results of ISAM, it was found that even though the Tagr NH3 only contributed 10.1 % of nitrate under current emissions scenarios, the reduction of nitrate could reach 98.8 % upon removal of the Tagr NH3 emissions. The main reason for this deviation could be that the NH3 contribution to nitrate is small under rich NH3 conditions and large in poor NH3 environments. Thus, the influence of NH3 on nitrate formation could be enhanced with the decrease of ambient NH3 mass concentration.

scholarly journals Issues in Simulating the Annual Precipitation of a Semiarid Region in Central Spain

2005 ◽  
Vol 6 (4) ◽  
pp. 409-422 ◽  
Author(s):  
N. Hasler ◽  
R. Avissar ◽  
G. E. Liston
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Domain Size ◽  
Atmospheric Modeling ◽  
Annual Rainfall ◽  
Semiarid Region ◽  
Central Spain ◽  
Small Domain ◽  
The Impact ◽  
Dry Bias

Abstract Running regional climate models at a high resolution may improve their ability to simulate regional precipitation patterns, making them suitable for studying the impact of human-induced land-cover changes on hydrometeorology. The performance of the Regional Atmospheric Modeling System (RAMS) run in the high-resolution climate mode (4-km grid mesh) has been tested over a small domain in a semiarid region in central Spain. Three 1-yr simulations representing dry, intermediate, and wet conditions were compared to observations collected in 35 rain gauges. The model captured general spatiotemporal features of precipitation, such as the timing of precipitation events and approximate location of storms. A high correlation (0.82) between monthly domain-averaged observed and modeled precipitation was obtained. However, the model had a systematic dry bias, averaging −0.29 mm day−1, equivalent to 26% of annual rainfall. The small domain size, chosen because of computational limits, induced strong lateral boundary forcing, which, combined with uncertainty in NCEP relative humidity fields, was a likely cause for this dry bias.

scholarly journals Using High-Resolution Numerical Weather Forecasts to Improve Remotely Sensed Rainfall Estimates: The Case of the 2013 Colorado Flash Flood

2015 ◽  
Vol 16 (4) ◽  
pp. 1742-1751 ◽  
Author(s):  
E. I. Nikolopoulos ◽  
N. S. Bartsotas ◽  
E. N. Anagnostou ◽  
G. Kallos
Keyword(s):  
High Resolution ◽  
Flash Flood ◽  
Weather Prediction ◽  
Stage Iv ◽  
Bias Reduction ◽  
Atmospheric Modeling ◽  
Limited Area ◽  
Numerical Weather ◽  
Satellite Rainfall ◽  
Rainfall Estimates

Abstract The September 2013 flash flood–triggering rainfall event in Colorado highlighted the strong underestimation of remote sensing techniques over mountainous terrain. In this work, the use of high-resolution rainfall forecasts for adjusting weather radar– [Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimation (Q3)] and satellite-based [CPC morphing technique (CMORPH) and TRMM 3B42RT] rainfall estimates is examined. Evaluation of the adjustment procedures is based on the NCEP Stage IV product. Results show that 1-km-grid-resolution rainfall forecasts provided by a numerical weather prediction model [Regional Atmospheric Modeling System and Integrated Community Limited Area Modeling System (RAMS-ICLAMS)] adequately captured total rainfall amounts during the event and could therefore be used to adjust biases in radar and satellite rainfall estimates. Two commonly used adjustment procedures according to 1) mean field bias and 2) probability density function matching are examined. Findings indicate that both procedures are successful in improving the original radar and satellite rainfall estimates, with the first method consistently providing the highest bias reduction while the second exhibits higher improvement in RMSE and correlation.

scholarly journals Shallow cumulus properties as captured by adiabatic fraction in high-resolution LES simulations

2021 ◽  
Author(s):  
Eshkol Eytan ◽  
Alexander Khain ◽  
Mark Pinsky ◽  
Orit Altaratz ◽  
Jacob Shpund ◽  
...  
Keyword(s):  
High Resolution ◽  
Hydrological Cycle ◽  
Atmospheric Modeling ◽  
Trade Wind ◽  
Cumulus Clouds ◽  
Complex Processes ◽  
Cloud Properties ◽  
Shallow Cumulus ◽  
Hebrew University ◽  
Bin Microphysics

Abstract Shallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions, due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high-resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the cloud’s dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20-40 m in width) are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplets-size-distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplets size distributions at different cloud regions (e.g. core, edge) are analyzed in relation to AF.

scholarly journals Combined stellar evolution and spectroscopic modeling of massive stars

2015 ◽  
Vol 11 (A29B) ◽  
pp. 455-457
Author(s):  
Jose H. Groh
Keyword(s):  
High Resolution ◽  
Stellar Evolution ◽  
Massive Star ◽  
Massive Stars ◽  
Atmospheric Modeling ◽  
High Resolution Spectrum ◽  
Morphological Appearance ◽  
First Time

AbstractThe morphological appearance of massive stars during their evolution and at the pre-SN stage is very uncertain, both from theoretical and observational perspectives. We recently developed coupled stellar evolution and atmospheric modeling of stars done with the Geneva and CMFGEN codes, for initial masses between 9 and 120 M⊙. We are able to predict the observables such as the high-resolution spectrum and broadband photometry. Here I discuss how the spectrum of a massive star changes across its evolution and before death. Our models allow, for the first time, direct comparison between predictions from stellar evolution models and observations of SN progenitors.

scholarly journals Image Collection Simulation Using High-Resolution Atmospheric Modeling

2020 ◽  
Vol 12 (19) ◽  
pp. 3214
Author(s):  
Andrew Kalukin ◽  
Satoshi Endo ◽  
Russell Crook ◽  
Manoj Mahajan ◽  
Robert Fennimore ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Atmospheric Model ◽  
Remote Sensing Image ◽  
Atmospheric Modeling ◽  
Atmospheric Effects ◽  
Multispectral Imagery ◽  
Modeling Framework ◽  
Voxel Grid ◽  
Image Collection

A new method is described for simulating the passive remote sensing image collection of ground targets that includes effects from atmospheric physics and dynamics at fine spatial and temporal scales. The innovation in this research is the process of combining a high-resolution weather model with image collection simulation to attempt to account for heterogeneous and high-resolution atmospheric effects on image products. The atmosphere was modeled on a 3D voxel grid by a Large-Eddy Simulation (LES) driven by forcing data constrained by local ground-based and air-based observations. The spatial scale of the atmospheric model (10–100 m) came closer than conventional weather forecast scales (10–100 km) to approaching the scale of typical commercial multispectral imagery (2 m). This approach was demonstrated through a ground truth experiment conducted at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. In this experiment, calibrated targets (colored spectral tarps) were placed on the ground, and the scene was imaged with WorldView-3 multispectral imagery at a resolution enabling the tarps to be visible in at least 9–12 image pixels. The image collection was simulated with Digital Imaging and Remote Sensing Image Generation (DIRSIG) software, using the 3D atmosphere from the LES model to generate a high-resolution cloud mask. The high-resolution atmospheric model-predicted cloud coverage was usually within 23% of the measured cloud cover. The simulated image products were comparable to the WorldView-3 satellite imagery in terms of the variations of cloud distributions and spectral properties of the ground targets in clear-sky regions, suggesting the potential utility of the proposed modeling framework in improving simulation capabilities, as well as testing and improving the operation of image collection processes.

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