The Advanced Regional Prediction System (ARPS) - A multi-scale nonhydrostatic atmospheric simulation and prediction model. Part I: Model dynamics and verification

2000 ◽  
Vol 75 (3-4) ◽  
pp. 161-193 ◽  
Author(s):  
M. Xue ◽  
K. K. Droegemeier ◽  
V. Wong
Keyword(s):  
Prediction Model ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Multi Scale ◽  
Regional Prediction ◽  
Model Dynamics

scholarly journals Basic Diagnosis and Prediction of Persistent Contrail Occurrence Using High-Resolution Numerical Weather Analyses/Forecasts and Logistic Regression. Part II: Evaluation of Sample Models

2009 ◽  
Vol 48 (9) ◽  
pp. 1790-1802 ◽  
Author(s):  
David P. Duda ◽  
Patrick Minnis
Keyword(s):  
Relative Humidity ◽  
Wind Direction ◽  
Meteorological Data ◽  
Logistic Models ◽  
Satellite Observations ◽  
Lapse Rate ◽  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Surface Models

Abstract A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

Parallel weather modeling with the advanced regional prediction system

1997 ◽  
Vol 23 (14) ◽  
pp. 2243-2256 ◽  
Author(s):  
A. Sathye ◽  
M. Xue ◽  
G. Bassett ◽  
K. Droegemeier
Keyword(s):  
Prediction System ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Weather Modeling

scholarly journals Precipitation simulation of synoptic scale systems over western Himalayan region using Advanced Regional Prediction System (ARPS) model

MAUSAM ◽  
2021 ◽  
Vol 58 (4) ◽  
pp. 471-480
Author(s):  
GIRISH SEMWAL ◽  
R. K. GIRI
Keyword(s):  
Numerical Models ◽  
Weather Prediction ◽  
Himalayan Region ◽  
Prediction System ◽  
Lateral Boundary ◽  
Weather System ◽  
Advanced Regional Prediction System ◽  
Western Himalayan Region ◽  
Lateral Boundary Conditions ◽  
Regional Prediction

Operational weather prediction over western Himalayan region is a challenging job due to scarcity of data and complex topography that interacts with approaching weather system. Accurate prediction of complex weather phenomena requires dense data network which is difficult to establish in mountain due to complex terrain and hostile weather conditions over Himalaya. The alternate method to overcome this problem is by ingesting three-dimensional meteorological variables from global model’s analysis and forecast values as initial and lateral boundary conditions in meso-scale numerical models. Simultaneously, data assimilation is a potential tool in which non-conventional [satellite, radar and Automatic Weather Station (AWS)] and conventional (surface and upper air observations) data are ingested in the numerical models to generate high resolution and accurate initial fields for the initialization of the mesoscale model. In the present study, Advanced Regional Prediction System (ARPS) model has been used for the prediction of synoptic weather system known as Western Disturbance (WD) that affects the weather of western and central Himalaya during winter period (November – April).The ARPS model has been selected for this study because the model has its own objective analysis and quality control system. It has the capacity to ingest the satellite, Doppler weather radar data and other types of observations. Its assimilation system can also be used to overcome the problem of data scarcity in Himalayan region. In this study, initial and lateral boundary fields are taken from the T-80 spectral global model operationally used at National Centre for Medium Range Prediction (NCMRWF), Noida (UP), India. The global model’s analysis was taken as the initial condition and 24 hour’s interval forecasts as lateral boundary conditions. The model has been used for the simulation of few WDs for 96 hours (Four days). The comparison of ARPS simulation with T-80 forecast shows that the ARPS model could produce better results in respect of the circulation of WDs and hence it can be utilized for the operational weather prediction over the Indian region.  

scholarly journals Relating observations of contrail persistence to numerical weather analysis output

2009 ◽  
Vol 9 (4) ◽  
pp. 1357-1364 ◽  
Author(s):  
D. P. Duda ◽  
R. Palikonda ◽  
P. Minnis
Keyword(s):  
High Resolution ◽  
Wind Shear ◽  
Meteorological Data ◽  
Atmospheric Stability ◽  
Satellite Observations ◽  
Prediction System ◽  
Atmospheric Conditions ◽  
Advanced Regional Prediction System ◽  
Numerical Weather ◽  
Regional Prediction

Abstract. The potential for using high-resolution meteorological data from two operational numerical weather analyses (NWA) to diagnose and predict persistent contrail formation is evaluated using two independent contrail observation databases. Contrail occurrence statistics derived from surface and satellite observations between April 2004 and June 2005 are matched to the humidity, vertical velocity, wind shear and atmospheric stability derived from analyses from the Rapid Update Cycle (RUC) and the Advanced Regional Prediction System (ARPS) models. The relationships between contrail occurrence and the NWA-derived statistics are analyzed to determine under which atmospheric conditions persistent contrail formation is favored within NWAs. Humidity is the most important factor determining whether contrails are short-lived or persistent, and persistent contrails are more likely to appear when vertical velocities are positive. The model-derived atmospheric stability and wind shear do not appear to have a significant effect on contrail occurrence.

Application of ARPS–CMAQ modeling system for urban air pollutant emission abatementA paper submitted to the Journal of Environmental Engineering and Science.

2010 ◽  
Vol 37 (2) ◽  
pp. 323-334
Author(s):  
X. Y. Zhao ◽  
S. Y. Cheng ◽  
J. B. Li ◽  
H. Y. Wang ◽  
X. R. Guo
Keyword(s):  
Air Quality ◽  
Northern China ◽  
Air Pollutant ◽  
Decision Makers ◽  
Pollutant Emission ◽  
Advanced Regional Prediction System ◽  
Multi Scale ◽  
Regional Prediction ◽  
Quality Guideline ◽  
Quality Objective

A coupled advanced regional prediction system – community multi-scale air quality (ARPS–CMAQ) modeling system was applied to develop an abatement strategy for air pollutant emission in the Handan region of the northern China. The system was evaluated by comparing the simulated concentrations of particulate matter less than 10 µm (PM10) with the observed results in the study area during the four representative months in 2005. A process of planning emission abatement was applied by gradually reducing PM10 emissions from the original GIS-based emission inventory until a modeling emission scenario was obtained under which the simulated PM10 concentrations could satisfy the desired air quality objective. The air quality objective was represented by an air quality guideline satisfaction ratio of 80% to reach a daily PM10 concentration of 150 µg/m3 after the year 2010. The modeling system and results could provide sound basis for decision makers to develop an effective air quality management strategy.

scholarly journals Assimilating remotely sensed cloud optical thickness into a mesoscale model

2011 ◽  
Vol 11 (5) ◽  
pp. 13355-13380 ◽  
Author(s):  
D. Lauwaet ◽  
K. De Ridder ◽  
P. Pandey
Keyword(s):  
Water Content ◽  
Optical Thickness ◽  
Prediction System ◽  
Model Simulations ◽  
Advanced Regional Prediction System ◽  
Air Temperatures ◽  
Regional Prediction ◽  
Ice Water Content ◽  
Cloud Optical Thickness ◽  
Ice Water

Abstract. The Advanced Regional Prediction System, a mesoscale atmospheric model, is applied to simulate the month of June 2006 with a focus on the near surface air temperatures around Paris. To improve the simulated temperatures which show errors up to 10 K during a day on which a cold front passed Paris, a data assimilation procedure to calculate 3-D analysis fields of specific cloud liquid and ice water content is presented. The method is based on the assimilation of observed cloud optical thickness fields into the Advanced Regional Prediction System model and operates on 1-D vertical columns, assuming that there is no horizontal background error covariance. The rationale behind it is to find vertical profiles of cloud liquid and ice water content that yield the observed cloud optical thickness values and are consistent with the simulated profile. Afterwards, a latent heat adjustment is applied to the temperature in the vertical column. Data from 4 meteorological surface stations around Paris are used to verify the model simulations. The results show that the presented assimilation procedure is able to improve the simulated 2 m air temperatures and incoming shortwave radiation significantly during cloudy days. The scheme is able to alter the position of the cloud fields significantly and brings the simulated cloud pattern closer to the observations. As the scheme is rather simple and computationally fast, it is a promising new technique to improve the surface fields of retrospective model simulations for variables that are affected by the position of the clouds.

scholarly journals Assimilating remotely sensed cloud optical thickness into a mesoscale model

2011 ◽  
Vol 11 (19) ◽  
pp. 10269-10281
Author(s):  
D. Lauwaet ◽  
K. De Ridder ◽  
P. Pandey
Keyword(s):  
Water Content ◽  
Optical Thickness ◽  
Prediction System ◽  
Model Simulations ◽  
Advanced Regional Prediction System ◽  
Air Temperatures ◽  
Regional Prediction ◽  
Ice Water Content ◽  
Cloud Optical Thickness ◽  
Ice Water

Abstract. The Advanced Regional Prediction System, a mesoscale atmospheric model, is applied to simulate the month of June 2006 with a focus on the near surface air temperatures around Paris. To improve the simulated temperatures which show errors up to 10 K during a day on which a cold front passed Paris, a data assimilation procedure to calculate 3-D analysis fields of specific cloud liquid and ice water content is presented. The method is based on the assimilation of observed cloud optical thickness fields into the Advanced Regional Prediction System model and operates on 1-D vertical columns, assuming that the horizontal background error covariance is infinite, i.e. an independent pixel approximation. The rationale behind it is to find vertical profiles of cloud liquid and ice water content that yield the observed cloud optical thickness values and are consistent with the simulated profile. Afterwards, a latent heat adjustment is applied to the temperature in the vertical column. Data from several meteorological stations in the study area are used to verify the model simulations. The results show that the presented assimilation procedure is able to improve the simulated 2 m air temperatures and incoming shortwave radiation significantly during cloudy days. The scheme is able to alter the position of the cloud fields significantly and brings the simulated cloud pattern closer to the observations. As the scheme is rather simple and computationally inexpensive, it is a promising new technique to improve the surface fields of retrospective model simulations for variables that are affected by the position of the clouds.

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